This version: June, 2007
Monitoring the Socio-Economic
Conditions in Argentina
1992-2006
#
Leonardo Gasparini *
CEDLAS **
Universidad Nacional de La Plata
Abstract
This report documents the socio-economic situation in Argentina. The report is
based on a wide range of distributional, labor and social statistics computed
from microdata of the Encuesta Permanente de Hogares (EPH) from 1992 to
2006. We also draw from other data sources and the existing literature.
Argentina has witnessed dramatic changes in its distributional, labor and social
conditions in the last three decades. Over that period Argentina has had one of
the most disappointing social performances in the region. The social situation
has substantially improved since the crisis of 2002, although poverty and
inequality are still at levels comparable to those of the 1990s.
Keywords: poverty, inequality, education, labor, wages, employment, Argentina
#
This document is an updated version of a paper with the same title published as working paper by the World
Bank and CEDLAS, 2005. The study is part of the project “Monitoring the socio-economic conditions in
Argentina, Chile, Paraguay and Uruguay”, CEDLAS-The World Bank. The CEDLAS team is comprised by
Leonardo Gasparini (director), Georgina Pizzolito and Leopoldo Tornarolli. We are grateful to the very
helpful comments of Jesko Hentschel, Evelina Bertranou, Evelyn Vezza, Pablo Gluzmann, Ignacio Bayugar
and seminar participants at the World Bank and UNLP.
*
E-mail: [email protected].
**
CEDLAS is the Center for Distributional, Labor and Social Studies at Universidad Nacional de La Plata.
www.depeco.econo.unlp.edu.ar/cedlas
1. Introduction
Argentina was traditionally one of the Latin American countries with better social
indicators. Poverty and inequality were very low compared to most countries in the region.
Also, unemployment was low and social and labor protection was widespread. However,
since the 1970s the socioeconomic situation has been deteriorating, being the sharp increase
in poverty the most dramatic sign of this fall. After the last deep crisis in 2001/02 the
economy has recovered and the main social indicators are significantly improving.
Since the 1970s Argentina has experienced several major macroeconomic crisis and
episodes of structural changes. A severe macroeconomic crisis in mid 1970s under the
Peronist administration was followed by some structural reforms carried out by the military
regime. The debt crisis of the early 1980s hit the Argentina’s economy, which enter a phase
of deep recession. The lost decade of the 1980s, characterized by poor economic
performance, finished with a major macroeconomic crisis, including two episodes of
hyperinflation in 1989 and 1990. The Peronist administration that took power in 1989
introduced in the early 1990s a wide range of macro and market-based reforms. Despite an
impressive macroeconomic record, the social situation significantly deteriorated. The 1990s
ended with another recession, which was followed by a major breakdown: the 2001/02
crisis implied a fall in the GDP of more than 15%. The economy has strongly recovered
since then, reaching in 2006 levels of activity higher than those in the 1990s.
The social situation in the country has worsened over the last three decades. Poverty and
inequality have increased even during some periods of economic expansion. The labor
market performance has also been extremely weak. Argentina, traditionally a country of
nearly full employment and widespread social protection, became an economy with
persistent high unemployment and informality rates.
This document shows evidence on the socio-economic performance of Argentina in the last
three decades. The report is mostly focused on the period 1992-2006, and especially draws
from statistics constructed from microdata of the Encuesta Permanente de Hogares (EPH).
All the statistics presented in this report and computed by our team can be shown and
downloaded from www.depeco.econo.unlp.edu.ar/cedlas/monitoreo.htm. All the indicators
are updated as new information is released.
The rest of the document is organized as follows. In section 2 we present the main sources
of information used in this report. The next nine sections show and analyze information on
incomes, poverty, inequality, aggregate welfare, the labor market, education, housing and
2
social services, demographics, and poverty-alleviation programs. Section 12 closes with an
assessment of the results.
