Operationalising Pro-Poor Growth-A country case study on Bolivia

“Operationalising Pro- Poor Growth”
A joint initiative of
AFD, BMZ (GTZ, KfW Development Bank), DFID, and the World Bank
A Country Case Study on Bolivia
Stephan Klasen, Melanie Grosse, Rainer Thiele, Jann Lay, Julius Spatz,
Manfred Wiebelt
October 2004
This paper belongs to a series of 14 country case studies spanning Africa,
Asia, Latin America and Eastern Europe. The series is part of the
‘Operationalising Pro-Poor Growth (OPPG)’ work programme, a joint initiative
of AFD, BMZ (GTZ, KfW Development Bank), DFID and the World Bank. The
OPPG work programme aims to provide better advice to governments on
policies that facilitate the participation of poor people in the growth process.
Other outputs of the OPPG initiative include a joint synthesis report, a note on
methodological approaches to analysing the distributional impact of growth,
cross-country econometric work, literature reviews, and six synthesis papers
on: macroeconomics and structural policies, institutions, labour markets,
agriculture and rural development, pro-poor spending, and gender. The
country case studies and synthesis papers will be disseminated in 2005.
The entire set of country case studies can be found on the websites of the
participating organisations: BMZ www.bmz.de, DFID www.dfid.gov.uk, GTZ
www.gtz.de,
KfW
Development
Bank
www.kfwentwicklungsbank.de/EN/Fachinformationen
and
the
World
Bank
www.worldbank.org.
For further information, please contact:
AFD: Jacky Amprou [email protected]
BMZ: Birgit Pickel [email protected]
DFID: Manu Manthri [email protected] and Christian Rogg [email protected]
GTZ: Hartmut Janus [email protected]
KfW Development Bank: Annette Langhammer [email protected]
World Bank: Louise Cord [email protected] and Ignacio Fiestas [email protected]
Stephan Klasen
Melanie Grosse
Department of Economics
University of Göttingen
Rainer Thiele
Jann Lay
Julius Spatz
Manfred Wiebelt
Kiel Institute for World Economics
Operationalizing Pro-Poor Growth
Country Case Study: Bolivia
Final Report, September 28, 2004
Table of Contents
Executive Summary
i
Chapter 1: Historical Context
1
Chapter 2: Analysis of Growth and Its Distributional and Poverty Impact 9
Chapter 3: Factors Affecting the Participation of the Poor in Growth
25
Chapter 4: Possible Trade-Offs between Growth and Poverty Reduction 44
Chapter 5: Recommendations for Policy-Making
46
References
50
Acknowledgements
We would like to thank Juan-Carlos Aguilar and Stefan Zeeb for generous support during two
visits to Bolivia as well as for providing valuable inputs, comments, and documentation. We
also want to thank Annette Langhammer and Louise Cord for valuable comments throughout
the drafting of this document. In addition, we like to thank Berk Ozler, Omar Arias, Fernando
Landa, Wilson Jimenez, Sara Calvo, participants and discussants at workshops at the World
Bank, in Frankfurt, and in La Paz for valuable comments and discussion. Funding from the
German Federal Ministry for Economic Cooperation and Development via the KfW
Entwicklungsbank (KfW Development Bank) is gratefully acknowledged.
i
Executive Summary
Introduction
1. This case study examines to what extent Bolivia has been able to achieve pro-poor
growth, what the mechanisms of achieving (or failing to achieve) pro-poor growth have been,
and what options are available to ensure higher rates of pro-poor growth. The analysis focuses
on the period from 1989 to 2003, which spans a time of relatively high growth in the 1990s,
and low growth with social and political turmoil in the past few years. In contrast, there have
been notable and sustained improvements in social indicators which continued to improve
despite the economic slowdown.
2. Bolivia, a landlocked country with poorly developed infrastructure and a very uneven
population distribution, has had a legacy of high economic and social inequality with a strong
ethnic dimension. The political system has always been dominated by an urban-based elite
and has only recently opened to serious indigenous representation. After a disastrous bout of
hyperinflation, Bolivia embarked on a path of structural reforms in the late 1980s, which
brought stability and fairly high growth throughout most of the 1990s. Growth decelerated
since as a result of external shocks, which reversed some of the gains made in the previous
decade. A large share of the population is dependent on subsistence agriculture and informal
activities (some illegal including the production of coca leaves), with a small modern
agricultural sector, a small formal sector, and a capital-intensive natural resource sector,
which generates a large share of export earnings.
Poverty Trends, Profiles, and Pro-Poor Growth
3. As there are no national poverty data before 1997, we have created a new time series of
poverty data from 1989 to 2003 by linking information from urban household surveys with
nationally representative Demographic and Health Surveys. The new time series, which is
robust to different sensitivity analyses, indicates large differentials in poverty between urban
and rural areas. In addition, poverty rates in urban areas responded rapidly to economic
opportunities (and the recent slowdown), while poverty in rural areas followed its own
dynamic. The extent of poverty reduction in rural areas was moderate, did not affect the
headcount ratio much and is partly sensitive to the assumption made in the data matching
exercise. Using the Ravallion-Chen measure of pro-poor growth, we find that growth was
pro-poor but relatively low throughout the 1990s, but became sharply anti-poor in urban areas
since then. In rural areas, growth was slower, but generally more pro-poor. Due to the recent
slowdown, pro-poor growth over the entire 1989-2002 period was too slow to lead to
significant poverty reduction. A decomposition of poverty reduction shows that about 2/3 of
poverty reduction was due to income growth with the remaining share being allocated to a
redistribution component which, however, also includes the effect of favorable price shifts for
the goods consumed by the poor.
4. A poverty profile shows considerable regional inequality, with the central highland and
valley provinces being affected by much higher poverty, compared to the outlying valley and
lowland provinces. The most important correlates of poverty are, apart from the urban/rural
divide, ethnic background and education. There is comparatively little gender bias in
education (but serious gender gaps persist elsewhere in the formal economy and in the home).
5. We link the record of pro-poor growth to the sectoral composition of growth and find that
urban incomes were closely tied to macroeconomic developments, while rural incomes were
more dependent on weather conditions and the coca economy. Consistent with the poverty
profile, we also find that Bolivia is a highly segmented society with relatively sharp
segmentations along a formal-informal divide, a rural-urban divide, and an ethnic divide. The
formal-informal divide is related, among other things, to tight labor market regulation in the
ii
urban formal market, poor credit access for informal producers and other barriers to
formalization, relatively little opportunities for migrant workers to gain entry into the formal
economy, and the small inherent size of the formal sector. The urban-rural and the ethnic
divide are closely related and are partly a legacy of strong discrimination against the
indigenous population, little success in modernizing highland agriculture, and little success in
generating an income base in rural areas of the highlands and central valleys beyond the coca
economy.
Initial Conditions, Policies, and Pro-poor Growth
6. Initial conditions were unfavorable for linking the poor to the growth process. Among
them are an uneven population distribution, high initial inequalities (of land, other assets,
human capital, and incomes), and comparative advantages in highly capital-intensive
agricultural and resource extraction activities. Moreover, poor governance and the divisive
and strife-torn political economy of Bolivia have made stable economic policy-making
difficult.
7. Bolivia’s macro policies were narrowly focused on stability, liberalization, and growth
with little direct concern for distributional issues. Such a policy stance was feasible as long as
the policy environment produced stable growth and some poverty reduction. In the current
slowdown, which is largely caused by events beyond Bolivia’s control but amplified by its
liberalized economy, the legitimacy of this economic model has been seriously questioned.
8. The tax system is not progressive and the expenditure system generally reaches the poor
but is not particularly well targeted. Despite this, a rapid expansion of social sector spending
beginning in the mid-1990s, aided by funds freed from the HIPC II debt reduction initiative,
has contributed to rapid improvements in health and education indicators (from a relatively
low level). Unfortunately, the sustainability of this expansion is highly doubtful given the
economic slowdown, the associated decline in tax revenues, and the emergence of huge
budget deficits. Buying support for economic reforms through an expansion in social sector
spending does not seem to be feasible anymore.
9. Using a dynamic CGE model we then assess the impact of shocks and policies on propoor growth, both to account for the developments of the past and to investigate policy
options for the future. In an optimistic baseline scenario, Bolivia could achieve a sustainable
4.7% rate of growth per year with moderate poverty reduction, but a widening urban-rural
gap. External shocks such as terms-of-trade shocks, El Niño, and declining capital inflows all
served to lower economic growth in the latter half of the 1990s and contributed to rising
poverty. Given Bolivia’s high degree of dollarization and its dependence on foreign capital,
exchange rate and monetary policies can do little to cushion the blow from external shocks.
10. As far as forward-looking policies are concerned, expansion of natural gas exports will
boost growth and reduce urban poverty somewhat, but will lead to rising inequality and rising
rural poverty. Labor market and tax reforms have the potential to increase growth and urban
poverty reduction, with relatively little impact on rural poverty. The combination of gas
exports and labor market and tax reforms would yield the highest outcome in terms of
economic growth. If they were combined with transfer programs targeted at the rural poor,
they would also lead to significant poverty reduction there. Other targeted interventions in
favor of the poor such as improvements in credit access, agricultural technologies, and rural
infrastructure have only a small impact on poverty reduction in the medium term, although the
impacts are likely to be larger over a longer time horizon.
Institutions and Pro-Poor Growth
11. Bolivia’s institutional environment is difficult and has recently deteriorated considerably
given the political uncertainty and social instability. Bolivia scores particularly low on
iii
political stability and government effectiveness which is largely due to high perceived levels
of corruption and low judicial reliability. Lack of transparency and voice in the public sector
appears to be the main factor responsible for the high levels of corruption. Well-intentioned
decentralization aimed to bring the government closer to the people and involve the poor have
not (yet?) had the desired outcome due to difficulties in implementation, the loss of fiscal
control, and the inability to manage the high expectations of the population. Bolivia’s PRSP
process, once hailed as a model and enshrined in a permanent National Dialogue Law, is now
largely seen as a failure. The goals were too ambitious, there was a serious disconnect
between the consultation and the write-up of the strategy, it was too focused on determining
how to allocate HIPC resources, there was no thorough discussion of economic policymaking, there was too little emphasis on strengthening the productive capacities of the poor,
and by now nobody seems to own this document. As a result, revisions of the PRSP and the
associated National Dialogue have stalled. It thus appears that the pay-off to the ambitious
decentralization and PRPS processes has been quite low in Bolivia and might have
contributed to some of the polarized political debates that currently undermine Bolivia’s
political and social stability.
Trade Offs between Growth and Poverty Reduction
12. Using the CGE model, we investigate trade-offs and win-win situations for growth and
poverty reduction. Among the win-win scenarios would be a reform of urban labor markets
and a tax reform, although the urban poor would benefit more than their rural counterparts.
But both policies might face stiff opposition from interest groups and thus are not easily
implemented.
13. The expansion of the natural gas sector appears to cause a trade-off between growth and
rural poverty reduction. It raises the growth rate but leads to sharply increasing inequality so
that nationwide poverty would fall only moderately, while rural poverty would actually go up.
Only if the receipts of gas were channeled as transfer or investment programs into rural areas,
could this trade-off be mitigated. The largest effect for pro-poor growth could be achieved if
the gas exports, tax and labor market reform were combined with transfer programs that are
better targeted to the rural poor than currently.
14. More fundamentally, the model-based assessments suggest that incremental reforms will
have a limited impact on putting Bolivia on a sustainable pro-poor growth trajectory. In
particular, it highlights the fundamental constraint imposed by the very low domestic savings
rate, which limits growth, increases vulnerability to external events, and limits opportunities
for pro-poor policy-making. In addition, the high dualism of the economy is sharply reducing
the poverty impact of growth. It thus appears to be necessary to confront some of the deepseated inequalities in opportunities, resources, and power.
Recommendations for Policy-Making
15.
We find that there is a range of incremental policies that could lift growth and poverty
reduction in urban areas, where, in the absence of shocks, poverty reduction is expected to
continue in coming years. Among them are policies to develop the gas sector, deregulation of
urban labor markets, and income tax reform. The options to reduce rural poverty are much
more limited. Our model-based estimates suggest that transfer programs (such as a demandside transfer program linked to human capital investments) might be the best option, although
a combination of investments in rural infrastructure, micro-credit, and agricultural
productivity might also be of some help. A combination of such transfer and investment
programs with gas exports, tax and labor market reforms might be a politically and
economically feasible option.
iv
16.
In addition, there are clear opportunities for improvements in policy-making at the
macro and fiscal level. At the macro level, it is critical to develop policies that raise the
domestic savings rate. They could include institutional reforms to widen the coverage of
savings, greater public savings (e.g. from the proceeds of gas exports) and, at the international
level, further debt relief. In addition, it is necessary to implement, to the extent feasible,
policies to reduce dollarization of the economy in order to increase the room to maneuver for
an active monetary and exchange rate policy that could support growth and poverty reduction.
17.
Similarly, there are opportunities to increase the progressivity of the tax system and
improve the poverty impact of public spending. In addition, policies to strengthen the
productive capacities of the poor (such as the Cadenas Productivas Initiative and other proactive policies) should receive the same attention as the expansion of social sector spending
has.
18.
Apart from these incremental reforms, it appears urgently necessary to confront some
of the deep-seated inequalities in assets, opportunities, resources, and power in Bolivia.
Among the policies to consider are revisiting the stalled land reform program, policies to
transfer proceeds from natural gas directly to the poor, and policies to increase the voice of
Bolivia’s marginalized indigenous communities.
1
Chapter 1: Historical Context
1.
Bolivia is a large land-locked country with low population density (8 people per km2),
difficult terrain, and consequently poorly developed transport and communications
infrastructure (see Table 1). It is characterized by great economic and social inequalities with
deep historical roots. Apart from a Spanish-speaking population (consisting of people of
Spanish and mixed descent) that has dominated political and social affairs since independence
in the early 19th century, Bolivia also has a very large indigenous population that comprises
Aymara-speaking people in the highlands, Quechua-speaking people in the valleys, and
smaller ethnic groups in the lowlands and the rainforest. Consequently, Bolivia is one of the
most ethnically diverse countries in Latin America. Its index of ethnic fractionalization in
1998 stood at 0.74, compared to an average for Latin America and the Caribbean of 0.42
(Alesina et al. 2003).1
2.
Until the revolutionary government of Victor Paz Estenzoro installed in 1952, most
indigenous people lived in serf-like arrangements in rural areas. The agrarian reform in 1953
freed the peasants in the highlands and gave them access to land. Since then, subdivisions of
land and population pressure have created smaller and smaller land-holdings (minifundismo)
and landlessness has recently become a problem. In other parts of the country, particularly the
lowlands, large estates dedicated to commercial farming predominate. As a result, the Gini
coefficient for land inequality stood at 0.768 in 1989, indicating overall high land
concentration similar to other Latin American countries (Deininger and Squire 1998). The
other main source of incomes in the highlands, tin and silver mining, became progressively
less lucrative and was sharply curtailed in the 1980s. Also here, the indigenous people had
been used as forced labor for many centuries and as free miners since the 1950s, who
organized themselves in unions. The mines became the breeding ground for considerable
labor unrest throughout much of the 1970s and 1980s.
3.
In contrast, the previously largely unpopulated lowlands surrounding Santa Cruz have
become the focus of settlement and growth in recent decades, fuelled by a large-scale farming
sector as well as the discovery of natural resources (oil and gas).
4.
Starting in the 1970s, Bolivia became a major exporter of coca leaves, the input to
cocaine, which became Bolivia’s most lucrative cash crop. The coca growing regions
(Chapare and Yungas) became the focus of much in-migration (temporary and permanent)
from other rural areas, generating considerable remittances. At the same time, under pressure
from the United States, Bolivian governments promised coca eradication and pursued it with
varying degrees of intensity. In the late 1990s and early 2000s, coca eradication was pursued
much more vigorously, leading to a decline in production of some 80% (World Bank 2004b).
The ebb and flow of these eradication efforts have played a significant role in the income
sources of poor rural households.
1
The index measures the likelihood that two randomly drawn people from the population belong to different
ethnic groups.
Table 1: Bolivia in a Comparative Latin American Perspective, 2001
Bolivia
Argentina
Brazil
Chile
Ecuador
Guatemala
Paraguay
Economic Indicators
GNI per capita (PPP $)
2240.00
10980.00
7070.00
8840.00
2960.00
4380.00
5180.00
Average GDP Growth 1994–2001 (%)
3.46
1.48
2.86
5.14
1.64
3.85
1.76
Average Population Growth 1994–2001 (%)
2.30
1.26
1.30
1.34
1.95
2.63
2.55
Population density (people per km2)
7.85
13.70
20.39
20.57
46.52
107.75
14.18
Average Inflation 1999–2001
2.79
-1.06
6.25
3.58
62.00
6.16
7.67
Average GDP Shares 1999–2001 of
Agriculture
15.27
4.84
7.99
8.57
10.98
22.83
20.85
Industry
28.92
27.36
29.88
34.53
36.86
19.82
26.08
Services
55.81
67.80
62.13
56.90
52.16
57.35
53.07
Exports
17.66
10.70
11.58
30.44
36.89
19.30
22.42
Current Account Deficit
-4.97
-3.01
-4.52
-1.24
3.01
-5.72
-2.92
Budget Deficit
-4.14
-2.82
n.a.
-0.49
n.a.
n.a.
-2.71
Gross Domestic Savings
7.80
15.73
19.74
23.07
24.85
7.72
10.24
Aid
7.23
0.04
0.05
0.08
1.04
1.36
0.97
External Debt
65.03
51.15
43.72
51.21
95.52
22.49
41.03
Human Development and Infrastructure
Life Expectancy at birth (years)
63.06
74.08
68.31
75.79
70.04
65.23
70.58
Immunization, DPT (% of children under 12
months)
81.00
82.00
97.00
97.00
90.00
82.00
66.00
Hospital beds (per 1,000 people)
1.67
3.29
3.11
2.67
1.55
0.98
1.34
Total Years of Schooling (15+) 2000
5.58
8.83
4.88
7.55
6.41
3.49
6.18
Adult Illiteracy (%)
14.00
3.09
12.70
4.10
8.16
30.79
6.50
Female Illiteracy (%)
20.06
3.09
12.75
4.26
9.75
38.21
7.55
Roads, paved (% of total roads)
6.50
29.40
5.50
19.40
18.90
34.50
9.50
Roads to surface area (%)
4.90
7.75
20.18
10.55
15.23
12.97
7.25
Roads to total population (per ‘000)
6.46
5.89
10.14
5.25
3.42
1.27
5.73
Telephone mainlines (per 1,000 people)
62.21
223.83
217.84
232.51
103.71
64.68
51.24
Poverty and Inequality Data
Year
1997
2001
2001
2000
1998
2000
1999
PPP $1 Poverty Incidence
29.40
3.33
8.17
0.97
17.67
15.95
14.86
PPP $2 Poverty Incidence
51.69
14.31
22.43
9.58
40.77
37.36
30.29
Gini Coefficient
0.585
0.522
0.585
0.571
0.522
0.483
0.568
Source: http://www.worldbank.org/research/povmonitor/regional/Latin_America_and_the_Caribbean.htm; Barro and Lee (2000); World Bank (2003a).
Peru
4470.00
4.30
1.69
20.58
3.07
8.58
29.90
61.51
15.53
-2.61
-1.98
18.11
0.82
53.73
69.57
85.00
1.47
7.58
9.80
14.27
12.80
5.67
2.85
77.50
2000
9.14
37.71
0.498
3
Table 2: Basic Economic and Human Development Indicators for Bolivia
1985-1989
1989-1994
1994-1999
1999-2002
Economic Indicators
Real GDP growth
1.62
4.08
3.93
2.18
Agriculture excluding mining
0.33
4.10
2.08
2.38
Mining
-0.16
4.07
2.36
2.80
Services excluding public administration
1.21
4.94
6.93
1.47
Public Administration
-0.98
1.88
3.93
2.44
Industry - Manufacturing
2.02
4.40
3.80
1.94
Export growth (goods and services)
15.56
4.08
1.54
0.02
Export growth (merchandise)
5.04
5.89
-0.89
0.09
Export growth (mineral and hydrocarbon)
-0.81
-2.49
-2.81
0.18
Ave. share of mineral and hydrocarbon exports to GDP
13.68
10.17
7.57
7.65
Ave. share of agricultural exports to GDP
2.14
3.87
5.16
5.28
Current Account Deficit
-5.28
-3.53
-6.05
-4.38
Budget Balance
-0.38
-1.92
-2.33
-5.06
Inflation
2414.35
13.41
7.43
3.10
Savings Rate (domestic)
10.91
9.05
10.53
7.52
Investment Rate
14.42
15.15
18.70
15.09
Human Development Indicators
Population Growth
2.18
2.41
2.33
2.16
Child Mortality
146
122
97
80
Life Expectancy
56.19
58.81
61.03
62.56
Primary Enrollment (male)
100.81
103.29
111.34
116.66
Primary Enrollment (female)
89.80
94.71
106.26
115.07
Secondary Enrollment (male)
42.16
41.69
60.28
81.34
Secondary Enrollment (female)
35.92
35.74
54.54
77.87
Note: Data on GDP growth and current account is taken from UDAPE (various issues) and INE (various issues). Data
on exports is taken from UDAPE (various Issues) and WDI (2003). All other data are taken from WDI (World Bank
2003a), covering the years up to 2001.
Source: WDI 2003; UDAPE (various issues); INE (various issues).
5. Politically, Bolivia oscillated between military dictatorships and civilian rule between the 1950s
and the early 1980s when the latest military government was replaced with a democratic one, and
democracy has persisted ever since. Bolivia’s politics were dominated by three main political
parties (MNR, MIR, and ADN) and a few smaller ones and all governments since 1982 have been
coalition governments, where the coalitions only lasted for one term and then were replaced by
another coalition among the three major parties (or coalitions involving smaller ones; all possible
permutations of coalitions among the three major parties existed in the past 20 years); this was
aided by the constitutional provision that a president can only serve one term in office. All three
parties represented the Spanish-speaking population with little representation from the indigenous
populations. As a result of these arrangements, horse-trading and patronage became central
elements in Bolivia’s political system, both to ensure the support of indigenous populations in
elections and to generate coalition governments between groups with substantially different
ideological agendas (Kaufman et al. 2003). This led to an increasing alienation and frustration of
the population with the political process and led to the rise of powerful extra-parliamentary
opposition forces, such as the coca growers’ union and other civil society groups, which were in
hostile opposition to the government.
6. The latest election in 2002 brought major breakthroughs for new parties aligned with indigenous
groups, which for the first time have a major representation in parliament. In particular, a party
allied to coca growers (MAS) was able to gain major representation in parliament. Apart from
representing coca growers, they have also taken on a range of populist positions on macro and trade
issues. In this new environment, politics as usual continued and a coalition between MNR and MIR
brought Gonzalo Sanchez de Lozada back into power (he had been president before between 1993
4
and 1997). Some of the proposed reforms and measures of the government, in particular a poorly
communicated tax reform in early 2003 and a proposal to sell liquefied natural gas via Chile to the
USA, led to such opposition (within and outside of parliament) and civil unrest that the government
was forced out of power in October 2003 and the vice-president, Carlos Mesa, took over as the
constitutional successor to form an independent government. Despite enjoying some popular
support (based on his background in media and his strong stance against corruption), he has little
support in parliament and it is unclear whether he will be able to bring back stability to the country.
A constitutional assembly has been called for 2005 tasked reassessing the entire political and
economic model that has been followed in the past, with great uncertainties about what outcome
this might generate.
7. Regarding economic policies, Bolivia had pursued a state-led import-substitution regime until
the 1980s, which was largely financed through the export of raw materials (tin and silver). The first
democratic government under Siles-Zuazo (1982-85) faced a very difficult internal (drought, social
unrest) and external environment (debt crisis, global recession and collapse in tin prices in 1985)
and was unable to stabilize the country but instead allowed a hyperinflation to develop which led to
a collapse of the government in 1985. Victor Paz Estenssoro took over and first undertook a strict
stabilization plan, which ended hyperinflation and brought back internal and external stability (for
details see Sachs and Larrain 1998).
8. In addition, the Paz Estenzoro government designed and began implementation of a Nueva
Politica Economica, which included a wide range of structural reforms, which were supported
thereafter by structural adjustment programs of the World Bank and the IMF. These reforms , which
in the early 1990s shifted to second generation structural reforms, were continued by most of the
successive governments so that Bolivia stands out as a country having undertaken more structural
reforms in line with the so-called ‘Washington Consensus’ than most developing countries (Rodrik
2003; Lora 2001). They included:
⎯ Product market deregulation (freeing of prices, regulation of natural monopolies)
⎯ Capital market deregulation (freeing of interest rates, reduction in reserve requirements,
liberalization of the external capital market)
⎯ Fiscal reforms involving the simplification of the tax structure where a value-added tax and an
income tax (both at 13% where individuals could deduce value-added tax payments from the
income tax bill) became the central revenue source and tax collection increased significantly as
a share of GDP. On the expenditure side, there was a considerable expansion of expenditure in
the social sectors (health and education), while expenditures on state-owned companies were
sharply reduced through the privatization program.
⎯ Trade liberalization (simplification and sharp reduction of import tariffs, elimination of nontariff barriers, efforts to promote non-traditional exports)
⎯ Liberalization of the FDI regime (regulatory framework, investor protection, equal treatment of
domestic and foreign investors)
⎯ Restructuring, closing, and ‘capitalization’ of the large state-owned companies. The latter refers
to a scheme where public companies sold a 50% stake to strategic investors (where the proceeds
remained with the companies to finance a pre-specified investment program). The proceeds
from the remaining shares are being used to finance an annual old age pension (the Bonosol) for
all citizens over the age of 65. This way, electricity, railway, telecommunications, mining, the
national airline, and the national hydrocarbon company were transferred to (mostly foreign)
strategic investors who took management control of these companies.
9.
The one area where there were only few reforms was the labor market. Here, only
government intervention in wage setting was reduced and there was some reduction in wages and
benefits for public sector employees. The Labor Law of 1942 is still largely in force with quite high
5
costs of dismissal, few options for temporary work, substantial requirements to meet occupational
health and safety standards, a prohibition of employment agencies, and other regulations which
were aimed primarily at the mining sector but have since become a stumbling bloc for a smoother
operation of the formal labor market.
10.
In addition, the first government of Sanchez de Lozada (1993-97) undertook an ambitious
decentralization program in the 1994 Popular Participation Law and the 1995 Decentralisation Law,
which transferred a considerable amount of resources (and responsibilities) to Bolivia’s 314
municipalities. In addition, the municipalities were also awarded all additional resources that were
freed up as a result of the HIPC II initiative which were the focus of attention in Bolivia’s first
PRSP, concluded in 2000.
11.
In several dimensions, Bolivia’s structural reforms produced positive outcomes.
Macroeconomic stability was achieved and maintained throughout the period with low inflation,
low fiscal deficits, and a relatively stable exchange rate. The fiscal reforms, combined with the
reform of the state sector, ensured that the fiscal situation improved dramatically over the 1990s.
Exports, including non-traditional exports, improved, and there were significant improvements in
human development indicators, particularly education (see Tables 1 and 2). While Bolivia remains a
lot poorer than all of its neighbors, has higher poverty rates and lower life expectancy, it compares
favorably in education indicators with some richer Latin American countries such as Guatemala or
even Brazil (see Table 1).2 Economic growth also improved and Bolivia grew at around 4% per
year from 1990-1998, but only about 1.5% in per capita terms. This relatively positive performance
was aided by a favorable external environment, with high growth of Bolivia’s main trading partners,
the expansion of natural resource exports, and a surge in foreign direct investment that accompanied
the capitalization process. The combination of strong memories of the 1985 hyperinflation, an open
capital account, and high political and economic uncertainty of a small open economy led to high
and increasing dollarization in the economy, which permeates the financial system and significantly
limits the options for an active monetary and exchange rate policy. There were few attempts to
combat dollarization, which is extremely high to this day.
12.
Exports, while improving throughout the 1990s, remained largely focused on primary
products with the mix shifting from a heavy reliance on minerals to a much greater importance of
hydrobarbons and agricultural cash crops produced by commercial agriculture (i.e. soybeans, sugar,
and wood). The lack of diversification and the failure to develop manufactured exports appears to
be due to a combination of geographical factors (land-locked country, poor infrastructure, high
transport costs), economic risks and volatility (i.e. exchange rate risks and volatility vis-à-vis
trading partners), Dutch disease problems associated with the primary exports, and institutional
constraints (weak protection of property rights, high corruption, contraband economy, high
regulatory burden for start-ups, high informality of the economy, e.g. Kaufman et al. 2001; World
Bank 2004b). A continuing concern is also the very low domestic savings rate (see Tables 1, 2 and
below), making Bolivia heavily dependent on capital inflows to finance investment.
13.
Since 1998, economic growth has decelerated to an average of only about 1.5% per year and
has become negative in per capita terms. The main causes for this slowdown are a series of external
economic shocks that have affected the economy, including particularly the strong devaluations and
recessions in Brazil and Argentina in 1999 and 2002, respectively, while the Boliviano appreciated
significantly alongside the US$. This led to a sharply overvalued currency and the (independent)
monetary authorities did little to combat this due to the risks of devaluations in a dollarized
economy, but instead stuck to their policy of allowing only very small devaluations against the
2
One should note that the findings on poverty and inequality are quite sensitive to the choice of the survey, and to
whether income or expenditure is being used as the indicator. When one uses expenditures and the 1999 MECOVI
survey, the Gini stands at only 0.45 and the poverty headcount of below $1 a day falls to 14.4%. We report the
income-based figures in Table 1 as the data from the other countries are also based on incomes.
6
dollar (some 8% in 2001, falling to 4% in 2002). Instead, the economy slowed down considerably,
credit contracted sharply as the financial sector experienced build-up of non-performing loans; as a
result of the recession and costly amendments to a pension reform, budget deficits have soared to
unsustainable levels, adding economic uncertainty to the already existing explosive political and
social situation (World Bank 2004a). The financing of the large budget deficit through domestic
and international borrowing has placed Bolivia in an increasingly vulnerable situation where rising
shares of government spending must be allocated to debt service payments, thereby partially wiping
out some of the gains realized by the HIPC debt relief (World Bank, 2004a). As the dollar has
fallen recently against the currencies of Bolivia’s main trading partners and raw material prices
have increased, the external environment has improved somewhat and growth is projected to at
3.8% and 4.5% for 2004 and 2005, respectively.
.
14.
Regarding poverty and inequality trends, one first has to note that nationally representative
household surveys with income and expenditure information are only available from 1997
onwards.3 Before, there are income surveys for departmental capitals (plus El Alto) going back to
1989, and some spotty survey information from non-urban areas (see Annex 1). Thus rural areas
(comprising about 40% of the population in 1994, with the share falling over time) and towns
(comprising 12% of the population in 1994 with the share rising over time) were excluded from
these surveys. In addition, there are three national censuses (1976, 1992, and 2001) and three
nationally representative Demographic and Health Surveys (DHS in 1989, 1994, and 1998) none of
which contain income information.4 As a result there have been considerable disagreements about
the actual trends in poverty in Bolivia as shown in Tables 1 and 2 in Annex 1 which compiles all
poverty estimates we could find. Nevertheless, most of the studies agree on the following three
stylized facts: First, in the late 1990s, poverty is much higher in rural than urban areas; second,
there was some decline in poverty in capital cities since 1989 with an upturn in poverty again after
1997; third, non-income measures of poverty have declined stronger than income measures
throughout the 1990s, particularly in urban areas.
15.
For the purposes of this study, it was critical to generate nationally representative poverty
data going as far back as 1989. In order to achieve this, we employed two alternative
methodologies to generate national poverty data and poverty profiles for the time prior to 1997.
The first uses information from the DHS to generate an asset index as a proxy for income following
proposals from Sahn and Stiefel (2003) and Pritchett and Filmer (2001). Due to limitations in the
data, we can do this only for 1994 and 1998.5 The second combines information from the urban
household surveys with the DHS to generate income and poverty information for the entire country
from 1989 to 2002. The precise methodology and all of the statistical and econometric issues are
discussed in Annex 1.
16.
The most important results regarding poverty and inequality, based on the second
methodology, are summarized in Table 3 below. We present our main estimates but also include (in
brackets) the results of a sensitivity analysis of one of our key assumptions underlying the
simulation which might lead to an overestimate in the decline of poverty in rural areas.6 Moreover,
3
The 1997 survey is also not comparable to later surveys so that a consistent national time series only emerges in
1999.
4
There are further restrictions on the DHS. The 1989 DHS only includes households with women of reproductive
age (15-49), while the later ones include a representative sample. The 2003 DHS is due to be out in June. We will
be able to report on some summary information from the survey which was made available to us below.
5
We are also not convinced that this approach will be appropriate for inter-temporal comparisons of welfare and
poverty as changes in tastes and relative prices might systematically distort such an inter-temporal assessment. See
Annex 1 for a further discussion. We nevertheless used this method primarily as a robustness check on our other
approach.
6
In particular, we assume that the difference in returns to assets and endowments between rural, urban, and capital
cities did not change between 1989 and 1999. In our sensitivity analyses we replace the fixed difference assumption
7
one should note that the use of consumption (including auto-consumption) as the welfare measure
in rural areas and income as the welfare measure in capital cities (the nine departmental capitals and
the city of El Alto) and towns (all other cities and towns), as is standard practice in Bolivia (e.g.
INE-UDAPE, 2002), will lead to lower levels of inequality compared to using incomes in rural
areas which are reported to be considerably smaller. Using incomes for rural areas as well would
raise the Gini in 2002 to about 0.598. But as incomes in rural areas are implausibly low (about 25%
lower than consumption with many households reported extremely low incomes--including incomes
from own-consumed goods--that are impossible to survive on), we believe that it is preferable to
stick to the mixed definition.7 Lastly, we should point out that the poverty lines used here are based
a regionally differentiated basket of goods that allows sufficient caloric consumption which has
been updated using local price data on these goods. The extreme poverty line is derived by just
allowing for enough caloric consumption while the moderate poverty line also makes allowance for
non-food items (see annex 1 for further discussion). As will be shown below (and in annex 1), the
updating of the poverty line is not in line with the developments of overall prices as the prices of the
poor have risen less than the overall CPI.
17.
With these caveats in mind, the following observations are noteworthy:
First, using our methodology, we are able to reproduce actual poverty trends in capital cities (where
we have actual data for comparison) fairly well, particularly for the poverty gap measure, which is
quite reassuring. We tend to slightly underpredict the headcount ratio (poverty rate) most of the
time but also here, the most important trends (in capital cities where we can make a comparison) are
accurately reflected.8 Second, consistent with other studies, there is a steep gradient in poverty
levels between capital cities, towns, and rural areas, with poverty being much higher in the latter.
As far as the poverty rate is concerned, this differential between capital cities and rural areas gets
larger over time (from about 25 percentage points in 1989 to nearly 29 percentage points in 2002).
This is not true, however, when we consider the poverty gap, for which the differential gap has
somewhat narrowed. This suggests that the very poor have been able to make some gains in the
1990s while rural dwellers close to the poverty line did not benefit as much. Third, there is a clear
poverty trend in capital cities, which closely mirrors macroeconomic conditions. Thus poverty
(using the headcount or the poverty gap measure) declines considerably between 1989 and 1999 and
then increases again between 1999 and 2002. In towns and rural areas, in contrast, the dynamics of
poverty are not as closely aligned to macroeconomic developments. In particular, there is no
poverty reduction at all in rural areas between 1989 and 1994, then considerable poverty reduction
between 1994 and 1999, and stagnation (headcount) or slight further reductions (poverty gap)
between 1999 and 2002. Note also that the pace of poverty reduction in rural areas is smaller in our
sensitivity analysis but does not change the general picture (see figures in brackets).
18.
Using the first approach (see Annex 1 for tables and discussion) to generate poverty data
largely confirms the findings above for the time period 1994 to 1998, but with some slightly
different nuances. While the asset index which we use as a proxy for incomes increases overall and
in all three regions, which is consistent with the findings above, the increase in the asset index is
largest in towns, followed by capital cities, and smallest in rural areas (see Annex 1), suggesting
that rural poverty reduction measured this way has been somewhat smaller than urban poverty
reduction.
with the assumption that the difference in the impact of assets moved in accordance with the overall growth rates or
rural areas, towns, and capital cities which show that rural incomes increased more slowly than incomes elsewhere.
7
At the same time, we acknowledge that using consumption in one area and income in another may also lead to
biases that are hard to quantify. It is not possible to use expenditure throughout as expenditure data are not available
prior to 1999.
8
In Annex 1 (Table 5), we show that most of the differences in our prediction are due to our specification of the error
term in the underlying regression where we assume a normal distribution. We will experiment with other
distributional assumption to address this issue.
8
19.
Regarding inequality, the trends follow closely the poverty discussion, but with some
additional features. In particular, the sharp increase in inequality in capital cities between 1999 and
2002 is noteworthy. Measures that are more sensitive to the bottom of the distribution, such as the
Atkinson measure with e=2, show even more dramatic deteriorations (see Annex 1) suggesting that
the urban poor have fared particularly badly in the last few years. In other areas, inequality seems to
have fallen and thereby somewhat offsetting the dramatic worsening of inequality in capital cities.
Overall, the Gini in 2002 is similar to 1989. It thus appears that the fate of the urban population,
including the urban poor, has been closely linked to macro developments and has recently led to a
significant deterioration in poverty and inequality. In contrast, the much poorer rural poor have
been more detached from improvements and deteriorations in the overall economic environment
and their poverty trends have followed another logic.
Table 3: Poverty and Inequality Trends using Moderate Poverty Line*
1989
Observed
1994
Simulated
1999
2002
Observed Simulated Observed Simulated Observed
Headcount
Capital Cities**
67.2
Towns
64.8
59.5
81.1
(80.7)+
89.7
(87.8)
76.9
(76.0)
Rural
Total
57.4
51.1
48.1
55.1
75.1
(74.3)
89.6
(87.8)
72.4
(71.6)
69.1
64.2
67.7
83.4
79.1
83.8
65.2
60.3
67.2
25.3
21.0
21.3
24.4
44.7
(44.0)
60.9
(58.2)
41.9
(40.7)
34.7
33.6
32.9
47.7
43.1
44.9
32.5
30.1
32.9
0.455
0.480
0.488
0.540
Poverty Gap
Capital Cities**
32.9
Towns
32.9
25.7
51.3
(50.7)
58.3
(55.2)
45.5
(44.1)
Rural
Total
Gini Coefficient
Capital Cities**
0.505
0.497
0.481
Towns
0.547
0.537
0.455
0.500
0.452
Rural
0.475
0.497
0.423
0.443
0.421
Total
0.555
0.555
0.525
0.531
0.551
*The moderate poverty line is, in line with standard practice in Bolivia, applied to income in urban areas, and
consumption in rural areas (as income data are considered not to be reliable there and consumption data are not
available for the urban household surveys prior to 1997). While the extreme poverty line in Bolivia is only based on
ensuring adequate nutrition, the moderate poverty line also makes allowance for some non-food expenditures. The
moderate poverty line stood at about US$40 per capita and month, the extreme poverty line at about US$20. For details
on the poverty lines and the results for the extreme poverty line, refer to annex 1.
**Capital cities refer to the 9 departmental capitals and El Alto (the city adjacent to La Paz).
+ The figures in brackets refer to sensitivity analyses which no longer assume that the impact of endowments on growth
did not change between urban and rural areas between 1989 and 1998 but that it changed in proportion with the
differential in aggregate growth performance in the three areas. See Annex 1 for details and full results.
20. One should point out that Bolivia’s record in non-income dimensions of poverty is considerably
more favorable than its record in income poverty reduction. As shown in Table 2, Bolivia has
9
achieved impressive improvements in the reduction of child mortality and the expansion of primary
and secondary education. More recent data suggest that the decline in infant and child mortality as
well as the expansion of reproductive services and immunization coverage has continued at a rapid
pace, including in rural areas (INE, 2004), while education data suggest that the poorest quintile
have (in contrast to richer groups) suffered from slight declines in enrolment and attendance rates
(World Bank 2004b). The index of unsatisfied basic needs which combines information on
housing, sanitation, education, and health care, also shows strong improvements between 1992 and
2001; but the improvements are much smaller in rural areas where in 2001 91% of the population
continues to suffer from unsatisfied basic needs (see Annex 1 and World Bank, 2004b). The
apparent disconnect between rapidly improving social indicators and only moderate improvements
in income poverty are one of the conundrums of Bolivia’s economy (see below).
Chapter 2: Analysis of Growth and Its Distributional and Poverty Impact
21. Growth Decomposition and Pro-Poor Growth. Two ways to provide further insights about
the links between poverty, inequality, and growth trends is to do a decomposition of the observed
poverty reduction and provide estimates of the rates of pro poor growth (Datt and Ravallion 1992;
Ravallion and Chen, 2003). The decomposition of the observed poverty reduction into a growth
and an inequality contribution (and an interaction term which cancels if one the average of a
‘forward’ and ‘backward’ decomposition) is using the methods proposed by Ravallion and Datt
(1992). As discussed in detail in the Grimm and Günther (2004), the distribution component in this
decomposition also implicitly includes the impact of changes in the real value of the poverty line
(i.e. how prices paid by the poor have moved relative to the overall price level). As shown in Table
4 of Annex 1, the prices paid by the poor (in particular food prices) have risen somewhat less than
the overall price level (particularly in recent years) so that the purchasing power of the poor has
increased by more than suggested by the change in their real incomes. This is implicitly captured in
the decomposition as a distributional shift favoring the poor.
Table 4 – Growth Inequality Decompostion of Poverty Changes (Moderate Poverty)
1989–1999
Change in poverty
Growth component
Redistribution component
-0.118
-0.080
-0.038
Change in poverty
Growth component
Redistribution component
-0.163
-0.105
-0.057
Change in poverty
Growth component
Redistribution component
-0.117
-0.067
-0.050
Change in poverty
Growth component
Redistribution component
-0.068
-0.041
-0.028
1999–2002
1989–2002
Total Bolivia
0.020
-0.099
0.018
-0.064
0.002
-0.035
Departmental Capitals
0.040
-0.123
0.025
-0.080
0.015
-0.043
Other Urban Areas
-0.015
-0.132
0.017
-0.074
-0.032
-0.058
Rural Areas
0.005
-0.064
-0.005
-0.039
0.010
-0.025
Notes: Calculated using the Datt-Ravaillion (1992) method of growthinequaltiy decomposition.
Source: Own calculations. For the extreme poverty line, see Table 12 in Annex 1.
10
22. The result of the decomposition analysis (Table 4) for the entire period show that about twothirds of the 10 percentage point decline in poverty for total Bolivia is attributable to growth, and
about one-third to a distributional shift favoring the poor. 9 As the income distribution hardly
shifted between the two periods (see Table 3)10, most of this distributional shift is actually due to
the poverty line effect which increased the real purchasing power of the poor. Considering subperiods and different parts of the country shows a more differentiated picture. In the period 198999 both the growth and redistribution (and/or poverty line) effect served to reduce poverty in all
parts of the country. In the latter three years, the picture has changed drastically. Now poverty has
increased in capital cities nationally, and particularly in capital cities where 60% is due to falling
incomes and 40% due to adverse distributional shifts.
23. When one splits out this poverty line effect from the distributional component (results not
shown), we find that ‘pure’ redistribution helped to lower poverty in all of Bolivia between 1989
and 1999 as well as capital cities and towns, while the redistribution component was essentially
zero in rural areas. Between 1999 and 2002, the redistribution component served to increase
poverty in all regions and Bolivia as a whole. For the overall period (1989-2002), this ‘pure’
redistribution effect had a slightly poverty-increasing effect for Bolivia as a whole so that the
poverty decline that happened occurred mostly due to growth and a favorable development of the
prices paid by the poor. This adverse distributional effect is entirely driven by an adverse
distributional shift in capital cities which dominates a favorable distributional shift in towns and
rural areas.
24. A second way to examine the linkages between growth, inequality, and poverty is the RavallionChen measure of Pro-poor Growth which takes the average of growth rates of the quantiles of the
population that were poor in the initial period (see Ravallion and Chen 2003).11 The growth
incidence curves underlying this analysis are shown below for the entire period (1989-2002); for
sub-periods they are available in Annex 1. For the entire country and the entire period, they are
above 0 for all groups, and moderately downward sloping from the 10th to the 90th percentile
suggesting that, on the whole, the poor gained proportionately more from growth than the rich.
This is not true below the 10th percentile and above the 90th percentile suggesting that the extremely
poor were not benefiting as much and that the very rich were benefiting more from growth.12
Matters are different when one considers the different parts of the country. In departmental capitals
(and El Alto), growth over the period was anti-poor with the poor gaining less than the rich
(particularly due to the influence of the period after 1999), while it was strongly pro-poor in towns,
and moderately pro poor in rural areas.
25. The annual rate of pro poor growth, shown in Table 5, summarizes the information provided in
the growth incidence curves.13 We also show the results of our sensitivity analysis for towns and
9
This changes very slightly in our sensitivity analysis which is available on request.
10
Whether income distribution in Bolivia worsened between 1989 and 2002 is sensitive to the choice of inequality
indicators which give different weights to different parts of the distribution. But all show that whatever
distributional shifts occurred were small.
11
There are various criticisms of this approach of measuring pro-poor growth some of which can be found in Klasen
(2004).
12
One should note that measurement error might have a considerable influence at the two tails of the distribution so that
these results should be treated with some caution.
13
We should point out that Jimenez and Landa (2004) from UDAPE have, for the World Bank poverty assessment
(World Bank 2004b), also been calculating rates of pro poor growth using the Ravallion and Chen method whose
results, on the surface are quite different from ours. Their growth incidence curves for 1999-2002 point to sharply
rising inequality in rural areas and somewhat rising inequality in urban areas (combining capital cities and towns);
the calculated annual rates of pro poor growth are -6% per year. Where we use the same information (per capita
incomes for capital cities between 1999 and 2002), our findings are virtually identical. The most important reasons
for the discrepancy appear to be that they use income as the welfare indicator in rural areas while we use
11
rural areas (and by implication, all Bolivia) in brackets. The most important findings are the
following. Overall, there was Pro Poor Growth between 1.9 and 2.2% per year between 1989 and
2002, which was mostly due to high pro poor growth in towns and some pro poor growth in rural
areas, while pro poor growth in capital cities was negligible. As before, it is useful to consider subperiods. Between 1989 and 1999, there was a considerable amount of pro-poor growth in total
Bolivia, in capital cities, towns, and rural areas, regardless of the poverty line. Also, the rate of propoor growth exceeded the growth rate in the mean, suggesting that growth was accompanied by
falling inequality. The particularly high growth rate in total Bolivia (2.23%) is due to growth in the
three areas plus a shift in the composition of the population from the poorer rural areas to the richer
urban areas. Between 1999 and 2002, we find that there was a strongly anti-poor contraction in
capital cities, wiping out most of the gains the urban poor have made in the ten previous years. In
other urban areas, the contraction was not particularly anti-poor so that the poor had roughly
stagnant incomes. In rural areas, incomes continued to rise, although very slowly, and growth
continued to be somewhat higher for the poor than for the non-poor. Given that the rural poor
predominate among the poor, overall growth was only slightly anti-poor between 1999 and 2002,
and this finding is sensitive to the choice of the poverty line. In the sensitivity analysis, growth and
pro poor growth is somewhat smaller in total Bolivia and more significantly so in rural areas which
hardly experienced any growth mean income growth between 1989 and 2002; but the rates of propoor growth remain between 1.2 and 1.4% suggesting that the poor were able to make some gains
over
the
period.
Figure 1 — Growth Incidence Curve for Bolivia, 1989 to 2002
Annual
Growth Rate %
8
P0ex
P0mod
6
4
2
0
-2–2
0
10 Growth20Incidence30Curve
Growth Rate in Mean
40
50
60Mean of70
80 for Poorest
90
Growth Rates
% 100
Percentiles
consumption, in line with the usual practice in Bolivia. Using the income indicator for rural areas shows massive
declines in per capita income which are implausible in two ways. First, they imply income levels in rural areas that
are unlikely to assure basic survival and second the growth rates, -20% per year for the poorest quintile over three
years, is not consistent with all the known information about economic developments between 1999 and 2002
(where per capita incomes declined slightly, but not by these magnitudes). For the period prior to 1999 (19931999), they calculate only very moderate pro poor growth rates in capital cities, in contrast to our higher figures; this
discrepancy is probably largely due to the different time periods considered. Beginning in 1993 omits high (and
pro-poor) growth from 1989 to 1993. Thus we find those figures to be roughly consistent with ours (which they
should given that we both use incomes and use a similar income definition).
12
Figure 2 —
Growth Incidence Curve for the Departmental Capitals of Bolivia, 1989 to 2002
Annual Growth Rate %
8
0
P
P0m od
ex
6
4
2
0
-2
–2
0
10
20
30
40
50
60
70
80
90
100
Percentiles
Growth Incidence Curve
Mean of Growth Rates for Poorest %
Growth Rate in Mean
Figure 3 — Growth Incidence Curve for Other Urban Areas of Bolivia, 1989 to 2002
Annual Growth Rate %
8
P
0
P
ex
0
mod
6
4
2
0
–2
-2
0
10
20
30
Growth Incidence Curve
Growth Rates in Mean
40
50
60
70
80
90
100
Percentiles
Mean of Growth Rates for Poorest %
13
Figure 4 — Growth Incidence Curve for Rural Areas of Bolivia, 1989 to 2002
Annual Growth Rate %
8
P
0
ex
P
0
mod
6
4
2
0
-2
–2
0
10
20
30
Growth Incidence Curve
Growth Rate in Mean
40
50
60
70
80
90
100
Percentiles
Mean of Growth Rates for Poorest %
26. With the exception of the strongly anti-poor growth in capital cities in recent years, it appears
that growth has been quite pro-poor throughout most of the last 15 years, and particularly so in
towns and (moderately so) in rural areas. One may wonder how this squares with the results in
Table 3 which showed only slowly falling poverty rates in rural areas in the 1990s. But these
results are entirely consistent with each other when one notes that the depth of poverty in rural areas
is so large that even considerable pro-poor growth does not lift many of the poor above the poverty
line (but does reduce the poverty gap as indeed happened, particularly between 1994 and 1999).
Thus the problem of Bolivia’s poverty is not so much that growth in the 1990s has been biased
against the poor, but that overall growth has not been very high throughout the period and that the
initial inequality was so large that the poor remained poor despite some improvements in incomes.
It would probably have taken another decade of such growth to make serious inroads into poverty,
particularly in rural areas. Unfortunately, that did not happen. With the type of growth experienced
since 1999, rural poverty will not change much and urban poverty is on a sharply increasing trend.
14
Table 5: Annual Pro-poor Growth Rates (per Capita)
1989 - 2002
1989 – 1999
1999 - 2002
Total Bolivia
Growth Rate in the Mean
Mean of Growth Rates for
Extremely Poor
Moderately Poor
All
Growth Rate in the Mean
Mean of Growth Rates for
Extremely Poor
Moderately Poor
All
Growth Rate in the Mean
Mean of Growth Rates for
Extremely Poor
Moderately Poor
All
Growth Rate in the Mean
Mean of Growth Rates for
Extremely Poor
Moderately Poor
All
Source:
1.41
(1.25)
2.16
(1.74)
1.85
(1.49)
1.67
(1.34)
2.23
(2.02)
-1.29
-0.88
1.19
3.39
(2.81)
3.21
(2.74)
2.98
(2.56)
Departmental Capitals
2.01
0.44
0.48
0.69
2.56
2.58
2.50
-6.30
-6.44
-5.01
Other Urban Areas
2.89
(2.64)
-1.90
1.76
(1.58)
4.70
(4.53)
4.22
(4.03)
3.75
(3.56)
0.87
(0.17)
2.07
(1.40)
1.86
(1.18)
1.73
(1.02)
6.23
(6.01)
5.80
(5.55)
5.25
(5.00)
Rural Areas
0.94
(0.02)
2.31
(1.39)
2.18
(1.28)
1.99
(1.06)
-2.22
-2.56
-1.51
0.48
-0.22
-1.03
0.59
1.86
0.99
0.86
Own calculations. Growth rates use the actually observed levels of income/expenditure where
available (in capital cities throughout and elsewhere from 1999 onwards). Figures in brackets are
based on sensitivity analysis as discussed in text (footnote 6) and in Annex 1.
