CHAPTER ONE
INTRODUCTION
1.1 Background to the Study
Economic growth and development processes affect and are affected by
migration of people. In traditional viewpoint, people migrate when they are
both pushed by lack of opportunities at home and pulled by the hope of
economic gains elsewhere. Thus, the hope that migration will help associate
migrants more closely with available economic opportunities, employment and
services elsewhere is a major incentive for migration. Arguably, migration is
necessarily a part of a family strategy to raise income, obtain new funds for
investment, and insure against risks.
It is not surprising therefore that
thousands of African workers with relevant skill endowments leave their home
country yearly to pursue better economic prospects within or outside Africa.
However, migration of skilled workers could potentially hurt the sending
countries if not well managed by appropriate policies.
As populations in advanced countries continue to age, shortage of labour in
sectors such as health care continue to attract relatively cheap but qualified
labour from these developing countries of Africa. Migration of skilled workers
in this sense contributes to the economic growth of receiving countries by
responding to real labour needs in receiving countries. In addition, migrant
workers help fulfill unmet labour requirements in many lower-pay and lowskill jobs such as those associated with domestic and agricultural work in
developed countries. Migrants also contribute to the scientific and
technological development of host countries. These factors partly provide the
necessary impetus for international migration flows to continue to increase,
1
and for the process of globalization and the interdependence of nations to
continue to deepen.
While the positive aspects of migration can lead to economic gains for the
receiving countries, it can also lead to “unintended consequences” in both the
sending and receiving countries. Some of these consequences include an
outright deprivation of vital human resources in sending countries, and by
implication the adverse impact of migration of skilled workers on the brain
drain phenomenon in sending countries. Such deprivation of vital human
resources is rather very alarming given that the United Nations predicts that
the net number of migrants from developing to developed countries will
increase by 2.2 million people annually, from 191 million or 3 per cent of the
world population in 2005 (United Nations, 2004). This problem is even further
compounded when the long gestation period for training skilled workers is
taken into account by the migrant sending countries of Africa. There are also
the issues of cultural conflicts in receiving countries, human trafficking,
economic exploitation of migrants, sending country dependency patterns,
delayed economic growth in sending countries, etc. In this case, a vicious cycle
is easily perpetuated.
The emigration of people with scarce skills, such as entrepreneurs, scientists,
technicians and health professionals reduces both the stock of human
capital and the overall labour productivity of developing countries. However,
if these highly skilled migrants return, they bring with them experience,
knowledge contacts and capital, which have a positive impact on development.
Thus, gains and losses from migration depend on whether it is temporary or
permanent (Rena, 2008). In essence, African countries stand to benefit from
2
migration through the African Diaspora1 expertise, knowledge, technology,
professional capacity building and a great potential for trade and investment
links. The migrant sending countries of Africa can also benefit economically
from migration through the inflow of workers’ remittances. Given these
possibilities, migration is increasingly being regarded as an important
instrument for growth and development in Sub-Saharan Africa (SSA).
Migrant remittances seem to have contributed to poverty reduction throughout
Sub-Saharan Africa, leading to increased household investments in education,
entrepreneurship and health. At the household level, remittances are spent
primarily on general consumption items in local communities which can
contribute to local economies by supporting small businesses. This in general,
has its employment generation implications in these critical services sectors. In
addition to supporting domestic consumption, remittances can also promote
investments in real assets including building schools and clinics. Remittances
flow is directly to households and they are widely distributed in small amounts
throughout the economy. This makes remittances capable of having a much
broader effect on home country economies than either FDI or official
development assistance.
Official data on remittances inflow to Sub-Saharan Africa reveal that, the flow
of remittances to the region has been far more stable than official aid flows and
foreign direct investment (FDI). Besides, remittances do not decline even in
conditions of instability and poor governance. Hence, remittance flows
represent one of the least volatile sources of foreign exchange earnings.
1
The African Diaspora consists of peoples of African origin that are living outside the continent,
irrespective of their citizenship and nationality.
3
They are also more evenly spread among developing countries than capital
flows. Workers’ remittances represent one of the largest private sources of
external finance for developing countries; thus, remittances are the main
transmitter of migration’s development benefits to sending country economies.
Workers’ remittances are inter-household transfer of money within or across
national boundaries. According to Reinke and Patterson (2005), workers’
remittances cover current transfers by migrants who are employed in new
economies and are considered residents there.
Workers’ remittances flow has steadily increased since the mid 1980s.
Officially recorded remittances were an estimated US$206 billion in 2006,
compared to US$19.6 billion in 1985 (World Development Indicators 2006).
Remittances have been the second most important source of external finance
for developing countries, being twice the size of Official Development Aid
(ODA) and almost as large as Foreign Direct Investment (FDI). World Bank
(2009) reports that recorded remittances to developing countries in 2008 were
estimated to have reached US$305 billion. This is equivalent to nearly two
percent of aggregate developing country GDP and well over half of estimated
FDI inflows (US$490 billion). The 2008 estimated remittances to developing
countries are over twice as large as official development aid of US$119 billion
received by developing countries.
In absolute terms, big developing countries like India, China, Mexico and the
Philippines receive the largest shares of remittances in the world. However, in
relative terms, small and poor countries tend to be much more dependent on
remittances. For many countries with large Diasporas, workers’ remittances
often amount to at least, 15% of Gross Domestic Product (GDP). Tonga for
example had a share of remittances to GDP of 39% and Haiti and Lesotho of
27% in 2003. Actual figures are even higher than this, because unrecorded
4
remittances in cash or kind are often brought by migrants themselves or sent
through third parties, and are not declared when entering the country.
Remittance receipt in relative terms is expressed as a percentage of GDP for
the top 25 recipients in SSA in 2008 and is reported in figure 1.1.
Figure 1.1: Sub-Saharan Africa: top 25 recipients of remittances in 2008
Source: International Monetary Fund (IMF) (2009). Regional Economic Outlook:
Sub-Saharan Africa.
Figure 1.1 shows that Lesotho tops remittance recipients in SSA with
remittance inflow amounting to about 27.5 percent of GDP. This is followed
closely by Comoros with about 25 percent of GDP. Mozambique and Cote
d’Ivoire are the least in terms of relative importance of remittances with about
1 percent of GDP in each of the countries.
5
When considered as a share of GDP, workers’ remittances can in fact be
conveniently regarded as a vital source of finance for many developing
countries. These flows contribute to the poverty reduction process by
enhancing the living standards of the beneficiaries. Workers’ remittances can
also contribute to the poverty reduction process through the multiplier effects
of flows which create additional demand, employment and income. Page and
Adams (2003) estimate that a 10% increase of remittances per capita would
lead to a decline of the poverty head count by 3.5%, due to multiplier effects
on GDP growth. Despite their positive impact on poverty rates, the way in
which remittances contribute to economic growth and development is still an
open question. Even if we take account of multiplier effects, poverty reduction
through remittances is, in principle, a one-time effect. From a development
perspective the question must be whether remittances have, beyond their
immediate impact on poverty, an effect on the long-term growth of a country.
Most remittances are made in the form of cash rather than as goods. Therefore,
remittances are financial flows made up of private and unilateral transfers of
money by a migrant worker resident in a foreign country (host country) to a
person (most often a family member of the migrant) living in the migrant’s
country of origin (home country). In principle, there are three ways of
measuring remittance inflows in countries. According to Addison (2004), the
first approach is the balance of payments (BOP) estimates. Other
methodologies include micro or household surveys of recipients of such flows
e.g. inference from the Ghana Living Standard Survey (GLSS). The third
method is through banks or financial institutions in origin countries i.e.
focusing on resource transfer institutions.
In terms of relative accuracy and level of coverage, the micro or household
surveys of recipients approach is likely to be the least. The obvious explanation
6
will be the problems of non-disclosure by respondents and general costs
associated with micro or household surveys respectively. The BOP approach
tends to be most reliable for macro studies since aggregated data are usually
compiled and reported by the various monetary authorities under this approach.
Thus, the size of the remittances flows employed in this study are based on
BOP estimates reported by the various central banks of the IMF member
countries. For obvious reasons, the cross–country nature of this study demands
that relevant data are drawn from a common source to allow for uniformity of
measurement standard as well as easy comparism. The World Bank Africa
Development Indicators satisfactorily meets these requirements.
The importance of remittances for some countries in the SSA region can be
best illustrated by expressing them as a ratio to GDP, while in others the
absolute total of per-capita value of remittances flows are more revealing.
Remittance flows is widely believed to be much more sustainable as a source
of development finance to many countries around the world. Two major forces
are expected to ensure the growth and sustenance of these flows: Globalization
and the aging populations (Olayiwola et al, 2008 and Olayiwola, 2010).
Globalization and the aging of developed economy populations will ensure that
demand for migrant workers remains robust for years to come. Consequently,
the volume of remittances will most likely continue to grow, since migrants
will continue to support the elderly and other dependants in their countries of
origin. However, challenges remain in determining how best to channel the
flow of remittances through formal financial institutions to promote economic
growth and development in sending countries (Chami, Barajas, Cosimano,
Fullenkamp, Gapen, and Montiel, 2008). This study empirically examines this
challenge, and sheds more light on several possible options open to some
7
selected SSA countries in the effort to harness maximum societal benefits from
workers’ remittances inflow.
1.2 Statement of the Problem
The major research issue in this study bothers on the determination of the
nature of relationship between remittances and economic growth in SSA.
There is so far no conclusive answer in the literature to the question of whether
workers’ remittances constitute at the aggregate level, a vital source of
development finance to the developing countries of the Sub-Saharan African
region.
The literature on the potential developmental impact of remittances in an
economy is quite vast but mixed and can be divided into two separate strands.
One strand takes a microeconomic approach and examines the causes and uses
of remittances using household surveys and aggregate data (Taylor, 1999). The
other strand focuses on the effects of remittances and uses macroeconomic
models (that are not based on individual maximizing behavior) to estimate the
impact of remittances. While the micro dimension of remittances is often
closely associated with the “dependency framework”, the macro dimension is
often associated with the “developmental framework”.
In other words, workers’ remittances seen from the perspective of individual to
individual transfers often connote a relationship between two parties that
allows for regular financial support from one party. Such support is often to
meet the consumption, medical and/or education needs of the dependant party.
However, when workers’ remittances is taken from the perspective of group to
group transfers, it connotes an arrangement that allows for group or societal
support often to meet the developmental needs of the benefiting party. The
8
likely negative impact of remittances associated with the dependency
framework is that it may engender a culture of dependency among the
economically active population that benefit from remittances flows.
Workers’ remittances may on the other hand generate a number of important
positive contributions to economic growth and development. In particular,
remittances tend to reduce poverty and inequality in recipient countries, as well
as increase aggregate investment and growth. Moreover, when perceived to
behave counter – cyclically, remittances may significantly reduce growth
volatility and help countries adjust to external and macroeconomic policy
shocks. At the microeconomic level, remittances allow poor recipient
households to increase their savings, spend more on consumer durables and
human capital, and improve children’s health and educational outcomes.
Consequently, the net impact of workers’ remittances is that it is beneficial to
the recipient party if properly managed.
Workers’ remittances are important source of finance and foreign exchange for
many African countries. They help the countries to stabilize irregular incomes
and also assist communities to build human and social capital. Remittances
receivers in many cases are typically or financially better off than their peers
who lack this source of income (Sander and Maimbo, 2003). In this sense,
remittances are private and family funds, which may be construed as
constituting some form of familial support that does not create any future
liabilities such as debt servicing or profit transfer for the recipient.
These transfers have been a critical means of financial support to many poor
families in developing economies for generations and have helped them
significantly in confronting the plague of poverty. Thus, remittances reduce the
problem of income inequality in many societies. Within this perspective, there
9
are at least four identifiable motives for remittances in the literature; these
include (i) altruism (ii) self interest (iii) implicit family contract: - loan
repayment, and (iv) implicit family contract: - co-insurance (Solimano, 2003).
At the macroeconomic level, remittances have a substantial positive effect on
the balance of payments and on foreign exchange revenues. This however may
not be true for net remittances. More importantly, remittance inflows, unlike
oil windfalls do not weaken institutional capacity. This is because remittances
are widely dispersed with the great bulk allocated in small amounts to the
recipients while the governments are precluded from playing the role of
“middlemen”.
The role of workers’ remittances in economic growth and development
continues to be an important issue for researchers and policymakers. One strand
of studies relates to the understanding of the determinants and factors that
shape the transfer of funds by migrants. It also explains the amount, frequency,
volume, and duration of such transfers (Lee, Bokkerink, Smallwood, and
Hermandez-Coss, 2005). The other strand concentrates on the causes and or
uses of remittances while only a few made efforts to directly address the
macroeconomic effects of remittance transfers (Chami, Fullenkamp, and
Jahjah , 2003). This limited research effort did not give Africa, and particularly
SSA, much attention on the issue of remittances (Sander 2003). This
development is traceable to the relatively low share of remittances going to the
African continent (15 percent of total flows to developing countries) and the
even lower share going to Sub-Saharan Africa (5 percent), and by the relatively
small number of international migrants from Africa, as well as their greater
dispersion, compared to migrants from other developing regions (sander and
Maimbo, 2003).
10
Workers’ remittances to Africa are nevertheless an important financial flow—
with perhaps, significant developmental effects. As shown in figure 1.1,
workers’ remittance as a percentage of GDP in many SSA countries is quite
significant averaging about 8 percent for these countries. Thus, these realities
make a study on the subject worth embarking on. Moreover, their level is
probably much higher than official data indicates (Sander and Maimbo, 2003).
Anecdotal reports support the fact that many transactions go unrecorded or
unreported, this in large part is because financial systems and services are
weak in much of Africa. The weakness of financial systems brings about the
problem of remittance leakages as it creates obstacles for the efficient transfer
of remittances through formal money transfer services and limits the potential
of remittances to contribute to development (Gupta, Pattilo and Wagh, 2007).
The weak financial systems and services in Africa has been a major stimulus
for the sustenance of the informal transfer systems which includes personal
carriage of cash or goods by migrants, their relatives, their friends, or trusted
agents. Other informal services operate as a side business to an import-export
operation, retail shop, or currency dealership. Most of them keep little paper or
electronic documentation. Transactions are communicated by phone, fax, or email to a counterpart who will make the payment (El-Qorchi, Maimbo, and
Wilson, 2002). The best known of the informal services are hawala and hundi,
which operate in a similar fashion. The terms can be used interchangeably, but
hawala is typically used in the context of the Middle East and Arab countries
and their migrant populations, whereas hundi is usually connected with South
Asia especially Bangladesh (Sander and Maimbo, 2003).
Workers’ remittances as a potential source of external development finance for
many developing countries provide a much more stable source of foreign
exchange than other foreign currency flows to developing countries. This is
11
especially relevant to SSA, where official aid flows have fluctuated over the
years. The increasing attention is also due to the growing volume of official
financial remittances to low income countries and their potential contribution
to the development of the receiving regions. But despite the large interest in
remittances, their role in economic growth and development remains unclear.
First, it is extremely difficult to gather accurate data on remittances. This is
because many remittances are not channeled through the payment system and
are left outside the official statistics. In addition, most studies on workers’
remittances flows to Africa tend to be on a single country or one migrant group
at a time and this does not allow for any form of general inference.
At the macro level, the economic growth and developmental impact of
remittances on the economy attracts two opposing views in the literature.
Within the first perspective, remittances often provide a significant source of
foreign currency, increase national income, finance imports and contribute to
the balance of payments. Remittances also contributed to the expansion of wire
transfer and courier companies as well as money exchanges (Russell 1986;
Keely and Tran 1989; Massey 1992; Taylor et al. 1996a and 1996b). Other
studies with contrary views believe that remittances decrease the likelihood of
an improved economy. Their argument is that, the inflow of funds can be
deceptive if it creates dependence among the recipients, encourages the
continued migration of the working age population and decreases the
likelihood of investment by the government or foreign investors because of an
unreliable workforce (Pastor and Rogers 1985; Pastor 1989). Another possible
negative effect of remittances is the possibility that they produce a “Dutch
disease” effect. For countries that receive important sums of remittances, there
is a tendency for the real exchange rate to appreciate, penalizing non-
12
traditional exports and hampering the development of the tradable goods sector
(Solimano, 2003).
Remittances can also be countercyclical or procyclical with the GDP in
recipient countries. On the one hand, remittances motivated largely by
altruism, are argued to have a tendency to move counter-cyclically with the
GDP in recipient countries. The reasoning here is that migrant workers are
expected to increase their support to family members during down cycles of
economic activity back home. This expectedly will compensate the remittances
beneficiaries for lost family income due to unemployment or other crisisinduced reasons. However, remittances conceived as procyclical with output in
recipient countries may act as a destabilizing force. In this case, procyclical
remittances increase the capacity of swings in remittance flows to produce
additional fluctuations in output or current account balances, with serious
macroeconomic effects (Sayan, 2004). It is quite obvious from the foregoing
that, despite the increasing importance of remittances in total international
capital flows, the direct or indirect relationship between remittances and
economic growth and development has not been adequately studied.
This study sheds additional insight into the inconclusive debate on the
remittance – growth nexus by exploring the macroeconomic impact of
remittances on economic growth and development in some selected SSA
countries. It does this within the extended neoclassical growth framework
using a balanced panel data set spanning from 2000 to 2007 for twenty one
SSA countries.
13
1.3 Research Questions
Given the various issues relating to the growth and developmental role of
workers’ remittances flows to SSA, a number of research questions arise as
follows:
(i)
What are the roles or contributions of remittances to output growth
within the SSA?
(ii)
What is the contribution of remittances to private investment?
(iii)
To what extent do remittances contribute to foreign trade balance?
(iv)
What are the various policy options that can be adopted to better
manage the macroeconomic effects of remittances in SSA?
Any research effort that provides satisfactory answers or at the least, shed
some meaningful insights into the above questions represents a valuable
guide to the understanding of the economic growth and development role of
workers’ remittances inflows to SSA. Therefore, in this empirical study, no
effort is spared in providing meaningful answers to the above questions.
1.4 Objectives of the Study
The overall objective of this study is to investigate the economic growth and
developmental role of workers’ remittances in selected Sub-Saharan African
(SSA) countries. The specific objectives are to:
(i)
Determine the contributions of remittances to output growth in SSA
(ii) Analyze the importance of remittances to the level of domestic
investment in SSA
(iii) Investigate the effects of remittances on trade balance in SSA.
14
1.5 Statement of Research Hypotheses
The following testable hypotheses which are implied in the research questions
are considered appropriate for this study and are therefore subjected to
empirical investigation. These hypotheses are stated in their null context as
follows:
1. Workers’ remittances do not significantly promote economic growth in
selected SSA countries.
2. Workers’ remittances do not significantly impact positively on domestic
investment in selected SSA countries.
3. Workers’ remittances inflow has no significant impact on foreign trade
balance in the selected SSA countries.
1.6 Scope of the Study
The study employs data covering a period of eight years (2000-2007). The
choice of this period is explained by the availability of data across the selected
countries as well as the fact of a dramatic rise in recorded remittance flows to
the region over this period. The study is limited to the twenty one SSA
countries that reported inward remittances receipts for the period- 2000 and
2007. These countries are:
Benin, Botswana, Cameroon, Cape Verde, Djibouti, Ethiopia, Gabon, Ghana,
Guinea, Kenya, Lesotho, Malawi, Mali, Namibia, Niger, Nigeria, Senegal,
Seychelles, Sierra Leone, Togo, Uganda.
Remittance flows will be restricted to inter-household unilateral and unrequited
transfer of cash earnings, meaning that such transfer is void of any form of
quid pro quo terms, across national boundaries only. The implication is that
remittances in forms of material transfers by migrant workers to their home
15
countries, compensation of employees, or unrequited inter-household cash
transfers within each economy under investigation, are not covered in this
study. It is important to clarify here that the study is restricted to the
macroeconomic impact of remittances on the receiving economies and not on
their microeconomic impact.
1.7 Justification of the Study
A common theme motivating much of the research on remittances is the better
understanding of their role as promoter of economic growth and
development. This also includes the question of how remittances flows
can be channeled into productive investments by appropriate policies. Black
(2003) noted that despite the glaring evidence on the extent of the flow of
remittances, gaps still remain in the understanding of how remittances are or
can be used to promote growth and development, especially given that existing
policy incentives are not generally considered as having been very effective in
channeling remittances towards economic growth. The study is considered
important to SSA countries in several ways as follows: the SSA region is
widely regarded least among remittances recipients in the world. A good
knowledge of the growth and developmental role of remittances will help
encourage regional and national policies that will further boost the inflow of
this very important source of foreign exchange to the region. In other words,
this study helps policymakers in the various SSA countries to better understand
the phenomenon of remittances flows to the region and how best to manipulate
related policies to optimize these flows. This hopefully will help loosen the
foreign exchange constraint that has so far weakened the capacity of most of
these African economies to operate effectively in the international market.
16
The literature on remittances is replete with inadequacies regarding an
appropriate measure of remittances. Many researchers make use of an
aggregate measure of remittances and this at best exhibit characteristics that
are different from those which they intend to study. According to Chami et al
(2008), the category ‘workers’ remittances’ in the balance of payments best
represents what economists have in mind when modeling remittances. The
properties of this series differ significantly from those of ‘employee
compensation’ and ‘migrants’ transfers’, so combining these three items into a
single measure of remittances, as is common practice in the literature, can lead
to invalid conclusions about the properties of remittances and, in turn,
suboptimal policy decisions. Again, effort is made in this study to correct this
inadequacy by isolating data on workers’ remittances from the aggregate
measure commonly used in the remittances literature.
The resource-gap syndrome is more pronounced in SSA countries than
anywhere else in the world. As a consequence, the region is often not able to
meet up with its foreign exchange requirements for imports. A stable
remittances inflow can reasonably fill the foreign exchange gap in SSA. There
is however the need to properly channel remittances into growth and
development. The overall understanding of remittances and economic
development is inadequate given the importance of this economic
phenomenon. The debate on the growth and development impact of migrants’
remittances, which is based largely on evidence from proximate economies, is
rather inconclusive. Sub-Saharan Africa has unfortunately been grossly underresearched in this respect. Situating the SSA countries properly on the growth
and development impact of remittance inflows remains a major gap in the
literature. This study is an attempt to further close this identified gap. The
study therefore is a contribution to the inconclusive debate on the growth and
17
developmental role of workers’ remittances and it provides empirical evidence
based on data from Sub-Saharan Africa which hopefully will further clarify the
issues.
1.8 Structure of the Study
The study is divided into six chapters. The first chapter deals with general
introduction, and the second chapter focuses on patterns of economic growth,
investment, foreign trade and remittances in SSA. The third chapter is the
review of the theoretical literature, the empirical literature, and methodological
issues in the literature. The fourth chapter comprises of the theoretical
framework and methodology. The fifth chapter is model estimation and
analysis of results. Chapter six comprises of the summary of findings,
recommendations, conclusion, as well as limitations of the study and
suggestions for further research.
18
CHAPTER TWO
PATTERNS AND TRENDS OF REMITTANCES AND ECONOMIC
GROWTH IN SSA
2.0 Introduction
This chapter provides background information on patterns and trend of
economic growth, investment, trade and remittance flows to SSA. The focus
here is to determine the existence of any pattern, distribution and trend in the
identified variables that characterize the SSA region. Such characterization
helps in the identification of necessary links among the variables of interest
within the SSA economies. The chapter is also aimed at helping the reader
form expectations on the various relationships among the variables of choice
and across the study group.
2.1 Patterns and Trends of Economic Growth in SSA
Recent trends in growth rates in SSA suggest that a large majority of the
countries in the region experienced significant improvements in their overall
growth performance since year 2000. However, growth performance across the
SSA countries selected for this study exhibits substantial disparities over this
period. Economic growth rates for each of the sampled countries and for each
of the years within the scope of this study are presented in table 2.1. These
values are compared using the average values for SSA as a benchmark value
for each year.
19
Table 2.1: GDP Growth Rate in Selected SSA Countries
Country/Year
2000
SSA
4
Benin
6
Percent of SSA (%) 150
Botswana
8
Percent of SSA (%) 200
Cameroon
4
Percent of SSA (%) 100
Cape Verde
7
Percent of SSA (%) 175
Djibouti
0
Percent of SSA (%)
0
Ethiopia
6
Percent of SSA (%) 150
Gabon
-2
Percent of SSA (%) -50
Ghana
4
Percent of SSA (%) 100
Guinea
2
Percent of SSA (%)
50
Kenya
1
Percent of SSA (%)
25
Lesotho
5
Percent of SSA (%) 125
Malawi
2
Percent of SSA (%)
50
Mali
3
Percent of SSA (%)
75
Namibia
3
Percent of SSA (%)
75
Niger
-1
Percent of SSA (%) -25
Nigeria
5
Percent of SSA (%) 125
Senegal
3
Percent of SSA (%)
75
Seychelles
4
Percent of SSA (%) 100
Sierra Leone
4
Percent of SSA (%) 100
Togo
-1
Percent of SSA (%) -25
Uganda
6
Percent of SSA (%) 150
2001
4
5
125
5
125
5
125
4
100
2
50
8
200
2
50
4
100
4
100
4
100
3
75
-5
-125
12
300
1
25
7
175
3
75
5
125
-2
-50
18
450
0
0
5
125
2002
3
5
167
3
100
4
133
5
167
3
100
2
67
0
0
4
133
4
133
1
33
2
67
-4
-133
4
133
5
167
3
100
2
67
1
33
1
25
27
900
4
133
6
200
2003
4
4
100
6
150
4
100
6
150
3
75
-2
-50
2
50
5
125
2
50
3
75
4
100
6
150
7
175
4
100
4
100
10
250
7
175
-6
-150
9
225
3
75
6
150
2004 2005
6
6
3
3
50
50
7
5
117
83
4
2
67
33
-1
7
-16.7 117
4
3
67
50
14
12
233
200
1
3
16.7
50
6
6
100
100
3
3
50
50
5
6
83
100
5
1
83 16.7
6
3
100
50
2
6
33
100
12
3
200
50
-1
7
-16.7 117
11
5
183
83
6
6
100
100
-3
7
-50
117
8
7
133
117
3
1
50 16.7
7
6
117
100
2006
6
4
67
3
50
3
50
11
183
4
67
11
183
1
16.7
6
100
2
33
6
100
8
133
8
133
5
83
7
117
6
100
6
100
2
33
8
133
7
117
4
67
11
183
2007
6
5
83
4
67
3
50
7
117
4
67
11
183
6
100
6
100
2
33
7
117
5
83
9
150
3
50
4
67
3
50
6
100
5
83
7
117
7
117
2
33
9
150
Source: Author’s calculation based on data from World Bank, Africa Development
Indicators online
20
Overall, SSA recorded a 4 percent growth rate in 2000, 2001 and 2003. The
lowest growth rate of 3 percent was recorded for the region in 2002. Growth
rate rose in 2004 to 6 percent for the region and this was sustained till 2007.
For the year 2000, Botswana recorded the highest growth rate of 8 percent
representing 200 percent of SSA average for that year. In sharp contrast,
Gabon, Niger, and Togo all reported negative growth rates for the same year.
Gabon however had the worst growth rate of -2 percent which represented -50
percent of the SSA average for that year. The year 2001 was by no means less
dramatic in terms of recorded GDP growth rates for the selected SSA
countries. For example, Sierra Leone just recovering from long years of civil
war topped the study group at 18 percent growth rate. This figure represented
450 percent of the average growth rate for SSA in that year. Malawi and
Seychelles reported negative GDP growth rates for 2001. But Malawi was at
the bottom as she had a negative growth rate of -5 percent which was -125
percent of SSA average for the year.
Sierra Leone continued to be the best performer in 2002 among the study
group as the country again recorded a spectacular growth rate figure of 27
percent representing 900 percent of SSA average for that year. Malawi again
was in the negative region with a -4 percent GDP growth rate for the year 2002
which represented -133 percent of the SSA average for the year. Surprisingly,
Malawi was the only country within the study group that actually reported a
deceleration in GDP growth rate for year 2002. Nigeria was at the top in the
year 2003 with a GDP growth rate of 10 percent representing 250 percent of
the SSA average for that year. Seychelles came out worst performer in 2003
with a recorded deceleration of GDP growth rate of -6 percent representing 150 percent of the SSA average for the year.
21
The impressive performances of majority of the SSA economies continued in
2004 with Ethiopia taking the lead position for the year. The Ethiopian
economy grew at 14 percent representing 233 percent of the SSA average for
the year. Seychelles unfortunately could not catch up with the momentum of
growth across the region as the country again was confined to the bottom
position with a recorded negative GDP growth rate of -3 percent which
represented -50 percent of the SSA average for that year. Economic growth
figures for year 2005 revealed impressive economic performances across the
sampled SSA countries. The Ethiopian economy was again in the lead with a
growth rate of 12 percent representing 200 percent of the SSA average for the
year. Lesotho and Togo on the other hand, trailed every other country within
the sampled group as each of these countries recorded a growth rate of 1
percent in 2005. This represented 16.7 percent of the SSA average for the year.
In 2006, three countries, Cape Verde, Ethiopia and Uganda tied in the lead
with each recording a growth rate of 11 percent for the year. This value stood
at 183 percent of the average growth rate for the SSA region in 2006. At the
bottom was Gabon with a 1 percent growth rate for 2006 which represented a
meager 16.7 percent of the SSA average for the same year. In 2007, the
Ethiopian economy maintained its leading role at 11 percent growth rate and
this amounted to 183 percent of the SSA average economic performance for
the year. During the same year, Guinea and Togo tied in the bottom position as
each of the two countries recorded a 2 percent growth rate representing 33
percent of the SSA average for the year.
A quick remark here is to observe that on the average, economic performance
remained robust in SSA over the study period. In view of this fact, Rena
(2008) pointed out that growth in most of SSA was driven essentially by
production and exports of primary commodities. This unfortunately exposes
22
the continent to external shocks which consequently compels growth policies
that encourages economic diversification in the continent. Moreover, it is also
noted here that those economies that initially recorded negative growth rates
began to pick up by year 2005 and no SSA economy within the study group
reported a negative growth rate between 2005 and 2007.
2.2 Patterns and Trends of Domestic Investment in SSA
The difficulties in raising domestic savings to support rapid capital
accumulation and growth account for the inability of SSA to provide the basic
needs for their population. Within sustainable growth framework, appropriate
policies are needed especially in raising saving rate. In some countries,
sizeable increases in domestic savings cannot be expected to take place as a
pre-condition for acceleration of investment and growth, (United Nations
Conference on Trade and Development (UNCTAD), 2001).
Capital accumulation is very vital for a sustainable process of economic
growth. It is note-worthy that though considerable productivity gains could be
attained by more intensive and efficient use of existing resources, such gains
would be one-off and may not lead to rapid and sustained growth unless
translated into investment in productive capacity, including physical and
human infrastructure. Every economy (including those of SSA) therefore
makes investment in productive capacity a major policy goal for all time. Table
2.2 captures the trend of domestic investment in SSA between the periods 2002
and 2007.
23
Table 2.2: Domestic Investment in Selected SSA Countries (US$’ billion)
Country/Year
2000 2001 2002 2003 2004 2005 2006
2007
SSA
58.07 57.08 58.67 78.64 98.63 115.22 138.46 168.64
Benin
0.43 0.46 0.50 0.67 0.74 0.84 0.00 0.00
Percent of SSA (%) 0.74 0.81 0.85 0.85 0.75 0.73
0.00 0.00
Botswana
2.16 2.40 2.42 3.46 4.01 3.70 3.30 5.01
Percent of SSA (%) 3.72 4.20 4.12 4.40 4.07 3.21
2.38 2.97
Cameroon
1.68 1.95 2.15 2.38 2.98 3.16 3.02 3.58
Percent of SSA (%) 2.89 3.42 3.66 3.03 3.02 2.74
2.18 2.12
Cape Verde
0.10 0.10 0.13 0.15 0.35 0.37 0.45 0.58
Percent of SSA (%) 0.17 0.18 0.22 0.19 0.35 0.32
0.33 0.34
Djibouti
0.05 0.05 0.06 0.09 0.14 0.13 0.23 0.32
Percent of SSA (%) 0.09 0.09 0.10 0.11 0.14 0.11
0.17 0.19
Ethiopia
1.66 1.75 1.86 1.87 2.56 2.83 3.67 4.84
Percent of SSA (%) 2.86 3.07 3.17 2.38 2.60 2.46
2.65 2.87
Gabon
1.11 1.21 1.21 1.45 1.75 1.85 2.34 3.03
Percent of SSA (%) 1.91 2.12 2.06 1.84 1.77 1.61
1.69 1.80
Ghana
1.19 1.41 1.21 1.75 2.52 3.21 3.87 5.10
Percent of SSA (%) 2.05 2.47 2.06 2.23 2.56 2.79
2.80 3.02
Guinea
0.61 0.47 0.43 0.37 0.45 0.46 0.43 0.58
Percent of SSA (%) 1.05 0.82 0.73 0.47 0.46 0.40
0.31 0.34
Kenya
2.21 2.44 1.99 2.46 2.75 3.17 4.04 5.44
Percent of SSA (%) 3.81 4.27 3.39 3.13 2.79 2.75
2.92 3.23
Lesotho
0.40 0.33 0.30 0.32 0.40 0.40 0.38 0.44
Percent of SSA (%) 0.69 0.58 0.51 0.41 0.41 0.35
0.27 0.26
Malawi
0.24 0.26 0.00 0.44 0.53 0.67 0.72 0.93
Percent of SSA (%) 0.41 0.46 0.00 0.56 0.54 0.58
0.52 0.55
Mali
0.60 0.82 0.62 1.06 1.02 1.20 1.34 1.60
Percent of SSA (%) 1.03 1.44 1.06 1.35 1.03 1.04
0.97 0.95
Namibia
0.67 0.79 0.62 0.96 1.26 1.43 1.82 1.83
Percent of SSA (%) 1.15 1.38 1.06 1.22 1.28 1.24
1.31 1.09
Niger
0.21 0.24 0.31 0.39 0.42 0.77 0.00 0.00
Percent of SSA (%) 0.36 0.42 0.53 0.50 0.43 0.67
0.00 0.00
Nigeria
6.78 7.26 7.65 9.29 11.0 13.0 15.7 19.9
Percent of SSA (%) 11.6 12.7 13.0 11.8 11.1
2 11.3
5 11.3
1 11.8
5
Senegal
0.96
0.90
0.92
1.43
1.67
2.58
2.64
8
2
4
1
7
3
5 3.49
3
Percent of SSA (%) 1.65 1.58 1.57 1.82 1.69 2.24
1.91 2.07
Seychelles
0.15 0.25 0.18 0.07 0.09 0.22 0.25 0.30
Percent of SSA (%) 0.26 0.44 0.31 0.09 0.09 0.19
0.18 0.18
Sierra Leone
0.04 0.05 0.09 0.14 0.11 0.21 0.22 0.22
Percent of SSA (%) 0.07 0.09 0.15 0.18 0.11 0.18
0.16 0.13
Togo
0.24 0.27 0.27 0.33 0.37 0.39 0.00 0.00
Percent of SSA (%) 0.41 0.47 0.46 0.42 0.38 0.34
0.00 0.00
Uganda
1.21 1.13 1.25 1.39 1.60 2.07 2.11 2.63
Percent of SSA (%) 2.08 1.98 2.13 1.77 1.62 1.80
1.52 1.56
Source: Author’s calculation based on data from World Bank, Africa Development
Indicators online
24
Sub-Saharan Africa recorded some sizeable domestic investment between
2000 and 2007 as shown in table 2.2. The figures ranged from approximately
US$58.07 billion in 2000 to US$168.64 billion in 2007 representing a change
in domestic investment level of about 190.4 percent. However, another look at
individual country investment levels for each year reveals that improvement in
private investment was driven mainly by some few countries within the
sampled group. Notable among these countries (in alphabetic order) were
Botswana, Cameroon, Ethiopia, Gabon, Ghana, Kenya and Nigeria.
Nigeria for instance remained on top of all other economies within the study
group throughout the study period. This is in terms of capacity of the economy
to mobilize private investment internally. The country recorded a total of
US$6.78 billion domestic investment in year 2000 and this represented about
11.6 percent of total domestic investment in SSA for that year. This figure
steadily grew to US$7.26 billion representing 12.7 percent of SSA average in
2001, US$7.65 billion representing 13.0 percent of SSA average in 2002,
US$9.29 billion representing 11.0 percent of SSA average in 2003, US$11.0
billion representing 11.1 percent of SSA average in 2004, US$13.0 billion
representing 11.3 percent of SSA average in 2005, US$19.9 billion
representing 11.3 percent of SSA average in 2006, and US$7.26 billion
representing 11.8 percent of SSA average in 2007.
Sierra Leone appeared to be in the rear for the greater part of the period under
review. This of course is not surprising considering the fact that this country is
just recovering from a civil war that lasted for many years. What is surprising
is the high economic growth rate recorded by the country during the same
period covered by this study. The question here is what could have driven this
growth outside of domestic investment in the economy? Precisely, domestic
investment figures for Sierra Leone range from US$0.04 billion in 2000, to
25
US$0.05 billion in 2001, US$0.09 billion in 2002, US$0.14 billion in 2003,
US$0.11 billion in 2004, US$0.21 billion in 2005, US$0.22 billion in 2006,
and US$0.22 billion in 2007. These figures represented 0.07 percent, 0.09
percent, 0.15 percent, 0.18 percent, 0.11 percent, 0.18 percent, 0.16 percent,
and 0.13 percent of the SSA average for the years 2000, 2001, 2002, 2003,
2004, 2005, 2006 and 2007 respectively.
A good number of the selected SSA countries never recorded up to US$1.00
billion for any year during the study period. Though most of these economies
are small by most standards, it is equally disturbing that private investment
drive does not occupy any priority place in these countries. These details are
also indicative of the predicament of resource gap among SSA economies and
probably policy misdirection for the region in its drive for sustainable growth
through investment in productive capacity.
2.3 Patterns and Trends of Foreign Trade in SSA
Export growth supports investment because it helps to earn foreign exchange
needed for capital goods imports and advanced technology. Investment
supports exports by providing the basis for productivity growth and increased
competitiveness. Investment also allows for production to be shifted towards
products with high income elasticity, thereby helping to avert terms of trade
losses. Successful examples of industrialization and growth are thus
underpinned by rising rates of savings, investment and exports. While African
countries have in the past experienced surges of investment and growth, they
have not in general been able to establish a virtuous circle of investment,
savings and exports (UNCTAD, 2001). Pattern of trade are captured for all
countries within the study by each country’s real external balance and this can
be seen in table 2.3.
26
Table 2.3: Real External Balance in Selected SSA Countries (US$’ billions)
Country/Year
SSA
Benin
Percent of SSA (%)
Botswana
Percent of SSA (%)
Cameroon
Percent of SSA (%)
Cape Verde
Percent of SSA (%)
Djibouti
Percent of SSA (%)
Ethiopia
Percent of SSA (%)
Gabon
Percent of SSA (%)
Ghana
Percent of SSA (%)
Guinea
Percent of SSA (%)
Kenya
Percent of SSA (%)
Lesotho
Percent of SSA (%)
Malawi
Percent of SSA (%)
Mali
Percent of SSA (%)
Namibia
Percent of SSA (%)
Niger
Percent of SSA (%)
Nigeria
Percent of SSA (%)
Senegal
Percent of SSA (%)
Seychelles
Percent of SSA (%)
Sierra Leone