2. The data
Distributional, labor and social conditions can be monitored with the help of the Encuesta
Permanente de Hogares (EPH), the main household survey in Argentina. The EPH is
carried out by the Instituto Nacional de Estadística y Censos (INDEC). It now covers 31
urban areas (all the urban areas with more than 100,000 inhabitants) which are home of
71% of the Argentine urban population. Since the share of urban areas in Argentina is
87.1% (one of the largest in the world), the sample of the EPH represents around 62% of
the total population of the country.1 The EPH gathers information on individual
sociodemographic characteristics, employment status, hours of work, wages, incomes, type
of job, education, and migration status. The microdata of the EPH is available for the
Greater Buenos Aires (GBA) since 1974. The rest of the urban areas have been added
during the last three decades. The EPH has been traditionally carried out twice a year, in
May and October. During 2003 a major methodological change was implemented by
INDEC, including changes in the questionnaires and in the frequency of the survey visits.
The number of observations (individuals) has changed from around 90,000 in the late 1990s
to around 60,000 in the early 2000s, and more than 100,000 in the new EPHC.
In the last decade Argentina has conducted two Living Standard Surveys. The first survey,
known as Encuesta de Desarrollo Social (EDS), was carried out in 1996/7 and includes
around 75,000 individuals (representing 83% of total population) living in urban areas. The
second survey, Encuesta de Condiciones de Vida (ECV), with similar coverage and
questionnaires, was conducted in 2001. Both surveys include questions on housing, some
assets, demographics, labor variables, health status and services, and education. The EDS
and ECV were sponsored by the World Bank and have questionnaires similar to those in
other countries. However, they are not part of the Living Standard Measurement Surveys
(LSMS) program, and they do not include questions on expenditures as the LSMS surveys
do.2 Although with a richer questionnaire and a somewhat larger geographical coverage, the
ECV is of lower quality than the EPH.
The World Bank has also carried out other surveys to characterize the socio-economic
situation of the country. In particular, during 2002 the Bank conducted the Encuesta de
1
Although the EPH does not meet one of the Deininger and Squire (1996) criteria since it is an urban survey,
it represents a reasonably large share of Argentina’s population. Additionally, the missing population does not
seem to affect some results. For instance, using a recent survey conducted by the World Bank (2002) that
include small towns in rural areas, we find only a negligible difference in all inequality measures when we
include or ignore rural areas.
2
They are usually labeled as quasi-LSMS.
3
Impacto Social de la Crisis en Argentina (ISCA) to learn about the consequences of the
deep economic crisis of 2001-2002, and the strategies used by households to cope with that
crisis.3
Expenditures are reported in the Encuesta Nacional de Gastos de los Hogares (ENGH)
conducted every 10 years (1986, 1996/7). Although the last ENGH includes some questions
on socio-economic issues, we do not use this survey, since social topics are better covered
in the EPH and the ECV.
The EPH does not allow the closely monitoring of labor statistics, as results are available
every six months (every three months in the EPHC). To fill part of this gap the Labor
Department carries out the Encuesta de Indicadores Laborales (EIL). It is a survey of firms
in the private sector in Greater Buenos Aires, Córdoba, Mendoza and Rosario. The survey
covers only large firms (more than 10 workers) in the formal sector (workers should be
registered in the SIJP). The EIL has around 800 observations in the Greater Buenos Aires
and less than 200 in each of the other three largest cities in the country.
There are other two surveys to firms with some information on labor indicators. The
Encuesta Mensual Industrial (EMI) records information from firms in the manufacturing
sector and includes some labor statistics. The Encuesta Nacional de Grandes Empresas
(ENGE) is a panel of the largest 500 firms in the formal sector surveyed since 1993. It also
contains information on some labor variables.
Argentina has a program of conducting census every ten years. The last available census
are for 1980, 1991 and 2001. Besides basic demographic variables, the census includes
information on housing, education and some basic labor variables. Given the main
objective of this report -monitoring the socioeconomic situation on a yearly basis- we use
the census only as a reference. Besides, census data does not include information on
monetary variables. It is however possible to assess some non-monetary dimensions of
poverty using census data (see a forthcoming Report of the World Bank (2007) for a
comprehensive analysis of rural poverty using Census 2001).
Summarizing, the EPH is the best data source for monitoring the distributional, labor and
social conditions in Argentina on a yearly basis. The ECV provides useful information on
some issues not well-captured or not captured at all by the EPH (e.g. health and social
programs), while the EIL helps monitoring the labor situation in the formal sector on a
monthly basis. The ENGH is the only survey that records expenditures but has been carried
out every ten years. Some specific surveys (e.g. ISCA of The World Bank) are useful to
3
See Fiszbein and Giovagnoli (2003).