27. (Sectoral) Sources and Proximate Determinants of Growth. Before discussing the
determinants of pro-poor growth, it is important to first discuss the sources of overall growth in
Bolivia in the past 15 years. Table 6 gives an overview over the sectoral composition of GDP and
its growth. Regarding the sectoral composition of GDP in 2002, agriculture makes up about 14%,
about half of which is subsistence agriculture where many of the rural poor live. About 10% of
GDP is generated by mines, oil, and gas and only about 16% by manufacturing. Most of this
manufacturing consists of food processing and the processing of raw materials (wood, oil, and
minerals), with hardly any light or heavy industry present in the country. The remainder of GDP
consists of services of various kinds, which includes mostly services that involve the rural and
urban poor (such as trade and transport services). Employment shares differ radically from this
sectoral composition of GDP (see Table 7). Agriculture employs 60% of the workforce, sales
employs another 10% of the workforce, while manufacturing, oil and gas, and high-value services
employ only a small fraction of the workforce. Thus Bolivia is a highly dualistic economy with a
15
large employment in low value agriculture and the small-scale service sector and very small
employment in manufacturing.
28. Overall GDP growth between 1989 and 1999 was driven largely by sharp growth in commercial
agriculture, oil and gas production (and associated construction and production in the electricity,
gas and water sector), some small-scale food processing industries, and some services. In contrast,
subsistence agriculture, mining, hotels and restaurants, and public administration grew less than
proportionately. Between 1999 and 2002, virtually all sectors grew slower, with the exception of
oil and gas, which expanded production due to enhanced exports to Brazil. The figures for coca
production show a continuous and sharp decline between 1989 and 2002. This decline in reported
coca production is very likely overstating the actual decline. While eradication efforts were
intensified throughout the 1990s, the enforcement varied considerably. It was particularly strong
under the Banzer regime (1997-2002), but it is still likely that clandestine production is much larger
than reported here (and it is also likely that coca production increased considerably recently as
enforcement has flagged).
29. One should also note that the oil, gas and mineral sectors only account for about 10% of
Bolivia’s GDP and less than 1% of its employment, but more than 40% of Bolivia’s exports, so that
the importance of these sectors for Bolivia’s external position is much larger than its GDP share.
Thus we find that Bolivia has a highly dualistic economy, with the most dynamic sectors being the
oil and gas sector, industrial agriculture (concentrated in the lowlands) and some high-value service
sectors. The remainder of the economy showed a much more moderate evolution.
30. TFP Analysis. Another way to examine the proximate sources of growth is to examine the
influence of input factors (labor, capital, human capital) and the residual component, total factor
productivity (TFP). This can be done using a growth accounting framework based on the Solow
growth model and is such an analysis was done by Loayza et al. (2002). Depending on whether
human capital and the input factors are adjusted for capital utilization, the results show that the
contribution of capital to GDP growth was negative on average in the 1981-1990 period (-0.26 to 0.31% per year) indicating very low investment rates. Similarly, TFP growth was negative
indicating worsening efficiency. In the 1991-2000 period, things turned around with capital
contributing about 0.45% to annual growth and TFP contributing about 1.23-1.66% per year
depending on the assumptions. Labor throughout both periods contributed about 1.4-1.7% per year
and its contribution was very stable. While the findings for the crisis-ridden 1980s are to be
expected, the remarkable finding for the 1990s is the very low capital contribution to growth,
suggesting very low investment rates that are barely able to make up for depreciation. This, in turn,
is related to Bolivia’s very low domestic savings rate (Table 1) which, even with generous aid and
capital inflows, leads to only a moderate investment rate and thus quite low growth attributable to
capital deepening (see Table 2 and below).
16
Table 6: Sectoral Composition of GDP and its Growth, 1989–2002
Production in 1990 Bs ('000)
Annual Growth
1989
1999
2002
1989-99 1999-2002
A. PRIVATE SECTOR
11876
18054
19209
4.3
2.1
1. AGRICULTURE
2267
3071
3296
3.1
2.4
- Non-industrial Agricultural Products
1062
1358
1437
2.5
1.9
- Industrial Agricultural Products
212
558
605
10.2
2.7
- Coca
193
74
39
-9.1
-18.9
- Cattle and other Livestock
669
896
1005
3.0
3.9
- Forestry, Hunting and Fishing
130
185
211
3.6
4.5
2. MINING AND QUARRYING
1470
2017
2191
3.2
2.8
- Crude Oil and Natural Gas
644
978
1189
4.3
6.7
- Metal and Non-Metal Minerals
826
1039
1002
2.3
-1.2
3. MANUFACTURING
2430
3633
3849
4.1
1.9
- Food, Drinks and Tobacco
1109
1745
1975
4.6
4.2
- Other Industries
1321
1889
1874
3.6
-0.3
4. ELECTRICITY, GAS, AND WATER
235
452
475
6.7
1.7
5. CONSTRUCTION
462
819
819
5.9
0.0
6. TRADE AND COMMERCE
1270
1820
1937
3.7
2.1
7. LOGISTICS & COMMUNICATIONS
1365
2331
2562
5.5
3.2
8. FINANCIAL AND BUSINESS SERVICES
1528
3161
3103
7.5
-0.6
- Financial Services
242
974
914
14.9
-2.1
- Business Services
382
1113
1040
11.3
-2.2
- Real Estate
904
1075
1149
1.7
2.3
9. PERSONAL SERVICES (INCKL. DOMESTIC SERVICES)
667
973
1073
3.8
3.3
10. RESTAURANTS Y HOTELS
507
688
734
3.1
2.2
11. IMPUTED BANKING SERVICES
-234
-911
-830
14.5
-3.1
B. PUBLIC SECTOR
1570
1991
2140
2.4
2.4
TOTAL A AT FACTOR COSTS
13537
20045
21350
4.0
2.1
INDIRECT TAXES
1222
1764
1916
3.7
2.8
TOTAL AT MARKET PRICES
14759
21809
23266
4.0
2.2
Source: UDAPE (various isues).
Table 7: Employment Shares, 1999
A. PRIVATE SECTOR
2528708
1. AGRICULTURE
1598358
2. MINING AND QUARRYING
44051
3. MANUFACTURING
249167
4. ELECTRICITY, GAS, AND WATER
3986
5. CONSTRUCTION
116845
6. TRADE AND COMMERCE
253974
7. LOGISTICS & COMMUNICATIONS
66776
8. FINANCIAL AND BUSINESS SERVICES
12802
9. PERSONAL SERVICES (INCKL. DOMESTIC SERVICES) 107401
10. RESTAURANTS AND HOTELS
75348
B. PUBLIC SECTOR
TOTAL
95.1%
60.1%
1.7%
9.4%
0.1%
4.4%
9.5%
2.5%
0.5%
4.0%
2.8%
131464
4.9%
2660172
100.0%
Source: MECOVI survey.
31. Poverty Profile. These analyses have so far provided quite an aggregative picture of
developments in poverty as well as of GDP growth. We now turn to a detailed poverty profile to
17
give a better sense of who and where the poor are, and what they mainly live off. In Tables 8 and 9,
we present our results for the poverty gap, which also captures the depth of poverty.14 Apart from
the already noted rural-urban divide, there are very large regional variations in the poverty gap in
the different departments. In particular, poverty gaps are very high in the two highland and valley
departments of Chuquisaca and Potosi, while they are much lower in the lowland departments of
Santa Cruz, Beni, Pando, and the valley department of Tarija. The former two provinces are
particularly dependent on subsistence agriculture, while the latter three are the home to large-scale
farming, as well as most oil and gas production. The three provinces La Paz, Oruro, and
Cochabamba take on an intermediary position.
Table 8: Regional Disaggregation of the Poverty Gap
Moderate Poverty Gap
Total
Extreme Poverty Gap
1989
1994
1999
2002
1989
1994
1999
2002
45.45
(0.35)
41.89
(0.25)
32.53
32.94
27.53
(0.34)
25.21
(0.22)
15.73
15.32
32.92
51.31
(0.92)
58.30
(0.50)
25.74
44.68
(0.69)
60.90
(0.34)
21.02
34.70
24.37
32.88
9.79
13.10
44.86
9.58
27.02
(0.63)
43.33
(0.38)
8.00
13.97
47.71
15.29
34.10
(0.90)
39.13
(0.57)
27.37
23.88
58.81
(0.81)
45.19
(0.70)
43.02
(0.83)
48.27
(0.82)
64.69
(0.73)
50.78
(0.75)
31.41
(0.81)
47.05
(0.84)
60.79
(0.70)
37.11
(0.50)
41.97
(0.76)
49.55
(0.70)
63.87
(0.58)
50.27
(0.74)
28.16
(0.57)
50.11
(0.83)
53.94
49.16
29.12
33.53
18.04
16.48
30.20
36.30
12.44
17.14
34.57
36.15
15.76
18.36
50.53
47.24
30.24
26.99
28.92
28.67
12.19
9.21
20.47
23.97
6.92
8.44
20.03
26.66
44.86
(0.74)
20.09
(0.46)
23.68
(0.62)
33.34
(0.69)
50.62
(0.64)
30.46
(0.62)
12.48
(0.46)
31.05
(0.78)
35.43
35.12
40.34
(0.90)
26.48
(0.66)
24.66
(0.81)
30.67
(0.79)
49.40
(0.93)
31.16
(0.75)
14.84
(0.66)
26.90
(0.80)
4.20
8.77
By Type of Municipality
City
Town
Rural
By Department
Chuquisaca
La Paz
Cochabamba
Oruro
Potosí
Tarija
Santa Cruz
Beni & Pando
Source: Own calculations. Standard errors are in brackets (only applicable to the simulated poverty rates). For 1999 and 2002, we use
the actual poverty rates.
32. Regarding household characteristics of the poor, large households, those with many dependents,
and those with a young head are significantly poorer, although the latter influence is quite small.
This suggests an important influence of fertility on pro-poor growth, where fertility decline could
make a significant contribution to the decline of poverty and inequality (see Box 1). Particularly
striking are the very large differences in poverty by language and education. The poverty gap of
those speaking an indigenous language is nearly twice as large when the moderate poverty line is
applied, and three times as large when the extreme poverty line is used. Similarly, there is hardly
any poverty among those with more than completed secondary education, while there are very high
poverty rates among those with less than 5 years of schooling. Given the differences between
employment shares and sectoral contributions to GDP (as shown in Tables 5 and 6), it is not
14
The poverty gap index (or P1 from the FGT family of indices) divides the percentage average shortfall of the poor
from the poverty line with the total population. Other results can be found in Annex 1. We should note that the
surveys were not designed to be representative at the level of departments so that the results presented here should
be treated with some caution (particularly in the case of the smaller departments such as Beni and Pando).
18
surprising to find considerable differences in poverty rates by the sectoral employment of the
household head. In particular, those working in agriculture have a much larger poverty gap than
those working in any other profession. Unemployed heads also have very large poverty rates while
the poverty rate among white-collar workers is predictably low. It is also interesting to note that the
gender of the household head does not appear to have a big impact on poverty. If anything, femaleheaded households are less poor than male-headed households, a finding common to many Latin
American countries (see Marcoux 1998). Similarly, education gaps by gender, an important cause
of poverty, have largely disappeared. But females continue to be disadvantaged in other ways,
particularly in the labor market but also in the household (see Box 2).
33. There are no dramatic trends in terms of changes of the characteristics of the poor over time.15
But a few changes are noteworthy. In particular, the poverty gap in Chuquisaca appears to have
declined the least so that it surpassed Potosi as the poorest province by 2002. In contrast, in Tarija,
Beni and Pando, poverty reduction appears to have been particularly rapid. As far as household
characteristics are concerned, small households appear to have reduced poverty more rapidly than
large households, particularly in relative terms. While the absolute reduction for the poorly
educated and those speaking indigenous languages were larger than for others, in relative terms it
was smaller so that the relative gap between them and the rest has widened. Similarly, the relative
gap between farming households and while-collar households has widened considerably in the past
15 years even if the poverty gap was reduced considerably in farming households.
34. Using the asset index confirms most of the results shown above, but in a somewhat more
accentuated fashion (see Annex 1). The difference between rural areas, towns, and capital cities in
the asset index is larger than in the simulated incomes leading to starker differences in poverty rates
between the three areas. The poverty profile confirms that larger households16 and those with less
educated and younger household heads are poorer, and find even stronger differences in poverty
rates by language and education of the household head and spouse. Also here, female-headed
households are less poor than male-headed ones. Thus we find large and significant differences in
poverty rates among different groups.
35. Accounting for Inequality Change. It is useful to further examine the causes of the observed
changes in inequality over the past 10 years. Here we draw on findings from Gasparini et al (2003),
which decompose changes in inequality in (equivalized) household labor income in capital cities
between 1993 and 1997 and urban and rural areas from 1997 to 2002. Between 1993 and 1997,
they find a slight increase in household labor income in capital cities which is mostly driven by a
rising employment gap between the highly educated and the less educated, a slight shift in
educational inequality, and a significant increase of in the returns to unobservable characteristics,
while returns to education were equalizing. Between 1997 and 2002, inequality in household labor
incomes increased considerably in capital cities and here all factors (returns to education, inequality
in employment, inequality in education, and inequality in returns to unobservables) all contributed
to this rise in inequality.17 The importance of the rising inequality in unobservables points to
increasing disparities in the returns to characteristics such as educational quality, labor market
connections, and unmeasured skills. While reducing educational and employment inequality would
serve to reduce inequality and thus help with poverty reduction, the high returns to inequality in
unobservables points to deeper segmentations of the Bolivian economy, to which we turn now.
15
To a limited extent, this is true by construction as we use correlates of incomes to simulate incomes which include
the characteristics listed in the table. But since we allow these correlates to vary over time, we would be able to
discern if there have been significant changes in the determinants of poverty.
16
The effect of household size on poverty is found to be smaller using the asset index than with the simulated per
capita incomes. This is to be expected given that large households are likely to possess more assets and thus appear
less poor in an asset-based index than in an income-based one. See also the discussion below.
17
In rural areas they find declines in inequality between 1997 and 2002.
only captures a small portion of the rural economy.
As this is based on reported labor income, it
19
Table 9: Disaggregation of the Poverty Gap by Household Characteristics (Total Bolivia)
Moderate Poverty Gap
1989
Total
By Hh Size
<=3
4-6
>=7
1994
1999
2002
1989
1994
1999
2002
45.45
(0.35)
41.89
(0.25)
32.53
32.94
27.53
(0.34)
25.21
(0.22)
15.73
15.32
38.52
(0.83)
42.88
(0.45)
54.88
(0.67)
31.35
(0.60)
40.86
(0.31)
53.74
(0.47)
19.48
17.21
5.70
30.17
13.93
13.34
43.48
42.76
16.19
(0.45)
24.14
(0.29)
35.79
(0.46)
7.24
29.51
20.94
(0.78)
25.09
(0.44)
36.50
(0.71)
22.56
21.75
28.48
(0.60)
28.12
(0.52)
25.16
(0.77)
25.78
(1.33)
24.30
(0.36)
26.22
(0.35)
23.46
(0.47)
30.39
(0.89)
16.47
14.78
16.37
16.97
12.43
12.65
17.80
12.98
33.27
(0.42)
19.67
(0.50)
32.00
(0.30)
16.59
(0.29)
20.83
19.97
9.66
9.82
21.40
(0.34)
45.08
(0.78)
16.39
(0.26)
45.83
(0.48)
7.80
8.30
24.00
21.83
28.31
(0.38)
23.55
(0.85)
26.11
(0.25)
20.91
(0.52)
16.06
15.55
13.91
13.90
39.07
(0.52)
18.06
(0.50)
4.55
(0.59)
42.14
(0.39)
15.48
(0.34)
2.96
(0.33)
28.28
26.03
11.23
10.76
1.10
1.83
11.16
(0.58)
24.62
(0.67)
46.15
(0.72)
19.11
(0.89)
29.80
(2.32)
23.55
(0.79)
5.79
(0.37)
19.59
(0.49)
50.73
(0.47)
13.19
(0.50)
24.05
(1.49)
20.95
(0.50)
3.68
2.62
11.28
13.40
31.60
26.52
8.72
6.07
19.15
13.67
n.a.
n.a.
By Age of Hh Head
<=34
47.04
41.79
33.79
(0.62)
(0.41)
35-49
45.92
42.89
33.45
(0.52)
(0.36)
50-65
42.78
39.03
27.74
(0.79)
(0.61)
>=66
41.73
44.57
34.33
(1.45)
(0.95)
By # of Hh Members Between 15 and 65 Years to Total Hh Members
<= 0.5
52.02
50.23
40.15
(0.41)
(0.30)
> 0.5
36.45
31.29
23.45
(0.54)
(0.41)
By Language of Hh Head
Spanish
38.80
32.51
21.34
(0.40)
(0.33)
Indigenous
64.48
63.80
44.18
(0.67)
(0.42)
By Gender of Hh Head
Male
46.23
42.80
32.87
(0.40)
(0.27)
Female
41.45
37.49
30.62
(0.78)
(0.62)
By Average Education of Respondents and Partners
<=5
58.88
60.30
49.35
(0.48)
(0.37)
6-12
35.61
32.98
28.29
(0.59)
(0.46)
>=13
13.44
10.12
6.33
(1.00)
(0.59)
Sectoral Employment of Head
White Collar
Blue Collar
Agriculture
Sales & Services
Not Employed
No Partner
Source:
Extreme Poverty Gap
24.06
(0.72)
43.84
(0.75)
65.51
(0.59)
37.27
(1.06)
48.52
(2.33)
41.42
(0.89)
15.60
(0.62)
39.06
(0.56)
68.22
(0.38)
30.26
(0.69)
43.16
(1.63)
36.80
(0.61)
33.59
34.97
27.66
30.57
40.90
23.52
23.03
42.14
33.61
28.81
47.76
27.97
7.52
13.17
9.68
30.31
32.63
52.21
48.35
22.80
19.45
32.45
29.57
n.a.
n.a.
Own calculations. Standard errors are in brackets (only applicable to the simulated poverty rates). For 1999 and 2002, we use the actual
poverty rates. In the DHS, the employment of the head is not listed directly and can only indirectly be inferred by the employment of the
partner of the women who was the respondent. In some cases, this partner of the respondent might not be the head of household so that
there might be some inaccuracies here. For 1999 and 2002, the head’s employment is listed in the data and we do not need to rely on the
partner’s employment status (and thus the option of ‘no respondent’ no longer exists). The change in poverty between 1994 and 1999 by
head’s employment should therefore be interpreted with some caution.
20
Box 1:
Population Growth, Household Size, Poverty, and Pro-poor Growth
Bolivia has a surprisingly high population growth rate. The intercensal annual growth rate of the population
went up from 2.1% between 1976 and 1992 to over 2.7% between 1992 and 2001 (INE 2003a). Based on
revised census counts, officials at INE argue that the correct intercensal growth rates would be 2.4% for both
periods, which still indicate very high population growth by South American standards (see Table 1). Part of
the high population growth is due to continued high fertility. The 2001 census estimates the TFR to be at
4.4, while the DHS 2003 reports it to be at 3.8 (INE, 2003b; INE, 2004). The impact of this TFR (and the
much higher levels of TFR in the past) generates a considerable demographic momentum through the impact
of large increases in the number of women of reproductive age. The second source of high population
growth has been a sharp fall in mortality levels in the past 15 years and as such is a welcome development
(see Table 2).
There has been a considerable fertility decline in the past 20 years, which is now clearly visible in
the age structure of the population where the absolute number of 0-4 year olds has recently begun to decline.
If these trends continue, Bolivia will soon be about to enter the phase which has been referred to as a
‘demographic gift’ by Bloom and Williamson (1998), where the share of the working age population will be
particularly large (and dependency rates correspondingly low), enabling the country to save more, to invest
more in the quality of children, and, if employment opportunities are there for the large working age
population, to boost growth of per capita incomes.
The ‘demographic gift’ is likely to make growth more pro-poor as it is particularly the poor who are
now in the process of further reducing their household size and thus benefiting from reduced dependency
rates (see also Klasen 2003; Klasen and Woltermann, 2004; Eastwood and Lipton 2001). This can be seen
when considering two factors. First, as shown in Table 8, poverty rates are highly correlated with household
size. This also holds if we calculate poverty rates based on adult equivalents (rather than based on per capita
incomes), which assumes that children need fewer resources and that households benefit from considerable
economies of scale (see Annex 2). More importantly, it appears that the poverty risk of household size has
sharply increased over time. Based on adult equivalent incomes, the poverty rate of households with more
than 6 members was nearly 20 percentage points higher in 2002 than of those with less than 4 members, up
from a difference of less than 10 percentage points in 1989. The differential has similarly widened when
using per capita incomes.
Second, the poor have much larger families and thus disproportionately suffer the costs of large
families. Using the unsatisfied basic needs index and applying it to the 2002 Census, ‘marginal’ households
have a TFR of 6.9, compared to a rate of 2.1 for those with satisfied basic needs. If fertility decline reaches
the poor, it is likely to have a major impact on poverty reduction as it did elsewhere in recent years (e.g. in
East Asia and in countries such as Brazil).
Policies that would further such a development would be a combination of further improvements in
education and health access for the poor, combined with the availability of low-cost family planning for the
poor which still appears to be a problem among uneducated women in some rural areas (see INE 2004).
21
Box 2:
Gender and Pro-poor Growth
Compared to other Latin American countries, Bolivia had considerable gender inequality in a variety
of indicators of well-being, human capital, access to resources, and income earning opportunities. For
example, as late as 1976, there was a 24 percentage point gap in literacy rates among adults (INE, 2003). On
many fronts, there has been considerable progress in closing these gaps. The gap in education has closed the
fastest. In 2001, the gap in literacy rates has narrowed to 12 percentage points (with the remaining gap being
largely due to past discrimination) and gaps in enrolments or progression are now limited to a few pockets in
more remote municipalities (Anderson and Molina, 2004).
As shown in the international literature on the subject, the closing of the gender gap in education
could have important positive effects for growth and human development (e.g. World Bank, 2001; Klasen,
2002). This is due to the direct impact of the removal of an artificial distortion that limits the potential of
women to contribute to economic development and through the indirect impact female education has on
fertility, mortality, and education of children. The impact of female education on fertility is welldocumented in Bolivia. Females with more than 12 years of education have only 1.9 children, compared to
6.7 for (the by now very few) females with no education (INE 2003b). Thus the closing of the gender gap
will further accelerate the on-going fertility decline with the potential benefits described in Box 1.
As shown in the poverty profile below, it is also noteworthy that female-headed households are
generally less poor than male-headed households, which is a common finding in Latin America but much
rarer elsewhere (e.g. Marcoux, 1998). But one should caution that female-headed households represent a
very heterogeneous group of households (e.g. single female elderly, single professional women, divorced
women with or without children, women of migrant workers with or without children, etc) so that it may
well be that sub-groups are particularly vulnerable to poverty (an issue that deserves further examination).
Less positive is the record on female opportunities in employment. Here we find that females have
much fewer employment opportunities, making up only about a third of formal sector employees, while
constituting about 50% of informal and self-employment. In all three sectors they then suffer from
considerably wage gaps with gender having one of the largest effects on wages (Tannuri-Pianto et al. 2004);
these are also considerably larger than in other Latin American regions (World Bank 2004b). Also, female
migrants (who are over-represented among rural to urban migrants) were, prior to 2002, had to accept lower
wages than the (already depressed) female wages of non-migrant counterparts in urban areas (Pianto et al.
2004).
As shown in Klasen and Lamanna (2004), such discrimination in employment has also been found to
reduce economic growth due to the distortion such discrimination brings about. Moreover, it is likely to
have an adverse effect on poverty reduction as female earnings increase their bargaining power within
households which has been found to increase investments in education, health, and nutrition of children (e.g.
Thomas, 1997).
Lastly, a particularly worrying development is the high incidence of domestic violence in Bolivian
households, particularly against women. About 10% of women report being beaten regularly, nearly half
occasionally, and some 15% of women report occasional incidences of forced sexual relations (INE, 2004).
These problems are not only well-being issues for the women concerned, but also clearly affect their and
their children’s ability to contribute to, and profit from economic development opportunities.
In sum, there has been notable progress in closing gender gaps, particularly in education, but gender
gaps in employment and pay as well as an unusually high incidence of domestic violence continue to
generate formidable obstacles for women to contribute to pro-poor growth.
22
36. Segmentation of the Bolivian Economy and its Impact on Growth and Poverty. Based on
the findings above, particularly the large gaps by region, education, and economic sector, it is clear
that Bolivia’s economy suffers from considerable segmentation, with the poor being largely
separated from the income-generating and growth processes that tend to favor urban areas as well as
resource-based sectors and modernized agriculture. In this section we want to discuss various
forms of segmentation of the economy in some more detail.
(i) Urban-rural and formal-informal divide
37. The urban-rural divide in Bolivia is particularly strong as was shown by the poverty rates, the
depth of poverty, and the poverty profile. The particular mechanisms leading to this large divide
relate to initial conditions, the dynamics of internal migration, the educational system, and the urban
labor market.
38. The initial conditions relate largely to Bolivia’s population distribution. Bolivia’s poor are
heavily concentrated in the rural areas of the altiplano (highlands) and the valles, following
Bolivia’s historical settlement pattern which focused on these areas. As these rural areas face
difficult ecological and climatic conditions for agricultural production, and suffer from the
proliferation of tiny plots, it is not surprising that poverty rates are higher there. In addition,
Bolivia’s poor have been relatively slow to settle in the areas of dynamic economic development in
the lowlands, partly for climate and health reasons as well as the lack of support networks in these
areas. Thus much migration of the poor has involved moving to urban areas in the altiplano and
valles as well as rural-rural migration within these areas (with particular emphasis on migration
related to the coca economy) (CODEPO 2002, Pianto et al. 2004). In 1997, 46% of recent rural
migrants went to other rural areas, presumably due to agricultural employment (esp. coca
production) as well as family reasons. By 2002, this share had dropped to 37% (with metropolitan
areas taking in a larger share of migrants), probably in line with the sharp decline in coca
production (Pianto et al. 2004). Probably as a result of the economic crisis which is concentrated in
the capital cities, return migration from them to rural areas (as well as to towns) was between 1997
and 2002 very large, making up about 43% of total migration flows between the three regions. It is
important to note that female heads of households are over-represented among the rural-urban
migrants who apparently see better economic opportunities in large cities; at the same time,
migrants do not come from the poorest areas of the country. As far as the economic success of
migrants are concerned, they are mostly able to earn as well as their urban non-migrant
counterparts, which then raises the question why there is not more migration to equate the large
earnings differentials between the regions (World Bank 2004b).18
39. In sum, migration currently does not appear to be a reliable mechanism for ensuring quick
convergence of regional disparities and thus is currently unlikely to contribute much towards
poverty reduction; this might be an issue for the attention of policy-makers concerned about the
poverty impact of these regional disparities (see below).19 Another point of note is that rural-urban
migrants retain a connection to rural areas to which they can return to, suggesting that the
segmentation between the regions is not so relevant for this group. Lastly, not only has economic
performance of smaller towns has outperformed rural areas and departmental capitals (also in terms
of pro poor growth), but they are also the beneficiaries of considerable in-migration. If the past
performance is any guide, encouraging and supporting migration from rural areas to these towns
could make a significant contribution to poverty reduction.
18
It is important to note that females were not able to benefit economically as much from migration in the 1990s
although things appear to have improved (Pianto et al. 2004).
19
This is also borne out by regional growth figures which show that the departments with the lowest poverty rates grew
the fastest. It is true, that these departments (i.e. Tarija, Santa Cruz, Pando) were the targets of considerable inmigration from poorer departments but this did not enough to reduce disparities (see World Bank 2004b).
23
40. The large educational divide between urban and rural areas amplified this distinction. In 1976,
the average years of schooling of the rural adult population stood at a dismal 1.8 years, compared to
6.1 years in urban areas (INE 2003a). While investments in rural education have led to better
outcomes, the differences remain substantial. In 2001, average years of schooling were 9.2 in urban
and 4.2 in rural areas (INE 2003a). In addition, analyses of the selectivity of migrants clearly show
that young, well-educated people speaking Spanish as their main language have a much higher
likelihood to migrate, thus contributing to a drain of skills from rural areas (Pianto et al. 2004).
Taken together, these differentials continue to seriously compromise the economic opportunities of
the poor population.
41. Third, tight regulation of the formal labor market especially with respect to dismissal protection
and high costs of formality restrict the access of rural workers and the urban poor, who predominate
in the informal and self-employed sectors, to income-earning jobs and keep employment in the
formal labor market below levels that would otherwise be possible. This is exacerbated by
particularly high institutional and regulatory barriers to formalization in Bolivia, which sharply
reduces the incentive for firms to formalize (Kaufmann et al. 2001). As a result of these two
problems, the share of informal employment in total employment is among the highest in Latin
America (World Bank 2004b). In the 1990s, this was less of a concern since, as a result of other
macroeconomic reforms, the demand for labor in the formal sector grew nonetheless and the
participation rates in urban areas increased despite considerable rural-urban migration (Jimenez,
Pereira, and Hernany 2001; Spatz 2004). However, when the Bolivian economy was hit by external
shocks, the tight regulation became a more serious problem and reduced the employment
opportunities for recent arrivals and urban informals, as evidenced by the drop of the formal sector
share in urban employment to only 50% of employment in 2001 (from 55% in 1997) (Spatz
2004).20
42. While one should not see the urban formal sector as completely closed to rural-urban migrants
and the urban poor (Pianto et al. 2004; Tannuri-Pianto et al. 2004), the conditions of entry into the
formal sector are distinctly less favorable for these groups. In particular, participation equations in
the formal sector suggest that formal sector employment is particularly difficult to achieve for
women, for people with a non-Spanish mother tongue, and for the poorly educated (Tannuri-Pianto
et al. 2004). In addition, selectivity-corrected earnings regressions show that earnings in the formal
sector are much lower for these same groups suggesting that their ability to enter formal sector
employment is restricted and happens under worse conditions, both factors that militate against
urban formal employment as being an important tool for poverty reduction.
43. A last barrier associated with the formal-informal and rural-urban divide is the very restricted
credit access for self-employed and informal producers. Despite the fact that some of Bolivia’s
microfinance institutions have been hailed as models to ensure sustainable credit access, data show
that the expansion of the portfolio of micro-credit institutions in recent years was associated with a
contraction in the portfolio of banks, and that it only covers about 10% of the population operating
in 68 of Bolivia’s 314 municipalities. In rural areas, credit is virtually unobtainable for anyone
except very large producers, and also in urban areas it is highly restricted. These problems are
exacerbated by little movement to restructure state wholesale finance institutions and years of
inconclusive debate about the possibilities for bringing in informal institutions into the regulatory
system.
20
Using different data and a slightly different definition, the formal sector share in urban employment dropped to only
32% of employment in 2002 (from 44% in 1997) (Tannuri-Pianto et al. 2004).
24
(ii) The ethnic divide
44. The urban-rural and formal-informal divide is posing particular problems for the non-Spanish
populations of Bolivia, who predominate in rural and urban informal sectors. As shown by
Andersen, Mercado, and Muriel (2003), there is very large inequality in educational attainments
between indigenous and non-indigenous populations. Analyses of earnings regressions show that
lower education levels and lower quality of education account for most of the earnings differences
in urban and rural areas between Spanish-speaking and indigenous populations. It is important to
note here that Bolivia has, compared to 13 other Latin American countries included in a
comparative study, the lowest educational output in public education in terms of language test
scores of fourth graders, while private educational institutions, which largely serve the Spanishspeaking urban populations, exhibit much higher scores (Mercado 2003). The earnings differentials
are further exacerbated by occupational crowding of indigenous people in sales, agricultural,
domestic service, and other low earnings occupations in the informal and self-employment sectors
(Andersen, Mercado and Muriel 2003; Tannuri-Pianto et al. 2004). It is particularly interesting to
note that this occupational crowding extends with equal force to public sector employment in
education, health, administration and the like (Anderson, Mercado, and Muriel 2003). This adds to
a perception of powerlessness among indigenous groups and their consequent mistrust of the
government, which until recently had hardly any indigenous representation. It also can explain the
finding that subjective poverty rates among particularly Quechua-speaking populations are even
higher than objective poverty rates, presumably due to a felt sense of discrimination and
powerlessness (World Bank 2004b). This sense is also supported by the finding of particularly low
social mobility in Bolivia, compared to other Latin American countries, which transmits poverty
intergenerationally (Andersen 2003).
(iii) Highlands agriculture versus the resource-based economy
45. These divides would be less important if it had been possible to ensure that productivity in
highlands agriculture, the mainstay of incomes for many of the poor, had improved in past decades.
But here, success has proved elusive for the majority of producers. Qualitative work by
Tuchschneider (2001) shows that about 90% of highland producers find that their yields have
deteriorated throughout the 1990s due to worsening climatic conditions (higher temperatures and
less rain), lack of irrigation, deterioration of soil quality due to overexploitation, lack of land,
population pressure and lack of modern inputs. Most development projects to support highland
agriculture (which, interestingly, were concentrated on areas that were better integrated avoiding the
most remote parts of rural areas) have been deemed unsuccessful by the respondents. Among the
reasons cited are that they were often not focused on the central problem of declining agricultural
productivity, did not take a holistic approach that addressed the technical, institutional, and
economic aspects of the problem, and had little local participation, input, and support. Those few
who claim to have benefited particularly value that the projects were focused on achieving
substantial changes in the production and crop systems, improved access to irrigation and improved
seed varieties and inputs. Given the importance of highland agriculture for employment and
incomes, the failure to improve productivity there is critical. It is therefore not surprising that there
was little improvement in rural incomes and poverty which is consistent with the international
experience that stresses the importance of smallholder agricultural productivity for pro-poor growth
(e.g. Eastwood and Lipton 2001; Timmer 1997, Klasen 2004).
46. In contrast, the most dynamic sectors of the economy have been capital-intensive, exportoriented lowlands agriculture, and the resource-based economy involving oil and gas, which are
also highly capital-intensive with little linkages to the poor. In this context it is of particular
importance to stress that the decline of tin mining since the mid-1980s (when the tin price collapsed
and the government largely abandoned tin mining) took away one form of income from which the
poor in some of Bolivia’s poorest provinces (particularly in Potosi) had benefited directly and
indirectly.
25
47. Linking Growth to Poverty Reduction. From the analysis above it is not surprising to find
high levels of inequality and poverty in Bolivia and the relatively poor record in poverty reduction
in the past 15 years. If anything, it is somewhat surprising that during the 1990s the poor were able
to improve their incomes somewhat (from a very low level), including in rural areas, which is true
even in our sensitivity analysis (although the rate of pro poor growth is very low). It is likely that
this is driven partly by temporarily favorable weather conditions in agriculture, a recovery from
particularly poor conditions in the late 1980s, some spill-overs of the growth in the urban formal
economy to the rural sector (through migration and remittance linkages), and the considerable
importance of the coca economy which is likely to have had a significant direct and indirect impact
on incomes and expenditures in some of the poorest rural areas (such as Chuquisaca, Potosi, and
Cochabamba).
Chapter 3: Factors Affecting the Participation of the Poor in Growth
48. In this chapter we are discussing the impact of initial conditions and policies on the participation
of the poor in the growth process. We approach this question using two different methods. First, we
discuss the role of initial conditions as well as macro and public spending policies on pro-poor
growth in the past 15 years. We then turn to a formal assessment of the role of policies using a
dynamic CGE model. While the particular policy simulations used are trying to assess the potential
for pro-poor policy reforms as we look into the future, we also use these simulations to explain the
record in the past.
a) Role of Initial Conditions
49. Bolivia’s initial conditions are generally unfavorable for achieving pro-poor growth. Bolivia
suffers from being a large land-locked country with a poorly developed infrastructure that increases
the remoteness of many of the poor from markets and potential income-earning opportunities.
Remoteness is also likely to account for the high poverty rates and the few changes in Bolivia’s
most remote and poorly developed highland and valley provinces (particularly Potosi and
Chuquisaca). For those poor the otherwise positive impact of a liberalizing and integrating economy
has been found to be much smaller (World Bank, 2004b), a finding similar to Christiaensen,
Demery and Paternostro for a sample of African countries (Christiaensen, Demery and Paternostro
2002). Thus whatever growth has taken place has been concentrated in areas with lower poverty,
while the poorest provinces have experienced below average growth and thus lower poverty
reduction. The high initial income, land, and ethnic inequality is making poverty-reducing growth
more difficult to begin with (World Bank 2000, Klasen, 2004) and the high ethnic fractionalization
is making policy-making for pro-poor growth particularly difficult (e.g. Alesina et al. 2003). In
fact, Alesina et al. (2003) cite Bolivia as a particular example of how high ethnic fractionalization
contributed to poor policy choices and poor quality of public goods in the 1980s. While governance
clearly improved after the crises and reforms of the mid-1980s, governance indicators remain weak
compared to Latin American averages in past years (Kaufmann et al. 2003, see also below). A
particular governance challenge that was inherited from decades of military rule and coalition
governments was the rise of patronage relationships and informal procedures and practices in the
public sector, which in turn supported a growing informalization of the private economy which
severely restricts its growth potential (Kaufman et al. 2001).
50. Lastly, Bolivia is hurt by an economic structure where (with the exception of coca) its most
lucrative exportable products, particularly oil, gas, some mining products, soya and other
agricultural exportables, are highly capital-intensive and provide few linkages to the poor. This is
partly due to nature and geography, but also linked to the inability to develop a dynamic
smallholder agricultural sector with a great potential for import substitution and exports. As a result
(as will be shown below in some of the simulations), the potential impact of these export products
for poverty reduction is small and can even be negative if the Dutch Disease effects dominate the
otherwise potential beneficial effects of greater exports for poverty reduction.
26
Box 3:
Land Distribution and Reform and Pro-poor Growth
As part of the revolutionary government of Victor Paz Estenzorro in the early 1950s, Bolivia enacted
comprehensive land reform that included a freeing of peasants from labor duties and gave them access to
some land. Despite some short-term success, the reform did not solve the problems of poverty in the
highlands. Instead, subdivisions and increasing land pressure led to increasingly smaller plots with little
modern inputs. The process largely stopped in the 1970s and due to the emergence of landed estates in the
lowlands, land inequality in Bolivia is as high as elsewhere in Latin America.
A new push for agrarian reform came with a new land reform law in 1996. The focus here is on titling
although the expectation was that this would lead to restoration of illegally seized land, redistribution of
public lands, and a renewed push for land reform. Unfortunately, despite relatively good progress in titling
(which is nevertheless somewhat behind schedule by now, see Anderson and Evia, 2003), little redistribution
has happened as a result of this process, leading to considerably disappointment with land reform process
and calls for redistributive land reforms.
Plan Tierra tried to address this by redistributing some public lands in specific projects, but this was widely
seen as too little, too late, and not broad enough. Landless movements have sprung up and demands for
further land reforms are considerable. At the same time, it is clear that there is not much land available for
redistribution in the highlands and valles (apart from some public lands) and most land for redistribution
would be in the lowlands. At the same time, there might be some scope for land reform by using land taxes
to bring underutilized private to the market, a subsidy and support program for emerging farmers, and further
redistribution of public lands.
Land reform is among the most pressing social and political issues currently under discussion and it is clear
that political and social stability will depend on successful resolution of these issues.
Source: Urioste (2003), World Bank (2003b).
b) Macro Policies and Pro-poor Spending
51. Bolivia’s macro and public expenditure approach to poverty reduction can be seen as closely
following the World Bank’s 1990 model for poverty reduction, which focused on developing a
growth-oriented strategy and accompany it with investment in human resources and safety nets for
the poor (World Bank 1990). Consequently, the growth agenda was largely seen as separate from a
poverty-reduction agenda in the belief that high growth would ensure sufficient poverty reduction.
This should be aided by investment in human resources that could then allow for a participation of
the poor in the growth process. For those who were left behind, safety nets (such as the Bolivian
Social Fund and public works programs) should try to address this problem. While this approach
worked as long as growth was satisfactory and there were increasing resources to be used for
expanding social sector spending, it failed to sufficiently promote the productive activities of the
poor and in addressing the large equity problems permeating Bolivia’s society.
52. Macro policies and poverty. As already discussed in Chapter 1, the macro policy agenda was
mainly one of stabilization and liberalization. One particular focus of macro policy was to ensure
low and stable inflation and this has been achieved for the past 20 years (see Table 2). Given that
inflation tends to hurt the poor disproportionately, this macro policy is likely to have supported
poverty reduction. In addition, the external capital account was liberalized and a friendly foreign
investment regime was established. While this allowed a significant increase in foreign direct
investments throughout the 1990s (Schweickert et al. 2002, World Bank 2004a) the liberalization of
the external capital account contributed to a further increase in dollarization of the economy which
severely limited the room to maneuver as far as exchange rate policies are concerned. There were
no efforts to combat dollarization, which remains high to this day. While the policy of allowing
27
dollarization per se is neither pro- nor anti-poor, it increases the vulnerability of the economy to
exchange rate shocks (such as the appreciation of the US$, or the sharp devaluation of the Brazilian
and Argentinian currencies as it occurred in the 1998-2001 period) which can hit the poor more as
they are unable to shield themselves against such shocks. Also, it was not possible to use exchange
rate policies to affect the distribution of income through the use of a real depreciation that would
favor poor export and import-competing producers at the expense of wealthier import-consuming
parts of the population (see Klasen 2004). In addition, strong trade liberalization, while improving
the allocative efficiency of Bolivia’s economy, further undermined the ability of Bolivia to manage
its external environment to support poor producers, particularly in an environment of sharply
fluctuating currency movements with its trading partners.
53. Fiscal and public expenditure policies. In the areas of fiscal and public expenditure policies,
the pro-growth agenda initially dominated policy-making and fiscal policy aimed at low budget
deficits, which was achieved through tax reforms, prudent expenditure policies, and divestiture
from loss-making state-owned enterprises. Tax reforms largely focused on broadening the tax base
through a value added tax and a transactions tax which together make up some 60% of tax revenues
in 2002. A hydrocarbons tax is the only other significant tax generating about 18% of revenues
(Servicio de Impuestos Internos 2003). As a result, there is no progressivity in the tax system,
which could be achieved via an income tax on those employed in the formal sector, a serious tax on
large land-holdings (also to facilitate the sale of underused land by large landowners) or other real
estate, or significant surcharges for particular items mainly consumed by the non-poor.21
54. As long as growth was relatively high in the 1990s, and tax revenues continued to rise, the
government was able to maintain relatively low budget deficits while at the same time ensuring
rising expenditures for priority social sectors, such as health and education. Public expenditures as
a share of GDP rose sharply in the 1990s, and also public capital expenditures were high by
international standards (World Bank 2004a); excluding social security, Bolivia now devotes the
second-highest share of its GDP to public social expenditures in all of Latin America (World Bank
2004c).
55. This was also supported by generous aid flows and complemented by funds made available by
HIPC I and II, with the latter being channeled entirely to the municipalities to fund priority
investments, mostly in the social sectors and recently also in infrastructure.22 Public expenditures in
health, education, and infrastructure do reach the poor, although to varying degrees (World Bank
2004a, c). In particular, while the poor get the same absolute amount of resources as the rich of
public health expenditures, they get slightly more than that in public primary education (the poorest
40% capture about 50% of public expenditures on education, presumably due to their larger
families and greater use of the public system). Public funds for secondary education are
proportional or slightly pro-rich depending on the study (World Bank 2004a, c), largely due to an
income gradient in enrolment rates, and public spending is sharply pro-rich at the tertiary level. As
universities take up 32% of total education spending in 2003, the total effect of education spending
is proportional, i.e. it reaches the poor about as much as the rich. Payments from the public pension
system are strongly pro-rich with the non-poor (who make up 40% of the population in the study)
collecting 83% of benefits. As a result of this strong bias, total current public social spending was
pro-rich, i.e. with 54% of the benefits going to the non-poor and only 46% to the poor.
21
Such taxes could, however, face compliance problems in an economy where contraband is widely available and tax
evasion is considerable (Delgadillo 2000). In 2003, a few taxes were changed to increase tax revenues but they did
not seriously affect the progressivity of the tax system.
22
The World Bank Public Expenditure Report notes that from 1996 to 2001, 75-80% of municipal investment was in
the social sectors, while in 2002 infrastructure received 34% with social sectors falling to 58% (World Bank 2004a).
Investments in productive activities were always low and only amounted to 3-6% in the period.
28
56. Investments in infrastructure at the municipal level are also strongly pro-rich, with the richest
quintile capturing about twice the absolute amount of subsidies as the poorest fifth in 2000 (World
Bank 2004a). This bias has been considerably reduced in the last several years, partly as a result of
the National Dialogue Law, which required spending from HIPCII resources to be targeted to
poorer communities (World Bank 2004c).
57. Thus public spending presents a mixed picture as far as its poverty and equity impact is
concerned. While it does reach the poor to some extent, it could do so much more than is currently
the case. Better targeting could be achieved through demanding greater co-payments for health and
education for the non-poor, as well as boosting access to health services and enrollments in
secondary and higher education for the poor (through dedicated programs including subsidies).
Moreover, as the pension system overwhelmingly benefits the non-poor, limiting the obligations
would automatically reduce the anti-poor bias of the overall social expenditure system.
58. The ability to combine the maintenance of fiscal discipline with rising social expenditures
collapsed in the late 1990s leading to ever-rising and now unsustainable budget deficits (reaching
9% of GDP in 2002). Three factors largely account for this deterioration (World Bank 2004a).
First, tax revenues plummeted in line with the economic slowdown that began in 1999, while
expenditures continued to rise. Second, a mismanaged pension reform led to much higher than
anticipated costs. It is now costing 5% of GDP while providing benefits to only 2% of the
population, most of them non-poor formal sector retirees in urban areas. Third, due to Bolivia’s
decentralization program and the dedication of HIPC funds to the municipalities, there is little
central control or even information over expenditures, thereby weakening the ability of the central
government to maintain fiscal discipline.
59. As will be discussed in more detail below, the bargain that maintained economic growth and
social stability in the 1990s and involved the continuation of an economic model fashioned on the
Washington Consensus, while ensuring social stability through significant transfers to the social
sectors, thus became unstuck. It appears that in a situation where inequality, social and ethnic
tension are high, such a bargain proves unsustainable and extremely fragile. It has contributed to
great opposition to the government, calls for populist reforms, and now has thrown the debate about
the appropriate economic model wide open in the form of the constitutional assembly that will now
address these issues. A continuation of the current bargain seems not possible, nor is it fiscally
feasible, and a new approach to economic policy-making is required.
c) Assessment of the Impact of Shocks and Policies on Pro-poor Growth
60. In this section, we make use of the dynamic CGE model described in Annex 2 to assess the
impact of shocks and policies on Pro-poor growth. While the general approach in this section is to
simulate forward-looking policies and focuses on the impact of shocks and policies on the ability to
reduce poverty and inequality, we will also comment on the extent to which they are able to explain
past performance in growth, inequality, and poverty reduction.
61. In its Poverty Reduction Strategy Paper (PRSP), which was completed in May 2001, the
Bolivian government formulated ambitious social goals to be achieved over the period 2001–2015
República de Bolivia 2001). Among the improvements the PRSP envisages are the following
targets with respect to income poverty:
¾ a reduction of the nationwide poverty incidence from 63 % to 41 %;
¾ a reduction of the urban poverty incidence from 47 % to 32 %;
¾ a reduction of the rural poverty incidence from 82 % to 52 %.
61. Success in reaching these and other social targets will to a large extent depend on Bolivia’s
ability to achieve higher growth. The PRSP calls for average growth in excess of 5 % over the
29
period under consideration, compared with an average growth rate of about 4 % in the 1990s. It
acknowledges that faster growth will require additional structural reforms – in particular a more
flexible labor market and a more efficient tax system – which enable the country to boost private
investment, and that only measures specifically tailored to poverty groups, such as investments in
rural infrastructure, can make growth more pro-poor than in the past.
62. So far, the expectations raised in the PRSP have not materialized. During the protracted
economic slowdown of the last 5 years, both per capita income and the incidence of poverty have
stagnated at best. In a recent revision of the PRSP, the Bolivian economy is projected to grow at an
average rate of 4.8% between 2006 and 2015 (UDAPE 2003a), which is somewhat below the
original projections. Moreover, poverty elasticities with respect to overall growth have been revised
downwards from -0.77 to -0.60 and from -0.54 to -0.26 for urban and rural areas, respectively.
Given these estimates, which are extremely low in international perspective, and the target growth
of 4.8%, the headcount index is now only expected to fall to 54 % nationwide and to 45 % and
75 % in urban and rural areas, respectively, until 2015.
63. Against the background of these fairly disparate projections, Bolivia’s prospects for achieving
pro-poor growth will be evaluated using the CGE model. In particular, it will be examined how
external shocks and policy reforms in different areas, ranging from macroeconomic stabilization to
poverty-focused interventions, might affect the trajectory of the Bolivian economy and the
evolution of poverty.
64. While assessing model results, two central characteristics of the model have to be kept in mind.
First, economic growth is determined by changes in the endowments of the primary factors capital
and labor as well as the efficiency with which these factors are used. As far as efficiency of using
factors is concerned, the model assumes an exogenously given rate of TFP growth of 2% per year.23
Thus all changes in the simulations will depend on changes in the primary factors capital and labor.
The major driving forces of labor dynamics are population change, migration, the rate of labor
productivity growth, and the change in human capital. Of these, the model only takes account of
population changes, which are kept constant over simulations, and migration. The driving forces for
capital accumulation are domestic savings and foreign capital inflows as well as relative returns on
financial (domestic and foreign) and physical assets. Since net capital inflows are exogenously
given in most simulations24, differences in growth rates across simulations are the result of changes
in total domestic savings. Second, the model assumes a full employment situation for all types of
labor and capital categories in each period of the simulation horizon. Hence, unlike other models for
Bolivia that analyze short-run issues (e.g. Jemio 2001; Jemio, Wiebelt 2003; Thiele, Wiebelt 2004),
this model neglects Keynesian multiplier effects that might result from changes in current and
investment expenditures. With the exception of some taxes and limited intersectoral mobility of
certain factor categories, there is essentially no difference between the technologically feasible
production possibility set and the resulting transformation set reflecting market behavior and
institutional characteristics of the economy. All markets are cleared and overall output is almost
fixed within individual periods. Taken together, these two model characteristics imply that the
model cannot be viewed as a short-run projections model and is not intended for that purpose. It is
better suited to explain medium- to long-term trends and structural responses to changes in external
conditions and development policy. But the problems facing pro-poor growth are inherently longer23
Two comments are in order. First, while the assumption of TFP growth of 2% is relatively high (slightly higher than
was found for the 1990s), for an optimistic baseline scenario it appears realistic. Second, one may speculate that
some of the policies proposed will have an impact on TFP growth. This is possible but there is very sparse literature
on this subject, both in an international as well as in a Bolivian context. One might therefore speculate that the
effects of some policies might be larger because of knock-on effects on TFP growth.
24
Exceptions are the simulations of declining capital inflows, where capital inflows are changed exogenously, and of a
devaluation, which changes the domestic currency value of capital inflows.
30
run problems, and the model thus naturally reflects an emphasis on the long run rather than on the
short run, on growth rather than stabilization, and on trends rather than cyclical variation.25
(i) An Optimistic Baseline Scenario
65. A scenario that describes how the Bolivian economy might evolve in the absence of shocks and
policy changes serves as a benchmark against which all alternative developments will be evaluated.
In this scenario, the economy exhibits smooth economic growth of about 4.7 percent on average
over a ten-year period (Table 10)26, where economic growth is driven by capital accumulation,
(exogenous) growth of the labor force, and (exogenous) technical progress (2% TFP growth). This
not only describes an optimistic forward-looking scenario, but is also a good description of the
record of Bolivia in the 1990s. The growth process is associated with roughly constant domestic
savings and investment ratios, which implies that the large savings gap is not closed over time. The
continuing savings gap corresponds to a persistent current account deficit, and both are reflected in
a fairly stable real exchange rate.