Percent of SSA (%)
Togo
Percent of SSA (%)
Uganda
Percent of SSA (%)
2000
2001
2002
2003
2004
2005
2006
2007
10.28 1.30 -3.65 -4.17 3.95 13.76 21.26 13.65
-0.29 -0.30 -0.39 -0.46 -0.52 -0.54 0.00 0.00
-2.84 -23.12 10.71 10.96 -13.06 -3.94 0.00 0.00
1.17
0.98 0.68 0.72 0.98 1.81 2.47 1.28
11.3 74.79 -18.65 -17.20 24.7 13.1 11.6 9.42
0.36
8 -0.12 -0.09 0.04 -0.07
4 -0.17
7 0.37
3 0.17
3.52 -9.53
2.33 -1.07 -1.72 -1.23 1.73 1.23
-0.18 -0.18 -0.23 -0.28 -0.36 -0.33 -0.39 -0.51
-1.75 -14.09 6.17 6.60 -9.11 -2.39 -1.86 -3.71
-0.08 -0.05 -0.03 -0.06 -0.11 -0.07 -0.13 -0.17
-0.82 -3.72 0.84 1.37 -2.90 -0.53 -0.63 -1.25
-0.98 -0.96 -1.09 -1.21 -1.68 -2.51 -3.44 -3.77
-9.50 -73.47 29.85 28.92 -42.45 -18.23 -16.19 -27.60
1.84
1.22 0.95 1.47 2.17 3.21 3.14 3.33
17.93 93.94 -25.94 -35.21 54.86 23.33 14.75 24.43
-0.92 -1.04 -0.75 -1.21 -1.87 -2.68 -3.14 -3.96
-8.93 -79.73 20.66 29.12 -47.32 -19.52 -14.78 -29.04
-0.13 -0.04 -0.13 -0.09 -0.16 -0.09 -0.09 -0.10
-1.29 -3.11 3.48 2.05 -3.97 -0.64 -0.42 -0.75
-1.29 -1.31 -0.71 -0.89 -1.01 -1.40 -2.22 -3.02
-12.51 -100.44 19.35 21.29 -25.50 -10.16 -10.46 -22.14
-0.52 -0.41 -0.42 -0.55 -0.63 -0.71 -0.70 -0.83
-5.02 -31.13 11.45 13.25 -15.94 -5.15 -3.31 -6.10
-0.17 -0.19 -0.66 -0.53 -0.48 -0.70 -0.76 -0.75
-1.65 -14.66 18.14 12.65 -12.12 -5.09 -3.56 -5.51
-0.31 -0.45 -0.25 -0.48 -0.60 -0.62 -0.48 -0.67
-2.97 -34.23 6.72 11.44 -15.29 -4.51 -2.24 -4.92
-0.14 -0.23 -0.07 -0.45 -0.15 0.01 0.31 -0.16
-1.40 -17.68 1.91 10.75 -3.79 0.07 1.46 -1.20
-0.14 -0.15 -0.19 -0.25 -0.30 -0.31 0.00 0.00
-1.38 -11.48 5.29 6.00 -7.69 -2.27 0.00 0.00
10.09 5.14
-0.41 1.53 11.33 17.39 22.12 17.42
98.21 394.12 11.10 -36.71 286.83 126.41 104.05 127.68
-0.44 -0.44 -0.56 -0.83 -1.04 -1.35 -1.64 -2.53
-4.24 -33.81 15.21 19.92 -26.29 -9.84 -7.70 -18.55
-0.02 -0.13 -0.01 0.08 0.01 -0.19 -0.17 -0.32
-0.20 -10.25 0.23 -1.89 0.35 -1.39 -0.82 -2.35
-0.13 -0.15 -0.17 -0.17 -0.12 -0.16 -0.11 -0.12
-1.31 -11.27 4.69 4.18 -3.03 -1.16 -0.51 -0.90
-0.27 -0.26 -0.26 -0.24 -0.28 -0.36 -0.43 -0.51
-2.58 -19.78 7.25 5.72 -7.03 -2.59 -2.04 -3.76
-0.73 -0.74 -0.88 -0.94 -0.83 -1.02 -1.35 -1.65
-7.13 -56.91 24.15 22.57 -21.03 -7.43 -6.37 -12.08
Source: Author’s calculation based on data from World Bank, Africa Development
Indicators online
27
Table 2.3 shows the real external balance on goods and services for all selected
SSA countries for this study. With the exception of Botswana, Gabon and
Nigeria, none of these countries performed impressively well as they all
remained in the negative region for most years within the study period. On the
average, SSA also performed well having negative entries for just two (2002
and 2003) of eight years covered by this study. The positive outlook of real
external balance for the overall SSA economy is undoubtedly as a result of the
overwhelming size of the Nigerian economy within the region.
A comparism of the figures in Table 2.3 reveals the predominance of the
Nigerian economy throughout the period under review. In a number of cases,
the real external balance (REB) for the country was higher than the net figures
for SSA as a region. In year 2000, REB for the country stood at US$10.09
billion or 98.21 percent of the net value for SSA. The year 2001 was US$5.14
billion or 394 percent of the net value for SSA. Year 2002 figures were in the
negative for the country at -US$0.41billion, but the economy still stood above
the SSA average at 11.1 percent. Interestingly, REB for all the selected SSA
countries (except Botswana) were the negative for this year meaning that it
was a particularly bad year for trade in the region. Nigeria’s REB picked again
in 2003 standing US$1.53billion, US$11.33 billion in 2004, US$17.39 billion
in 2005, US$22.12 billion in 2006, and US$17.42 billion in 2007. These
figures represented -36.71 percent, 286.83 percent, 126.41 percent, 104.05
percent, and 127.68 percent of the net REB for SSA in the years 2003, 2004,
2005, 2006 and 2007 respectively.
Benin, Cape Verde, Djibouti, Ethiopia, Ghana, Guinea, Kenya, Lesotho,
Malawi, Mali, Niger, Senegal, Sierra Leone, Togo and Uganda all had negative
real external balance figures for all the years covered by this study. What this
means is that each of these countries simply imported much more than they
28
exported during each year throughout the period under review. Again this is
indicative of the poor health of most Sub-Saharan African economies.
2.4 Patterns and Trends of Workers’ Remittances flow to SSA
Remittances flows are important and stable source of external finance for many
countries and constitute a substantial part of financial inflows for countries
with a large migrant labour force. Officially recorded remittances received by
developing countries are estimated to have exceeded US$93 billion in 2003
and have since increased dramatically totaling an estimated US$167 billion in
2005, according to World Bank (2006) estimates. Remittance flows to SSA
region have grown steadily from US$4.62billion in 2000 to US$4.66billion in
2001. The figures stood at US$5.03billion in 2002 and US$6.00billion in 2003.
It rose to US$8.05billion and US$9.41billion in 2005. And finally remittance
flows to SSA further rose to US$12.6billion in 2006 and US$18.6billion in
2007.
The explanations for this dramatic rise in remittance flows to SSA are quite
obvious. First, remittances through informal channels are being subjected to
greater scrutiny since the events of September 11, 2001. The discovery of the
large size of these flows has prompted governments worldwide to improve the
recording efforts. Second, reduction in remittance costs and expansion of
remittance networks have increased migrants’ disposable incomes and their
incentives to remit. Third, the depreciation of the U.S. dollar has raised the
value of remittances from Europe and Japan. The appreciation of the Euro
relative to the U.S. dollar may account for some 7 percent of the increase in
remittances to developing countries during 2001–2005 (Mohapatra and others,
2006). Finally, growth in migrant stocks (due to falling travel costs and
29
increased globalization) and an increase in migrant incomes have also
contributed to higher remittances. Table 2.4 provides details of remittance
flows to Sub-Saharan Africa and other developing countries.
30
Table 2.4: Remittance Flows to Selected SSA Countries, (US$ billions)
Country/Year
SSA
Benin
Percent of SSA (%)
Botswana
Percent of SSA (%)
Cameroon
Percent of SSA (%)
Cape Verde
Percent of SSA (%)
Djibouti
Percent of SSA (%)
Ethiopia
Percent of SSA (%)
Gabon
Percent of SSA (%)
Ghana
Percent of SSA (%)
Guinea
Percent of SSA (%)
Kenya
Percent of SSA (%)
Lesotho
Percent of SSA (%)
Malawi
Percent of SSA (%)
Mali
Percent of SSA (%)
Namibia
Percent of SSA (%)
Niger
Percent of SSA (%)
Nigeria
Percent of SSA (%)
Senegal
Percent of SSA (%)
Seychelles
Percent of SSA (%)
Sierra Leone
Percent of SSA (%)
Togo
Percent of SSA (%)
Uganda
Percent of SSA (%)
2000
2001
2002
2003
2004
2005
2006
2007
4.62
0.08
1.74
0.00
0.01
0.01
0.26
0.09
1.85
0.001
0.02
0.05
1.15
0.002
0.05
0.03
0.70
0.00
0.03
0.05
1.10
0.00
0.00
0.04
0.89
0.07
1.50
0.00
0.10
0.00
0.10
1.39
30.1
0.18
1
3.88
0.003
0.06
0.01
0.15
0.02
0.34
0.24
5.15
4.66
0.08
1.67
0.00
0.01
0.01
0.15
0.08
1.71
0.001
0.02
0.02
0.39
0.001
0.03
0.05
0.98
0.01
0.19
0.05
1.09
0.00
0.03
0.04
0.88
0.08
1.76
0.00
0.08
0.01
0.30
1.17
25.02
0.26
5.57
0.002
0.03
0.01
0.13
0.05
1.11
0.34
7.34
5.03
0.07
1.39
0.00
0.00
0.01
0.28
0.08
1.68
0.001
0.02
0.03
0.66
0.001
0.02
0.04
0.87
0.02
0.30
0.06
1.14
0.01
0.19
0.03
0.58
0.13
2.51
0.00
0.06
0.01
0.17
1.21
24.0
0.30
3
5.90
0.002
0.04
0.01
0.15
0.09
1.72
0.42
8.36
6.00
0.05
0.83
0.00
0.00
0.06
1.01
0.11
1.81
0.003
0.05
0.05
0.78
0.004
0.06
0.07
1.09
0.11
1.85
0.07
1.10
0.01
0.19
0.03
0.50
0.14
2.32
0.00
0.08
0.01
0.19
1.06
17.7
0.45
3
7.48
0.005
0.08
0.03
0.43
0.13
2.14
0.31
5.11
8.05
0.05
0.67
0.05
0.63
0.10
1.22
0.11
1.40
0.003
0.04
0.13
1.66
0.001
0.02
0.08
1.02
0.04
0.52
0.38
4.67
0.01
0.18
0.00
0.03
0.14
1.72
0.01
0.07
0.04
0.53
2.27
28.2
0.56
5
7.00
0.007
0.08
0.02
0.31
0.15
1.91
0.31
3.86
9.41
0.14
1.46
0.08
0.88
0.07
0.71
0.14
1.45
0.003
0.03
0.17
1.84
0.001
0.02
0.10
1.05
0.04
0.44
0.42
4.52
0.01
0.07
0.01
0.06
0.15
1.63
0.01
0.08
0.05
0.48
3.33
35.3
0.72
9
7.62
0.012
0.13
0.00
0.02
0.16
1.75
0.32
3.42
12.6
0.19
5
1.47
0.08
0.62
0.12
0.93
0.14
1.07
0.004
0.03
0.17
1.34
0.001
0.01
0.11
0.83
0.04
0.33
0.57
4.51
0.00
0.04
0.02
0.13
0.19
1.52
0.01
0.05
0.05
0.39
3.33
26.3
0.85
2
6.73
0.013
0.10
0.05
0.37
0.20
1.58
0.41
3.25
18.6
0.19
2
1.00
0.08
0.43
0.15
0.83
0.14
0.74
0.004
0.02
0.36
1.91
0.001
0.01
0.12
0.63
0.02
0.08
0.65
3.47
0.01
0.07
0.03
0.17
0.32
1.74
0.01
0.03
0.05
0.26
1.79
9.64
1.11
5.95
0.011
0.06
0.15
0.79
0.20
1.07
0.45
2.43
Source: Author’s calculation based on data from World Bank, Africa Development
Indicators online
31
A breakdown of the SSA remittance figures in Table 2.4 shows that flows to
Nigeria top the list of recipients within the study group throughout the study
period. The country recorded a total of about US$1.39billion in 2000,
US$1.17billion in 2001, US$1.21billion in 2002, US$1.06billion in 2003,
US$2.27billion in 2004, US$3.33billion in 2005, US$3.33billion in 2006 and
US$1.79billion in 2007. These figures represent 30.1 percent, 25.02 percent,
24.0 percent, 17.7 percent, 28.2 percent, 35.3 percent, and 9.64 percent of the
SSA total for the years 2000, 2001, 2002, 2003, 2004, 2005, 2006 and 2007
respectively.
Remittance flows to most of the countries covered in this study are actually
very small in when compared to the big recipients such as Nigeria. A further
examination of figures in table 2.4 reveals that a number of these countries
never received up to 1 percent of the recorded remittance flows to SSA at any
given year throughout the study period. Included in this group are: Botswana,
Djibouti, Gabon, Lesotho, Malawi, Namibia, Niger, Seychelles and Sierra
Leone. Despite the small amount flowing to these countries over the years, it is
often not surprising to see that these flows are quite significant when measured
as a ratio to receiving country’s Gross Domestic Product (GDP). Such realities
provide the necessary impetus to encourage remittance flows with relevant
policy measures in these SSA countries.
2.5 Trends in Workers’ Remittances and Growth Indicators in SSA
Workers’ remittances may exhibit trends and patterns with key development
and economic growth indicators such as output growth, investment and foreign
trade or real external balance. Such trends can help in predicting the path or
direction of any of these variables and this in turn can be a useful guide in
32
appropriate policy formulation. Figure 2.1 below shows trends in workers’
remittances, economic growth, investment and real external balance in SSA
between 2000 and 2007.
US$' Billions
Figure 2.1: Trends in Workers’ Remittances and Selected Economic Growth
Indicators in SSA
1000
900
800
700
600
GDP
500
INV
400
REB
300
Remittances
200
100
0
-100
2000 2001 2002 2003 2004 2005 2006 2007
Source: Plotted by author based on data from World Bank, Africa Development Indicators
online
Figure 2.1 suggests that investment and economic growth in SSA countries
have similar patterns of growth over the study period. A similar behavioural
pattern cannot be concluded for remittances on the one hand, and domestic
investment or economic growth on the other hand. No single pattern between
33
real external balance and the other three variables is observed throughout the
period covered by the study. These results are however not sufficient to
prescribe any policy direction for the SSA economies as they only indicate
some patterns in behaviour of the selected variables over time. It will require a
cause-effect analysis to determine the exact nature of relationships among
these variables.
2.6 Sources and Destination of Remittance Flows
Remittances flow to Africa represents the least in terms of relative share of
flows to the different regions of the world. Table 2.5 below provides estimates
of the regional distribution of remittances flow by sources and destination in
year 2000.
Table 2.5: Estimated flows of remittances by region, 2000. US$ billions
Sending Region
Receiving Region
Latin
America & North
Africa Asia Europe Caribbean America
Africa
0.5
0.1
0.0
0.0
3.7
Asia
3.4
3.4
0.5
0.2
31.5
Europe
2.6
3.2
0.4
0.4
9.5
Latin America & Caribbean
0.0
0.1
0.6
0.1
1.1
North America
0.7
7.9
5.7
14.2
0.9
Oceania
0.0
0.2
0.4
0.0
0.0
Total
10.4
43.4
19.6
16.2
1.6
Oceania
0.0
0.0
0.1
0.0
0.1
0.1
0.3
Bold figures indicate flows between countries in the same region.
Source: Harrison (2004). Adapted from Carling (2005)
Evidence from the above table reveals that about one third of global
remittances are estimated to flow between Asian countries. This places the
Asian region on top of all other regions in terms of intra-regional remittance
flows. Within Europe, intra-regional remittances flow is also quite substantial
34
Total
4.2
39.0
16.2
1.8
29.6
0.8
91.5
making this region the second largest. When inter-regional flows are
considered, North America to Latin America and the Caribbean top the list
while North America to Asia follows. Table 2.5 also shows that African
countries receive more remittances from elsewhere in Africa than they do from
other continents. However, the largest inter-continental sources are from Asia,
Europe and North America in that order. The relative dominance of Asia as
number one source region of remittance flow to Africa has since changed in
favour of North America. Fadayomi (2009: 15) stated that “almost ¾ of
remittances to Sub-Saharan Africa in 2007 were sent from the United States
and Western Europe, while the rest were sent Gulf States, other developed
countries and developing countries”.
2.7 Country Level Analysis of Distribution of Remittance Flows to SSA
At the country level, distributions of remittance flows to SSA countries are not
easily determined owing to the non-existent or scanty nature of available data.
In terms of volume and value of remittance flows to SSA, evidence from
available data show that no sub-region in SSA is left out from remittance
flows. However, the West and East African sub-regions dominate in terms of
concentration of remittance inflows while Central and Southern African subregions are barely represented with two and three countries respectively
reporting data on remittances for most of the periods covered in this study.
Details of volume and value of remittance flows to SSA by sub-region and by
country are presented in Table 2.6.
35
Table 2.6: Volume and Value of Remittance Flows to SSA by Sub-Region and by Country
Sub-region
Remittances
Sub-region
Remittances
and
and
US$ million
% of GDP
US$ million
% of GDP
Country
Country
2000 2006
2000 2006
2000
2006
2000 2006
Eastern Africa
Western Africa
Burundi
Benin*
80.48 186.19 3.57 4.03
Comoros
Burkina Faso 62.47
2.39
Djibouti*
0.72
3.66
0.13 0.48 Cape Verde* 85.69 135.83 16.13
11.30
Eritrea
Cote d’Ivoire
Ethiopia*
53.16 169.18 0.65 1.12 Gambia
62.87
12.38
Kenya*
584.85 570.46 4.61 2.54 Ghana*
32.40 105.25 0.65
0.83
Madagascar
Guinea*
1.17
41.64
0.04
1.30
Malawi*
3.62
17.17 0.21 0.54 Guinea
Bissau
Mauritius
Liberia
Mozambique
15.83
0.22 Mali*
69.18 192.73 2.86
3.29
Rwanda
3.62 17.17 0.21 0.61 Niger*
4.55
49.06
0.25
1.35
Seychelles* 2.98
13.08 0.49 1.35 Nigeria*
1391.79 3328.69 3.03
2.27
Somalia
Senegal*
179.2 850.58 3.82
9.08
2
Uganda*
238.10 411.00 3.84 4.13 Sierra
7.13
47.35
1.12
3.33
Leone*
Tanzania
8.99
0.06 Togo*
15.71 199.95 1.18
9.01
Central Africa
Southern Africa
Cameroon* 11.85 117.65 0.12 0.66 Angola
CA
Botswana*
0.35
78.74
0.01
0.72
Republic
Chad
Lesotho*
0.14
4.46
0.02
0.29
DR Congo
Namibia*
4.49
6.54
0.11
0.08
Eq. Guinea
South Africa
Gabon*
2.26
1.48
0.04 0.02 Swaziland
São Tomé
Zambia
and
Zimbabwe
Príncipe
Source: Author’s Computations based on Data from Africa Development Indicators online, 2010
*indicate countries included in this study
An effort to determine how much of remittance flows can be associated with
productive activities or economic growth in recipient economies of SSA
necessarily begins with the identification of top remittance recipients from the
36
four sub-regional blocks in SSA. Available data reveal that Nigeria tops in the
West African sub-region, Kenya tops in East Africa; Botswana tops the list in
Southern Africa sub-region and Cameroon occupies that position in the central
Africa sub-region. An annual classification (covering the study period, 2000 2007) of remittances and other major growth indicators for the identified top
remittance recipients are presented in Table 2.7.
37
Table 2.7: Major Growth Indicators of Sub-Regional Top Remittance
Recipients in SSA (US$’Million)
Country
year
2000
2001
Nigeria 2002
2003
(West
2004
Africa)
2005
2006
2007
2000
2001
Kenya 2002
(East 2003
2004
Africa)
2005
2006
2007
2000
2001
Botswana 2002
(Southern 2003
2004
Africa)
2005
2006
2007
2000
2001
Cameroon 2002
(Central 2003
2004
Africa)
2005
2006
2007
GDP
45983.6
47999.78
59116.85
67656.02
87845.42
112248.6
146869
165920.9
12691.28
12986.52
13149.26
14903.63
16091.63
18769.01
22478.65
26950.31
6177.184
6033.253
5933.281
8277.572
9827.417
10512.51
11006.46
12323.81
10075.04
9598.224
10879.78
13621.81
15775.36
16587.86
17956.99
20691.56
INV
9317.43
11563.31
12249.57
13910.33
16261.06
21071.68
25370.21
25370.21
2210.071
2440.211
1990.564
2456.439
2750.309
3169.203
4038.904
5437.966
2160.083
2397.494
2416.915
3455.007
4007.657
3699.524
3299.392
5010.711
1684.636
1949.526
2153.019
2383.246
2983.054
3163.024
3019.452
3582.002
REB
10092.99
5138.631
-405.57
1530.907
11326.93
17388.16
22123.33
17424.52
-1286.12
-1309.52
-707
-888.053
-1007.05
-1398
-2223
-3022
1169.572
975.1013
681.4528
717.251
976.8438
1811.447
2471.827
1284.973
361.8344
-124.231
-85.2043
44.62519
-67.8614
-168.54
368.2258
168.3954
WR
1391.79
1166.628
1208.94
1062.84
2272.701
3328.694
3328.694
17945.94
584.8543
50.91443
57.14348
65.8453
375.8113
424.991
570.4593
645.1811
0.352816
0.359518
0.015803
0.022223
50.82159
82.35632
78.74315
80.0393
11.84716
6.78832
14.14916
60.55743
98.38632
67.12822
117.648
154.0269
Source: World Bank, Africa Development Indicators online, 2010
38
An inspection of data in Table 2.7 reveals a steady rise in values of all
variables observed between 2000 and 2007. The only exception here has to do
with data on the variable - external balance which have mixed signs across
periods and countries. The observed relationships are further captured in
separate figures below for each of the top remittance recipients. This is to
allow for additional insights regarding the existence of any unique
characterization of remittance flows into SSA.
US$'Billions
Figure 2.2: Remittance Receipts and other Growth Indicators in Nigeria (2000 – 2007)
180
160
140
120
100
GDP
Investment
80
External Balance
60
Remittances
40
20
0
-20
2000 2001 2002 2003 2004 2005 2006 2007
Source: Plotted by author based on data from African Development Online, 2010
The volume of remittance flows to Nigeria continued to remain below the
investment and aggregate output (GDP) curves throughout the period under
review. However, remittances to Nigeria remained in the positive region and
39
exhibited an upward trend over time. Remittances flow to Nigeria continued to
grow as GDP and domestic investment rises. This pattern is particularly so
from the period 2004 and 2007. External balance, which captures the external
trade sector, failed to demonstrate a similar relationship with the other
variables over time. What is observed with this variable is a pattern of cycles
the investment and remittance curves throughout the study period.
The same set of variables is examined below in Figure 2.3 for Kenya which
represents the east African region. This is to verify whether a similar pattern of
behaviour exists among the four sub-regions.
US$'Billions
Figure 2.3: Remittance Receipts and other Growth Indicators in Kenya (2000 – 2007)
30
25
20
GDP
15
Investment
External Balance
10
Remittances
5
0
-5
2000 2001 2002 2003 2004 2005 2006 2007
Source: Plotted by author based on data from African Development Online, 2010
40
Relationships among the variables GDP, domestic investment, external balance
and remittance receipts for Kenya between 2000 and 2007 are shown in figure
2.3. One major contrasting observation in the figure from the case of Nigeria is
the behaviour of the variable, external balance which remained predominantly
in the negative region and maintains a downward trend throughout the study
period.
Remittances remain in the positive region throughout the study period but with
some downward trend observed between 2000 and 2003. Remittances however
rose sustainably between 2003 and 2007. Investment and GDP on the average
remained positive and upward sloping. One very interesting pattern noticed
here is the period 2003 upward. GDP, investment and remittances all exhibited
very similar swings during this period. This behaviour is quite similar to what
was noticed in the case of Nigeria. But can a similar relationship hold for
Botswana in southern Africa? Figure 2.4 below reveals the answer.
41
US$'Billions
Figure 2.4: Remittance Receipts and other Growth Indicators in Botswana (2000 –
2007)
14
12
10
GDP
8
Investment
External Balance
6
Remittances
4
2
0
2000 2001 2002 2003 2004 2005 2006 2007
Source: Plotted by author based on data from African Development Online, 2010
The case of Botswana appears quite interesting with all variables exhibiting a
marked difference from the case Kenya. Here, none of the variables appeared
in the negative region. Of particular note is the variable external balance which
is substantially positively sloped contrasting sharply the cases of Nigeria and
Kenya. This is indicative of a relatively healthy economy. While GDP,
investment and external balance on the average, grew in the same upward
direction from 2002, the variable external balance started fading in the
downward direction from the period 2006. Remittances inflow though positive,
remained minimal slightly rising above the horizontal axis from throughout the
study period. Cameroon in central Africa is next examined below in figure 2.6.
42
US$'Billions
Figure 2.5: Remittance Receipts and other Growth Indicators in Cameroon (2000 –
2007)
25
20
15
GDP
Investment
10
External Balance
Remittances
5
0
2000 2001 2002 2003 2004 2005 2006 2007
-5
Source: Plotted by author based on data from African Development Indicator, Online,
2010
The average pattern in the case of Cameroon is again different from the other
three top remittance recipients in SSA. While GDP and investment curves are
continuously upward sloping from the period 2001, remittances curve averages
out the eternal balance curve. Although both curves remained substantially in
the positive region, both were barely above the horizontal axis indicating a
rather negligible value for these variables. The remittances curve interestingly
is very similar to the case of Botswana which was stable throughout the period
under review around the horizontal axis meaning that inflows are minimal.
43
It is also compelling at this juncture to consider the least remittance recipients
from each of the SSA sub-regions in order to obtain a more balanced and fair
picture.
The overall idea here is to probe for the presence of similar
characterizations of remittance flows to SSA among these least remittance
recipients. Data on major growth indicators and remittances for the four least
remittance recipients are presented in Table 2.8.
44
Table 2.8: Growth Indicators of least Sub-Regional Remittance
Recipients in SSA (US$’Million)
Country
year
GDP
INV
REB
WR
2000
3112.363 613.2339
-132.777
1.166455
2001
3039.157 468.2449
-40.5415
8.71587
2002
3208.305 431.5194
-127.189
15.16
Guinea
2003
3619.436 368.5984
-85.68
111.046
(West
2004
3938.328 447.3895
-156.784
41.64
Africa)
2005
3260.598 458.0511
-88.4114
41.64
2006
3203.923 427.4501
-89.4554
41.64
2007
4563.586 575.8159
-102.179
15.07
2000
551.2309 48.45854
-84.4807
0.72023
2001
572.4174 45.0487
-48.5199
0.708976
2002
591.122
59.40604
-30.6098
0.787752
Djibouti
2003
622.0447 89.6557
-56.9657
2.909054
(East
2004
666.0721 143.2864
-114.553
2.970949
Africa)
2005
708.8436 134.4804
-73.2384
2.993456
2006
760.6529 227.2663
-134.115
3.657418
2007
817.6805 317.8634
-170.104
3.544882
2000
783.1093 395.5415
-515.861
0.138586
2001
711.0866 325.8268
-405.828
1.300182
2002
669.718
300.0478
-418.334
9.772631
Lesotho
2003
994.2572 323.4153
-552.491
11.46537
(Southern
2004
1289.785 404.1968
-629.386
14.37744
Africa)
2005
1375.998 401.0849
-707.811
6.948424
2006
1517.512 378.1838
-703.336
4.461993
2007
1669.564 442.8817
-832.964
12.87145
2000
5067.839 1109.961
1842.789
2.258502
2001
4712.84
1211.642
1224.853
1.23868
2002
4931.504 1208.803
947.9257
1.156404
Gabon
2003
6054.886 1450.454
1468.341
3.805917
(Central
2004
7178.136 1750.973
2166.439
1.431046
Africa)
2005
8665.739 1846.705
3209.592
1.478762
2006
9545.985 2340.1
3135.111
1.478762
2007
11567.59 3028.015
3333.316
1.478762
Source: World Bank, Africa Development Indicators online, 2010
45
For the purpose of providing additional insight, data covering the study period
(2000-2007), on all variables and for each of the four countries listed in Table
2.8 are plotted in the figures below as was done earlier on, in the cases of top
remittance recipients. These figures are presented and discussed in turn.
US$'Billions
Figure 2.6: Remittance Receipts and other Growth Indicators in Guinea (2000 – 2007)
5
4
3
GDP
INV
2
REB
WR
1
0
-1
2000
2001
2002
2003
2004
2005
2006
2007
Source: Plotted by author based on data from African Development Online, 2010
GDP trend for Guinea in West Africa follows a cyclical pattern in the period
under review while domestic investment and external balance curves, although
in the positive region are slightly negatively sloped. External balance curve is
completely in the negative region meaning that this country was never able to
meet its trading obligations to her trading partners during the period under
46
review. Remittances flow is stable, positive and minimal around the horizontal
axis. Djibouti in East Africa is considered in figure 2.7 below. Again the goal
here is to examine whether behaviour similar to those of Guinea is exhibited.
US$'Billions
Figure 2.7: Remittance Receipts and other Growth Indicators in Djibouti (2000 –
2007)
1
0.8
0.6
GDP
0.4
INV
REB
0.2
WR
0
-0.2
-0.4
2000
2001
2002
2003
2004
2005
2006
2007
Source: Plotted by author based on data from African Development Online, 2010
Evidence from Djibouti reveals that GDP and domestic investment exhibited upward
trend from 2000 up until 2007. Remittances again remained stable around the
horizontal axis demonstrating little or no improvement over time. External balance
variable is disturbingly in the negative region and negative sloping all through the
47
period of the review again indicating the inability of this country to settle its trading
obligations with her trading partners. What is rather striking in these behaviours is the
extremely weak nature of these least remittance recipients as revealed by their
negative external balances. What remains is to see whether Lesotho and Gabon will
also exhibit similar behavioural patterns. Figure 2.8 reveals the case of Lesotho.
US$'Billions
Figure 2.8: Remittance Receipts and other Growth Indicators in Lesotho (2000 –
2007)
2
1.5
1
GDP
INV
0.5
REB
WR
0
-0.5
-1
2000
2001
2002
2003
2004
2005
2006
2007
Source: Plotted by author based on data from African Development Online, 2010
Apart from GDP and investment which are largely in the positive region and upward
sloping, the other growth indicators for Lesotho are most unimpressive as shown in
48
figure 2.8. The variable - eternal balance exhibited predominantly downward trends
and remained in the negative region throughout the study period. One surprising
observation is the fact that the period of GDP and investment growth (2002)
unfortunately coincides with the period of further decline in external balance meaning
that the observed growth in GDP and investment did not translate into a healthy
foreign trade sector. The negative values observed for the variable - external balance
further add to the curiosity on whether any systematic link exists between a weak
foreign trade sector and low remittance inflows. An examination of the case of Gabon
in figure 2.9 below will shed more light on these relationships.
US$'Billions
Figure 2.9: Remittance Receipts and other Growth Indicators in Gabon (2000 – 2007)
14
12
10
GDP
8
INV
REB
6
WR
4
2
0
2000
2001
2002
2003
2004
2005
2006
2007
Source: Plotted by author based on data from African Development Online, 2010
49
All growth indicators for Gabon exhibit a more impressive pattern of behaviour than
the other three least remittance recipient economies in SSA. As can be seen, these
variables including remittances are in the positive region. GDP, investment, and
external balance clearly demonstrate upward trends on the average and this is
indicative of a much more healthy economy than the other three least remittance
recipient economies of SSA. Remittances itself though positive and stable around the
horizontal axis is quite minimal. The evidence provided by data on Gabon makes the
exact nature of relationship among remittances and growth indicators included in this
study rather unclear and inconclusive. This therefore calls for further investigation.
50
CHAPTER THREE
REVIEW OF THE LITERATURE
3.1 Conceptual and Measurement Issues
Remittances are defined by the World Bank (2007) as “the sum of workers’
remittances, compensation of employees, and migrant transfers”. The main
sources of official data on migrants' remittances are the annual balance of
payments records of countries, which are compiled in the Balance of Payments
Statistics Yearbook published by the International Monetary Fund (IMF). It is
therefore most logical to examine the definition of remittances as provided by
the IMF. The IMF Balance of Payments Manual 5 (BMP5) does not define
workers or migrants. According to the Balance of Payments Textbook,
“workers’ remittances consist of goods or financial instruments transferred by
migrants living and working in new economies to residents of the economies in
which the migrants formerly resided”. It further states that workers’
remittances are “transfers made by migrants who are employed by entities of
economies in which the workers are considered residents” and that transfers of
self-employed migrants “are not classified as workers’ remittances but as
current transfers”. This distinction is necessary since “workers’ remittances,
according to the balance of payments convention, arise from labour and not
from entrepreneurial income”.
Remittances may also be viewed as transfers of money, goods and diverse
traits by migrants or migrant groups back to their countries of origin or
citizenship. The notion of remittances often conjures only monetary aspect;
however, remittances embrace monetary and non-monetary flows, including
social remittances. Social remittances are defined as ideas, practices, mindsets, world views, values and attitudes, norms of behaviour and social capital
51
(knowledge, experience and expertise) that the diasporas mediate and either
consciously or unconsciously transfer from host to home communities (NorthSouth Centre of the Council of Europe, 2006 cited in Oucho, 2008).
There are divergent views regarding the concept of remittances on the part of
data users. Reinke and Patterson (2005) pointed out that studies of (Adams and
Page, 2003; Harrison, 2003; Migration Policy Institute, 2003) treated
remittances as certain transactions that are initiated by individuals living or
working outside their country of birth or origin and related to their migration.
In general, data on remittances are available from three items in Balance of
Payments (BOP) reports at country level as compiled in the IMF Balance of
Payments Statistics Yearbook: a) “Workers’ remittances” (money sent by
workers residing abroad for more than one year);
b) “Compensations of employees” (gross earnings of foreigners residing
abroad for less than a year; c) “Migrant transfer” (net worth of migrants
moving from one country to another) (Gammeltoft, 2002).
In the balance of payments framework, compensation of employees is a
component of income while workers’ remittances are a component of current
transfers; both are part of the current account. Migrants’ transfers are a
component of capital transfers, which is part of the capital account. The
definitions of these components, according to the BPM5, are:
Compensation of employees comprises wages, salaries, and other benefits
earned by Individuals—in economies other than those in which they are
residents—for work performed for and paid for by residents of those
economies.
52
Workers’ remittances cover current transfers by migrants who are employed in
new economies and considered residents there. A migrant is a person who
comes to an economy and stays there, or is expected to stay, for a year or more.
Workers’ remittances often involve related persons.
Migrants’ transfers are contra-entries to the flow of goods and changes in
financial items that arise from the migration of individuals from one economy
to another.
The concept of residence for households and individuals is based on their
center of economic interest. If a resident household member leaves the
economic territory where the household is based and returns to the household
after a limited period of time (of less than one year), the individual continues to
be a resident even if he or she makes frequent journeys outside the economic
territory. Individuals leaving their country with the intention of living in a new
economy for a year or longer will be considered residents of the new economy
(with a few exceptions, notably students, medical patients, diplomats and
military personnel). BPM5 does not specify a definition of migrants. Transfers
are offset entries in the balance of payments to the provision of a resource
(such as grants and gifts in kind or financial form) without a quid pro quo.
Depending on the nature and use of the resource, transfers are recorded as
current transfers in the current account or as capital transfers in the capital
account component of the capital and financial account (Reinke, 2007).
There are a number of conceptual limitations of remittance definitions in the
BMP5 which obviously create certain conceptual difficulties. For example;
there are currently two questions arising from the focus on employment and
lack of clarity of migration as emphasized by Reinke and Patterson (2005)
53
• First, with increasing international mobility and the breakdown of traditional
employment models, the focus on workers may be difficult to maintain. The
basic issue for consideration should the focused perhaps on all migrants,
regardless of status of employment and source of income?
• Second, there is no clear guidance on migrants, since the BPM5 distinguishes
only residents and nonresidents (visitors). Is a clear definition of migrants
needed, as the originating unit of remittances? As a result, should workers’
remittances be renamed migrants’ remittances?
These questions are currently being addressed in the context of on-going work
on the Balance of Payments Manual and in coordination with other fora, such
as the UN Technical Sub-Group on the Movement of Natural Persons—Mode
4.
The lack of an acceptable official definition of remittances and the lack of
clarity surrounding statistical compilation of a corresponding data series in the
balance of payments has been noted for some time and led to a call by the G-8,
during their 2004 meetings on Sea Island, to clarify the meaning of remittances
and improve the accuracy of measuring remittance flows. This in turn led to
the creation of a working group composed of the World Bank, IMF, and other
international financial institutions that was tasked with clarifying the definition
of remittances, offering guidance on how to collect and estimate remittance
statistics, and providing assistance on how to develop an inflow-outflow matrix
for tracking remittance flows.
A technical subgroup of the United Nations reported its findings to the IMF
Committee on Balance of Payments Statistics and the Advisory Expert Group
on National Accounts. According to Reinke (2007), the results of this process
is included in the revision of the BPM5 and the update of System of National
54
Accounts, 1993, both of which are scheduled for completion in 2008. The
proposed changes will include the introduction of four new categories related
to remittances, conceptual changes to the use of migration and residence status,
and the elimination of the use of migrants’ transfers in the reporting of balance
of payments flows. As discussed in Reinke (2007), the changes 2 include
several items of importance:
(i) “Personal Transfers” to replace “Workers’ Remittances” Personal transfers
will replace the existing workers’ remittances item in the balance of payments,
and will include all current transfers in cash or in kind between resident
households and non-resident households. Unlike workers’ remittances, the new
concept is based neither on employment nor migration status and thus resolves
inconsistencies associated with the previous concept.
(ii) Creation of a new item, “Personal Remittances” Personal remittances will
be defined as current and capital transfers in cash or in kind between resident
households and non-resident households, and “take-home” compensation of
employees earned by persons working in economies where they are not
resident.
(iii) Creation of a new item, “Total Remittances” This will include “Personal
Remittances” and social benefits. Intuitively, it includes all household income
obtained from working abroad.
(iv) Creation of a new item, “Total Remittances and Transfers to Nonprofit
Institutions serving Households”. This will include all components of “Total
Remittances” as well as both current and capital transfers to nonprofit
institutions serving households (NPISHs).
2
This proposed change is yet to be implemented as at April 2010 and so does not affect this
study.
55
(v) Removal of the concept of “migrants’ transfers” from the balance of
payments framework. Instead of recording changes of assets and liabilities
resulting from individuals moving their residence from one economy to
another in the capital account, they will be recorded as “other changes of assets
and liabilities”. The movement of personal effects that accompany a migrant
will be excluded from import and export data.
(vi) Abolition of the concept of “migrant” in the balance of payments
framework. Since the concepts of personal transfers and remittances are based
on the concept of residence rather than migration status, the concept of migrant
is no longer relevant. This is consistent with the use of residence criteria
elsewhere in the balance of payments and national accounts frameworks.
(vii) Reporting of remittance flows to and from major partner countries in
balance of payments data. This is a lower priority request of data users
compared to accurate reporting of aggregate remittance flows, but reporting of
bilateral flows will be encouraged.
Personal transfers are expected to be a standard item in the revised balance of
payments framework. All new definitions—i.e., personal remittances, total
remittances, and total remittances and transfers to nonprofit institutions serving
households—are expected to be supplementary items that compiling countries
are encouraged but not required to compile. It should be noted that they cut
across standard categories (income and transfers) and may entail asymmetries
between transacting countries due to sector allocation.
Chami, et al (2008) noted that the proposed changes to the balance of
payments and system of national accounts frameworks are welcome
developments regarding the true specification of remittances. The new
category personal transfers captures periodic, recurring, unrequited current
56
transfers between residents of different countries. Any prior confusion arising
from the distinction between transfers out of wage income and those out of
other income, or from the concept of migrant status, which led to grey areas
between the previous definitions of workers’ remittances and employee
compensation, are eliminated in this proposed version.
The main focus from a balance of payments perspective is to capture and
record transfers between persons in different countries, which coincides with
the generally accepted definition of remittances. The elimination of the concept
of migrants’ transfers and the inclusion of employee compensation in a
supplementary item are also welcome. As evidenced by the data, migrants’
transfers and employee compensation have characteristics more closely akin to
those of private capital flows than to those of personal transfers and as such
should be classified as items separate from workers’ remittances.
As already noted, the new category “personal transfers” captures periodic,
recurring, unrequited current transfers between residents of different countries;
this proposed changes constitute a useful guide in this study as data on
workers’ remittances corresponds to personal transfers. This component is
therefore isolated from migrants’ transfer and employee compensation and is
employed in this study.
3.2 Review of Theoretical Issues
The overriding essence of discussing the theoretical literature on remittances
here is to provoke, or at the least, motivate an empirical exercise which
ultimately will assist in the choice of empirical model(s) that can be employed
to analyze the macroeconomic effects of remittances. Many researchers have
57
informally suggested theories describing the role of remittances in the
economy in order to motivate an empirical exercise. Rapoport and Docquier
(2005) suggest that until the end of the 1980s the research on remittances
focused on their short run effects, that is, on its impact on domestic output and
prices. However, attention has shifted to the long run effects since the early
1990s by analyzing how remittances could alter the growth pattern of the
receiving country.
Approaches to the theory of remittances identified and described various
costs and benefits to remitting and these are well summarized in Russell
(1986). Stark and Bloom (1985) identify the family as the appropriate unit of
analysis in migration and remittance questions. This is because the entire
family is involved in sharing, and trading off, the costs and benefits of
remitting. The recent theoretical literature on the role of remittances has
therefore focused on the possible roles that the family or family
relationships can play in shaping remittance choices. While Johnson and
Whitelaw (1974) mention altruistic motivations for remittances, Lucas and Stark
(1985: 902) state that "certainly the most obvious motive for remitting is
pure altruism- the care of a migrant for those left behind. Indeed, this
appears to be the single notion underlying much of the remittance
literature."
Some theories also focus on the idea that there can be self-interested reasons for
remitting as well, which nevertheless center on the family. These self-interested
theories of remittances are still based on the family because they view the family
as a business or as a nexus of contracts that enables the members to enter into
Pareto-improving arrangements (Chami et al, 2003). Lucas and Stark
(1985) suggest that migrants may have investments that need to be tended
while they are away, so they will use other family members as their agents. In
58
cases like this, the remittances sent by the migrant are used to care for the
migrant's interests, but they certainly will also include some compensation for
the agents.
The family may also play the role of financial intermediary in the remittances
arrangement. Stark (1991), as well as Agarwal and Horowitz (2002) and Gubert
(2002), assert that the family can function as an insurance company that
provides members with protection against income shocks by diversifying
the sources of income. Poirine (1997) and Ilahi and Jafarey (1999) differ slightly
by modeling the family as a bank that finances migration for some members. The
borrowers remit funds in order to repay the loans, which are put toward
more loans to further the interests of other individual family members. Based on
altruistic motive, Olayiwola et al (2008) and Olayiwola (2010) also found
remittances as a major source of old-age support in Ghana.
In contrast, Chami and Fisher (1996) show that altruism can be a mechanism
by which independent agents find partners with whom to enter into risksharing arrangements. They pointed out that even if the remittance arrangements
are truly self-interested, the self-enforcing mechanism on which they depend
may actually be altruism-that is, the migrant will live up to her obligations
because she cares about the family members who are the counterparties to
the agreement. This idea is explored in Stark and Lucas (1988). The
important point here is that, although the motivations to remit are doubtlessly
complex, altruism
between
family
members
appears
to
be
the
overwhelming motivation for remittances and hence, a good benchmark
to use when modeling the interaction of causes and effects of remittances.
These models built on altruism, and which focus on the family as the
relevant unit of analysis in the migration process, attempt to analyze in a
59
unified framework the motivations that drive remittances and the economic
effects that these are likely to produce in the recipient country. Chami et al.
(2005) motivate remittances on the basis of altruism on the part of the migrant.
Altruism is modeled introducing in the utility function of the migrant the
expected utility of the remittee, discounted by a factor, alongside with the utility