4
study particular questions or periods. Administrative information is especially helpful to
portrait the educational, health, and security situation.
This document is especially based on information computed from microdata of the EPH.
Three panels are presented in most tables. The first one refers to the main 15 urban areas
with available microdata from the EPH since 1992 (Capital Federal and Conurbano
Bonaerense (known as Greater Buenos Aires or GBA), Comodoro Rivadavia, Córdoba,
Jujuy, La Plata, Neuquén, Paraná, Río Gallegos, Salta, San Luis, San Juan, Santa Rosa,
Santa Fe, Santiago del Estero and Tierra del Fuego). The second panel adds another 13
urban areas with microdata since 1998 (Bahía Blanca, Catamarca, Concordia, Corrientes,
Formosa, La Rioja, Mar del Plata, Mendoza, Posadas, Resistencia, Río Cuarto, Rosario and
Tucumán).4 To match both series we compute all statistics in 1998 with both samples of 15
and 28 cities. All statistics correspond to the October round of the EPH, with the exception
of 2003 since the microdata of the October wave is not available for that year.5 From 2003
on we include a third panel with information from the new EPHC. In this update we add to
the report information for 2006.
Unfortunately, the change from the EPH to the EPHC introduces noise in all the series.
INDEC has not released the microdata for the first quarter of the EPHC 2003, which could
have allowed studying the impact on the statistics introduced by the methodological
changes. However, INDEC has published statistics computed with the microdata of the first
half of the EPHC, which are close to our estimates with the May 2003 EPH. For instance,
we estimate a poverty headcount ratio of 54.7% using the official moderate poverty line in
May 2003, while INDEC publishes a value of 54% using the EPHC, first half of 2003.
Given this preliminary evidence, we interpret estimated changes between the EPH and the
EPHC as mostly driven by real facts more than by methodological changes.6
Among the modifications in the EPH Continua, the INDEC now reports population weights
that control for income non-response. Although this is clearly an improvement, the use of
these weights introduces a comparability problem with previous surveys. To assess the
impact of this change, we add a line to most tables, including statistics for the second half
of 2003 using the “old” weights, i.e. those that do not take income non-response into
account.
In the second half of 2006 the EPHC was extended to include three new urban areas: San
Nicolás-Villa Constitución, Rawson-Trelew, and Viedma-Carmen de Patagones. According
4
We do not include in the analysis Alto Valle del Río Negro and Interior de Mendoza which were covered in
some rounds of the EPH, and the recently (2002) incorporated areas of San Nicolás-Villa Constitución,
Rawson-Trelew and Viedma-Carmen de Patagones.
5
Given that surveys cover only urban areas, most statistics are not significantly affected by seasonality issues.
6
See a companion paper (Gasparini, 2004b) for further discussion on this issue.
5
to our estimates (see Box below), the addition of these areas does not affect the main social
national indicators in a significant way. All the statistics in this paper for the second half of
2006 include the 31 urban areas now surveyed in the EPHC.
Since 2003 the EPHC is divided into four quarters. INDEC provides the quarterly datasets,
and also two six-month datasets. INDEC estimates for poverty and other social indicators
are computed using these two larger datasets. The problem with this procedure is that given
the short panel structure of the EPHC, the six-month datasets include two observations (one
in each quarter) for some households, and one observation for the rest. This doublecounting may generate some biases. We prefer to work with the six-month datasets after
deleting the “duplicated” observations (and recomputing the weights). That implies, for
instance, a reduction in the number of observations from 129,410 to 99,768 individuals in
the second half of 2006.
3. Incomes
Real incomes are the arguments of all poverty, inequality, polarization and welfare
measures. Thus, before computing indicators of these distributional dimensions in the next
sections, we show some basic statistics on real incomes. All incomes are presented in real
values by deflating nominal incomes by the consumer price index of the month when
incomes reported in the survey were earned.