66. In line with past experience, the structural change projected in the baseline scenario is rather
moderate. The shares of the broad aggregates “Agriculture”, “Industry” and “Services” in total
value added barely change over time. More pronounced shifts of resources are taking place within
these three sectors. Within agriculture, for example, the more productive export-oriented segment
gains at the expense of the traditional, subsistence-like segment. The same pattern prevails in the
services sector, where higher productivity growth and a higher income elasticity of demand raise
the provision of formal relative to informal services.
67. From a distributional point of view, the baseline scenario suggests that without further policy
reforms and without external shocks the rise in urban inequality observed over the 1990s will
continue, and that the rural-urban gap in income levels will widen. In addition, inequality within
rural areas will also increase. In both urban and rural areas inequality is already at very high levels,
which is why aggregate growth in Bolivia barely translates into poverty reduction. As the following
figures indicate, this holds in particular for rural areas. In the course of the simulated 10-year
period, the national headcount merely declines from 63.6 percent to 55.3 percent. The moderate
reduction results from a decrease in the urban headcount from 49.7 percent to 38.9 percent, and a
reduction in rural poverty of only 4 percentage points from 86.9 percent to 82.8 percent. Even under
this optimistic scenario, Bolivia would just manage to reach the revised national poverty reduction
target. According to our model, however, poverty reduction in rural areas falls short of the reduction
predicted in the revised PRSP, while urban poverty declines faster.
(ii) Accounting for External Shocks
68. The assumption made in the baseline scenario that no external shocks occur during the
simulation period is highly unrealistic in the Bolivian context. Most predictably, the agricultural
sector is recurrently hit by the El Niño phenomenon. Furthermore, a resource-based economy such
as Bolivia’s has to cope with volatile terms of trade for primary goods. And as the Brazilian crisis
forcefully demonstrated, Bolivia’s small open economy can hardly avoid being affected by
instabilities in neighboring countries. In particular, the experience of the Brazilian crisis suggests
that the high level of foreign capital inflows realized in the mid-1990s should not be taken for
granted. Doubts about the sustainability of these inflows are reinforced by the fact that most foreign
25
As such, it is also not possible to use the model to track the actual development over the 1990s where short-run
cyclical variations play a considerable role. Instead, we can comment on the impact of the various shocks and
policies on past growth and poverty reduction.
26
For more detailed information, see Annex 4, which also includes other poverty measures and inequality measures. It
should be pointed out that the poverty gap often shows larger results than the poverty headcount, as has been the
case in the past.
31
direct investment was related to the capitalization process, which was more or less completed at the
end of the 1990s.
69. El Niño. Among the external shocks threatening Bolivia, El Niño carries the highest cost in
terms of short-run agricultural output losses. An El Niño shock of average size may lower GDP
growth by about one percentage point in the year of its occurrence. Since this is only partly
compensated by higher growth in subsequent periods, and since El Niño tends to occur every three
years, the losses add up to significantly lower average growth rates (Table 10). In addition, the fall
in agricultural exports during El Niño years leads to a deterioration of the current account balance
and a real depreciation of the Boliviano. The real depreciation, in turn, gives rise to a reallocation of
resources from inward-oriented sectors such as utilities, construction, and informal services to more
outward-oriented sectors such as intermediate goods and mining.
70. The direct distributional consequence of El Niño is that smallholders and agricultural workers
suffer income losses. The same is true for employers, who obtain a significant share of their capital
income from investments undertaken in modern agriculture. The negative impact on these three
household groups is somewhat dampened by a slight increase in domestic agricultural prices as a
result of supply shortages.27 The price increases are not strong enough, however, to show up in
lower real incomes for urban food consumers. In urban areas, the only major effect of El Niño on
household incomes runs via the real devaluation, which makes the providers of non-traded informal
services worse off. By contrast, the overall income position of non-agricultural workers and
employees is hardly affected as their gains in tradable sectors tend to offset their losses in nontradable sectors. The decline of urban informal income results in a quite considerable increase in the
urban poverty incidence. In some periods, the urban headcount increases by more than 1.5
percentage points compared to the baseline scenario. The rise in urban poverty is comparable to that
in rural areas where the headcount is on average about one percentage point higher than in the base
run. Inequality rises somewhat within urban areas, and quite considerably in rural areas, which is
mainly due to the fact that the losses of the employers in modern agriculture are less pronounced
than those for rural workers and smallholders.
71. Terms-of-Trade Shock. Terms of Trade shocks, such as the 10-percent decrease in world
market prices for agricultural and mineral products considered here, differ from supply shocks, such
as El Niño, in one main respect: they do not impair production capacities and thus do not lead to
major output losses as long as the economy operates at or near full employment.28 Rather, the direct
effect of the Terms of Trade shock is to reduce relative output prices and the incomes earned in the
affected sectors, which induces a shift of production towards non-affected sectors. Further
economy-wide repercussions are caused by the fall in agricultural prices, which lowers intermediate
input costs for processed agricultural products and thereby boosts output in the consumer goods
sector, and – analogous to the case of El Niño – by the real devaluation that follows the widening of
the current account deficit.
72. The negative income effect of the Terms of Trade shock is most strongly felt by agricultural
workers as the export-oriented modern agricultural sector, where they earn their living, experiences
a marked decline. For smallholders, who mainly serve the domestic market, the drop in real income
levels is much less severe. For the urban household groups, various factors work in opposite
directions, and they appear to cancel out. Urban informals, for instance, benefit from lower food
prices, while the real devaluation and an increase in rural-urban migration exert downward pressure
27
It is assumed that domestic agricultural prices are to a large extent determined by world market conditions so that
there is only limited scope for independent domestic price movements.
28
The assumption of full employment, which does not rule out the existence of underemployment, seems to be
justified at least in non-recession years where open unemployment tends to be very low. But even shocks which
mainly affect agriculture are unlikely to drive up open unemployment as the rural population cannot fall back on a
social safety net.
32
on their real income. Therefore, urban poverty remains virtually constant, whereas poverty in rural
areas increases slightly. In rural areas, the income distribution worsens to some extent because of
the income losses experienced by the workers in modern agriculture.
73. Declining Capital Inflows. If foreign direct investment (FDI) falls by almost a third, as has
been the case in Bolivia in the year 2000, this causes only about half the immediate output losses of
El Niño, but the impact turns out to be much more persistent.29 Even after ten years, growth has not
fully recovered. The fall in FDI lowers the domestic investment ratio by about 2.5 percentage
points, which narrows the savings gap because domestic savings hardly change. Correspondingly,
the current account deficit improves, with rising exports and falling imports. The losses incurred at
the macro level are spread over all sectors using formal capital, and over those producing capital
goods. Since traditional agriculture and informal services lack access to formal capital, smallholders
and urban informals are the only household groups, which do not immediately suffer from the
shock. A real devaluation of the Boliviano, however, indirectly hurts the urban informals, while it
benefits modern agriculture to such an extent that the sector’s loss of FDI is overcompensated.
74. Temporarily, smallholders and agricultural workers gain slightly from the shock, but lose
somewhat in the medium run as a result of lower economic growth. This drives the evolution of the
rural poverty incidence, which decreases slightly in the shock period, but increases by up to 0.7
percentage points afterwards. Over the entire simulation period, the rural income distribution
improves quite considerably. By contrast, all urban households are negatively affected by the
decrease in FDI. During adjustment to the shock, the urban headcount increases by almost two
percentage points compared to the baseline scenario. The urban income distribution worsens
somewhat, which is mainly due to the very pronounced decrease in urban informal income.
75. A similar drop in portfolio investment, which was the main consequence of the Brazilian crisis,
strongly reinforces the negative short-run impact of the fall in FDI. During the initial year, both
shocks combined drive the growth rate of real GDP down by about 1.5 percentage points. Since
lower portfolio investment reduces growth only temporarily, the medium-run impact is dominated
by the reduction of FDI. Average growth over the whole simulation period is about 0.3 percentage
points lower than in the baseline scenario. The medium-run poverty outcome for the combined
shocks is very similar to the FDI shock alone. In the short run, however, the impact on urban
poverty is much stronger, with an increase of the urban headcount by 3 percentage points during the
first period.
76. All in all, a realistic baseline scenario for Bolivia’s medium-run development prospects would
have to acknowledge that under the current policy framework average growth rates are unlikely to
lie markedly above 4 percent. Compared to the optimistic scenario, this implies worse prospects for
poverty reduction.
77. Apart from helping to arrive at a realistic assessment of Bolivia’s medium-run development
prospects, the simulations discussed above may also contribute to explain the past evolution of the
economy.30 Over the 1990s, the foreign investment boom triggered by the capitalization process has
in all likelihood been a key factor behind the comparably high growth rates until 1998. And
according to our results, the poverty impact of foreign-investment-driven growth tends to be biased
in favor of urban households, but does not completely bypass the rural economy. Compared to this
effect, agricultural shocks have only played a minor role between 1990 and 1998. The only major
exception is a severe El Niño, which in combination with declining prices for some primary
products led to negative per capita income growth in 1992 and thus may have worsened the overall
29
Since a dramatic fall in FDI can be expected to lead to temporary open unemployment on a significant scale, the true
short-run losses probably exceed those reported here.
30
With a grain of salt, the simulation results may be used for the explanation of past developments because the
baseline scenario considered here is basically an extrapolation of the situation prevailing in the 1990s before the
recent recession.
33
poverty outcome for rural areas in the early 1990s (as found above). A favorable external
environment for agricultural production may at least partly explain the result obtained in Chapter 2
that the rural poor fared exceptionally well over the period 1994 to 1998. The recession that started
in 1999 has in all likelihood hit urban households harder than rural households because the sharp
decline in foreign capital inflows in the year 2000 has clearly dominated the agricultural shocks (El
Niño, deterioration of terms of trade), which set in a year earlier.
(iii) Macro policies
78.
One of Bolivia’s biggest achievements since the beginning of reforms in 1985 has been the
containment of inflation by means of prudent monetary, fiscal and exchange rate policies.31 It might
be argued that now, with an internal equilibrium that is firmly established, the exchange rate could
be used to improve the external competitiveness of the Bolivian economy and affect its income
distribution, given that the Boliviano has always been quite strong (Schweickert et al. 2003). The
macroeconomic policy instruments are also still needed to bring about the real devaluations
required in the face of negative external shocks.
79.
Nominal Devaluation. A higher yearly devaluation of the Boliviano within the crawling
peg regime causes an almost complete exchange rate pass-through to domestic prices, which will
rise by nearly the same amount as the Boliviano is devalued as Bolivia’s ability to respond to higher
import prices with competitive import-substituting domestic production is quite limited.32 The
resulting real devaluation is therefore too small to provide the incentives for a significant
reallocation of resources, and the minor real adjustment that occurs has no discernible effect on
aggregate economic performance. Real effects, however, originate from the financial side of the
economy, which is strongly and directly affected by the devaluation. This is because in the highly
dollarized Bolivian economy the value of most assets and liabilities is indexed to the Dollar
exchange rate. As a consequence, the net wealth position of net creditors in the financial system
improves, while that of net debtors worsens. Since the economy as a whole – in particular the
government – is a strong net foreign debtor, the overall wealth effect of the devaluation is negative.
The deterioration of the domestic wealth position leads to a drop in aggregate real investment and a
fall in the growth rate, which accelerates over time due to a compound interest effect. Among the
household groups, the two richest, employers and employees are the only major actors on financial
markets. Both are net creditors and thus benefit from higher net wealth and interest income, i.e. the
revaluation of assets caused by the devaluation tends to reinforce existing wealth disparities. All
other household groups are adversely affected by the negative growth effect. Unskilled workers and
urban informals are most severely hurt because many of them are employed in the construction
sector where production growth is lower because of lower real investment demand. As a
consequence, urban inequality increases and urban poverty rises somewhat more than rural poverty.
Thus such a policy would be counter-productive and poverty-increasing in the current environment
of near complete pass-through to domestic prices, high dollarization, and large foreign debt.
Conversely, tackling any of these three constraints successfully could change the assessment of
such a policy considerably.
31
Since inflation is unlikely to be among the major policy issues in the foreseeable future, measures aimed at
containing inflation will not be analyzed here.
32
During recessions such as the current one, when capacities are underutilized, the pass-through will of course be
lower, but the question here is whether the exchange rate can contribute to improve Bolivia’s competitiveness in the
medium run.
34
Table 10: The Impact of Shocks and Policies on Growth and Poverty
Baseline Scenario
Average
Growth
(%)
4.7
Nationala
Urbana
Rurala
Headcount Headcount Headcount
55.3
38.9
82.8
Terms-of-Trade Shock
El Niño
Declining Capital Inflows
Nominal Devaluation
Real Devaluation (restrictive monetary
policy)
Labor Market Reform
Tax Reform (revenue-neutral)
4.7
4.4
4.4
4.5
4.7
55.6
56.3
56.3
56.7
54.8
39.1
39.7
40.3
40.4
38.1
83.3
84.1
83.3
83.9
82.9
5.0
5.0
54.4
53.9
37.4
37.0
82.8
82.4
Gas Projects (higher government
consumption)
Gas Projects (constant government
consumption)
Gas Projects (constant government
consumption) plus Labor Market
Reform plus Tax Reform
5.1
54.9
37.8
83.8
5.3
53.8
36.1
83.7
5.8
52.0
33.5
83.0
Improved Access to Credit for
4.7
55.2
38.8
82.8
Smallholders
Investment in Rural Infrastructure (high
4.8
55.0
38.6
82.4
productivity effect)
Investment in Rural Infrastructure (low
4.8
55.1
38.7
82.5
productivity effect)
Industrial Policy (modern agriculture)
4.7
55.7
39.6
82.7
Industrial Policy (consumer goods)
4.6
54.9
38.3
82.8
Transfer Program (lower government
4.7
53.8
37.9
80.5
consumption)
Transfer Program (lower public
4.5
54.6
38.9
81.1
investment)
Gas Projects plus Transfer Program
5.1
53.5
37.1
81.0
aRatio at the end of the 10-year simulation period. Please note that the initial poverty
headcounts are: 63.6% national, 49.7% urban, and 86.9% rural.
Source: Own calculations based on the CGE model.
80. Real Devaluation. A real devaluation can be achieved if the Central Bank conducts a
restrictive monetary policy. By constraining the opportunities of private banks to supply credit, such
a policy temporarily lowers aggregate real investment demand and thereby exerts downward
pressure on the domestic price level. The drop in real investment, in turn, causes a temporary
economic slowdown. Specifically, it has a contractionary impact on capital-intensive sectors and on
the sectors providing capital goods. This effect dominates the restructuring of production resulting
from the real devaluation, which brings about an improvement of the current account. Household
incomes are only moderately affected by these adjustments. While the investment slowdown makes
non-agricultural workers, in particular construction workers, slightly less well off, the real
depreciation entails minor losses for urban informals and minor gains for rural households. After
the short-run adjustments, the economy soon shifts back to the old growth path, and household
incomes evolve largely as in the base run. As construction output rebounds rather strongly, nonagricultural workers and urban informals even realize small medium-run gains so that urban poverty
35
declines somewhat towards the end of the simulation period. All in all, the negative impact on poor
households often attributed to real devaluations is unlikely to occur in Bolivia.
(iv) Structural reforms
81. By Latin American standards, Bolivia has also made remarkable progress in the area of
structural reforms (see, e.g., Lora 2001). The main exception is labor market reform, where Bolivia
lags behind most other Latin American countries. Among the labor market distortions that still
prevail, the segmentation of the urban labor market into formal and informal parts stands out. The
tax system is another area where further reforms may be warranted. In particular, the question arises
of whether the income tax, which hitherto has been of only marginal importance, should become a
major source of government revenues.
82. Labor Market Reform. If the government makes it easier for urban informals to be employed
as unskilled workers in the formal labor market, e.g. by lowering the costs of dismissal or by
granting more options for temporary work, the obvious direct effect is that average real wages go
down for unskilled workers and up for urban informals. Better earning opportunities in the urban
informal sector, in turn, induce rural-urban migration on a significant scale, which moderately
increases the incomes of those who stay in traditional agriculture. At the macro level, the efficiency
gains achieved by reducing labor market segmentation – the wage differential between informal
labor and unskilled labor is roughly halved – translate into average economic growth rates which
are more than 0.3 percentage points higher than in the base run.
83. On balance, these developments cause negligible distributional shifts in urban areas because
higher incomes for informals are offset by lower incomes for unskilled workers. Nonetheless, urban
poverty decreases because of higher growth, but it takes some periods for the positive growth effect
to materialize. The rural income distribution changes somewhat in favor of poorer groups due to the
gains experienced by smallholders. This change and a slight increase in rural growth do not show
up in the poverty headcount, but the rural poverty gap falls moderately (see Annex 2).
84. Tax Reform. A rise in income taxes for all household groups except smallholders and urban
informals directly forces the two richest household groups, employers and employees, to consume
and save less. For worker households the impact on disposable income is not strong enough to alter
consumption and savings significantly.33 The main indirect effect runs via a tax-induced fall in
aggregate private consumption, which lowers the prices received by smallholders and urban
informals und thus worsens their real income position. The growth effect of the tax increase
depends on how the receipts are allocated between consumption and investment. Under the
assumption that the government broadly retains the original structure of expenditures, it is likely to
be moderately contractionary in the medium run as the rise in public investment does not suffice to
fully offset the fall in private investment. This in turn would have a negative impact on the factor
incomes of all household groups.
85. If higher income taxes are combined with lower indirect taxes so as to arrive at a revenueneutral tax reform, the economy-wide outcome is different.34 Lower indirect tax rates cause an
expansion of capital-intensive industries (oil and gas, mining, intermediate goods), where the
indirect tax burden is highest, and thus boost investment and growth. Overall, given the current tax
structure, a revenue-neutral tax reform can be expected to improve Bolivia’s growth performance.
As for household incomes, the decrease in indirect taxes raises private consumption expenditures,
thereby offsetting the negative demand effect that higher income taxes have on smallholders and
33
The impact of the tax increase on aggregate poverty and income distribution cannot be calculated because the
household survey on which the distributional measures are based does not contain information on income tax
payments.
34
To arrive at a revenue-neutral tax reform, tax rates in those sectors that are mainly subject to indirect taxes have to be
lowered by roughly 20 percent.
36
urban informals. The main beneficiaries of the reform are non-agricultural workers, many of whom
work in the mining and the intermediate goods sector, as well as in construction, which benefits
from higher investment demand. The expansion of the construction sector additionally favors urban
informals so that on balance their incomes are also significantly higher than in the base run. The
gains of these two groups reduce the urban headcount ratio by up to 2 percentage points, and even
rural poverty falls a little due to the growth effect.
(v) Gas Projects
86. Perhaps more than any macroeconomic and structural policy reform, the development of the
natural gas sector promises to change the medium-run growth path of the Bolivian economy. Two
large export-oriented hydrocarbon projects with Brazil and Argentina are already being
implemented and another project involving the export of liquefied natural gas to North America has
entered the planning stage but is currently on hold (IMF 2004). Taken together, these projects could
roughly double the share of oil and gas in total domestic production from 5 to 10 percent within a
decade, oil and gas could finally account for as much as 50 percent of total exports. Since the sector
is an “enclave” in the sense that it uses negligible domestic inputs and generates little employment,
its main link to the economy is through the fiscal accounts via increased revenues from taxes, and
through its effects on the balance of payments – the current account improves and the exchange rate
appreciates in real terms.
87. The natural gas boom translates into markedly higher economic growth. In 2008 and 2009,
when the liquefied natural gas project is assumed to reach full capacity, the growth rate is likely to
approach 6 percent.35 These average gains are likely to be somewhat underestimated because the
upfront investment necessary to construct and develop large gas projects is not taken into account.36
The size of the growth effect will also depend on how the government uses its additional revenues.
If the receipts are channeled into consumption, the average gains over the simulation period will
only be about two thirds as large as if consumption growth is left constant and the resources are
instead used to prop up public savings. Choosing the latter option would increase the overall
domestic savings ratio by up to 3 percentage points compared to the base run, a remarkable
improvement which macroeconomic and structural reforms are unlikely to achieve.
88. The real appreciation of the Boliviano, which in the peak years of the resource boom might
reach 8 to 9 percent, leads to a contraction of export-oriented sectors such as modern agriculture,
mining, and consumer goods, and an expansion of nontradables, in particular construction. This is
the well-known Dutch Disease effect of resource booms, which, however, turns out to be rather
moderate except for the two peak years. By keeping consumption growth constant, the government
can slightly dampen the Dutch Disease effect. As a further economy-wide repercussion, lower
consumer goods production reduces intermediate demand for agricultural raw materials so that
modern agricultural activities contract even more, while smallholders suffer from declining prices
as they can hardly adjust supply. A restructuring of final demand away from private consumption
reinforces the pressure on smallholders’ prices and also hurts urban informals. Together with the
fact that rural-urban migration rises considerably, this explains that urban informals are slightly
worse off as a result of the gas projects even though they benefit from the real appreciation and the
expansion of the construction sector. Overall, rural areas, i.e. smallholders as well as agricultural
workers, suffer significant income losses, in particular in the two peak years. In urban areas, both
unskilled and skilled workers gain, with the gain of skilled workers, who are for the most part
employed in the public sector, being much more pronounced if government consumption expands.
35
The growth results obtained here come quite close to the projections reported in IMF (2004).
36
While most of the inputs, in particular capital goods, will need to be imported, some domestic activities such as
construction and business services might benefit during the early phases of the gas projects. The pipeline
construction to Brazil is estimated to have contributed some 1.5% of GDP in 1996-97.
37
89. These changes in relative factor prices induce major distributional and poverty changes. From a
national perspective, inequality increases substantially, which is due to both rising inequality
between and within urban and rural areas. In the scenario with higher government consumption, the
national Gini coefficient increases by about one percentage point. The results regarding the
evolution of poverty during the gas boom are disappointing. Despite considerably higher growth
rates, the decrease in nation-wide poverty is only moderate compared to the baseline scenario. More
remarkably, rural poverty even increases substantially, with a rural headcount that
falls by up to one percentage point. The rural poverty gap ratio, which during the second half of the
simulation period is about 2.5 points higher than in the baseline scenario, illustrates that many of
those who were already poor incur income losses.
90. A somewhat more favorable outcome could be expected if the government refrained from
raising consumption expenditures. In this case, the headcount would be significantly lower in urban
areas, but rural households would hardly benefit and thus would remain markedly worse off than
without the gas projects. In addition, the rise in inequality would be somewhat less severe due to the
dampened Dutch Disease effect, with an increase in the Gini coefficient of about 0.5 percentage
points.
91. A fairly large medium-term boost for the Bolivian economy might become possible if the gas
projects were combined with the structural reforms discussed above. Such a policy package could
raise average economic growth by more than 1 percentage point and lower the national headcount
by more than 3 percentage points. The gains would, however, exclusively accrue to urban
households. They would benefit from a substantial drop in the poverty rate by more than 5
percentage points compared to the base run, while rural poverty would even rise a little. Clearly,
such a strategy maximizes growth, but not pro-poor growth.
(vi) Targeted interventions in favor of the poor
92. The low poverty elasticities with respect to growth point to the problem that many of Bolivia’s
poor are not well integrated into the economy and are additionally too poor to be lifted above the
poverty line as a result of moderate economic growth. Among the policies which might help
increase the productivity of the poor, particularly in rural areas where poverty is most persistent,
improved access to credit for smallholders and investment in public goods such as rural
infrastructure and agricultural research figure prominently (Thiele 2003). More direct ways of
raising incomes of poor households could involve the subsidization of activities where many of the
poor are employed (Klasen 2004), or the implementation of traditional transfer programs.
93. Improved Access to Credit for Smallholders. Efforts to improve credit availability for
smallholders, e.g. by making land tenure more secure, are likely to raise investment in traditional
agriculture significantly, albeit from a very low base. The impact tends to decelerate over time, but
even after 10 years real investment could still exceed the base-run level by almost 50 percent.
However, since the contribution of capital to sectoral value added is very small (and is assumed to
have no impact on TFP growth), the investment boom only raises output by about 1 percent in the
short run and by about 3 percent in the medium run. This supply response is too moderate to induce
major adjustments in the rest of the economy. Aggregate investment rises slightly and average
economic growth is less than 0.05 percentage points higher than in the base run. Smallholders’ real
income position improves somewhat as a result of the output expansion, but this does not show up
in the rural headcount, and even the poverty gap falls only marginally. Hence, the loosening of
smallholders’ credit restrictions must be regarded as largely ineffective in the medium term, at least
without further complementary measures. In the long term, such a policy is likely to be more
beneficial as it allows the build-up of a capital stock and thus progressively raise the contribution of
capital to value added.
94. Investment in Rural Infrastructure. Specific investments in public goods – e.g. the
development of more productive crop varieties or the construction of rural roads – might constitute
38
one such complementary measure. However, even if public investments are tailored to
smallholders’ needs, its impact is constrained by the difficult natural conditions prevailing in the
Bolivian highlands.37 Here we consider two different scenarios: a fairly optimistic one where we
assume that smallholder’s average output is raised by about 12 percent compared to the base run,
and a more pessimistic one where the rise in output is only half that size. In both cases, the
expansion of smallholder agriculture comes partly at the expense of modern agriculture so that
smallholders realize income gains, whereas agricultural workers experience a less pronounced
decline in wages. Although there is a countervailing force in the form of a small price decrease,
smallholders benefit to such an extent that fewer of them migrate to urban areas. Together with a
small real appreciation, this slightly improves the income position of urban informals.
95. Despite considerably higher income gains in rural areas, reductions in the urban and rural
headcount are roughly equal. The difference between the two regions manifests itself in a
significantly higher fall of the rural poverty gap (see Annex 4), which again reveals that many
smallholders are far below the poverty line. In addition, inequality within rural areas decreases
slightly. As for a comparison between the optimistic and the pessimistic scenario, all the
mechanisms described above are more pronounced with stronger productivity effects of public
investment. While this does hardly show up in economic growth rates and the poverty incidence, it
is clearly reflected in the rural poverty gap, which goes down by more than 1 percentage point in
the optimistic scenario as compared to 0.5 percentage points in the pessimistic scenario.
96. Industrial Policy. While the measures just mentioned aim at augmenting the asset base of poor
households, a pro-poor industrial policy instead aims at raising the returns on existing assets, in
particular on unskilled labor. One option in this area would be to support the development of
modern agriculture. If the government, for instance, granted a 20 percent export subsidy, the sector
would become markedly more important, particularly in terms of its share in total exports, which
might increase from 15 to 25 percent. The expansion of modern agriculture is fuelled by rural
migrants who are attracted by steeply increasing agricultural wages, and by a reallocation of capital.
It is thus associated with lower output growth in traditional agriculture and in the capital-intensive
sectors. The policy-induced structural change leads to an improvement of the current account, a
small real appreciation of the Boliviano, and minor efficiency losses for the economy. With respect
to household incomes, the out-migration of smallholders to some extent benefits those who stay in
traditional agriculture. The migration effect on smallholders’ incomes remains limited because the
workforce required in modern agriculture is very small compared to the number of smallholders.
Lower production growth in capital-intensive sectors implies lower real incomes for workers and
urban informals, which translates into moderately higher urban poverty over the whole simulation
period. The rural headcount falls by almost one percentage point in the first period, but the
deviation from the base run gradually disappears as the gains of agricultural workers become
smaller over time.
97. An alternative option would be to support agriculture-based industrialization rather than primary
agricultural activities. Subsidizing the consumer goods industry would entail stronger economywide adjustments than subsidizing modern agriculture and, as a consequence, efficiency losses
would be higher. Most importantly, the Boliviano would appreciate considerably, and intermediate
demand for agricultural raw materials would go up. The backward linkage to agriculture boosts the
production of both agricultural sectors, but for modern agriculture this effect is overcompensated by
the loss of competitiveness caused by the real appreciation. This implies that smallholders receive
higher real incomes, whereas agricultural workers incur minor losses. The real appreciation
improves the income situation of urban informals. Non-agricultural workers benefit from the
expansion of the consumer goods industry, but as this expansion largely occurs at the expense of
37
Since little is known about the likely productivity effects of public investment in Bolivia’s highland agriculture, the
results presented here should be regarded as very tentative.
39
other sectors where they mainly work, their overall income position is not improved. Nonetheless,
urban poverty declines substantially due to the gains realized by urban informals. The reduction in
the rural headcount again tends to disappear over time, in this case because smallholders mainly
benefit in the short run.
98. Transfer Programs. Transfer payments constitute the most direct means of enhancing the real
income position of the poor. Here we assume that the government expands existing transfer
programs so that gross incomes of the poor household groups are raised every year by roughly five
percent compared to the base run. Thus these programs assume that it is possible to target such an
enhancement of transfers to the poor, but that among the poor, the transfers are distributed in line
with the receipts of transfers in 1999, i.e. no further improvement in targeting is assumed. As
shown above, transfer and social expenditures are not particularly well-targeted, that is the effects
could be more beneficial to the poor than shown here through better targeting (see also World Bank
2004a). Whether the impact of such programs goes beyond the direct beneficiaries largely depends
on how the government finances the outlays. If it substitutes transfer payments for consumption
expenditures, economy-wide repercussions are negligible and average growth is not affected. The
only significant change is the fall in consumption expenditures itself, which leads to somewhat
lower real incomes for public employees. As a consequence of the transfers that mainly benefit
smallholders and urban informals, both urban and rural poverty falls markedly. The evolution of
inequality appears to be less favorable. While the nation-wide Gini coefficient falls somewhat as the
income change is stronger in rural than in urban areas, urban inequality remains constant and rural
inequality even widens. These surprising regional results can be explained by the fact that the
transfers tend to reach richer rather than poorer segments of the smallholders and urban informals.
99. Financing transfer programs through cuts in investment spending has a much stronger impact on
the economy as it lowers aggregate investment and saving ratios by over one percentage point and
thereby leads to reduced economic growth. The investment slowdown is most strongly felt in the
construction sector, which implies that factor incomes of workers and urban informals decline. For
urban informals, the decline is cushioned by a restructuring of final demand towards private
consumption. The shift in final demand equally raises smallholders’ factor incomes so that they
enjoy direct and indirect benefits. Overall, the secondary effects via the fall in investment fully
offset the transfer-induced urban poverty reduction, whereas rural poverty alleviation remains
sizeable.
100. As transfer programs have the biggest impact on reducing rural poverty, the combination
with a natural gas project might yield a favorable scenario. We show such a combined scenario in
Table 10 and it indeed is able to deliver higher growth and lower poverty in urban and rural areas.
If such a policy package was also combined with labor market and tax reform as well as
improvements in the targeting of transfers among the poor, the poverty impact could be substantial
and conceivably allow the government to reach its PRSP targets also in rural areas.
101. To summarize the findings from the policy simulations, a few points are worth noting.
Regarding an explanation of the impact of shocks and policies on pro-poor growth in the past, the
following conclusions are warranted. First, one can nicely see how the evolution of poverty and
growth in urban areas has varied with foreign capital inflows. Rural development has been more
dependent on climatic conditions, and the lack of private and public capital. The failure to
implement labor market reforms appears to have held back growth and urban poverty reduction. A
deregulation of the urban labor market would also have had a positive if limited impact on rural
incomes by providing an incentive for additional rural-urban migration.38
38
There are indications that the model underestimates the response of migration to changes in wage differentials.
Additional empirical research is needed to see whether the modeling of migration assumes the right amount of
adjustment compared to actual migration patterns in Bolivia.
40
102. Looking forward, one can draw conclusions about the policy options for pro-poor growth as
well as the constraints of Bolivia’s economy that the model analysis has served to highlight.
Turning to the former issue, the main conclusion to be drawn from the model analysis is that,
currently, the opportunities for achieving pro-poor growth are much better in urban than in rural
areas. Given the available policy choices, Bolivia could clearly exceed the targets for urban poverty
reduction set in the revised PRSP. Rural poverty reduction, by contrast, risks falling short of the
targets due to a combination of recurrent external shocks and limited policy options. In particular,
the implementation of gas projects is likely to bypass rural areas and significantly increase
inequality. Improvements in access to credit and rural infrastructure have a positive but fairly small
effect on poverty (particularly on the poverty gap). Only a coordinated policy package involving
the gas project, labor market and tax reforms, and targeted transfer programs and interventions
would allow Bolivia to achieve higher pro-poor growth and allow significant poverty reduction,
also in rural areas.
103. Turning to the latter issue, the analysis has clearly shown up significant structural
weaknesses of Bolivia’s economy that also need to be addressed in order to accelerate pro-poor
growth. A critical weakness is Bolivia’s low domestic savings rate, which is contributes to Bolivia’s
reliance on foreign capital inflows, the high degree of dollarisation, its high foreign debt, its
vulnerability to external shocks, and its inability to manage its external trade and monetary
environment to support pro-poor growth. A related second weakness is Bolivia’s high dependence
on natural resources which have few linkages to the poor, but can have significant anti-poor effects.
Third, Bolivia’ economy exhibits such a great degree of dualism that well-managed policies to
generate higher growth do not reach the poor in rural areas or have little impact on their poverty.
Lastly, Bolivia’s high initial inequality militates against success in poverty reduction, particularly in
rural areas. While the policy packages discussed above can help with pro-poor growth, only
success in tackling these four deep-seated issues will enable Bolivia to enter a sustainable path
towards higher growth and poverty reduction.
d) Institutions, Political Economy, and Pro-poor Growth
104. This section will discuss selected institutional aspects of policy-making in Bolivia as they
relate to pro-poor growth. We will focus particularly on an assessment of institutional weaknesses,
then discuss the impact of decentralization on policy-making, and lastly consider the PRSP and
National Dialogue process and its impact on policy-making for pro-poor growth.
(i) Governance weaknesses and their link to poverty and inequality
105. Based on the most comprehensive source of governance indicators (Kaufman et al. 2003),
Table 11 shows the evolution of composite indicators of governance in the Bolivian case. The
indicators are scaled so that they have a mean of 0 and a standard deviation of 1 for all countries
included in the sample. The first group summarizes indicators of the political process, civil and
political rights. The political instability indicator measures the likelihood of government overthrow
or destabilization, the third cluster summarizes measures of public service provision, competence
and quality of the bureaucracy and the last one summarizes the incidence of market-unfriendly
policies. It is notable that in all four governance measures Bolivia exhibits a downward trend in the
last few years, ending up close to or below the average for all countries.39 This is particularly the
case for government effectiveness and regulatory quality. As most of these measures are based on
survey-based evidence, it appears that the trust in the policy-making apparatus and the state
bureaucracy has weakened already well before the recent protests over tax reform and gas exports.
106. More detailed investigations of the role of institutions in Bolivia by Kaufman et al (2002)
yield further important insights. In particular, business surveys reveal that corruption and the lack
39
Somewhat ironically, the deterioration is least apparent in the political stability measure, given that the government
was indeed forced out of office by popular protests in 2003.
41
of the rule of law stand out as particular constraints in Bolivia, while issues of macro management
and financing are not seen as a problem at all. Corruption, bribery, and a weak judiciary are also
named as significant barriers preventing sales growth. This suggests that the structural reforms of
the 1990s were having positive effects in their areas of focus but did not tackle the more deepseated problems of corruption and a weak judiciary. As far as causes of corruption and low
government effectiveness is concerned the results suggest that lack of transparency and voice of
citizens appear to be the most important reasons, according to surveys of public officials. Also
here, much work remains to be done.
107. These institutional weaknesses not only retard economic growth by preventing effective
policy-making (Rodrik, 2003), but they also are implicated in sustaining high inequality. A study
by Chong and Gradstein (2004) show that indicators of institutional weakness are strongly
associated with inequality in Latin America. The causality is bi-directional with the causal link
from inequality to institutions being somewhat stronger than the reverse one. The discussion
suggests that also here there are two sets of policy issues emerging. On the one hand, there is clear
need to tackle the institutional weaknesses in Bolivia, particularly corruption, lack of a reliable
judiciary, and lack of transparency and voice in the public sector. On the other hand, it is clear that
such reforms will be more difficult in an environment of high inequality that sustains these
institutional problems, which provides a further case to address these inequalities.
Table 11: Trends in Governance Indicators over Time
1996
Voice and Accountability
1998
2000
2002
0.10
0.35
0.23
0.01
Political Stability
-0.28
0.00
-0.42
-0.20
Government Effectiveness
-0.49
-0.09
-0.35
-0.53
0.66
0.90
0.65
-0.11
Regulatory Quality
Source: Kaufmann et al (2003).
(ii) Decentralization and pro-poor growth
108. Bolivia embarked in 1994–1996 on an ambitious decentralization program, which transferred
a large share of resources and associated responsibilities to Bolivia’s municipalities. Municipalities
were assigned responsibilities for investment expenditures in the social sectors and infrastructure. In
addition to funds from the central government, they were granted, as an outcome of the National
Dialogue Law, the entire amount of HIPC II debt relief for investments at the local level (targeted to
communities with higher rates of unsatisfied basic needs) and they have spent most of these funds,
as reported above, on social sector investments and most recently also on infrastructure. In addition,
an intermediate (centrally appointed) layer of government (prefectures) was introduced and also
given considerable spending and implementation authorities.
109. In view of the governance problems cited above, such decentralization could be of help to
address the problems of lack of transparency and voice as decisions and implementation are brought
closer to the people affected. This was also supported by popular oversight mechanisms. At the
same time, it is unclear whether such an ambitious decentralization program can tackle the
problems of low government effectiveness and corruption. Clearly, it can be only one avenue of
governance reforms and other items (including judicial reform and improvements in voice and
transparency throughout the public sector) remain of importance.
110. In addition, decentralization in its current form appears to be plagued by a number of
problems. First, the roles of municipalities versus prefectures are not clarified and lead to
cumbersome coordination and oversight problems. Secondly, the institutional capacity of smaller
municipalities is too weak to undertake many of tasks they are being charged with. Third, revenue
42
generation at the municipal level remained low, with the municipalities depending on average for
80% of their resources on the central government (World Bank 2004a). Moreover, they have no
control over the public sector wage bill for the services under their control (e.g. health and
education). This has effectively softened the budget constraint and led in some municipalities to the
build-up of considerable debt. The planning and oversight systems are not working well due to
weak institutional capacity and lead to very slow and cumbersome implementation of investment
projects.
111. It appears that the process of decentralization has proceeded too quickly and overtaxed the
capacity of the developing municipal authorities. This has led to capacity constraints, delays in
spending, complaints of poor and intransparent governance at the municipal level, and left the
central government with little ability to direct public policy towards priority sectors (World Bank
2004a). While many local investment projects which, if successfully executed, were found to have
had a significant positive impact on health outcomes and water access (Newman et al. 2002), the
cumbersome procedures, large overheads, and poor capacity have led to very slow implementation.
The capacity constraints might also explain the focus on social sector spending and infrastructure, at
the expense of efforts to promote productive sectors. Programs to promote productive sectors (such
as credit programs, cluster initiatives, subsidy programs) tend to be much harder to implement than
the expansion of health and education programs and the construction of physical infrastructure.
This surely contributed to the considerable improvement in social indicators over the past few
years, but did little to reduce income poverty. This focus on social sectors at the expense of
promoting productive sectors has become one of the main criticisms that have been voiced in the
current round of the National Dialogue centered on the revision of the PRSP.
112. As shown above, it has also, at least initially, led to an anti-poor spending pattern on
infrastructure. As far as the outcome of the decentralization process on poverty is concerned, there
is little reliable data to date. Viana Sarabia (2004) undertakes an econometric analysis (ordered
logit) of the impact of three indicators of decentralization on a non-income measure of poverty, the
so-called NBI (Unmet basic needs). She finds that municipalities with lower levels of unmet basic
needs have a larger resource envelope per capita to spend, confirming that decentralization did not
equate per capita spending (or target them to the poorest areas). In addition, the most important
determinant of the degree of unmet basic needs is the share of own resources spent by a
municipality. Given that this share is particularly large in richer municipalities, the analysis
supports the notion that a decentralization process that largely depends on central government
transfers for the poorest communities will not have a significant impact on poverty outcomes and
might in fact exacerbate existing inequalities. More recently, the situation appears to have
improved somewhat through the better targeting of HIPCII resources to communities and through a
greater focus on productive sectors (which mainly means infrastructure). In fact, some poor
communities have received much more funds and are having difficulties in spending it productively
(World Bank 2004a).
113. While decentralization might be a way to improve governance and can also enhance pro-poor
spending, it can generate new problems of capacity constraints, poor fiscal control and oversight,
and failure to tackle income poverty issues. If capacity constraints or other institutional weaknesses
are correlated with income poverty of a municipality, it can even exacerbate existing inequalities
unless ways are found to assist poorer and smaller municipalities. Alternatively, it is important to
keep options open for pro-poor interventions that are directed and supported from the central
government.
(iii) PRSP and national dialogue
114. Bolivia was among the first to complete an Interim PRSP and a full PRSP in 2000 and thus
to enjoy HIPC debt relief. The PRSP was the culmination of a systematic National Dialogue
Process, which was written into a law in 2001 as a permanent institution to take the PRSP process
forward. At the time, Bolivia’s PRSP has been widely lauded for both its content and process.
43
115. But soon after completion of the process, serious disappointment with the process and the
outcome emerged, and the PRSP began to be considered a ‘dead’ document by most stakeholders
(ISS 2003a, 2003b). Apart from the well-known problems with PRSPs elsewhere (overambitious
targets, ‘laundry lists’ with little prioritization, unclear relation to the government macro and fiscal
strategy, too focused on trying to please donors in order to get HIPC funds), there appear to have
been particular problems associated with the National Dialogue and the creation of the PRSP.
Among the problems encountered in the process, according to an analysis undertaken by the
Institute of Social Studies, was that the Interim PRSP was never fully discussed, and that there was
a significant disconnect between the National Dialogue, which was open, transparent, and had
significant non-governmental participation, and the writing of the PSRP that was then relegated to a
group of consultants who drafted a strategy that was only partly based on the outcomes of the
dialogue but more influenced by inputs from the donor community and the desire to please the
international community to get HIPC funds (ISS 2003a, 2003b). This sharply reduced the popular
ownership of the final strategy, which in addition became largely obsolete when the macroeconomic
conditions departed sharply from the rosy projections and plans included in the PRSP. As such, the
PRSP had little impact beyond the decision in the National Dialogue Law to transfer all HIPC
resources to the municipalities, which strengthened social sector investments at those levels.
116. Regarding the content of the strategy, there appears to have been considerable unease over
the almost exclusive focus on social sector spending as the route out of poverty, the neglect to
discuss macro issues and consider alternatives to the current economic model guiding Bolivia’s
economy, the overoptimistic macroeconomic projections, the unsustainable associated expenditure
plans, and the neglect to focus on strengthening the productive sectors as a means to achieve
sustainable poverty reduction.
117. The revised draft PRSP tabled by UDAPE in late 2003 already included some changes in
focus and put the development of micro, small and medium-sized enterprises at the heart of the
poverty reduction strategy. This development was supposed to be promoted through a combination
of a land policy focusing on titling and increased security of tenure, national productive clusters
promoting the supply chains for 14 products through joint public-private initiatives (cadenas
productivas, see Box 4), and through efforts to promote local economic development. While this
focus on productive sectors addressed one central complaint about the original PRSP, there
continued to be disappointment that the revised strategy did not consider alternative economic
models of development, did not include more radical land redistribution programs, and was not farreaching enough in focusing on poverty reduction through strengthening productive sectors
(including public support for productive sectors that go far beyond the cadenas productivas
approach). As a result, this draft is by now also seen as insufficient as the debate has now moved to
the constitutional assembly where the questions of the use of natural resources, the economic
model, and the land distribution are likely to figure prominently.
118. These (necessarily cursory) discussions of institutional issues in policy-making appear to
suggest the following conclusions for pro-poor policy-making. First, in a situation of poor and
deteriorating governance, it is critical to address public sector governance issues as a first priority.
A decentralization program can be of help in some aspects, but it is particularly risky to embark on
major new initiatives that further impair the management capacity of the public sector. The Bolivian
government appeared to have pushed institutional changes to policy-making at a speed that
eventually weakened its ability to implement effective policies. Second, decentralization need not
improve governance nor does it necessarily improve the poverty focus of public expenditures. It
merits consideration whether the central government should consider the build-up of a centrally
managed approach to promoting equity and assisting with pro-poor growth. In addition, it appears
that decentralization of the expenditure side without the necessary abilities to raise revenues can
undermine the success of a decentralization effort. Third, regarding pro-poor policy-making, it is
very risky and ultimately counter-productive to establish a participatory process of pro-poor policymaking and then strictly limit the agenda to certain priority actions. This is particularly risky if
44
there is significant mistrust of the government, which is grounded on long-standing inequalities and
social and political exclusion. If a process is to be participatory, all aspects of economic policymaking must be openly discussed even if this can lengthen the process and pose risks for the
outcome. The Bolivian case seems to suggest that the route taken here has now led to a very
polarized political debate that poses even greater risks for political and economic stability than an
early discussion of all aspects of economic policy-making (see also ISS 2003).
Box 4:
Cadenas Productivas
The idea was to promote the supply chain through coordinated public and private actions for the following
14 products: poultry, bananas, cows, Brazil nuts, leather, timber, oil products, palm heart, quinoa, textile and
cotton, wheat, grapes, (wines and liquors) and tourism (Sucre-Potosi-Uyuni salt lake circuit). These were
selected as they already made up a significant share of GDP, generated some 400,000 jobs, were often
activities undertaken by small farmers or entrepreneurs, and took place in many parts of the country. It was
intended to strengthen the supply chain by developing capacities to refine these products within the country,
and by improving national and particularly international sales opportunities. The plan was to generate publicprivate partnerships that would develop coordinated plans to achieve these goals. Specific measures would
include technical and technological assistance, support services for production, help with market access, and
productive infrastructure. The preparation of this strategy would involve three steps (strategic vision,
strategic plan of coordinated actions, and a conclusion of a public-private pact as part of the 2003 National
Dialogue). Initial documents set aside public funds from municipal and departmental budgets as well as
HIPC funds to support these investments. With the shift of the debate to the constitutional assembly, the
future of this approach to support productive sectors is uncertain.
Sources: Ministerio de la Presidencia (2003); (UDAPE 2003a).
Chapter 4: Possible Trade-Offs between Growth and Poverty Reduction
119. Based on the assessments from the CGE model, we can not only assess the impact of
individual policies on pro-poor growth, but also consider possible trade-offs and complementarities.
When it comes to choosing a policy package for pro-poor growth from the available options, it is
important to know whether particular measures promise to create win-win situations in that they
help achieve growth and distributional objectives at the same time, or whether there are trade-offs
involved.
120. In the area of macroeconomic policy, higher yearly devaluations of the Boliviano risk to fail
on both accounts as a result of adverse balance sheet effects. A tightening of monetary policy, by
contrast, may bring about the real devaluations that are regularly required to adjust to external
shocks at a negligible short-run cost for the poor.
121. Among the two structural reforms considered here, a deregulation of the urban labor market
carries the potential to make growth considerably more pro-poor by removing a substantial part of
the existing wedge between formal and informal wages. Such a measure would, however, meet with
strong resistance from formal workers, who arguably are much better organized than the diverse
group of people working in the informal sector. This difficult political situation is probably the key
factor behind the fact that profound labor market reforms have not yet been initiated. Also, it would
not do enough to reduce rural poverty.
122. Similar pressure from powerful interest groups – in this case mainly from public employees
– stands in the way of a comprehensive tax reform. Provided that this pressure can be overcome, the
introduction of a revenue-neutral tax reform may improve efficiency and reduce poverty. A pure
income tax increase, by contrast, is unlikely to serve these objectives. If income taxes are set at
moderate rates as assumed above, they are likely to be only mildly progressive and may even raise
poverty. And with substantially higher tax rates, the efficiency losses may well turn out to be
intolerable.
45
123. In the development of Bolivia’s gas sector, there appears to be a trade-off between growth
on the one hand and the participation of the poor – in particular the rural poor – in the growth
process on the other hand. Given the prospect that nation-wide poverty might decrease only
moderately as a result of the resource boom, and that rural poverty might even increase, the
rationale behind the recent social unrest becomes obvious. The trade-off is, however, hard to avoid
as the gas sector is highly capital intensive, generates little employment, and uses limited national
inputs. To what extent growth and poverty objectives can be reconciled depends on how the
government allocates the additional revenues it receives. While an increase in public savings might
cushion the trade-off, more specific pro-poor measures are likely to be required in order to make the
impact of the gas projects socially acceptable.
124. Given that rural poverty constitutes the most severe problem, measures targeted at
augmenting the asset base of smallholders suggest themselves as possible win-win options. It has to
be taken into account, however, that natural conditions in the highlands are not very favorable, and
that the growth process would have to start from very low initial capital endowments. This implies
that the medium-run supply response, and thus the impact on rural poverty, will probably remain
limited.
125. With respect to pro-poor industrial policies, the key question is whether favorable poverty
outcomes can be achieved at low efficiency losses. Our simulation results indicate that efficiency
losses may be kept at moderate levels, but that neither a strategy based on export-oriented
agriculture nor a strategy based on agricultural processing are likely to bring about lasting
improvements for the rural poor.
126. Transfer programs targeted towards the poor can in principle alleviate poverty without
compromising growth objectives, but the precondition for this to happen – a more or less complete
financing of the programs out of other current expenditures – appears to be very demanding. If
investment spending has to bear the lion’s share of the costs, the economic losses can become
considerable.
127. In the coming years, the gas receipts may provide a way out of this trade-off by loosening
the budget constraint of the Bolivian government. If gas revenues are used instead of public
investment funds to finance transfers, the combination of the gas projects and the transfer programs
produces a clear win-win situation, with higher growth and a marked alleviation of rural and urban
poverty. The only major drawback of this policy option is that both the gas projects and the transfer
programs lead to a significant increase in rural inequality, raising the rural Gini coefficient by
almost three percentage points in the second half of the simulation period. This is driven by the
(model) assumption that the transfer programs are targeted in the same way as existing programs. If
targeting were to be improved, this rise in inequality could be significantly reduced.
128. One way to summarize the trade-off is to examine the combined scenarios in Table 10. The
optimal pro-growth scenario combines a gas projects (with constant government consumption)
labor market and revenue-neutral tax reform. Growth is boosted to nearly 6% per year. But the
poverty impact is only moderate and entirely focused on urban areas; inequality and rural poverty
are expected to increase. At the other extreme, we have a pure transfer program which, if coming at
the expense of public investment reduces growth but also reduces rural and urban poverty, as well
as reducing overall inequality. To achieve high rates of pro-poor growth given current constraints,
the combination of both policy scenarios is likely to be best for sustainable poverty reduction in
urban and rural areas, with an added focus on improving the targeting of the transfer program.
129. Apart from these model-based assessments, it is also important to consider more
fundamentally the trade-offs involved between a narrow growth agenda that is largely guided by
policies informed by the Washington Consensus and its associated political economy risks. It
appears that given Bolivia’s unfavorable initial conditions as well as its history of high inequality
and large social and ethnic tensions, a technocratic focus on liberalization and macro stability might
not deliver benefits in terms of poverty and inequality reduction quickly enough to prevent serious
46
setbacks as have been experienced in Bolivia in recent years. Going further down that route and
hoping that the possible high growth associated with natural gas and commercial agriculture exports
will deliver enough benefits to maintain social stability are likely to prove elusive and might
provoke populist backlashes with serious consequences for growth and poverty reduction. Instead it
appears necessary to confront the issues of deep-seated inequalities in resources, opportunities, and
power more directly rather than hoping than one can grow out of them. Some of these questions
will be taken up below.
Chapter 5: Recommendations for Policy-Making
130. Bolivia is now facing a serious economic and political situation. It is in a state of serious
political and social unrest, economic conditions are not favorable (although they have stabilized
recently), and there are loud demands for more spending, more redistribution, and a total
abandonment of the current economic policy stance. In this situation, it is not easy to come up with
a policy framework that will successfully steer Bolivia to a path of significant pro-poor growth.
131. In this chapter we will start from a combination of incremental policies that all could serve
to improve the overall slow record of poverty reduction in Bolivia. We will then move towards
asking whether a more radical reform of economic policy-making is needed, both in terms of
content as well as process. We will begin by summarizing some of the conclusions from our modelbased assessments.