that the migrant derives from her own consumption. The remittee is employed in
the domestic production process, whose output level is influenced by the
(unobservable) work effort expended by the employees; domestic firms
differentiate wages according to actual output levels.
Remittances are driven by the desire to protect the relative from adverse
income shocks, and their optimal level is inversely related to the recipient
labour income in the bad state of nature. But the model predicts that
remittances induce moral hazard in the behaviour of the remittee, as they create
an incentive to substitute work effort, and hence labour income, with
remittance income. Even though domestic firms increase the dispersion of
wages among different output levels to induce greater work effort in risk
averse workers, they are not able to fully overcome the moral hazard
introduced by remittances, and domestic production falls.
Chami et al. (2005) further suggest that workers’ remittances are
compensatory transfers, that are likely to smooth household consumption,
but that could depress production in the home country. The conclusion of
their model reminds the implication of the early pessimistic view about
remittances, as remittances are predicted to create a dependency culture in the
receiving economy, which is induced to increase its reliance on the
remittance inflow. Moreover, remittances are expected to fluctuate countercyclically, as the need for compensatory transfer increases as economic
60
activity in the home country declines.
Remittances might also have significant indirect effect in the long run; IMF
(2005) argues that “banking the unbanked” could be one of the beneficial
side effect produced by workers’ remittances. The high profitability of the
remittance business that is currently reflected by the high costs charged by
private money transfer operators (MTOs), is driving banks’ attempts to widen
their market share (Solimano, 2003b). Banks are signing international
agreements whose aim is to increase the use of banking channels by the
remitters and the remittees, offering bank accounts that can be accessed in both
the host and home country. Thus, “remittances have the potential to bring a
larger share of the population into contact with the formal financial system,
expanding the availability of credit and saving products such as education
loans, mortgages, and savings accounts.” (IMF, 2005a).
In the short run, the main direct effect of remittances is to increase private
consumption expenditure, thus stimulating aggregate demand. This is the
unique stylized fact derived from micro surveys on remittees’ behaviour that is
included in the macroeconomic analysis of short run effect of remittances. In a
Keynesian framework, in the absence of supply constraints, the demand
stimulus that is created by workers’ remittances induces a multiplier effect on
domestic output. In the presence of slack productive capacity, remittances can
thus contribute to raise capacity utilization. The overall effect depends on the
propensity to consume out of remittances and on the import content of
consumption. Life-cycle theories of consumption suggest that the propensity
to consume depends on the remittees’ perception about the future flow of
remittances; if these are not expected to be a long lasting source of income,
then the theory predicts that households will not fully adjust current
consumption levels to the increase in current income. Thus, Keynesian models
61
suggest that it is the propensity to consume out of remittances that raises
domestic production.
The Mundell-Fleming model of a small open economy stresses that the impact
of remittances depends crucially on the exchange rate regime and on the
degree of capital mobility. Theoretically, a flexible exchange rate regime
considerably insulates the domestic economic system from an external shock.
The argument here is that the flow of remittances leads to an appreciation
of the local currency and this reduces foreign demand of domestic goods,
thus it counteracts the stimulus on national expenditure stemming from
remittances. The implication of this is that the domestic level of economic
activity is not altered by the flow of remittances. On the other hand, with a
fixed exchange rate regime, the positive demand shock can be transmitted over
to the domestic economy, and this may produce an increase in output coupled
with an increase in domestic prices. The inflow of remittances leads to an
increase in the money supply, and this monetary expansion consequently
accommodates the increase in domestic demand brought about by remittances
themselves.
When the short-run analysis is conducted within models that depart from the
assumption of a single composite goods, the focus shifts on how remittances
affect the sectoral distribution of resources. The Dutch Disease described in
Corden and Neary (1982) has been often recalled to describe the potential
adverse effects of aid inflows to developing countries, and has also been used
to interpret possible drawbacks of workers’ remittances. The increase in
demand coming from remittances is applicable to both tradable and non tradable
goods. However, the supply of the latter is usually constrained by the
availability of domestic resources. If factors are already fully employed - or
62
supply is not able to react to the demand stimulus, prices of non tradable goods
can be expected to increase to absorb excess demand. Tradable goods prices are
determined in the world markets and hence, are not altered by domestic
demand, which consequently causes a worsening in the trade balance. Thus
the excess demand for non tradable goods results in an increase of the
domestic price level. This - coupled with a possible nominal appreciation of
domestic currency due to remittance inflow - determines an appreciation of the
real exchange rate, that affects negatively the export and import competing
sector, drawing resources towards the non tradable sector.
The analysis of short run macroeconomic effects of remittances suggests that it
is actually the increase in private consumption that might trigger investments,
and thus possibly foster long run economic growth. Firms can also benefit
from the “extension of investment credit allowed by the increase in the
liquidity of banks from remittance deposits” (Glytsos, 2001). Hence, short
run effects depend crucially on the expected response of domestic supply: while
a lack of reaction of the latter would dissipate the remittance-induced
demand stimulus through inflation and import increase, there is a chance
that remittances might raise the level of domestic activity.
A bridge between short and long run effects is laid by structuralist growth
models that stress the interplay between current demand and future growth
prospects. In demand-led growth models, current demand influences the
level
of
capacity
utilization,
that
is
one
of
the determinants of
investments. An expansion of capital can be hindered by an insufficient level
of domestic demand; the increase in private consumption that remittances
generate can thus induce firms to undertake new investments. When the focus
of the economic analysis shifts from the short to the long-run, the literature
63
still presents a considerable diversity of interpretations about the effects
of
workers’ remittances, though a predominant view about a positive
development impact of remittances emerges. It is interesting to observe that
a significant part of the analysis also becomes remittance-specific, as
models tend to incorporate some characteristics that differentiate remittances
from other foreign exchange flows to developing countries.
The two-gap model proposed by Chenery and Bruno (1962) stresses the
critical role that is played by foreign transfers in determining the actual level
of investment in developing countries. Domestic investments have fixed import
content, and thus their level is limited by the amount of foreign exchange that
can be derived from exports and capital inflows, net of factor payments to
abroad. Remittances contribute to fill the foreign exchange gap that is the
shortfall of available foreign currency to the level that would be required to
undertake the investments allowed by the level of domestic savings. The
foreign exchange gap is presented as the binding constraint for investments
whenever output is below its potential level. This suggests that remittances are
likely to foster economic growth, as they expand receiving country’s capacity to
import capital goods. The short run increase in imports that is brought
about by remittance inflow is a precondition for subsequent growth.
3.3 Review of Methodological and Empirical Issues
The question of whether remittances promote economic growth has not been
conclusively answered by any theoretical or empirical study. Unarguably,
remittances lead to an increase in the level of income in the recipient country
and plausibly help reduce poverty (Gupta et al., 2007), but it is not at all
64
obvious that remittances increase output and promote long-term economic
growth.
Based on household survey data from various African countries, few empirical
studies have investigated the role of remittances in reducing poverty (Lucas
and Stark, 1985; Adams, 1991; Sander, 2004; Azam and Gubert, 2005; Adam,
2006). The macroeconomic impacts of remittances in Africa have not been
sufficiently explored by researchers for at least two reasons. One theoretical
strand suggests that workers’ remittances are mainly used for consumption
purposes and, hence, have minimal impact on investment. In other words,
remittances are widely viewed as compensatory transfers between family
members who lost skilled workers due to migration. Nevertheless, Stahl and
Arnold (1986) argue that the use of remittances for consumption may have a
positive effect on growth because of their possible multiplier effect. Moreover,
remittances respond to investment opportunities in the home country as much
as to charitable or insurance motives. Many migrants invest their savings in
small businesses, real estate or other assets in their own country because they
know the local markets better than their host countries, or probably expecting
to return in the future. In about two-thirds of developing countries, remittances
are mostly profit-driven and increase when economic conditions improve back
home.
There are a number of channels through which Workers’ remittances can
positively affect growth. At the household level, remittances may ease credit
constraint of households and encourage entrepreneurial activity and private
investment (Yang, 2004; Woodruff and Zenteno, 2004). Many households in
developing countries have very limited access to credit markets. Remittance
inflows could help such households to set up their entrepreneurial activity.
Apart from physical investment, remittances could also be used to finance
65
education and health, which are also key variables in promoting economic
growth.
At the aggregate level, remittances could improve a country’s
creditworthiness and thereby enhance its access to international capital
markets. In the view of World Bank (2006), the calculation of country credit
ratings by major international creditors depends in part on her volume of
remittance flows. The higher the volume of remittance flows the better the
credit rating rank the country could reach.
Unarguably, access to more
international credit potentially could increase both physical and human capital
investment in a country, thereby enhancing economic growth.
Rempel and Lobdell (1978) use household survey data from rural Kenya and
conclude that remittances from rural-to-urban migrants have little impact on
the development of the region of origin. By contrast, Collier and Lal (1984)
show in the case of rural Kenya again, that remittances enable the recipient
families to hold more productive capital than the others. They thus bring out
the role of migration and remittances as a means to overcome capital market
imperfection, and to bring home some capital for funding productive
investment. This fact had also been described to some extent by Bates (1976),
in the case of Zambian migrants. This effect is emphasized even more strongly
in Collier and Lal (1986), in the case of rural Kenya again.
Poirine (1997) provides some further analysis of “remittances as an implicit
family loan arrangement”, emphasizing both the collective organization of the
financial flows within the family. Remittances are then viewed as absorbing
random shocks, like bad crops or illness, thus providing some informal
insurance services (e.g. Gubert, 2002). It is fair to say that the empirical
literature on migration and remittances has devoted more attention to income
distribution issues. In his early study of migration from Kasumpa village in
Zambia, Bates (1976) shows that households earning lower incomes in the
66
village receive more remittances from town than richer ones, after controlling
for demographic composition. Stark, Taylor and Yitzhaki (1988) show that this
type of transfers reduces income inequality in a Mexican village having
migrants in the USA, but suggest that the poorest are excluded from migrating.
Banerjee and Kanbur (1981) and Faini and Venturini (1993) conclude, by
different routes, that migration benefits more the middle income classes of the
society of origin than the two extremes of the distribution, in India and
Southern Europe respectively.
By contrast, Gustafsson and Makonnen (1993) conclude that poverty in
Lesotho would go up by about 15% were the flow of transfers sent by the
migrants working in the mines in South Africa to stop. Azam and Gubert
(2004) show that this issue is probably more subtle than it looks, as the
correlation between poverty and low measured (earned) income can be
misleading. Lucas and Stark (1985) analyze various potential motivations
explaining why migrants transfer some income to their relatives remained in
the village, for testing various forms of altruistic or egoistic behaviour. Using
survey data on Botswana, they conclude that mixed motivations of moderate
altruism or enlightened egoism seem to prevail. Their empirical analysis
supports the view that the migrants do provide some insurance services, by
transferring more money when a drought threatens the livestock. They also
show that wealthier families receive more than poorer ones, suggesting that the
migrants are defending their inheritance rights or their ability to come back to
the village with dignity. Hoddinott (1992) gets a similar result using a
household survey conducted in Kenya.
Azam and Gubert (2005) in their paper on migrant remittances and economic
development in Africa emphasized that migration cannot be understood as an
individual decision, but must be regarded as a collective decision made by the
67
extended family or the village. The study also noted that remittances are to a
large extent a contingent flow, aimed at buttressing the family’s consumption
in case of adverse shock. However, this insurance system involves some moral
hazard, as those remaining behind tend to exert less effort to take care of
themselves, knowing that the migrants will compensate any consumption
shortfall, with a high probability. Their result was able to provide insight into a
puzzle that bugged the remittances-development literature for nearly three
decades: the rich families are more likely to send some migrant away, and thus
get more remittances, while they earn less income in the village, because of
moral hazard.
Wealth makes them lazy, while low income does not make them poor. Quartey
(2006) investigated the impact of migrant remittances on household welfare in
Ghana. The study employed micro data based on the Ghana Living Standard
Survey (1 to 4) and found that remittances improve household welfare and help
to minimize the effects of economic shocks to household welfare. This study
was limited to the individual beneficiary of remittances income and this can
hinder generalization. A considerable measure of aggregation will be required
for these findings to be able to provide adequate guide for macroeconomic
policy direction on the subject of remittances.
Empirical evidence from outside Africa reveals that remittances have a
potential, positive, impact as a development tool for the recipient countries. In
a study that relies on a definition of remittances that includes workers’
remittances and compensation of employees, Solimano (2003) noted that the
development effect of remittances can be decomposed into effects on savings,
investment, growth, consumption, and poverty and income distribution. The
impact on growth of remittances in receiving economies is likely to act through
savings and investment as well as short-run effects on aggregate demand and
68
output through consumption. The total saving effect of remittances comes from
the sum of foreign savings and domestic savings effects. Workers’ remittances
are a component of foreign savings and they complement national savings by
increasing the total pool of resources available to investment. The direct effects
of remittances on investment are bound to be on small community projects.
According to Buch et al (2002), remittances can influence economic growth
directly or indirectly. However, the degree of the latter channel strongly
depends on supporting governmental policies and a supporting economic
environment for investment activities. Glytsos (2005) analyzes the effect of
remittances on investment, consumption, imports and output. The author uses a
sample of five countries and estimates short and long run multipliers of
remittances. He finds that the effect of reducing remittances would be greater
than the effect of raising them. Ziesemer (2007) proposes a savings channel
that relates remittances with growth. He finds that remittances have a positive
impact on growth, due to the ability to increase saving rates in countries with a
per capita income of less than US $1200.
Funkhouser (1992) as well as Woodruff and Zenteno, (2004) identified a
number of channels through which remittances could raise economic growth
and these include: when an increase in remittances raises investment,
remittances could be expected to affect growth positively. If this effect is large
enough, then remittances could alleviate the credit constraints faced by most
people in developing countries.
The implication of this result is that the positive effect of remittances on
investment or on economic growth is likely to be larger for countries where the
financial system is relatively underdeveloped. This position of possible
substitutability between remittances and financial development is supported
69
empirically by other studies (for example, Fajnzylber and Lopez, 2007, and
Giuliano and Ruiz-Arranz, 2005). Singh, Haacker and Lee (2009) found an
overall effect is negative and significant. This result is consistent with the
finding of Chami, Fullenkamp, and Jahjah (2003) regress per capita real
growth on investment, change in remittances, and net private capital inflows as
well as regional dummy variables; they obtain positive coefficients for both
investment and net private capital inflows, but the coefficient of remittances
comes out negative. They therefore suggest that remittances are unlikely to
promote economic growth because of a moral hazard problem (i.e., reduced
labour market participation), as well as other factors. They therefore
questioned whether remittances can be a source of development capital.
Chami et al. (2009) found evidence supporting the notion that remittance flows
provide a stabilizing influence on output. If remittances are predominantly
consumed rather than invested, any growth effects through higher investment
could be subdued. Even in this case, however, remittances could foster
investment by reducing the volatility of consumption and contributing to a
more stable macroeconomic environment. Giuliano and ruiz-Arranz (2005)
provide evidence of the positive effects of remittances on the growth of
less developed countries. In a cross sectional study of 37 African countries,
Fayissa and Nsiah (2008) explored the aggregate impact of remittances on
economic growth and found that remittances boost growth in countries where
the financial systems are less developed by providing an alternative way to
finance investment and helping overcome liquidity constraints.
Similarly, Fayissa and Nsiah (2010) found that remittances have a positive and
significant effect on the growth of Latin American Countries where the
financial systems are less developed by providing an alternative way to
finance investment and helping overcome liquidity constraints. Most of the
70
empirical works have focused on migrant-exporting countries with rather
similar characteristics; however, the debate about the impact of remittances is
still ongoing. Chami et al (2005) report a negative effect of remittances on
growth and productivity using cross-country panel data. Their argument here is
that migration deprives the economy of the most productive workers, or that
remittances have adverse effects on those staying behind, or both.
Different researchers are not in agreement about whether or not remittances
serve as an important source of investment capital. The basic principle is that
either directly or through the process of intermediation and leverage,
remittances will tend to increase investment, thus increasing potential growth.
Durand, Kandel, Parrado, Massey (1996) noted that, in the case of Mexico,
under the right circumstances (a high-paying US job, secure attachment to the
US labour force, access to complementary resources in Mexico), the odds of
productive investment of remittances rise substantially.
Ratha (2003) cites positive effects of remittances on investment in receiving
countries such as Mexico, Egypt, and Sub-Saharan Africa. In these countries,
remittances have financed the building of schools, clinics and other
infrastructure. In addition, return-migrants bring fresh capital that can help
finance investment projects. The relationship between household investment
and workers’ remittances in developing countries are found to be positive in a
number of studies.
For example, Brown (1994) relying on a micro-level
analysis of the use of remittances by households, investigates the relationship
between remittances, savings and investment in Tonga and Samoa. The study
found that remittances make a significant contribution to savings and
investment in the island economies. Mesnard (2004) examines impacts of
remittances on Tunisia using a life-cycle model. The study reveals that workers
who have limited access to the financial market tend to invest their remittances
71
receipts. Yang (2004) finds that remittances lead to improved child schooling,
reduce child labour, increased education expenditure, and facilitate investment.
The major role of remittances in receiving countries is to stimulate
consumption and investment in those countries, help relax foreign exchange
constraints and contribute to poverty alleviation (Adams, 2007). Their
contribution to development depends on their macroeconomic impact and how
they are used in receiving countries. There is evidence that they are more
directed to consumption than investment, which perhaps explains why no link
between them and long-term growth has been found (IMF, 2005: chapter 2).
The focus of the recent remittances literature is therefore toward the macro and
micro implications of remittance flows.
Acosta et al (2007), finds that in addition to the usual nominal exchange rate
channel, remittances result in a shrinkage of, and resource re-allocations away
from, the tradable sector through (i) increasing prices in the non tradable
sector, and (ii) reducing the labour supply to, and thereby increasing the
production costs of, the otherwise labour-intensive non tradable sector. Using
micro data from Morocco, Van Dalen et al (2005) find that remittances have a
potential to stimulate further migration among the family members left behind.
These studies in all are quite emphatic on the possibility that the benefits of
remittances, if any, could be less pronounced.
Remittances also finance consumption; thus, private savings will increase less
than proportionally than an increase in income from external remittances.
Bendixen and others (2003) in a study of remittances for Ecuador shows that
around 60 percent of remittances in Ecuador are spent on food, medicines,
house rents and other basic commodities. The study shows that less than 5
percent of remittances are used in the acquisition of residential property. The
72
combined effects of remittances on investment and consumption can increase
output and growth. The sustainability of this effect is an open discussion. If
remittances are a response to recent migration, remittances may be transitory
and thus their effects on investment, consumption and growth can be more of a
temporary basis. In contrast, if migrants form associations and their
commitment to their home country becomes “institutionalized” then, their
positive developmental effects of remittances may become more permanent.
The impact of remittances on growth in cross country studies is inconclusive.
Studies that focus on the labour supply response of recipient households find
that remittances lower growth (Chami, Fullenkamp, and Jahjah, 2003; Azam
and Gubert, 2005). However, Fajnzylber and López (2008) in a cross-country
study of Latin American countries found evidence which suggests that indeed,
remittances are more effective in raising investment and enhancing growth in
countries with higher levels of human capital, strong institutions, and good
policy environments. They also found that increases in remittances apparently
have more of an investment and growth impact in countries with less
developed financial sectors. In general, studies that link remittances to
investment, where remittances either substitute for or improve financial access,
tend to conclude that remittances stimulate growth (Giuliano and Ruiz-Arranz,
2005; Toxopeus and Lensink, 2006). While the evidence on the
contemporaneous impact of remittances on growth may be mixed, it is likely
that remittances can affect long-term growth by fostering financial deepening.
Stark, (2004), and Mountford, (1997) highlight the positive impact of
remittances to include its impact on human capital development in home
countries, which often is linked to increased demand for and access to
education among those left behind. The positive impact of remittances is
73
broadened to include technology and knowledge transfer and other benefits of
brain circulation, and the potential benefits deriving from Diaspora links.
Docquier and Rapoport (2004:27) summarize the main effects of the successful
experience of migrants abroad: “successive cohorts adapt their education
decisions, and the economy-wide average level of education partly… or totally
catches up, with a possible net gain in the long run” and “the creation of
migrants’ networks that facilitate the movement of goods, factors and ideas
between migrants’ host and home countries”. It must be emphasized here that
the existence of a positive impact on countries of origin rests on the
assumption that a significant number of graduates of new courses and new
schools, who initially enrolled with the aim of going abroad eventually had a
change of orientation, thus, end up contributing to the provision of a higher
value of goods and services to the domestic economy.
Remittances can be expected to cause a widening of the external trade account
deficit (including services as travel), or a narrowing of the current account
surplus. As remittances increase purchasing power in the receiving country
they augment domestic demand. Bouhga-Hagbe found that in the case of
Morocco, “remittances almost cover the trade deficit and have contributed to
the recent surpluses of the external current account, as well as the overall BOP.
The BOP surpluses have contributed to the strengthening of Morocco’s
external position through the accumulation of reserves, which now cover the
external public debt (Bouhga-Hagbe, 2004).
The impact of remittances on the real exchange rate and export
competitiveness, and the Dutch disease effect, is another area of debate. In
countries receiving remittances the currencies could appreciate, which might
be harmful to their long-run economic growth (a Dutch disease effect). As in
74
the case of any other transfer (for instance, official aid), the effect depends on
the proportion of such flows spent on domestic goods, in particular nontradables (Gupta, et al, 2006). Since remittances are private transfers dispersed
over a large number of poor households it has been argued that their impact on
domestic demand differs from that of donor-funded infrastructure projects
(World Bank, 2006). Remittances may in fact be self-correcting as an
overvalued currency deters remittances, and hence Dutch disease effects are
not sustained (Rajan and Subramanian, 2005). However, studies in Latin
America (Amuedo-Dorantes and Pozo, 2004) and Cape Verde (Bourdet and
Falck, 2006) have found evidence that remittances do have Dutch disease
effects on the competitiveness of the tradable sector. In countries where
remittances inflows are large compared to the size of the economy, where
supply constraints are a significant hindrance to the expansion of the nontradables sector, and where a significant portion of remittances are spent on
domestic goods policymakers will need to be alert to the possibility of a Dutch
disease phenomenon. Moreover, remittances may reduce the labour supply or
labour market participation of recipients. If these negative factors dominate,
remittances could be detrimental to economic development in SSA (Chami,
Fullenkamp, and Jahjah, 2003).
Elbadawi and Rocha (1992) present a detailed theoretical review and insightful
analysis of the literature on the causes of immigrant remittances, which applies
well to all remittances. They divide this literature into two main strands:
the "endogenous migration" approach, and the "portfolio" approach. The
endogenous migration approach is based on the economics of the family, which
include but not limited to motivations based on altruism. The portfolio
approach isolates the decision to remit from the decision to migrate, and
likewise avoids issues of family ties. In this view, the migrant earns income
75
and decides how to allocate savings between host country assets and home
country assets.
Remittances are a result of deciding to invest in home country assets. The
portfolio view, therefore, is an informal theory of remittances that supports
the view that remittances behave like other capital flows. The rates of return
on various assets, or return differentials are regarded as important decision
variables affecting remittances in the portfolio view. The variables often
included in such studies are interest rate differentials on comparable deposit
accounts offered in the host and home (labour-sending) countries, incentive
interest rates offered on home country deposits, black market exchange premium
(if any), the return on real estate in the home country, inflation rates, and other
returns. In addition, political risk and uncertainty may also affect the decision to
remit.
The endogenous migration approach and the portfolio approach are the
most prominent approaches employed to perform empirical estimations of
remittance determination. Wahba (1991) introduced a dichotomy by
dividing remittances into "fixed" remittances, which go toward family
support, and "discretionary" remittances, which are investment flows. The
fixed remittances depend essentially on demographic and economic
factors including family characteristics such as size and income level, and
therefore may be explained by the endogenous migration view. In
general, empirical analyses include some demographic variables such as
the stock of migrants in the host country (or family characteristics in
studies that use micro data), economic variables such as wages or income,
and financial variables such as interest rates.
The demographic and income variables tend to be significant in nearly all
76
estimations, while the financial variables’ significance varies depending
on the sample and specification. Chami et al (2003) pointed out that this
is probably the most reliable stylized fact to come out of the empirical
literature on the causes of remittances. While most papers have found
evidence consistent with altruistic behavior, only a few papers such as
Lucas and Stark (1985) and Agarwal and Horowitz (2002) have tested
altruism against alternate family arrangements. Lucas and Stark (1985)
find evidence in favor of self-interested behavior in Botswana, while
Agarwal and Horowitz find evidence in favor of altruism in Guyana.
Chami, et al (2003) conducted panel regressions of growth in real GDP per
capita on both the workers’ remittances–to–GDP ratio and the change in that
ratio, conditioned on the investment rate, the rate of inflation, regional
dummies, and the ratio of net private capital flows to GDP. The study found
that domestic investment and private capital flows were positively related to
growth, the workers’ remittances–to–GDP ratio either was not significant or
were negatively related to growth. The IMF (2005) performed cross-country
growth regressions on a set of 101 countries measured over the 1970–2003
period. IMF (2005) used an aggregate remittance variable, or the sum of
workers’ remittances, employee compensation, and migrant transfers; a
measure of remittances which captures behavior not associated with workers’
remittances. The IMF study also used two instruments for remittances: distance
between the migrants’ home and main destination country, and a dummy
measuring whether the home and main destination country shared a common
language. Because the instruments did not vary over time, panel estimation
techniques could not be used. The IMF (2005) found no statistically significant
effect of total remittances on economic growth.
Faini (2006) estimated cross-sectional growth regressions on a set of 68
77
countries in which the dependent variable is the average annual per capita GDP
growth rate from 1980 to 2004. Faini (2006), like the IMF (2005), used an
aggregate measure of remittances obtained by summing workers’ remittances,
employee compensation, and migrant transfers. The estimated coefficient on
the total remittances–to–GDP ratio in Faini’s ordinary least-squares (OLS)
regression was positive and significant, both when average and when initial
remittances were used in the total remittances–to–GDP variable. Faini also
conducted instrumental variables estimation, using distance from the migrants’
main destination countries as the instrument for remittances. In this estimation,
the coefficient on total remittances remained positive but lost its significance.
3.4 Modeling Issues in the Remittances Literature
Modeling issues bothering on appropriate and adequate estimation of models
in cross country studies have also received considerable attention in the
literature. Models have to be estimated by methods that handle the problems
afflicting each model. For example, a constant coefficients model with residual
homogeneity and normality can be estimated with ordinary least squares
estimation (OLS). As long as there is no groupwise or other heteroskedastic
effects on the dependent variable, OLS may be used for fixed effects model
estimation (Sayrs, 1989). For OLS to be properly applied, the errors have to be
independent and homoskedastic. Those conditions are so rare that it is often
unrealistic to expect that OLS will suffice for such models (Davidson and
MacKinnon, 1993).
Heteroskedastic models are usually fitted with estimated or feasible
generalized least squares (EGLS or FGLS). Heteroskedasticity can be assessed
with a White or a Breusch-Pagan test. For the most part, fixed effects models
78
with groupwise heteroskedasticity cannot be efficiently estimated with OLS. If
the sample size is large enough and autocorrelation plagues the errors, FGLS
can be used. Random sampling and maximum likelihood iterated by
generalized least squares have also been used (Greene, 2002). Beck and Katz
(1995) reportedly found that if the sample size is finite or small, the total
number of temporal observations must be as large as the number of panels;
moreover they reportedly found that OLS with panel corrected errors provided
more efficient estimation than FGLS (Greenberg, 2003; STATA, 2003).
If the model exhibits autocorrelation and/or moving average errors, first
differences (Wooldridge, 2002) or GLS corrected for ARMA errors can be
used (Sayrs, 1989). Hausman and Taylor (1981) have used weighted
instrumental variables, based only on the information within the model, for
random effects estimation to be used when there are enough instruments for
the modeling. The instrumental variables, which are proxy variables
uncorrelated with the errors, are based on the group means. The use of these
instrumental variables allows researchers to circumvent the inconsistency and
inefficiency problems following from correlation of the individual variables
with the errors.
For dynamic panels with lagged dependent variables, Arellano, Bond, and
Bover have used generalized methods of moments (GMM), which are
asymptotically normal (Wooldridge, 2002). With greater numbers of moment
conditions, they are able to handle some missing data and they can attain gains
in efficiency as long as there are three or four periods of data (Greene, 2002).
The Seemingly Unrelated Regression (SUR) is another estimation procedure
which requires that the number of explanatory variables in each cross-section
is the same. In the SUR approach, variables are transformed with a form of
79
Cochrane-Orchutt correction to model the autocorrelation. Feasible generalized
least squares is used to estimate a covariance matrix.
If there are enough temporal observations, they can use either the lagged levels
or lagged differences as instruments, while the other variables serve as their
own instruments in an extension. Robust estimation, when one has
heteroskedasticity, autocorrelation, or outliers to contend with, may be
performed with the generalized methods of moments and combination of
White and Newey-West estimators to obtain robust panel standard errors.
GMM models tend to be robust with respect to heteroskedasticity and
nonnormality. The GMM based estimation techniques is considered quite
appropriate in cases involving the estimation of a dynamic panel data models.
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CHAPTER FOUR
THEORETICAL FRAMEWORK AND METHODOLOGY
4.1 Theoretical Framework
In the standard Neo-Classical Growth Model (Solow 1956, Barro and SalaiMartin 1995), economic growth derives from the capital accumulation per
capita and technology progress jointly in the transitional period, and solely
comes from the technology growth in the steady state.
Thus, we can
reasonably predict that there are two channels through which the development
in the financial sector may affect economic growth: the accumulation rate of
physical capital and total factor productivity growth (Spiegel 2001). On capital
accumulation, well functioning of financial system affects the rate of capital
formation either by altering the saving rate or by raising the investment rate.
In the transitional period, higher saving (investment) rate accelerates the
economy’s growth rate approaching to its steady state at a higher level of real
per capita income.
This study attempts to address the issue of finance-growth channels by
following an extended neo-classical growth model proposed by Mankiw,
Romer and Weil (1992). It introduces remittances into the model and then,
empirically tests the impacts of remittance flows on economic growth and
development through above-mentioned channels in a dynamic panel data
model.
Specifically, this study evaluates whether remittance flow is a
significant determinant of growth when it is integrated into the neo-classical
growth model. The study further investigates whether remittances exert a more
fundamental impact on capital accumulation within the sample group.
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The basic theoretical framework for this study is adapted from Chami et al
(2008). The first step in analyzing the macroeconomic effects of remittances is
to take a position about what drives remittance flows. This issue has serious
implications for results and could generate controversies empirically. The
question therefore is on how best to think about the factors driving these flows.
Assuming here that remittance flows are determined by altruistic motives on
the part of migrants, the utility of the recipients enters the remitters’ utility
function. To be concrete, let us suppose that migrants value the welfare of the
recipients as much as they do their own. The implication of this is that a
change in remittances must be caused by a change in some exogenous variable.
In any case, the arrival of remittances is first considered a basic input in the
economy whose impact may be viewed in terms of its micro or macro
dimension. The reasoning here is that such impact may be directly on income
when motivated by altruism, it may be on the level of domestic investment
when it occurs as a disguised capital flow, and it may even be on the trade
balance when it reduces the foreign trade competitiveness of the receiving
economy. Overall, the consequences will be evident in the rate of economic
growth, the level of domestic investment, and the foreign trade
competitiveness of a country’s exports as highlighted in figure 4.1.
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Figure 4.1: Schematic analysis of the remittances-development nexus
Remittances
Input
Basic Form
Strands
Nature and
Impact
Expected
Result
Initial Output
Remittances Inflow
Microeconomic Strand
 Remittances inflow
motivated by
altruism
 Expected impact is
directly on income
in the recipient
economy
Remittances Outflow
Macroeconomic Strand
 Remittances inflow
as disguised capital
flow
 Expected impact is
directly on domestic
investment in the
recipient economy
 Remittances inflow
reducing recipient
country’s trade
competitiveness
 Expected impact is
directly on the trade
balance in the
recipient economy
 Smoothen consumption
expenditure of recipient
 Increases per capita
income in the recipient
economy
 Boost output in
receiving economy
 Enhances
domestic
investment in the
recipient
economy
 Helps close the
investment-saving
gap in recipient
economy
 Strengthens the real
effective exchange
rate of the recipient
economy
 May weaken the
current account
balance position of
the recipient
economy
Output Growth
Domestic Investment
Depressed Trade
Balance
Overall Output
Economic Growth
Source: Author’s compilations based on hypothesized relationships among related variables
83
Figure 4.1 is a schematic analysis of the remittances-development nexus that
may be examined within the following transmission mechanism. First, the
arrival of workers’ remittances may affect the recipient economy in one of two
ways typically thought of under the microeconomic or macroeconomic strand.
Whether this happens depends on how remittance receipts are used and the
motives driving remittance flows.
Second, remittances motivated primarily by altruistic considerations tend to be
countercyclical in its effects on the receiving economy. In periods of economic
boom, less remittances is likely to be received and in periods of economic
downturn more remittances will be received to compensate loved ones of loss
in income and general wellbeing. Within this context, remittances flow is
likely to smoothen consumption expenditure of recipient households at all
times, increase per capita income and boost aggregate output in the receiving
economy. Overall, the occurrence of remittance receipts motivated by altruism
will positively impact on economic growth as well as the economic
development of the receiving economy
Third, remittances motivated essentially by migrant’s self interest will tend to
flow as disguised capital into the receiving economy. The overall assumption
here is that remittances flow responds to real investment opportunities in
migrants’ country of origin. Thus, it represents direct investments by migrants
in the receiving economy. The consequence here is that remittances as
disguised capital flow will enhance domestic investment in the recipient
economy and help close the investment-saving gap in recipient economy.
Overall, the occurrence of remittance receipts motivated by self interest will
positively impact on domestic investment as well as the economic
development of the receiving economy.
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Fourth, remittances inflow may be so significant in volume as to result in an
artificial appreciation of the real exchange rate of the receiving economy. In
this case, remittances inflow may reduce the foreign trade competitiveness
which in turn, weakens the real external balance and by implication, the
current account balance position of the recipient economy. Consequently,
remittances in this context halt the receiving country’s trade balance via a
reduction in exports of traded goods. Overall, the occurrence of remittance
receipts in volumes that reduce the foreign trade competitiveness of the
receiving economy, will adversely impact on the trade balance as well as the
economic development of the receiving economy.
4.1.1 Remittances and Growth
This section examines the channels through which remittance receipts may
affect an economy’s growth. Remittance receipts can in principle affect growth
through three channels:

Their effects on the growth of the economy’s technological capacity;

Their effects on the rate of accumulation of productive assets (i.e., the
level of domestic investment); and

Their effects on the efficiency of the allocation of new capital.

A familiar channel through which the arrival of workers’ remittances
can affect the rate of growth of an economy’s technological capacity is
through Dutch disease effects—that is, effects that operate through the
influence of remittances on the real exchange rate. Suppose, for
example, that the rate of growth of domestic technological capacity is
at least partly a function of the share of domestic traded goods
production in GDP. This could be the case if production in some
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component of the traded goods sector—for example, nontraditional
manufactures intended for export—increases the technological capacity
of other firms in the economy. This could come about as the result of
training, learning by doing, demonstration effects, “self-discovery,” or
similar dynamic production externalities. Since these externalities are
positive on firms outside the traded goods sector, in the absence of
corrective policy intervention the presence of such externalities creates
a distortion that renders the domestic traded goods sector sub-optimally
small.

The arrival of (or an increase in) workers’ remittances can affect the
severity of this distortion. To the extent that an increase in remittance
receipts results in an appreciation of the economy’s equilibrium real
exchange rate and causing a contraction in traded goods production.
Since the traded goods sector would in any case have been suboptimally small even without remittance inflows, the addition of
workers’ remittances aggravates a pre-existing distortion, reducing the
rate of growth of the economy’s technological capacity. This is
precisely the phenomenon that has come to be known as Dutch disease.
However, it is important to emphasize that this outcome is not a
necessary implication of the appreciation of the real exchange rate and
contraction of the traded goods sector associated with the arrival of (or
an increase in) remittances. There is no “disease” if there are no
distortions, because the real exchange rate appreciation is optimal in
that case.
4.1.2 Remittances and Domestic Investment
The presence of workers’ remittances may affect the rate of investment in the
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recipient economy. Whether this happens depends on how remittance receipts
are used. In turn, the disposition of remittance receipts depends on the motives
driving remittance flows. The overall assumption here is that remittances flow
as disguised capital flows, representing direct investments by migrants in the
receiving economy. Besides, the analysis of short run macroeconomic effects of
remittances suggests that it is actually the increase in private consumption that
might trigger investments, and thus possibly foster long run economic growth.
Firms can also benefit from the “extension of investment credit allowed by the
increase in the liquidity of banks from remittance deposits” (Glytsos, 2001).
Hence, short run effects depend crucially on the expected response of domestic
supply: while a lack of reaction
of
the
latter
would
dissipate
the
remittance-induced demand stimulus through inflation and import increase,
in this case, there is a chance that remittances might raise the level of
domestic activity.
Aside from their effects on the level of investment, remittance flows may
affect the efficiency of investment in the receiving country, both in the short
run and in the long run. In the short run, remittance flows can have an effect on
investment efficiency if remittances are transfers with a merit good component,
that merit good happens to be an investment good (e.g., education for the
children, residential investment), and the migrant can indeed enforce his or her
preferences on the recipient. Under these circumstances, remittance flows may
affect the efficiency of investment if the migrant is either more or less well
informed about relative rates of return among competing projects in the
domestic economy than is the recipient. To the extent that remittances
represent a disguised capital inflow, replacing other flows that would have
been intermediated differently in the domestic economy (e.g., through the
domestic banking system), they tend to have effects on the efficiency of
87
investment depending on whether the individual investing the funds on behalf
of the migrant is a more or less efficient intermediary than the alternative
intermediary in the domestic financial system.
4.1.3 Remittances, Real Exchange Rate, and Dutch Disease
López, Molina, and Bussolo (2008) provide a very useful discussion on the
channel through which remittances, real exchange rate, and Dutch disease may
be linked. According to them, workers’ remittances can be viewed as a capital
inflow, and therefore the theory of the Dutch disease phenomenon associated
with a surge in inflows (perhaps because of the discovery of new natural
resources) can also be applied in this context. In order to isolate the specific
channels transmitting remittances shocks through the economy, consider first a
small open economy model with no leisure-consumption trade-off. In this
setup, an increase in remittances is equivalent to a (permanent) increase in
incomes of the households. Assuming that non-tradables are normal goods, the
positive remittances income shocks result in extra spending on both tradables
and non-tradables. Because most SSA countries are price takers in
international markets, growing demand does not raise the prices of tradables.
However, because the prices of non-tradables are determined in the domestic
economy, they increase due to additional demand, or the so-called spending
effect. There is also a “resource movement effect.” The relative price change
between tradables and non-tradables makes production of the latter more
profitable.
Output growth in the non-tradable sectors will push up factor demands,
especially for those factors used intensively in these sectors. Increased factor
demand by the expanding sectors will be accommodated by factors released
88
from other sectors (the resource movement effect) and, depending on the
behavior of total supply of the factor, will normally result in higher factor
returns in the final equilibrium. The price shift and resource reallocation in
favor of non-tradables erode the competitiveness of export-oriented sectors and
hurt import-competing sectors. The final result of this real exchange rate
appreciation is normally increased import flows and lower export sales. When
the above assumption of no consumption-leisure trade-off in the household
utility function is removed, the above effects are exacerbated. Without this
assumption, an increase in non-labour income, as is the case with remittances,
influences household decisions to supply labour—namely, individuals can now
consume more of both goods and leisure (that is, the income effect dominates),
and thus their labour supply is reduced. In turn, reduced labour supply implies
rising wages, and this additional pressure on wages intensifies the effects of
real exchange rate appreciation described earlier.
There are a number of connected macroeconomic effects that can result from a
real exchange rate appreciation associated with remittances flows. They
include:
• Adverse effects on the tradable sector of the economy. Although remittances
flows are likely to lead to an expansion of the nontradable sector (as a result of
the increase experienced in domestic demand), both export- and importcompeting industries (that is, the tradable sector of the economy) would be
adversely affected by real exchange rate appreciation and the associated loss of
international competitiveness. The negative impact of remittances on the
tradable sector may be reinforced if they also fuel inflation and higher prices
result in higher economy wide wages. This effect would be further magnified if
remittances also reduce the labour supply. In these circumstances, the
nontradable sector may be in the position of passing some of the wage
89
pressures on to prices, but this is likely to be much more difficult for a tradable
sector facing international competition, which, as a result, will lose
competitiveness.
• Widening of the current account deficit. In principle, it is difficult to justify
that an increase in domestic demand will be passed in full to the non-tradable
sector. So, to the extent that some of the remittances induced consumption is
directed toward tradable goods, there will be an increase in the demand for
imports. This, coupled with the loss of international competitiveness for
domestic firms mentioned in the previous paragraph, would likely result in
deteriorations of the external position.
• Weaker monetary control, inflationary pressures, and the sectoral allocation
of investment. If remittances flows do not leave the country (at least in full)
through a widening of the current account, large flows will push up monetary
aggregates, potentially derailing inflation targets. Experience also indicates
that prices of financial assets, and particularly of real estate, can rise rapidly
following a surge in remittances, something that in turn may introduce
significant distortions in the economy and affect the sectoral allocation of
investment and lead to overinvestment in some sectors (for example, real
estate).
4.2 The Empirical Models
The empirical models of the study are derived from the analytical framework
discussed in section 4.1. Using a cross-country approach to look at the
development-remittances nexus, care is taken to correct for reverse causality
and other sources of endogeneity in remittance flows. As a starting point, it is
90
important to emphasize that macro-econometric modeling aims at explaining
the empirical behaviour of an actual economic system. Such models will be
systems of inter-linked equations estimated from either time-series data, cross
sectional data, or even a combination of the two using appropriate statistical or
econometric techniques. Instrumental Variable (IV) approach is in this case
employed to deal with problems of endogeneity that are often associated with
systems of inter-linked equations. The use of these instrumental variables
allows researchers to circumvent the inconsistency and inefficiency problems
following from correlation of the individual explanatory variables with the
errors.
In addition, emphasis is placed on the fact that many economic relationships
are dynamic in nature and that one of the advantages of panel data is that they
allow the researcher to better understand the dynamics of adjustment within
these economic relationships. Model specification begins with a set of
structural equations made up of three models of system equations. Each of
these systems of structural equations is thereafter linked respectively to a
specified linear dynamic panel data model to be estimated. These models
account for major economic growth indicators which include OutputRemittances Model (Model 1), Investment-Remittances Model (Model 2), and
Trade Balance-Remittances Model (Model 3). Data analysis are done using
STATA 10.1 statistical software which is a very recent version of the software
that is widely reputed for dealing with the problems that are unique to crosssectional and panel data studies.
91
4.2.1
Model 1: The Output-Remittances Model
The essential assumption in these relationships is that remittances are
motivated primarily by altruism and hence will most often exhibit
countercyclical characteristics. In line with Chami et al (2008) as discussed in
section 4.2, a positive productivity shock in the recipient economy will give
rise to an increase in domestic output and help transfer some of the resulting
benefits to the remitter by inducing him or her to reduce the amount remitted
periodically and to increase his or her own consumption and vice versa.
The assumption of altruistically motivated remittances is adequately captured
within a system of equations characterized by three endogenous variables in
three equations namely: growth rate of output (YGR), workers’ remittances
(WR), and per capita income (PCI). The first equation is a neoclassical
production function of the Cobb-Douglas form in which output (GDP) is
specified as a function of labour (L), capital (K), workers’ remittances, and a
technological factor or efficiency parameter (A). Two basic assumptions of the
neoclassical production function of the Cobb-Douglas form are:
1. Positive and diminishing returns to private inputs. For all K
> 0 and L > 0, the production function exhibits positive and
diminishing marginal products with respect to each input such
that:
dF dF
,
 0;
dK dL
d 2F
 0;
d ( LK )
and
d 2F d 2F
,
0
dK 2 dL2
Thus, the neoclassical technology assumes that, holding constant
the levels of technology and labour, each additional unit of
capital delivers positive additions to output, but these additions
decrease as the number of machines rises. The same property is
assumed for labour.
92
2. Inada conditions. The second defining characteristic of the
neoclassical production function is that the marginal product
of capital (or labour) approaches infinity as capital (or labour)
goes to 0 and approaches 0 as capital (or labour) goes to infinity:
 F 
 F 
lim 
 lim 