Table 3.1 shows real incomes by deciles for the aggregate of 15 urban areas for some
selected years from 1992 to 1998; and for the aggregate of 28 cities from 1998 to 2006.7
Real income reported in the EPH fell around 8% between 1992 and 1996. This change is in
sharp contrast with national accounts: per capita GDP increased in that period 8.9%. This
discrepancy may be due to increasing under-reporting in the EPH, or overestimation in the
GDP. It could also be the consequence of an increase in the share of sources not well
captured in the EPH: capital income, benefits, and rents. Between 1996 and 1998 the
economy enjoyed a phase of expansion: per capita income grew 10%. A similar number is
also reported by national accounts. The crisis 1998-2002 implied a fall of around 40% in
mean income reported in the EPH, which is higher than the figure from national accounts.
Since 2003 the economy is strongly recovering: real per capita income grew 41.6%
between the second half of 2003 and the second half of 2006, which is significantly more
than national accounts estimates (25%).
The second panel of Table 3.1 shows that income changes were not uniform across deciles.
Income changes between 1992 and 2000 were clearly unequalizing. The crisis 2000-2002
7
Recall that since the second half of 2006 the EPHC covers 31 urban areas (see Box below).
6
hit all the population, although the richest decile suffered somewhat less than the rest. The
recovery 2003/06 appears as clearly pro-poor.
The growth-incidence curves of Figure 3.1 present a more detailed picture of the income
change patterns. Each curve shows the proportional income change of each percentile in a
given time period. Ideally, we would like these curves to be (i) well above the horizontal
axis, implying income growth, and (ii) decreasing, implying pro-poor growth. In the
Argentina’s case, however, most curves are below the horizontal axis and have a positive
“slope”. The solid line labeled 1992-2006 summarizes the disappointing performance of the
last fourteen years: real incomes reported in the EPH have changed in a highly unequalizing
way. The situation since the 2002 crisis is depicted in Figure 3.2. Growth was pro-poor in
2003 and 2004. During 2005/06 the economy remained strong, although the growthincidence curves became nearly flat.
The income changes shown in this section suggest clear patterns for poverty, inequality and
welfare. Overall, the non-uniform changes in income since 1992 have likely implied an
increase in poverty, and surely a significant growth in inequality. The next three sections
provide more evidence on these issues.
4. Poverty 8
This report shows poverty computed with the most widely used poverty lines and poverty
indicators. The USD 1 a day and USD 2 a day at PPP prices are international poverty lines
extensively used by the World Bank (see World Bank Indicators, 2004).9 Most LAC
countries, including Argentina, calculate official moderate and extreme poverty lines based
on the cost of a basic food bundle and the Engel/Orshansky ratio of food expenditures.10
Table 4.1 presents the value of these poverty lines in local currency units for the period
1992-2006. We also consider the line set at 50% of the median of the household per capita
income distribution, which captures a relative rather than an absolute concept of poverty.
For each poverty line we compute three poverty indicators: the headcount ratio, the poverty
gap, and the FGT (2).11 We also calculate the number of poor people expanding the survey
to all the population by assuming that the income distribution of the areas not covered by
the survey mimics the distribution computed from the EPH.
8
See the Appendix for a poverty profile for year 2006.
See the methodological document for details.
10
See the methodological document and INDEC (2003).
11
See Foster, Greer and Thornbecke (1984) for references.
9
7
Tables 4.2 to 4.6 show various poverty measures with alternative poverty lines.12 Argentina
has witnessed a sizeable increase in income poverty in the last fourteen years, despite a
strong recovery after the 2001/02 crisis. All indicators shown in Tables 4.2 to 4.6 and
Figures 4.1 to 4.2 agree with this statement. According to the USD1 line, the headcount
ratio increased from 1.4 in 1992 to 3.2 in the second half of 2006.13 Poverty substantially
increased between 1992 and 1996, despite a significant growth in GDP reported by
National Accounts. After a temporary reduction around 1998, poverty increased again
fueled by the economic recession that started in the second half of 1998. In 2002 the
headcount ratio reached the record level of 9.9. The latest available value (second half of
2006) suggests a significant reduction in poverty. Between 1992 and 2005 around one
million Argentineans (out of a population of around 40 millions) crossed the USD1-a-day
poverty line.14 The patterns for the other poverty indicators (poverty gap and FGT(2)) are
similar.