132. The main general conclusion to be drawn from the foregoing model-based analysis is that in
Bolivia the opportunities for achieving pro-poor growth differ enormously between urban and rural
areas. This has been true for the 1990s, where until the outbreak of the recent crisis urban
households have benefited disproportionately from foreign investment led growth. And it is also
true with respect to future prospects, which are less favorable for rural households due to several
factors. To support our argument, we reorder in Table 12 the main policy experiments from Table
10 according to whether the impact is mainly on urban or rural households. First, the rural
economy will inevitably have to cope with recurrent disruptions caused by external shocks. Second,
difficult natural conditions in combination with very low initial capital endowments will limit the
impact of efforts to increase the asset base of poor farmers. Nonetheless, investments in public
goods such as rural infrastructure or agricultural research should be taken into consideration as they
could at least entail some productivity improvements.40 If it turns out that significant productivity
gains can be expected, measures aimed at improving smallholders’ access to credit such as
increased tenure security or additional micro-credit initiatives (including tackling issues of nontraditional forms of guarantees), might also have a positive pay-off in that they help realize
complementary private investment. Also, community-driven investments in irrigation, improved
seed varieties, and modern inputs should remain firmly on the agenda.
133. Third, the modern, dynamic segment of the agricultural sector is too small to absorb a
sizeable part of the poor rural workforce so that a pro-poor industrial policy based on modern
agriculture does not appear to be promising. Finally, the development of the gas sector will largely
bypass the rural economy, and will even raise rural poverty via the economy-wide repercussions it
entails. As the recent protests have shown forcefully, such an uneven distribution of benefits will
meet strong resistance. This does of course not imply that Bolivia should forego the gains to be
expected from the gas exports, but rather that rural households should be able to share in the gains
to an extent that more than compensates the expected losses to them. To achieve this, direct
transfers constitute the only realistic option as only direct transfers can raise incomes significantly
in the short to medium run. Such transfers should, however, be targeted very carefully. By simply
expanding existing transfer programs, as assumed in the simulation reported above, the government
40
Unfortunately, evaluations of the impact of public investments in Bolivia are lacking. Hence, their productivity
effects can only be guessed, and priority areas for public investment can hardly be identified.
47
will miss most of the poorest households. As a further caveat, the financing of transfers that are
large enough to accomplish a significant poverty reduction is only sustainable as long as the gas
boom endures. All in all, given current constraints, it is thus likely that the prospects of fostering
rural development will to a large extent rely on dynamic growth of the urban economy, which
would indirectly raise rural incomes via increased rural-urban migration, higher intermediate
demand for agricultural raw materials, and higher consumption demand for food. In this context,
the dynamic development, in terms of population growth, economic growth, and poverty reduction,
of small and medium-sized towns in Bolivia could provide one avenue for providing employment
and income-earning opportunities for the rural poor. Supporting such growth centers through
infrastructure investments and support for decentralized industrial activities might be one option to
consider.
Table 12:
Shocks, Policies and Pro-Poor Growth in Rural and Urban Areas
Shock/Policy
Main Effect on Rural Households
El Niño
Terms-of-Trade Shock
Investment in Rural Infrastructure
Improved Access to Credit for
Smallholders
Industrial Policy (modern agriculture)
Transfer Program
Main Effect on Urban Households
Gas Projects
Labor Market Reform
Tax Reform
Gas Projects + Labor
Market Reform + Tax Reform
Declining Capital Inflows
aPercentage points deviation from base run.
Source: Based on Table 10.
Average
Growth (%)a
Rural
Headcounta
Urban
Headcounta
-0.3
0.0
0.1
0.0
1.3
0.5
-0.4
0.0
0.8
0.2
-0.3
-0.1
0.0
0.0
-0.1
-2.3
0.7
-1.0
0.6
0.3
0.3
1.1
1.0
0.0
-0.4
0.2
-2.8
-1.5
-1.9
-5.4
-0.3
0.5
1.4
134. Several options can be pursued to raise urban growth and to alleviate urban poverty. Despite
limited linkages to the rest of the economy, the development of the gas sector will benefit urban
areas. The positive effect will be the stronger the more the gas projects boost domestic investment.
Beside the funds earmarked for pro-poor spending, a substantial part of the gas revenues should
thus be used to prop up public savings. The difficult task for the government then is to withstand
pressures and keep public consumption under control.
135. In addition, the two big remaining structural reforms, a deregulation of the urban labor
market and an income tax reform, would both have a significantly positive impact on growth and
poverty and should thus be initiated. The main problem with these reforms is that the potential
losers – non-agricultural workers and employees, respectively – can effectively lobby against them.
Perhaps it will become somewhat easier to overcome their resistance if the structural reforms are
carried out in combination with the gas projects as all urban household groups stand to benefit from
the latter. If this were further combined with transfer programs aimed at the rural poor, it might be
possible to generate a politically and economically feasible policy package for pro-poor growth.
136. Beyond the model-based simulations, there are further policy options to consider. As
mentioned above, there is great need to switch to a tax regime that is more progressive than the
current system and could also go beyond the revenue neutral system that is proposed in the model.
On the expenditure side, there is considerable scope for increasing the poverty focus of public
48
expenditures. In health and education sectors, this can be achieved by requiring larger co-payments
from the better-off and ensuring better access and utilization of higher education and higher order
health facilities for the poor. In order to free up resources, reforms of the pension system must
proceed in a way that limits government spending on this program that has little poverty impact.
Expenditure reform should also include a greater poverty focus of decentralized public
expenditures. This can either be ensured through greater equity in inter-governmental transfers, with
a particular emphasis on increasing the resource envelope of poorer municipalities (using formulas
that are not only based on population), or through additional central government programs in these
areas.
137. In line with the demands made from many sides, it is important to overcome the disconnect
between social sector performance and economic performance. While the social sector
improvements are impressive and need to be sustained as important achievements in their own
right, their impact on income poverty will largely materialize in the longer term and will require
complementary measures to support the productive sectors. Consequently, public expenditures
should not only focus on social sectors (important as they are for reaching the MDGs), but include
rising expenditures for measures to strengthen production among the poor. The planned efforts to
strengthen the cadenas productivas appear to be a step in the right direction. In addition,
municipalities should be encouraged to experiment with various ways of strengthening their
productive sectors through infrastructure, credit, and subsidy programs. This is particularly
important as neither the national nor the international experience suggest clear best-practice models.
Based on evaluations of successes and failures, better-managed programs should then be
mainstreamed across the country.
138. Success on such ventures will greatly depend on the ability to overcome institutional
weaknesses within the public sector. In particular, control of corruption and low government
effectiveness should be the primary focus of public sector reform efforts. This will require greater
transparency and voice in all tiers of government. Decentralization can support such a process but
only if the current defects of the process are fixed. They include unclear roles for municipalities
versus prefectures, capacity constraints in the poorer and smaller municipalities, too little central
oversight and fiscal control, too little ability to affect pro-poor spending and implementation at the
local level.
139. But the analysis has also shown that there are a range of basic constraints that need to be
addressed if Bolivia is to succeed in developing and implementing a pro-poor growth agenda. A
first critical constraint is the low domestic savings rate which leads to dependence on foreign
capital, leads to foreign debt, and contributes to the vulnerability of the economy to external shocks
against which it cannot act due to the high degree of dollarization. Here a combination of policy
measures ought to be considered. First, at the international level the question should be re-opened
whether debt relief in the case of Bolivia was deep enough. After several years of low growth, the
foreign debt burden is high, putting pressure on the fiscal side and sharply curtailing any room for
devaluations to improve the competitiveness of Bolivia’s economy. Second, measures to raise the
domestic savings should be strengthened. They should include both institutional strengthening of
the financial sector, particularly the availability of reliable savings institutions also in small towns
and rural areas, policies to shield savings from the risks of inflation (through index-linked products
and possibly a complete indexation of the economy), and policies to promote public savings
(through limiting obligations on the pension system, savings proceeds from the gas project, and
further debt relief or an increasing share of grant aid). Third, measures to reduce dollarization
should be pursued more vigorously to increase the ability of the monetary authorities to engage in
pro-poor monetary policies. Given the by now long record of low inflation, it should be in the
interest of the government to begin pushing back the dollarization of its economy. This could be
done via a set of incentives such as the recently passed financial transactions tax which is only
levied on $-denominated transactions, differential reserve requirements for $ versus Boliviano
denominated assets, and a push to popularize inflation-indexed securities as the main form of
49
issuing government debt. Once dollarization has been reduced, a much more active management of
the exchange rate would become possible to ensure international competitiveness and also address
distributional issues. To maintain this flexibility, controls on capital inflows might be needed to
ensure that they do not destabilize the currency and financial markets. Should natural gas exports
increase to the level envisaged, management of the exchange rate through sterilization policies
would be critical to limit Dutch disease effects.
140. Secondly, it appears urgently necessary to confront some of the more deep-seated
inequalities in Bolivia’s economy. As part of the on-going discussions in the National Dialogue as
well as in the Constitutional Assembly, a national plan for the redistribution of assets should be
considered. Elements of such a plan could include greater attention to land redistribution (in
addition to titling) from public lands, market and subsidy-based land reforms using land taxes to
increase the land brought to the market. In addition, such a strategy could include a mechanism that
would transfer part of the benefits from natural resources directly to the poor to ensure their direct
access to the proceeds from these assets. The use of demand-side transfers such as those pioneered
in Mexico (Progresa and Oportunidad) or Brazil (bolsa escolar) might be a good way to proceed.
Third, it appears that much of Bolivia’s current social and political turmoil stems from the fact that
its indigenous population was largely excluded from the political process. Measures to increase
their voice and power through quotas and other mechanisms might be considered to involve them
more directly in policy-making.
141. Given Bolivia’s history and its current problems, achieving sustained high rates of pro-poor
growth will be very difficult unless these deep-seated inequalities are addressed.
50
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Stephan Klasen
Department of Economics
University of Göttingen
Rainer Thiele
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Melanie Grosse, University of Göttingen
Jann Lay, Kiel Institute
Julius Spatz, Kiel Institute
Manfred Wiebelt, Kiel Institute
Operationalizing Pro Poor Growth
Country Case Study: Bolivia
ANNEXES
Annex 1
Creating National Poverty Profiles and Growth Incidence Curves With
Incomplete Income or Expenditure Data
1
Annex 2
The CGE Model
75
Annex 3
Description of Policy Simulations
79
Annex 4
Simulation Results
82
September 28, 2004
1
Annex 1 1
Creating National Poverty Profiles and Growth Incidence Curves With
Incomplete Income or Expenditure Data
Motivation
A review of the literature reveals that it is not possible to obtain consistent time series of basic
poverty measures1 – let alone inter-temporally comparable poverty profiles – for total Bolivia
drawing on the results of previous empirical studies (Tables 1 and 2). The main obstacle to this
exercise is the lack of reliable pre-1997 microdata on incomes and/or consumption expenditures
and, thus, reliable pre-1997 estimates of basic poverty measures outside the departmental capitals of
Bolivia.2 Until 1997, the Bolivian living standard measurement surveys (LSMS), which include
reasonable proxies of incomes and/or consumption expenditures, cover only the departmental
capitals of Bolivia. At the same time, the three rounds of the Bolivian Census of 1976, 1992, and
2001, and the three rounds of the Bolivian Demographic and Health Surveys (DHS) of 1989, 1994
and 1998, which cover the entire country, did not collect information on incomes, consumption
expenditures, or any other monetary welfare indicator. Additionally, the construction of consistent
time series of basic poverty measures out of the numerous point estimates and time series fractions
of previous empirical studies is prevented by the use of different analysis units (households versus
household members) and different welfare indicators (income versus adjusted income versus
observed consumption expenditure).
However, the compilation of poverty headcount indices in Tables 1 and 2 can at least be used to
derive two tentative hypotheses on the evolution of poverty in Bolivia in the era of structural
reforms. First, there is a substantial urban-rural divide. In 2001, the difference in the incidence of
poverty between households in rural areas and those in departmental capitals was 51.8 percentage
points according to the unsatisfied basic needs approach, 27.2 percentage points if the poor are
identified with a moderate poverty line, and 37.4 percentage points if the poor are identified with an
extreme poverty line. Second, this divide seems to have widened in the era of structural reforms.
The incidence of poverty fell – at least until 1999 – in departmental capitals, but remained more or
less constant in rural areas.
A simple exercise based on growth differentials in GDP per capita provides further evidence that
rural areas may have under-performed in the era of structural reforms. Since the national accounts
of Bolivia do not report separate growth rates of GDP per capita for departmental capitals, other
urban areas, and rural areas, we impute them in Table 3 by multiplying for each economic sector the
average annual growth rate of value added per capita over the period 1989 to 2002 (taken from the
national accounts) by the employment shares of those sectors in departmental capitals, other urban
areas, and rural areas, respectively (estimated from the LSMS 1999).3 We find that the imputed
average annual growth rate of GDP per capita between 1989 and 2002 was 1.25 percent in
departmental capitals, 1.09 percent in other urban areas, but only 0.62 percent in rural areas.4 This
result is mainly driven by slow growth in agriculture and fast growth in services.
1
A brief description of the poverty and inequality measures used can be found in Box A1 in the Appendix. We define
an individual as poor if her income (urban areas) or her consumption expenditure (rural areas) is below the official
poverty line. For an overview of alternative concepts of poverty, see Hemmer and Wilhelm (2000).
2
Pre-1997 microdata on incomes and/or consumption expenditures for other urban areas and rural areas of Bolivia
are, if at all available, derived from low-quality household surveys. See, for instance, Burger and Pradhan’s (1998)
criticism on the Encuesta Socio-Económica de Hogares 1995, which collected microdata on only 381 rural
households in only four departments of Bolivia (La Paz, Oruro, Potosí, and Cochabamba).
3
For comparison, we also report the sectoral GDP share in 1999 in Table 3.
4
If we account for changes in the population shares of the three regions between 1989 and 2002, the imputed growth
rate of GDP per capita was 1.24 percent in departmental capitals, 0.86 in other urban areas, and 0.67 in rural areas.
Table 1 — Poverty Headcount Indices in the Departmental Capitals of Bolivia
Source
Analysis Unit
Welfare Indicator
1976
INE (2001)
Household Members
Unsat. Basic Needs
66.3
CEPAL (2002)
Pereira and Jiménez (1998)
Jiménez and Yañez (1997)
Gray-Molina et al. (1999)
Antelo (2000)
UDAPSO (1995)
World Bank (1996)
Vos et al. (1998)
Jiménez and Landa (2004)
Wodon (2000)
Psacharopoulos et al. (1993)
Hernany et al. (2001)
World Bank (2000)
Vos et al. (1998)
World Bank (1996)
Households
Households
Households
Households
Households
Households
Households
Household Members
Household Members
Household Members
Household Members
Household Members
Household Members
Household Members
Household Members
Adj. Incomea
Adj. Incomea
Adj. Incomea
Adj. Incomea
Adj. Incomea
Consumption Exp.
Consumption Exp.
Income
Income
Adj. Incomeb
Adj. Incomec
Adj. Incomea
Adj. Incomea
Consumption Exp.
Consumption Exp.
CEPAL (2002)
Pereira and Jiménez (1998)
Jiménez and Yañez (1997)
Gray-Molina et al. (1999)
Antelo (2000)
World Bank (1996)
UDAPSO (1995)
Vos et al. (1998)
Jiménez and Landa (2004)
Wodon (2000)
Psacharopoulos et al. (1993)
Hernany et al. (2001)
World Bank (2000)
Vos et al. (1998)
World Bank (1996)
Households
Households
Households
Households
Households
Households
Households
Household Members
Household Members
Household Members
Household Members
Household Members
Household Members
Household Members
Household Members
Adj. Incomea
Adj. Incomea
Adj. Incomea
Adj. Incomea
Adj. Incomea
Consumption Exp.
Consumption Exp.
Income
Income
Adj. Incomeb
Adj. Incomec
Adj. Incomea
Adj. Incomea
Consumption Exp.
Consumption Exp.
1986
1989
1990
1991
1992
1993
1994
1995
1996
1997
1999
2000
2001
2002
Unsatisfied Basic Needs
Notes:
a
53.1
49.4
52.9
51.6
70.8
70.0
51.5
53.3
53.3
53.3
53.3
55.2
49.0
49.0
49.0
51.2
51.2
51.2
39.0
Moderate Poverty Line
45.6
49.1 45.1
49.1 45.1 47.8
49.1 45.1 47.8
49.1
46.8
46.9
53.3
52.6
56.9
52.0
73.6
54.0
59.7
73.4
59.3
75.5
60.9
60.1
22.1
21.1
21.1
21.1
24.0
24.0
24.0
21.8
50.7
46.4
52.4
50.7
48.5c
19.2
16.4
52.0
50.5
51.0
25.7
22.3
23.9
64.5
52.0
60.3
61.6
26.2
26.2
26.2
24.5
42.3
Extreme Poverty Line
16.8
22.3 17.9
22.3 18.0 20.8
22.3 18.0 20.8
20.9
22.4
19.3
23.1
46.0
41.2
22.3
52.0
23.2
29.3
27.9
28.1
30.0
23.7
42.0
32.2
46.0
25.5
28.3
29.3
21.3
20.7
23.3
21.5
22.5d
32.3
Incomes are adjusted according to the methodology of CEPAL (1995). b The adjustment factor is equal to the ratio of consumption expenditure per capita from the national accounts to the
mean income per capita from the LSMS. c The adjustment factor is equal to the ratio of GDP (taken from the national accounts) to aggregate household income (estimated from the LSMS). d
Arithmetic mean of the poverty indices estimated from the LSMS of March 1999 and Nov. 1999. The welfare indicator of the LSMS of Nov. 1999 is consumption expenditure per capita.
Source: Own compilation.
Table 2 — Poverty Headcount Indices in the Rural Areas of Bolivia
Source
Analysis Unit
Welfare Indicator
1976
1991
INE (2001)
Household Members
Unsat. Basic Needs
98.6
UDAPSO (1995)
World Bank (1996)
Vos et al. (1998)
Jiménez and Landa (2004)
World Bank (2000)
Vos et al. (1998)
World Bank (1996)
World Bank (2000)
Households
Households
Household Members
Household Members
Household Members
Household Members
Household Members
Household Members
Consumption Exp.
Consumption Exp.
Income
Income
Adj. Income b
Consumption Exp.
Consumption Exp.
Consumption Exp.
68.8a
UDAPSO (1995)
World Bank (1996)
Vos et al. (1998)
Jiménez and Landa (2004)
World Bank (2000)
Vos et al. (1998)
World Bank (1996)
World Bank (2000)
Households
Households
Household Members
Household Members
Household Members
Household Members
Household Members
Household Members
Consumption Exp.
Consumption Exp.
Income
Income
Adj. Incomeb
Consumption Exp.
Consumption Exp.
Consumption Exp.
58.6a
1992
1995
1997
1999
2000
2001
2002
Unsatisfied Basic Needs
Notes:
a
95.3
90.8
Moderate Poverty Line
82.4a
77.1a
88.3a
87.7a
78.0
77.3
84.0
87.0
77.7
83.4
75.0
59.7
66.8
81.7
Extreme Poverty Line
72.7a
73.3a
85.8a
79.1a
59.0
58.2
69.9
58.8
Microdata are derived from low-quality income and expenditure surveys. b Incomes are adjusted according to the methodology of CEPAL (1995).
Source: Own compilation.
4
Table 3 — Imputation of Average Annual Growth Rates of GDP per Capita, 1989 to 2002
Value Added Growth
1989 to 2002
Total
Bolivia
Agriculture
Mining and Quarrying
Manufacturing
Electricity, Gas, and Water
Construction
Trade and Commerce
Logistics and Communication
Financial Services
Hotels and Restaurants
Personal Servicesd
Public Administration
0.54
0.73
1.20
3.11
2.08
0.91
2.53
3.16
0.51
1.33
0.04
X
Employment Share
in 1999
DepartOther
Rural
mental
Urban
Areasc
Capitalsa
Areasb
1.44
0.88
17.23
0.31
8.69
28.48
9.23
5.34
6.21
10.64
11.55
||
12.10
0.72
22.16
0.15
8.72
22.29
6.35
1.19
6.38
9.70
10.25
||
85.64
2.16
2.55
0.15
2.09
2.76
0.46
0.11
0.95
1.03
2.11
||
Total
Bolivia
41.56
1.45
11.07
0.22
5.65
15.87
4.84
2.42
3.80
6.47
6.66
GDP Share
in 1999
Total
Bolivia
14.66
9.62
17.34
2.15
3.91
8.68
11.12
15.09
3.28
4.64
9.50
||
Imputed GDP Growth 1989 to
1.25
1.09
0.62
0.95e
2002
Notes: a Comprise the cities of Sucre, La Paz (incl. El Alto), Cochabamba, Oruro, Potosí, Santa Cruz, Tarija, Trinidad,
and El Alto. – b Municipalities outside the departmental capitals with more than 10,000 inhabitants. – c
Municipalities with less than 10,000 inhabitants. – d Including domestic services. e The observed average
annual growth rate of GDP per capita for total Bolivia is 1.17 percent.
Source: Own calculations.
To explore the trends in the urban-rural divide as well as other dimensions of poverty in more
depth and detail irrespective of the above mentioned data constraints, we set up a dynamic crosssurvey microsimulation methodology. In Section 2, we start by developing the methodology and
describing the data used. The empirical application for the case of Bolivia in Section 3 is carried out
in three steps. First, we generate an inter-temporally comparable microdata set of simulated
incomes for total Bolivia (i.e., departmental capitals, other urban areas, and rural areas) between
1989 and 2002, and check the consistency between observed and simulated incomes where the
former are available. Second, we use the simulated incomes to estimate detailed national poverty
profiles by place of residence and by household characteristics to track the evolution of poverty for
different subgroups of the population over time. Third, we evaluate the “pro-poorness” of the
simulated 1989-to-2002 income changes with the help of growth incidence curves. In Section 2.3,
sensitivity analyses are performed (a) to check the robustness of our results to two alternative model
specifications and (b) to compare our results with those derived from the asset-index (or wealthindex) approach developed by Filmer and Pritchett (2001), and Sahn and Stifel (2000, 2003).
Section 4 discusses the results.
2
Data and Approach
Our methodology to create national poverty profiles and growth incidence curves with incomplete
income or consumption expenditure data builds upon the static cross-survey microsimulation
methodology of Hentschel et al. (2000) and Elbers et al. (2003). Their objective is to analyze the
spatial dimension of poverty in detailed poverty maps of national coverage for Ecuador. Their
problem is that the Ecuadorian LSMS did not collect consumption expenditures for all households
but only for a nationally representative sample of two-stage randomly selected households. The
two-stage sample design, first selecting clusters and then households within the selected clusters,
generates a sample in which the households are not randomly distributed over space, but are
geographically grouped. Their solution to this problem is to combine the LSMS data with
concurrent unit-record Census data of all Ecuadorian households and impute consumption
5
expenditures for those municipalities which were not included in the LSMS sample. To this end,
they estimate a consumption expenditure model in the LSMS data restricting the set of covariates to
those which are also available in the Census data. Then they multiply for each household in the
Census its covariates with the corresponding regression coefficient from the consumption
expenditure model and add a randomly distributed error term.
We have a similar objective but face more severe data constraints. The pre-1997 LSMS of Bolivia
are not even nationally representative, but cover only the departmental capitals. Additionally, the
concurrent Bolivian rounds of LSMS and Census are only available for 1992 and 2001, but not for
the early years of the structural reform process. To overcome these data constraints, we extend the
static cross-survey microsimulation methodology of Hentschel et al. (2000) and Elbers et al. (2003)
by a dynamic component and replace the Census data by DHS data. The analysis proceeds in three
steps. First, we choose a base period t in which we dispose of a nationally representative LSMS as
well as a nationally representative DHS, and develop an empirical model of a monetary welfare
indicator y (hereafter referred to as income) using the LSMS data. Similar to above, the set of
covariates X is restricted to those which are also available in the corresponding DHS. The choice of
the covariates is further guided by (a) the highest possible consistency between LSMS and DHS
data as well as over time, and (b) the best possible fit of the regression model. We then construct a 3
x 3 block diagonal structure of the covariates by interacting them with three regional dummies, and
run the weighted standard semi-log OLS regression model
⎛ ytC ⎞ ⎡ X tC
⎜ T⎟ ⎢
⎜ yt ⎟ = ⎢ 0
⎜ yR ⎟ ⎢ 0
⎝ t ⎠ ⎣
0
X tT
0
0 ⎤ ⎛ β tC ⎞
⎟
⎥ ⎜
0 ⎥ ⋅ ⎜ β tT ⎟ + ε t .
X tR ⎥⎦ ⎜⎝ β tR ⎟⎠
(1)
where the indices C, T and R stand for departmental capitals, other urban areas, and rural areas,
respectively, β are coefficient vectors, and ε is an independent error term. We account for
heteroskedasticity using the covariance matrix estimator proposed by White (1980).5
Second, we check the consistency between the observed incomes of the LSMS and the simulated
incomes of the DHS in period t. To this end, we apply the coefficient estimates β̂ from regression
~
model (1) to the DHS covariates X and generate simulated incomes
~
⎛~
y C ⎞ ⎡X C
0
0 ⎤ ⎛⎜ βˆ tC0 ⎞⎟ ⎛ utC ⎞
⎜ tT ⎟ ⎢ t
⎜ ⎟
⎥
~
(2)
yt ⎟ = ⎢ 0
X tT
0 ⎥ ⋅ ⎜ βˆ tT ⎟ + ⎜ utT ⎟ .
⎜~
~
⎜
⎟
⎜~
⎜
⎟
R ⎟
R
R
R
⎢
0 X t ⎥⎦ ⎜⎝ βˆ t ⎟⎠ ⎝ ut ⎠
⎝ yt ⎠ ⎣ 0
Since the regression model explains only a fraction of the variance, we add the realization of
normally distributed random variables uC, uT, and uR with mean zero and a variance equal to the
variance of the error term in the respective region. This simulation procedure is repeated 200 times
yˆ ) be a poverty or
to create 200 nationally representative samples of simulated incomes. Letting P( ~
inequality measure based on the simulated income distribution, we can then generate the conditional
yˆ ) , in particular, its mean point estimate and its prediction error, from the 200
distribution of P ( ~
samples of simulated incomes. The fit of the imputation can be evaluated by comparing the poverty
5
Unfortunately, the primary sample units of the pre-1997 LSMS are not available in Bolivia so that we cannot split
the error term into a spatial and an idiosyncratic component as in Elbers et al. (2003).
6
and inequality measures estimated from observed incomes of the LSMS, P ( y ) , with those
estimated from simulated incomes of the DHS, P ( ~
yˆ ) .
Third, we choose an earlier period t–1 in which the LSMS covers only the departmental capitals
and partially re-run our regression model
ytC−1 = X tC−1 ⋅ β tC−1 + ε tC−1
(3)
to obtain the coefficient estimates and the variance of the error term for the departmental capitals in
period t–1. We assume that the absolute differences in the regression coefficients between
departmental capitals on the one hand, and other urban areas and rural areas on the other hand,
remain constant between period t–1 and t,6 and arrive at the coefficient estimates for other urban
areas and rural areas, respectively, in period t–1
βˆ tT−1 = βˆ tC−1 + ( βˆ tT − βˆ tC ) and βˆ tR−1 = βˆ tC−1 + ( βˆ tR − βˆ tC ) .
(4)
In a similar vein, by assuming that the relative change in the variances of the error terms between
period t–1 and t is identical for all three regions, we obtain the variances of the error terms for other
urban areas and rural areas, respectively, in period t–1
var(ε tC−1 )
var(ε tC−1 )
R
R
and var(ε t −1 ) = var(ε t ) ⋅
.
var(ε ) = var(ε ) ⋅
var(ε tC )
var(ε tC )
T
t −1
T
t
(5)
Repeating the simulation exercise (2) with the coefficient estimates from equations (3) to (5) and
the DHS data in period t–1, we can create 200 nationally representative samples of simulated
incomes in period t–1. Again we can compare the poverty and inequality measures between the two
household surveys. In contrast to above, however, this exercise is only possible for the departmental
capitals where observed incomes are available. After this consistency check, we can use the
simulated incomes (a) to construct inter-temporally comparable poverty profiles of national
coverage for Bolivia and (b) to evaluate the “pro-poorness” of changes in the distribution of
simulated incomes over time with the help of growth incidence curves.
Our set of LSMS consists of four multi-purpose household surveys conducted by the Instituto
Nacional de Estadísticas de Bolivia (National Statistical Office of Bolivia): the 2nd round (Nov.
1989) and the 7th round (July to Dec. 1994) of the Encuesta Integrada de Hogares (EIH), and the 1st
round (Nov. 1999) and the 4th round (Nov. 2002) of the Encuesta Continua de Hogares (ECH). The
EIH cover only the departmental capitals of Bolivia, while the ECH are nationally representative.
Two-stage sampling techniques were used in selecting the sample of households, and sampling was
done in a way to ensure self-weighting. The purpose of the LSMS is to collect individual,
household, and community level data to measure the welfare level of the sampled population and its
changes over time. In addition to income and/or expenditure data, the LSMS provide information on
demographics, asset ownership, education, employment, and health.
In order to be able to compare our results with earlier empirical studies, we largely use per capita
incomes as our income indicator. Only when we turn to the impact of household size on poverty, do
we check the sensitivity of our results by also using equivalized incomes. As welfare indicator, we
use monthly consumption expenditures including own consumption (excluding annualized costs for
6
We check the robustness of our results to an alternative assumption on the evolution of the regression coefficients
between period t–1 and t in Section 4.1.
7
durable consumer goods) for rural areas, and monthly labor income (excluding fringe benefits)7 plus
monthly capital income for urban areas. The choice of the mixed measurement unit, which is
common for Bolivia (see, for instance, INE-UDAPE 2002), can be justified by that (a) an allexpenditure specification is not possible since the EIHs collected only income but no expenditure
data, and (b) an all-income specification is not preferable since incomes only poorly reflect the
long-term welfare in rural areas due to large seasonal income fluctuations, a high degree of own
consumption in agricultural households (Deaton and Zaidi 2002). On the practical level, it appears
that incomes in rural areas are seriously under-estimated leading to implausible poverty figures, a
common finding in many developing countries. In order to account for non-declaration of incomes,
we apply a statistical matching approach similar to Hernany (1999). By contrast, we do not adjust
for sub-declaration (under-reporting) of incomes (e.g. by scaling up the mean income and mean
consumption expenditures in the LSMS to those in the national accounts) because (a) it is a priori
not clear whether national account data or LSMS data are more accurate, and (b) Bolivia does not
report separate national account data for departmental capitals, other urban areas, and rural areas.8
To identify the poor, we use the two sets of poverty lines provided by the Unidad de Análisis de
Políticas Sociales y Económicas (UDAPE) (Table 4). The extreme poverty lines are given by the
costs of food baskets which reflect (a) the nutritional requirements of adults, and (b) the local eating
habits of the middle quintile of the income distribution. The moderate poverty lines additionally
include the costs of non-nutritional basic needs and are obtained by multiplying the extreme poverty
lines by the inverse of local Engel coefficients. Since no rural poverty lines are available for 1989
and 1994, we extrapolate the relative difference between the rural poverty line and the weighted
average urban poverty line of 1999. The poverty lines are updated using the (regionallydisaggregated) prices for the goods included in the poverty basket.9
In Table 4 we also report a poverty line that simply takes the 1989 values and inflates them to
2002 using the Consumer Price Index (2002cpi) to see whether the prices paid by the poor have
developed differently to the overall price level. The results show that overall prices have risen
faster in all parts of the country than the prices faced by the poor. The difference between 1989 and
2002 is on the order of 20-30%. To the extent that the poverty basket has not changed over this
time period, 10 this implies that the poor have been benefiting from falling relative prices of the
goods they consume most and this will have enabled some of the poor to escape poverty.
Our set of Demographic and Health Surveys (DHS) consists of the first three Bolivian rounds
which were conducted in 1989, 1994, and 1998.11 Two-stage sampling techniques were used to
select nationally representative samples of women aged between 15 and 49 who serve as
respondents of the DHS. The main objective of the DHS is to collect information on health and
fertility trends. Additionally, the questionnaire includes some questions on the educational
attainment and the employment situation of the respondent and her partner, as well as on the asset
ownership of the household.
7
Only if we exclude fringe benefits is the measurement unit inter-temporally comparable between 1989 and 2002.
This is because the EIHs collected, if at all, only the incidence and type of fringe benefits but not their monetary
equivalent. As a consequence, our poverty estimates for 1999 and 2002 are somewhat higher than the official figures
provided by INE (var. iss.).
8
For an description and evaluation of, and an analysis of the sensitivity of poverty measures to, different adjustment
methods see Székely et al. (2000).
9
Further information on the construction of the Bolivian poverty lines can be found in Box A2 in the Appendix.
10
One should bear in mind that the poverty line is a Laspeyres Index using a fixed basket. As a result of changing
preferences and prices, the poor might have changed their consumption habits over time which would obviously
affect the assessment of the differences between the poverty line escalation and the development of the CPI.
11
The fourth Bolivian DHS round of 2003 has not been made publicly available until finishing this study.
8
The covariates taken from the two data sources and their sample means are listed in Tables A1
and A2 in the Appendix to this Annex. They can be grouped into five categories: information on (a)
demographics of the household, (b) asset ownership of the household, (c) educational attainment of
adult men and women, (d) employment situation of adult men and women, and (e) health situation
of children. By choosing suitable variables and dummy categories, we obtained a high degree of
consistency both across surveys and over time.
Table 4 — Poverty Lines for Bolivia (in current Bolivianos)
Moderate Poverty Line
Extreme Poverty Line
a
1989
1994
1999
2002
2002 cpi
Urban Areas
Chuquisaca
La Paz (Capital City)
La Paz (El Alto)
Cochabamba
Oruro
Potosí
Tarija
Santa Cruz
Beni
Pando
138.5
135.3
116.6
142.1
123.0
113.1
144.3
141.8
141.8
141.8
241.8
227.9
192.6
253.2
207.1
190.5
257.3
237.8
237.8
237.8
335.4
324.0
270.4
351.1
294.7
271.0
356.8
354.7
354.7
354.7
335.6
327.0
272.6
351.3
297.4
273.5
351.3
343.9
343.9
343.9
Rural Areas
96.9b
164.4b
233.6
Pop. Weighted Average
119.5
204.8
299.3
a
c
a
1989
1994
1999
2002
2002 cpic
395.5
383.3
332.9
405.8
351.1
323.0
412.1
404.8
404.8
404.8
73.3
75.2
70.7
71.8
75.2
75.2
71.8
72.0
72.0
72.0
127.9
126.6
116.7
127.6
126.6
126.6
127.9
120.7
120.7
120.7
169.4
180.1
164.1
177.3
163.9
150.7
178.6
180.2
180.2
180.2
169.5
181.8
165.5
177.4
165.3
152.1
177.4
174.7
174.7
174.7
209.2
214.6
201.8
204.9
214.6
214.6
204.9
205.5
205.5
205.5
233.4
276.6
55.2b
93.4b
131.2
133.0
157.6
298.1
351.2
65.9
112.3
160.6
160.3
190.5
nd
Notes: Since no poverty lines are available for the 2 round (Nov. 1989) of the EIH, they are constructed as the
arithmetic mean of the poverty lines for the 1st round (March 1989) and the 3rd round (Sept. 1990) of the EIH. –b
Constructed by extrapolating the relative difference between the rural poverty line and the weighted average
urban poverty line of 1999. –c 1989 poverty lines inflated with the CPI.
Source: Own compilation based on unpublished data of UDAPE.
3
Empirical Results
We build our methodology around the base period 1999 and then apply it to the earlier periods 1989
and 1994. Additional data constraints impede our empirical analysis in three respects. First, to
create inter-temporally comparable samples of simulated incomes for Bolivia it would be ideal to
use a set of covariates which is available in all three pairs of concurrent household surveys of 1989,
1994, and 1999. At the same time, however, the availability of covariates in the LSMS and the DHS
changes over time due to changes in their questionnaires. In order to avoid too small a set of
covariates we, thus, decided to use three different sets of covariates to (a) check the consistency
between the LSMS and the DHS data in 1999, (b) to create 200 samples of simulated incomes in the
DHS 1989 data, and (c) to create 200 samples of simulated incomes in the DHS 1994 data.12
Second, since no Bolivian DHS round was conducted in 1999, we have to use the DHS 1998 data
for our consistency check. That is, we compare the poverty and inequality measures based on
observed incomes of the LSMS 1999 with those based on simulated incomes of the DHS 1998,
assuming that the distribution of the covariates remained reasonably constant in between. By
contrast, for 1989 and 1994 we dispose of concurrent rounds of LSMS and DHS. Third, due to its
focus on health and fertility trends, the DHS data (in 1989) only include households with at least
12
To put it more formally, we only require that the set of covariates be identical for the LSMS and the DHS in period
t–1 as well as for the LSMS in period t. To check for robustness, we also performed our subsequent empirical
analysis for the smaller set of common covariates. While, as expected, the consistency check performed worse, the
empirical results did not change qualitatively.
9
one woman of reproductive age (i.e., aged between 15 and 49). We, thus, have to replicate this
implicit sample selection in the LSMS data.13
3.1 Consistency Check
In Tables 5 and 6, we provide four sets of poverty and inequality measures: (a) their point estimates
from observed incomes of all households in the LSMS, (b) their point estimates from observed
incomes of households with at least one woman of reproductive age in the LSMS, (c) their mean
point estimates and standard deviations from 200 samples of predicted incomes in the LSMS, and
(d) their mean point estimates and standard deviations from 200 samples of simulated incomes in
the DHS.14 Taking differences between successive members of this series enables us to decompose
the overall difference between observed and simulated poverty and inequality measures into three
components related to (a-b) the implicit sample selection in the DHS data, (b-c) the specification of
the error term in the underlying regression model, and (c-d) differences in the distribution of the
covariates between LSMS and DHS.
For 1989 and 1994, for which the consistency check is limited to departmental capitals, the
results are very encouraging. Restricting the sample to households with at least one woman of
reproductive age does not induce a serious bias in estimating poverty and inequality measures.
Using a normally distributed error term (rather than drawing observed residuals) to create 200
samples of predicted incomes in the LSMS, only slightly understates the poverty headcount, renders
a very close fit for the poverty gap, and only slightly overstates the squared poverty gap.15 It also
only slightly understates income inequality as evidenced by lower values of the Gini coefficient and
the Atkinson indices. The transition from LSMS data to DHS data does, if at all, only slightly
reduce the poverty and inequality measures.
For 1999, the situation is somewhat less favorable. Only the inequality measures continue to be
unbiased by sample selection, while the poverty measures seem to be upward biased. Our
specification of the error term seriously underestimates the Atkinson index with ε = 2 in
departmental capitals. Most striking, however, are the large differences between predicted and
simulated poverty indices, particularly so in rural areas. The underlying reason is most probably the
lack of consistency with respect to the collection period of the two underlying household surveys.
The DHS 1998 data, the covariates of which were used to create the simulated incomes, were
collected during an economic boom. By contrast the observed incomes of the LSMS 1999 were
collected after a sharp economic downturn when Bolivia experienced strongly negative growth in
GDP per capita.
These inconsistencies notwithstanding, we are confident that the conditions for applying our
dynamic cross-survey microsimulation methodology are fulfilled for the case of Bolivia. First, the
simulations can accurately reproduce the observed poverty trends in departmental capitals, where
we have observed incomes for comparison. The differences between observed and simulated
poverty measures are small compared to their changes over time. Second, the DHS 1998 data,
which are least consistent to those of the corresponding LSMS, are not used in the subsequent
analysis. Only the poverty profiles and growth incidence curves for 1989 and 1994 draw on
13
For 1994 and 1998 (but not for 1989), the DHS provide an additional data module on – and responded by – male
adults. We opted against using this data module for two reasons: (a) the information was not collected for the
husbands and partners of all women included in the main module so that we would have had to reduce the sample
size and possibly would have introduced another sample-selection bias, and (b) our microdata set of simulated
incomes would no longer be inter-temporally comparable over the whole observation period from 1989 to 2002.
14
The underlying regression results are not reported here, but are available upon request.
15
This is because the distributions of the error terms is slightly skewed to the right. The kernel density graphs of the
errors terms are not reported here, but are available upon request.
10
simulated incomes of the DHS. Those for 1999 and 2002 are based on observed incomes of the
LSMS.
In Section 2, we assumed that the absolute difference in the regression coefficients between
departmental capitals on the one hand, and other urban areas and rural areas on the other hand,
remained constant between 1989 and 1999. If this assumption does not hold, i.e., if the coefficients
in rural areas deteriorated relative to those in urban areas, the decline in poverty in rural areas
shown in the subsequent analysis would be overstated. We address this potential bias in Section 4.1.
Another factor that may contribute to overstating the decline in poverty – albeit in this case not
limited to rural areas – is that the degree of underreporting, which is common to all income and
expenditure surveys, may have fallen over time due to improvements in the questionnaire design.
Taken together, we, thus, caution to treat the reduction in poverty as an upper bound, and
particularly so in rural areas.
3.2
Poverty Profiles
After having completed this consistency check, we can proceed to construct inter-temporally
comparable poverty profiles of national coverage for Bolivia to get an understanding of where and
who the poor are. Where possible – in departmental capitals throughout the entire observation
period and in the rest of the country for 1999 and 2002 – we use poverty measures estimated from
observed incomes of the LSMS. The remaining gaps are filled with the mean point estimates and
the standard deviations of poverty measures from 200 samples of simulated incomes in the DHS. In
what follows, we focus on delineating major poverty trends of Bolivia during the era of structural
reforms. The discussion of their underlying causes is deferred to Section 5.
We start our empirical analysis with a disaggregation of the poverty headcount by place of
residence in Table 7.16 Between 1989 and 2002, total Bolivia experienced a significant reduction in
the incidence of poverty. Moderate poverty decreased from three quarters to two thirds of the
population. The reduction in extreme poverty was even more spectacular; it decreased by 17
percentage points. Yet, the picture is not all favorable. In the late 1990s, the poverty trend reversed
and the incidence of moderate and extreme poverty in total Bolivia started to increase again.
As expected, rural households were more likely to be poor than those in departmental capitals and
other urban areas, even after controlling for local cost-of-living differences. What is more of
concern here is that rural households did not fully participate in the reduction of moderate poverty
between 1989 and 1999. Departmental capitals and other urban areas could reduce the incidence of
moderate poverty by 16 and 12 percentage points, respectively. In rural areas, this reduction was
only 6 percentage points – despite starting from a higher level of poverty.17 By contrast, households
in departmental capitals were most affected by the economic downturn in the late 1990s, accounting
for almost the entire increase in the incidence of moderate and extreme poverty in total Bolivia
between 1999 and 2002. Taken together, the poverty trends suggest that rural areas were quite
detached from improvements and deteriorations in the overall economic environment.
16
For the corresponding tables for the poverty gap and the squared poverty gap see Tables A3 and A4 in the
Appendix.
17
That is, in relative terms, the performance of rural areas was even worse. As concerns extreme poverty, rural areas
also experienced the lowest absolute (!) reduction the poverty headcount index between 1989 and 1999.
11
Table 5 — Comparison of Poverty Indices Based on Observed and Simulated Incomes
1989
LSMS Data
All Hh. Sample Prediction
DHS
Data
Simulation
1994
LSMS Data
All Hh. Sample Prediction
DHS
Data
Simulation
1999
LSMS Data
All Hh. Sample Prediction
DHS
Data
Simulationa
Moderate Poverty Line
Departmental Capitals
Headcount
66.60
Gap
33.31
Squared Gap
20.78
67.21 65.42*
(0.70)
32.92 33.14*
(0.43)
19.96 20.62*
(0.35)
Other Urban Areas
Headcount
n.a.
n.a.
Gap
n.a.
n.a.
Squared Gap
n.a.
n.a.
Rural Areas
Headcount
n.a.
n.a.
Gap
n.a.
n.a.
Squared Gap
n.a.
n.a.
Total Bolivia
Headcount
n.a.
n.a.
Gap
n.a.
n.a.
Squared Gap
n.a.
n.a.
64.81
(0.83)
32.92*
(0.52)
20.57*
(0.42)
58.09
n.a. 81.05
(1.32)
n.a. 51.31
(0.92)
n.a. 37.28
(0.82)
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a. 89.66
(0.59)
n.a. 58.30
(0.50)
n.a. 42.21
(0.49)
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a. 76.88
(0.50)
n.a. 45.45
(0.35)
n.a. 31.37
(0.31)
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
25.15
13.91
59.49 58.06
(0.64)
25.74 25.92*
(0.31)
14.16 14.67
(0.23)
57.35
(0.75)
25.33*
(0.41)
14.25*
(0.30)
48.73
n.a. 75.13
(1.16)
n.a. 44.68
(0.69)
n.a. 31.38
(0.58)
66.92
n.a. 89.55
(0.47)
n.a. 60.90
(0.34)
n.a. 45.83
(0.33)
81.64
n.a. 72.37
(0.45)
n.a. 41.89
(0.25)
n.a. 28.94
(0.21)
63.69
20.28
11.39
33.64
20.71
46.02
30.39
31.85
19.85
51.05 50.53*
(1.49)
21.02 22.48*
(0.87)
11.60 12.82*
(0.68)
48.05
(0.68)
21.28*
(0.37)
12.17
(0.28)
69.09 67.59*
(2.32)
34.70 35.25*
(1.51)
21.12 22.52*
(1.23)
64.17
(1.12)
33.59*
(0.67)
21.69*
(0.53)
83.37 84.31*
(1.10)
47.71 48.74*
(0.82)
31.85 32.47*
(0.79)
79.07
(0.62)
43.10
(0.41)
27.67
(0.34)
65.21 65.03*
(0.92)
32.53 33.67
(0.58)
20.19 21.22
(0.49)
60.33
(0.43)
30.06
(0.27)
18.52
(0.20)
24.22 25.30*
(1.53)
8.00 9.01*
(0.70)
3.94 4.43*
(0.45)
23.10*
(0.65)
8.24*
(0.27)
4.06*
(0.16)
34.31 39.51
(2.60)
13.97 16.56
(1.26)
8.01 9.26*
(0.89)
38.09
(1.28)
16.60
(0.52)
9.54
(0.35)
59.98 62.58*
(1.51)
27.37 27.87*
(0.93)
15.65 15.58*
(0.71)
54.79
(0.76)
22.94
(0.37)
12.32
(0.25)
38.35 40.58
(1.00)
15.73 16.79
(0.53)
8.68 9.09*
(0.38)
35.43
(0.46)
14.16
(0.20)
7.51
(0.13)
Extreme Poverty Line
Departmental Capitals
Headcount
39.44
Gap
16.26
39.38 39.62*
(0.73)
15.29 16.19
(0.36)
8.05 8.77
(0.26)
Squared Gap
9.30
Other Urban Areas
Headcount
n.a.
n.a.
Gap
n.a.
n.a.
Squared Gap
n.a.
n.a.
Rural Areas
Headcount
n.a.
n.a.
Gap
n.a.
n.a.
Squared Gap
n.a.
n.a.
Total Bolivia
Headcount
n.a.
n.a.
Gap
n.a.
n.a.
Squared Gap
n.a.
n.a.
38.78*
(0.92)
15.92*
(0.43)
8.65
(0.30)
28.04
n.a. 62.84
(1.44)
n.a. 34.10
(0.90)
n.a. 22.52
(0.71)
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a. 74.59
(0.92)
n.a. 39.13
(0.58)
n.a. 24.59
(0.47)
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a. 56.24
(0.61)
n.a. 27.53
(0.34)
n.a. 16.78
(0.25)
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
9.47
4.57
28.78 29.66*
(0.54)
9.58 10.26
(0.25)
4.51 4.90
(0.16)
28.34*
(0.73)
9.66*
(0.29)
4.56*
(0.18)
23.01
n.a. 53.31
(1.22)
n.a. 27.02
(0.63)
n.a. 17.17
(0.49)
33.10
n.a. 76.05
(0.62)
n.a. 43.33
80.38)
n.a. 28.84
(0.34)
57.93
n.a. 50.43
(0.45)
n.a. 25.21
(0.22)
n.a. 15.79
(0.17)
37.48
8.00
4.20
13.93
8.29
25.88
14.55
15.52
8.66
Notes: Poverty indices are calculated using income data for departmental capitals and other urban areas, expenditure data for rural
areas, and mixed income-expenditure data for total Bolivia. Standard errors of the poverty indices in brackets (only applicable to
those based on predicted and simulated incomes). – a The covariates for the simulation exercise are taken from the third Bolivian
DHS round, which was conducted in 1998. * denotes that the 95-percent confidence interval includes the corresponding index
value in the “Sample” column.
Source: Own calculations.
12
Table 6 — Comparison of Inequality Indices Based on Observed and Simulated Incomes
1989
1994
Prediction
DHS
Data
Simulation
0.492*
(0.007)
0.196
(0.006)
0.350*
(0.008)
0.566*
(0.008)
LSMS Data
All
Obs.
Sample
Departmental Capitals
Gini
0.512
0.505
A(0.5)
0.222
0.211
A(1.0)
0.348
0.364
A(2.0)
0.568
0.582
Other Urban Areas
Gini
n.a.
n.a.
A(0.5)
n.a.
n.a.
A(1.0)
n.a.
n.a.
A(2.0)
n.a.
n.a.
Rural Areas
Gini
n.a.
n.a.
A(0.5)
n.a.
n.a.
A(1.0)
n.a.
n.a.
A(2.0)
n.a.
n.a.
Total Bolivia
Gini
n.a.
n.a.
A(0.5)
n.a.
n.a.
A(1.0)
n.a.
n.a.
A(2.0)
n.a.
n.a.
All
Obs.
Sample
0.497*
(0.008)
0.200*
(0.007)
0.357*
(0.009)
0.574*
(0.010)
0.493
0.481
0.202
0.190
0.341
0.329
0.537
0.523
0.547
(0.015)
n.a. 0.244
(0.014)
n.a. 0.428
(0.018)
n.a. 0.667
(0.017)
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
0.475
(0.010)
n.a. 0.184
(0.009)
n.a. 0.321
(0.011)
n.a. 0.510
(0.012)
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
0.555
(0.006)
n.a. 0.250
(0.006)
n.a. 0.433
(0.007)
n.a. 0.657
(0.006)
1999
Prediction
DHS
Data
Simulation
0.470
(0.005)
0.179
(0.004)
0.318*
(0.006)
0.513*
(0.007)
0.455
(0.006)
0.166
(0.005)
0.300
(0.007)
0.495
(0.008)
0.487
0.480
0.197
0.188
0.340
0.340
0.646
0.650
0.537
(0.012)
n.a. 0.236
(0.012)
n.a. 0.419
(0.014)
n.a. 0.668
(0.013)
0.457
0.455
0.176
0.171
0.312
0.323
0.615
0.626
n.a.
0.497
(0.006)
n.a. 0.199
(0.006)
n.a. 0.349
(0.007)
n.a. 0.545
(0.008)
0.436
0.423
0.155
0.145
0.281
0.267
0.471
0.458
n.a.
0.530
0.525
0.232
0.225
0.400
0.399
0.658
0.661
LSMS Data
n.a.
0.550
(0.004)
n.a. 0.248
(0.004)
n.a. 0.443
(0.005)
n.a. 0.689
(0.004)
DHS
Data
Predic- Simulat
Sample
tion
iona
LSMS Data
All
Obs.
0.491*
(0.011)
0.195*
(0.009)
0.350*
(0.014)
0.568
(0.017)
0.488*
(0.006)
0.193*
(0.005)
0.348*
(0.007)
0.570
(0.008)
0.482*
(0.020)
0.189*
(0.017)
0.345*
(0.024)
0.580*
(0.029)
0.500
(0.010)
0.204
(0.009)
0.371
(0.011)
0.615*
(0.012)
0.444*
(0.012)
0.159*
(0.009)
0.283*
(0.013)
0.459*
(0.016)
0.443*
(0.006)
0.158
(0.005)
0.284
(0.006)
0.465*
(0.007)
0.538*
(0.008)
0.234*
(0.008)
0.410*
(0.010)
0.632
(0.011)
0.531*
(0.005)
0.229*
(0.004)
0.404*
(0.005)
0.629
(0.005)
Notes: Inequality indices are calculated using income data for departmental capitals and other urban areas, expenditure data for rural
areas, and mixed income-expenditure data for total Bolivia. Standard errors of the inequality indices in brackets (only applicable
to those based on predicted and simulated incomes). – a The covariates for the simulation exercise are taken from the third
Bolivian DHS round, which was conducted in 1998. * denotes that the 95-percent confidence interval includes the corresponding
index value in the “Sample” column.