K  0 K

 L 0  L 
and
 F 
 F 
lim 
 lim 

0
K  K

 L   L 
This equation can be written explicitly as follows:
GDP  f ( A, L, K ,WR, PCI )
(1a)
Where A is the technological factor of the efficiency factor within the system
and relation (1a) can be re-written even in more explicit terms as:

GDP  AL K (1 )WR, PCI
(0    1)
Where α is the relative share of labour in total output and (1-α) is the relative
share of capital in total output.
On a priori ground, the following are expected:
GDP GDP GDP PCI
,
,
,
0
L
K
WR WR
In turn, the second equation endogenizes PCI as a function of REER, INF, INV
and the one period lag values of growth (𝑌𝐺𝑅𝑖,𝑡−1 ) as follows:
PCI  f (YGRt 1 , REER, INF , INV )
The a priori expectations are:
(1b)
PCI PCI
PCI PCI
,
 0 and
,
0
YGRt 1 INV
INF REER
93
The structural forms of Equations (1a – 1b) are rewritten in their linear forms
as shown below.
𝑌𝐺𝑅𝑖𝑡 = 𝛿11𝑖 + 𝛿12𝑖 𝐿𝐿𝑖𝑡 + 𝛿13𝑖 𝐿𝐾𝑖𝑡 + 𝛿14𝑖 𝑊𝑅𝑖𝑡 + 𝛿15𝑖 𝑃𝐶𝐼𝑖𝑡 + 𝜀1𝑖𝑡
(2𝑎)
𝑃𝐶𝐼𝑖𝑡 = 𝛿21𝑖 + 𝛿22𝑖 𝑌𝐺𝑅𝑖,𝑡−1 + 𝛿23𝑖 𝑅𝐸𝐸𝑅𝑖𝑡 + 𝛿24𝑖 𝐼𝑁𝐹𝑖𝑡 + 𝛿25𝑖 𝐼𝑁𝑉𝑖𝑡 + 𝜀2𝑖𝑡
(2𝑏)
𝑖 = 1, 2, … 21 (𝑐𝑜𝑢𝑛𝑡𝑟𝑖𝑒𝑠); 𝑡 = 1, 2, … ,8 (𝑦𝑒𝑎𝑟𝑠)
The labour and capital input variables in equation (2a) are now in their log
forms. Model 1 is intended to capture the role played by remittances in the
economic growth of the remittances recipient economy as well as the
distributional effect of previous period growth levels on the economy. By
substituting equation (2b) into equation (2a), a single equation of the linear
dynamic panel data model type is obtained in equation (3) as follows:
Substitute equation (2b) into equation (2a) to obtain the following:
𝑌𝐺𝑅𝑖𝑡 = 𝛿11𝑖 + 𝛿12𝑖 𝐿𝐿𝑖𝑡 + 𝛿13𝑖 𝐿𝐾𝑖𝑡 + 𝛿14𝑖 𝑊𝑅𝑖𝑡 + 𝛿15𝑖 (𝛿21𝑖 + 𝛿22𝑖 𝑌𝐺𝑅𝑖,𝑡−1 +
𝛿23𝑖 𝑅𝐸𝐸𝑅𝑖𝑡 + 𝛿24𝑖 𝐼𝑁𝐹𝑖𝑡 + 𝛿25𝑖 𝐼𝑁𝑉𝑖𝑡 + 𝜀2𝑖𝑡 ) + 𝜀1𝑖𝑡
(2c)
Equation (2c) may be expanded to obtain the following:
𝑌𝐺𝑅𝑖𝑡 = (𝛿11𝑖 + 𝛿15𝑖 𝛿21𝑖 ) + 𝛿12𝑖 𝐿𝐿𝑖𝑡 + 𝛿13𝑖 𝐿𝐾𝑖𝑡 + 𝛿14𝑖 𝑊𝑅𝑖𝑡 +
𝛿15𝑖 (𝛿22𝑖 𝑌𝐺𝑅𝑖,𝑡−1 +
(𝛿15𝑖 𝜀2𝑖𝑡 + 𝜀1𝑖𝑡 )
𝛿23𝑖 𝑅𝐸𝐸𝑅𝑖𝑡 + 𝛿24𝑖 𝐼𝑁𝐹𝑖𝑡 + 𝛿25𝑖 𝐼𝑁𝑉𝑖𝑡 ) +
(2d)
Rearrange equation (2d) to obtain the following:
𝑌𝐺𝑅𝑖𝑡 = 𝛿15𝑖 𝛿22𝑖 𝑌𝐺𝑅𝑖,𝑡−1 + 𝛿12𝑖 𝐿𝐿𝑖𝑡 + 𝛿13𝑖 𝐿𝐾𝑖𝑡 + 𝛿14𝑖 𝑊𝑅𝑖𝑡 +
𝛿15𝑖 𝛿23𝑖 𝑅𝐸𝐸𝑅𝑖𝑡 + 𝛿15𝑖 𝛿24𝑖 𝐼𝑁𝐹𝑖𝑡 + 𝛿15𝑖 𝛿25𝑖 𝐼𝑁𝑉𝑖𝑡 + {(𝛿11𝑖 + 𝛿15𝑖 𝛿21𝑖 ) +
(𝛿15𝑖 𝜀2𝑖𝑡 + 𝜀1𝑖𝑡 )} (2e)
From equation (2e), the following dynamic panel data model may be obtained
𝑌𝐺𝑅𝑖𝑡 = 𝜋1 𝑌𝐺𝑅𝑖.𝑡−1 + π′2 𝑋𝑖𝑡 + 𝜋3′ 𝑊𝑖𝑡 + 𝑈𝑖𝑡
(3)
𝑊ℎ𝑒𝑟𝑒: 𝜋1 = 𝛿15𝑖 𝛿22𝑖 𝑎𝑛𝑑 ,
94
𝑈𝑖𝑡 = {(𝛿11𝑖 + 𝛿15𝑖 𝛿21𝑖 ) + (𝛿15𝑖 𝜀2𝑖𝑡 + 𝜀1𝑖𝑡 )} = (vi + 𝑒𝑖𝑡 )
(4)
𝐹𝑜𝑟: 𝑣𝑖 = (𝛿11𝑖 + 𝛿15𝑖 𝛿21𝑖 ) 𝑎𝑛𝑑 𝑒𝑖𝑡 = (𝛿15𝑖 𝜀2𝑖𝑡 + 𝜀1𝑖𝑡 )
𝑋𝑖𝑡 is a vector of strictly exogenous covariates which include the following
variables:
𝑋𝑖𝑡′ = (𝐿𝐾, 𝐿𝐿, 𝑅𝐸𝐸𝑅, 𝐼𝑁𝐹, 𝐼𝑁𝑉)′
𝑊𝑖𝑡 on the other hand, is a vector of endogenous and predetermined covariates
which include the following variables:
𝑊𝑖𝑡′ = (𝑌𝐺𝑅𝑡−1 , 𝑊𝑅)′
𝜋𝑖 are vectors of parameters to be estimated.
The assumption of altruistically motivated remittances is thus adequately
captured within the resulting linear dynamic panel data model in equation (3).
𝑣𝑖 + 𝑒𝑖𝑡 is the usual error component decomposition of the error term;
𝑣𝑖 are unobserved individual-specific effects;
𝑒𝑖𝑡 are the observation-specific (idiosyncratic) errors;
𝜋𝑖 are vectors of parameters to be estimated.
The individual-specific effects, 𝑣𝑖
are assumed to be uncorrelated across
individuals, {𝐸(𝑣𝑖 , 𝑣𝑗 ) = 0; ∀𝑖 ≠ 𝑗} and with the disturbance of any individual
at all leads and lags {𝐸(𝑣𝑖 𝑒𝑗 ) = 0; ∀ 𝑖, 𝑗}, but may be correlated with the
explanatory variables {𝐸(𝑋𝑖𝑡 𝑣𝑗 ) = 𝑢𝑛𝑘𝑛𝑜𝑤𝑛, ∀ 𝑖, 𝑡}. The mean of 𝑣𝑖 is zero
2
{𝐸(𝑣𝑖 ) = 0, ∀𝑖} and its variance (𝜎𝑣𝑖
) may differ across individuals. The
observation-specific disturbance has mean zero {𝐸(𝑒𝑖𝑡 ) = 0, ∀𝑖, 𝑡} and is
uncorrelated across individuals and {𝐸(𝑒𝑖𝑡 𝑒𝑗𝑠 ) = 0 ∀𝑖 ≠ 𝑗, 𝑡 ≠ 𝑠}. In general,
2
) may differ across both individuals and periods. The initial
its variance (𝜎𝑒𝑖𝑡
95
observation 𝑌𝐺𝑅𝑖0 is uncorrelated with the disturbance of any individual for all
periods {𝐸(𝑌𝐺𝑅𝑖0 𝑒𝑗𝑡 ) = 0 ∀𝑖, 𝑗, 𝑡} but may be correlated with the individual
effects
{𝐸(𝑌𝐺𝑅𝑖0 𝑣𝑗 ) = 𝑢𝑛𝑘𝑛𝑜𝑤𝑛 ∀𝑖, 𝑗}.
The
autoregressive
parameter
satisfies |𝛿1 | < 1 (dynamic stability). The vector xit may include lags of
explanatory variables. It may also include covariates that are fixed over time
for a given individual, and/or covariates that vary over time but are shared by
all individuals.
All 𝑋𝑖𝑡 variables are defined as follows:
𝐿𝐿 = 𝑡ℎ𝑒 𝑙𝑜𝑔𝑎𝑟𝑖𝑡ℎ𝑚 𝑜𝑓 𝑡𝑜𝑡𝑎𝑙 𝑙𝑎𝑏𝑜𝑢𝑟 𝑓𝑜𝑟𝑐𝑒;
𝐿𝐾 = 𝑡ℎ𝑒 𝑙𝑜𝑔𝑎𝑟𝑖𝑡ℎ𝑚 𝑜𝑓 𝑐𝑎𝑝𝑖𝑡𝑎𝑙 𝑠𝑡𝑜𝑐𝑘;
𝑅𝐸𝐸𝑅 = 𝑟𝑒𝑎𝑙 𝑒𝑓𝑓𝑒𝑐𝑡𝑖𝑣𝑒 𝑒𝑥𝑐ℎ𝑎𝑛𝑔𝑒 𝑟𝑎𝑡𝑒;
𝐼𝑁𝐹 = 𝑖𝑛𝑓𝑙𝑎𝑡𝑖𝑜𝑛 𝑟𝑎𝑡𝑒;
𝐼𝑁𝑉 = 𝑑𝑜𝑚𝑒𝑠𝑡𝑖𝑐 𝑖𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡
𝑊𝑖𝑡 variables on the other hand are defined as follows:
𝑌𝐺𝑅𝑡−1 = 𝑡ℎ𝑒 𝑓𝑖𝑟𝑠𝑡 𝑝𝑒𝑟𝑖𝑜𝑑 𝑙𝑎𝑔 𝑜𝑓 𝑡ℎ𝑒 𝑑𝑒𝑝𝑒𝑛𝑑𝑒𝑛𝑡 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒 𝑌𝐺𝑅
𝑊𝑅 = 𝑤𝑜𝑟𝑘𝑒𝑟𝑠 ′ 𝑟𝑒𝑚𝑖𝑡𝑡𝑎𝑛𝑐𝑒𝑠
In order to get a consistent estimate of δ as N →∞ with T fixed, equation (3)
may be rewritten in first differenced notations. This also eliminates the
individual effects as follows:
𝐷. 𝑌𝐺𝑅𝑖𝑡 = 𝛿1 𝐷. 𝑌𝐺𝑅𝑖.𝑡−1 + δ′2 𝐷. 𝑋𝑖𝑡 + δ′3 𝐷. 𝑊𝑖𝑡 + 𝐷. 𝑒𝑖𝑡
(5)
The implication of transforming equation (3) into (5) is that the unobserved
individual-level effects, 𝑣𝑖 has disappeared from the differenced equation (5)
96
because it does not vary over time. In this way, differencing has successfully
dealt with the issue of country or individual specific effect also known as fixed
effect. The Ds are the first difference operators.
4.2.2
Model 2: Investment-Remittances Model
The assumption of assumption of altruistically motivated remittances in model
1 is now modified to accommodate a new working assumption that remittances
are motivated by self interest. In other words, remittance flows is viewed here
as disguised capital flows, representing investments by migrants in the
receiving economy. The focus here is not to investigate the role remittance
plays in the relationship between growth and investment (which ordinarily can
be captured using the conventional growth model). The emphasis rather is to
verify empirically whether remittances exhibit a crowding-out effect on
domestic investment in recipient economies of SSA. The very essence of this
model stems from recent theories which have focused on the idea that there can
be self-interested reasons for remitting as well. This according to Chami et al
(2003) nevertheless center on the family. These self-interested theories of
remittances are still based on the family because they view the family as a
business or as a nexus of contracts that enables the members to enter into
Pareto-improving arrangements. Several different types of businesses or
contracts are possible, which has led to various self-interested models of
remittances.
Lucas and Stark (1985) suggest that migrants may have investments that
need to be tended while they are away, so they will use other family members as
their agents. The remittances sent by the migrant are used to care for the
migrant's interests, but they also contain some compensation for the agents. In
97
what follows, the study examines the impact of remittance inflows on the
overall level of domestic investment in the recipient economies. The
assumption of remittances inflow motivated by self interest is again captured in
a system of equations comprising of two endogenous variables which include
INV and aggregate output (GDP) in two equations.
The first equation expresses domestic investment as a function of its
hypothesized determinants which include WR, interest rate (INT), and INF.
INV  f (WR, INT , FD, GDP)
(6a)
These variables are expected on a priori grounds to be signed as follows:
INV INV
INV INV
,
 0 and
,
0
FD GDP
WR INT
The second equation in turn endogenizes output (GDP) which is expressed as a
function of its hypothesized determinants including: INF and 𝐼𝑁𝑉𝑖,𝑡−1 .
GDP  f ( INF , INVt 1 )
(6b)
On the basis of a priori expectation, the sign of the explanatory variables are as
follows:
GDP
 0 and
INF
GDP
0
INVt 1
The explicit forms of the relations in model 2 are provided below:
𝐼𝑁𝑉𝑖𝑡 = 𝜂11𝑖 + 𝜂12𝑖 𝑊𝑅𝑖𝑡 + 𝜂13𝑖 𝐼𝑁𝑇𝑖𝑡 + 𝜂14𝑖 𝐹𝐷𝑖𝑡 + 𝜂15𝑖 𝐺𝐷𝑃𝑖𝑡 + 𝜀1𝑖𝑡
(7a)
𝐺𝐷𝑃𝑖𝑡 = 𝜂21𝑖 + 𝜂22𝑖 𝐼𝑁𝐹𝑖𝑡 + 𝜂23𝑖 𝐼𝑁𝑉𝑖,𝑡−1 + 𝜀2𝑖𝑡
(7b)
𝑖 = 1, 2, … 21 (𝑐𝑜𝑢𝑛𝑡𝑟𝑖𝑒𝑠); 𝑡 = 1, 2, … ,8 (𝑦𝑒𝑎𝑟𝑠)
98
Without any loss of generality, equations (7a and 7b) may now be transformed,
again by simple substitution, into a single linear dynamic panel data model to
be estimated using the system GMM estimation technique to obtain equation
(8) as follows:
Substitute equation (7b) into equation (7a) to obtain the following:
𝐼𝑁𝑉𝑖𝑡 = 𝜂11𝑖 + 𝜂12𝑖 𝑊𝑅𝑖𝑡 + 𝜂13𝑖 𝐼𝑁𝑇𝑖𝑡 + 𝜂14𝑖 𝐹𝐷𝑖𝑡 + 𝜂15𝑖 (𝜂21𝑖 + 𝜂22𝑖 𝐼𝑁𝐹𝑖𝑡 +
𝜂23𝑖 𝐼𝑁𝑉𝑖,𝑡−1 + 𝜀2𝑖𝑡 ) + 𝜀1𝑖𝑡
(7c)
Equation (7c) may be expanded to obtain the following:
𝐼𝑁𝑉𝑖𝑡 = (𝜂11𝑖 + 𝜂15𝑖 𝜂21𝑖 ) + 𝜂12𝑖 𝑊𝑅𝑖𝑡 + 𝜂13𝑖 𝐼𝑁𝑇𝑖𝑡 + 𝜂14𝑖 𝐹𝐷𝑖𝑡 +
𝜂15𝑖 𝜂22𝑖 𝐼𝑁𝐹𝑖𝑡 + 𝜂15𝑖 𝜂23𝑖 𝐼𝑁𝑉𝑖,𝑡−1 + (𝜂15𝑖 𝜀2𝑖𝑡 + 𝜀1𝑖𝑡 )
(7d)
Rearrange equation (7d) to obtain the following:
𝐼𝑁𝑉𝑖𝑡 = 𝜂15𝑖 𝜂23𝑖 𝐼𝑁𝑉𝑖,𝑡−1 + 𝜂12𝑖 𝑊𝑅𝑖𝑡 + 𝜂13𝑖 𝐼𝑁𝑇𝑖𝑡 + 𝜂14𝑖 𝐹𝐷𝑖𝑡 +
𝜂15𝑖 𝜂22𝑖 𝐼𝑁𝐹𝑖𝑡 + {(𝜂11𝑖 + 𝜂15𝑖 𝜂21𝑖 ) + (𝜂15𝑖 𝜀2𝑖𝑡 + 𝜀1𝑖𝑡 )}
(7e)
From equation (7e), the following dynamic panel data model may be obtained.
𝐼𝑁𝑉𝑖𝑡 = 𝛾1 𝐼𝑁𝑉𝑖.𝑡−1 + γ′2 𝑋𝑖𝑡 + γ′2 𝑊𝑖𝑡 + 𝑈𝑖𝑡
(8)
𝑊ℎ𝑒𝑟𝑒: 𝛾1 = 𝜂15𝑖 𝜂23𝑖 𝑎𝑛𝑑
𝑈𝑖𝑡 = {(𝜂11𝑖 + 𝜂15𝑖 𝜂21𝑖 ) + (𝜂15𝑖 𝜀
2𝑖𝑡
+ 𝜀1𝑖𝑡 )} = (vi + 𝑒𝑖𝑡 )
𝐹𝑜𝑟: 𝑣𝑖 = (𝜂11𝑖 + 𝜂15𝑖 𝜂21𝑖 ) 𝑎𝑛𝑑 𝑒𝑖𝑡 = (𝜂15𝑖 𝜀
2𝑖𝑡
(9)
+ 𝜀1𝑖𝑡 )
𝑋𝑖𝑡 is a vector of strictly exogenous covariates (ones dependent on neither
current nor past 𝑒𝑖𝑡 ); such that:
𝑋𝑖𝑡′ = (𝐼𝑁𝑇, 𝐼𝑁𝐹, 𝐹𝐷)′
𝑊𝑖𝑡 is a vector of predetermined covariates (which may include the lag of
INV) and endogenous covariates, all of which may be correlated with the 𝑣𝑖 .
𝑊𝑖𝑡 comprises of:
99
𝑊𝑖𝑡′ = (𝐼𝑁𝑉𝑡−1 , 𝑊𝑅)′
𝛾𝑖 are vectors of parameters to be estimated.
𝑣𝑖 + 𝑒𝑖𝑡 is the usual error component decomposition of the error term;
𝑣𝑖 are unobserved individual-specific effects;
𝑒𝑖𝑡 are the observation-specific (idiosyncratic) errors;
The variables in 𝑋𝑖𝑡 are defined as follows:
𝐼𝑁𝑇 = 𝑟𝑎𝑡𝑒 𝑜𝑓 𝑖𝑛𝑡𝑒𝑟𝑒𝑠𝑡;
𝐼𝑁𝐹 = 𝑖𝑛𝑓𝑙𝑎𝑡𝑖𝑜𝑛 𝑟𝑎𝑡𝑒;
𝐹𝐷 = 𝑓𝑖𝑛𝑎𝑛𝑐𝑖𝑎𝑙 𝑑𝑒𝑒𝑝𝑒𝑛𝑖𝑛𝑔;
Variables in 𝑊𝑖𝑡 on the other hand are defined as follows:
𝐼𝑁𝑉𝑡−1 = 𝑓𝑖𝑟𝑠𝑡 𝑝𝑒𝑟𝑖𝑜𝑑 𝑙𝑎𝑔 𝑜𝑓 𝑡ℎ𝑒 𝑑𝑒𝑝𝑒𝑛𝑑𝑒𝑛𝑡 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒 𝐼𝑁𝑉
𝑊𝑅 = 𝑤𝑜𝑟𝑘𝑒𝑟𝑠 ′ 𝑟𝑒𝑚𝑖𝑡𝑡𝑎𝑛𝑐𝑒𝑠
In order to get a consistent estimate of 𝛾 as N →∞ with T fixed, equation (8)
may be rewritten in first differenced notations as follows:
𝐷. 𝐼𝑁𝑉𝑖𝑡 = 𝛾1 𝐼𝑁𝑉𝑖.𝑡−1 + γ′2 𝐷. 𝑋𝑖𝑡 + γ′2 𝐷. 𝑊𝑖𝑡 + 𝐷. 𝑒𝑖𝑡
(10)
The unobserved individual-level effects, 𝑣𝑖 again has disappeared from the
differenced equation (10) because it does not vary over time. The Ds are the
first difference operators. This effectively has once again removed the fixed
effect elements from the model.
100
4.2.3 Model 3: Trade Balance-Remittances Model
The overall development impact of remittances via the external sector is
examined in this model following the Mundell-Flemming model of small open
economies. The underlying assumption here is that remittances may be
positively correlated with real exchange rate appreciation in the recipient
economy and thereby hindering the external foreign trade competitiveness of
the recipient economy. Significant inflows of remittances may result in the
artificial appreciation of the real exchange rate of the receiving economy and
consequently penalize the traded goods sector (since its exportables now
become more expensive and less competitive) in the other economies. The
model is made up of a system of equations comprising of two endogenous
variables, which are: real external balance (REB) and INF in two equations.
The first equation is an attempt to verify empirically whether remittances
inflow brings about an improvement in the real external balance (REB) which
is the endogenous variable in this equation. The regressors in this relation are
trade openness (OPEN), WR, INTR and INF. The specification follows.
REB  f (WR, OPEN , INT , INF )
(10a)
The a priori expectations in this case are:
REB REB
REB REB
,
 0 and
,
0
WR OPEN
INT INF
In what follows, inflation rate (INF) now appears as the endogenous variable in
the second equation and the objective here is to address possible
macroeconomic stabilizing role of the external sector proxied by lag period
levels of real external balance, in the recipient economies. The regressors
101
included in this equation are current account balance (CAB), REER and first
period lagged levels of real external balance (𝑅𝐸𝐵𝑖,𝑡−1 ).
INF  f (CAB, REER, REBt 1 )
(10b)
The related a priori expectations are:
INF INF
INF
,
 0 and
0
CAB REBt 1
REER
The structural form of the relations in model 3 is provided below.
𝑅𝐸𝐵𝑖𝑡 = 𝜆11𝑖 + 𝜆12𝑖 𝑊𝑅𝑖𝑡 + 𝜆13𝑖 𝑂𝑃𝐸𝑁𝑖𝑡 + 𝜆14𝑖 𝐼𝑁𝑇𝑅𝑖𝑡 + 𝜆15𝑖 𝐼𝑁𝐹𝑖𝑡 + 𝜀1𝑖𝑡
(11𝑎)
𝐼𝑁𝐹𝑖𝑡 = 𝜆21𝑖 + 𝜆22𝑖 𝐶𝐴𝐵𝑖𝑡 + 𝜆23𝑖 𝑅𝐸𝐸𝑅𝑖𝑡 + 𝜆24𝑖 𝑅𝐸𝐵𝑖,𝑡−1 + 𝜀2𝑖𝑡
(11b)
𝑖 = 1, 2, 3, … ,21 (𝑐𝑜𝑢𝑛𝑡𝑟𝑖𝑒𝑠)
𝑡 = 1, 2, 3, … ,8 (𝑦𝑒𝑎𝑟𝑠)
Equations (11a and 11b) are further transformed as in previous cases by simple
substitution into a single equation of the linear dynamic panel data model form
and this equation is an attempt to verify empirically whether remittances
inflow brings about an improvement of the external real external balance
(REB) as follows:
Substitute equation (11b) into equation (11a) to obtain the following
𝑅𝐸𝐵𝑖𝑡 = 𝜆11𝑖 + 𝜆12𝑖 𝑊𝑅𝑖𝑡 + 𝜆13𝑖 𝑂𝑃𝐸𝑁𝑖𝑡 + 𝜆14𝑖 𝐼𝑁𝑇𝑅𝑖𝑡 + 𝜆15𝑖 (𝜆21𝑖 + 𝜆22𝑖 𝐶𝐴𝐵𝑖𝑡 +
𝜆23𝑖 𝑅𝐸𝐸𝑅𝑖𝑡 + 𝜆24𝑖 𝑅𝐸𝐵𝑖,𝑡−1 + 𝜀2𝑖𝑡 ) + 𝜀1𝑖𝑡
(11c)
Equation (11c) may be expanded to obtain the following
𝑅𝐸𝐵𝑖𝑡 = (𝜆11𝑖 + 𝜆15𝑖 𝜆21𝑖 ) + 𝜆12𝑖 𝑊𝑅𝑖𝑡 + 𝜆13𝑖 𝑂𝑃𝐸𝑁𝑖𝑡 + 𝜆14𝑖 𝐼𝑁𝑇𝑅𝑖𝑡 +
𝜆15𝑖 (𝜆22𝑖 𝐶𝐴𝐵𝑖𝑡 + 𝜆23𝑖 𝑅𝐸𝐸𝑅𝑖𝑡 + 𝜆24𝑖 𝑅𝐸𝐵𝑖,𝑡−1 ) + (𝜆15𝑖 𝜀2𝑖𝑡 + 𝜀1𝑖𝑡 )
(11d)
Rearrange equation (11d) to obtain the following
𝑅𝐸𝐵𝑖𝑡 = 𝜆15𝑖 𝜆24𝑖 𝑅𝐸𝐵𝑖,𝑡−1 + 𝜆12𝑖 𝑊𝑅𝑖𝑡 + 𝜆13𝑖 𝑂𝑃𝐸𝑁𝑖𝑡 + 𝜆14𝑖 𝐼𝑁𝑇𝑅𝑖𝑡 +
𝜆15𝑖 𝜆22𝑖 𝐶𝐴𝐵𝑖𝑡 + 𝜆15𝑖 𝜆23𝑖 𝑅𝐸𝐸𝑅𝑖𝑡 + {(𝜆11𝑖 + 𝜆15𝑖 𝜆21𝑖 ) + (𝜆15𝑖 𝜀2𝑖𝑡 + 𝜀1𝑖𝑡 )}
(11e)
From equation (7e), the following dynamic panel data model may be obtained
102
𝑅𝐸𝐵𝑖𝑡 = 𝛽1 𝑅𝐸𝐵𝑖.𝑡−1 + β′2 𝑋𝑖𝑡 + β′3 𝑊𝑖𝑡 + 𝑈𝑖𝑡
(12)
𝑊ℎ𝑒𝑟𝑒: 𝛽1 = 𝜆15𝑖 𝜆24𝑖 𝑎𝑛𝑑 𝑈𝑖𝑡 = {(𝜆11𝑖 + 𝜆15𝑖 𝜆21𝑖 ) + (𝜆15𝑖 𝜀2𝑖𝑡 + 𝜀1𝑖𝑡 )}
= (vi + 𝑒𝑖𝑡 )
𝐹𝑜𝑟: 𝑣𝑖 = (𝜆11𝑖 + 𝜆15𝑖 𝜆21𝑖 ) 𝑎𝑛𝑑 𝑒𝑖𝑡 = (𝜆15𝑖 𝜀2𝑖𝑡 + 𝜀1𝑖𝑡 )
𝑋𝑖𝑡 is a vector of strictly exogenous covariates (ones dependent on neither current nor
past 𝑒𝑖𝑡 ); and may be written as:
𝑋𝑖𝑡′ = (𝐼𝑁𝑇𝑅, 𝐼𝑁𝐹, 𝑂𝑃𝐸𝑁, )′
𝑊𝑖𝑡 is a vector of predetermined covariates (which may include the lag of
REB) and endogenous covariates, all of which may be correlated with the 𝑣𝑖 .
These include:
𝑊𝑖𝑡′ = (𝑅𝐸𝐵𝑡−1 , 𝑅𝐸𝐸𝑅, 𝑊𝑅, 𝐶𝐴𝐵)′
𝛽𝑖 are vectors of parameters to be estimated.
𝑣𝑖 + 𝑒𝑖𝑡 is the usual error component decomposition of the error term;
𝑣𝑖 are unobserved individual-specific effects;
𝑒𝑖𝑡 are the observation-specific (idiosyncratic) errors;
𝑋𝑖𝑡 comprises variables that are defined as follows:
𝐼𝑁𝑇𝑅 = 𝑟𝑎𝑡𝑒 𝑜𝑓 𝑖𝑛𝑡𝑒𝑟𝑒𝑠𝑡;
𝐼𝑁𝐹 = 𝑖𝑛𝑓𝑙𝑎𝑡𝑖𝑜𝑛 𝑟𝑎𝑡𝑒;
𝑂𝑃𝐸𝑁 = 𝑡𝑟𝑎𝑑𝑒 𝑜𝑝𝑒𝑛𝑛𝑒𝑠𝑠
𝑊𝑖𝑡 on the other hand comprises variables that are defined as follows:
𝑅𝐸𝐵 = 𝑟𝑒𝑎𝑙 𝑒𝑥𝑡𝑒𝑟𝑛𝑎𝑙 𝑏𝑎𝑙𝑎𝑛𝑐𝑒
103
𝑊𝑅 = 𝑤𝑜𝑟𝑘𝑒𝑟𝑠 ′ 𝑟𝑒𝑚𝑖𝑡𝑡𝑎𝑛𝑐𝑒𝑠
𝐶𝐴𝐵 = 𝑐𝑢𝑟𝑟𝑒𝑛𝑡 𝑎𝑐𝑐𝑜𝑢𝑛𝑡 𝑏𝑎𝑙𝑎𝑛𝑐𝑒
Equation (12) may be rewritten in first differenced notations in order to get a
consistent estimate of 𝛽 as N →∞ with T fixed as follows:
𝐷. 𝑅𝐸𝐵𝑖𝑡 = 𝛽1 𝐷. 𝑅𝐸𝐵𝑖.𝑡−1 + β′2 𝐷. 𝑋𝑖𝑡 + β′3 D. 𝑊𝑖𝑡 + 𝐷. 𝑒𝑖𝑡
(13)
The unobserved individual-level effects, 𝑣𝑖 again has been eliminated from the
differenced equation (13) in line with the earlier argument that it does not vary
over time. The Ds are the first difference operators.
4.3 Model Estimation Technique
There are at least two sources of endogeneity that may bias estimates of how
the explanatory variables in equations (3) and (8) and (12) affect the dependent
variable in each of the three specified models: first is the unobservable
heterogeneity (which arises if there are unobservable factors that affect both
the dependent and explanatory variables) and, second is simultaneity (which
arises if the independent variables are a function of the dependent variable or
expected values of the dependent variable). One other likely important source
of endogeneity often overlooked in most empirical researches arises because of
the fact that the relations among individual’s or country’s (in this case)
observable characteristics are likely to be dynamic. That is, a country’s current
performance will affect her future realizations, which will in turn affect her
future performance.
Working within the context of remittance flows, current country remittance
realizations will affect future economic performance and this may, in turn,
104
affect future country remittance realizations. Thus, giving rise to what may be
termed as “dynamic endogeneity”. The argument here centers on the fact that
cross-sectional variation in observed country economic structures is driven by
both unobservable heterogeneity and the country’s history. As such, any
attempt to explain the role of remittance flows or its effect on economic
performances of selected countries that does not recognize these sources of
endogeneity may be biased.
The emphasis on unobservable heterogeneity in the literature as the major
source of endogeneity often accounts for the widespread use of panel data and
fixed-effects estimator. However, traditional fixed-effects (or “within”)
estimates that eliminate unobservable heterogeneity are only consistent under
the assumption that country characteristics or structures are strictly exogenous.
That is, that they are purely random observations through time and are
unrelated to the country’s history. This is a strong assumption that is unlikely
to hold in practice. So, while OLS estimation may be biased because it ignores
unobservable heterogeneity, fixed-effects estimation may be biased since it
ignores dynamic endogeneity.
The problem of endogeneity that is often associated with the use panel data
analysis are thus resolved in this study by the choice of the System GMM
Estimator to estimate the relation between remittance flows and country
economic performance in three different Dynamic Panel Data Model
framework. This methodology not only eliminates any bias that may arise from
ignoring dynamic endogeneity, but also provides theoretically based and
powerful instruments that accounts for simultaneity while eliminating any
unobservable heterogeneity. Dynamic panel estimation is most useful in
situations where some unobservable factor affects both the dependent variable
and the explanatory variables, and some explanatory variables are strongly
105
related to past values of the dependent variable. This is likely to be the case in
regressions of remittance flows on economic performance. This is because
remittance flows tend to exert a strong, immediate and persistent effect on
economic performance.
The dynamic panel data regression models described in equations (3) and (8)
and (12) are in fact characterized by another source of persistence over time.
That is the problem of autocorrelation which is due to the presence of a lagged
dependent variable among the regressors. There are also two major and
important complications arising from efforts to estimate models (3, 8 and 12)
using macroeconomic panel data: first, the presence of endogenous and/or
predetermined covariates, and second, the small time-series and cross-sectional
dimensions of the typical panel data set. These identified complications may be
addressed using the Arellano and Bond (1991) generalized method of moments
(GMM) estimator (usually called standard first-differenced GMM estimator) or
the augmented version proposed by Arellano and Bover (1995) and Blundell
and Bond (1998), known as (system GMM estimator).
The dynamic structure of equations 3, 8 and 12 suggests that the OLS
estimator will be upward biased and inconsistent, this is because the lagged
level of income is correlated with the error term. The problem will not be
solved even if the within transformation is applied owing to a downward bias
(Nickell, 1981) and inconsistency. The Generalized Method of Moments
(GMM) technique turns out to be the possible solution. Blundell and Bond
(1998) show that when α (the coefficient of the lagged dependent variable in
the dynamic model) approaches one, so that the dependent variable follows a
path close to a random walk, the Differenced–GMM (Arellano and Bond,
1991) has poor finite sample properties and it is downwards biased, especially
when T is small. Bond, Hoeffler and Temple (2001) argue that this is likely to
106
be a serious issue for autoregressive models like equations 3, 8 and 12.
Therefore, the Blundell and Bond (1998) System–GMM — derived from the
estimation of a system of two simultaneous equations, one in levels (with
lagged first differences as instruments) and the other in first differences (with
lagged levels as instruments) becomes a more viable estimator.
As emphasized by Bun and Windmeijer (2009), the good performance of the
system GMM estimator relative to the difference GMM estimator in terms of
finite sample bias and root mean square error, has made it the estimator of
choice in many applied panel data settings. In multivariate dynamic panel
models, the System–GMM estimator is also shown to perform better than the
Differenced–GMM when series are persistent (α close to unity) and there is a
dramatic reduction in the finite sample bias due to the exploitation of
additional moment conditions (Blundell, Bond and Windmeijer, 2000).
In the presence of heteroscedasticity and serial correlation, the two-step
System–GMM uses a consistent estimate of the weighting matrix, taking the
residuals from the one-step estimate (Davidson and MacKinnon, 2004).
Though asymptotically more efficient, the two-step GMM presents estimates
of the standard errors that tend to be severely downward biased. However, it is
possible to solve this problem using the finite-sample correction to the two–
step covariance matrix derived by Windmeijer (2005), which can make twostep robust GMM estimates more efficient than one-step robust ones,
especially for System–GMM (Roodman, 2006).
Bond, Hoeffler and Temple (2001) provide a useful insight in the GMM
estimation of dynamic growth models, arguing that the pooled OLS and the
LSDV estimators should be considered respectively as the upper and lower
bound. As a result, whether the Differenced–GMM coefficient is close to or
107
lower than the within group one; this is likely a sign that the estimates are
biased downward (maybe because of a weak instrument problem). Thus, if this
is the case, the use of System– GMM is highly recommended and its estimates
should lie between OLS and LSDV. Unarguably, there is evidence that the
System GMM produces results that: (1) lies between the upper and lower
bound represented by OLS and LSDV, (2) shows an efficiency gain, and (3)
has valid instrument set (see: Presbitero, 2006).
The first-differenced GMM estimator simply transforms the original model to
eliminate the unobserved effects and rely on limited serial correlation in the
transformed error process to obtain valid moment conditions or instrumental
variables. The extended GMM (system GMM) estimators incorporate
additional moment conditions for the untransformed equations in levels, and it
relies on instrumental variables that are orthogonal to the individual-specific
effects. Blundell and Bond (1998) show that an additional mild stationarity
restriction on the initial conditions process allows the use of an extended
system GMM estimator that uses lagged differences of the dependent variable
as instruments for equations in levels, in addition to lagged levels of dependent
variable as instruments for equations in first differences (Baltagi, 2005).
108
4.4 Definition of Variables and Data Sources
The summary of definition of variables employed in the study and data sources
are presented in the Table 4.1.
Table 4.1: Summary of Definition of Variables and Data Sources
Variable
Real Gross
Definition
The broadest quantitative measure of a
Source of Data
World Bank
domestic
nation’s total economic activity. It
(2010), Africa
product (GDP)
measures, in constant (2000 US dollars)
development
prices, the value of economic activity
indicators online
within a country’s geographic borders,
including all final goods and services
produced over a period of time (usually a
year).
Growth rate of
This is the annual percentage change in
real GDP (YGR) the value of the real GDP.
World Bank
(2010), Africa
development
indicators online
109
Labour Force
Total labour force, also called the
World Bank
(L)
economically active population,
(2010), Africa
"comprises all persons of either sex who
development
furnish the supply of labour for the
indicators online
production of economic goods and
services." Labour force includes people
ages 15 and older who meet the
International Labour Organization (ILO)
definition of the economically active
population.
Stock of
Stock of physical capital input per
World Bank
physical capital worker. The proxy for this variable is the
(2010), Africa
input (K)
development
gross fixed capital formation
indicators online
Workers’
Workers’ remittances received comprise
World Bank
remittances
of current transfers by migrant workers.
(2010), Africa
(WR)
It is measured as a ratio of GDP
development
indicators online
Investment
Gross Domestic Investment also known
World Bank
(INV)
as Gross Capital Formation. This is the
(2010), Africa
total change in the value of fixed assets
development
plus change in stocks.
indicators online
African Countries
Real effective
Real effective exchange rate index
World Bank
exchange rate
represents the relative importance of
(2010), Africa
(REER)
each selected currency to all other
development
currencies.
indicators online
Countries
110
Inflation rate
This is the annual percentage change in
World Bank
(INF)
consumer price index (CPI)
(2010), Africa
development
indicators online
Trade openness The sum of imports and exports of goods
World Bank
(OPEN)
and services divided by GDP in constant
(2010), Africa
2000 prices
development
indicators online
Financial
The degree of financial deepening
deepening (FD) variable is the ratio of broad money
World Bank
(2010), Africa
supply (M2) to GDP. It is a measure of
development
the size of the banking sector rather than
indicators online
the overall performance.
Real Interest
Real interest rate is the lending interest
World Bank
rate (INT)
rate adjusted for inflation as measured by (2010), Africa
the gross domestic product (GDP)
development
deflator.
indicators online
Real external
Exports minus imports or the difference
World Bank
balance (REB)
between free on board exports and cost,
(2010), Africa
insurance, and freight imports of goods
development
and service
indicators online
Current Account Current account balance is the sum of net World Bank
Balance (CAB)
exports of goods and services, net
(2010), Africa
income, and net current transfers.
development
indicators online
111
CHAPTER FIVE
DISCUSSION OF EMPIRICAL RESULTS
5.1 Introduction
Three sets of empirical results are presented in this chapter. These results are
the outcome of the estimation exercises involving models (3), (8) and (12)
using the system GMM estimation technique. The ordinary least square (OLS)
and the least square dummy variable (LSDV) results are also presented in each
model. The goal here is to verify the position of Bond, Hoeffler and Temple
(2001) that the pooled OLS and the LSDV estimators should be considered
respectively as the upper and lower bound for the system GMM coefficients.
The first set of results is on the remittances-growth nexus and it involves
measuring the impact of remittances and other control variables on the
economic growth of the 21 selected SSA countries. The second set of results is
on the remittances-investments nexus and it involves measuring the impact of
remittances and other control variables on domestic investment in the 21
selected SSA countries. Finally, the third set of results is on the remittancestrade balance nexus and it involves measuring the impact of remittances and
other control variables on foreign trade balance in the 21 selected SSA
countries. STATA 10.1 statistical software was used for the data analysis. The
XTABOND2 command was engaged in the implementation of the model
estimation.
The empirical results for each model estimated are presented in turn after
interpreting the one presented. The discussion of results places much emphasis
on the system GMM using the one-step and two-step options. The idea here is
to draw from the strength of the two-step GMM estimates since in the presence
of heteroskedasticity and serial correlation, the two-step System–GMM uses a
112
consistent estimate of the weighting matrix. This usually is done by taking the
residuals from the one-step estimate (Davidson and MacKinnon, 2004). For
one-step estimation, the robust estimator of the covariance matrix of the
parameter estimates yield a resulting standard error estimates that are
consistent in the presence of any pattern of heteroskedasticity and
autocorrelation within panels. In two-step estimation, the standard covariance
matrix is already robust in theory; but typically yields standard errors that are
downward biased. Thus, though the two-step GMM estimates may be
asymptotically more efficient; extreme caution must be exercised here since
this option often presents standard errors that tend to be severely downward
biased. Fortunately, Roodman (2006) has clarified that it is possible to solve
this problem using the finite-sample correction to the two–step covariance
matrix derived by Windmeijer (2005), which interestingly can make the twostep robust GMM estimates more efficient than the one-step robust ones,
especially for System–GMM.
Time dummies are included in all model estimates because the autocorrelation
test and the robust estimates of the coefficient standard errors assume no
correlation across individuals in the idiosyncratic disturbances. Time dummies
make this assumption more likely to hold (Roodman, 2006). Country dummies
are also included in the case of LSDV models. The country dummies are
however absorbed in the presented results so as to allow for space and easy
estimation. Considerable attention is given to the various specifications and
diagnostic test results in the interpretation of results. This is deliberately so
since only satisfactory specifications and diagnostic test results will give
credibility to whatever coefficient estimates as well as t-statistics and standard
errors that are obtained. In addition, results of each coefficient estimate may
never make complete sense without an alignment of empirical findings with
113
practical realism. In view of this fact, effort is made to identify possible
transmission mechanism within the economy in each case of interpreted
results. Implications of findings for policy are also dealt with in this chapter.
5.1 Presentation of Estimated Empirical Results in the GrowthRemittances Model
Dynamic panel data model estimation results for equation 3 in model 1 are
presented in appendices 1 through 6 and are reported in table 5.1. This includes
four separate results in columns 1 to 4 of the table. Column 1 comprises the
OLS estimated results, column 2 has the one-step system GMM results and
column 3 includes the two-step system GMM results. Column 4 is made up of
the results for the LSDV estimated model. An underlying advantage of the
dynamic system GMM estimation is that all variables from the regression that
are not correlated with the error term (including lagged and differenced
variables) can be potentially used as valid instruments (Greene, 2008). Optimal
set of internal instruments were utilized by engaging the collapse option in the
system GMM results. All estimations are robust to heteroskedasticity or
autocorrelation. This is irrespective of whether they are considered under OLS,
system GMM, or LSDV.
In this model specification, lagged YGR and WR are predetermined and
endogenous variables respectively. Hence, I control for the endogeneity of
these variables in its lagged form as regressors by using internal instruments;
namely, lagged levels of the standard differenced equation (equation 5) and
lagged differences of the levels equation (equation 3). The list of these internal
instruments can be found in appendices 1 and 3. As an additional check of
potential endogeneity problems I investigate the correlation coefficients (see
114
appendix 2) between residuals from the base regression and independent
variables. The coefficients of correlations suggest that none of the independent
variables is highly correlated with predicted residuals.
115
Table 5.1: Estimated Empirical Results in the Output-Remittances Model (Model 1)
Dependent Variable: YGR
OLS
SYSTEM-GMM
LSDV
One-Step
Two-Step
Instrument Weight
collapsed
collapsed
Regressors
(1)
(2)
(3)
(4)
YGR(-1)
0.33958**
0.26652**
0.26529*
0.03141
(0.136)
(0.105)
(.064)
(0.129)
Log(labour)
0.64111***
0.67894***
0.74795*
-9.40942
(0.361)
(0.362)
(0.199)
(19.829)
Log(capital)
-1.68758***
-2.04399
-1.87093***
-0.81266
(0.911)
(1.352)
(0.983)
(1.643)
REER
-0.00026
1.12000
0.0003
0.00389
(0.002)
(0.001)
(0.001)
(0.003)
INF
-0.09096**
-0.10451**
-0.10773*
-0.15324*
(0.035)
(0.040)
(0.037)
(0.041)
INV
0.00111**
0.00146***
0.0015**
0.00117
(0.001)
(0.0007)
(0.001)
(0.001)
WR
-0.00026
-0.00001
-0.00112
0.00034
(0.002)
(0.002)
(0.001)
(0.003)
Constant
13.162
15.41628
14.2918**
19.06527
(5.678)
(7.948)
(5.746)
(22.631)
Time Dummy
Yes
Yes
Yes
Yes
Country Dummy
No
No
No
Yes
Observations
133
133
133
133
No. of countries
19
19
19
19
Instrument count
18
18
F-stat (Wald χ2 )
4.24
43.10
158.89
3.45
F-stat (p-value)
[0.0000]
[0.0000]
[0.0000]
[0.0002]
AR(2)
[0.394]
[0.411]
AR(3)
[0.231]
[]0.220
Sargan Test (OIR)
[0.930]
[0.930]
Hansen Test (OIR)
[0.870]
[0.870]
Notes: Robust standard errors, consistent in the presence of any pattern of heteroskedasticity
and autocorrelation within panels are reported in curly brackets.
Robust standard errors are with Windmeijer (2005) finite-sample correction for the
two-step covariance matrix
P-values are reported in square brackets
* indicates significant at 1 percent level
** indicates significant at 5 percent level
*** indicates significant at 10 percent level
116
An examination of results in Table 5.1 begins with some specification or
diagnostic tests. As a starting point, the system GMM estimators assume that
the idiosyncratic errors 𝑣𝑖𝑡 are serially uncorrelated for consistent estimations.
The presence of autocorrelation will indicate that lags of the dependent
variable (and any other variables used as instruments that are not strictly