When using the USD2 line results are also similar: poverty has dramatically increased
during the last decade. The headcount ratio rose from 4.2 in 1992 to 8.5 in 2006, which
means that the estimated number of poor increased in around two millions. Poverty
increased 4.1 points from 1992 to 1998, 6.2 points during the stagnation of 1998-2001,
around 9.1 points during the crisis 2001-2002, and then substantially fell between October
2002 and the second half of 2006.
The official poverty line in Argentina is set at higher levels than USD 2 a day at PPP, a fact
that reveals that Argentina is a middle-income country. Although the level of official
poverty is higher than poverty computed with international lines, the patterns shown in
Tables 4.4 and 4.5 for official poverty are similar to those commented above. The dramatic
increase in poverty is captured by all indicators. According to the official line, extreme
poverty increased from 3.8% in 1992 to 8.2% in 1996. After a fall around 1998, extreme
poverty increased again to 13.7% in 2001 and reached 27.6% in 2002. Extreme poverty has
been falling since then, reaching 8.6% in the second half of 2006.
The headcount ratio computed using the moderate poverty line is the most extensively cited
poverty measure in policy discussions and the media in Argentina. Table 4.5 shows a large
increase in this indicator over the last fourteen years. The headcount ratio increased around
12
See the web page for an analysis of statistical significance of poverty changes based on bootstrapping
techniques.
13
Notice that the difference from taking 28 instead of 15 cities in 1998 is small. Also the change in the survey
in 2003 does not seem to greatly affect the income statistics. In all cases when differences are small, we will
not mention the change in sample or methodology when commenting on the statistics.
14
That value is the net increase in poverty, which is the consequence of people jumping out and into poverty.
8
7 points between 1992 and 2006, which means around 4 million “new poor” individuals.15
About 3.5 millions entered poverty during the economic growth period of the 90s, another
3.5 joined that group in the first phase of the recession (1998-2001), while about 7.5
millions crossed the poverty line during the crisis 2001-2002. The economic recovery
substantially reduced the number of poor in around 10.5 million individuals.
It is interesting to notice that the moderate official poverty line is close to the mode of the
income distribution (see Figure 4.3). When that occurs, the poverty-growth elasticity is
large: changes in income generate a large impact on the poverty rate. This fact implies that
a relatively small improvement in economic conditions may lead to a large fall in the
official measure of poverty. The particular location of the poverty line close to the mode
partially explains the huge increase in official poverty during the crisis and the sharp fall
during the recovery.
Figure 4.4 shows poverty computed with the official moderate poverty line for the Greater
Buenos Aires area. Restricting the analysis to this area, which is home of 1/3 of the
Argentine population, allows a more historical perspective, since the EPH was initially
conducted only in that metropolitan area. Poverty slowly increased during the first half of
the 1980s, and skyrocketed during the hyperinflation crisis. After a sharp fall in the early
1990s, the poverty headcount ratio increased around 10 points between 1993 and 1999, and
jumped 28 points during the crisis. Since 2002 poverty went substantially down, being in
2006 at roughly the same level as in 1998.
The increasing trend in poverty in Argentina is not well documented in the international
literature. In some datasets Argentina is discarded for having household surveys covering
only urban areas (e.g. Chen and Ravallion, 2003, Sala-i-Martin, 2000), while in those
where Argentina is included, the number of observations is small, and generally belong to
the second half of the 1990s, when poverty was rather stable (Székely, 2003, Wodon, 2001,
WDI, various years).
The increase in income poverty in Argentina during the last 3 decades contrasts with the
performance of most Latin American countries. Although the region has not been very
successful in fighting poverty, the record of most of the Latin American countries is much
better than the Argentine performance. The contrast with Chile and Brazil, for instance, is
notorious, as poverty significantly decreased in these two countries during the last decades.
Figure 4.5, based on Gasparini et al. (2005), shows that the poverty increase in Argentina
15
Notice that changing the sample from 15 to 28 cities in 1998 implies an increase in poverty of around 2
points. Also, by changing the weights to consider unit non response the new EPHC implies a fall in recorded
poverty of around 2 points.
9
was particularly harsh. Argentina ranks third according to points of poverty increase, and
first if the increase is measured in percentages.