Source: Own calculations.
13
There are also substantial differences in the incidence of poverty across the nine departments of
Bolivia. The moderate poverty headcount in 1989 ranged from 62 percent in Santa Cruz to 92
percent in Potosí. The corresponding figures for the extreme poverty headcount were 31 percent and
79 percent, respectively. The departmental distribution of the poverty headcount index was also
very stable in Bolivia. While Santa Cruz, which is a major host of commercial agriculture and foodprocessing industry, had the lowest incidence of poverty throughout the entire observation period, it
was highest in Potosí, followed by Chuquisaca, which are particularly dependent on subsistence
agriculture.
To gain insights into other dimensions of poverty, we proceed with a disaggregation of the
poverty headcount index by household characteristics for total Bolivia as well as for its
departmental capitals, other urban areas, and rural areas in Tables 8 to 11.18 By far the most
important determinant of poverty and its change over time is education. Households where the
average education of adult members was primary schooling or less (i.e., <= 5 years) rarely escaped
poverty, even in departmental capitals. Secondary schooling (i.e., 6 to 12 years) and tertiary
schooling (i.e., 13 years and above) substantially reduced the likelihood of poverty. Their
contribution to reducing the incidence of poverty (relative to the next lower schooling category) was
highest in rural areas and lowest in other urban areas. Over time, the distribution of the poverty
headcount indices across schooling groups changed significantly. While the incidence of poverty
fell in all three schooling groups, the returns to secondary schooling declined somewhat while the
returns to tertiary schooling increased substantially.
Table 7 — Spatial Disaggregation of the Poverty Headcount in Bolivia, 1989 to 2002
Moderate Poverty Line
Extreme Poverty Line
1989
1994
1999
2002
1989
1994
1999
2002
76.88
(0.50)
72.37
(0.45)
65.21
67.22
56.24
(0.61)
50.43
(0.45)
38.35
39.24
Departmental Capitals
67.21
59.49
51.05
55.13
39.38
28.78
24.22
27.03
Other Urban Areas
81.05
(1.32)
89.66
(0.59)
75.13
(1.16)
89.55
(0.47)
69.09
67.70
36.65
83.83
53.31
(1.22)
76.05
(0.62)
34.31
83.37
62.84
(1.44)
74.59
(0.92)
59.98
57.24
88.09
(0.97)
78.48
(0.99)
74.04
(1.21)
82.01
(1.16)
91.85
(0.83)
81.44
(1.06)
61.62
(1.33)
80.22
(1.28)
86.02
(1.06)
69.52
(0.87)
74.27
(1.32)
80.96
(1.00)
88.18
(0.91)
81.67
(1.22)
58.11
(1.14)
80.35
(1.22)
84.15
79.66
64.28
69.05
46.33
42.53
64.69
70.66
31.70
42.58
68.64
71.61
47.63
43.64
84.66
82.68
63.01
59.55
61.68
65.36
26.39
30.52
50.59
56.26
21.66
25.55
53.00
63.87
73.18
(1.12)
45.82
(0.89)
49.34
(1.36)
64.22
(1.25)
79.39
(1.01)
58.75
(1.32)
31.14
(1.00)
59.56
(1.43)
64.34
68.55
73.14
(1.39)
57.12
(1.28)
51.82
(1.29)
63.07
(1.39)
83.27
(1.19)
60.49
(1.25)
35.64
(1.31)
56.38
(1.46)
14.73
27.29
Total
By Region
Rural Areas
By Department
Chuquisaca
La Paz
Cochabamba
Oruro
Potosí
Tarija
Santa Cruz
Beni & Pando
Notes: Poverty indices are calculated using income data for departmental capitals and other urban areas, expenditure data for rural
areas, and mixed income-expenditure data for total Bolivia. Standard errors of the poverty indices in brackets (only
applicable to those based on simulated data).
Source: Own calculations.
18
For the corresponding tables for the poverty gap and the squared poverty gap see Tables A5 to A12 in the Appendix.
14
Table 8 —
Disaggregation of the Poverty Headcount in Bolivia by Household Characteristics,
1989 to 2002
Moderate Poverty Line
Total
By Hh Size
<=3
4-6
>=7
By % of Hh Members
between 15 and 65
years
<= 50
> 50
By Age of Hh Head
<=34
35-49
50-65
>=66
By Language of Hh
Head
Spanish
Indigenous
By Gender of Hh Head
Male
Female
By Average Years of
Schooling of Adultsa
<=5
6-12
>=13
By Profession of
Principal Wage Earnerb
White-Collar Worker
Blue-Collar Worker
Agriculture
Sales & Services
Not Employed
c
By % of Adult Women
in Employment
>0
=0
Extreme Poverty Line
1989
1994
1999
2002
1989
1994
1999
2002
76.88
(0.50)
72.37
(0.45)
65.21
67.22
56.24
(0.61)
50.43
(0.45)
38.35
39.24
71.41
(1.25)
74.47
(0.67)
85.08
(0.80)
61.72
(1.14)
71.56
(0.59)
83.83
(0.75)
47.35
43.30
17.91
63.87
34.28
35.25
80.35
80.84
37.16
(1.00)
49.16
(0.56)
65.29
(0.87)
22.02
61.01
47.91
(1.45)
53.03
(0.79)
67.84
(1.12)
52.61
52.93
83.41
(0.59)
67.94
(0.82)
81.71
74.93
78.70
64.85
60.94
48.79
50.69
60.50
(0.74)
53.64
53.64
44.46
(0.89)
37.07
(0.71)
25.91
25.69
79.12
(0.91)
76.99
(0.77)
74.54
(1.16)
70.80
(2.45)
74.00
(0.73)
72.98
(0.69)
67.96
(1.10)
70.43
(1.90)
67.29
69.44
39.95
69.39
40.43
42.67
57.86
58.72
31.56
31.28
63.66
68.30
50.05
(0.78)
51.90
(0.66)
47.04
(1.03)
53.27
(1.62)
39.02
66.97
58.06
(1.00)
56.92
(0.90)
53.17
(1.34)
50.96
(2.30)
39.13
33.41
70.69
(0.62)
94.59
(0.66)
63.72
(0.60)
92.57
(0.54)
51.27
54.20
23.98
79.31
38.00
(0.59)
79.48
(0.75)
22.27
79.75
47.07
(0.69)
82.51
(1.14)
55.11
53.42
77.50
(0.54)
73.69
(1.26)
73.15
(0.48)
68.57
(1.17)
65.64
68.66
40.31
58.38
51.59
(0.51)
44.82
(1.12)
38.82
62.82
57.27
(0.68)
50.98
(1.38)
35.73
32.69
90.76
(0.55)
68.89
(0.94)
34.50
(2.17)
89.61
(0.50)
67.15
(0.82)
28.69
(1.44)
86.04
85.61
60.68
63.60
32.01
31.02
20.11
24.61
74.37
(0.67)
38.71
(0.79)
9.68
(0.97)
61.53
63.14
74.54
(0.83)
42.61
(1.04)
13.91
(1.51)
4.65
5.57
49.67
(1.31)
78.39
(1.08)
95.22
(0.54)
68.87
(1.48)
80.14
(1.30)
37.11
(1.42)
73.86
(0.93)
94.80
(0.42)
63.49
(1.30)
71.16
(1.55)
33.84
28.96
9.68
70.42
30.80
37.81
88.11
87.15
65.56
61.91
53.30
45.69
29.74
19.81
53.82
62.95
15.88
(0.92)
45.55
(1.10)
84.40
(0.65)
34.01
(1.20)
44.73
(1.53)
14.82
69.23
26.81
(1.07)
53.99
(1.28)
83.51
(1.03)
42.87
(1.53)
58.06
(1.63)
32.02
31.45
59.57
(1.14)
84.30
(0.53)
69.72
(0.55)
77.94
(0.82)
63.95
65.55
38.47
70.77
49.02
(0.51)
53.39
(0.92)
37.27
67.95
35.46
(1.02)
65.15
(0.75)
40.69
40.89
Notes: Poverty indices are calculated using mixed income-expenditure data. Standard errors of the poverty indices in brackets
(only applicable to those based on simulated data). – a Women aged between 15 and 49 and their husbands and partners. – b
In the case of DHS: Husband or partner of the oldest woman aged between 15 and 49. If she is single, this woman herself.
In the case of LSMS: Household head. – c Women aged between 15 and 49.
Source: Own calculations.
15
Table 9 —
Disaggregation of the Poverty Headcount in the Departmental Capitals of Bolivia by
Household Characteristics, 1989 to 2002
Moderate Poverty Line
Total
By Hh Size
<=3
4-6
>=7
By % of Hh Members
between 15 and 65
years
<= 50
> 50
By Age of Hh Head
<=34
35-49
50-65
>=66
By Native Language of
Hh Head
Spanish
Indigenous
By Gender of Hh Head
Male
Female
By Average Years of
Schooling of Adultsa
<=5
6-12
>=13
By Profession of
Principal Wage Earnerb
White-Collar Worker
Blue-Collar Worker
Agriculture
Sales & Services
Not Employed
By % of Adult Womenc
in Employment
>0
=0
Extreme Poverty Line
1989
1994
1999
2002
1989
1994
1999
2002
67.21
59.49
51.05
55.13
39.38
28.78
24.22
27.03
49.96
64.95
76.67
40.36
57.22
71.84
39.40
47.67
65.80
36.21
53.35
67.96
19.76
36.38
50.83
14.03
26.42
39.42
14.93
22.90
32.69
11.68
25.14
38.35
76.57
57.00
73.30
46.21
60.46
43.87
69.85
42.17
51.37
26.29
39.96
18.02
34.03
16.75
39.07
16.44
71.97
69.28
57.89
53.73
65.13
60.28
51.35
48.08
53.34
54.90
41.66
33.20
59.07
57.20
45.32
51.15
42.86
42.67
29.45
24.12
32.45
29.56
22.29
23.84
25.17
27.04
18.43
10.66
29.73
29.84
18.00
18.53
60.26
76.80
52.65
68.10
43.53
66.36
45.23
69.69
30.40
51.76
22.95
36.11
18.51
35.85
17.74
40.71
66.97
68.89
59.57
58.98
50.82
52.15
56.24
49.81
39.75
36.71
28.61
29.79
24.13
24.68
27.44
25.08
85.01
66.50
37.91
80.40
64.80
30.49
74.74
57.57
20.06
75.42
58.67
24.36
62.60
36.09
13.13
47.87
29.77
11.05
44.87
28.22
4.48
42.85
28.30
5.24
45.00
79.94
70.46
68.38
67.76
31.79
76.12
55.11
60.89
71.27
30.04
64.71
71.96
53.00
46.67
26.82
66.83
82.14
44.37
58.16
18.89
49.19
40.26
41.65
45.56
10.69
38.68
30.33
28.19
45.41
13.53
26.22
41.83
31.48
25.57
8.78
35.89
38.85
20.23
24.78
59.44
77.35
53.46
69.68
47.43
57.95
50.70
64.09
30.91
50.42
23.22
38.17
19.54
33.16
22.47
36.26
Notes: Poverty indices are calculated using income data. a Women aged between 15 and 49 and their husbands and partners. b In
the case of DHS: Husband or partner of the oldest woman aged between 15 and 49. If she is single, this woman herself. In
the case of LSMS: Household head. c Women aged between 15 and 49.
Source: Own calculations.
16
Table 10 —
Disaggregation of the Poverty Headcount in Other Urban Areas of Bolivia by
Household Characteristics, 1989 to 2002
Moderate Poverty Line
Total
By Hh Size
<=3
4-6
>=7
By % of Hh Members
between 15 and 65
years
<= 50
> 50
By Age of Hh Head
<=34
35-49
50-65
>=66
By Native Language of
Hh Head
Spanish
Indigenous
By Gender of Hh Head
Male
Female
By Average Years of
Schooling of Adultsa
<=5
6-12
>=13
By Profession of
Principal Wage Earnerb
White-Collar Worker
Blue-Collar Worker
Agriculture
Sales & Services
Not Employed
By % of Adult Womenc
in Employment
>0
=0
Extreme Poverty Line
1989
1994
1999
2002
1989
1994
1999
2002
81.05
(1.32)
75.13
(1.16)
69.09
67.70
62.84
(1.44)
53.31
(1.22)
34.31
36.65
77.71
(3.32)
78.04
(1.72)
88.55
(1.69)
62.36
(3.21)
75.73
(1.84)
82.72
(1.93)
44.66
40.84
18.37
65.32
26.36
34.74
84.93
78.94
37.81
(2.63)
53.38
(1.94)
63.65
(2.25)
15.41
64.93
57.64
(3.59)
58.77
(2.01)
73.42
(2.73)
54.61
44.70
83.61
(1.56)
77.04
(2.25)
82.49
(1.47)
64.89
(2.10)
77.07
78.16
50.09
55.40
62.34
(1.69)
40.78
(1.98)
41.56
60.11
66.61
(1.74)
56.93
(2.31)
26.15
20.83
82.60
(2.08)
81.10
(1.82)
78.87
(2.71)
79.62
(6.61)
78.71
(2.06)
72.93
(1.82)
74.63
(2.96)
69.50
(5.44)
75.11
70.49
40.18
72.05
37.90
42.65
61.26
54.17
23.54
21.23
82.48
74.34
56.64
(2.15)
51.68
(1.85)
51.34
(3.21)
49.63
(5.17)
35.45
67.01
64.59
(2.62)
63.47
(2.14)
60.42
(3.27)
56.57
(7.79)
36.47
36.44
79.77
(1.35)
90.56
(4.17)
73.99
(1.25)
84.78
(3.47)
65.14
64.80
32.09
73.34
51.60
(1.27)
67.92
(4.40)
29.50
76.60
61.02
(1.40)
76.38
(4.69)
43.45
45.48
82.02
(1.40)
76.82
(3.60)
76.46
(1.27)
69.70
(2.77)
70.86
68.30
37.02
64.68
54.82
(1.33)
47.21
(2.63)
34.01
59.87
64.72
(1.62)
54.67
(3.82)
35.89
34.76
91.40
(1.75)
77.66
(2.07)
49.51
(5.75)
86.40
(1.58)
73.04
(1.72)
42.20
(4.64)
81.71
85.44
59.43
68.52
34.17
33.13
21.24
28.74
67.67
(2.24)
48.82
(1.73)
21.69
(3.00)
50.65
73.29
77.20
(2.39)
57.45
(2.13)
26.78
(4.45)
4.71
8.20
66.45
(3.53)
87.77
(1.87)
91.97
(2.48)
71.31
(3.10)
93.34
(2.85)
50.02
(3.31)
78.56
(2.24)
95.21
(1.83)
69.14
(2.40)
87.85
(3.71)
38.14
34.22
9.13
76.69
38.23
43.50
92.38
71.25
60.27
43.09
56.03
53.59
24.90
24.32
70.42
68.69
24.09
(2.74)
56.83
(2.29)
81.94
(3.02)
43.21
(2.74)
68.80
(3.83)
10.94
78.03
42.29
(3.47)
72.52
(2.20)
80.00
(3.82)
48.95
(3.34)
77.85
(4.33)
44.75
36.70
62.60
(2.57)
92.21
(1.22)
66.45
(1.68)
90.55
(1.69)
60.48
63.84
33.50
74.74
42.72
(1.52)
72.14
(1.91)
23.46
81.84
38.27
(2.43)
77.70
(1.84)
50.36
42.38
Notes: Poverty indices are calculated using income data. Standard errors of the poverty indices in brackets (only applicable to those
based on simulated data). – a Women aged between 15 and 49 and their husbands and partners. – b In the case of DHS:
Husband or partner of the oldest woman aged between 15 and 49. If she is single, this woman herself. In the case of LSMS:
Household head. – c Women aged between 15 and 49.
Source: Own calculations.
17
Table 11 —
Disaggregation of the Poverty Headcount in Rural Areas of Bolivia by Household
Characteristics, 1989 to 2002
Moderate Poverty Line
Total
By Hh Size
<=3
4-6
>=7
By % of Hh Members
between 15 and 65
years
<= 50
> 50
By Age of Hh Head
<=34
35-49
50-65
>=66
By Native Language of
Hh Head
Spanish
Indigenous
By Gender of Hh Head
Male
Female
By Average Years of
Schooling of Adultsa
<=5
6-12
>=13
By Profession of
Principal Wage Earnerb
White-Collar Worker
Blue-Collar Worker
Agriculture
Sales & Services
Not Employed
By % of Adult Womenc
in Employment
>0
=0
Extreme Poverty Line
1989
1994
1999
2002
1989
1994
1999
2002
89.66
(0.59)
89.55
(0.47)
83.37
83.83
74.59
(0.92)
76.05
(0.62)
59.98
57.24
84.94
(1.78)
88.26
(0.92)
95.09
(0.86)
80.42
(1.27)
90.22
(0.64)
95.03
(0.68)
65.39
57.51
29.31
80.90
55.97
52.44
92.53
93.69
61.54
(1.70)
76.85
(0.86)
85.22
(1.08)
40.41
80.33
64.90
(2.51)
72.45
(1.20)
84.40
(1.43)
70.97
69.62
91.16
(0.72)
86.66
(1.19)
92.36
(0.54)
84.64
(0.90)
86.65
87.82
62.63
76.59
80.63
(0.75)
68.01
(1.16)
63.91
75.49
77.74
(1.01)
68.27
(1.69)
50.54
47.46
86.74
(1.15)
90.56
(0.93)
91.29
(1.55)
95.44
(2.22)
86.98
(0.85)
90.55
(0.70)
90.74
(1.12)
95.49
(1.56)
82.25
82.70
53.35
85.39
60.15
60.50
82.24
80.69
56.90
55.69
88.69
89.45
71.55
(1.12)
77.86
(0.91)
77.95
(1.49)
86.46
(2.41)
59.17
83.82
71.54
(1.45)
75.47
(1.45)
75.34
(2.20)
85.07
(3.43)
73.08
53.69
82.21
(0.99)
96.59
(0.59)
80.37
(0.96)
94.92
(0.50)
65.95
72.54
35.95
87.63
61.89
(1.04)
84.32
(0.80)
28.76
88.31
62.31
(1.33)
86.01
(1.18)
68.83
64.41
89.63
(0.63)
89.87
(1.80)
90.25
(0.51)
85.59
(1.44)
82.91
84.41
57.69
77.61
77.17
(0.66)
69.66
(1.76)
60.15
86.86
74.72
(0.96)
73.64
(2.79)
58.63
52.41
94.49
(0.55)
73.19
(2.05)
31.15
(9.23)
94.56
(0.45)
80.42
(1.34)
36.73
(6.80)
89.45
89.50
67.64
73.72
43.32
37.10
17.86
10.62
84.93
(0.69)
57.82
(1.51)
13.75
(4.21)
67.18
70.94
81.67
(1.00)
48.66
(2.32)
10.80
(6.17)
7.62
1.28
66.02
(3.83)
77.98
(2.19)
95.77
(0.55)
71.91
(2.86)
93.51
(1.70)
62.91
(3.59)
83.31
(1.45)
95.04
(0.45)
70.79
(2.79)
83.89
(2.53)
52.96
38.49
17.62
77.29
37.70
38.44
88.27
88.47
66.49
64.37
46.25
43.03
22.33
6.55
79.22
83.81
43.45
(2.99)
62.63
(1.83)
85.15
(0.69)
46.32
(2.65)
65.72
(2.85)
28.09
74.27
43.85
(3.59)
53.91
(2.59)
84.52
(1.08)
49.11
(3.66)
78.10
(2.63)
60.12
64.59
79.07
(2.09)
91.62
(0.59)
90.19
(0.54)
87.86
(1.02)
85.08
85.66
61.26
79.37
78.06
(0.69)
70.73
(1.30)
63.46
77.84
57.97
(2.66)
77.66
(0.99)
48.71
47.45
Notes: Poverty indices are calculated using expenditure data. Standard errors of the poverty indices in brackets (only applicable to
those based on simulated data). – a Women aged between 15 and 49 and their husbands and partners. – b In the case of DHS:
Husband or partner of the oldest woman aged between 15 and 49. If she is single, this woman herself. In the case of LSMS:
Household head. – c Women aged between 15 and 49.
Source: Own calculations.
18
We find that a large number of children is also an important factor in shaping the distribution of
poverty, namely in two respects. First, large households were on average poorer than small
households and the relationship between poverty and household size strengthened over time, above
all in rural areas where large households did not participate at all in the overall reduction of
poverty.19 Second, households where the share of members in working age was below 50 percent
were more likely to be poor than other households. The relationship between the age composition of
households and poverty is strongest in the departmental cities and weakest in rural areas, but its
strength increased over time in all three regions.
To analyze the impact of employment on poverty, we first look at the profession of the principal
wage earner.20 Given the large differences between the sectoral employment shares and the sectoral
GDP shares (as shown in Table 3), it is not surprising to find a steep gradient in the poverty
incidence across professions. White-collar workers were by far least likely to be poor in 1989,
followed by workers in sales & services.21 At the other end of the spectrum were agricultural and
blue-collar workers. Like above, we find that the differences in the poverty incidence across
professions increased over time. The absolute (!) poverty headcount index of the relatively rich
white collar workers and workers in sales & services fell more than twice as much as the poverty
headcount index of the relatively poor agricultural and blue collar workers. The figures on
unemployed principal wage earners are less straightforward to interpret. In departmental capitals,
households with unemployed principal wage earners took an intermediate position in the poverty
ranking throughout the entire observation period. This may be due to two opposing factors. On the
one hand, total household income is reduced if the principal wage earner does not have gainful
employment. On the other hand, in the absence of unemployment and other social benefits, only
rich households can afford long unemployment spells (or long schooling periods) of the principal
wage earner. By contrast, in other urban areas and rural areas, they started from being most likely to
be poor in 1989, and ended up in an intermediate position in the poverty ranking in 2002. However,
we caution not to read too much into this finding. The group of households with unemployed
principal wage earners is so small (especially outside departmental capitals) that this finding is very
sensitive and its information content is very low. Second, we turn to female labor market
participation. Households where no adult woman had gainful employment were more likely to be
poor than other households in departmental capitals and other urban areas, but less likely to be poor
than other households in rural areas (except in 1989). Female labor market participation, thus,
seemed to be a successful strategy to lift households out of poverty in the former two regions. By
contrast, in rural areas, poverty seems to have forced women to work.
The role of the age of the household head in shaping the distribution of poverty is small and not
straightforward. In 1989, older household heads tended to be richer than younger household heads
in departmental capitals and other urban areas but poorer in rural areas. Between 1989 and 2002,
households with heads aged between 50 and 65 outperformed the other age groups in all three
regions. As a result, the relationship between poverty and the age of the household head turned Ushaped in departmental cities and other urban areas. As expected, the incidence of poverty was
smaller among households with Spanish-speaking heads. Additionally, their welfare seemed to be
more volatile. They benefited more from the economic boom between 1989 and 1999, but also
suffered more from the subsequent economic downturn. This finding is partly due to that Spanishspeaking household heads are over-represented in departmental capitals. The explanatory power of
the gender of the household head is negligible. If at all, households headed by women were slightly
19
We check the robustness of this result using incomes per adult equivalent (rather than income per capita) as welfare
indicator in Section 3.2.
20
Unfortunately, data constraints prevent us from further disaggregating the professional categories. Our
disaggregation is most problematic in the case of “sales & services” where we have to lump together bankers with
street vendors. For the exact definition of the term “principal wage earner” see the notes of Tables 8 to 11.
21
It could be argued that the poverty headcount index in the latter category is downward biased since the incomes of
self-employed, who are over-represented in sales & services, may not always be measured net of costs. However,
we find the same ranking in rural areas, where we use consumption expenditures rather than incomes.
19
better off, a finding common to many Latin American countries (see Marcoux 1998). But we
caution that female-headed households represent a very heterogeneous group (e.g., single female
elderly, single female professionals, divorced women, and women of migrant workers) so that it
may well be that sub-groups of female-headed households are particularly vulnerable to poverty.
3.3 Growth Inequality Decomposition and Growth Incidence Curves
Poverty profiles are suitable means to track the evolution of the incidence, intensity, and severity of
poverty for different subgroups of the population over time. However, they can only poorly
disentangle to what extent the observed poverty trends are due (a) to changes in mean income or (b)
to changes in the relative income distribution. Two ways to provide further insights about the links
between poverty, inequality, and growth trends: the first is to do a growth inequality decomposition
of the observed poverty reduction (Datt and Ravallion 1992) and the second is to estimate the rates
of pro-poor growth and the growth incidence curves (Ravallion and Chen 2003).
The decomposition of the observed poverty reduction into a growth and an inequality
contribution (and an interaction term which cancels if one calculates the average of a ‘forward’ and
‘backward’ decomposition) is using the methods proposed by Datt and Ravallion (1992). As
discussed in detail in Grimm and Günther (2004), the distribution component in this decomposition
also implicitly includes the impact of changes in the real value of the poverty line (i.e., how prices
paid by the poor have moved relative to the overall price level). As shown in Table 4, the prices
paid by the poor (in particular food prices) have risen somewhat less than the overall price level
(particularly in recent years) so that the purchasing power of the poor has increased by more than
suggested by the change in their real incomes. This is implicitly captured in the decomposition as a
distributional shift favoring the poor.
Table 12a — Growth Inequality Decompostion of Poverty Changes (Moderate Poverty)
1989–1999
1999–2002
1989–2002
Change in poverty
Growth component
Redistribution component
-0.118
-0.080
-0.038
Total Bolivia
0.020
0.018
0.002
-0.099
-0.064
-0.035
Change in poverty
Growth component
Redistribution component
-0.163
-0.105
-0.057
Departamental Capitals
0.040
0.025
0.015
-0.123
-0.080
-0.043
Change in poverty
Growth component
Redistribution component
-0.117
-0.067
-0.050
Other Urban Areas
-0.015
0.017
-0.032
-0.132
-0.074
-0.058
Change in poverty
Growth component
Redistribution component
-0.068
-0.041
-0.028
Rural Areas
0.005
-0.005
0.010
-0.064
-0.039
-0.025
Notes: Calculated using the Datt-Ravaillion (1992) method of growth-inequaltiy
decomposition.
Source: Own calculations.
20
Table 12b — Growth Inequality Decompostion of Poverty Changes (Extreme Poverty)
1989–1999
1999–2002
1989–2002
Change in poverty
Growth component
Redistribution component
-0.181
-0.090
-0.091
Total Bolivia
0.008
0.019
-0.011
-0.173
-0.075
-0.098
Change in poverty
Growth component
Redistribution component
-0.157
-0.077
-0.079
Departamental Capitals
0.027
0.015
0.012
-0.130
-0.073
-0.056
Change in poverty
Growth component
Redistribution component
-0.270
-0.136
-0.135
Other Urban Areas
0.021
0.038
-0.017
-0.250
-0.080
-0.170
Change in poverty
Growth component
Redistribution component
-0.157
-0.056
-0.100
Rural Areas
-0.027
-0.008
-0.020
-0.184
-0.071
-0.113
Notes: Calculated using the Datt-Ravaillion (1992) method of growth-inequaltiy
decomposition.
Source: Own calculations.
The result of the decomposition analysis (Table 12a) for the entire period show that about two
thirds of the 10 percentage points decline in poverty for total Bolivia is attributable to growth, and
about one third to a distributional shift favoring the poor. As the income distribution hardly shifted
between the two periods (Table 3 of the main document), most of this distributional shift is actually
due to the poverty line effect which increased the real purchasing power of the poor.22 Considering
sub-periods and different parts of the country shows a more differentiated picture. In the period
1989-1999 both the growth and redistribution (and/or poverty line) effect served to reduce poverty
in all parts of the country. In the latter three years, the picture has changed drastically. Now poverty
has increased nationally, and particularly in capital cities where 60% is due to falling incomes and
40% due to adverse distributional shifts. For the extreme poverty line (Table 12b), the growth
component seems to be less important in poverty reduction, but the redistribution component
becomes more important. In the period 1989-2002, of the 17 percentage points poverty reduction,
more than one half is due to redistribution (and/or the poverty line effect which is even larger here)
and less than one half is due to growth.
22
When we additionally split out the poverty line effect (results are not shown here, but are available upon request),
we find for the period 1989 to 1999 the “pure” redistribution to contribute to poverty reduction in departmental
capitals and other urban areas and zero for rural areas in the case of the moderate poverty line. For the extreme
poverty line, the “pure” redistribution also becomes positive in rural areas. From 1999 to 2002, the “pure”
redistribution effect leads to a poverty increase in all regions for both poverty lines. For the whole period from 1989
to 2002, the “pure” redistribution was poverty increasing in nearly all regions, except using the extreme poverty line
for other urban areas and rural areas. The “poverty line shift” redistribution is poverty decreasing in all areas for all
periods and both poverty lines. As explained above, this is due to the slower increase of food prices compared to
overall prices.
21
To evaluate whether the simulated income changes over time were “pro-poor” in the sense that
the poor benefited more from economic growth than the rich, we apply the methodology of growth
incidence curves (GIC) developed by Ravallion and Chen (2003). Comparing two periods, t-1 and t,
the growth incidence curve plots the cumulative share of the population (depicted on the x-axis)
against the income growth rate of the ξ-th quantile (depicted on the y-axis) when the analysis units
are ranked in ascending order of their income. It is given by
g t (ξ ) :=
y t (ξ )
y
L ' (ξ )
−1 = t ⋅ t
−1,
y t −1 (ξ )
y t −1 Lt −1 ' (ξ )
(6)
where L' (ξ ) is the slope of the Lorenz curve at the ξ-th quantile, and y is the mean income. It can
be shown that the area under the GIC up to the poverty headcount index P 0 gives (minus one
times) the rate of change of the Watts index23 over time
dWt
−
=
dt
Pt0
d log y t (ξ )
∫0 dt ⋅ dξ =
Pt0
∫ g (ξ ) ⋅ dξ .
t
(7)
0
The desirable axiomatic properties of the Watts index motivate evaluating the “pro-poorness” of
economic growth by comparing the growth rate of mean income with the mean of the income
growth rates of the poor in period t–1
1
PPGt := 0 ⋅
Pt −1
Pt0−1
∫ g (ξ ) ⋅ dξ
t
(8)
0
which Ravallion and Chen (2003) coined the “rate of pro-poor growth”.24
The comparison of the growth rates25 is shown in Table 13. Between 1989 and 1999, economic
growth in Bolivia can be classified as pro-poor. For both poverty lines and for all three regions, the
rates of pro-poor growth exceeded the growth rate of mean income suggesting that economic
growth was accompanied by falling inequality.26 For departmental capitals, the income distribution
of 1999 even first-order dominates the income distribution of 1989 as evidenced by that the GIC
lies above 0 for all ξ (Figure A2 in the Appendix). For other urban areas and rural areas, this
condition is met at least for all poor. That is, abstracting from individual income mobility across
quantiles, the welfare of all citizens in departmental capitals, and of all poor citizens in the rest of
the country, improved during the 1990s.
Between 1999 and 2002, the economic growth performance differed substantially between the
three regions. The departmental capitals experienced a strongly anti-poor contraction, which wiped
out a substantial part of the gains the poor had made in the previous ten years. In other urban areas,
this contraction was pro-poor so that, despite negative growth rates in mean income, the poor could
more or less keep their living standard. In rural areas, incomes even continued to rise (albeit very
slowly), and income growth continued to be somewhat higher for the poor than for the non-poor.
Given that (a) most income is generated in urban areas, but (b) most poor live in rural areas,
economic growth in total Bolivia was negative between 1999 and 2002, but only slightly anti-poor
or even pro-poor depending on the choice of the poverty line.
23
See Box A3 in the Appendix for a description of the Watts index and of its axiomatic properties.
24
Alternative approaches of measuring pro-poor growth can be found in Klasen (2004) and Son (2003).
25
For the corresponding growth incidence curves see Figures A1 to A12 in the Appendix.
26
The particularly high growth rate of mean income for total Bolivia (2.23 percent) is due to a shift in the composition
of the population from poorer rural areas to richer urban areas.
22
Table 13 — Annual Average Income Growth per Capita, 1989 to 2002
1989–1999
Growth Rate of Mean Income
Mean of Income Growth Rates of
Extremely Poor
Moderately Poor
All
2.23
3.39
3.21
2.98
1999–2002
Total Bolivia
-1.29
-0.88
-2.22
-2.56
1989–2002
1.41
2.16
1.85
1.67
Departmental Capitals
Growth Rate of Mean Income
Mean of Income Growth Rates of
Extremely Poor
Moderately Poor
All
2.01
-1.51
1.19
2.56
2.58
2.50
-6.30
-6.44
-5.01
0.44
0.48
0.69
Other Urban Areas
Growth Rate of Mean Income
Mean of Income Growth Rates of
Extremely Poor
Moderately Poor
All
2.89
-1.90
1.76
6.23
5.80
5.25
0.48
-0.22
-1.03
4.70
4.22
3.75
Rural Areas
Growth Rate of Mean Income
0.94
0.59
0.87
Mean of Income Growth Rates of
Extremely Poor
2.31
1.86
2.07
Moderately Poor
2.18
0.99
1.86
All
1.99
0.86
1.73
Notes: Annual average income growth rates are calculated using income data for departmental
capitals and other urban areas, expenditure data for rural areas, and mixed incomeexpenditure data for total Bolivia.
Source: Own calculations.
With the exception of the strongly anti-poor contraction in departmental capitals in recent years,
economic growth in Bolivia has been pro-poor since 1989, and particularly so in rural areas.27 This
result seems to be at odds with Table 7 which shows only slowly falling poverty rates in rural areas
since 1989. However, this puzzle resolves when taking into account that the depth of poverty in
rural areas is so large that even substantial pro-poor growth did not lift the poor above the poverty
line.28 Hence, the prime concern is not that economic growth in the 1990s was anti-poor, but that it
was so low and that the initial income inequality was so high that the poor remained poor despite
some welfare improvements. It would take another decade of such economic growth to make
serious inroads into poverty. Unfortunately, the future prospects are even bleaker. If the meager
growth performance of the Bolivian economy since 1999 continues, rural poverty will decline even
less and urban poverty will rise sharply.
27
Jimenez and Landa (2004) also provide pro-poor growth rates for total Bolivia between 1999 and 2002. They find
that the anti-poorness of the recent contraction was not restricted to departmental capitals. The main difference to
our analysis is that they rely on incomes (rather than consumption expenditure) as welfare indicator in rural areas.
Rural per-capita income exhibits much lower and falling levels between 1999 and 2002, while rural per–capita
consumption expenditure remained roughly constant.
28
But it did reduce the poverty gap in rural areas as evidenced in Table A3 in the Appendix.
23
4
Sensitivity Analyses
Before drawing conclusions from the national poverty profiles and growth incidence curves, we
perform three sensitivity analyses. First, we check the robustness of our results to alternative
assumptions on the dynamics of the cross-survey microsimulation methodology. Second, we
analyze how our results change if welfare is measured by income per adult equivalent rather than
income per capita. Third, we contrast our results with those derived from the asset-index (or wealthindex) approach developed by Filmer and Pritchett (2001), and Sahn and Stifel (2000, 2003).
4.1 Accounting for Growth Differentials in GDP per Capita between Urban and Rural Areas
One of the basic assumptions of our dynamic cross-survey microsimulation methodology is that the
absolute difference in the regression coefficients between departmental capitals on the one hand,
and other urban areas and rural areas on the other hand, remained constant between 1989 and 1999.
The widening of the urban-rural divide during that time is, thus, entirely attributed (a) to changes in
the endowment of covariates in favor of urban areas, and (b) to nationwide changes in the return to
covariates in favor of those covariates which are relatively abundant in urban areas. If this
assumption does not hold, i.e., if additionally (c) the returns to covariates in rural areas deteriorated
relative to those in urban areas, the widening of the urban-rural divide would be understated. To get
an idea of the possible size of this bias we have to simulate the opposite scenario where we assume
that the widening of the urban-rural divide between 1989 and 1999 is entirely due to a deterioration
of the returns to covariates in rural areas relative to those in urban areas. Since it is a priori not clear
which covariates are affected and to what extent, we take a rather simple approach and attribute the
regional growth differentials in GDP per capita to growth differentials in the regression coefficients
of the regional dummies.
This sensitivity analysis proceeds in three steps. First, using the same approach as in Table 3, we
impute the 1989-to-1994 and the 1994-to-1999 cumulative growth differentials in GDP per capita
between departmental capitals on the one hand, and other urban areas and rural areas on the other
hand. We find that the economic growth performance was nearly identical across the three regions
in the first half of observation period, but it differed substantially thereafter. Between 1989 and
1994, departmental capitals (cumulatively) grew by only 0.3 percent faster than other urban areas
and also by only 0.3 percent faster than rural areas. The corresponding figures for the period from
1994 to 1999 are 2.13 percent and 9.19 percent, respectively. Second, we sterilize the growth
differentials in GDP per capita by adding (a) for other urban areas and (b) for rural areas, the 1994to-1999 growth differential in GDP per capita (relative to departmental capitals) to the 1994
regression coefficient of the corresponding regional dummy, and sum of the 1989-to-1994 and the
1994-to-1999 growth differential in GDP per capita (relative to departmental capitals) to the 1989
regression coefficient of the corresponding regional dummy. Third, we partially re-run our
simulation with the adjusted coefficients to generate an adjusted spatial disaggregation of the
poverty headcount in Bolivia in Table 14a.29
Comparing the results with the corresponding entries in Table 7 reveals that the bias of neglecting
a possible deterioration of the returns to covariates in rural areas relative to those in urban areas is
small. Sterilizing the regional growth differentials in GDP per capita reduces the incidence of
moderate poverty in rural areas in 1989 by less than 2 percentage points and the incidence of
extreme poverty by less than 4 percentage points. This implies that the inferior performance of rural
areas in reducing the poverty headcount index is not primarily due to urban-rural growth
differentials in GDP per capita. Instead, due to high initial inequality, only relatively few rural
households were initially just below the poverty lines so that a given growth of GDP per capita
between 1989 and 2002 lifted only relatively few rural households over the poverty lines.
Table 14b calculates the corresponding rates of pro-poor growth for the various regions. Due to
lower growth in rural areas and towns, overall (mean) growth in Bolivia is now smaller between
29
For the corresponding tables for the poverty gap and the squared poverty gap, see Tables A13 and A14 in the
Appendix.
24
1989 and 1999, and the growth is also less pro-poor as the rate of growth in rural areas, whose
population predominates among the poor, is now estimated to have been lower. But the qualitative
results from above do not change.
Table 14 a— Adjusted Spatial Disaggregation of the Poverty Headcount in Bolivia, 1989 to 2002
Moderate Poverty Line
Extreme Poverty Line
1989
1994
1999
2002
1989
1994
1999
2002
75.96
(0.48)
71.60
(0.46)
65.21
67.22
54.62
(0.58)
49.21
(0.45)
38.35
39.24
Departmental Capitals
67.21
59.49
51.05
55.13
39.38
28.78
24.22
27.03
Other Urban Areas
80.69
(1.26)
87.76
(0.60)
74.34
(1.15)
87.81
(0.49)
69.09
67.70
36.65
83.83
52.56
(1.21)
73.18
(0.65)
34.31
83.37
62.10
(1.61)
70.88
(0.90)
59.98
57.24
Total
By Region
Rural Areas
Notes: Only poverty indices based on simulated data changed relative to Table 7. Poverty indices are calculated using income data
for departmental capitals and other urban areas, expenditure data for rural areas, and mixed income-expenditure data for total
Bolivia. Standard errors of the poverty indices in brackets (only applicable to those based on simulated data).
Source: own calculations.
Table 14.b — Adjusted Annual Average Income Growth per Capita, 1989 to 2002
1989–1999
Growth Rate of Mean Income
Mean of Income Growth Rates of
Extremely Poor
Moderately Poor
All
2.02
2.81
2.74
2.56
1999–2002
Total Bolivia
-1.29
-0.88
-2.22
-2.56
1989–2002
1.25
1.74
1.49
1.34
Departmental Capitals
Growth Rate of Mean Income
Mean of Income Growth Rates of
Extremely Poor
Moderately Poor
All
2.01
-1.51
1.19
2.56
2.58
2.50
-6.30
-6.44
-5.01
0.44
0.48
0.69
Other Urban Areas
Growth Rate of Mean Income
Mean of Income Growth Rates of
Extremely Poor
Moderately Poor
All
2.64
-1.90
1.58
6.01
5.55
5.00
0.48
-0.22
-1.03
4.53
4.03
3.56
Rural Areas
Growth Rate of Mean Income
Mean of Income Growth Rates of
Extremely Poor
Moderately Poor
All
0.02
0.59
0.17
1.39
1.28
1.06
1.86
0.99
0.86
1.40
1.18
1.02
Notes: Annual average income growth rates are calculated using income data for departmental capitals
and other urban areas, expenditure data for rural areas, and mixed income-expenditure data for
total Bolivia.
Source: Own calculations.
25
4.2 Adult Equivalent Scales
To this point, welfare was measured by income per capita in departmental capitals and other urban
areas, and consumption expenditure per capita in rural areas. To account for different preferences
and needs of adults and children as well as for economies of scale within the household, we check
the robustness of the impact of household size on poverty using income per adult equivalent and
consumption expenditure per adult equivalent, respectively. The number of adult equivalents is
defined as
AE = (adu + κ 1 ⋅ chi1 + κ 2 ⋅ chi2 )θ ,
(9)
where adu is the number of adults (age ≥ 15 years), chi1 the number of children aged between 6 and
14 years, and chi2 the number of children aged 5 years and below. κ 1 and κ 2 reflect the costs of
children relative to the costs of an adult and, thus, correct for the age composition of the household,
and θ controls the extent of economies of scale within the household (National Research Council
1995). In line with Gasparini et al. (2003), we set κ 1 = 0.75 and κ 2 = 0.5 . Since our objective is to
check the robustness of the relationship between poverty and household size, we choose a rather
low value of θ = 0.75 .30 We partially re-run our simulations to generate an adjusted disaggregation
of the poverty headcount by household size in Table 15.31
The comparison of the results with the corresponding entries in Tables 8 to 11 shows that, as
expected, the differences in the poverty headcount index across the household size categories
decline substantially. However, the use of adult equivalent scales does not qualitatively change our
findings. Large households are still more likely to be poor than small households, and the
relationship between poverty and household size strengthened over time.
4.3 The Asset Index Approach
The asset-index approach to construct national time series of basic poverty measures goes back to
Filmer and Pritchett (2001) and Sahn and Stifel (2000, 2003). To proxy welfare in the absence of
income or expenditure data, they assume that the asset ownership of households closely reflects
their living standard. Using DHS data, they define a set of assets32 and construct a metric asset
index
AI j =
s1 (a j1 − a1 )
σ1
+K+
s k (a jk − a k )
σk
+K+
s n (a jK − a K )
σK
,
(10)
where s k is the “scoring factor” or the weight of the asset k, a jk takes the value of 1 if household j
owns asset k and 0 otherwise, ak is the mean value of a jk over all households, and σ k is its
standard deviation.
Following Filmer and Pritchett (2001), we use the principal component analysis (rather than the
closely related factor analysis as in Sahn and Stifel (2000, 2003)) to determine the asset weights s k .
The underlying idea is to find a linear combination of the variables – the principal component or the
asset index – which contains most of the common information of the variables and is interpreted as
a background variable contained in all of them. Hence, the asset-index approach is valid if (and only
if) welfare is indeed the main determinant of asset variability among households. We apply the
asset-index approach to track the evolution of poverty between period t–1 and t. Since the mean
30
For comparison, Gasparini et al. (2003) set θ=0.9.
31
For the corresponding tables for the poverty gap and the squared poverty gap see Tables A15 and A16 in the
Appendix.
32
The asset definition is rather broad and includes not only real estate and financial assets, but also consumer durables
and the household’s endowment with human capital.
26
value of the asset index is zero by construction, we do not estimate equation (10) for each period
separately but over a pooled sample of the periods t–1 and t.
Table 15 — Influence of Adult Equivalent Scales on the Poverty Headcount Disaggregated by
Household Size
Moderate Poverty Line
1989
1994
1999
Extreme Poverty Line
2002
1989
1994
1999
2002
Bolivia
Total
58.67
(0.61)
53.32
(0.46)
40.11
39.87
34.27
(0.59)
31.65
(0.39)
17.16
15.18
57.49
(1.50)
55.41
(0.83)
65.81
(1.03)
46.84
(0.99)
51.55
(0.69)
63.15
(0.97)
31.16
28.29
8.95
37.30
15.80
14.68
49.93
47.90
25.25
(0.81)
30.17
(0.57)
40.74
(0.83)
11.06
36.81
32.60
(1.45)
31.27
(0.77)
41.20
(1.16)
22.19
18.15
Total
By Hh Size
<=3
4-6
>=7
41.57
30.04
25.95
7.03
9.63
30.03
39.48
48.87
23.12
28.48
35.87
21.53
24.37
32.27
7.68
6.80
7.24
5.75
9.75
11.31
Total
65.60
(1.54)
56.84
(1.28)
39.83
16.15
12.03
65.93
(3.39)
61.62
(2.15)
72.38
(2.61)
47.42
(2.80)
56.49
(1.94)
63.77
(2.34)
28.42
31.51
8.71
7.57
35.67
38.38
12.75
13.40
51.02
41.97
24.60
11.42
57.17
(0.67)
31.83
24.13
48.35
(1.67)
57.57
(0.96)
62.88
(1.27)
19.39
15.46
31.46
23.52
35.62
26.99
By Hh Size
<=3
4-6
>=7
By Hh Size
<=3
4-6
>=7
Total
By Hh Size
<=3
4-6
>=7
Departmental Capitals of Bolivia
28.93
15.32
8.75
19.41
27.63
36.19
7.82
13.49
20.88
6.72
8.41
10.24
Other Urban Areas of Bolivia
38.96
43.41
33.49
(1.51)
(1.15)
43.94
(3.96)
39.21
(2.17)
50.46
(2.77)
Rural Areas of Bolivia
55.43
49.96
(1.04)
75.34
(0.96)
77.53
(0.63)
60.10
73.70
(2.04)
72.77
(1.34)
80.92
(1.50)
70.28
(1.50)
77.67
(0.86)
82.57
(1.12)
52.74
43.50
57.04
53.06
66.49
61.07
47.09
(2.50)
47.10
(1.43)
56.81
(1.74)
25.65
(2.22)
32.86
(1.77)
39.80
(2.00)
Notes: Poverty indices are calculated using income data for departmental capitals and other urban areas, expenditure data for rural
areas, and mixed income-expenditure data for total Bolivia. Standard errors of the poverty indices in brackets (only
applicable to those based on simulated data).
Source: Own calculations.
In contrast to our dynamic cross-survey microsimulation methodology, the creation of national
poverty profiles on the basis of the asset index requires a common set of assets for all observation
years. Unfortunately, there was a change in the DHS questionnaire design: the DHS 1994 and 1998
collected information on more and other assets than the DHS 1989.33 The set of common assets
over all three Bolivian DHS rounds would have been very small so that we decided to restrict our
empirical analysis to the years 1994 and 1998. The derivation of the asset index and the summary
statistics of the assets included therein are shown in Table 16. We use 25 assets – 17 tangible assets
33
The DHS 1989 was conducted under the DHS1 questionnaire design, the DHS 1994 and 1998 under the DHS3
questionnaire design. The lack of consistency applies especially to consumer durables (Table A2 in the Appendix).
27
and 8 human capital variables – to capture the welfare of households.34 The eigenvalues of the
principal component analysis suggest that the asset index is indeed an important determinant for the
asset distribution among households. The first principal component explains 21.7 percent of total
asset variability.
Since all tangible assets are dummy variables, their scoring factors have a simple interpretation. A
move from “non-ownership” to “ownership” of the asset changes the asset index by s k / σ k . For
example, having private telephone connection increases the asset index by 0.83 in 1994 and 0.59 in
1998.35 In the case of the human capital variables, s k / σ k gives the change in the asset index if the
average education of adult household members switches from the reference state (“less than
complete basic schooling or unknown”) to the respective schooling category.
Table 16 — The Derivation of the Asset Index, 1994 and 1998
1994
Tangible Assets
Telephone
Radio
Television
Fridge
House
Plot of Agricultural Land
In-house Access to Electricity
In-house Access to Public Water
Use of Other (Non-open) Water Source
High-quality Cooking Materiala
Shared Toilet
Private Toilet
Cement Floor
Brick Floor
Other (Non-earth) Floor
2-3 Sleeping Rooms
>= 4 Sleeping Rooms
Human Capital
% of Adult Menb with
Complete Basic Schooling
Lower Secondary Schooling
Higher Secondary Schooling
Tertiary Education
% of Adult Womenc with
Complete Basic Schooling
Lower Secondary Schooling
Higher Secondary Schooling
Tertiary Education
Asset Index
Notes:
1998
ak
σk
sk
sk / σ k
ak
σk
sk
sk / σ k
0.106
0.852
0.582
0.297
0.671
0.285
0.676
0.561
0.143
0.641
0.358
0.240
0.326
0.117
0.180
0.411
0.057
0.308
0.355
0.493
0.457
0.470
0.451
0.468
0.496
0.350
0.480
0.480
0.427
0.469
0.322
0.384
0.492
0.232
0.254
0.180
0.351
0.285
-0.109
-0.299
0.342
0.307
-0.084
0.335
-0.002
0.243
0.098
0.055
0.197
0.102
0.113
0.826
0.508
0.711
0.625
-0.233
-0.662
0.731
0.618
-0.239
0.699
-0.005
0.570
0.209
0.171
0.511
0.208
0.487
0.250
0.881
0.684
0.377
0.650
0.213
0.757
0.698
0.109
0.718
0.194
0.483
0.376
0.076
0.260
0.346
0.062
0.433
0.324
0.465
0.485
0.477
0.409
0.429
0.459
0.312
0.450
0.396
0.500
0.484
0.265
0.439
0.476
0.240
0.254
0.180
0.351
0.285
-0.109
-0.299
0.342
0.307
-0.084
0.335
-0.002
0.243
0.098
0.055
0.197
0.102
0.113
0.587
0.557
0.755
0.589
-0.229
-0.730
0.798
0.668
-0.268
0.745
-0.006
0.487
0.202
0.208
0.448
0.215
0.470
0.119
0.136
0.242
0.107
0.321
0.341
0.425
0.307
-0.084
-0.033
0.092
0.193
-0.261
-0.098
0.215
0.629
0.095
0.115
0.235
0.156
0.290
0.316
0.420
0.360
-0.084
-0.033
0.092
0.193
-0.289
-0.106
0.218
0.536
0.125
0.137
0.254
0.080
0.315
0.326
0.410
0.255
-0.075
-0.012
0.198
0.185
-0.238
-0.036
0.483
0.726
0.101
0.133
0.301
0.139
0.287
0.317
0.427
0.325
-0.075
-0.012
0.198
0.185
-0.261
-0.037
0.464
0.570
-0.371
2.281
0.383
2.317
a
Gas, kerosene, and electricity. – b Husbands and partners of women aged between 15 and 49. – c Women
aged between 15 and 49.
Source: Own calculations.
34
To check the robustness of our empirical results, we also estimated the asset index without human capital variables.
The empirical results, which are available upon request, do not change qualitatively.
35
The reduction in the asset weight reflects the fact that private telephone connection has become more affordable and,
thus, more widespread in Bolivia (from 11 percent of all households in 1989 to 25 percent in 1998).
28
As expected, consumer durables, such as telephone, radio, television, and fridge, have high
scoring factors suggesting that they are powerful welfare predictors. By contrast, the ownership of a
house or of a plot of agricultural land indicates poverty, which can mainly be explained by the
widespread subsistence agriculture in rural areas of Bolivia. The quality of the dwelling unit also
provides information on the welfare of households. Access to public utilities, high quality cooking
materials, high quality toilet facilities, high quality floor materials, and a large number of sleeping
rooms all increase the asset index. The scoring factors of the human capital variables are more
difficult to reconcile. We find negative returns to schooling up to lower secondary schooling (9
years of schooling), which we attribute to that (a) our reference state includes “unknown” and that
(b) the returns to basic and secondary schooling are indeed very small in Bolivia.