exogenous), are in fact endogenous, thus bad instruments. Arellano and Bond
develop a test for this phenomenon that would potentially render some lags
invalid as instruments. Of course, the full disturbance 𝜀𝑖𝑡 is presumed
autocorrelated because it contains fixed effects, and the estimators are designed
to eliminate this source of trouble.
The Arellano-Bond test for autocorrelation is applied to the differenced
residuals in order to purge the unobserved and perfectly autocorrelated
idiosyncratic errors. These results are reported as AR(2) and AR(3) in the
lower portion of table 5.1. The null hypothesis here that 𝑐𝑜𝑣(∆𝑣𝑖𝑡 , ∆𝑣𝑖,𝑡−𝑘 ) =
0 for k = 1, 2 and 3 is rejected at a level of 0.05 if 𝑝 < 0.05. If 𝑣𝑖𝑡 are serially
uncorrelated, then the null of no serial correlation will be rejected at order 1
but not at higher orders. This indeed is the case with results in columns 2 and
3. Here, it can be concluded that there is no evidence of serial correlation at the
five percent level of significance. Given this results, the estimates can be
regarded as consistent.
The next specification test is a test of overidentifying restrictions of whether
the instruments, as a group, appear exogenous. This test of instrument validity
has to do with a comparism of the number of instruments used in each case and
the related number of parameters. It is implemented by the Sargan and Hansen
J tests. For one-step, non-robust estimation, the Sargan statistic which is the
minimized value of the one-step GMM criterion function, is applicable. The
Sargan statistic in this case is however not robust to autocorrelation. So for
117
one-step, robust estimation (and for all two-step estimation), the xtabond2
command also reports the Hansen J statistic, which is the minimized value of
the two-step GMM criterion function, and is robust to autocorrelation. In
addition, xtabond2 still reports the Sargan statistic in these cases because the
Hansen J test has its own problem: it can be greatly weakened by instrument
proliferation. Only the respective p-values are reported for this test results in
the lower part of table 1. Here, the null hypothesis that the population moment
condition is valid is not rejected if 𝑝 > 0.05. The summary statistics in
columns 2 and 3 indicate that the one-step and two-step system GMM dynamic
panel models of the selected 21 SSA countries have 18 instruments and 14
parameters each. This represents a total of 4 overidentifying restrictions in
each case. In both specifications, the Hansen–J statistic does not reject the
Over-Identifying Restrictions (OIR), thus confirming that the instrument set
can be considered valid.
The F-statistic is the small-sample counterpart of the Wald (Chi Squared)
statistic and it is a measure of the overall significance of the estimated models
and the values here in each of the specifications are considerably satisfactory
with level of significance being one percent in each case. This of course is
indicative that all the exogenous variables jointly explained significantly, the
economic growth process across the sampled SSA countries over the study
period.
Results on the control variables are broadly and satisfactorily consistent with
theoretical expectations. The Blundell–Bond (system-GMM) robust estimates
(in specifications 2 and 3) indicate that growth dynamics are crucial and
significant across the sampled SSA countries. An inspection of these results
reveals that past realizations of economic growth produced some
contemporaneous positive impact on economic growth. Precisely, a 100
118
percent increase in the past realizations of growth explained positively, about
27 percent of current growth levels. This is irrespective of whether the one-step
or two-step collapsed instruments options is considered. In both cases
therefore, it cannot be concluded that growth dynamics do not retard economic
growth in the study group over the study period. This finding clearly agrees
with that of Ahortor and Adenutsi (2009).
Size of labour force also produced some very meaningful and interesting
results in the Blundell–Bond robust estimates. One striking observation here is
that labour input produced a contemporaneous positive impact on economic
growth across the sampled countries over the study period. This variable is also
highly significant at the one percent level in the two–step system GMM option.
In more definitive terms, a one hundred percent increase in size of labour force
under the two –step system GMM estimates, explains about 74.80 percent of
the increase in economic growth across the study group. This result is not
surprising since labour supply is in relative abundance in most of the SSA
countries. It is therefore expected that the average production function in these
economies will be characterized by enormous labour intensity. The
transmission mechanism here is such that additional labour input in any of the
selected SSA countries will directly impact on output growth. However, this
argument will only hold as long as these economies operate within the positive
region of the production function (that is before diminishing returns set in).
The implication of this result for theory is that economic growth inducing role
of labour input is mostly applicable in the selected SSA economies.
Surprisingly, capital input is negatively signed and weakly significant at the
ten percent level when the two-step system GMM with collapsed instrument
option is considered. This result indicates that a one hundred percent increase
in capital input in these SSA economies will explain about 187 percent
119
reduction in economic growth rate for these sampled economies. Capital input
in this sense turns out not to be a major consideration in driving economic
growth in the sampled SSA economies. This fact may not be unconnected with
the relative dominance of the labour intensive sectors in most SSA economies.
Real effective exchange rate (REER) is found to be insignificant as an
explanatory factor of economic growth in SSA. This of course is not
unexpected given the small size of the foreign trade sector in most SSA
economies. The foreign traded goods in SSA countries are dominated by
imports of consumption goods which in the long-run do not bring about much
economic growth.
Inflation rate variable should attract some comments as it explains economic
growth in the sampled group over the study period significantly at the one
percent level. This variable under the two-step system GMM with collapsed
instrument option specifications produced a contemporaneous negative impact
on economic growth across the sampled countries over the study period. As
can be seen, a 100 percent increase in inflation rate explains about 108 percent
reduction in economic growth in the selected SSA economies. A negatively
signed coefficient for the variable, inflation rate is of course not unexpected as
can be explained by the following transmission mechanism.
It is well known that borrowers benefit from major episodes of inflation, but
lenders of loanable funds (that also double as profit maximizers) do counter the
tide of inflation to minimize loss from the phenomenon. They achieve this by
frequently adjusting the rate of interest upward probably to compensate
themselves for loss of value in loaned funds and to keep track with the trend of
inflation. The consequence of this kind of behaviour is that rising interest rate
will constitute a disincentive to investment in these SSA economies and of
120
course lead to a decline in economic growth. Theoretically, this result confirms
that while some mild inflation rate may be consistent with the goal of
economic growth, persistently high inflation rate definitely will impede
economic growth.
Given the two-step collapsed instruments option in the Blundell–Bond
estimates, the domestic investment variable has a significant (at the five
percent level) contemporaneous positive impact on economic growth across
the sampled countries over the study period. Precisely, a 100 percent increase
in domestic investment will explain about 0.15 percent increase in economic
growth. The very low nature of share of economic growth explained by
domestic investment may actually be explained by the high dependence on,
and of course, the dominance of foreign investment component of total
investment in many of the SSA economies. Overall, this relationship is not
unexpected from the viewpoint of theory as investment remains a traditional
driver of economic growth in every economy. This finding is similar to those
of Chami et al (2003) and Faini (2006) who also found that domestic
investment and private capital flows were positively related to growth.
However,
the
workers’
remittances
variable
has
an
insignificant
contemporaneous negative impact on economic growth across the sampled
countries over the study period. What this finding suggests is that a significant
proportion of remittances inflow to SSA is directed (intentionally or otherwise)
at some economically unproductive uses. This result is in agreement with
findings in Chami et al (2005). It is however in contrast with Ahortor and
Adenutsi (2009). The relatively small volume of workers’ remittances inflows
to SSA countries could actually be the explanation for the insignificant result
obtained for this variable. In terms of basis for comparism of the two works,
Ahortor and Adenutsi (2009) used the Blundell and Bond GMM on a set of
121
dynamic panel models while Chami et al (2003) employed panel data
methodology or the fixed effect estimation procedure. On grounds of data type,
the outcomes of the two works may be comparable but this may not be true in
the case of estimation techniques. The policy implication of this result is that
for now, workers’ remittances inflows may not be effectively relied on in
driving economic growth in these SSA economies.
5.2 Presentation of Estimated Empirical Results in the InvestmentRemittances Model
Dynamic panel data model estimation results for equation 8 in model 2 are
presented in appendix 5 to 8 and reported in Table 5.2. The system GMM
estimator is categorized into the one-step and two-step option, and is reported
in columns 2 and 3 respectively. The OLS estimates and the LSDV estimates
are reported in columns 1 and 4 respectively. All estimations are again in this
case robust to heteroskedasticity or autocorrelation. This is irrespective of the
option under which the estimates are considered. Details of these results are in
appendices 7 to 11.
The related predetermined and endogenous variables on the right hand side of
this specification include the lagged INV and WR respectively. To control for
endogeneity of these variables that appear as regressors, internal instruments
are again utilized; and these include the lagged levels of the standard
differenced equation (equation 10) and lagged differences of the levels
equation (equation 8). The list of these internal instruments are included in the
results output and can be found in appendices 7 and 9. Correlation coefficients
(see appendices 8 and 10) between residuals from the base regression and
independent variables were computed as an additional check of potential
122
endogeneity problems. An investigation of these coefficients of correlations
suggests that none of the independent variables is highly correlated with
predicted residuals.
Table 5.2: Estimated Empirical Results in the Investment-Remittances Model (Model 2)
Dependent Variable: INV
OLS
SYSTEM-GMM
LSDV
One-Step
Two-Step
Instrument Weight
collapsed
collapsed
Regressors
(1)
(2)
(3)
(4)
INV(-1)
1.49215*
1.59850*
1.52204*
1.66606*
(0.067)
(0.079)
(0.079)
(0.112)
WR
-2.04091*
-2.21179*
-2.09074*
-2.19928*
(0.103)
(0.124)
(0.135)
(0.093)
INTR
-17.550
-22.14462
-4.80923
3.64175
(11.838)
(26.458)
(22.0004)
(10.959)
INF
3.81952
-1.51909
0.95473
3.78888
(6.265)
(4.505)
(3.862)
(5.518)
FD
1.07333*
1.33819**
0.87714***
-0.24794
(0.262)
(0.639)
(0.484)
(0.299)
Constant
-225.1606
-131.8035
-70.604
-476.703
(106.741)
(162.038)
(178.967)
(150.954)
Time Dummy
Yes
Yes
Yes
Yes
Country Dummy
No
No
No
Yes
Observations
147
147
147
147
No. of countries
21
21
21
21
Instrument count
13
13
F-stat (Wald χ2 )
95.80
2378
260.57
108.82
F-stat (p-value)
[0.0000]
[0.0000]
[0.0000]
[0.0000]
AR(2)
[0.146]
[0.196]
AR(3)
[0.127]
[0.225]
Sargan Test (OIR)
[0.000]
[0.000]
Hansen Test (OIR)
[0.262]
[0.262]
Notes: Robust standard errors, consistent in the presence of any pattern of heteroskedasticity
and autocorrelation within panels are reported in curly brackets.
Robust standard errors are with Windmeijer (2005) finite-sample correction for the
two-step covariance matrix
P-values are reported in square brackets
* indicates significant at 1 percent level
** indicates significant at 5 percent level
*** indicates significant at 10 percent level
123
Some specification tests are again examined as a starting point to determine the
reliability of coefficient estimates reported in Table 5.2. The working
assumption that the idiosyncratic errors 𝑣𝑖𝑡 in the system GMM estimators are
serially uncorrelated for consistent estimations is retained. The assumption that
the full disturbance 𝜀𝑖𝑡 is autocorrelated because it contains fixed effects is not
relaxed hence; the system GMM estimator remains the most appropriate tool to
eliminate this source of trouble. The Arellano-Bond test for autocorrelation are
reported as AR(2) and AR(3) in the lower portion of table 5.2. The p-values
are greater than 0.05 in the one-step and two-step system GMM estimates
indicating that there is no evidence of serial correlation at the five percent level
of significance. Given these results, the estimates can be regarded as consistent
and the instruments are not endogenous.
The test of overidentifying restrictions of whether the instruments, as a group,
appear exogenous is implemented by the Sargan and Hansen J tests. Here the
Hansen J statistic, which is the minimized value of the two-step system GMM
criterion function, and is robust to autocorrelation, is of tremendous
importance. Only the respective p-values are reported for this test results in
the lower part of table 5.2. of course, the null hypothesis that the population
moment condition is valid is not rejected if 𝑝 > 0.05. In columns 2 and 3, the
summary statistics indicate that the system dynamic panel model of the
selected 21 SSA countries has 13 instruments and 12 parameters in both the
one-step and two-step system GMM options. This represents a total of 1
overidentifying restrictions in each case. Thus, the Hansen–J statistic does not
reject the Over-Identifying Restrictions (OIR), thus confirming that the
instrument set can be considered valid. The F-statistic which measures the
overall significance of all regressors in the estimated model is satisfactorily
significant at the one percent level. This of course is indicative of the fact that
124
all the exogenous variables, in each estimated result, jointly explained
significantly, the systematic variations in domestic investment across the
sampled SSA countries over the study period.
A look at the control variables reveals the coefficient estimates are sufficiently
consistent with theoretical expectations. The Blundell–Bond robust estimates
of lagged domestic investment are positively signed. As can be seen in
columns 2 and 3 of table 5.2, past realizations of domestic investment
positively impact on its contemporaneous levels. These domestic investment
dynamics are significant at the 1 percent level in these specifications. In
specific terms, a 10 percent increase in domestic investment dynamics will
explain about 15.98 percent and 15.22 percent of the increase in the
contemporaneous realizations of domestic investment within the sampled SSA
countries. This of course is when the one-step and two-step collapsed
instruments options are considered respectively. Domestic investment
dynamics suggest here that domestic investment in SSA has a way of feeding
on its past values.
Workers’ remittance variable also has highly significant results in both the
one-step and two-step system GMM robust estimates. In all, this set of results
for both specifications produced a contemporaneous negative impact on
domestic investment across the sampled countries over the study period. The
levels of significance here are all one percent. In more definitive terms, a 10
percent increase in workers’ remittances under the Blundell–Bond estimates,
will explain negatively about 20.9 percent, of the changes in domestic
investment across the study group. This is suggestive of the possibility of a
crowding-out of domestic investment role for remittances in the selected SSA
economies. The negative nature of this relationship is suggestive of the fact
125
that remittances flow to SSA is basically a financial flow and does not
necessarily double as capital flows.
Financial deepening variable has some significant results ranging from the five
percent to the ten percent levels. Given the collapse option in the one-step and
two-step system GMM, a 10 percent increase in financial deepening will
produce about 13.38 percent and 8.77 percent increase in domestic investment
respectively. Given this finding, it can be remarked here that policies which
encourage banks to increase provision of financial services that have wider
choice of services geared to all levels of society will help attract more
remittances to Africa. Remittances related banking products or services will be
immensely useful in this regards.
5.3 Presentation of Estimated Empirical Results in the Trade BalanceRemittances Model
Dynamic panel data model estimation results for equation 13 in model 3 are
reported in Table 5.3. The results of the Blundell–Bond system GMM
estimator as in previous cases are reported in columns 2 and 3 of the table. In
addition to the system GMM estimator, the OLS and the LSDV estimators are
also reported in columns 1 and 4 of the table respectively. The complete results
are presented in appendices 9 to 12. As usual, all estimations are robust to
heteroskedasticity and autocorrelation. Details of these results are in
appendices 9 to 12.
The related predetermined and endogenous variables on the right hand side of
this specification include the lagged REB on the one hand, and REER, CAB as
well as WR on the other hand respectively. As in previous two cases, I control
126
for endogeneity of these variables that appear as regressors by utilizing internal
instruments that include the lagged levels of the standard differenced equation
and lagged differences of the levels equation. The list of these internal
instruments can be found in appendices 14 and 16. Correlation coefficients
(see appendices 15 and 17) between residuals from the base regression and
independent variables were computed as an additional check of potential
endogeneity problems. An investigation of these coefficients of correlations
suggests that none of the independent variables is highly correlated with
predicted residuals.
127
Table 5.3: Estimated Results in the Trade Balance-Remittances Model (Model 3)
Dependent Variable: REB
OLS
SYSTEM-GMM
LSDV
One-Step
Two-Step
Instrument Weight
collapsed
collapsed
Regressors
(1)
(2)
(3)
(4)
REB(-1)
0.54847*
0.32606*
0.33445*
0.36634*
(0.093)
(0.055)
(0.056)
(0.093)
WR
-0.44741*
-0.20740**
-0.22063*
-0.22396**
(0.101)
(0.074)
(0.072)
(0.102)
CAB
0.60073*
0.63834*
0.63423*
0.65873*
(0.072)
(0.019)
(0.026)
(0.064)
INTR
-22.0679***
-20.4288
-9.8458
7.78239
(11.175)
(11.842)
(10.463)
(8.989)
OPEN
-13.9869
54.7389
-82.0211
147.7737
(69.580)
(177.83)
(131.248)
(265.901)
REER
0.07964
0.090
0.03815
-0.60125
(0.124)
(0.210)
(0.1548)
(0.433)
Constant
57.036
-301.372
-184.3704
-171.851
(124.148)
(291.56)
(273.769)
(169.054)
Time Dummy
Yes
Yes
Yes
Yes
Country Dummy
No
No
No
Yes
Observations
146
146
146
146
No. of countries
21
21
21
21
Instrument count
19
19
F-stat (Wald χ2 )
135.08
5881.31
3994.26
60.27
F-stat (p-values)
[0.0000]
[0.0000]
[0.0000]
[0.0000]
AR(2)
[0.277]
[0.378]
AR(3)
[0.146]
[0.267]
Sargan Test (OIR)
[0.000]
[0.000]
Hansen Test (OIR)
[0.219]
[0.219]
Notes: Robust standard errors, consistent in the presence of any pattern of heteroskedasticity
and autocorrelation within panels are reported in curly brackets.
Robust standard errors are with Windmeijer (2005) finite-sample correction for the
two-step covariance matrix
P-values are reported in square brackets
* indicates significant at 1 percent level
** indicates significant at 5 percent level
*** indicates significant at 10 percent level
Relevant specification tests results are first examined in Table 5.3. The
working assumption that the idiosyncratic errors 𝑣𝑖𝑡 are serially uncorrelated
128
for consistent estimations under the system GMM estimators is still retained.
The Arellano-Bond test for autocorrelation AR(2) and AR(3), are reported in
the lower portion of Table 5.3. Interestingly from these results, there is no
evidence of serial correlation at the five percent level of significance. This
provides sufficient basis to conclude that the coefficient estimates can be
regarded as consistent. The p-values of the Hansen J tests statistic indicate that
the system dynamic panel of the selected 21 SSA countries has 19 instruments
and 13 parameters. This represents a total of 6 overidentifying restrictions in
each of the options in columns 2 and 3 of table 5.3. Consequently, the Hansen–
J statistic does not reject the Over-Identifying Restrictions (OIR), thus
confirming that the instrument set can be considered valid. The F-statistic is
highly significant at the one percent level. This result is indicative of the fact
that all the regressors jointly explained significantly, the systematic variations
in real external balance (REB) across the sampled SSA countries over the
study period.
The control variables in the estimated results are next considered. These
figures reveal some impressive and striking results which are largely
significant and sufficiently consistent with theoretical expectations. The
Blundell–Bond two-step system GMM robust estimate of lagged real external
balance is positively signed and significant at the one percent level. As can be
seen, this result indicates that past realizations of real external balance
positively impact on its contemporaneous levels. In specific terms, a 10 percent
increase in real external balance dynamics will explain about 3.34 percent
increase in the contemporaneous realizations of real external balance within
the sampled SSA countries. The applicable level of significance here is one
percent. And the collapsed instruments option was utilized.
129
Workers’ remittances variable produced a highly significant result in the
Blundell–Bond system GMM two-step robust estimates. A particularly striking
thing about this result is that it is negatively signed and significant at the one
percent level. This clearly suggests that workers’ remittances inflow depresses
foreign trade balance in SSA. Contemporaneously, real external balance in the
selected SSA countries decline by about 2.21 percent as workers’ remittance
inflows into SSA rise by 10 percent. This finding suggests that the bulk
receipts from workers’ remittances flows to SSA may actually be channeled
into the consumption of imported goods. This gives the impression that
workers’ remittances flows are potentially harmful to the economies of the
receiving SSA countries if deliberate policies to channel such flows into
productive uses are not formulated and enforced.
Current account balance (CAB) is another variable that is positively signed and
highly significant at the one percent level. Interestingly, this result is not
unexpected since a positive CAB is largely indicative of healthy domestic
economy which provides a platform for a favourable trade balance. In more
definitive terms, real external balance will increase by about 6.34 percent for
every 10 percent rise in current account balance. By implication, policies
aimed at boosting the current account balance of SSA economies will also
assist in boosting the trade balance of these economies.
The interest rate variable is negatively signed and insignificant even at the ten
percent level. The meaning of this is that every rise in the domestic interest
(lending) rate in the SSA economies increases the cost of production and by
implication reduces competitiveness of all exported goods. The direct
consequence of this will be a depressed trade balance for these SSA
economies. Trade openness and real effective exchange rate variables are also
insignificant explanatory factors of the changes in real external balance. This is
130
not surprising given the very low size of the external sector and the
preponderance of primary products in the total exports of most SSA
economies. However, the negative signs of the openness variable in two out of
the four specifications call for some form of guided deregulation in SSA
economies seeking to completely open up to the rest of the world economies.
A quick remark that must be made regarding the results in the three estimated
models is the failure of the system GMM coefficient estimates in some cases,
to lie within the boundaries created by the OLS and LSDV estimates as
prescribed by Bond, Hoeffler and Temple (2001). In checking for the source of
this discrepancy, the validity of all instruments used in the model estimation
was examined and found to be satisfactory. The only logical explanation for
the failure of the Bond, Hoeffler and Temple (2001) simulation prescription to
apply to the analysis in this work is the possibility that the simulation exercise
is very likely sensitive to data employed. This openly calls to question the
validity of the position of Bond, Hoeffler and Temple (2001) regarding the
boundaries created by OLS and LSDV estimates for the system GMM
estimates.
5.4 Policy Implications of Findings
A number of policy issues naturally arise from the empirical findings in this
thesis. First, the positive role of labour in the economic growth process is
highlighted in the results. The relative abundance of labour supply in most
SSA economies can be taken advantage of as a viable demographic dividend or
surplus to accelerate the process of economic growth in these countries.
Relevant authorities can in this wise embark on policy measures that tackles
the recurring low economic growth problem in SSA through sustained
131
investment human capital. This will hopefully harness the abundant labour
resource and boost the human capital base of these economies. This policy
option will consequently raise the productivity of labour in these economies.
Overall, this will help address the concern for non-inclusive growth (by
increasing local content of employee within the industry in SSA) and also
drive a sustained economic growth agenda for SSA countries.
The cycles of persistent inflation which tend to discourage domestic
investment and consequently economic growth in the SSA region is another
area requiring policy attention. The monetary authorities as well as the rest of
the financial sector in the SSA countries must exercise greater caution in
matters involving money supply and interest rates respectively. The long term
economic goals of these economies should regularly be the overriding
consideration on decisions involving money supply and interest rates. This
implies constantly subjecting the exigencies of time to the long term economic
vision of the countries. A situation whereby the banking system freely adjust
the rate of interest on account of market realities should as a matter of policy
be regarded as unacceptable in these economies.
Given the significant positive relationship between investment and economic
growth, investment in both physical asset and human capital must officially be
recognized as a major driver of economic growth in the SSA countries. This
calls for policies that encourage private sector participation in the formation of
physical assets and human capital for greater efficiency. The governments
must withdraw from areas of investment she has failed over the course of time
to demonstrate unarguable managerial competencies. At the same time, she
must provide total incentive for private sector participation in such areas of the
economy. Typical examples include the establishment and/or management of
banks, factories, farms, etc. The involvement of government in these areas
132
should be limited to the provision of a conducive policy framework or
environment, and occasionally some form of subsidies to encourage optimal
production.
Workers’ remittances flow to SSA is found to negatively impact on economic
growth in the selected SSA countries over the study period suggesting that
most remittance receipts are not channeled into productive uses. Policy
measures must therefore be put in place to enhance tracking of these flows and
to encourage its channeling into more economically productive uses. This
could be achieved by enacting laws that require mandatory documentation of
all remittances flows at the point of collection by recipients.
Such
documentation should require information on share of receipts for specific uses
such as: investment in stocks, education, health, feeding, housing/rent, building
or construction activities, social or community projects, etc. With this policy in
place, all remittances flow through formal channels will be adequately tracked.
A policy that enables the banking system to use current and future remittances
flow as security in extending credit facilities to potentially enterprising
remittance recipients will encourage channeling of remittances into more
economically productive uses. In the alternative, SSA monetary authorities
should design a policy that guarantees loan facility to every regular remittances
recipient who indicates interest to use such credit for investment purposes
only. Such recipients must of course demonstrate convincing viable business
ideas to the participating banks (in the credit guarantee scheme) and the stream
of flows to the beneficiary must have been regular and stable over a specified
minimum period of time.
It has been shown from the empirical results that remittances flow to SSA
countries is predominantly financial flows rather than capital flows. This
means that the African Diaspora has not demonstrated sufficient interest in
133
investment opportunities back home. To stimulate the commitments of the
African Diaspora to invest at home, policies that encourage them to invest
directly without passing through third parties should be put in place. This will
hopefully guide against the recurring problem of information asymmetries and
associated moral hazards problems in transactions of this nature. The full
advantage of modern Information and Communication Technology (ICT)
infrastructure must be explored in implementing this policy. For example, the
various SSA countries could establish agencies to coordinate a one-stop,
regularly updated, online shop where all investment opportunities in the region
are showcased. This online shop should be freely accessible to all and
participation in all investment offers must be facilitated through a simplified
and secured software programme that enhances confidence in the system. This
call for an aggressive investment drive from remittances inflow to the SSA
region is further necessitated by the finding that remittances crowd-out
domestic investment in SSA and by the potential of workers’ remittances flow
to contribute meaningfully to the growth of SSA economies.
Policies aimed at attracting workers’ remittances may not be relied on to
enhance the trade balance of the SSA economies. This is in view of the finding
that workers’ remittances depress trade balance in these economies. To ensure
that workers’ remittances flow to SSA becomes more useful in promoting trade
balance, policy that encourage beneficiaries to productively engage these flows
must first be implemented. On the other hand, policies aimed at improving the
current account balance as well as trade balance in SSA will be largely
complementary and mutually beneficial.
134
CHAPTER SIX
SUMMARY, CONCLUSION AND RECOMMENDATIONS
6.1 Summary
Workers’ remittances are unarguably a vital source of finance for many
individuals and families in developing countries. Remittances recipients in
comparism with their peers, who do not receive such flows, have a higher
living standard. It is a common belief that workers’ remittances contribute to
the reduction of poverty, either when the poor receive remittances directly or
through multiplier effects which create additional demand, employment and
income. However, a number of researchers hold divergent views on the
question of whether remittances also constitute, at the aggregate level, a vital
source of development finance to many developing countries that attract
significant workers’ remittances flow. This study sheds new light on this
question by examining the economic growth consequences of workers’
remittances in some selected Sub-Saharan African (SSA) countries using the
macroeconomic approach.
The thesis is a cross-country study comprising of a panel of 21 countries within
the SSA region and the data cover the period 2000 to 2007. The study
examines three vital aspects of the research problem. These include the
remittances – output nexus, remittances – investment nexus, and remittances –
trade balance nexus. The overall goal of course is to determine the existence
any unique link between remittances flow and the above indicators of
economic growth taking the affected SSA countries as a group with similar
characteristics. The dynamic panel data modeling technique was engaged in
the study and it uses the Blundell-Bond (1998) system Generalized Methods of
Moment (GMM) estimator to provide additional insight into the problem. The
135
dynamic panel data model has a number of estimation problems which are
readily overcome by the above estimators.
The major empirical findings are quite striking and they suggest that the
central objective of this thesis has been empirically explored. For instance, the
results revealed that;