Figure 4.6, also taken from Gasparini et al. (2005), places Argentina as a low-poverty
country compared to the rest of LAC, due to its relatively high per capita GDP and still
relatively low inequality. Notice that Argentina had poverty levels similar to Uruguay and
much lower than Chile, which it is not the case anymore.
Some countries (e.g. those in the European Union) use a relative rather than an absolute
measure of poverty. According to this view, since social perceptions of poverty change as
the country develops and living standards go up, the poverty line should increase along
with economic growth. Probably the most popular relative poverty line is 50% of median
income. The relevant scenario for justifying this kind of poverty measure does not apply to
Argentina, since the economy is stagnant since the 1970s. Anyway, we show in Table 4.6
and Figure 4.7 poverty indicators computed with the 50% median income line. Relative
poverty increased in the 1990s, and was not greatly affected by the last economic crisis.
The main reason behind this latter fact lies on the generalized income fall across income
strata occurred during the crisis: in this scenario relative poverty does not go up.
There are convincing arguments for considering poverty as a multidimensional issue.16
Insufficient income is just one of the manifestations of a more complex problem. Given the
availability of information for the countries in the region we construct an indicator of
poverty according to the characteristics of the dwelling, access to water, sanitation,
education (of household head and children) and dependency rates.17 Table 4.7 and Figure
4.8 suggests that poverty did not increase when defined in the space of those variables.18
However, there was not much improvement either. Indicators of endowments or basic
needs usually fall, since over time people improve their dwellings and governments invest
in water, sanitation and education, even in stagnant economies. The constant pattern for the
poverty indicator in Table 4.7 should be interpreted more as a negative sign of sluggish
social development, than as a positive sign of no increase in poverty.
16
Bourguignon (2003) discusses the need and the problem of going from income poverty to a
multidimensional approach of endowments. Attanasio and Székely (eds.) (2001) show evidence of poverty as
lack of certain assets for LAC countries.
17
An individual is poor if she lives in a household meeting at least one of the following conditions: (i) 4 or
more persons per room, (ii) dwelling in a shantytown or other inconvenient place, (iii) walls of chapa, adobe,
or cartón, (iv) unavailability of water in lot, (v) unavailability of hygienic restroom, (vi) children aged 7 to 11
not attending school, (vii) household head without a primary education degree, (viii) household head with no
more than a primary education degree, and more than 4 persons per income earner.
18
There is not enough information in the dataset released for the EPHC to present comparable data for the
2003-2005 period.
10
In column (ii) of Table 4.7 we define poverty as a situation where an individual is poor
according to both the endowment and the income criteria. We take the USD2 line for the
computation of this column. The pattern from column (ii) follows that of income poverty in
Table 4.3. The level, however, is lower. While in May 2003 23.7% of the population had a
per capita income lower than USD2-a-day, 16.3% were poor also according to the
endowment criterion.
INDEC computes a basic-needs indicator of poverty (Necesidades Básicas Insatisfechas –
NBI) with census data. An individual is poor if she lives in a household meeting at least
one of the following conditions: (i) more than 3 persons per room, (ii) dwelling in a
shantytown or other inconvenient place, (iii) unavailability of hygienic restroom (without
retrete), (iv) children aged 6 to 12 not attending school, (v) household head who has not
completed three years of primary school, and household with more than 4 persons per
income earner. According to the Census 1980, 27.7% of the individuals lived in households
that met at least one of these criteria. In the Census of 1991 that proportion was 19.9%,
while in 2001 was 17.7%. The small fall in NBI in the 1990s leads to the same two
conclusions stated above. The optimistic result is the fall in basic-needs poverty, despite a
dramatic increase in income poverty. People now have less income than a decade ago, but
they are (slightly) better-off in terms of housing, sewerage and education. The pessimistic
view underlines the very modest fall in the NBI indicator in one decade. In fact, the number
of poor people according to this definition was almost the same in 1991 (6,427,257) and
2001 (6,343,589). Basic needs indicators of poverty are usually decreasing over time, even
in stagnant economies: an example is Argentina in the 1980s.19
5. Inequality and polarization
In Table 5.1 we present the most tangible measures of inequality: the shares of each decile
and some income ratios. These measures are computed over the distribution of household
per capita income. The income share of the poorest decile fell from 1.8 in 1992 to 1 in
2001, and increased to 1.2 by 2006. In the other extreme, the income share of the richest
decile increased from 34.1 in 1992 to 35.7 in 2006. Notice the heterogeneous pattern of
changes across deciles. As mentioned above the income share of decile 1 fell over the last
decade and a half. That was also the situation for deciles 2 to 5. The shares of deciles 6 to 9
stayed roughly unchanged, while the share of decile 10 went up around 3 points in fourteen
years.20 While income distribution changes were unequalizing over the period 1992-2001/2,
they turned equalizing during the recovery 2002-2006.