The asset-index value of the individual household is obtained by multiplying the deviation of the
household asset endowment from the mean asset endowment with the vector of normalized scoring
factors according to equation (10). Aggregating the asset-index values over all households, we find
an increase in the mean asset index from –0.37 in 1994 to 0.38 in 1998 suggesting a favorable
development of the living standard in Bolivia, which is consistent with findings using the Unmet
Needs indicator (see Table 1). Based on the estimates of the asset-index values at household level,
we can carry out two consistency checks between our dynamic cross-survey microsimulation
methodology and the asset-index approach of Filmer and Pritchett (2001), and Sahn and Stifel
(2000, 2003). First, we rank the households according to (a) their simulated incomes and (b) their
asset-index values, and calculate the Spearman rank correlation coefficient between the two welfare
indicators. We find a close relationship between the simulated incomes and the asset-index values.
The Spearman rank correlation coefficient is 0.834 in 1994, and 0.792 in 1998.
Second, we construct poverty profiles based on asset-index values and compare them to those in
Section 3.2. To this end, we again rank the households according to their asset-index values and
calibrate the thresholds (i.e., poverty lines) between extremely poor, moderately poor, and non-poor
so as to ensure that the incidence of poverty at the aggregated national level (i.e., in the first line of
the poverty profile) in 1994 coincides with the one of the dynamic cross-survey microsimulation
methodology, which is shown in Table 7.36 We keep this threshold level of 1994 constant and apply
it also to the 1998 data. The spatial poverty profile based on asset-index values is shown in Table
17.
Although the direction of change and determinants are qualitative similar to our findings using
the microsimulation approach, there are some differences. The most striking difference between the
asset index and the microsimulation methodology is that overall poverty reduction from 1994 to
1998 appears much stronger using the asset index. Keeping the threshold of 1994 constant yields a
5.1 percentage points higher poverty reduction using the moderate poverty line and 2.0 percentage
points using the extreme poverty line compared to the results shown in Table 7. We suspect that this
sharper reduction in poverty using the asset index is due to a combination of changes in preferences
favoring some assets (e.g. telephones and televisions), relative price reductions of some assets (e.g.
telephones), and public investments in education which have not (yet) translated into income gains.
Thus the sharper poverty reduction using the asset index says more about developments in nonincome dimensions of well-being than being the most reliable proxy for the income dimension.
Furthermore, taking the corresponding results of the dynamic cross-survey microsimulation
methodology in Table 7 as reference point, we find that the asset-index approach strongly
underpredicts poverty in departmental capitals and other urban areas, and strongly overpredicts
poverty in rural areas as the asset endowments there are much lower. In doing so, the results of the
asset-index approach are closer to those of the unsatisfied-basic-needs approach37 (see first entries
36
The distribution of the assets among extremely poor, moderately poor and non-poor are given in Table A17 in the
Appendix.
37
The unsatisfied-basic-needs approach is very similar to the asset-index approach. It generates a weighted average of
welfare indicators (e.g., educational attainment, housing quality, access to public utilities, and access to basic health
services, in the case of Bolivia) and classifies households as poor if their weighted average indicator value is below
29
in Tables 1 and 2) than those of the dynamic cross-survey microsimulation methodology.
Additionally, not only the level but also the change in the incidence of poverty is more unevenly
distributed across the three regions. While, according to the dynamic cross-survey microsimulation
methodology rural areas participated –albeit less than proportionately– in the overall poverty
reduction, they experienced nearly no progress in reducing poverty according to the asset-index
approach. These differences are partly due to that only the dynamic cross-survey microsimulation
methodology accounts for differences in the local price levels (Table 4); they also show that
progress in improving the asset base in rural areas have been much slower in the 1990s.
Table 17 — Spatial Disaggregation of the Poverty Headcount Based on Asset-Index Values in
Bolivia, 1994 and 1998
Moderate Poverty Line
Extreme Poverty Line
1994
1998
1994
1998
Total
72.37
60.13
50.44
36.40
By Type of Municipality
Departmental Capitals
Other Urban Areas
Rural Areas
51.20
71.06
98.17
38.61
57.87
97.01
19.14
36.21
92.27
8.91
22.74
88.37
By Department
Chuquisaca
La Paz
Cochabamba
Oruro
Potosí
Tarija
Santa Cruz
Beni & Pando
78.57
69.91
76.08
69.75
83.92
65.99
66.40
81.25
70.48
61.15
56.72
58.27
76.74
55.54
51.38
66.04
68.54
46.57
57.65
39.92
67.85
45.61
39.11
62.69
57.60
33.08
37.05
28.18
54.99
34.82
26.42
47.00
Source: Own calculations.
By contrast, Table 17 shows less variation in the incidence of poverty across departments. The
1994 moderate poverty headcount index ranged only from 66 percent in Santa Cruz and Tarija to 84
percent in Potosí. For comparison, the corresponding figures of the dynamic cross-survey
microsimulation methodology were 58 percent and 88 percent, respectively. As concerns the
departmental poverty ranking, we find greater consistency between the two approaches.38 Santa
Cruz is the richest department and Potosí and Chuquisaca are the poorest departments. The notable
exception is Oruro, which is relatively poor according to the dynamic cross-survey microsimulation
methodology, and relatively rich according to the asset-index approach.
The disaggregation of the poverty headcount index based on asset-index values by household
characteristics is shown in Tables 18 to 21. To facilitate the comparison with the corresponding
results of the dynamic cross-survey microsimulation methodology in Tables 8 to 11, we calibrate
the thresholds between extremely poor, moderately poor, and non-poor for each poverty profile
anew. The poverty profile for total Bolivia is calibrated to match the poverty headcount index for
total Bolivia in 1994 (keeping the threshold constant for 1998), while the poverty profiles for
departmental capitals, other urban areas, and rural areas are calibrated to match the poverty
a certain threshold. In contrast to the asset-index approach, the indicator weights are set arbitrarily. For a more
detailed description of the unsatisfied-basic-needs approach and its application to Bolivia, see Hernany (1999).
38
This result becomes even more obvious when we compare the departmental disaggregation of the poverty headcount
by quintiles rather than only at the thresholds between extremely poor, moderately poor, and non-poor (results are
not reported here, but are available upon request).
30
headcount indices in the respective region in 1994 (again keeping the threshold constant for
1998).39
Table 18 — Disaggregation of the Poverty Headcount Based on Asset-Index Values in Bolivia by
Household Characteristics, 1994 and 1998
Moderate Poverty Line
Extreme Poverty Line
1994
1998
1994
1998
Total
72.37
60.13
50.44
36.40
By Hh Size
<=3
4-6
>=7
71.95
69.40
79.40
63.03
56.49
66.22
49.29
46.57
60.12
35.21
33.41
44.88
By % of Hh Members between 15 and 65 Years
<= 0.5
> 0.5
79.39
63.46
71.45
47.72
58.06
40.75
47.08
24.69
By Age of Hh Head
<=34
35-49
50-65
>=66
76.42
72.56
66.18
61.63
70.97
57.58
49.51
47.87
51.84
50.98
47.37
46.02
39.53
35.95
32.16
34.30
By Language of Hh Head
Spanish
Indigenous
61.06
98.82
50.06
96.78
33.05
91.08
22.68
86.37
By Gender of Hh Head
Male
Female
72.96
69.52
61.15
55.32
51.48
45.42
37.62
30.63
By Average Years of Schooling of Adultsa
<=5
6-12
>=13
97.36
64.29
8.77
93.43
51.37
9.19
83.82
32.08
1.22
72.45
21.41
1.93
By Profession of Principal Wage Earnerb
White-Collar Worker
Blue-Collar Worker
Agriculture
Sales & Services
Not Employed
28.06
79.94
99.00
64.54
52.39
18.38
68.08
96.86
49.38
44.43
10.61
45.51
95.32
29.67
26.99
6.83
27.81
90.93
15.84
19.55
By % of Adult Womenc out of Employment
< 100
= 100
72.71
71.66
56.34
66.95
51.61
47.97
33.41
41.78
Notes:
a
Women aged between 15 and 49 and their husbands and partners. – b Husband or partner of the oldest woman aged
between 15 and 49. If she is single, this woman herself. – c Women aged between 15 and 49.
Source: Own calculations.
39
The poverty headcount indices for 1994 for the calibration exercise are taken from Table 7.
31
Table 19 — Disaggregation of the Poverty Headcount Based on Asset-Index Values in the
Departmental Capitals of Bolivia by Household Characteristics, 1994 and 1998
Moderate Poverty Line
Extreme Poverty Line
1994
1998
1994
1998
Total
59.49
47.76
28.79
16.93
By Hh Size
<=3
4-6
>=7
63.65
55.69
66.02
54.54
44.38
49.53
30.08
27.32
31.67
19.74
15.72
17.07
By Age of Hh Head
<=34
35-49
50-65
>=66
67.89
59.31
47.93
30.84
65.54
42.85
32.60
26.84
37.16
27.01
18.72
9.48
26.13
14.20
8.64
9.09
By % of Hh Members between 15 and 65 Years
<= 0.5
> 0.5
67.70
51.62
58.84
39.66
36.82
21.07
25.34
10.78
By Language of Hh Head
Spanish
Indigenous
56.69
95.58
46.31
85.29
25.43
71.96
15.74
47.60
By Gender of Hh Head
Male
Female
58.93
61.99
48.72
43.68
29.03
27.72
17.03
16.49
By Average Years of Schooling of Adultsa
<=5
6-12
>=13
95.03
62.49
11.38
88.62
49.78
10.44
68.52
24.29
0.42
51.23
13.03
0.53
By Profession of Principal Wage Earnerb
White-Collar Worker
Blue-Collar Worker
Agriculture
Sales & Services
Not Employed
24.65
80.18
86.69
67.18
48.30
14.50
70.81
62.99
52.90
41.09
8.20
45.66
70.33
28.78
14.23
1.99
29.98
35.95
18.03
8.88
By % of Adult Womenc in Employment
>0
57.93
44.59
27.34
16.30
=0
62.36
54.61
31.44
18.27
Notes: a Women aged between 15 and 49 and their husbands and partners. – b Husband or partner of the oldest woman
aged between 15 and 49. If she is single, this woman herself. – c Women aged between 15 and 49.
Source: Own calculations.
32
Table 20 — Disaggregation of the Poverty Headcount Based on Asset-Index Values in Other
Urban Areas of Bolivia by Household Characteristics, 1994 and 1998
Moderate Poverty Line
Extreme Poverty Line
1994
1998
1994
1998
Total
75.15
61.94
53.43
37.47
By Hh Size
<=3
4-6
>=7
70.46
72.91
81.96
64.86
55.97
72.64
53.47
49.02
60.62
39.74
30.83
50.17
By Age of Hh Head
<=34
35-49
50-65
>=66
77.75
75.30
72.45
64.76
73.15
58.74
55.50
45.35
53.83
56.42
48.71
42.16
45.83
34.07
35.06
25.17
By % of Hh Members between 15 and 65 Years
<= 0.5
> 0.5
79.57
69.00
69.02
52.64
58.81
45.96
44.51
28.22
By Language of Hh Head
Spanish
Indigenous
72.90
94.33
59.05
91.28
50.22
80.81
34.08
71.95
By Gender of Hh Head
Male
Female
75.49
73.75
61.70
62.92
53.14
54.63
36.91
39.75
By Average Years of Schooling of Adultsa
<=5
6-12
>=13
95.95
70.33
20.39
91.27
58.08
17.19
82.32
43.01
2.85
72.67
27.13
6.54
By Profession of Principal Wage Earnerb
White-Collar Worker
Blue-Collar Worker
Agriculture
Sales & Services
Not Employed
48.85
87.56
95.73
67.41
65.76
26.29
75.96
88.58
61.99
48.80
26.76
61.32
90.50
42.17
39.75
10.33
47.16
74.50
31.46
24.63
By % of Adult Womenc in Employment
>0
73.01
58.95
50.72
34.46
=0
78.94
66.66
58.25
42.22
Notes: a Women aged between 15 and 49 and their husbands and partners. – b Husband or partner of the oldest woman
aged between 15 and 49. If she is single, this woman herself. – c Women aged between 15 and 49.
Source: Own calculations.
33
Table 21 — Disaggregation of the Poverty Headcount Based on Asset-Index Values in Rural
Areas of Bolivia by Household Characteristics, 1994 and 1998
Moderate Poverty Line
Extreme Poverty Line
1994
1998
1994
1998
Total
89.55
84.27
76.11
67.60
By Hh Size
<=3
4-6
>=7
87.78
89.23
91.42
82.28
84.22
85.79
69.51
76.11
80.94
65.12
67.07
70.40
By Age of Hh Head
<=34
35-49
50-65
>=66
88.55
89.70
91.81
87.90
84.00
84.47
84.01
85.08
73.06
78.16
77.87
75.61
68.03
65.82
69.90
72.19
By % of Hh Members between 15 and 65 Years
<= 0.5
> 0.5
90.70
87.54
86.12
80.51
78.07
72.68
70.27
62.17
By Language of Hh Head
Spanish
Indigenous
78.11
96.23
75.13
91.19
59.52
85.81
57.21
75.47
By Gender of Hh Head
Male
Female
90.25
85.58
85.21
78.32
77.76
66.69
69.02
58.71
By Average Years of Schooling of Adultsa
<=5
6-12
>=13
95.95
77.83
18.00
92.21
69.23
30.54
85.89
56.91
3.50
79.51
45.40
1.57
By Profession of Principal Wage Earnerb
White-Collar Worker
Blue-Collar Worker
Agriculture
Sales & Services
Not Employed
59.98
80.57
96.36
70.37
77.75
49.31
67.29
93.89
53.66
75.35
34.28
58.56
88.27
39.10
56.28
21.41
41.57
80.90
30.02
53.02
By % of Adult Womenc in Employment
>0
90.03
85.34
77.06
69.73
=0
88.28
82.74
73.62
64.57
Notes: a Women aged between 15 and 49 and their husbands and partners. – b Husband or partner of the oldest woman
aged between 15 and 49. If she is single, this woman herself. – c Women aged between 15 and 49.
Source: Own calculations.
34
Education continues to be the most important determinant of poverty and its changes over time.40
The distribution of the headcount index across schooling groups is even more polarized according
to the asset-index approach. By contrast, we find a strikingly different pattern for the changes in the
distribution of the headcount indices across schooling groups between 1994 and 1998. For tertiary
schooling, not only did the returns to schooling decrease over time, we also find that the incidence
of poverty among household where the average education of adult members was 13 years of
schooling or more rose in absolute (!) terms.41
The impact of household size on poverty is found to be smaller for asset-index values than for
simulated per-capita incomes. The relationship between poverty and household size is U-shaped in
departmental capitals and other urban areas. In rural areas, large households continue to be poorer
than small households, but the relationship was relatively weak in 1994, and became even weaker
(not stronger as in the dynamic cross-survey microsimulation methodology) in 1998. We attribute
these inconsistencies, which cannot be reconciled by the use of realistically defined equivalent
scales (Table 15), to that the strong reliance on tangible assets in the asset-index approach may
overstate the economies of scale within the household. As concerns the age composition of the
household, the asset-index approach corroborates the earlier findings. Households where the share
of members in working age was below 50 percent were more likely to be poor – particularly so in
departmental capitals and less so in rural areas. Additionally, we again find that this relationship
strengthened over time.
With respect to the impact of employment on poverty, there is also much agreement between the
dynamic cross-survey microsimulation methodology and the asset-index approach. First, the
incidence of poverty and its change over time are more favorable for white-collar workers and
workers in sales & services than for agricultural and blue-collar workers. Second, female labor
market participation was a successful strategy to lift households out of poverty in departmental
capitals and other urban areas, but not in rural areas. However, like in the case of education, we find
that the distribution of the poverty headcount index across professions was more accentuated
according to the asset-index approach.
The asset-index approach depicts the poverty incidence of households with old heads in a more
favorable light than the dynamic cross-survey microsimulation methodology. From a static
perspective, the gradient in the poverty incidence based on asset-index values of 1994 was steeper
across age groups in departmental capitals and other urban areas, and the relationship between
poverty and the age of the household was flat rather than increasing in rural areas. From a dynamic
perspective, Tables 18 to 21 show that – depending on the poverty line – households with heads
aged 34 or below only less than proportionately participated in the overall poverty reduction
between 1994 and 1998. A plausible explanation for these differences between the dynamic crosssurvey microsimulation methodology and the asset-index approach is that household heads may
accumulate tangible assets over the life cycle so that once they are old they possess more but less
valuable assets.42 With respect to the other characteristics of the household head, we find more
similarities between the two approaches. Being Non-Spanish speaking substantially increases the
likelihood of being poor. The gradient in the poverty incidence between Spanish and Non-Spanish
speaking household heads was again even steeper according to the asset-index approach. The
explanatory power of the gender of the household head continues to be negligible.
40
This finding continues to hold if we exclude the human capital variables from the estimation of the asset-index
values.
41
However, especially concerning tertiary education, the sample size in some cases becomes very small, so one should
not interpret the numbers carefully.
42
The DHS data do not contain information on the age of the tangible assets so that we cannot check the validity of
this hypothesis.
35
5
Discussion
In the preceding sections, we developed a new methodology to create national poverty profiles and
growth incidence curves with incomplete income or expenditure data, and applied it to the case of
Bolivia between 1989 and 2002. The analysis revealed that there are four main determinants of
poverty and its changes over time. First, there is evidence for a large urban-rural divide in Bolivia.
Following the historical settlement patterns, Bolivia’s poor are still concentrated in the rural areas
of the highlands (altiplano and valles), where they face difficult ecological and climatic conditions
for agricultural production, and suffer from the proliferation of tiny plots. The migration of the poor
to urban areas and the more dynamic lowlands (llanos) has been limited due to reasons of disease
ecology as well as the lack of support networks.43 The large and persistent differences in education
levels between urban and rural areas have perpetuated the urban-rural divide. In 1976, the average
years of schooling of the adult rural population stood at only 1.8 years, compared to 6.1 years in
urban areas. While recent investments in rural education have led to some improvements, the
differences remain substantial. In 2001, average years of schooling were 4.2 in rural areas and 9.2 in
urban areas (INE var. iss.). Additionally, there is very restricted credit access for informal
enterprises, and particularly so in rural areas. Despite the fact that some of Bolivia’s microfinance
institutions have been hailed as models to ensure sustainable credit access, they still operate in only
68 of Bolivia’s 329 municipalities and cover only about 10 percent of the population. In rural areas,
credit is virtually unavailable for anyone except very large producers. These problems are
exacerbated by little progress in restructuring Bolivia’s wholesale financial institutions, and by
years of inconclusive debate about the possibilities for incorporating microfinance institutions into
the national regulatory system.
However, what is more of concern here is that the urban-rural divide seems to have widened over
the last 20 years. An obvious explanation for the inferior performance of rural areas in reducing the
poverty incidence is that the agricultural sector suffered most from the adverse El Niño/La Niña
weather phenomenon, which hit Bolivia twice during the 1990s, and from the continuing decline in
world-market prices for agricultural products. Eastwood and Lipton (1999, 2004) offer three
additional explanations: (a) structural reforms might have contributed to the widening of the urbanrural divide because well educated households, which are concentrated in urban areas, might be
better at exploiting economic opportunities following the structural reform process, and urban
economic activity, which had been most regulated before the structural reforms, had most to gain.
(b) there might have been two pro-urban demographic trends: selective rural-urban migration might
have left behind a core of old, poorly educated individuals in rural areas who are weak in reaping
“trickle down effects” from economic growth, and faster fertility transition might have increased
per-capita income of urban households. (c) there might have been a persistent urban bias in public
spending – at least until the transfer of large parts of the tax revenues from the national to the
municipal level which marked the start of the decentralization process in 1994.44 While these
explanations played a role in widening the urban-rural divide,45 our results suggest that neither antirural economic growth, nor anti-poor economic growth within rural areas were the main reasons for
the inferior performance of rural areas in reducing the poverty headcount index. It is rather because
due to high income inequality in rural areas, only relatively few rural households were initially just
43
Only very recently have the migration patterns of Bolivia’s poor changed. In 1997, 46 percent of the rural migrants
went to other rural areas, presumably due to agricultural employment (especially coca production) and family
reasons. By 2001, this share has dropped to 37 percent with departmental capitals taking in the larger share of
migrants (Tannuri-Pianto et al. 2004).
44
Table 7 provides some supportive evidence for this hypothesis. Before the start of the decentralization process in
1994, there was no progress in reducing the incidence of rural poverty. Thereafter, it declined more or less in line
with the incidence of urban poverty – at least in absolute terms.
45
See, for instance, Tannuri-Pianto el al. (2004) who provide evidence on that there has indeed been selective ruralurban migration in Bolivia. Young, well-educated people speaking Spanish as their mother tongue have a much
higher likelihood to migrate, thus, contributing to the brain drain from rural areas.
36
below the poverty lines so that a given growth of GDP per capita between 1989 and 2002 lifted only
relatively few rural households over the poverty lines.
Second, a large number of children has become an increasingly powerful poverty predictor in
Bolivia as evidenced by that (a) the incidence of poverty was higher among large households and
among households with few members in working age, and that (b) these relationships strengthened
over time. These trends reflect the considerable fertility decline in Bolivia over the past 20 years,
which is now clearly visible in the age structure of the population where the absolute number of 0-4
year olds has recently begun to decline. If the fertility decline continues, the country can expect two
types of welfare improvements: (a) Bolivia is likely to enter the phase which has been referred to as
“demographic gift” by Bloom and Williamson (1998), where the share of the working age
population will be particularly large. Under these conditions, the country can save more, invest
more in physical and human capital, and, if sufficient employment opportunities are available, spur
growth of GDP per capita. (b) economic growth is likely to become more pro-poor as it is
particularly the poor who are now in the process of further reducing their household size and of
benefiting from lower dependency rates (Klasen 2004; Eastwood and Lipton 2000).46 Once the
fertility decline has reached the poor in Bolivia, it can be a major driving force of poverty reduction,
as it was elsewhere in recent years (e.g., in East Asia and Brazil).
Third, average education of adult household members strongly shaped the likelihood of being
poor. There is a steep gradient in the poverty headcount index between basic, secondary, and
tertiary schooling. However, the two approaches to estimate basic poverty measures with
incomplete income or expenditure data applied in Sections 3 and 4 yield conflicting evidence on the
changes in the returns to schooling (in terms of reducing the incidence of poverty relative to the
next lower schooling category) between 1989 and 2001. According to our dynamic cross-survey
microsimulation methodology the returns to secondary schooling declined somewhat while the
returns to tertiary schooling increased substantially. Using the asset-index approach developed by
Filmer and Pritchett (2001), and Sahn and Stifel (2000, 2003), we find that not only did the returns
to tertiary schooling decrease over time, but the incidence of poverty among household where the
average education of adult members was 13 years of schooling or more also rose in absolute (!)
terms. These inconsistencies deserve more attention. Data constraints prevent us from exploring
them in more depth and detail on a national scale. This is because the DHS data on education of
male adults are deficient: (a) they provide the schooling category only for husbands and partners of
women aged between 15 and 49, but not for single, divorced, or widowed men or men whose wife
or partner is aged 50 or more, (b) the information on the education of male adults rely on the recall
of their wives and partners, who serve as respondents of the DHS so that many men are classified to
have “non or unknown schooling”, and (c) the schooling categories asked in the DHS are rather
broad – it would at least be desirable to distinguish between educación intermedia (lower secondary
schooling) and educación media (higher secondary schooling).
Fourth, we find that the explanatory power of the profession of the principal wage earner has
considerably increased since 1989. The poverty headcount index of the relatively rich white-collar
workers and workers in sales & services fell more than twice as much as the poverty headcount
index of the relatively poor agricultural and blue-collar workers. This finding, however, can be
criticized on several grounds. Like above, the DHS data of the mainly male principal wage earner
rely on the recall of the female respondent. Additionally, the professional categories (a) represent
very heterogeneous groups (e.g., “sales and services” can include anything from bankers to street
vendors), and (b) are highly correlated to education level of the job holder since they reflect a
mixture of academic credentials, job requirements, and industry characteristics.
46
The poor still have much larger families. Using the unsatisfied-basic-needs approach and applying it to the 2001
Census, extremely poor households in Bolivia had a total fertility rate of 6.9, compared to 2.1 for households with
satisfied basic needs.
37
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41
7
Appendices to Annex 1
42
– Boxes –
43
Box A1 — A Brief Overview of Poverty and Inequality Measures
(a) Poverty Measures
There are three dimensions of poverty which have to be captured by the poverty measures
(sometimes labeled the three I’s of poverty): (a) the incidence or extent of poverty (how many
individuals are poor?), (b) the intensity or depth of poverty (how poor are the poor?), and (c) the
inequality or severity of poverty (how is poverty distributed among the poor?) (Jenkins and Lambert
1997, 1998).
The most widely used approach to analyze the so-called three I’s of poverty is the family of poverty
indices proposed by Foster et al. (1984). Let p ( y i ≤ z ) denote an indicator function that takes the
value of 1 if income y i is less than the poverty line z, and 0 otherwise, and let
g yi =
z − yi
⎧ z − yi ⎫
⋅ p( y i ≤ z ) = max ⎨
, 0⎬ .
z
⎭
⎩ z
(A1)
denote the relative poverty gap. The poverty index family of Foster et al. (1984) can then be written
as
Pλ =
1 I
λ
⋅ ∑ (g yi ) ,
I i =1
(A2)
where λ measures the degree of poverty aversion. For λ = 0 , equation (A2) takes into account only
the incidence of poverty and simplifies to the poverty headcount index
P0 =
1 I
∑ p( yi ≤ z )
I i =1
(A2a)
which measures the proportion of the population which receives incomes below the poverty line. To
provide information on the intensity of poverty, we set λ = 1 and arrive at the poverty gap index
P1 =
1 I
⋅ ∑ g yi .
I i =1
(A2b)
It simply represents the sum of all poverty gaps divided by the total population and measures how
much would have to be transferred from each individual (including the poor) to the poor to bring
their incomes up to the poverty line.47 In the calculation of P 1 , each relative poverty gap (and, thus,
each poor) is equally weighted. To additionally penalize the inequality of poverty requires the
assignment of higher weights to greater poverty gaps (and, thus, to poorer poor). In other words, the
degree of poverty aversion has to be set to λ > 1 . The most widely-used approach is to take the
47
However, this figure represents only the minimum cost of eliminating poverty as the transfers would have to be
perfectly targeted and would have to be collected and distributed perfectly efficiently.
44
value of the poverty gap itself as the corresponding weight, which leads to the squared poverty gap
index
P2 =
1 I
2
⋅ ∑ (g yi ) .
I i =1
(A2c)
(b) Inequality Measures
The most widely used inequality measure48 is the Gini coefficient
G=
I
I
1
⋅ ∑∑ y i − y j ,
2 ⋅ y ⋅ I ⋅ ( I − 1) i =1 j =1
(A3)
which is defined as the average difference between every pair of incomes divided by two times the
mean income y and represents two times the area between the 45° line and the Lorenz curve.49 Due
to its intuitive graphical interpretation, it has become by far the most commonly used inequality
measure. However, the main disadvantage of Gini coefficient is that it places rather arbitrary
weights to income transfers that occur in different parts of the income distribution. The distance
between two income units depends only on their rank ordering but not on their income difference.
As a result, assuming a bell-shaped income distribution, the Gini coefficient is relative insensitive to
transfers within the group of low-ranking or within the group of high-ranking income recipients.
The Atkinson (1970) index measures the social welfare loss associated with a certain level of
income inequality compared to a situation where the same total income is equally distributed.
Assuming that the underlying social welfare function has a constant degree of relative inequality
aversion ζ, it is given by
1
⎧
1−ζ 1−ζ
I
⎡
⎤
⎛ yi ⎞
⎪
1
⎪1 − ⎢ I ⋅ ∑ ⎜⎜ y ⎟⎟ ⎥
⎪
A(ζ ) = ⎨ ⎢⎣ i =1 ⎝ ⎠ ⎥⎦
1
⎪
I
I
y
⎛
⎞
⎪
1 − ⎜⎜ ∏ i ⎟⎟
⎪⎩
⎝ i =1 y ⎠
if ζ ≠ 1
(A4)
if ζ = 1.
Its implicit weighting scheme to income transfers depends on the difference in the marginal social
utility between the conceding and the receiving income unit, and can be controlled for by the choice
of ζ. This parameter reflects the relative sensitivity to redistribution from the rich to the not-so-rich
vis-à-vis to redistribution from the not-so-poor to the poor. The higher the value of ζ, the more
sensitive is the Atkinson index to income transfers at the lower tail of the income distribution.
48
For a more detailed description of the theoretical foundations and the underlying axioms of inequality measures see
Cowell (1995, 2000), and Wolff (1997).
49
The Lorenz curve plots the cumulative share of the population (depicted on the x-axis) against the corresponding
cumulative share of total income (depicted on the y-axis), when the individual income units are ranked in ascending
order of their incomes.
45
Box A2 — The Construction of the Bolivian Poverty Lines
According to Ravallion (1998), “a credible measure of poverty can be a powerful instrument for
focusing the attention of policy makers on the living conditions of the poor.” A controversial issue
in this respect is the construction of an “objective” poverty line.50 The most commonly accepted
methodology, which was also adopted by Bolivia, is the cost-of-basic-needs approach:
(1) For well-defined geographical entities, time-invariant basic food baskets are defined which
reflect (a) the average nutritional requirements of adults – e.g., 2135 kilocalories per day in the
case of Bolivia – and (b) the local eating habits of a reference group – e.g., the middle quintile
of the income distribution in the case of Bolivia. The extreme poverty lines (líneas de
indigencia) are obtained by valuing each item of the basic food baskets with its average local
price paid by the reference group, and they are updated using the information of disaggregated
local Consumer Price Indices (CPI).
(2) To obtain moderate poverty lines (líneas de pobreza) which additionally include the cost of
non-nutritional basic needs, local Engel coefficients (average expenditure shares devoted to
food) of the reference group are estimated and their inverse is multiplied by the extreme
poverty lines.
In Bolivia, the construction of poverty lines was compounded by data constraints. The national CPI
is estimated on the basis of market prices of only four cities – La Paz, El Alto, Cochabamba, and
Santa Cruz – while no price data are available for the rest of the country. As a result, the cost-ofbasic-needs approach had to be modified. For the urban areas, a two-step procedure was applied.
First, the poverty lines for La Paz, El Alto, Cochabamba, and Santa Cruz were constructed using
local basic food baskets defined on the basis of the Encuesta de Presupuestos Familiares of 1990
and price data taken from the local CPI. Second, the poverty lines were extrapolated to the urban
areas of the other departments using ad-hoc adjustment factors (Gray-Molina et al. 1999). For the
rural areas, a single basic food basket was defined on the basis of the Encuesta de la Evaluación del
Fondo de Inversión Social (EVI-FIS) of 1997. The absence of a rural price data required to derive a
“CPI proxy” from the EVI-FIS and update it on the basis of the expenditure modules of the
Bolivian LSMS (INE-UDAPE 2000).
50
See WBI (2003) for a detailed description and evaluation of different methodologies to construct poverty lines.
46
Box A3 — The Watts Index and Its Axiomatic Properties
The Watts (1968) index is defined as
P0
W = ∫ − log
0
y (ξ )
⋅ dξ ,
z
(A5)
where ξ is the cumulative population share if the analysis units are ranked in ascending order of
their incomes. The Watts index is the only poverty measure that gives the absolute amount of social
welfare loss due to poverty, is additive decomposable,51 and satisfies all standard axioms of poverty
measurement (Zheng 1993, 1997):
1) Focus Axiom: The poverty measure is only a function of the poor and, thus, invariant to income
changes of the non-poor.
2) Symmetry Axiom (= Anonymity Axiom): Re-labeling the income recipients should not affect the
poverty measure.
3) Population Principle: The poverty measure is invariant if two or more identical populations are
pooled.
4) Monotonicity Axiom: The poverty measure increases when a poor person gets poorer.
5) Transfer Axiom: A transfer from poorer poor to richer poor increases the poverty measure.
6) Monotonicity Sensitivity Axiom: The poorer an individual is, the larger is the increase in the
poverty measure due to this income transfer.
7) Transfer Sensitivity Axiom: The increase in the poverty measure is higher if a poor person with
a larger distance to the poverty line makes this transfer.
51
That is, the poverty measure for a population can be written as a weighted average of the poverty measures of a set
of mutually exclusive and collectively exhaustive subpopulations.
47
– Tables –
Table A1 — Sample Means of the Variables Taken from the Living Standard Measurement Surveys
Total Bolivia
EIH89
EIH94
Deparatmental Capitals
ECH99
EIH89
EIH94
Other Urban Areas
ECH99
EIH89
EIH94
Rural Areas
ECH99
EIH89
EIH94
ECH99
Demographics
Place of Residence
City
Town
Rural
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
49.31
15.70
34.99
100.00
0.00
0.00
100.00
0.00
0.00
100.00
0.00
0.00
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
0.00
100.00
0.00
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
0.00
0.00
100.00
Department
Chuquisaca
La Paz
Cochabamba
Oruro
Potosí
Tarija
Santa Cruz
Beni and Pando
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
6.95
29.09
18.06
4.48
8.95
4.84
22.44
5.20
4.59
40.48
14.70
6.71
4.30
3.18
23.90
2.14
4.59
39.63
14.22
6.19
3.81
3.24
26.29
2.04
5.01
38.41
15.23
6.48
4.55
2.71
22.96
4.65
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
0.92
12.26
18.77
1.34
6.40
10.93
41.90
7.49
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
12.39
23.51
21.74
3.06
16.30
5.10
12.97
4.93
Number of
Elderly (age>=66 or unknown)
Adult Men (15>=age>=65)
Adult Women (15>=age>=65)
Youngsters (6>=age>=14)
Children (age<=5)
All household members
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
0.09
1.43
1.63
1.58
0.96
5.70
0.10
1.48
1.76
1.55
0.95
5.84
0.09
1.49
1.74
1.40
0.98
5.70
0.08
1.53
1.73
1.37
0.71
5.42
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
0.10
1.42
1.79
1.59
1.04
5.94
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
0.11
1.29
1.42
1.88
1.29
5.99
n.a.
n.a.
56.33
57.18
58.74
61.94
n.a.
n.a.
56.45
n.a.
n.a.
48.37
n.a.
n.a.
n.a.
n.a.
51.06
15.14
58.00
12.38
55.75
13.85
67.07
17.32
n.a.
n.a.
n.a.
n.a.
65.36
16.01
n.a.
n.a.
n.a.
n.a.
22.10
11.66
Age of Household Head
<=24
25 - 34
35 - 44
45 - 54
55 - 65
>=66 or Unknown
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
4.63
21.99
32.28
26.92
9.48
4.70
3.73
26.32
33.37
20.73
11.52
4.33
4.51
25.57
32.60
22.89
10.31
4.12
4.47
21.17
33.85
26.42
9.91
4.17
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
6.74
22.05
29.87
24.48
11.08
5.78
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
3.92
23.10
31.16
28.71
8.14
4.97
Tangible Assets
Water Source
Inhouse Access to Public Water
Open Water Source
Other Water Source
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
66.05
27.12
6.83
71.75
7.62
20.63
79.05
4.93
16.02
93.39
2.02
4.60
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
77.72
18.07
4.21
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
22.28
66.55
11.17
Age Composition of Hha
Language of Hh Head (Spanish)
Gender Hh Head (Female)
49
Table A1 continued
Total Bolivia
EIH89
EIH94
Deparatmental Capitals
ECH99
EIH89
EIH94
Other Urban Areas
ECH99
EIH89
EIH94
Rural Areas
ECH99
EIH89
EIH94
ECH99
Toilet Facility
No Toilet
Shared Toilet
Private Toilet
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
31.50
16.66
51.84
32.79
67.21
n.a.
25.34
26.24
48.42
11.38
26.99
61.63
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
17.55
12.61
69.84
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
66.11
3.94
29.95
House
Electricity
Telephone
Radio
Television
Fridge
Car
Family Land
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
67.37
72.94
25.30
79.57
66.15
35.24
11.48
n.a.
58.94
n.a.
n.a.
n.a.
n.a.
n.a.
18.82
n.a.
56.02
95.76
20.34
89.19
91.59
46.36
n.a.
n.a.
56.91
98.65
43.02
86.91
94.86
52.79
18.24
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
63.35
96.54
23.91
78.97
84.42
45.33
9.13
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
83.92
26.12
0.93
69.51
17.47
5.97
3.00
n.a.
Main Floor Material
Earth
Cement
Brick
Other Floor
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
34.82
37.67
5.95
21.57
n.a.
n.a.
n.a.
n.a.
11.41
43.47
10.79
34.33
7.59
49.17
6.80
36.44
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
24.76
51.86
10.81
12.57
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
77.69
15.10
2.56
4.64
High-quality Cooking Materialb
n.a.
n.a.
66.56
n.a.
96.98
97.40
n.a.
n.a.
81.28
n.a.
n.a.
16.48
Number of Sleeping Rooms
0-1
2-3
>= 4
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
58.35
35.58
6.07
n.a.
n.a.
n.a.
43.28
46.01
10.71
47.18
42.55
10.27
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
57.19
38.18
4.63
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
74.61
24.58
0.81
Educational Attainment of Adults
Men
No Schooling
Incomplete Basic Schooling
Complete Basic Schooling
Lower Secondary Schooling
Higher Secondary Schooling
Tertiary Education
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
5.18
25.82
11.41
15.33
28.36
13.90
2.72
15.66
11.86
16.60
32.28
20.89
1.27
13.08
10.88
17.55
35.75
21.47
0.67
12.54
8.98
14.39
39.28
24.14
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
3.75
24.46
10.15
15.07
36.01
10.56
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
11.96
44.53
15.27
16.74
10.14
1.36
Women
No Schooling
Incomplete Basic Schooling
Complete Basic Schooling
Lower Secondary Schooling
Higher Secondary Schooling
Tertiary Education
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
12.52
23.08
9.43
14.65
28.52
11.80
6.35
18.79
9.36
14.37
35.79
15.34
4.52
15.62
10.24
15.37
39.89
14.36
3.82
13.84
7.62
15.42
38.57
20.74
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
4.89
17.97
9.27
19.27
39.70
8.90
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
28.22
38.41
12.04
11.50
9.35
0.49
50
Table A1 continued
Total Bolivia
EIH89
EIH94
Deparatmental Capitals
ECH99
EIH89
EIH94
Other Urban Areas
ECH99
EIH89
EIH94
Rural Areas
ECH99
EIH89
EIH94
ECH99
Employment
Men
High-skilled White Collar
Medium-skilled White Collar
Skilled Manual
Unskilled Manual
Agriculture: Employed
Agriculture: Self-employed
Sales & Services
Never Worked / Don't Know
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
7.54
8.89
27.49
5.10
5.15
23.97
17.49
4.37
10.50
8.48
34.32
2.71
1.10
2.44
24.38
16.06
12.12
11.46
33.33
7.95
0.85
0.53
26.91
6.84
11.90
13.39
34.94
5.99
1.17
0.11
26.26
6.25
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
6.45
8.76
37.93
5.62
6.02
4.48
25.53
5.21
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
2.04
2.83
12.84
3.64
10.21
64.92
2.07
1.45
Women
High-skilled White Collar
Medium-skilled White Collar
Skilled Manual
Unskilled Manual
Agriculture: Employed
Agriculture: Self-employed
Sales & Services
Never Worked / Don't Know
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
3.39
5.13
6.92
6.75
3.42
18.53
15.48
40.39
1.83
8.77
5.08
0.84
0.23
0.36
26.89
55.99
2.31
9.12
7.40
9.34
0.30
0.13
23.45
47.95
5.15
7.93
7.22
9.72
0.34
0.33
22.30
47.00
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
3.55
4.30
11.64
8.27
0.57
2.65
17.72
51.29
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
0.83
1.55
4.36
1.87
9.04
51.31
4.87
26.17
Health
>=1 Hh Member Covered by Social
Security
n.a.
n.a.
23.70
34.01
n.a.
34.05
n.a.
n.a.
28.02
n.a.
n.a.
7.19
Birth in Last 12 Months
thereof: Attended by Doctor
thereof: Delivered in Hospital
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
15.72
55.47
40.97
15.63
65.00
52.53
15.25
72.26
58.36
10.40
83.65
61.35
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
16.22
82.06
55.18
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
23.00
29.00
23.52
Child under 4 Years
thereof: Has First Polio Vaccination
thereof: Has Triple DPT Vaccination
thereof: Incidence of Diarrhea
thereof: Incidence of Cough/Fever
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
46.56
89.22
71.13
31.49
48.73
48.06
88.60
33.69
16.25
16.46
46.03
n.a.
n.a.
8.28
16.32
37.28
89.30
75.19
22.45
45.09
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
49.21
93.29
67.85
35.09
43.55
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
58.47
87.60
68.74
38.24
53.96
Notes: a Ratio of hh members aged between 15 and 65 to all hh members. – b Gas, kerosene or electricity.
Source: Own calculations.
51
Table A2 — Sample Means of the Variables Taken from the Demographic and Health Surveys
Total Bolivia
Deparatmental Capitals
Other Urban Areas
Rural Areas
DHS 89
DHS 94
DHS 98
DHS 89
DHS 94
DHS 98
DHS 89
DHS 94
DHS 98
DHS 89
DHS 94
DHS 98
Demographics
Place of Residence
City
Town
Rural
47.55
11.24
41.21
47.96
12.06
39.98
53.46
14.46
32.08
100.00
0.00
0.00
100.00
0.00
0.00
100.00
0.00
0.00
0.00
100.00
0.00
0.00
100.00
0.00
0.00
100.00
0.00
0.00
0.00
100.00
0.00
0.00
100.00
0.00
0.00
100.00
Department
Chuquisaca
La Paz
Cochabamba
Oruro
Potosí
Tarija
Santa Cruz
Beni and Pando
5.68
36.05
17.20
6.28
9.79
3.90
18.25
2.87
5.96
31.94
17.55
6.20
9.72
4.50
20.91
3.22
6.61
30.60
17.31
4.97
9.01
5.31
22.04
4.14
3.25
42.47
16.45
6.93
3.87
2.93
22.44
1.67
4.16
40.72
14.30
7.00
4.50
3.15
24.49
1.67
5.20
38.77
14.14
6.26
4.35
4.32
24.77
2.20
3.25
21.16
11.77
5.20
18.88
8.04
23.25
8.46
1.34
13.15
11.89
7.68
10.37
10.07
33.83
11.67
1.97
11.77
23.49
4.26
10.93
9.30
26.57
11.70
9.15
32.70
19.55
5.82
14.13
3.88
12.06
2.72
9.50
27.07
23.15
4.80
15.80
4.43
12.72
2.52
11.05
25.49
19.81
3.15
15.92
5.16
15.45
3.97
0.10
1.30
1.53
1.42
1.00
5.35
0.09
1.21
1.48
1.32
1.02
5.12
0.11
1.25
1.53
1.29
0.93
5.10
0.11
1.38
1.64
1.35
0.84
5.32
0.08
1.24
1.56
1.17
0.88
4.93
0.10
1.31
1.65
1.09
0.76
4.91
0.11
1.25
1.52
1.50
0.99
5.37
0.11
1.26
1.52
1.48
1.01
5.38
0.14
1.24
1.52
1.46
0.95
5.31
0.09
1.21
1.39
1.49
1.18
5.36
0.10
1.16
1.38
1.46
1.19
5.29
0.11
1.15
1.34
1.55
1.20
5.35
56.30
56.54
58.23
59.41
60.11
63.22
55.48
55.34
55.86
52.94
52.63
50.96
74.13
15.14
70.04
17.15
78.46
17.45
93.30
18.17
92.79
18.38
96.27
19.08
88.14
16.02
89.50
19.78
91.05
19.71
48.18
11.40
36.88
14.89
43.09
13.71
6.04
26.82
30.17
19.92
10.67
6.38
8.62
28.91
28.01
20.04
9.65
4.77
7.37
26.36
30.52
20.41
9.63
5.71
5.75
27.33
29.93
19.40
10.68
6.90
8.95
29.84
27.67
20.04
9.47
4.03
7.81
25.86
30.13
21.23
9.67
5.29
5.82
24.55
31.98
20.56
10.61
6.48
8.11
28.17
30.16
19.60
8.55
5.41
6.34
26.33
30.70
19.69
10.01
6.94
6.45
26.84
29.97
20.34
10.67
5.74
8.38
28.02
27.76
20.18
10.21
5.46
7.10
27.21
31.10
19.35
9.40
5.84
47.36
29.39
23.24
56.08
29.63
14.29
69.75
19.31
10.94
67.63
6.72
25.64
77.03
4.93
18.04
88.48
1.76
9.76
58.86
12.73
28.40
79.09
11.94
8.98
84.05
8.49
7.47
20.84
60.09
19.07
24.01
64.60
11.39
32.09
53.44
14.48
Number of
Elderly (age>=66 or unknown)
Adult Men (15>=age>=65)
Adult Women (15>=age>=65)
Youngsters (6>=age>=14)
Children (age<=5)
All household members
Age Composition of Hh a
Language of Hh Head (Spanish)
Gender Hh Head (Female)
Age of Household Head
<=24
25 - 34
35 - 44
45 - 54
55 - 65
>=66 or Unknown
Tangible Assets
Water Source
Inhouse Access to Public Water
Open Water Source
Other Water Source
52
Table A2 continued
Total Bolivia
Deparatmental Capitals
Other Urban Areas
Rural Areas
DHS 89
DHS 94
DHS 98
DHS 89
DHS 94
DHS 98
DHS 89
DHS 94
DHS 98
DHS 89
DHS 94
DHS 98
Toilet Facility
No Toilet
Shared Toilet
Private Toilet
49.72
50.28
40.19
35.83
23.98
32.25
19.41
48.34
26.51
73.49
n.a.
26.32
30.03
43.65
16.27
28.04
55.69
40.46
59.54
n.a.
26.29
53.57
20.14
22.60
21.37
56.02
79.02
20.98
n.a.
61.02
37.43
1.55
63.22
4.15
32.63
House
Electricity
Telephone
Radio
Television
Fridge
Car
Family Land
63.83
n.a.
n.a.
n.a.
n.a.
n.a.
12.07
n.a.
67.06
67.61
10.59
85.17
58.19
29.69
n.a.
28.46
64.98
75.73
24.96
88.08
68.39
37.67
n.a.
21.27
53.02
n.a.
n.a.
n.a.
n.a.
n.a.
19.60
n.a.
52.54
95.00
20.20
94.74
88.32
45.56
n.a.
0.95
54.55
98.41
40.87
95.64
93.46
53.36
n.a.
0.55
59.58
n.a.
n.a.
n.a.
n.a.
n.a.
10.80
n.a.
60.65
86.17
6.66
85.74
72.15
35.91
n.a.
9.77
60.97
90.43
19.89
88.93
81.03
43.32
n.a.
6.63
77.46
n.a.
n.a.
n.a.
n.a.
n.a.
3.73
n.a.
86.43
29.16
0.25
73.53
17.83
8.78
n.a.
67.10
84.18
31.31
0.74
75.11
20.91
8.96
n.a.
62.40
Main Floor Material
Earth
Cement
Brick
Other Floor
n.a.
n.a.
n.a.
n.a.
37.63
32.64
11.72
18.01
28.84
37.57
7.58
26.01
n.a.
n.a.
n.a.
n.a.
14.56
41.62
15.98
27.84
7.42
43.51
9.36
39.71
n.a.
n.a.
n.a.
n.a.
26.30
39.76
21.61
12.33
19.89
51.01
11.08
18.02
n.a.
n.a.
n.a.
n.a.
68.73
19.72
3.62
7.93
68.58
21.62
3.04
6.76
High-quality Cooking Materialb
n.a.
64.10
71.77
n.a.
96.22
98.29
n.a.
75.18
83.92
n.a.
22.22
22.09
Number of Sleeping Rooms
0–1
2–3
>= 4
n.a.
n.a.
n.a.
53.15
41.13
5.73
59.25
34.60
6.16
n.a.
n.a.
n.a.
47.39
44.48
8.13
50.19
40.11
9.70
n.a.
n.a.
n.a.
49.94
42.87
7.19
58.85
36.57
4.58
n.a.
n.a.
n.a.
61.02
36.58
2.40
74.55
24.52
0.97
Educational Attainment of Adults
Men
No Schooling
Incomplete Basic Schooling
Complete Basic Schooling
Lower Secondary Schooling
Higher Secondary Schooling
Tertiary Education
14.21
23.99
17.67
13.34
17.77
13.02
5.48
22.84
14.12
16.16
28.74
12.67
4.24
24.18
11.29
13.71
28.03
18.55
9.55
11.33
14.68
16.22
25.58
22.64
2.27
11.10
10.67
14.66
39.58
21.72
1.92
13.69
7.25
14.12
34.81
28.21
11.98
18.90
18.89
13.97
27.08
9.18
4.23
20.12
15.23
15.58
34.97
9.87
2.69
22.28
11.58
16.40
30.67
16.39
19.99
39.39
20.62
9.99
6.58
3.45
9.74
37.79
17.91
18.15
13.72
2.69
8.64
41.84
17.64
11.85
16.01
4.02
Women
No Schooling
Incomplete Basic Schooling
Complete Basic Schooling
Lower Secondary Schooling
Higher Secondary Schooling
Tertiary Education
18.69
29.75
13.87
14.12
16.38
7.19
13.43
27.02
12.49
13.74
25.36
7.96
9.32
23.33
10.10
13.29
30.09
13.86
8.03
21.17
13.54
18.63
25.94
12.68
4.65
18.09
9.63
14.65
38.84
14.14
3.13
14.70
7.02
12.72
40.82
21.61
12.22
26.22
15.60
19.11
20.66
6.19
9.94
22.95
12.11
17.35
31.54
6.11
4.94
18.15
9.61
16.98
38.33
11.98
32.74
40.60
13.77
7.57
4.19
1.12
25.01
38.97
16.04
11.55
7.33
1.10
21.62
40.05
15.46
12.58
8.50
1.80
53
Table A2 continued
Total Bolivia
Deparatmental Capitals
Other Urban Areas
Rural Areas
DHS 89
DHS 94
DHS 98
DHS 89
DHS 94
DHS 98
DHS 89
DHS 94
DHS 98
DHS 89
DHS 94
DHS 98
Employment
Men
High-skilled White Collar
Medium-skilled White Collar
Skilled Manual
Unskilled Manual
Agriculture: Employed
Agriculture: Self-employed
Sales & Services
Never Worked / Don't Know
9.56
8.45
25.04
5.06
4.37
27.55
16.85
3.11
6.70
9.11
25.79
4.29
6.01
25.12
19.34
3.64
8.68
8.63
24.91
4.16
4.33
22.26
20.29
6.73
16.82
12.54
32.95
6.91
0.48
2.15
23.50
4.65
12.18
13.41
35.13
5.67
0.98
0.76
26.54
5.33
13.89
11.16
31.12
5.75
0.77
0.99
27.71
8.61
8.31
10.95
33.97
6.35
4.10
9.92
24.87
1.52
4.96
12.09
28.75
4.59
8.95
9.62
27.21
3.83
7.19
9.45
27.82
4.88
6.91
8.32
26.11
9.31
1.89
3.24
13.86
2.64
8.77
60.47
7.32
1.83
0.66
2.98
13.65
2.54
11.14
59.31
8.19
1.53
0.98
4.20
13.67
1.27
8.91
62.58
5.81
2.59
Women
High-skilled White Collar
Medium-skilled White Collar
Skilled Manual
Unskilled Manual
Agriculture: Employed
Agriculture: Self-employed
Sales & Services
Never Worked / Don't Know
1.43
5.39
3.58
0.42
0.50
0.80
13.59
74.28
1.42
7.14
6.53
9.47
6.32
15.01
17.21
36.89
3.07
8.17
6.99
7.95
0.92
12.18
19.09
41.64
2.58
8.38
3.93
0.23
0.13
0.04
18.75
65.97
2.39
11.30
8.25
14.18
0.42
0.13
21.96
41.37
4.93
11.29
8.18
11.10
0.01
0.10
25.06
39.33
0.67
8.29
3.43
1.94
0.25
0.10
18.81
66.52
1.34
8.90
7.10
11.69
1.54
2.26
24.69
42.48
2.40
9.37
7.53
8.19
0.91
1.40
24.77
45.42
0.31
1.16
3.22
0.23
1.01
1.86
6.22
85.99
0.30
1.61
4.30
3.15
14.85
36.70
9.26
29.83
0.28
2.41
4.76
2.60
2.43
37.15
6.59
43.80
Health
>=1 Hh Member Covered by Social
Security
21.44
n.a.