Workers’ remittances have a significant contemporaneous negative
impact on economic growth across the sampled countries over the
study period. This finding suggests that a significant proportion of
remittances inflow to SSA is directed (intentionally or otherwise) at
some non-economically productive uses. By implication, policy
incentives designed to attract more workers’ remittances to SSA
countries are not likely to help in promoting economic growth in these
economies except such flows are deliberately channeled into
economically productive ventures.

Past realizations of economic growth overall produced some
contemporaneous positive impact on economic growth itself thereby
indicating no empirical evidence that growth dynamics retard economic
growth within the study group over the study period. By implication,
this finding suggests that economic growth policies in SSA should of
necessity be guided by the integrated growth theory.
This theory
postulates that previous period growth largely explains current period
growth and current period growth again explains next period growth
and so on.

Contemporaneously, labour force has a positive impact on economic
growth suggesting that policies that are aimed at harnessing the
abundant labour supply in SSA countries will directly boost economic
growth in the selected SSA economies. A further implication of this
136
finding is that failure on the part of policy makers to develop capacity
of available labour force will directly hurt the economies of SSA
countries. In view of this, Ike (2000:149) cautioned that “the proportion
of non-workers to workers has important economic and social
repercussions” for any economy.

Inflation rate has a contemporaneous negative impact on economic
growth across the sampled countries over the study period. This is
suggestive of the possible role of high inflation rate as a disincentive to
investment in these SSA economies which in turn leads to a decline in
economic growth. As a consequence, the effectiveness of any policy
measure to encourage rapid domestic investment will partly depend on
the satisfactory performance of policies that counter high inflation rate
in SSA countries.

Expectedly, domestic investment has a significant contemporaneous
positive impact on economic growth across the sampled countries over
the study period. This is quite consistent with theoretical prediction and
is interestingly, a reassertion of the role of investment as a traditional
source of economic growth in every economy.

Capital and real effective exchange rate variables are insignificant
explanatory factors of economic growth in the SSA region considering
all available specifications.

Workers’ remittance variable has a significant contemporaneous
negative impact on domestic investment across the sampled countries
over the study period. The negative nature of this relationship is
suggestive of the fact that remittances flow to SSA is in the form of
financial flows rather than capital flows.

Financial deepening variable is a significant explanatory factor of
variations in domestic investment. This means that banks can help
137
attract more remittance flows to SSA by providing additional banking
products in the SSA economies.

Workers’ remittances have a negative and highly significant impact on
real external balance. This means that workers’ remittances inflow
depresses or weakens trade balance in SSA countries.

The variable current account balance is positively signed and
significantly impact on real external balance. This means that as the
economies of SSA countries become healthier, trade balance also
improves in these economies.
Policy options for governments and relevant monetary authorities were
suggested on the basis of each of the above empirical findings.
It is
believed that the identified negative impact of workers’ remittances inflow
on economic growth will be reversed if these policy options are embraced
by the various SSA economies.
6.2 Conclusion
The question of whether workers’ remittances constitute at the aggregate level,
a vital source of development finance to a selected developing countries within
the Sub-Saharan African region has been extensively explored in this study.
The application of the system GMM estimators to a set of dynamic panel data
models in investigating the research problem has proved quite intuitive and
immensely suitable. The empirical study sheds new light on the growthremittances nexus that are useful in the design of macroeconomic policies in
the SSA region and also provide the basis on which the policies can be
evaluated.
138
The results of this study clearly highlight the role of workers’ remittances in
the growth of the economies of the selected SSA countries and the policy
options available to the governments of these countries. The study also shows
the role of “integrated growth theory” in explaining economic growth within
the selected countries. And workers’ remittances may not be relied upon for
now to promote economic growth in the SSA region. Besides, it also reveals
that workers’ remittance inflows depress the external trade balance of the
recipient SSA economies. Another striking finding in this study is the fact that
workers’ remittance inflows to SSA exist only in the form of financial flows
considering its significant negative contribution to domestic investment in the
region.
The sustenance of workers’ remittances inflows and the productive use of all
such flows to SSA region, demand a central role for governments and
monetary authorities in terms of the provision of relevant policy direction.
Every related policy measure therefore should be targeted towards the
reorientation of senders and recipients of remittances so as to ensure that these
flows are regularly engaged productively. Moreover, it will not be out of place
if policy incentives are given a sectoral focus such that remittances are used
productively in sectors that are of greatest interest to the recipients. This will
hopefully allow for a stable and sustainable economic growth and development
in the SSA region.
6.3 Recommendations
Over the years, governments across countries have introduced a number of
policy measures to affect migrants' decisions to maximize the flow of
remittances back to the labour-sending country and to direct these flows to
139
socially
and
economically
optimal
ends.
A
major
difficulty
with
implementation of these policy measures is that remittances are essentially
private transfers, and so policy measures on these transfers must most often
take the form of incentives, rather than mandatory requirements. Given this
understanding and on the basis of findings in this study, the following
recommendations are considered necessary.

The need to engage all remittances recipients for enhanced data
gathering and management system is recommended. To this end, the
various monetary authorities in SSA should set up data gathering desk
in every bank serving as a remittance payment outlet within the region.
The policy should compulsorily require every remittance recipient to
declare and document the expected expenditure details of amount
received. Such details should include information on share of receipts
for specific uses such as: investment in stocks, education, health,
feeding, housing/rent, building, construction activities, social or
community projects, etc.

The establishment of a credit guarantee scheme by the individual
monetary authorities in SSA is recommended. This policy should be
designed to divert remittances receipt into more productive uses. To
ensure this objective is achieved, every regular remittances recipient
who indicates interest to use such credit for investment purposes only
should be extended the facility. Such recipients must however
demonstrate convincing viable business ideas to the participating banks
(in the credit guarantee scheme) and the stream of remittances flows to
the beneficiary must have been regular and stable over a specified
minimum period of time.
140

The joint venture approach to financing community development
projects by all of SSA is strongly recommended. Such joint venture
should be between individual government and the Diaspora and may be
coordinated by various leaders in the benefiting communities. The idea
here is that this strategy will encourage migrants and the beneficiaries
to use a proportion of remittances to fund community development
projects, with joint-financing provided in the form of public
subvention by the government.

Development of unique Banking Products for Migrants. Banks can
design ‘Real Estate Investment Products’ that are attractive to migrants
in their country of origin. The real estate market hopefully will
constitute the main investment niche for migrants in their country of
origin.

The governments of SSA countries should embark on policies that
facilitate investment by migrants in enterprise creation in labourexporting countries. One way this can be done is through the creation
of an investment opportunities directory for SSA countries.
Governments can make it a responsibility to regularly update this
directory which must be made available on the internet for ease of
access. This will hopefully create job opportunities to absorb the
surplus labour.

The various commercial banks should develop banking products that
will encourage migrants to maintain bank accounts in the labourexporting country. Such accounts should be denominated in applicable
foreign currency of choice to the migrant and must be made operational
in the migrant’s country of residence through the use of modern
information and communication technology.
141

The option of using Diaspora bonds to raise needed vital foreign
exchange for development in these SSA economies may be considered
by the various governments. However, this option will only be
attractive to the Diaspora if the issues of good governance,
transparency, the rule of law, etc are made sacrosanct by the various
SSA governments.
6.4 Limitations of the Study
The limitations encountered in the course of the study relate to the observed
difficulties encountered by the author and are considered sufficient to alter the
findings in this study in terms of outcome or timing if they were never
encountered. These limitations are outlined below.

The study would have been much more robust and encompassing if it
could be extended to cover the current global economic and financial
crisis vis-à-vis workers’ remittances flow to SSA. Many of the SSA
countries covered in this study do not promptly report remittances
inflow data to the International Monetary Fund (IMF) for compilation.
Consequently, this non availability of data up to the very recent time
greatly limited inclusion of this potentially interesting and important
aspect of the study.

The study could have been expanded to cover the expenditure pattern
of remittance receipts by recipients, as well as obstacles to free flow of
workers’ remittances in order to adequately capture the possible
economic benefits of flows. The requirement of a field survey
involving all sampled countries to accomplish this task was clearly
142
outside the scope of this thesis and hence constituted a limitation to the
study.

Inadequate funding also constituted a major limitation to this study. For
instance the inability of the researcher to acquire in time relevant
econometric software and other related manuals for data analysis
delayed the scheduled or timely completion of this PhD thesis.

The reliability of results in this study is limited by the accuracy of data
employed in the empirical analysis. The author’s role here is only to
ensure that to all intents and purposes, all secondary data employed for
the empirical analysis were sourced from well established and
internationally recognized credible sources.
6.5 Suggestions for Future Researches

A study on the impact of the current global economic and financial
crisis on workers’ remittances flow either to any of the individual SSA
countries or SSA as a group is suggested. Such study should address
the role of motives for remitting in sustaining remittances flow during
periods of global economic or financial crisis. This however is subject
to availability of the most recent data on workers’ remittances.

There is the need to investigate further the determinants of workers’
remittances flow to SSA at the micro level. Such study should also
account for the motives for remitting that are peculiar to SSA countries
from the perspective of the remitter as well as the recipient. This
suggestion is in view of the finding suggesting that only those
remittances recipients that are able to demonstrate adequate capacity to
manage current receipts satisfactorily can guarantee future flows.
143

A study on the expenditure pattern of remittances flow to individual
SSA countries will be a most important and interesting study. It is
therefore suggested that a study dedicated to this subject and based on a
field survey at both individual and household level be conducted in
future researches. This study should necessarily shed more light and
additional insights on the developmental roles and potentials of
remittances receipts in SSA at the micro level.

A study on labour force growth, labour productivity and remittance
flows in any specific SSA country is suggested. This of course will be
at the micro level and should further explore the finding suggesting that
labour force growth contribute positively to economic growth perhaps
through workers’ remittances.

A study that sheds more light on the poverty reducing role or potential
of workers’ remittances to SSA is suggested. Analytical tool involving
the use of a poverty transition matrix for the affected community is
highly recommended. What this means is that the research should be
conducted within the framework of a household survey.