19
20
See World Bank (2007) for a study of rural poverty using the 2001 Census.
These comparisons take into account changes in results due to changes in methodology.
11
In Table 5.2 we compute several inequality indices: the Gini coefficient, the Theil index,
the coefficient of variation, the Atkinson index, and the generalized entropy index with
different parameters. All measures of inequality suggest the same increasing pattern over
the last fourteen years. The Gini coefficient, for instance, increased from 0.450 in 1992 to
0.528 in May, 2003, with a peak of 0.533 in 2002.21 This change is not only statistically
significant but, according to historical records, very high.22
Table 5.2 reports a large drop in inequality between 2003 and 2006. Two clarifications are
in order. First, the use of the new weights included in the EPHC that consider unit nonresponse implies a very substantial drop in recorded inequality measures. For instance, the
change in the Gini in 2003 is about 1 point. Second, there was a very large drop in
inequality between 2003 and 2004. Although expected, the fall seems very large and might
be due in part to undetected methodological issues.
After taking account changes in the EPH, we conclude that inequality is today (second half
of 2006) at around the same levels as before the latest economic crisis (1998/99). The Gini
coefficient is at the level of 1999, while the estimated levels of the income ratio 10/1, and
the Atkinson with inequality-aversion parameter 2 are close to the values in 1998.
In Tables 5.3 and 5.4 we extend the analysis to the distribution of equivalized household
income. Equivalized income takes into account the fact that food needs are different across
age groups – leading to adjustments for adult equivalent scales – and that there are
household economies of scale.23 The introduction of these adjustments do not imply
significant changes in the assessments of the results.
In Tables 5.5 and 5.6 we consider the distribution of a more restricted income variable: the
equivalized household labor monetary income. Again, the inequality patterns are similar
than in previous tables. One exception is the period 2001-2003. By focusing on labor
income, capital income and transfers are ignored. In particular, incomes from the Programa
Jefes de Hogar are excluded from the statistics. When doing that, incomes in the first
deciles go down between 2001 and 2003, in contrast to the situation when including
transfers. Therefore, all indices in Table 5.6 show a sizeable increase in inequality between
2001 and 2003.
Table 5.7 is aimed at assessing the robustness of the results by presenting the Gini
coefficient over the distribution of several income variables. The different columns
21
Notice that changing the sample in 1998 does not significantly modify the value of any inequality index.
An analysis of statistical significance of inequality changes based on bootstrapping techniques is presented
in the web page of this project. See also Gasparini and Sosa Escudero (2001).
23
See Deaton and Zaidi (2002) and the methodological appendix for details on the implementation for
Argentina.
22
12
consider different adult equivalent scales, consider total household income without
adjusting for family size, and restrict the analysis to people in the same age bracket to
control for life-cycle factors. All the main results drawn from previous tables hold when
making these adjustments.
The increase in inequality was not a distinctive feature only of the 1990s. Figure 5.1 shows
the Gini coefficient for the distribution of per capita household income in the Greater
Buenos Aires from 1974 to 2006. This inequality measure climbed from 0.336 in 1974 to
0.487 in 2006. Inequality greatly increased in the second half of the 1970s, remained stable
in the first half of the 1980s and substantially increased during the macroeconomic crisis of
the late 1980s. After stabilization, inequality went down, although did not reach the precrisis levels. The 1990s were again times of increasing inequality: the Gini climbed 6 points
from 1992 to 1998. The recent macroeconomic crisis pushed the Gini another 4 points up.
This inequality indicator went down 4 points during the recent recovery.