21.31
29.19
n.a.
31.11
30.19
n.a.
23.18
10.10
n.a.
4.12
Birth in Last 12 Months
thereof: Attended by Doctor
thereof: Delivered in Hospital
19.83
40.29
36.86
18.64
42.06
31.17
17.08
56.73
42.62
16.30
63.31
56.56
15.57
63.20
46.37
14.15
76.54
51.45
20.22
49.36
50.79
18.61
57.50
40.73
15.58
72.66
60.59
23.80
20.00
17.98
22.34
20.50
16.03
22.63
31.24
27.79
Child under 4 Years
thereof: First Polio Vaccination
thereof: Triple DPT Vaccination
thereof: Incidence of Diarrhea
thereof: Incidence of Cough/Fever
51.02
70.64
30.22
29.26
40.93
50.08
56.13
26.32
21.45
30.35
47.31
76.16
44.09
20.84
48.17
43.90
76.67
39.50
28.38
37.31
44.75
62.39
32.54
21.34
31.80
41.27
79.23
48.46
19.02
47.13
50.64
72.35
30.65
30.98
39.71
49.26
56.31
27.49
24.08
31.85
45.08
76.86
46.58
19.92
46.78
59.34
65.07
22.19
29.61
44.29
56.73
50.13
20.13
20.84
28.56
58.39
72.31
38.07
23.29
49.89
Notes: a Ratio of hh members aged between 15 and 65 to all hh members. – b Gas, kerosene or electricity.
Source: Own calculations.
54
Table A3 — Spatial Disaggregation of the Poverty Gap in Bolivia, 1989 to 2002
Moderate Poverty Line
Extreme Poverty Line
1989
1994
1999
2002
1989
1994
1999
2002
45.45
(0.35)
41.89
(0.25)
32.53
32.94
27.53
(0.34)
25.21
(0.22)
15.73
15.32
Departmental Capital
32.92
25.74
21.02
24.37
15.29
9.58
8.00
9.79
Other Urban Areas
51.31
(0.92)
58.30
(0.50)
44.68
(0.69)
60.90
(0.34)
34.70
32.88
13.10
44.86
27.02
(0.63)
43.33
(0.38)
13.97
47.71
34.10
(0.90)
39.13
(0.57)
27.37
23.88
Total
By Type of Municipality
Rural Areas
By Department
Chuquisaca
58.81
60.79
53.94
49.16
40.34
44.86
35.43
29.12
(0.81)
(0.70)
(0.90)
(0.74)
La Paz
45.19
37.11
35.12
33.53
26.48
20.09
18.04
16.48
(0.70)
(0.50)
(0.66)
(0.46)
Cochabamba
43.02
41.97
30.20
36.30
24.66
23.68
12.44
17.14
(0.83)
(0.76)
(0.81)
(0.62)
Oruro
48.27
49.55
34.57
36.15
30.67
33.34
15.76
18.36
(0.82)
(0.70)
(0.79)
(0.69)
Potosí
64.69
63.87
50.53
47.24
49.40
50.62
30.24
26.99
(0.73)
(0.58)
(0.93)
(0.64)
Tarija
50.78
50.27
28.92
28.67
31.16
30.46
12.19
9.21
(0.75)
(0.74)
(0.75)
(0.62)
Santa Cruz
31.41
28.16
20.47
23.97
14.84
12.48
6.92
8.44
(0.81)
(0.57)
(0.66)
(0.46)
Beni & Pando
47.05
50.11
20.03
26.66
26.90
31.05
4.20
8.77
(0.84)
(0.83)
(0.80)
(0.78)
Notes: Poverty indices are calculated using income data for departmental capitals and other urban areas, expenditure data for rural
areas, and mixed income-expenditure data for total Bolivia. Standard errors of the poverty indices in brackets (only
applicable to those based on simulated data).
Source: Own calculations.
55
Table A4 — Spatial Disaggregation of the Squared Poverty Gap in Bolivia, 1989 to 2002
Moderate Poverty Line
Extreme Poverty Line
1989
1994
1999
2002
1989
1994
1999
2002
31.37
(0.31)
28.94
(0.21)
20.19
20.04
16.78
(0.25)
15.79
(0.17)
8.68
8.19
Departmental Capitals
19.96
14.16
11.60
14.00
8.05
4.51
3.94
5.13
Other Urban Areas
37.28
(0.83)
42.21
(0.49)
31.38
(0.58)
45.83
(0.33)
21.12
19.82
6.95
28.53
17.17
(0.49)
28.84
(0.34)
8.01
31.85
22.52
(0.71)
24.59
(0.47)
15.65
12.94
Total
By Type of Municipality
Rural Areas
By Department
Chuquisaca
43.60
47.22
39.22
33.65
26.27
31.20
22.68
16.24
(0.80)
(0.68)
(0.77)
(0.69)
La Paz
30.42
23.99
21.82
20.07
15.45
11.38
9.43
8.60
(0.60)
(0.42)
(0.49)
(0.33)
Cochabamba
29.32
28.41
17.68
22.70
14.71
14.42
6.66
9.41
(0.75)
(0.61)
(0.64)
(0.48)
Oruro
33.10
35.16
20.78
22.76
18.67
21.15
7.30
10.50
(0.72)
(0.62)
(0.62)
(0.55)
Potosí
49.55
50.68
34.86
31.58
33.61
36.39
18.58
16.28
(0.79)
(0.55)
(0.85)
(0.59)
Tarija
36.32
35.84
17.17
15.46
19.63
19.40
6.76
3.81
(0.68)
(0.60)
(0.62)
(0.49)
Santa Cruz
19.79
17.20
11.33
13.45
8.20
6.71
3.25
4.14
(0.64)
(0.45)
(0.44)
(0.31)
Beni & Pando
32.41
36.14
9.71
14.54
16.29
19.95
2.15
3.99
(0.72)
(0.72)
(0.58)
(0.62)
Notes: Poverty indices are calculated using income data for departmental capitals and other urban areas, expenditure data for rural
areas, and mixed income-expenditure data for total Bolivia. Standard errors of the poverty indices in brackets (only
applicable to those based on simulated data).
Source: Own calculations.
56
Table A5 — Disaggregation of the Poverty Gap in Bolivia by Household Characteristics, 1989 to
2002
Moderate Poverty Line
Total
By Hh Size
<=3
4-6
>=7
By % of Hh Members
between 15 and 65 Years
<= 0.5
> 0.5
By Age of Hh Head
<=34
35-49
50-65
>=66
By Language of Hh Head
Spanish
Indigenous
By Gender of Hh Head
Male
Female
By Average Years of
Schooling of Adultsa
<=5
6-12
>=13
By Profession of Principal
Wage Earnerb
White Collar Worker
Blue Collar Worker
Agriculture
Sales & Services
Not Employed
By % of Adult Womenc in
Employment
>0
=0
Extreme Poverty Line
1989
1994
1999
2002
1989
1994
1999
2002
45.45
(0.35)
41.89
(0.25)
32.53
32.94
27.53
(0.34)
25.21
(0.22)
15.73
15.32
38.52
(0.83)
42.88
(0.45)
54.88
(0.67)
31.35
(0.60)
40.86
(0.31)
53.74
(0.47)
19.48
17.21
5.70
30.17
13.93
13.34
43.48
42.76
16.19
(0.45)
24.14
(0.29)
35.79
(0.46)
7.24
29.51
20.94
(0.78)
25.09
(0.44)
36.50
(0.71)
22.56
21.75
52.02
(0.41)
36.45
(0.54)
50.23
(0.30)
31.29
(0.41)
40.15
40.90
19.97
23.52
32.00
(0.30)
16.59
(0.29)
20.83
23.45
33.27
(0.42)
19.67
(0.50)
9.66
9.82
47.04
(0.62)
45.92
(0.52)
42.78
(0.79)
41.73
(1.45)
41.79
(0.41)
42.89
(0.36)
39.03
(0.61)
44.57
(0.95)
33.79
33.59
14.78
34.97
16.37
16.97
27.74
27.66
12.43
12.65
34.33
30.57
24.30
(0.36)
26.22
(0.35)
23.46
(0.47)
30.39
(0.89)
16.47
33.45
28.48
(0.60)
28.12
(0.52)
25.16
(0.77)
25.78
(1.33)
17.80
12.98
38.80
(0.40)
64.48
(0.67)
32.51
(0.33)
63.80
(0.42)
21.34
23.03
8.30
42.14
16.39
(0.26)
45.83
(0.48)
7.80
44.18
21.40
(0.34)
45.08
(0.78)
24.00
21.83
46.23
(0.40)
41.45
(0.78)
42.80
(0.27)
37.49
(0.62)
32.87
33.61
15.55
28.81
26.11
(0.25)
20.91
(0.52)
16.06
30.62
28.31
(0.38)
23.55
(0.85)
13.91
13.90
58.88
(0.48)
35.61
(0.59)
13.44
(1.00)
60.30
(0.37)
32.98
(0.46)
10.12
(0.59)
49.35
47.76
26.03
27.97
11.23
10.76
6.33
7.52
42.14
(0.39)
15.48
(0.34)
2.96
(0.33)
28.28
28.29
39.07
(0.52)
18.06
(0.50)
4.55
(0.59)
1.10
1.83
23.42
(0.66)
44.06
(0.73)
65.53
(0.58)
35.72
(0.94)
46.54
(0.93)
15.10
(0.59)
38.18
(0.55)
67.92
(0.35)
29.62
(0.68)
37.33
(0.87)
12.55
9.68
2.62
32.63
11.10
13.40
52.26
48.35
31.18
26.52
23.55
19.45
9.74
6.07
27.37
29.57
5.47
(0.34)
19.10
(0.45)
50.25
(0.43)
12.77
(0.47)
19.80
(0.77)
3.40
30.28
10.72
(0.51)
24.79
(0.66)
46.15
(0.72)
17.95
(0.77)
27.61
(0.93)
13.51
13.67
29.82
(0.59)
52.15
(0.43)
40.86
(0.31)
44.05
(0.53)
32.01
32.20
15.05
34.51
25.27
(0.26)
25.10
(0.44)
15.93
33.64
14.59
(0.49)
33.08
(0.43)
15.31
15.89
Notes: Poverty indices are calculated using mixed income-expenditure data. Standard errors of the poverty indices in brackets
(only applicable to those based on simulated data). a Women aged between 15 and 49 and their husbands and partners. b In
the case of DHS: Husband or partner of the oldest woman aged between 15 and 49. If she is single, this woman herself. In
the case of LSMS: Household head. c Women aged between 15 and 49.
Source: Own calculations
57
Table A6 — Disaggregation of the Poverty Gap in the Departmental Capitals of Bolivia by
Household Characteristics, 1989 to 2002
Moderate Poverty Line
Total
By Hh Size
<=3
4-6
>=7
Extreme Poverty Line
1989
1994
1999
2002
1989
1994
1999
2002
32.92
25.74
21.02
24.37
15.29
9.58
8.00
9.79
18.62
30.70
41.33
14.50
23.95
33.83
13.91
19.66
28.33
12.62
22.97
32.95
5.88
13.24
21.85
4.26
8.61
13.64
4.70
7.55
10.98
3.71
8.99
14.38
41.13
23.96
34.37
17.44
27.54
16.05
33.22
16.58
21.06
9.00
13.97
5.36
11.62
5.25
14.27
5.85
35.27
35.18
25.79
24.06
28.91
26.41
20.62
19.27
22.46
22.55
17.21
10.94
26.53
26.16
18.13
18.26
15.75
17.40
10.99
8.61
11.14
9.88
7.06
6.72
8.66
8.91
5.91
2.51
10.63
11.07
6.35
5.36
27.37
40.58
21.68
30.85
16.67
29.88
18.53
32.98
11.08
21.10
7.18
12.61
5.85
12.38
6.43
14.74
32.93
32.85
25.60
26.58
20.89
21.64
24.68
22.91
15.37
14.73
9.42
10.59
7.88
8.57
9.84
9.55
48.07
31.26
13.90
39.37
27.66
10.49
34.89
24.08
6.29
36.88
25.79
7.31
26.40
13.49
3.99
17.63
9.69
2.85
15.19
9.27
1.26
16.04
10.25
1.89
17.56
39.96
34.77
33.79
38.32
10.88
34.10
25.83
25.58
38.27
10.69
26.13
36.31
23.32
22.01
8.53
30.88
38.52
19.18
25.55
6.45
18.30
14.25
15.91
22.50
2.73
12.60
9.94
9.35
20.13
2.72
9.28
18.25
10.35
10.20
2.38
13.06
15.45
6.11
11.04
27.22
40.36
21.69
32.58
18.13
26.54
21.19
30.82
12.06
19.51
7.33
13.38
6.33
11.20
7.37
14.69
By % of Hh Members
between 15 and 65 Years
<= 0.5
> 0.5
By Age of Hh Head
<=34
35-49
50-65
>=66
By Language of Hh Head
Spanish
Indigenous
By Gender of Hh Head
Male
Female
By Average Years of
Schooling of Adultsa
<=5
6-12
>=13
By Profession of
Principal Wage Earnerb
White Collar Worker
Blue Collar Worker
Agriculture
Sales & Services
Not Employed
By % of Adult Womenc
in Employment
>0
=0
Notes: Poverty indices are calculated using income data. a Women aged between 15 and 49 and their husbands and partners. b In
the case of DHS: Husband or partner of the oldest woman aged between 15 and 49. If she is single, this woman herself. In
the case of LSMS: Household head. c Women aged between 15 and 49.
Source: Own calculations.
58
Table A7 — Disaggregation of the Poverty Gap in Other Urban Areas of Bolivia by Household
Characteristics, 1989 to 2002
Moderate Poverty Line
Total
By Hh Size
<=3
4-6
>=7
By % of Hh Members
between 15 and 65 Years
<= 0.5
> 0.5
By Age of Hh Head
<=34
35-49
50-65
>=66
By Language of Hh Head
Spanish
Indigenous
By Gender of Hh Head
Male
Female
By Average Years of
Schooling of Adultsa
<=5
6-12
>=13
By Profession of Principal
Wage Earnerb
White Collar Worker
Blue Collar Worker
Agriculture
Sales & Services
Not Employed
c
By % of Adult Women in
Employment
>0
=0
Extreme Poverty Line
1989
1994
1999
2002
1989
1994
1999
2002
51.31
(0.92)
44.68
(0.69)
34.70
32.88
34.10
(0.90)
27.02
(0.63)
13.97
13.10
45.77
(2.11)
48.12
(1.27)
60.58
(1.61)
32.54
(1.67)
44.32
(1.13)
53.45
(1.30)
18.90
18.18
5.24
31.44
10.02
12.27
49.05
39.21
17.08
(1.26)
26.31
(0.99)
34.88
(1.27)
6.33
29.58
28.88
(2.03)
30.94
(1.20)
43.10
(1.70)
23.42
16.58
54.48
(1.08)
46.35
(1.49)
51.89
(0.94)
34.67
(1.15)
40.50
41.52
18.18
22.71
33.36
(0.90)
18.22
(0.95)
16.33
28.16
37.36
(1.14)
28.98
(1.43)
11.31
7.12
52.88
(1.58)
51.84
(1.28)
48.94
(2.08)
46.60
(4.49)
47.02
(1.27)
43.55
(1.04)
42.93
(1.89)
43.11
(2.94)
37.25
35.18
13.75
36.24
15.91
14.84
26.91
23.49
9.37
9.07
40.33
33.89
28.55
(1.17)
26.58
(0.98)
24.61
(1.71)
27.53
(2.49)
14.09
35.71
35.46
(1.51)
35.03
(1.32)
31.48
(2.05)
28.18
(3.79)
13.36
13.10
50.01
(0.92)
60.96
(3.03)
43.44
(0.71)
55.30
(2.47)
31.13
30.31
11.51
37.86
25.60
(0.65)
39.16
(2.37)
11.29
41.48
32.67
(0.91)
44.72
(3.06)
19.06
16.17
52.98
(1.03)
44.06
(2.21)
45.94
(0.77)
39.59
(1.58)
34.94
32.97
12.74
32.42
28.04
(0.72)
22.89
(1.36)
13.79
33.43
35.94
(1.04)
26.06
(1.98)
14.90
14.94
62.78
(1.41)
46.74
(1.34)
23.47
(2.6)
56.17
(1.23)
40.99
(0.99)
20.16
(1.96)
45.14
49.07
25.70
31.13
14.04
10.43
6.17
9.75
37.20
(1.29)
22.93
(0.82)
9.66
(1.36)
23.02
36.36
44.88
(1.55)
29.46
(1.23)
10.80
(2.12)
0.14
1.82
35.65
(2.06)
58.50
(1.34)
67.53
(2.43)
40.11
(2.00)
61.37
(2.47)
22.49
(1.59)
46.81
(1.34)
68.84
(1.71)
36.42
(1.51)
55.55
(2.25)
13.70
11.81
2.02
37.34
16.23
14.12
54.53
40.49
28.63
22.16
25.62
24.02
8.01
7.96
38.32
33.29
9.01
(1.10)
28.81
(1.09)
49.37
(1.98)
18.24
(1.21)
36.32
(2.28)
2.96
40.20
19.55
(1.71)
41.27
(1.51)
50.12
(2.81)
23.13
(1.82)
42.00
(2.81)
18.65
14.65
32.31
(1.42)
62.80
(1.12)
36.16
(0.93)
59.82
(1.12)
28.11
30.72
12.12
36.81
19.96
(0.74)
39.56
(1.16)
10.13
44.44
16.61
(1.10)
44.68
(1.19)
19.65
14.89
Notes: Poverty indices are calculated using income data. Standard errors of the poverty indices in brackets (only applicable to those
based on simulated data). a Women aged between 15 and 49 and their husbands and partners. b In the case of DHS: Husband
or partner of the oldest woman aged between 15 and 49. If she is single, this woman herself. In the case of LSMS:
Household head. c Women aged between 15 and 49.
Source: Own calculations.
59
Table A8 — Disaggregation of the Poverty Gap in Rural Areas of Bolivia by Household
Characteristics, 1989 to 2002
Moderate Poverty Line
Total
By Hh Size
<=3
4-6
>=7
By % of Hh Members
between 15 and 65 Years
<= 0.5
> 0.5
By Age of Hh Head
<=34
35-49
50-65
>=66
By Language of Hh Head
Spanish
Indigenous
By Gender of Hh Head
Male
Female
By Average Years of
Schooling of Adultsa
<=5
6-12
>=13
By Profession of Principal
Wage Earnerb
White Collar Worker
Blue Collar Worker
Agriculture
Sales & Services
Not Employed
c
By % of Adult Women in
Employment
>0
=0
Extreme Poverty Line
1989
1994
1999
2002
1989
1994
1999
2002
58.30
(0.50)
60.90
(0.34)
47.71
44.86
39.13
(0.57)
43.33
(0.38)
27.37
23.88
50.09
(1.29)
56.29
(0.65)
66.94
(0.89)
48.40
(0.89)
61.42
(0.48)
69.10
(0.52)
31.39
25.34
9.61
41.70
25.84
21.11
55.86
53.29
30.20
(0.92)
43.68
(0.53)
52.31
(0.61)
13.02
45.06
29.86
(1.38)
36.94
(0.71)
48.78
(1.08)
33.36
30.56
60.99
(0.57)
52.89
(0.98)
65.13
(0.38)
53.48
(0.62)
50.87
48.43
26.31
38.38
48.04
(0.46)
35.08
(0.64)
30.26
40.12
42.19
(0.66)
32.99
(1.06)
20.43
19.47
56.19
(0.85)
58.93
(0.81)
58.35
(1.22)
67.21
(2.21)
57.67
(0.58)
62.19
(0.49)
61.73
(0.82)
70.09
(1.35)
47.30
42.30
20.65
46.78
26.97
25.92
45.15
43.72
24.52
23.66
57.97
46.65
40.31
(0.59)
44.57
(0.59)
43.57
(0.89)
53.57
(1.66)
28.01
47.77
37.77
(0.95)
39.53
(0.92)
38.22
(1.28)
48.80
(2.65)
37.61
24.66
48.96
(0.68)
66.98
(0.71)
49.42
(0.59)
67.60
(0.43)
28.32
30.21
11.07
49.80
31.99
(0.57)
49.96
(0.51)
11.49
53.21
30.14
(0.67)
47.49
(0.85)
31.87
28.19
58.43
(0.53)
57.37
(1.52)
61.86
(0.37)
55.38
(0.90)
47.72
45.07
23.75
42.58
44.35
(0.41)
37.55
(0.95)
27.75
47.59
39.33
(0.60)
37.68
(1.79)
24.45
25.28
63.67
(0.58)
38.66
(1.24)
10.66
(3.69)
68.28
(0.37)
45.53
(0.78)
13.05
(2.56)
53.51
51.69
29.87
31.65
14.96
12.47
7.47
2.04
51.19
(0.44)
26.26
(0.79)
3.96
(1.29)
32.25
33.68
44.02
(0.69)
20.76
(1.20)
2.92
(2.04)
0.86
0.40
35.11
(2.08)
42.56
(1.46)
65.97
(0.62)
38.76
(1.93)
60.43
(1.59)
34.71
(1.71)
49.61
(0.95)
68.30
(0.36)
37.57
(1.38)
52.14
(1.50)
23.09
15.80
5.41
33.81
11.13
13.90
52.54
49.39
31.67
27.39
18.23
13.04
7.40
1.84
49.82
48.72
20.03
(1.62)
30.65
(0.96)
50.88
(0.44)
20.68
(1.22)
33.85
(1.57)
8.35
32.57
19.24
(1.89)
23.55
(1.36)
46.59
(0.77)
21.11
(1.87)
39.97
(1.72)
31.06
28.34
45.27
(1.51)
60.70
(0.55)
62.70
(0.39)
56.13
(0.65)
50.11
47.19
26.17
39.19
45.59
(0.45)
37.37
(0.67)
29.52
39.93
26.46
(1.46)
41.47
(0.62)
20.42
18.28
Notes: Poverty indices are calculated expenditures data. Standard errors of the poverty indices in brackets (only applicable to those
based on simulated data). a Women aged between 15 and 49 and their husbands and partners. b In the case of DHS: Husband
or partner of the oldest woman aged between 15 and 49. If she is single, this woman herself. In the case of LSMS:
Household head. c Women aged between 15 and 49.
Source: Own calculations.
60
Table A9 — Disaggregation of the Squared Poverty Gap in Bolivia by Household Characteristics,
1989 to 2002
Moderate Poverty Line
Total
By Hh Size
<=3
4-6
>=7
By % of Hh Members
between 15 and 65 Years
<= 0.5
> 0.5
By Age of Hh Head
<=34
35-49
50-65
>=66
By Language of Hh Head
Spanish
Indigenous
By Gender of Hh Head
Male
Female
By Average Years of
Schooling of Adultsa
<=5
6-12
>=13
By Profession of Principal
Wage Earnerb
White Collar Worker
Blue Collar Worker
Agriculture
Sales & Services
Not Employed
By % of Adult Womenc in
Employment
>0
=0
Extreme Poverty Line
1989
1994
1999
2002
1989
1994
1999
2002
31.37
(0.31)
28.94
(0.21)
20.19
20.04
16.78
(0.25)
15.79
(0.17)
8.68
8.19
24.92
(0.70)
29.05
(0.40)
40.01
(0.64)
19.88
(0.44)
27.94
(0.26)
39.39
(0.41)
10.64
9.08
11.81
(0.54)
14.91
(0.33)
23.62
(0.58)
9.24
(0.32)
14.91
(0.22)
23.70
(0.38)
3.72
2.61
18.07
17.90
7.57
6.97
28.06
27.20
12.79
12.04
37.01
(0.37)
23.65
(0.47)
35.86
(0.27)
20.14
(0.30)
25.73
25.48
20.83
(0.33)
11.23
(0.37)
20.62
(0.24)
9.65
(0.20)
11.59
10.67
13.59
13.61
5.22
5.26
32.54
(0.54)
31.87
(0.46)
28.96
(0.70)
29.08
28.41
(0.33)
29.85
(0.31)
26.86
(0.47)
33.01
21.02
19.92
17.38
(0.46)
17.23
(0.39)
14.98
(0.60)
16.20
15.02
(0.27)
16.45
(0.26)
14.58
(0.37)
20.69
9.34
7.64
20.85
21.72
8.87
9.23
16.76
16.68
6.91
6.85
22.09
17.89
9.98
6.57
25.62
(0.33)
47.84
(0.68)
20.64
(0.26)
48.35
(0.42)
11.86
12.87
12.53
(0.24)
28.97
(0.66)
9.37
(0.17)
30.80
(0.44)
3.92
4.15
28.88
26.71
13.65
11.94
32.09
(03.5)
27.69
(0.73)
29.78
(0.23)
24.91
(0.49)
20.48
20.39
17.36
(0.29)
13.82
(0.65)
16.47
(0.19)
12.49
(0.37)
8.96
8.28
18.58
17.90
7.15
7.63
42.60
(0.46)
22.56
(0.47)
7.09
(0.65)
45.19
(0.35)
20.16
(0.34)
5.02
(0.37)
33.14
31.16
24.64
(0.42)
10.11
(0.34)
2.14
(0.34)
28.10
(0.33)
8.33
(0.22)
1.36
(0.19)
16.41
14.63
16.17
15.83
5.65
5.34
2.79
3.48
0.46
0.91
14.12
(0.51)
29.37
(0.62)
48.97
(0.62)
22.59
(0.75)
31.62
(0.84)
8.26
(0.37)
24.10
(0.43)
52.53
(0.37)
17.47
(0.48)
24.02
(0.70)
6.04
4.70
5.78
(0.35)
14.59
(0.48)
29.90
(0.62)
9.91
(0.53)
16.47
(0.71)
2.70
(0.20)
10.62
(0.30)
34.26
(0.41)
6.57
(0.31)
11.40
(0.53)
1.13
1.35
16.89
19.07
5.64
6.78
35.81
31.44
18.53
14.77
13.54
10.32
4.54
3.01
17.47
17.87
7.76
7.38
18.52
(0.47)
36.87
(0.39)
28.60
(0.25)
29.67
(0.42)
20.07
19.65
7.95
(0.33)
20.57
(0.33)
16.13
(0.21)
15.08
(0.30)
9.07
8.01
20.46
20.88
7.86
8.58
Notes: Poverty indices are calculated using mixed income-expenditure data. Standard errors of the poverty indices in brackets
(only applicable to those based on simulated data). a Women aged between 15 and 49 and their husbands and partners. b In
the case of DHS: Husband or partner of the oldest woman aged between 15 and 49. If she is single, this woman herself. In
the case of LSMS: Household head. c Women aged between 15 and 49.
Source: Own calculations.
61
Table A10 — Disaggregation of the Squared Poverty Gap in the Departmental Capitals of Bolivia
by Household Characteristics, 1989 to 2002
Moderate Poverty Line
Total
By Hh Size
<=3
4-6
>=7
By % of Hh Members
between 15 and 65 Years
<= 0.5
> 0.5
By Age of Hh Head
<=34
35-49
50-65
>=66
By Language of Hh Head
Spanish
Indigenous
By Gender of Hh Head
Male
Female
By Average Years of
Schooling of Adultsa
<=5
6-12
>=13
By Profession of Principal
Wage Earnerb
White Collar Worker
Blue Collar Worker
Agriculture
Sales & Services
Not Employed
By % of Adult Womenc in
Employment
>0
=0
Extreme Poverty Line
1989
1994
1999
2002
1989
1994
1999
2002
19.96
14.16
11.60
14.00
8.05
4.51
3.94
5.13
9.47
18.03
26.66
7.19
12.95
19.37
7.19
10.85
15.91
6.36
12.99
19.75
2.68
6.76
11.99
1.95
4.03
6.48
2.44
3.70
5.37
1.74
4.72
7.63
26.14
13.22
19.75
8.78
15.85
8.36
19.66
9.01
11.42
4.38
6.78
2.32
5.86
2.47
7.48
3.07
21.17
21.85
15.07
13.08
16.26
14.52
10.87
10.28
12.33
12.64
9.31
4.91
15.08
15.40
9.95
9.24
8.03
9.32
5.97
3.85
5.48
4.63
3.09
2.80
4.51
4.23
3.06
0.82
5.47
5.92
3.30
2.13
15.90
25.56
11.51
17.49
8.94
17.02
10.26
19.50
5.59
11.45
3.28
6.05
2.81
6.23
3.35
7.77
19.99
19.77
14.00
15.11
11.44
12.39
14.12
13.38
8.03
8.23
4.41
5.12
3.84
4.38
5.20
4.82
31.39
18.49
6.81
23.38
14.90
5.07
20.55
13.30
2.81
22.00
14.78
3.39
14.23
7.07
1.73
8.82
4.47
1.12
7.45
4.49
0.56
8.57
5.35
0.97
9.38
24.13
20.47
20.58
26.13
5.10
18.72
15.01
14.00
24.74
4.98
14.12
24.02
13.51
13.69
4.11
18.13
23.27
10.22
14.91
3.18
9.38
6.58
8.13
13.82
0.99
5.68
5.35
4.50
11.63
0.85
4.79
11.74
4.66
6.18
1.33
6.73
8.57
3.12
6.10
16.04
25.07
11.48
18.68
9.67
15.30
11.61
18.84
6.34
10.29
3.38
6.42
3.24
5.26
3.61
8.21
Notes: Poverty indices are calculated using income data. a Women aged between 15 and 49 and their husbands and partners. b In
the case of DHS: Husband or partner of the oldest woman aged between 15 and 49. If she is single, this woman herself. In
the case of LSMS: Household head. c Women aged between 15 and 49.
Source: Own calculations.
62
Table A11 — Disaggregation of the Squared Poverty Gap in Other Urban Areas of Bolivia by
Household Characteristics, 1989 to 2002
Moderate Poverty Line
Total
By Hh Size
<=3
4-6
>=7
By % of Hh Members
between 15 and 65 Years
<= 0.5
> 0.5
By Age of Hh Head
<=34
35-49
50-65
>=66
By Language of Hh Head
Spanish
Indigenous
By Gender of Hh Head
Male
Female
By Average Years of
Schooling of Adultsa
<=5
6-12
>=13
By Profession of Principal
Wage Earnerb
White Collar Worker
Blue Collar Worker
Agriculture
Sales & Services
Not Employed
By % of Adult Womenc in
Employment
>0
=0
Extreme Poverty Line
1989
1994
1999
2002
1989
1994
1999
2002
37.28
(0.83)
31.38
(0.58)
21.12
19.82
22.52
(0.71)
17.17
(0.49)
8.01
6.95
31.59
(1.82)
34.37
(1.11)
46.17
(1.55)
21.10
(1.25)
30.68
(0.93)
39.46
(1.14)
10.40
9.86
2.32
18.72
5.13
6.37
32.19
24.29
10.13
(0.90)
16.30
(0.71)
23.34
(1.00)
4.30
16.87
18.07
(1.55)
19.97
(0.93)
29.94
(1.44)
14.24
9.12
40.36
(1.01)
32.47
(1.32)
37.67
(0.83)
22.64
(0.92)
24.83
26.20
9.64
12.32
21.88
(0.73)
10.64
(0.67)
8.91
16.94
25.24
(0.94)
18.27
(1.13)
7.00
3.78
38.72
(1.42)
37.98
(1.20)
34.90
(1.89)
32.15
(3.65)
33.01
(1.08)
30.73
(0.89)
29.35
(1.60)
31.71
(2.39)
22.24
21.25
7.39
22.11
9.31
7.62
15.56
13.72
5.85
5.40
22.70
20.14
18.02
(0.92)
17.08
(0.74)
15.00
(1.25)
18.88
(1.80)
7.95
22.58
23.60
(1.23)
23.41
(1.13)
20.14
(1.71)
17.69
(2.75)
5.26
6.19
36.13
(0.83)
45.84
(2.80)
30.22
(0.59)
41.29
(2.18)
18.21
17.91
6.14
23.52
16.01
(0.51)
27.09
(1.88)
6.06
26.64
21.43
(0.72)
30.66
(2.60)
11.73
8.51
38.98
(0.95)
29.90
(1.81)
32.45
(0.66)
27.03
(1.30)
21.15
19.73
6.71
20.31
17.94
(0.57)
14.07
(1.02)
8.11
20.95
24.07
(0.84)
15.80
(1.43)
7.50
8.15
47.66
(1.36)
32.86
(1.14)
14.25
(1.98)
41.54
(1.16)
27.60
(0.80)
12.61
(1.40)
29.94
33.23
15.36
17.66
8.01
4.99
2.53
4.54
24.87
(1.00)
13.79
(0.64)
5.77
(0.94)
13.90
21.77
30.71
(1.25)
18.78
(0.97)
5.96
(1.41)
0.01
0.75
23.45
(1.64)
43.65
(1.33)
53.67
(2.53)
27.00
(1.67)
44.73
(2.46)
13.11
(1.12)
32.77
(1.06)
54.11
(1.72)
23.27
(1.19)
39.99
(2.04)
6.47
5.47
0.94
22.17
8.89
6.88
37.94
28.03
21.73
15.17
14.48
13.33
4.45
3.63
24.62
20.51
4.73
(0.72)
18.23
(0.79)
34.71
(1.68)
10.11
(0.82)
23.17
(1.85)
0.92
24.42
11.70
(1.22)
27.95
(1.28)
35.96
(2.59)
14.05
(1.29)
27.16
(2.37)
9.41
7.61
20.60
(1.08)
47.38
(1.08)
24.03
(0.72)
44.44
(1.02)
16.36
18.45
6.42
22.31
12.04
(0.54)
26.29
(0.96)
6.20
28.15
9.46
(0.77)
30.42
(1.00)
10.70
7.91
Notes: Poverty indices are calculated using income data. Standard errors of the poverty indices in brackets (only applicable to those
based on simulated data). a Women aged between 15 and 49 and their husbands and partners. b In the case of DHS: Husband
or partner of the oldest woman aged between 15 and 49. If she is single, this woman herself. In the case of LSMS:
Household head. c Women aged between 15 and 49.
Source: Own calculations
63
Table A12 — Disaggregation of the Squared Poverty Gap in Rural Areas of Bolivia by
Household Characteristics, 1989 to 2002
Moderate Poverty Line
Total
By Hh Size
<=3
4-6
>=7
By % of Hh Members
between 15 and 65 Years
<= 0.5
> 0.5
By Age of Hh Head
<=34
35-49
50-65
>=66
By Language of Hh Head
Spanish
Indigenous
By Gender of Hh Head
Male
Female
By Average Years of
Schooling of Adultsa
<=5
6-12
>=13
By Profession of Principal
Wage Earnerb
White Collar Worker
Blue Collar Worker
Agriculture
Sales & Services
Not Employed
c
By % of Adult Women in
Employment
>0
=0
Extreme Poverty Line
1989
1994
1999
2002
1989
1994
1999
2002
42.21
(0.49)
45.83
(0.33)
31.85
28.53
24.59
(0.47)
28.84
(0.34)
15.65
12.94
33.84
(1.19)
40.17
(0.61)
51.02
(0.94)
33.61
(0.81)
46.17
(0.47)
54.15
(0.53)
17.97
13.84
4.35
25.78
14.83
11.02
38.11
35.29
18.21
(0.71)
28.92
(0.47)
36.47
(0.61)
6.07
30.05
17.15
(1.00)
22.70
(0.60)
32.59
(0.96)
19.34
17.23
44.97
(0.57)
36.66
(0.93)
50.16
(0.40)
38.23
(0.56)
34.54
31.11
14.24
23.83
32.86
(0.43)
21.80
(0.51)
17.43
25.38
27.07
(0.56)
19.63
(0.79)
11.37
10.58
40.73
(0.83)
42.64
(0.79)
41.61
(1.14)
51.12
(2.29)
42.99
(0.53)
46.97
(0.50)
46.15
(0.78)
55.25
(1.43)
32.05
25.81
10.60
30.36
15.18
14.38
29.41
28.04
13.67
12.80
41.44
29.27
26.85
(0.50)
29.63
(0.51)
28.46
(0.77)
37.56
(1.54)
16.42
31.61
23.87
(0.78)
24.80
(0.73)
23.46
(1.01)
32.49
(2.29)
22.93
13.76
33.73
(0.60)
50.09
(0.74)
35.11
(0.50)
52.09
(0.44)
15.93
16.14
4.70
32.70
20.08
(0.43)
33.96
(0.48)
5.83
36.36
18.12
(0.49)
30.61
(0.73)
18.43
15.71
42.38
(0.52)
41.01
(1.51)
46.78
(0.36)
40.41
(0.84)
32.09
28.51
12.73
28.73
29.68
(0.37)
24.08
(0.83)
16.04
30.02
24.77
(0.49)
23.32
(1.38)
12.69
15.11
46.91
(0.59)
24.71
(1.04)
5.04
(2.21)
53.07
(0.38)
30.30
(0.69)
6.41
(1.50)
36.71
34.32
16.82
17.40
6.99
5.59
3.22
0.73
35.06
(0.42)
15.14
(0.58)
1.63
(0.73)
18.98
19.71
28.06
(0.57)
11.51
(0.85)
1.17
(1.11)
0.10
0.13
22.67
(1.72)
27.65
(1.22)
49.23
(0.66)
24.97
(1.65)
43.33
(1.52)
23.09
(1.41)
34.31
(0.85)
52.88
(0.38)
24.39
(1.09)
37.06
(1.36)
12.31
8.23
2.03
18.98
4.62
6.83
35.99
32.10
18.51
15.07
9.88
5.26
3.00
0.73
34.63
32.12
11.72
(1.21)
18.59
(0.76)
34.70
(0.42)
11.94
(0.86)
21.08
(1.23)
3.20
17.31
10.89
(1.45)
13.26
(0.98)
30.05
(0.67)
11.73
(1.27)
24.62
(1.37)
17.91
14.87
30.27
(1.32)
44.41
(0.54)
47.82
(0.39)
40.56
(0.58)
33.92
30.63
14.35
23.41
30.76
(0.41)
23.78
(0.54)
17.08
25.15
15.26
(1.03)
26.32
(0.52)
11.00
9.49
Notes: Poverty indices are calculated using expenditure data. Standard errors of the poverty indices in brackets (only applicable to
those based on simulated data). a Women aged between 15 and 49 and their husbands and partners. b In the case of DHS:
Husband or partner of the oldest woman aged between 15 and 49. If she is single, this woman herself. In the case of LSMS:
Household head. c Women aged between 15 and 49.
Source: Own calculations
64
Table A13 — Adjusted Spatial Disaggregation of the Poverty Gap in Bolivia, 1989 to 2002
Moderate Poverty Line
Extreme Poverty Line
1989
1994
1999
2002
1989
1994
1999
2002
44.06
(0.34)
40.74
(0.25)
32.53
32.94
26.02
(0.32)
23.94
(0.24)
15.73
15.32
Departmental Capitals
32.92
25.74
21.02
24.37
15.29
9.58
8.00
9.79
Other Urban Areas
50.67
(0.97)
55.17
(0.53)
44.03
(0.74)
58.21
(0.35)
34.70
32.88
13.10
44.86
26.48
(0.65)
40.30
(0.41)
13.97
47.71
33.46
(0.98)
35.66
(0.57)
27.37
23.88
Total
By Type of Municipality
Rural Areas
Notes: Only poverty indices based on simulated data changed relative to Table A2.3. Poverty indices are calculated using income
data for departmental capitals and other urban areas, expenditure data for rural areas, and mixed income-expenditure data for
total Bolivia. Standard errors of the poverty indices in brackets (only applicable to those based on simulated data).
Source: Own calculations.
Table A14 —
Adjusted Spatial Disaggregation of the Squared Poverty Gap in Bolivia, 1989 to
2002
Moderate Poverty Line
Extreme Poverty Line
1989
1994
1999
2002
1989
1994
1999
2002
29.98
(0.29)
27.77
(0.21)
20.19
20.04
15.58
(0.24)
14.69
(0.18)
8.68
8.19
Departmental Capitals
19.96
14.16
11.60
14.00
8.05
4.51
3.94
5.13
Other Urban Areas
36.66
(0.89)
39.05
(0.50)
30.82
(0.61)
43.03
(0.35)
21.12
19.82
6.95
28.53
16.75
(0.51)
26.20
(0.34)
8.01
31.85
22.01
(0.76)
21.81
(0.45)
15.65
12.94
Total
By Type of Municipality
Rural Areas
Notes: Only poverty indices based on simulated data changed relative to Table A2.4. Poverty indices are calculated using income
data for departmental capitals and other urban areas, expenditure data for rural areas, and mixed income-expenditure data for
total Bolivia. Standard errors of the poverty indices in brackets (only applicable to those based on simulated data).
Source: Own calculations.
65
Table A15 — Influence of Adult Equivalent Scales on the Poverty Gap Disaggregated by
Household Size
Moderate Poverty Line
Extreme Poverty Line
1989
1994
1999
2002
28.29
(0.34)
26.42
(0.24)
15.92
Bolivia
15.25
13.27
(0.27)
26.97
(0.80)
26.06
(0.45)
33.50
(0.65)
21.60
(0.43)
25.27
(0.34)
33.34
(0.47)
11.38
9.71
14.72
14.20
20.01
18.83
Total
By Hh Size
<=3
4-6
>=7
15.52
9.66
9.88
14.19
19.64
Total
Total
By Hh Size
<=3
4-6
>=7
By Hh Size
<=3
4-6
>=7
Total
By Hh Size
<=3
4-6
>=7
1999
2002
13.25
(0.19)
5.92
5.12
9.92
(0.32)
12.51
(0.25)
17.91
(0.38)
3.94
2.52
5.30
4.72
7.89
6.65
Departmental Capitals of Bolivia
8.40
10.19
4.94
2.37
2.50
3.20
7.31
9.24
11.42
7.31
8.08
9.79
2.59
2.27
2.99
1.64
3.29
3.77
35.25
(0.88)
28.74
(0.64)
15.28
5.67
4.52
34.65
(2.28)
32.25
(1.28)
40.92
(1.68)
22.51
(1.20)
28.00
(0.97)
34.14
(1.23)
9.73
10.41
4.54
2.21
12.27
14.61
3.42
4.37
22.39
16.00
10.62
(0.93)
13.49
(0.78)
18.09
(0.96)
9.89
5.41
39.07
(0.57)
44.38
(0.37)
26.76
25.75
(0.37)
10.84
8.02
37.14
(1.34)
36.94
(0.79)
44.05
(0.95)
37.87
(0.79)
44.61
(0.50)
48.73
(0.70)
20.75
15.52
6.44
4.27
26.38
21.25
10.95
7.27
28.89
25.68
20.40
(0.73)
25.98
(0.48)
29.23
(0.73)
11.82
9.81
6.43
9.89
12.63
1989
12.26
(0.58)
11.75
(0.35)
16.87
(0.56)
2.37
4.36
6.77
1994
1.87
2.29
2.73
Other Urban Areas of Bolivia
14.67
19.75
14.30
(0.76)
(0.47)
19.59
(1.92)
17.34
(1.04)
24.09
(1.52)
Rural Areas of Bolivia
22.51
19.84
(0.49)
18.13
(1.16)
18.20
(0.69)
23.81
(0.88)
Notes: Poverty indices are calculated using income data for departmental capitals and other urban areas, expenditure data for rural
areas, and mixed income-expenditure data for total Bolivia. Standard errors of the poverty indices in brackets (only
applicable to those based on simulated data).
Source: Own calculations.
66
Table A16 — Influence of Adult Equivalent Scales on the Squared Poverty Gap Disaggregated by
Household Size
Moderate Poverty Line
Total
By Hh Size
<=3
4-6
>=7
Total
By Hh Size
<=3
4-6
>=7
Total
By Hh Size
<=3
4-6
>=7
Total
By Hh Size
<=3
4-6
>=7
1999
Extreme Poverty Line
1989
1994
2002
1989
17.00
(0.25)
16.42
(0.18)
8.68
Bolivia
7.99
6.88
(0.18)
15.89
(0.57)
15.40
(0.34)
20.84
(0.52)
12.83
(0.30)
15.60
(0.24)
21.48
(0.36)
5.91
4.63
1999
2002
7.23
(0.13)
2.95
2.59
5.21
(0.22)
6.78
(0.17)
10.08
(0.28)
2.00
1.15
8.00
7.41
2.71
2.32
11.08
10.08
3.79
3.53
1.35
1.73
1.21
1.29
1.58
0.81
1.76
2.12
Other Rural Areas of Bolivia
8.78
7.80
11.60
8.11
(0.56)
(0.32)
3.36
2.46
5.76
4.90
3.21
1.00
6.46
7.73
1.95
2.16
13.79
8.76
5.95
(0.66)
7.42
(0.53)
10.70
(0.68)
5.81
3.31
14.46
(0.28)
5.01
3.85
11.02
(0.52)
14.59
(0.38)
16.74
(0.57)
2.98
1.84
5.30
3.33
5.11
4.95
6.25
80.38)
5.97
(0.23)
9.07
(0.37)
1994
7.90
4.40
Departmental Capitals of Bolivia
4.09
5.21
2.40
1.04
4.57
7.10
10.34
3.37
4.25
5.12
3.70
3.94
4.67
3.07
5.13
6.40
23.01
(0.71)
18.17
(0.46)
22.22
(1.84)
20.70
(1.00)
27.58
(1.40)
13.76
(0.87)
17.38
(0.73)
22.43
(0.94)
24.25
(0.45)
29.53
(0.32)
15.07
22.62
(1.07)
22.59
(0.63)
28.22
(0.79)
24.24
(0.64)
29.74
(0.43)
33.02
(0.63)
10.59
7.43
15.13
11.09
16.17
14.02
1.18
2.18
3.17
11.30
(1.44)
10.01
(0.75)
14.59
(1.13)
Rural Areas of Bolivia
11.94
10.32
(0.34)
9.26
(0.79)
9.29
(0.46)
12.83
(0.61)
0.83
1.05
1.13
Notes: Poverty indices are calculated using income data for departmental capitals and other urban areas, expenditure data for rural
areas, and mixed income-expenditure data for total Bolivia. Standard errors of the poverty indices in brackets (only
applicable to those based on simulated data).
Source: Own calculations.
67
Table A17 — Asset Endowment Among Extremely Poor, Moderately Poor and Non-poor (in
Percent), 1994 and 1998
Extremely
Poor
1994
Moderately
Poor
Non-poor
0.02
72.93
21.55
4.57
79.91
54.95
36.51
22.72
22.19
0.29
79.67
42.39
11.58
72.17
39.07
55.28
40.27
19.18
31.59
36.73
0.71
20.43
5.10
6.83
32.74
1.93
Tangible Assets
Telephone
Radio
Television
Fridge
House
Plot of Agricultural Land
In-house Access to Electricity
In-house Access to Public Water
Use of Other (Non-open) Water
Source
High-quality Cooking Materiala
Shared Toilet
Private Toilet
Cement Floor
Brick Floor
Other (Non-earth) Floor
2-3 Sleeping Rooms
>= 4 Sleeping Rooms
Human Capital
% of Adult Menb with
Complete Basic Schooling
Lower Secondary Schooling
Higher Secondary Schooling
Tertiary Education
% of Adult Womenc with
Complete Basic Schooling
Lower Secondary Schooling
Higher Secondary Schooling
Tertiary Education
Notes:
a
Gas, kerosene, and electricity. –
aged between 15 and 49.
Source: Own calculations.
Extremely
Poor
1998
Moderately
Poor
Non-poor
37.58
99.59
99.57
77.13
53.70
0.67
99.93
97.49
1.46
0.40
73.19
21.85
4.46
77.45
53.36
37.04
31.25
21.36
2.13
82.63
51.90
12.82
66.27
32.34
62.82
54.53
16.12
67.81
98.31
99.31
84.28
62.57
0.50
99.95
98.30
1.22
50.76
39.73
6.80
30.14
9.24
9.15
36.04
2.00
99.06
25.61
69.00
39.19
18.23
41.23
54.44
15.49
30.04
9.58
26.91
22.20
4.88
6.05
20.10
0.84
56.99
21.27
30.64
37.85
8.20
10.29
23.45
1.12
99.50
15.92
81.54
37.06
6.42
55.50
55.51
15.62
16.85
16.69
12.71
1.27
15.49
17.14
19.51
2.15
2.43
4.34
36.44
32.98
16.25
11.49
13.55
1.86
13.31
14.00
20.84
3.65
2.29
6.84
28.61
37.95
17.14
13.24
6.33
0.23
16.00
16.01
13.80
1.09
3.28
7.78
55.66
25.96
16.71
14.63
7.22
0.86
14.08
16.41
19.76
2.99
2.64
7.45
49.48
34.25
b
Husbands and partners of women aged between 15 and 49. – c Women
68
– Figures –
69
Figure A1 — Growth Incidence Curve for Bolivia, 1989 to 1999
Annual Growth Rate %
12
P
0
P
ex
0
mod
10
8
6
4
2
0
-2
–2
0
10
20
30
40
50
Growth Incidence Curve
Growth Rate in Mean
60
70
80
90
100
Percentiles
Mean of Growth Rates for Poorest %
Figure A2 — Growth Incidence Curve for the Departmental Capitals of Bolivia,
1989 to 1999
Annual Growth Rate %
12
P
10
0
P
ex
0
mod
8
6
4
2
0
-2
–2
0
10
20
30
Growth Incidence Curve
Growth Rate in Mean
40
50
60
70
80
90
100
Percentiles
Mean of Growth Rates for Poorest %
70
Figure A3 — Growth Incidence Curve for Other Urban Areas of Bolivia, 1989 to 1999
Annual Growth Rate %
12
P
0
P
ex
0
mod
10
8
6
4
2
0
-2
–2
0
10
20
30
40
50
Growth Incidence Curve
Growth Rates in Mean
60
70
80
90
100
Percentiles
Mean of Growth Rates for Poorest %
Figure A4 — Growth Incidence Curve for Rural Areas of Bolivia, 1989 to 1999
Annual Growth Rate %
12
P
0
ex
P
0
mod
10
8
6
4
2
0
-2
–2
0
10
20
30
Growth Incidence Curve
Growth Rate in Mean
40
50
60
70
80
90
100
Percentiles
Mean of Growth Rates for Poorest %
71
Figure A5 — Growth Incidence Curve for Bolivia, 1999 to 2002
Annual Growth Rate %
16
P
0
P
ex
0
mod
12
8
4
0
-4
-8
-12
0
10
20
30
40
50
60
Growth Incidence Curve
Growth Rate in Mean
70
80
90
100
Percentiles
Mean of Growth Rates for Poorest %
Figure A6 — Growth Incidence Curve for the Departmental Capitals of Bolivia,
1999 to 2002
Annual Growth Rate %
16
P
0
P
ex
0
mod
12
8
4
0
-4
-8
-12
0
10
20
30
Growth Incidence Curve
Growth Rate in Mean
40
50
60
70
80
90
100
Percentiles
Mean of Growth Rates for Poorest %
72
Figure A7 — Growth Incidence Curve for Other Urban Areas of Bolivia, 1999 to 2002
Annual Growth Rate %
16
P
0
P
ex
0
mod
12
8
4
0
-4
-8
-12
0
10
20
30
40
50
60
Growth Incidence Curve
Growth Rate in Mean
70
80
90
100
Percentiles
Mean of Growth Rates for Poorest %
Figure A8 — Growth Incidence Curve for Rural Areas of Bolivia, 1999 to 2002
Annual Growth Rate %
16
P
0
P
ex
0
mod
12
8
4
0
-4
-8
-12
0
10
20
30
Growth Incidence Curve
Growth Rate in Mean
40
50
60
70
80
90
100
Percentiles
Mean of Growth Rates for Poorest %
73
Figure A9 — Growth Incidence Curve for Bolivia, 1989 to 2002
Annual Growth Rate %
8
P
0
P
ex
0
mod
6
4
2
0
-2
–2
0
10
20
30
40
50
Growth Incidence Curve
Growth Rate in Mean
60
70
80
90
100
Percentiles
Mean of Growth Rates for Poorest %
Figure A10 — Growth Incidence Curve for the Departmental Capitals of Bolivia, 1989 to
2002
Annual Growth Rate %
8
P
0
P
ex
0
mod
6
4
2
0
-2
–2
0
10
20
30
Growth Incidence Curve
Growth Rate in Mean
40
50
60
70
80
90
100
Percentiles
Mean of Growth Rates for Poorest %
74
Figure A11 — Growth Incidence Curve for Other Urban Areas of Bolivia, 1989 to 2002
Annual Growth Rate %
8
P
0
P
ex
0
mod
6
4
2
0
–2
-2
0
10
20
30
40
50
Growth Incidence Curve
Growth Rates in Mean
60
70
80
90
100
Percentiles
Mean of Growth Rates for Poorest %
Figure A12 — Growth Incidence Curve for Rural Areas of Bolivia, 1989 to 2002
Annual Growth Rate %
8
P
0
ex
P
0
mod
6
4
2
0
-2
–2
0
10
20
30
Growth Incidence Curve
Growth Rate in Mean
40
50
60
70
80
90
100
Percentiles
Mean of Growth Rates for Poorest %
75
Annex 2 – The CGE Model
The model employed in the policy analysis is a dynamic real-financial CGE model which
combines neoclassical and structuralist characteristics, but does not account for Keynesian
multiplier effects. The production structure and product market conditions, for example,
correspond with standard neoclassical theory. As an important structuralist element, the
segmentation of labor markets observable in Bolivia is taken into account. In addition, the
savings and investment behavior of different economic agents is modeled explicitly via the
specification of a financial market, which allows for the existence of credit rationing. In the
following, the major components of the modeling framework will be described in a nontechnical manner. A full mathematical documentation can be found in Wiebelt (2004).