A future research on the developmental impact of workers’ remittances
to SSA countries is suggested and such study should specifically cover
other indicators of growth and development such as health, investment
in human capital, housing, etc.
144
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APPENDIX 1: Two-Step System GMM Dynamic Panel Data Estimation (YGR)
. xtabond2 ygr l.ygr ll lk reer inf inv wr yr*, gmm(ygr l.ygr wr, lag(2 2) collapse equation
> (both)) iv( ll lk inv inf reer yr*) small robust artests(3) twostep
Favoring space over speed. To switch, type or click on mata: mata set matafavor speed, perm.
yr1 dropped due to collinearity
yr8 dropped due to collinearity
Warning: Two-step estimated covariance matrix of moments is singular.
Using a generalized inverse to calculate optimal weighting matrix for two-step estimation.
Difference-in-Sargan/Hansen statistics may be negative.
Dynamic panel-data estimation, two-step system GMM
Group variable: countryid
Time variable : year
Number of instruments = 18
F(13, 18)
=
158.89
Prob > F
=
0.000
ygr
Coef.
ygr
L1.
ll
lk
reer
inf
inv
wr
yr2
yr3
yr4
yr5
yr6
yr7
_cons
.2652887
.7479525
-1.870935
.0002908
-.1077338
.0014758
-.0011254
-.1041676
-.6190384
-.5167044
.440047
-.2591498
.9251511
14.29186
Number of obs
Number of groups
Obs per group: min
avg
max
Corrected
Std. Err.
.0640402
.1994866
.9835047
.0013007
.0369841
.0006263
.0013628
1.272746
.9749005
.6135131
.862063
.7781206
.5866291
5.746709
t
4.14
3.75
-1.90
0.22
-2.91
2.36
-0.83
-0.08
-0.63
-0.84
0.51
-0.33
1.58
2.49
P>|t|
0.001
0.001
0.073
0.826
0.009
0.030
0.420
0.936
0.533
0.411
0.616
0.743
0.132
0.023
=
=
=
=
=
133
19
7
7.00
7
[95% Conf. Interval]
.1307452
.3288467
-3.937202
-.0024419
-.1854345
.00016
-.0039885
-2.778109
-2.667228
-1.805648
-1.37108
-1.893921
-.307311
2.218477
.3998322
1.167058
.1953315
.0030234
-.0300331
.0027916
.0017377
2.569773
1.429152
.7722387
2.251174
1.375621
2.157613
26.36525
Instruments for first differences equation
Standard
D.(ll lk inv inf reer yr1 yr2 yr3 yr4 yr5 yr6 yr7 yr8)
GMM-type (missing=0, separate instruments for each period unless collapsed)
L2.(ygr L.ygr wr) collapsed
Instruments for levels equation
Standard
_cons
ll lk inv inf reer yr1 yr2 yr3 yr4 yr5 yr6 yr7 yr8
GMM-type (missing=0, separate instruments for each period unless collapsed)
DL.(ygr L.ygr wr) collapsed
Arellano-Bond test for AR(1) in first differences: z =
Arellano-Bond test for AR(2) in first differences: z =
Arellano-Bond test for AR(3) in first differences: z =
Pr > z =
Pr > z =
Pr > z =
0.009
0.411
0.220
Prob > chi2 =
0.930
Prob > chi2 =
0.870
Difference-in-Hansen tests of exogeneity of instrument subsets:
GMM instruments for levels
Hansen test excluding group:
chi2(1)
=
0.05 Prob > chi2 =
Difference (null H = exogenous): chi2(3)
=
1.20 Prob > chi2 =
0.830
0.753
Sargan test of
(Not robust,
Hansen test of
(Robust, but
overid. restrictions: chi2(4)
=
0.86
but not weakened by many instruments.)
overid. restrictions: chi2(4)
=
1.25
can be weakened by many instruments.)
-2.60
0.82
-1.23
161
APPENDIX 2: Correlation Coefficients for the Two-Step System GMM Dynamic Panel
Data Estimation (YGR)
. correlate
(obs=133)
resid l.ygr ll lk reer inf inv wr yr*
resid
resid
ygr
L1.
ll
lk
reer
inf
inv
wr
yr1
yr2
yr3
yr4
yr5
yr6
yr7
yr8
yr1
yr2
yr3
yr4
yr5
yr6
yr7
yr8
L.
ygr
ll
lk
reer
inf
inv
wr
1.0000
0.0734
-0.0387
-0.0540
-0.0282
0.0323
-0.0661
0.0108
.
-0.0044
-0.0084
-0.0056
-0.0348
0.0274
0.0247
0.0011
1.0000
0.0966
0.0257
-0.0287
-0.0941
0.1004
0.1135
.
-0.1485
0.0153
-0.0411
-0.0186
0.0436
0.0210
0.1283
1.0000
0.5340
0.0620
0.1537
0.4077
0.3662
.
-0.0238
-0.0159
-0.0078
0.0001
0.0079
0.0156
0.0239
1.0000
-0.1640
-0.0137
0.8616
0.4227
.
-0.1762
-0.1519
-0.0657
0.0163
0.0748
0.1145
0.1882
1.0000
-0.1631
-0.2448
-0.1362
.
0.0816
0.0674
0.0233
0.0061
0.0074
0.0070
-0.1928
1.0000
0.0124
-0.1257
.
-0.0574
0.0482
-0.0872
-0.0252
0.0275
0.0849
0.0092
1.0000
0.4120
.
-0.1572
-0.1592
-0.0895
-0.0082
0.0503
0.1042
0.2595
1.0000
.
-0.1475
-0.1198
-0.0822
-0.0077
0.0378
0.1095
0.2099
yr1
yr2
yr3
yr4
yr5
yr6
yr7
yr8
.
.
.
.
.
.
.
.
1.0000
-0.1667
-0.1667
-0.1667
-0.1667
-0.1667
-0.1667
1.0000
-0.1667
-0.1667
-0.1667
-0.1667
-0.1667
1.0000
-0.1667
-0.1667
-0.1667
-0.1667
1.0000
-0.1667
-0.1667
-0.1667
1.0000
-0.1667
-0.1667
1.0000
-0.1667
1.0000
162
APPENDIX 3: One-Step System GMM Dynamic Panel Data Estimation (YGR)
. xtabond2 ygr l.ygr ll lk reer inf inv wr yr*, gmm(ygr l.ygr wr, lag(2 2) collapse equation
> (both)) iv( ll lk inv inf reer yr*) small robust artests(3)
Favoring space over speed. To switch, type or click on mata: mata set matafavor speed, perm.
yr1 dropped due to collinearity
yr8 dropped due to collinearity
Warning: Two-step estimated covariance matrix of moments is singular.
Using a generalized inverse to calculate robust weighting matrix for Hansen test.
Difference-in-Sargan/Hansen statistics may be negative.
Dynamic panel-data estimation, one-step system GMM
Group variable: countryid
Time variable : year
Number of instruments = 18
F(13, 18)
=
43.10
Prob > F
=
0.000
ygr
Coef.
ygr
L1.
ll
lk
reer
inf
inv
wr
yr2
yr3
yr4
yr5
yr6
yr7
_cons
.2665238
.6789443
-2.043988
1.12e-06
-.1045146
.0014578
-.000013
-.1284378
-.6871525
-.5309716
.1960627
-.0095523
1.13749
15.41628
Number of obs
Number of groups
Obs per group: min
avg
max
Robust
Std. Err.
.1045137
.3622308
1.352894
.0017436
.0404259
.0007404
.0019914
1.223976
1.129847
.7044212
.98732
.7414303
.6897335
7.948597
t
2.55
1.87
-1.51
0.00
-2.59
1.97
-0.01
-0.10
-0.61
-0.75
0.20
-0.01
1.65
1.94
P>|t|
0.020
0.077
0.148
0.999
0.019
0.065
0.995
0.918
0.551
0.461
0.845
0.990
0.116
0.068
=
=
=
=
=
133
19
7
7.00
7
[95% Conf. Interval]
.0469486
-.0820743
-4.886312
-.003662
-.1894463
-.0000977
-.0041967
-2.699915
-3.060873
-2.010906
-1.87822
-1.56724
-.3115868
-1.283102
.486099
1.439963
.7983367
.0036642
-.0195828
.0030133
.0041707
2.44304
1.686569
.9489623
2.270345
1.548135
2.586566
32.11566
Instruments for first differences equation
Standard
D.(ll lk inv inf reer yr1 yr2 yr3 yr4 yr5 yr6 yr7 yr8)
GMM-type (missing=0, separate instruments for each period unless collapsed)
L2.(ygr L.ygr wr) collapsed
Instruments for levels equation
Standard
_cons
ll lk inv inf reer yr1 yr2 yr3 yr4 yr5 yr6 yr7 yr8
GMM-type (missing=0, separate instruments for each period unless collapsed)
DL.(ygr L.ygr wr) collapsed
Arellano-Bond test for AR(1) in first differences: z =
Arellano-Bond test for AR(2) in first differences: z =
Arellano-Bond test for AR(3) in first differences: z =
Pr > z =
Pr > z =
Pr > z =
0.014
0.394
0.231
Prob > chi2 =
0.930
Prob > chi2 =
0.870
Difference-in-Hansen tests of exogeneity of instrument subsets:
GMM instruments for levels
Hansen test excluding group:
chi2(1)
=
0.05 Prob > chi2 =
Difference (null H = exogenous): chi2(3)
=
1.20 Prob > chi2 =
0.830
0.753
Sargan test of
(Not robust,
Hansen test of
(Robust, but
overid. restrictions: chi2(4)
=
0.86
but not weakened by many instruments.)
overid. restrictions: chi2(4)
=
1.25
can be weakened by many instruments.)
-2.45
0.85
-1.20
163
APPENDIX 4: Correlation Coefficients for the One-Step System GMM Dynamic Panel
Data Estimation (YGR)
. correlate resid l.ygr ll lk reer inf inv wr yr*
(obs=133)
resid
resid
ygr
L1.
ll
lk
reer
inf
inv
wr
yr1
yr2
yr3
yr4
yr5
yr6
yr7
yr8
yr1
yr2
yr3
yr4
yr5
yr6
yr7
yr8
L.
ygr
ll
lk
reer
inf
inv
wr
1.0000
0.0966 1.0000
0.0257 0.5340 1.0000
-0.0287 0.0620 -0.1640 1.0000
-0.0941 0.1537 -0.0137 -0.1631
0.1004 0.4077 0.8616 -0.2448
0.1135 0.3662 0.4227 -0.1362
.
.
.
.
-0.1485 -0.0238 -0.1762 0.0816
0.0153 -0.0159 -0.1519 0.0674
-0.0411 -0.0078 -0.0657 0.0233
-0.0186 0.0001 0.0163 0.0061
0.0436 0.0079 0.0748 0.0074
0.0210 0.0156 0.1145 0.0070
0.1283 0.0239 0.1882 -0.1928
1.0000
0.0124
-0.1257
.
-0.0574
0.0482
-0.0872
-0.0252
0.0275
0.0849
0.0092
1.0000
0.4120
.
-0.1572
-0.1592
-0.0895
-0.0082
0.0503
0.1042
0.2595
1.0000
.
-0.1475
-0.1198
-0.0822
-0.0077
0.0378
0.1095
0.2099
yr6
yr7
yr8
1.0000
-0.1667 1.0000
-0.1667 -0.1667 1.0000
-0.1667 -0.1667 -0.1667 1.0000
-0.1667 -0.1667 -0.1667 -0.1667
1.0000
1.0000
0.0681
0.0004
-0.0095
-0.0100
0.0287
-0.0332
-0.0196
.
0.0000
0.0000
0.0000
-0.0000
0.0000
-0.0000
-0.0000
yr1
yr2
yr3
.
.
.
.
.
.
.
.
1.0000
-0.1667
-0.1667
-0.1667
-0.1667
-0.1667
-0.1667
1.0000
-0.1667
-0.1667
-0.1667
-0.1667
-0.1667
yr4
yr5
.
164
APPENDIX 5: LSDV Linear Regression Result (YGR)
. areg ygr l.ygr ll lk reer inf inv wr yr*, absorb(countryid) robust
(dropping yr1 because it does not vary within category)
Linear regression, absorbing indicators
ygr
Coef.
ygr
L1.
ll
lk
reer
inf
inv
wr
yr1
yr2
yr3
yr4
yr5
yr6
yr7
yr8
_cons
.0314063
-9.40942
-.8126612
.0038955
-.1532422
.0011784
.0003433
(dropped)
-2.040307
-1.913353
-1.905739
-.9680189
-.8609837
.4679434
(dropped)
19.06527
countryid
absorbed
Robust
Std. Err.
Number of obs
F( 13,
101)
Prob > F
R-squared
Adj R-squared
Root MSE
t
P>|t|
=
=
=
=
=
=
133
3.45
0.0002
0.4772
0.3167
3.0981
[95% Conf. Interval]
.1292819
19.82997
1.642863
.003133
.0412981
.0007783
.0028244
0.24
-0.47
-0.49
1.24
-3.71
1.51
0.12
0.809
0.636
0.622
0.217
0.000
0.133
0.904
-.2250541
-48.74675
-4.07166
-.0023195
-.2351664
-.0003656
-.0052597
.2878667
29.92791
2.446338
.0101105
-.0713179
.0027224
.0059462
2.910312
2.122244
1.887166
1.651482
1.18654
.9945623
-0.70
-0.90
-1.01
-0.59
-0.73
0.47
0.485
0.369
0.315
0.559
0.470
0.639
-7.813583
-6.123314
-5.649368
-4.244114
-3.21476
-1.505001
3.732969
2.296608
1.83789
2.308076
1.492793
2.440887
22.63085
0.84
0.402
-25.82824
63.95879
(19 categories)
165
APPENDIX 6: OLS Linear Regression Result (YGR)
. reg
ygr l.ygr ll lk reer inf inv wr yr*,
robust
Linear regression
Number of obs
F( 13,
119)
Prob > F
R-squared
Root MSE
ygr
Coef.
ygr
L1.
ll
lk
reer
inf
inv
wr
yr1
yr2
yr3
yr4
yr5
yr6
yr7
yr8
_cons
.3395813
.6411125
-1.687579
-.0002664
-.0909555
.0011134
-.0002634
(dropped)
(dropped)
-.7256122
-.4956724
.2135507
-.0372017
1.136257
.0324898
13.16178
Robust
Std. Err.
t
P>|t|
=
=
=
=
=
133
4.24
0.0000
0.2774
3.3555
[95% Conf. Interval]
.1359736
.3606137
.9112346
.0018056
.0350804
.0005007
.0016269
2.50
1.78
-1.85
-0.15
-2.59
2.22
-0.16
0.014
0.078
0.067
0.883
0.011
0.028
0.872
.0703399
-.0729387
-3.491914
-.0038418
-.1604182
.000122
-.0034848
.6088227
1.355164
.1167568
.0033089
-.0214928
.0021048
.002958
1.32397
1.066972
1.288655
1.083236
1.074868
1.003711
5.677946
-0.55
-0.46
0.17
-0.03
1.06
0.03
2.32
0.585
0.643
0.869
0.973
0.293
0.974
0.022
-3.347204
-2.608384
-2.338115
-2.182116
-.9920885
-1.954959
1.918876
1.89598
1.617039
2.765216
2.107713
3.264603
2.019939
24.40468
166
APPENDIX 7: Two-Step System GMM Dynamic Panel Data Estimation (INV)
. xtabond2 inv l.inv wr intr inf fd yr*, gmm( inv l.inv wr, lag(3 3) collapse equation(level
> )) iv( intr inf fd yr*) small robust twostep artests(3)
Favoring space over speed. To switch, type or click on mata: mata set matafavor speed, perm.
yr1 dropped due to collinearity
yr8 dropped due to collinearity
Warning: Two-step estimated covariance matrix of moments is singular.
Using a generalized inverse to calculate optimal weighting matrix for two-step estimation.
Difference-in-Sargan/Hansen statistics may be negative.
Dynamic panel-data estimation, two-step system GMM
Group variable: countryid
Time variable : year
Number of instruments = 13
F(11, 20)
=
260.57
Prob > F
=
0.000
inv
Coef.
inv
L1.
wr
intr
inf
fd
yr2
yr3
yr4
yr5
yr6
yr7
_cons
1.522043
-2.09074
-4.809234
.9547287
.8771388
-204.5687
-302.9658
-56.01824
-57.57085
-208.501
-218.9
-70.6041
Number of obs
Number of groups
Obs per group: min
avg
max
Corrected
Std. Err.
.0799817
.1346747
22.00037
3.861875
.4840851
136.043
149.0769
119.1201
115.592
170.9897
133.9801
178.967
t
19.03
-15.52
-0.22
0.25
1.81
-1.50
-2.03
-0.47
-0.50
-1.22
-1.63
-0.39
P>|t|
0.000
0.000
0.829
0.807
0.085
0.148
0.056
0.643
0.624
0.237
0.118
0.697
=
=
=
=
=
147
21
7
7.00
7
[95% Conf. Interval]
1.355205
-2.371666
-50.7012
-7.101001
-.1326449
-488.3494
-613.9347
-304.4984
-298.6915
-565.1793
-498.3776
-443.9228
1.688882
-1.809813
41.08274
9.010458
1.886923
79.21205
8.003134
192.4619
183.5498
148.1772
60.57767
302.7146
Instruments for first differences equation
Standard
D.(intr inf fd yr1 yr2 yr3 yr4 yr5 yr6 yr7 yr8)
Instruments for levels equation
Standard
_cons
intr inf fd yr1 yr2 yr3 yr4 yr5 yr6 yr7 yr8
GMM-type (missing=0, separate instruments for each period unless collapsed)
DL3.(inv L.inv wr) collapsed
Arellano-Bond test for AR(1) in first differences: z =
Arellano-Bond test for AR(2) in first differences: z =
Arellano-Bond test for AR(3) in first differences: z =
Sargan test of
(Not robust,
Hansen test of
(Robust, but
overid. restrictions: chi2(1)
= 26.49
but not weakened by many instruments.)
overid. restrictions: chi2(1)
=
1.26
can be weakened by many instruments.)
-0.59
-1.29
-1.21
Pr > z =
Pr > z =
Pr > z =
0.554
0.196
0.225
Prob > chi2 =
0.000
Prob > chi2 =
0.262
167
APPENDIX 8: Correlation Coefficients for the Two-Step System GMM Dynamic Panel
Data Estimation (INV)
. correlate resid l.inv wr intr inf fd yr*
(obs=147)
resid
resid
inv
L1.
wr
intr
inf
fd
yr1
yr2
yr3
yr4
yr5
yr6
yr7
yr8
yr3
yr4
yr5
yr6
yr7
yr8
L.
inv
wr
intr
inf
fd
yr1
yr2
.
.
.
.
.
.
.
.
1.0000
-0.1667
-0.1667
-0.1667
-0.1667
-0.1667
-0.1667
1.0000
-0.1040
-0.0204
-0.1034
-0.0404
0.0617
.
0.0180
-0.0687
-0.0682
0.0039
0.1097
0.0017
0.0035
1.0000
0.7618
0.2224
0.1835
-0.1983
.
-0.0745
-0.0537
-0.0509
-0.0193
0.0208
0.0678
0.1099
1.0000
0.0970
0.0331
-0.0551
.
-0.0558
-0.0523
-0.0502
-0.0269
-0.0087
-0.0011
0.1950
1.0000
0.6208
0.0035
.
0.1672
0.0618
0.0355
-0.0247
-0.0285
-0.1000
-0.1113
1.0000
-0.0978
.
-0.0654
0.0672
-0.0923
-0.0198
0.0547
0.0713
-0.0157
1.0000
.
-0.0183
-0.0097
0.0001
0.0049
0.0541
-0.0179
-0.0132
yr3
yr4
yr5
yr6
yr7
yr8
1.0000
-0.1667 1.0000
-0.1667 -0.1667 1.0000
-0.1667 -0.1667 -0.1667 1.0000
-0.1667 -0.1667 -0.1667 -0.1667
1.0000
1.0000
-0.1667
-0.1667
-0.1667
-0.1667
-0.1667
168
APPENDIX 9: One-Step System GMM Dynamic Panel Data Estimation (INV)
. xtabond2 inv l.inv wr intr inf fd yr*, gmm( inv l.inv wr, lag(3 3) collapse equation(level
> )) iv( intr inf fd yr*) small robust artests(3)
Favoring space over speed. To switch, type or click on mata: mata set matafavor speed, perm.
yr1 dropped due to collinearity
yr8 dropped due to collinearity
Warning: Two-step estimated covariance matrix of moments is singular.
Using a generalized inverse to calculate robust weighting matrix for Hansen test.
Difference-in-Sargan/Hansen statistics may be negative.
Dynamic panel-data estimation, one-step system GMM
Group variable: countryid
Time variable : year
Number of instruments = 13
F(11, 20)
=
2378.62
Prob > F
=
0.000
inv
Coef.
inv
L1.
wr
intr
inf
fd
yr2
yr3
yr4
yr5
yr6
yr7
_cons
1.598502
-2.211787
-22.14462
-1.51909
1.338188
-118.2094
-376.0929
-145.5531
-58.18054
-71.65533
-275.3189
-131.8035
Number of obs
Number of groups
Obs per group: min
avg
max
Robust
Std. Err.
.07937
.1239623
26.45814
4.505236
.6378451
146.5662
155.3274
142.1335
104.0838
207.0516
138.2551
162.038
t
20.14
-17.84
-0.84
-0.34
2.10
-0.81
-2.42
-1.02
-0.56
-0.35
-1.99
-0.81
P>|t|
0.000
0.000
0.413
0.739
0.049
0.429
0.025
0.318
0.582
0.733
0.060
0.426
=
=
=
=
=
147
21
7
7.00
7
[95% Conf. Interval]
1.432939
-2.470368
-77.33532
-10.91685
.0076665
-423.9411
-700.1002
-442.0384
-275.2955
-503.5575
-563.714
-469.8089
1.764065
-1.953207
33.04609
7.878668
2.66871
187.5223
-52.08567
150.9322
158.9344
360.2468
13.07615
206.2019
Instruments for first differences equation
Standard
D.(intr inf fd yr1 yr2 yr3 yr4 yr5 yr6 yr7 yr8)
Instruments for levels equation
Standard
_cons
intr inf fd yr1 yr2 yr3 yr4 yr5 yr6 yr7 yr8
GMM-type (missing=0, separate instruments for each period unless collapsed)
DL3.(inv L.inv wr) collapsed
Arellano-Bond test for AR(1) in first differences: z =
Arellano-Bond test for AR(2) in first differences: z =
Arellano-Bond test for AR(3) in first differences: z =
Sargan test of
(Not robust,
Hansen test of
(Robust, but
overid. restrictions: chi2(1)
= 26.49
but not weakened by many instruments.)
overid. restrictions: chi2(1)
=
1.26
can be weakened by many instruments.)
-0.98
-1.45
-1.53
Pr > z =
Pr > z =
Pr > z =
0.326
0.146
0.127
Prob > chi2 =
0.000
Prob > chi2 =
0.262
169
APPENDIX 10: Correlation Coefficients for the One-Step System GMM Dynamic Panel
Data Estimation (INV)
. correlate resid l.inv wr intr inf fd yr*
(obs=147)
resid
resid
inv
L1.
wr
intr
inf
fd
yr1
yr2
yr3
yr4
yr5
yr6
yr7
yr8
yr3
yr4
yr5
yr6
yr7
yr8
L.
inv
wr
intr
inf
fd
yr1
yr2
.
.
.
.
.
.
.
.
1.0000
-0.1667
-0.1667
-0.1667
-0.1667
-0.1667
-0.1667
1.0000
-0.2338
-0.0253
-0.0057
0.0271
0.0185
.
0.0000
0.0000
-0.0000
0.0000
0.0000
-0.0000
-0.0000
1.0000
0.7618
0.2224
0.1835
-0.1983
.
-0.0745
-0.0537
-0.0509
-0.0193
0.0208
0.0678
0.1099
1.0000
0.0970
0.0331
-0.0551
.
-0.0558
-0.0523
-0.0502
-0.0269
-0.0087
-0.0011
0.1950
1.0000
0.6208
0.0035
.
0.1672
0.0618
0.0355
-0.0247
-0.0285
-0.1000
-0.1113
1.0000
-0.0978
.
-0.0654
0.0672
-0.0923
-0.0198
0.0547
0.0713
-0.0157
1.0000
.
-0.0183
-0.0097
0.0001
0.0049
0.0541
-0.0179
-0.0132
yr3
yr4
yr5
yr6
yr7
yr8
1.0000
-0.1667 1.0000
-0.1667 -0.1667 1.0000
-0.1667 -0.1667 -0.1667 1.0000
-0.1667 -0.1667 -0.1667 -0.1667
1.0000
1.0000
-0.1667
-0.1667
-0.1667
-0.1667
-0.1667
170
APPENDIX 11: OLS Linear Regression Result (INV)
. reg
inv l.inv wr is intr inf fd yr*, robust
Linear regression
Number of obs
F( 12,
134)
Prob > F
R-squared
Root MSE
inv
Coef.
inv
L1.
wr
is
intr
inf
fd
yr1
yr2
yr3
yr4
yr5
yr6
yr7
yr8
_cons
1.492146
-2.040912
.1690263
-17.55043
3.819515
1.073328
(dropped)
(dropped)
-251.7033
-6.962524
81.52272
81.68152
-100.1055
55.34653
-225.1606
Robust
Std. Err.
t
P>|t|
=
=
=
=
=
147
95.80
0.0000
0.9721
601.43
[95% Conf. Interval]
.0672773
.1030068
.0526384
11.83806
6.265001
.2620202
22.18
-19.81
3.21
-1.48
0.61
4.10
0.000
0.000
0.002
0.141
0.543
0.000
1.359083
-2.244642
.0649168
-40.96405
-8.571565
.5550982
1.625209
-1.837183
.2731358
5.863192
16.2106
1.591559
145.1386
134.8978
100.377
204.77
160.4065
138.3133
106.7409
-1.73
-0.05
0.81
0.40
-0.62
0.40
-2.11
0.085
0.959
0.418
0.691
0.534
0.690
0.037
-538.7622
-273.7669
-117.0055
-323.3178
-417.3617
-218.2132
-436.2754
35.35552
259.8418
280.051
486.6808
217.1507
328.9062
-14.04577
171
APPENDIX 12: LSDV Linear Regression Result (INV)
. areg inv l.inv wr is intr inf fd yr*, absorb(countryid) robust
(dropping yr1 because it does not vary within category)
Linear regression, absorbing indicators
inv
Coef.
inv
L1.
wr
is
intr
inf
fd
yr1
yr2
yr3
yr4
yr5
yr6
yr7
yr8
_cons
1.66606
-2.199281
.2287761
3.64175
3.78888
-.2479393
(dropped)
(dropped)
-248.3985
1.684435
73.07941
45.9227
-217.4991
-17.93526
-476.7028
countryid
absorbed
Robust
Std. Err.
Number of obs
F( 12,
114)
Prob > F
R-squared
Adj R-squared
Root MSE
t
P>|t|
=
=
=
=
=
=
147
108.82
0.0000
0.9805
0.9750
544.96
[95% Conf. Interval]
.1124533
.093338
.095027
10.9598
5.517684
.2997467
14.82
-23.56
2.41
0.33
0.69
-0.83
0.000
0.000
0.018
0.740
0.494
0.410
1.443291
-2.384183
.0405284
-18.06952
-7.141609
-.8417352
1.88883
-2.014379
.4170239
25.35302
14.71937
.3458566
148.9315
148.7504
99.2898
174.4334
157.3187
154.8281
150.9538
-1.67
0.01
0.74
0.26
-1.38
-0.12
-3.16
0.098
0.991
0.463
0.793
0.170
0.908
0.002
-543.4306
-292.989
-123.6129
-299.6285
-529.1462
-324.6486
-775.7411
46.6336
296.3579
269.7717
391.4739
94.14808
288.778
-177.6644
(21 categories)
172
APPENDIX 13: Two-Step System GMM Dynamic Panel Data Estimation (REB)
. xtabond2 reb l.reb wr cab intr open reer yr*, gmm(l.reb wr cab, lag(. 2) collapse equatio
> n(both)) iv( intr reer open yr*) small robust twostep artests(3)
Favoring space over speed. To switch, type or click on mata: mata set matafavor speed, perm.
yr1 dropped due to collinearity
yr8 dropped due to collinearity
Warning: Two-step estimated covariance matrix of moments is singular.
Using a generalized inverse to calculate optimal weighting matrix for two-step estimation.
Difference-in-Sargan/Hansen statistics may be negative.
Dynamic panel-data estimation, two-step system GMM
Group variable: countryid
Time variable : year
Number of instruments = 19
F(12, 20)
=
3994.26
Prob > F
=
0.000
Number of obs
Number of groups
Obs per group: min
avg
max
=
=
=
=
=
146
21
6
6.95
7
Corrected
Std. Err.
t
P>|t|
[95% Conf. Interval]
.3344472
.055633
6.01
0.000
.2183989
.4504955
-.220629
.6342295
-9.845849
-82.02106
.0381551
220.0341
165.0725
153.8099
161.446
104.2631
128.1684
-184.3704
.0725187
.0260773
10.46355
131.248
.1548049
203.2634
153.4548
115.6127
111.203
88.58727
89.07543
273.769
-3.04
24.32
-0.94
-0.62
0.25
1.08
1.08
1.33
1.45
1.18
1.44
-0.67
0.006
0.000
0.358
0.539
0.808
0.292
0.295
0.198
0.162
0.253
0.166
0.508
-.3719003
.5798331
-31.67243
-355.7995
-.2847622
-203.966
-155.0285
-87.35405
-70.5193
-80.52671
-57.63964
-755.4425
-.0693577
.6886259
11.98073
191.7574
.3610724
644.0342
485.1735
394.9739
393.4113
289.0529
313.9765
386.7017
reb
Coef.
reb
L1.
wr
cab
intr
open
reer
yr2
yr3
yr4
yr5
yr6
yr7
_cons
Instruments for first differences equation
Standard
D.(intr reer open yr1 yr2 yr3 yr4 yr5 yr6 yr7 yr8)
GMM-type (missing=0, separate instruments for each period unless collapsed)
L(1/2).(L.reb wr cab) collapsed
Instruments for levels equation
Standard
_cons
intr reer open yr1 yr2 yr3 yr4 yr5 yr6 yr7 yr8
GMM-type (missing=0, separate instruments for each period unless collapsed)
D.(L.reb wr cab) collapsed
Arellano-Bond test for AR(1) in first differences: z =
Arellano-Bond test for AR(2) in first differences: z =
Arellano-Bond test for AR(3) in first differences: z =
Pr > z =
Pr > z =
Pr > z =
0.330
0.378
0.267
Prob > chi2 =
0.000
Prob > chi2 =
0.219
Difference-in-Hansen tests of exogeneity of instrument subsets:
GMM instruments for levels
Hansen test excluding group:
chi2(3)
=
6.38 Prob > chi2 =
Difference (null H = exogenous): chi2(3)
=
1.88 Prob > chi2 =
0.094
0.597
Sargan test of
(Not robust,
Hansen test of
(Robust, but
overid. restrictions: chi2(6)
= 203.17
but not weakened by many instruments.)
overid. restrictions: chi2(6)
=
8.26
can be weakened by many instruments.)
-0.97
-0.88
1.11
173
APPENDIX 14: Correlation Coefficients for the Two-Step System GMM Dynamic Panel
Data Estimation (REB)
. correlate resid l.reb wr cab intr open reer yr*
(obs=146)
resid
resid
reb
L1.
wr
cab
intr
open
reer
yr1
yr2
yr3
yr4
yr5
yr6
yr7
yr8
yr2
yr3
yr4
yr5
yr6
yr7
yr8
L.
reb
wr
cab
intr
open
reer
yr1
.
.
.
.
.
.
.
.
1.0000
0.2648
0.0001
0.0862
-0.0760
0.0969
0.0035
.
0.0239
-0.0336
-0.0038
0.0384
-0.0086
0.0065
-0.0227
1.0000
0.8083
0.7850
0.1471
0.0319
-0.0298
.
0.0468
-0.0053
-0.0518
-0.0448
0.0273
-0.0730
0.0993
1.0000
0.7270
0.0894
-0.0447
-0.0202
.
-0.0510
-0.0475
-0.0453
-0.0217
-0.0032
-0.0353
0.2033
1.0000
0.1746
0.0157
-0.0351
.
-0.0440
-0.0539
-0.0324
0.0430
0.0908
-0.0654
0.0606
1.0000
-0.0202
-0.1734
.
0.1694
0.0638
0.0374
-0.0229
-0.0267
-0.1136
-0.1097
1.0000
-0.2851
.
-0.0024
0.0047
0.0016
-0.0162
-0.0077
-0.0191
0.0387
1.0000
.
0.0760
0.0625
0.0205
0.0047
0.0076
0.0089
-0.1801
yr2
yr3
yr4
yr5
yr6
yr7
yr8
1.0000
-0.1680
-0.1680
-0.1680
-0.1680
-0.1633
-0.1680
1.0000
-0.1680
-0.1680
-0.1680
-0.1633
-0.1680
1.0000
-0.1680
-0.1680
-0.1633
-0.1680
1.0000
-0.1680
-0.1633
-0.1680
1.0000
-0.1633
-0.1680
1.0000
-0.1633
1.0000
174
APPENDIX 15: One-Step System GMM Dynamic Panel Data Estimation (REB)
. xtabond2 reb l.reb wr cab intr open reer yr*, gmm(l.reb wr cab, lag(. 2) collapse equatio
> n(both)) iv( intr reer open yr*) small robust artests(3)
Favoring space over speed. To switch, type or click on mata: mata set matafavor speed, perm.
yr1 dropped due to collinearity
yr8 dropped due to collinearity
Warning: Two-step estimated covariance matrix of moments is singular.
Using a generalized inverse to calculate robust weighting matrix for Hansen test.
Difference-in-Sargan/Hansen statistics may be negative.
Dynamic panel-data estimation, one-step system GMM
Group variable: countryid
Time variable : year
Number of instruments = 19
F(12, 20)
= 5881.31
Prob > F
=
0.000
Number of obs
Number of groups
Obs per group: min
avg
max
=
=
=
=
=
146
21
6
6.95
7
Robust
Std. Err.
t
P>|t|
[95% Conf. Interval]
.3260587
.0550705
5.92
0.000
.2111836
.4409338
-.2074022
.6383358
-20.42885
54.7389
.0900303
334.3751
180.5206
205.9587
264.3118
137.9955
181.4249
-301.3717
.0739689
.0196731
11.84277
177.831
.2102059
209.6102
198.7275
144.034
144.1988
127.7382
106.9296
291.5617
-2.80
32.45
-1.73
0.31
0.43
1.60
0.91
1.43
1.83
1.08
1.70
-1.03
0.011
0.000
0.100
0.761
0.673
0.126
0.374
0.168
0.082
0.293
0.105
0.314
-.3616985
.5972985
-45.13244
-316.2101
-.3484516
-102.8641
-234.0176
-94.49102
-36.48155
-128.4617
-41.62634
-909.5588
-.0531058
.6793732
4.27474
425.6878
.5285122
771.6144
595.0589
506.4084
565.1052
404.4527
404.4761
306.8153
reb
Coef.
reb
L1.
wr
cab
intr
open
reer
yr2
yr3
yr4
yr5
yr6
yr7
_cons
Instruments for first differences equation
Standard
D.(intr reer open yr1 yr2 yr3 yr4 yr5 yr6 yr7 yr8)
GMM-type (missing=0, separate instruments for each period unless collapsed)
L(1/2).(L.reb wr cab) collapsed
Instruments for levels equation
Standard
_cons
intr reer open yr1 yr2 yr3 yr4 yr5 yr6 yr7 yr8
GMM-type (missing=0, separate instruments for each period unless collapsed)
D.(L.reb wr cab) collapsed
Arellano-Bond test for AR(1) in first differences: z = -1.22 Pr > z = 0.223
Arellano-Bond test for AR(2) in first differences: z = -1.09 Pr > z = 0.277
Arellano-Bond test for AR(3) in first differences: z = 1.45 Pr > z = 0.146
Sargan test of
(Not robust,
Hansen test of
(Robust, but
overid. restrictions: chi2(6)
= 203.17 Prob > chi2 = 0.000
but not weakened by many instruments.)
overid. restrictions: chi2(6)
= 8.26 Prob > chi2 = 0.219
can be weakened by many instruments.)
Difference-in-Hansen tests of exogeneity of instrument subsets:
GMM instruments for levels
Hansen test excluding group:
chi2(3)
= 6.38 Prob > chi2 = 0.094
Difference (null H = exogenous): chi2(3)
= 1.88 Prob > chi2 = 0.597
175
APPENDIX 16: Correlation Coefficients for the One-Step System GMM Dynamic Panel
Data Estimation (REB)
. correlate resid l.reb wr cab intr open reer yr*
(obs=146)
resid
resid
reb
L1.
wr
cab
intr
open
reer
yr1
yr2
yr3
yr4
yr5
yr6
yr7
yr8
yr2
yr3
yr4
yr5
yr6
yr7
yr8
L.
reb
wr
cab
intr
open
reer
yr1
1.0000
-0.2851 1.0000
.
.
-0.0024 0.0760
0.0047 0.0625
0.0016 0.0205
-0.0162 0.0047
-0.0077 0.0076
-0.0191 0.0089
0.0387 -0.1801
.
.
.
.
.
.
.
.
1.0000
0.2701
-0.0008
0.0849
0.0135
0.0091
-0.0090
.
0.0001
0.0001
0.0003
0.0009
0.0022
-0.0026
-0.0011
1.0000
0.8083
0.7850
0.1471
0.0319
-0.0298
.
0.0468
-0.0053
-0.0518
-0.0448
0.0273
-0.0730
0.0993
1.0000
0.7270
0.0894
-0.0447
-0.0202
.
-0.0510
-0.0475
-0.0453
-0.0217
-0.0032
-0.0353
0.2033
1.0000
0.1746
0.0157
-0.0351
.
-0.0440
-0.0539
-0.0324
0.0430
0.0908
-0.0654
0.0606
1.0000
-0.0202
-0.1734
.
0.1694
0.0638
0.0374
-0.0229
-0.0267
-0.1136
-0.1097
yr2
yr3
yr4
yr5
yr6
1.0000
-0.1680
-0.1680
-0.1680
-0.1680
-0.1633
-0.1680
1.0000
-0.1680
-0.1680
-0.1680
-0.1633
-0.1680
yr7
yr8
1.0000
-0.1680 1.0000
-0.1680 -0.1680 1.0000
-0.1633 -0.1633 -0.1633 1.0000
-0.1680 -0.1680 -0.1680 -0.1633
1.0000
176
APPENDIX 17: OLS Linear Regression Result (REB)
. reg
reb l.reb wr cab intr
open reer yr*,
robust
Linear regression
Number of obs
F( 12,
133)
Prob > F
R-squared
Root MSE
reb
Coef.
reb
L1.
wr
cab
intr
open
reer
yr1
yr2
yr3
yr4
yr5
yr6
yr7
yr8
_cons
.5484697
-.4474112
.6007272
-22.06792
-13.98695
.079638
(dropped)
(dropped)
-85.90667
8.837841
98.45347
-91.45896
4.528824
-154.7017
57.0378
Robust
Std. Err.
t
P>|t|
=
=
=
=
=
146
135.08
0.0000
0.9606
530.29
[95% Conf. Interval]
.0928024
.1011547
.0724322
11.17454
69.58047
.1236633
5.91
-4.42
8.29
-1.97
-0.20
0.64
0.000
0.000
0.000
0.050
0.841
0.521
.3649102
-.6474913
.457459
-44.17074
-151.6144
-.1649632
.7320293
-.2473311
.7439954
.0348897
123.6405
.3242392
187.8241
107.8441
123.1865
130.3115
156.0889
188.431
124.1483
-0.46
0.08
0.80
-0.70
0.03
-0.82
0.46
0.648
0.935
0.426
0.484
0.977
0.413
0.647
-457.4155
-204.4735
-145.2046
-349.21
-304.2089
-527.411
-188.5228
285.6022
222.1492
342.1115
166.2921
313.2665
218.0076
302.5984
177
APPENDIX 18: LSDV Linear Regression Result (REB)
. areg reb l.reb wr cab intr open reer yr*, robust absorb( countryid)
(dropping yr1 because it does not vary within category)
Linear regression, absorbing indicators
reb
Coef.
reb
L1.
wr
cab
intr
open
reer
yr1
yr2
yr3
yr4
yr5
yr6
yr7
yr8
_cons
.3663364
-.2239559
.6587274
7.782394
147.7737
-.6012485
(dropped)
(dropped)
-106.6473
-76.12383
-10.63278
-157.1491
-81.68543
-332.4017
-171.8505
countryid
absorbed
Robust
Std. Err.
Number of obs
F( 12,
113)
Prob > F
R-squared
Adj R-squared
Root MSE
t
P>|t|
=
=
=
=
=
=
146
60.27
0.0000
0.9820
0.9769
389.07
[95% Conf. Interval]
.0926983
.1016109
.0639868
8.989553
265.9012
.4330388
3.95
-2.20
10.29
0.87
0.56
-1.39
0.000
0.030
0.000
0.388
0.579
0.168
.1826844
-.4252654
.531958
-10.02753
-379.0246
-1.459176
.5499885
-.0226464
.7854968
25.59232
674.5719
.2566794
153.3732
101.6442
107.5454
116.07
138.6195
226.7708
169.0538
-0.70
-0.75
-0.10
-1.35
-0.59
-1.47
-1.02
0.488
0.455
0.921
0.178
0.557
0.145
0.312
-410.5072
-277.4992
-223.6996
-387.1047
-356.3156
-781.6757
-506.7767
197.2127
125.2516
202.4341
72.80655
192.9447
116.8722
163.0756
(21 categories)
.
178