Production and Trade
The model distinguishes 12 sectors (see Table A1) which produce a characteristic but not
necessarily homogenous good. Rather, it is assumed for exporting sectors that, e.g. due to
quality differences, domestically sold and exported goods are not identical. This is modeled
by means of a Constant Elasticity of Transformation (CET) function. The exceptions are
mining and oil&gas, where exports are assumed to be exogenously determined by world
market conditions or by long-term contracts as in the case of gas exports to Brazil.
Domestically produced and imported goods of the same category are also treated as different,
which is modeled by means of a Constant Elasticity of Substitution (CES) function
(Armington assumption). Finally, some sectors (utilities, construction, public services)
produce pure non-tradables. This rather strong differentiation in production allows, for
instance, to capture in a realistic way the impact external shocks may have on the earning
opportunities of different households.
A distinctive feature of the model is the explicit treatment of traditional agriculture and
(urban) informal services as informal production sectors, where most of Bolivia’s poor earn
their living. Workers in these sectors are considered self-employed; they for the most part rely
on their own labor inputs and use only small amounts of capital. This implies that, over one
year, supply is almost constant for a given number of workers and given factor productivities;
and if demand slackens, adjustment will mainly run through a fall in prices and incomes of
those employed in these sectors. By contrast, formal sectors tend to produce with modern,
more capital-intensive techniques and, like the government, hire skilled and unskilled
workers, which provides them with greater adjustment flexibility on the supply side.
Throughout the formal economy, primary factors are combined via CES production functions,
while the production technology of the two informal sectors is represented by a CobbDouglas function to account for the fact that labor can fairly easily substitute for the very
basic capital goods used in these sectors. Both formal and informal sectors use intermediate
inputs in fixed proportions to production.
76
Table A1 — Classification of the CGE Model
tActivities/Goods and Services
Traditional agriculture
Modern agriculture
Oil& gas
Mining
Consumer goods
Intermediate goods
Capital goods
Utilities
Construction
Informal services
Formal services
Public services
Production Factors
Skilled labor
Agricultural unskilled labor
Non-agricultural unskilled
labor
Smallholder labor
Urban informal labor
Corporate (formal) capital
Employers’ capital
Urban informals’ capital
Smallholders’ capital
Public (infrastructure) capital
Economic Agents
Households
– Smallholders
– Agricultural workers
– Non-agricultural
workers
– Employees
– Urban informals
– Employers
Public enterprises
Private
enterprises
Government
Rest of the world
Financial institutions
– Commercial
banks
– Central Bank
Factor markets
To capture the reality of Bolivian employment and to keep track in a detailed manner of the
poor’s main income flows, the model assumes a high degree of labor market segmentation
(see Table A1). Beside the self-employed labor of smallholders and urban informals, two
types of unskilled labor (agricultural and non-agricultural) as well as skilled labor are
distinguished. Labor markets are linked via rural-rural and rural-urban migration. While the
former involves smallholders becoming hired workers in modern agriculture, the latter
involves the absorption of smallholders by the urban informal sector. Along the lines of the
Harris-Todaro model, the decision to migrate depends on wage differentials. In the urban
labor market, the limited possibilities of informal workers to enter the formal workforce are
taken into account by assuming that despite an existing wage differential migration is
constrained. The informal sector then absorbs all those who fail to obtain formal employment
at the prevailing wage. The model does allow for underemployment in the sense that people
are stuck in low-paid informal sector jobs, but not for open unemployment of unskilled labor,
which appears to be an accurate characterization of the Bolivian labor market except for
recession years where rates of open unemployment tend to rise to non-negligible levels. Wage
adjustments also ensure that all other labor markets clear.
The model also assumes segmented capital markets, with a distinction made between
unincorporated and corporate capital. Three household groups (smallholders, urban informals,
and employers) own unincorporated capital. While smallholders and urban informals invest
almost exclusively in traditional agriculture and informal services, respectively, employers
receive capital income from all formal sectors with the exception of utilities. Corporate
capital, by contrast, is owned by private and public enterprises, which invest in all formal
sectors and retain the respective factor income. Finally, the model separates public
infrastructure capital, which is assumed to affect the level of sectoral production. This is
specified by means of a CES function where public capital and aggregate private value added
77
enter as arguments. Thus, by determining its investment focus, the government can influence
the income generation possibilities in different sectors and regions.
Income and Expenditures
The model identifies six representative private households groups, which are basically
characterized by their distinct factor endowments (Table A1). This is justified because factor
income is the single-most important income source in Bolivia given the low degree of
redistribution. In addition, workers and the self-employed are disaggregated regionally as
their earning possibilities and consumption patterns tend to vary between regions. Four of the
six household groups (smallholders, urban informals, and agricultural and non-agricultural
workers) can be considered as poor. Depending on factor endowments, households receive
labor or capital income as well as (net) interest payments on financial assets. Moreover, they
receive transfer income from the state and from relatives living abroad. They use their gross
income to pay for taxes and consumption as well as for savings. The allocation of private
consumption expenditures on different goods is modeled employing a Linear Expenditure
System (LES), where poorer households devote a larger budget share to price-independent
subsistence consumption than do richer households.
The government finances its current and capital expenditures out of direct and indirect tax
revenues, operating surpluses of public enterprises, and capital inflows from abroad. Private
and public enterprises receive capital income, subsidies and net interest payments on financial
assets; they use this income to pay corporate taxes and to save in the form of retained
earnings. Since financial institutions are assumed to act as mere intermediaries, their current
transactions (interest payments) are also allocated to the two kinds of enterprises. Finally, the
rest of the world imports and exports goods from and to Bolivia, undertakes direct and
portfolio investments in the country, and provides development aid.
Financial markets
The specification of the model’s financial sector is based on Tobin’s portfolio-theoretic
framework, where the interaction of stocks and flows plays a decisive role. Starting from the
beginning-of-period stocks of assets and liabilities, financial markets match the savings and
investment decisions of all economic agents over the period, comprising the accumulation of
both physical and financial assets and liabilities. The financial markets handle simultaneously
the flows arising from savings and financial accumulation, and those arising from the
reshuffling of existing portfolios due to changes in asset returns. For the latter, it is assumed
that individual agents have only limited possibilities to substitute among different assets,
which is captured by CES functions. A further characteristic of the financial sector is that
specific economic agents, e.g. smallholders, may be constrained in their access to credit,
which is clearly the case for most of Bolivia’s informal producers. This is modeled by
determining bank credit to the respective agent residually after all other agents’ credit demand
is satisfied.
The identification of stocks in the model makes it possible to account for the revaluation of
assets and liabilities, which is of great importance in the highly dollarized Bolivian economy
where the value of most domestic assets is at least partially indexed to movements in the
exchange rate. Together with the accumulation occurring over the period, these revaluations
determine the end-of-period stocks of assets, liabilities and net wealth for each economic
agent.
78
Dynamics
An important feature of the model is its recursive-dynamic nature, which means that the
model is solved for a sequence of static equilibria connected through capital accumulation and
labor growth. The dynamics of the model are based on assumptions concerning exogenous
growth rates for different variables such as labor supply and government expenditures, as well
as the endogenous savings and investment behavior of economic agents. A general advantage
of the dynamic specification is the possibility to generate a medium to long run growth path.
Moreover, structural change over time can be analyzed. Finally, dynamic effects running
through the financial sector can be captured. For example, it can be investigated how the debt
relief granted under the HIPC initiative reduces Bolivia’s debt service.
Implementation of the model
In using the model for policy simulations, 1997 was chosen as the base year, for two different
reasons. First, crucial data, in particular an Input-Output Table , are available for that year.
Second, 1997 appears to be a fairly “normal” year for the Bolivian economy in the sense that
no major shocks occurred, rendering it an appropriate benchmark against which to evaluate
counterfactual simulations.
In specifying the model numerically, the first step was to compile the (real and financial)
transactions between the sectors, production factors and economic agents identified in Table
A1 in a Social Accounting Matrix (SAM) for 1997 (see Thiele and Piazolo 2003). The SAM
provides the statistical backbone for the calibration of the model. From the information given
in the SAM, many parameters, such as tax and subsidy rates, can readily be calculated. Other
parameters, such as trade elasticities and income elasticities of private demand, have to be
taken from external sources. Here, the choice of parameters is based on the stylized facts
known from the existing empirical literature and on what is known about Bolivia’s economic
structure, not on specific estimations performed for Bolivia. Armington elasticities, for
instance, are assumed to be considerably higher for agriculture than for intermediate and
capital goods, where import substitution is only possible to a limited extent because Bolivia’s
own production of these goods is very small and of low sophistication compared to the
relevant import substitutes.
In a final step, the calibrated model was updated so as to generate a fairly smooth growth path
over ten years. To achieve this, the first two years have to be left out of the simulation
analysis because the dynamics of the model only stabilize after three years.
Link between the model and household data
The CGE model is linked to household survey data in order to obtain detailed results on the
poverty and distributional impact of the simulated policies. The starting point for linking
survey data and the CGE model is household income, split up into (1) individual factor
incomes, (2) household net interest income and transfers from abroad, and (3) household
public transfers including pensions. These components of household income can be identified
in the CGE model (see above) as well as in the household survey.52
Households receive factor income from different sources, i.e. the individuals of a household
may earn different factor incomes. The household head may be self-employed (e.g. urban
informal in the CGE model) and his/her spouse may be employed as a worker (e.g. unskilled
worker in the CGE model). The link between CGE and the survey is simply sequential: each
52
The household survey used is the 1999 MECOVI. This survey is to be preferred over the 1997 employment
survey as it contains more detailed and more reliable information on household incomes.
79
individual factor income in the household survey is scaled up or down according to the CGE
results for the eight production factors owned by households (Table A1). This is how changes
in real factor prices in the CGE model affect the distribution of income.
The remaining two components of household income and the changes therein are given by
household group in the CGE model. These changes from the CGE model are applied to the
survey information at the household level. The household types in the survey are classified
according to the occupation of the household head, in line with the classification used in the
SAM.
Annex 3 – Description of Policy Simulations
Decisions by the government, commercial banks and the Central Bank provide the policy
framework for domestic activities. The main domestic policy instruments included in the
model are listed in Table A2. The effectiveness of domestic policies depends on external
events, such as changing world market prices for exports and/or imports and changing
international interest rates, as well as the rest of the world’s decisions about private and public
capital flows to Bolivia. Moreover, various parameters, such as sectoral factor productivities
and factor substitution elasticities, and institutional characteristics, such as the process by
which wages are determined, affect the behavior of economic agents and thus their response
to policy reforms.
Table A2 — Domestic Policy Variables and External Parameters
Government
Banking System
Income/corporate taxes
Central Bank
Export subsidies
Import tariffs
Excise taxes
Production subsidies
Value added taxes
Rest of the World
Development aid (including
Minimum reserves (in relation grant element of concessional
lending)
to imports)
Foreign portfolio investment
Central Bank interest rate
Foreign direct investment
Nominal exchange rate
Net credit to government
Debt relief (HIPC)
Transfers to households and
enterprises
Commercial banks
Foreign interest rate
Real government consumption
Access to credit
Factor income from abroad
Real government investment
Flexibility in credit allocation
Remittances from abroad
(e.g. in infrastructure)
World prices for exports
World prices for imports
Various of the policy variables and parameters identified in the model have been used for the
analysis of shocks and policies presented in Chapter 3. The remainder of this annex will
provide a short description of how the simulation experiments underlying this analysis were
performed.
External Shocks
80
In order to gauge the impact of the El Niño phenomenon, a simulation is undertaken which
assumes that total factor productivity in the two agricultural sectors decreases every three
years so as to produce a reduction of agricultural output in the order of 3 percent, which
roughly represents the impact of an El Niño of medium severity. A similar scenario is also
considered in the Bolivian PRSP.
A negative Terms-of-trade shock is modeled as a 10-percent decrease in the world market
prices for agricultural and mining exports (except for oil&gas where prices are assumed to be
fixed by contracts).
To capture the impact of the Brazilian crisis and the completion of the capitalization process,
both portfolio and foreign direct investment flows are reduced in line with the fall that
actually happened.
The simulation of the combined effect of external shocks simply involves the simultaneous
change of parameters.
Macro Policies
To investigate whether exchange rate policy might contribute to higher competitiveness of the
Bolivian economy, a simulation with a higher yearly depreciation of the Boliviano in the
crawling peg regime is compared to the base run.
The real depreciation required in case of a negative shock is simulated as an increase in the
minimum reserves the Central Bank holds to cover imports. This results in an endogenous
reduction of Central Bank credit to the private banking system, which in turn means lower
credit supply for non-financial institutions.
Structural Reforms
A less severe segmentation of urban labor markets is modeled by assuming that urban
informals are allowed to migrate into the formal unskilled labor market. The extent of the
migration is calibrated so as to reduce the wage differential between the two labor markets by
roughly 50 percent.
The two alternative tax reforms are both simulated by increasing income tax rates for all
household groups except smallholders and urban informals. In one option, the tax increase is
financed by a reduction of current and/or capital expenditures, in the other option indirect
(value added and excise) tax rates are reduced so as to arrive at a revenue-neutral tax reform.
Natural Resource Policies
The gas contracts imply in the model that export volumes of the oil&gas sector are raised
exogenously. One simulation assumes that as a response to higher tax revenues from gas
government consumption expenditures are also raised exogenously so as to keep public
savings roughly constant. In an alternative scenario, government consumption expenditures
are kept constant, and public savings are allowed to adjust.
Targeted Interventions in Favor of the Poor
Improved access of smallholders to credit is modeled via two mechanisms. First, the credit
constraint for smallholders is relaxed by assuming that their credit is no longer determined
residually by the banking system after all other agents’ demand has been satisfied, but rather
according to rentability criteria. Second, substitution elasticities of portfolio selection are
increased for banks, which implies that their credit allocation becomes more sensitive to
differences in sectoral rentabilities. To assess how this might impact on smallholders’ ability
to invest it is additionally assumed that a positive temporary terms-of-trade shock in the form
of higher export prices raises traditional agriculture’s rentability.
81
Public investment tailored to the needs of smallholders is modeled by increasing the
substitution elasticity in the CES function that combines public capital and aggregate private
value added.
A pro-poor industrial policy is alternatively simulated for modern agriculture and for the
consumer goods sector. In both cases, this involves the introduction of export subsidies as a
means to increase competitiveness.
A transfer program is simply modeled as an increase in direct government payments to poor
household groups. The programs are either financed by a decrease in government
consumption or by a decrease in public investment.
82
Annex 4 – Simulation Results
Table A3 — Baseline Scenario
Period
Indicator
0
Real GDP Growth
1
2
3
4
5
6
7
8
9
10
4.7
4.7
4.7
4.7
4.7
4.7
4.7
4.7
4.7
4.7
Real Factor Prices
Smallholders
100
101
102
104
105
107
109
110
112
114
116
Agr. Workers
100
101
102
104
106
107
109
111
113
115
118
Non-Agr. Workers
100
104
107
111
114
117
121
124
128
131
135
Urban Informals
100
102
103
105
106
108
109
111
112
114
116
Employers
100
103
105
108
110
112
114
116
117
119
120
Employees
100
104
108
112
116
120
124
128
133
138
142
National
63.6
62.6
61.6
61.0
60.2
59.2
58.1
57.2
56.6
56.1
55.3
Urban
49.7
48.1
47.0
46.1
45.1
43.6
42.1
41.0
40.3
39.7
38.9
Rural
86.9
86.8
86.1
85.9
85.7
85.3
84.9
84.4
83.9
83.4
82.8
National
37.5
36.9
36.3
35.8
35.2
34.6
34.1
33.6
33.1
32.6
32.1
Urban
21.9
21.2
20.5
19.8
19.2
18.6
18.0
17.5
17.0
16.4
15.9
Rural
63.7
63.3
62.9
62.5
62.0
61.6
61.1
60.6
60.2
59.7
59.2
National
62.7
62.8
62.9
63.0
63.0
63.1
63.2
63.2
63.3
63.4
63.4
Urban
54.4
54.4
54.5
54.5
54.6
54.6
54.7
54.7
54.8
54.8
54.9
Rural
64.5
64.6
64.7
64.8
64.9
65.0
65.0
65.1
65.2
65.2
65.3
Poverty Headcount
Poverty Gap
Gini Coefficient
Source: Own calculations based on the CGE model.
83
Table A4 — Terms-of-Trade Shock
Period
1
2
3
4
5
6
7
8
9
10
4.5
4.7
4.7
4.7
4.7
4.7
4.7
4.7
4.7
4.7
Smallholders
-4
-4
-5
-4
-5
-5
-5
-5
-5
-5
Agr. Workers
-9
-9
-10
-11
-11
-11
-12
-12
-13
-14
Non-Agr. Workers
-1
0
-1
0
0
0
0
0
0
0
0
1
0
0
0
0
0
1
0
0
Employers
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
Employees
-1
-1
-1
-1
-1
0
0
-1
-1
0
National
0.3
0.4
0.3
0.2
0.1
0.0
0.2
0.3
0.2
0.3
Urban
0.4
0.2
0.3
0.0
0.2
-0.1
0.1
0.0
0.1
0.2
Rural
0.3
0.7
0.4
0.5
0.1
0.2
0.4
0.8
0.6
0.5
National
0.3
0.3
0.3
0.3
0.4
0.3
0.3
0.3
0.3
0.3
Urban
0.1
0.0
0.1
0.1
0.1
0.1
0.1
0.0
0.1
0.1
Rural
0.7
0.7
0.7
0.7
0.7
0.8
0.8
0.7
0.8
0.8
National
0.2
0.1
0.1
0.2
0.2
0.1
0.2
0.1
0.1
0.2
Urban
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Rural
0.2
0.3
0.3
0.3
0.2
0.3
0.3
0.3
0.3
0.3
Indicator
Real GDP Growth
Real Factor Pricesa
Urban Informals
Poverty Headcountb
Poverty Gapb
Gini Coefficientb
a Points deviation from base run. – b Percentage points deviation from base run.
Source: Own calculations based on the CGE model.
84
Table A5 — El Niño
Period
1
2
3
4
5
6
7
8
9
10
3.6
4.8
4.8
3.7
4.8
4.8
3.7
4.8
4.7
4.7
Smallholders
-3
-3
-3
-6
-7
-7
-9
-10
-10
-11
Agr. Workers
-5
-5
-5
-9
-9
-12
-19
-19
-20
-21
0
0
-1
-2
0
0
-3
-2
-2
-2
Urban Informals
-2
-2
-1
-5
-4
-4
-6
-6
-6
-6
Employers
-2
-1
-2
-4
-4
-4
-6
-6
-7
-6
Employees
-1
-1
-1
-2
-2
0
-4
-3
-4
-3
National
0.7
0.2
0.5
1.0
1.1
1.2
1.8
1.8
1.4
1.0
Urban
0.8
0.2
0.6
1.2
1.3
1.3
2.0
1.8
1.2
0.8
Rural
0.3
0.3
0.3
0.6
0.8
1.2
1.3
1.6
1.9
1.3
National
0.4
0.4
0.4
0.9
0.8
0.8
1.2
1.1
1.2
1.2
Urban
0.4
0.3
0.3
0.7
0.6
0.6
1.0
0.9
0.9
0.9
Rural
0.6
0.6
0.6
1.1
1.1
1.1
1.5
1.6
1.6
1.6
National
0.1
0.1
0.0
0.2
0.2
0.1
0.2
0.2
0.2
0.2
Urban
0.0
0.0
0.0
0.0
0.1
0.0
0.1
0.1
0.1
0.1
Rural
0.2
0.1
0.1
0.3
0.3
0.2
0.3
0.3
0.3
0.4
Indicator
Real GDP Growth
Real Factor Pricesa
Non-Agr. Workers
Poverty Headcountb
Poverty Gapb
Gini Coefficientb
a Points deviation from base run.– b Percentage points deviation from base run.
Source: Own calculations based on the CGE model.
85
Table A6 — Declining Capital Inflows
Period
1
2
3
4
5
6
7
8
9
10
3.3
4.9
4.7
4.7
4.6
4.5
4.5
4.5
4.5
4.5
Smallholders
10
3
0
0
0
1
2
1
1
0
Agr. Workers
5
1
0
-1
0
1
1
1
1
0
-11
-4
-3
-2
-3
-5
-5
-6
-6
-7
Urban Informals
-9
-4
-4
-2
-4
-5
-6
-6
-6
-7
Employers
-1
-1
-1
-1
-1
-2
-2
-2
-2
-2
Employees
-2
-2
-2
-2
-2
-2
-2
-3
-4
-4
National
1.7
0.8
0.6
0.7
0.8
1.2
1.1
0.9
0.9
1.0
Urban
3.0
1.2
0.9
1.0
1.2
1.7
1.5
1.3
1.1
1.4
Rural
-0.3
0.2
0.2
0.1
0.3
0.4
0.4
0.5
0.6
0.5
National
0.7
0.4
0.4
0.4
0.5
0.6
0.6
0.6
0.7
0.7
Urban
1.5
0.7
0.7
0.6
0.6
0.8
0.8
0.9
1.0
1.0
Rural
-0.7
-0.1
0.1
0.3
0.2
0.2
0.2
0.2
0.2
0.3
-0.1
-0.1
-0.1
0.0
0.0
0.0
0.0
0.0
-0.1
0.0
Urban
0.4
0.1
0.1
0.0
0.1
0.1
0.2
0.1
0.2
0.2
Rural
-0.7
-0.3
-0.1
-0.1
-0.2
-0.2
-0.2
-0.3
-0.2
-0.2
Indicator
Real GDP Growth
Real Factor Pricesa
Non-Agr. Workers
Poverty Headcountb
Poverty Gapb
Gini Coefficientb
National
a Points deviation from base run.– b Percentage points deviation from base run.
Source: Own calculations based on the CGE model.
86
Table A7 — Nominal Devaluation
Period
1
2
3
4
5
6
7
8
9
10
4.7
4.6
4.6
4.6
4.5
4.5
4.4
4.4
4.3
4.3
Smallholders
0
0
-1
-1
-1
-2
-2
-3
-3
-4
Agr. Workers
0
0
-1
-1
-1
-2
-2
-3
-4
-6
Non-Agr. Workers
-1
-1
-2
-2
-3
-4
-5
-7
-7
-9
Urban Informals
-1
-1
-2
-2
-4
-4
-5
-6
-7
-9
Employers
0
-1
1
2
3
4
5
6
7
9
Employees
0
0
-1
-1
-1
0
0
-1
-2
-2
National
0.1
0.2
0.2
0.4
0.6
0.9
0.9
0.9
1.0
1.4
Urban
0.3
0.2
0.4
0.4
0.8
1.3
1.1
0.9
1.1
1.5
Rural
0.0
0.2
0.1
0.2
0.3
0.5
0.6
0.9
1.0
1.1
National
0.1
0.1
0.2
0.3
0.4
0.5
0.6
0.8
0.9
1.1
Urban
0.0
0.1
0.3
0.3
0.4
0.6
0.7
0.8
1.0
1.2
Rural
0.1
0.1
0.1
0.3
0.3
0.4
0.6
0.7
0.8
1.0
National
0.0
0.0
0.0
0.2
0.2
0.2
0.3
0.3
0.3
0.4
Urban
0.0
0.0
0.1
0.1
0.2
0.2
0.3
0.4
0.5
0.5
Rural
0.0
0.0
0.0
0.0
0.0
0.1
0.1
0.1
0.2
0.2
Indicator
Real GDP Growth
Real Factor Pricesa
Poverty Headcountb
Poverty Gapb
Gini Coefficientb
a Points deviation from base run.– b Percentage points deviation from base run.
Source: Own calculations based on the CGE model.
87
Table A8 — Real Devaluation (Restrictive monetary policy)
Period
1
2
3
4
5
6
7
8
9
10
4.5
4.7
4.7
4.8
4.8
4.8
4.8
4.8
4.8
4.8
Smallholders
1
0
-1
0
-1
-1
-1
-1
-1
-1
Agr. Workers
1
0
0
-1
0
-1
-1
-1
-1
-1
Non-Agr. Workers
-2
0
0
1
2
1
2
2
3
3
Urban Informals
-2
-1
-1
0
0
1
1
1
1
2
Employers
0
0
0
0
0
0
-1
0
-1
0
Employees
-1
-1
-1
-1
0
0
0
0
0
1
National
0.2
0.2
0.1
0.1
-0.1
-0.2
-0.1
-0.2
-0.3
-0.5
Urban
0.6
0.2
0.1
0.0
-0.1
-0.3
-0.3
-0.3
-0.3
-0.8
Rural
-0.3
0.1
0.1
0.1
0.0
0.1
0.1
0.2
0.1
0.1
National
0.1
0.1
0.0
0.0
0.1
0.0
0.0
-0.1
-0.1
-0.2
Urban
0.2
0.1
0.1
0.0
0.0
0.0
-0.1
-0.2
-0.2
-0.2
Rural
0.0
0.0
0.0
0.1
0.1
0.1
0.1
0.0
0.0
0.0
National
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
-0.1
0.0
Urban
0.1
0.0
0.0
-0.1
0.0
-0.1
-0.1
-0.1
-0.1
-0.1
Rural
-0.1
0.0
0.0
0.0
0.0
0.1
0.1
0.0
0.1
0.1
Indicator
Real GDP Growth
Real Factor Pricesa
Poverty Headcountb
Poverty Gapb
Gini Coefficientb
a Points deviation from base run.– b Percentage points deviation from base run.
Source: Own calculations based on the CGE model.
88
Table A9 — Labor Market Reform
Period
1
2
3
4
5
6
7
8
9
10
5.0
5.0
5.0
5.0
5.0
5.0
5.0
5.0
5.0
5.1
Smallholders
0
1
1
2
2
3
4
5
6
6
Agr. Workers
0
1
1
1
2
2
2
3
3
3
-4
-7
-11
-14
-16
-20
-23
-27
-29
-33
Urban Informals
2
6
8
9
13
17
19
22
25
28
Employers
0
0
-1
-1
-2
-2
-3
-2
-3
-3
Employees
0
-1
-1
-1
-1
-1
0
-1
-1
0
-0.1
0.0
-0.2
-0.5
-0.5
-0.5
-0.5
-0.6
-1.0
-0.9
Urban
0.1
0.2
-0.2
-0.7
-0.6
-0.7
-0.8
-1.0
-1.3
-1.5
Rural
-0.4
-0.2
-0.2
-0.2
-0.2
-0.1
0.0
0.1
-0.2
-0.1
National
-0.1
-0.1
-0.3
-0.3
-0.3
-0.4
-0.5
-0.6
-0.7
-0.8
Urban
-0.2
-0.2
-0.3
-0.4
-0.5
-0.5
-0.7
-0.8
-0.8
-0.9
Rural
0.0
0.0
-0.1
-0.1
-0.2
-0.3
-0.3
-0.4
-0.5
-0.5
National
0.0
0.0
0.0
0.1
0.1
0.1
0.2
0.1
0.1
0.2
Urban
0.0
0.0
0.0
0.0
0.1
0.0
0.1
0.1
0.1
0.1
Rural
0.0
0.0
-0.1
-0.1
-0.2
-0.1
-0.2
-0.2
-0.2
-0.2
Indicator
Real GDP Growth
Real Factor Pricesa
Non-Agr. Workers
Poverty Headcountb
National
Poverty Gapb
Gini Coefficientb
a Points deviation from base run.– b Percentage points deviation from base run.
Source: Own calculations based on the CGE model.
89
Table A10 — Tax Reform (Revenue-neutral)
Period
1
2
3
4
5
6
7
8
9
10
5.5
4.9
4.8
4.8
4.8
4.8
4.8
4.8
4.7
4.7
Smallholders
0
1
0
1
1
1
2
2
2
2
Agr. Workers
-1
0
0
0
1
1
1
1
1
1
Non-Agr. Workers
5
5
5
5
6
5
6
6
7
7
Urban Informals
6
6
5
6
5
6
6
6
6
6
Employers
1
2
1
1
1
1
0
1
1
0
Employees
2
2
2
2
2
3
3
3
3
4
National
-1.0
-0.8
-1.3
-1.5
-1.2
-1.0
-1.0
-1.1
-1.2
-1.4
Urban
-1.3
-1.1
-1.7
-2.3
-1.7
-1.2
-1.5
-1.3
-1.5
-1.9
Rural
-0.3
-0.3
-0.4
-0.4
-0.4
-0.6
-0.3
-0.9
-0.5
-0.4
National
-0.7
-0.7
-0.8
-0.7
-0.7
-0.7
-0.7
-0.7
-0.8
-0.7
Urban
-1.0
-1.0
-0.9
-0.9
-0.9
-0.8
-0.9
-0.9
-0.8
-0.8
Rural
-0.2
-0.3
-0.4
-0.4
-0.5
-0.5
-0.5
-0.6
-0.5
-0.5
0.0
0.0
-0.1
0.0
-0.1
-0.1
0.0
-0.1
-0.1
-0.1
Urban
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
Rural
0.2
0.1
0.1
0.1
0.0
0.1
0.1
0.0
0.1
0.1
Indicator
Real GDP Growth
Real Factor Pricesa
Poverty Headcountb
Poverty Gapb
Gini Coefficientb
National
a Points deviation from base run.– b Percentage points deviation from base run.
Source: Own calculations based on the CGE model.
90
Table A11 — Gas Projects (higher government consumption)
Period
1
2
3
4
5
6
7
8
9
10
5.1
5.0
5.3
5.0
5.9
5.6
4.7
4.7
4.8
4.8
Smallholders
-1
-2
-5
-5
-13
-21
-21
-20
-20
-19
Agr. Workers
-1
-3
-7
-9
-16
-24
-26
-28
-30
-31
0
0
0
0
3
5
6
7
8
8
-1
-1
-1
-1
-2
-3
-4
-2
-2
-2
Employers
0
1
1
1
1
1
3
4
5
6
Employees
1
1
2
3
6
8
9
8
8
9
National
-0.1
0.1
0.0
-0.2
-0.5
0.0
0.0
0.1
-0.7
-0.4
Urban
-0.1
-0.1
-0.1
-0.6
-1.1
-0.5
-0.6
-0.4
-1.4
-1.1
Rural
0.0
0.3
0.4
0.4
0.7
0.7
0.9
1.1
0.7
1.0
0.0
0.1
0.1
0.1
0.5
0.9
0.9
0.8
0.7
0.6
Urban
-0.1
-0.1
-0.1
-0.1
-0.2
-0.1
-0.2
-0.3
-0.3
-0.3
Rural
0.2
0.2
0.5
0.6
1.6
2.6
2.7
2.5
2.5
2.3
National
0.1
0.1
0.2
0.4
0.6
0.9
1.0
0.9
0.9
0.9
Urban
0.0
0.0
0.1
0.1
0.3
0.3
0.4
0.3
0.4
0.3
Rural
0.1
0.2
0.4
0.4
1.0
1.7
1.7
1.7
1.7
1.6
Indicator
Real GDP Growth
Real Factor Pricesa
Non-Agr. Workers
Urban Informals
Poverty Headcountb
Poverty Gapb
National
Gini Coefficientb
a Points deviation from base run.– b Percentage points deviation from base run.
Source: Own calculations based on the CGE model.
91
Table A12 — Gas Projects (constant government consumption)
Period
1
2
3
4
5
6
7
8
9
10
5.1
5.1
5.3
5.1
6.1
5.8
5.0
5.0
5.1
5.1
Smallholders
-1
-2
-5
-5
-13
-20
-19
-18
-17
-15
Agr. Workers
-1
-2
-6
-8
-15
-24
-26
-26
-26
-27
Non-Agr. Workers
1
2
3
5
11
15
18
19
21
22
Urban Informals
0
1
1
2
3
4
5
7
8
9
Employers
0
1
1
2
2
2
4
6
7
8
Employees
0
0
0
1
2
3
4
3
3
4
National
-0.2
0.1
-0.1
-0.4
-0.8
-0.8
-0.7
-1.1
-1.2
-1.5
Urban
-0.2
0.0
-0.2
-0.8
-1.5
-1.4
-1.5
-2.0
-2.4
-2.8
Rural
0.0
0.3
0.3
0.1
0.6
0.5
0.7
0.5
0.9
0.9
0.0
0.0
-0.1
-0.1
0.2
0.4
0.3
0.1
0.0
-0.2
Urban
-0.1
-0.2
-0.3
-0.4
-0.7
-0.8
-0.9
-1.1
-1.1
-1.3
Rural
0.2
0.2
0.5
0.6
1.4
2.4
2.3
2.1
1.9
1.7
National
0.1
0.1
0.1
0.2
0.3
0.5
0.5
0.5
0.4
0.4
Urban
0.0
-0.1
-0.1
-0.1
-0.2
-0.3
-0.2
-0.3
-0.3
-0.4
Rural
0.1
0.2
0.3
0.4
0.9
1.6
1.6
1.5
1.5
1.4
Indicator
Real GDP Growth
Real Factor Pricesa
Poverty Headcountb
Poverty Gapb
National
Gini Coefficientb
a Points deviation from base run.– b Percentage points deviation from base run.
Source: Own calculations based on the CGE model.
92
Table A13 — Gas Projects (constant government consumption) plus Labor Market Reform
plus Tax Reform
Period
1
2
3
4
5
6
7
8
9
10
6.0
5.5
5.7
5.5
6.5
6.4
5.4
5.4
5.5
5.5
Smallholders
-1
0
-2
-1
-8
-14
-12
-10
-7
-5
Agr. Workers
-2
-2
-4
-5
-12
-19
-20
-20
-20
-20
Non-Agr. Workers
2
-1
-3
-6
-4
-4
-6
-9
-11
-14
Urban Informals
8
12
15
19
23
28
31
36
41
45
Employers
1
2
1
1
1
2
3
4
4
5
Employees
2
2
3
3
5
7
8
8
8
10
National
-1.7
-1.2
-2.0
-2.3
-2.6
-2.6
-2.3
-2.4
-2.9
-3.3
Urban
-3.0
-2.1
-3.4
-4.1
-4.9
-5.1
-4.7
-4.6
-4.9
-5.4
Rural
0.5
0.3
0.4
0.7
1.1
1.5
1.6
1.2
0.8
0.2
National
-0.6
-0.6
-0.6
-0.7
-0.6
-0.5
-0.6
-0.8
-0.9
-1.1
Urban
-1.5
-1.7
-2.0
-2.1
-2.5
-2.7
-2.8
-3.0
-3.2
-3.3
Rural
1.0
1.2
1.5
1.6
2.4
3.2
3.2
3.0
2.8
2.5
0.5
0.5
0.6
0.6
0.9
1.2
1.2
1.2
1.2
1.1
Urban
-0.2
-0.2
-0.1
-0.2
-0.2
-0.1
-0.2
-0.1
-0.2
-0.1
Rural
1.0
1.1
1.3
1.4
2.0
2.6
2.6
2.6
2.5
2.3
Indicator
Real GDP Growth
Real Factor Pricesa
Poverty Headcountb
Poverty Gapb
Gini Coefficientb
National
a Points deviation from base run.– b Percentage points deviation from base run.
Source: Own calculations based on the CGE model.
93
Table A14 — Improved Access to Credit for Smallholders
Period
1
2
3
4
5
6
7
8
9
10
4.7
4.8
4.8
4.8
4.7
4.7
4.7
4.7
4.7
4.7
Smallholders
1
1
1
1
2
2
2
2
2
2
Agr. Workers
0
-1
0
-1
-1
0
0
0
0
0
Non-Agr. Workers
1
1
1
1
1
1
1
1
1
1
Urban Informals
1
-1
0
1
0
1
1
0
1
1
Employers
1
0
0
0
0
0
1
0
1
0
Employees
0
0
0
0
0
0
0
0
0
0
National
-0.1
-0.1
0.0
-0.1
0.0
-0.2
0.0
-0.1
-0.1
-0.1
Urban
-0.1
-0.2
0.0
-0.1
0.0
-0.2
-0.1
0.0
0.0
-0.1
Rural
0.0
-0.1
0.0
-0.1
0.0
-0.1
0.0
-0.2
0.0
0.0
National
-0.1
0.0
0.0
-0.1
-0.1
-0.1
-0.1
ß-2
-0.1
-0.2
Urban
-0.1
-0.1
-0.1
-0.1
-0.1
-0.1
-0.1
-0.1
-0.1
-0.1
Rural
-0.1
-0.1
-0.1
-0.1
-0.2
-0.2
-0.2
-0.2
-0.2
-0.3
National
0.0
0.0
0.0
-0.1
0.0
0.0
-0.1
0.0
0.0
-0.1
Urban
0.0
-0.1
0.0
-0.1
0.0
0.0
0.0
0.0
0.0
0.0
Rural
0.0
0.0
0.0
-0.1
-0.1
0.0
0.0
0.0
0.0
-0.1
Indicator
Real GDP Growth
Real Factor Pricesa
Poverty Headcountb
Poverty Gapb
Gini Coefficientb
a Points deviation from base run.– b Percentage points deviation from base run.
Source: Own calculations based on the CGE model.
94
Table A15 — Investment in Rural Infrastructure (high productivity effect)
Period
1
2
3
4
5
6
7
8
9
10
5.8
4.7
4.7
4.7
4.7
4.7
4.7
4.7
4.7
4.7
Smallholders
9
9
9
10
9
10
10
10
10
10
Agr. Workers
-6
-5
-6
-6
-7
-7
-7
-7
-7
-6
Non-Agr. Workers
1
1
0
0
1
1
0
0
0
0
Urban Informals
2
2
2
1
2
2
2
2
2
2
Employers
0
1
0
0
1
0
0
1
0
1
Employees
0
1
1
0
0
0
1
1
0
1
National
-0.5
-0.5
-0.4
-0.3
-0.6
-0.3
-0.5
-0.3
-0.2
-0.3
Urban
-0.6
-0.3
-0.3
-0.2
-0.5
-0.2
-0.5
-0.3
0.1
-0.3
Rural
-0.6
-0.7
-0.6
-0.5
-0.7
-0.4
-0.5
-0.6
-0.3
-0.4
National
-0.6
-0.5
-0.5
-0.5
-0.6
-0.5
-0.5
-0.6
-0.5
-0.5
Urban
-0.3
-0.3
-0.3
-0.2
-0.3
-0.2
-0.2
-0.2
0.2
-0.2
Rural
-1.0
-1.0
-1.0
-1.0
-1.0
-1.0
-1.0
-1.0
-1.1
-1.0
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
Urban
0.0
-0.1
0.0
-0.1
0.0
-0.1
-0.1
0.0
-0.1
0.0
Rural
-0.5
-0.6
-0.5
-0.5
-0.5
-0.6
-0.5
-0.5
-0.6
-0.5
Indicator
Real GDP Growth
Real Factor Pricesa
Poverty Headcountb
Poverty Gapb
Gini Coefficientb
National
a Points deviation from base run.– b Percentage points deviation from base run.
Source: Own calculations based on the CGE model.
95
Table A16 — Investment in Rural Infrastructure (low productivity effect)
Period
1
2
3
4
5
6
7
8
9
10
5.3
4.8
4.7
4.7
4.7
4.7
4.7
4.7
4.7
4.7
Smallholders
4
5
4
5
5
5
5
5
5
5
Agr. Workers
-3
-3
-3
-3
-4
-4
-3
-3
-3
-3
Non-Agr. Workers
1
1
0
0
1
1
0
0
0
0
Urban Informals
1
1
1
1
1
1
1
1
1
1
Employers
0
1
0
0
0
0
0
0
0
0
Employees
0
1
0
0
0
0
0
1
0
1
National
-0.2
-0.3
-0.2
-0.2
-0.2
-0.2
-0.4
-0.1
-0.1
-0.2
Urban
-0.2
-0.2
-0.2
-0.1
-0.1
-0.1
-0.3
-0.1
0.0
-0.2
Rural
-0.3
-0.3
-0.3
-0.4
-0.3
-0.3
-0.4
-0.2
-0.3
-0.3
National
-0.3
-0.3
-0.3
-0.3
-0.2
-0.3
-0.3
-0.3
-0.2
-0.2
Urban
-0.1
-0.1
-0.1
-0.1
-0.2
-0.1
-0.1
-0.1
-0.1
-0.1
Rural
-0.5
-0.4
-0.5
-0.5
-0.5
-0.5
-0.5
-0.5
-0.6
-0.5
-0.1
-0.1
-0.1
-0.1
-0.1
-0.1
-0.1
-0.1
-0.1
-0.2
Urban
0.0
-0.1
0.0
0.0
0.0
0.0
-0.1
0.00
-0.1
0.0
Rural
-0.2
-0.3
-0.3
-0.2
-0.2
-0.3
-0.3
-0.2
-0.3
-0.3
Indicator
Real GDP Growth
Real Factor Incomea
Poverty Headcountb
Poverty Gapb
Gini Coefficientb
National
a Points deviation from base run.– b Percentage points deviation from base run.
Source: Own calculations based on the CGE model.
96
Table A17 — Industrial Policy (modern agriculture)
Period
1
2
3
4
5
6
7
8
9
10
4.6
4.7
4.7
4.7
4.7
4.7
4.7
4.7
4.6
4.6
Smallholders
2
2
2
2
2
2
3
3
2
2
Agr. Workers
31
32
32
33
35
36
37
38
39
39
Non-Agr. Workers
-2
-2
-2
-2
-2
-3
-3
-4
-4
-5
Urban Informals
-3
-3
-3
-3
-4
-3
-4
-3
-4
-5
Employers
2
3
2
2
2
2
2
2
2
2
Employees
-1
-2
-2
-2
-2
-1
-1
-2
-2
-2
National
0.2
0.1
0.2
0.2
0.1
0.5
0.3
0.3
0.1
0.4
Urban
0.8
0.5
0.8
0.7
0.6
1.1
0.8
0.7
0.4
0.7
Rural
-0.8
-0.5
-0.6
-0.7
-0.6
-0.5
-0.4
-0.4
-0.1
-0.1
National
0.1
0.1
0.0
0.1
0.1
0.1
0.1
0.1
0.1
0.2
Urban
0.3
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.5
0.5
Rural
-0.4
-0.4
-0.5
-0.4
-0.5
-0.4
-0.4
-0.4
-0.4
-0.3
-0.1
-0.1
-0.1
-0.1
-0.1
-0.1
-0.1
-0.1
-0.1
0.0
Urban
0.1
0.0
0.1
0.0
0.1
0.1
0.1
0.1
0.2
0.2
Rural
0.1
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Indicator
Real GDP Growth
Real Factor Pricesa
Poverty Headcountb
Poverty Gapb
Gini Coefficientb
National
a Points deviation from base run.– b Percentage points deviation from base run.
Source: Own calculations based on the CGE model.
97
Table A18 — Industrial Policy (consumer goods)
Period
1
2
3
4
5
6
7
8
9
10
4.6
4.6
4.6
4.6
4.6
4.6
4.6
4.6
4.6
4.6
Smallholders
5
5
6
6
6
5
6
6
5
5
Agr. Workers
0
0
-1
-2
-1
-2
-2
-3
-3
-4
Non-Agr. Workers
1
1
1
2
2
1
2
1
2
1
Urban Informals
5
6
5
6
5
6
6
6
5
5
Employers
1
2
2
2
2
2
2
3
2
3
Employees
-3
-3
-3
-3
-3
-3
-3
-3
-4
-3
National
-0.6
-0.3
-0.5
-0.5
-0.3
0.1
-0.2
-0.3
-0.6
-0.4
Urban
-0.4
-0.2
-0.5
-0.6
-0.2
0.2
0.0
-0.4
-0.7
-0.6
Rural
-0.6
-0.4
-0.4
-0.4
-0.3
-0.1
-0.3
0.0
0.0
0.0
National
-0.5
-0.4
-0.5
-0.4
-0.3
-0.4
-0.3
-0.3
-0.3
-0.2
Urban
-0.5
-0.4
-0.3
-0.4
-0.3
-0.3
-0.3
-0.3
-0.2
-0.2
Rural
-0.6
-0.6
-0.5
-0.5
-0.5
-0.4
-0.3
-0.4
-0.3
-0.2
National
-0.2
-0.3
-0.3
-0.2
-0.2
-0.3
-0.2
-0.2
-0.3
-0.2
Urban
-0.2
-0.3
-0.2
-0.2
-0.3
-0.3
-0.2
-0.3
-0.2
-0.3
Rural
-0.3
-0.3
-0.3
-0.3
-0.3
-0.2
-0.3
-0.3
-0.2
-0.2
Indicator
Real GDP Growth
Real Factor Pricesa
Poverty Headcountb
Poverty Gapb
Gini Coefficientb
a Points deviation from base run.– b Percentage points deviation from base run.
Source: Own calculations based on the CGE model.
98
Table A19 — Transfer Program (lower government consumption)
Period
1
2
3
4
5
6
7
8
9
10
4.6
4.8
4.8
4.8
4.7
4.7
4.7
4.7
4.7
4.7
Smallholders
2
1
1
2
2
1
2
2
2
2
Agr. Workers
0
1
1
0
1
1
1
1
1
1
-1
0
-1
0
0
-1
0
-1
0
0
Urban Informals
0
1
0
1
0
1
0
1
1
1
Employers
0
0
0
0
0
0
0
0
0
0
Employees
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2
National
-1.4
-1.4
-1.3
-1.2
-1.3
-1.3
-1.0
-1.4
-1.4
-1.5
Urban
-0.8
-1.0
-0.6
-0.6
-0.6
-0.8
-0.4
-0.6
-0.8
-1.0
Rural
-2.4
-2.2
-2.2
-2.4
-2.3
-2.2
-2.1
-2.5
-2.6
-2.6
National
-1.1
-1.1
-1.2
-1.1
-1.1
-1.1
-1.1
-1.2
-1.2
-1.2
Urban
-0.4
-0.3
-0.3
-0.3
-0.3
-0.3
-0.3
-0.3
-0.3
-0.3
Rural
-2.4
-2.4
-2.5
-2.5
-2.5
-2.5
-2.5
-2.6
-2.6
-2.6
National
-0.7
-0.7
-0.7
-0.7
-0.7
-0.7
-0.7
-0.7
-0.7
-0.7
Urban
-0.3
-0.3
-0.3
-0.3
-0.3
-0.3
-0.3
-0.3
-0.3
-0.3
Rural
0.4
0.5
0.5
0.5
0.5
0.6
0.7
0.7
0.8
0.8
Indicator
Real GDP Growth
Real Factor Pricesa
Non-Agr. Workers
Poverty Headcountb
Poverty Gapb
Gini Coefficientb
a Points deviation from base run.– b Percentage points deviation from base run.
Source: Own calculations based on the CGE model.
99
Table A20 — Transfer Program (lower public investment)
Period
1
2
3
4
5
6
7
8
9
10
4.6
4.6
4.6
4.6
4.6
4.5
4.5
4.5
4.5
4.4
Smallholders
2
1
1
1
1
0
0
0
-1
-1
Agr. Workers
-1
-1
-2
-1
0
0
-1
-1
-1
-2
Non-Agr. Workers
-4
-3
-4
-4
-4
-5
-6
-7
-7
-8
Urban Informals
-2
-3
-3
-3
-4
-4
-5
-5
-5
-6
Employers
0
0
-1
-1
-1
-1
-1
-1
-1
-2
Employees
0
0
-1
-1
-1
0
0
-1
-1
-1
National
-1.1
-1.0
-1.2
-1.1
-0.7
-0.4
-0.8
-0.7
-0.7
-0.7
Urban
-0.2
-0.4
-0.6
-0.5
0.0
0.5
-0.1
0.0
0.2
0.0
Rural
-2.5
-2.1
-2.1
-2.1
-1.9
-1.9
-2.1
-1.8
-1.9
-1.7
National
-1.0
-1.0
-1.0
-0.9
-0.8
-0.8
-0.7
-0.7
-0.6
-0.5
Urban
-0.2
-0.1
0.0
0.0
0.1
0.2
0.2
0.2
0.4
0.5
Rural
-2.4
-2.5
-2.5
-2.4
-2.3
-2.2
-2.2
-2.2
-2.1
-2.0
National
-0.6
-0.6
-0.6
-0.5
-0.5
-0.5
-0.4
-0.5
-0.5
-0.4
Urban
-0.1
-0.1
-0.1
-0.1
0.0
-0.1
0.0
0.0
0.1
0.1
Rural
0.6
0.6
0.7
0.7
0.7
0.9
0.9
0.9
1.1
1.1
Indicator
Real GDP Growth
Real Factor Pricesa
Poverty Headcountb
Poverty Gapb
Gini Coefficientb
a Points deviation from base run.– b Percentage points deviation from base run.
Source: Own calculations based on the CGE model.
100
Table A21 — Gas Projects plus Transfer Program
Period
1
2
3
4
5
6
7
8
9
10
5.1
5.3
5.0
5.9
5.7
4.8
4.8
4.8
4.8
4.9
Smallholders
1
0
-3
-3
-11
-19
-19
-18
-17
-17
Agr. Workers
-1
-2
-6
-8
-16
-25
-27
-28
-28
-29
Non-Agr. Workers
-1
0
0
0
4
5
7
7
9
9
Urban Informals
0
0
0
0
-1
-2
-2
0
0
0
Employers
0
1
0
1
1
1
3
4
5
6
Employees
-1
-1
0
1
3
6
6
6
5
5
National
-1.4
-1.4
-1.3
-1.4
-1.7
-1.3
-1.4
-1.6
-1.9
-1.8
Urban
-0.8
-1.0
-0.9
-1.2
-1.8
-1.1
-1.5
-1.5
-1.8
-1.8
Rural
-2.4
-2.2
-1.8
-2.1
-1.4
-1.5
-1.2
-1.8
-1.8
-1.8
National
-1.1
-1.1
-1.1
-1.0
-0.7
-0.4
-0.4
-0.5
-0.6
-0.7
Urban
-0.4
-0.4
-0.4
-0.5
-0.6
-0.5
-0.6
-0.7
-0.6
-0.7
Rural
-2.3
-2.3
-2.1
-2.0
-1.1
-0.2
-0.1
-0.3
-0.4
-0.6
National
-0.6
-0.6
-0.5
-0.4
-0.1
0.1
0.2
0.1
0.1
0.1
Urban
-0.2
-0.3
-0.2
-0.2
-0.1
0.0
0.0
0.0
0.0
0.0
Rural
0.6
0.7
1.0
1.1
1.9
2.7
2.8
2.7
2.8
2.7
Indicator
Real GDP Growth
Real Factor Pricesa
Poverty Headcountb
Poverty Gapb
Gini Coefficientb
a Points deviation from base run.– b Percentage points deviation from base run.
Source: Own calculations based on the CGE model.