Argentina has traditionally been one of the most equal countries in Latin America, along
with Costa Rica and Uruguay (Londoño and Székely, 2000). The presence of a large
middle-class was a distinctive feature of Argentina’s economy. Figure 5.2 shows the Gini
coefficient for the distribution of equivalized income for most Latin American economies.
In the early 1990s and despite 15 years of increasing inequality, Argentina remained as one
of the low-inequality countries in the region. The Argentina’s distributional story in the last
decade and a half was substantially different from the rest of the region. Although
inequality increased in many countries, especially in South America, changes have been
small compared to the ones experienced by Argentina. The second panel of Figure 5.2
suggests that Argentina no longer belongs to the low-inequality group of LAC. It is
interesting the comparison with Uruguay: once almost identical, the distributions of these
two neighbor countries are now clearly different, after three decades of relative
distributional stability in Uruguay and turbulence in Argentina.
Figure 5.3 shows again the disappointing distributional performance of Argentina,
compared to the rest of Latin America. The raise in the Gini in Argentina was almost
double the one in Venezuela, which ranks second according to inequality increases.
Polarization is a dimension of equity that has recently received attention in the literature. It
refers to homogeneous clusters that antagonize each other. Table 5.8 shows the Wolfson
(1994) and Esteban, Gradín and Ray (1999) indices of bipolarization. Polarization and
inequality can go in different directions. This was not the case in Argentina, where the
distribution became more unequal and more polarized at the same time. Horenstein and
Olivieri (2004) compute the new generalized polarization index of Duclos, Esteban and
Ray (2004), finding similar results.
13
As commented above all the surveys in Argentina have only urban coverage. The World
Bank’s Encuesta de Impacto Social de la Crisis en Argentina (ISCA) included some small
towns in rural areas. From the information of that survey the income distribution in rural
areas turns out to be not significantly different from the income distribution in urban areas.
The Gini coefficient for the distribution of household per capita income is 0.474 in urban
areas, 0.482 in rural areas, and 0.475 for the whole country. This fact suggests that the
urban inequality statistics can be taken as a good approximation for the national figures.
14
Box: Robustness of the results to the EPH methodological changes
As discussed above, the main household survey in Argentina was significantly changed in 2003. In this box
we assess the effect of three of these changes on the poverty and inequality results. Among the innovations,
the new EPHC has a new weighting scheme to take income non-response into account. In addition to the
usual weights, since 2003 INDEC provides (and uses for official poverty calculations) adjusted weights to
consider those people who refuse to respond income questions.
Second, INDEC now provides microdata for individuals surveyed in a window of six months, instead of just
one month. As discussed above, given the short panel structure of the survey, some individuals show up twice
in the six-month dataset, since they are surveyed in two consecutive quarters. In our calculations we delete
one of these two observations randomly and recompute the weights, while INDEC includes them all.
Finally, three new urban areas were added to the survey: San Nicolás-Villa Constitución, Rawson-Trelew and
Viedma-Carmen de Patagones. They became fully part of the new EPHC in the second half of 2006.
The table shows the poverty headcount ratio (for both the USD2 and the moderate national poverty lines) and
the Gini coefficient in each of eight scenarios. Each scenario is the result of (i) using the new weights or the
old ones, (ii) accepting the “duplicated” observations or deleting them, and (iii) adding the new three urban
areas or not.
P overty
US$ 2
W ithout duplicated observations
31 urban areas
N ew w eights
8.5
O ld weights
9.5
28 urban areas
N ew w eights
8.5
O ld weights
9.6
W ith duplicated observations
31 urban areas
N ew w eights
8.6
O ld weights
9.7
28 urban areas
N ew w eights
8.7
O ld weights
9.7
M oderate
Inequality
G ini
26.7
28.8
0.483
0.487
26.8
28.9
0.483
0.487
26.8
28.8
0.485
0.488
26.8
28.9
0.485
0.488
Adding the new three urban areas has a very minor effect on the poverty and inequality estimations. Given
that result, in our tables we add the second half of 2006 with the new areas without repeating the calculations
for the smaller set of 28 cities, as we do for the change from 15 to 28 cities in 1998.
Cleaning the duplicated observations from the dataset has also a minor effect on the poverty and inequality
estimates. As discussed above, we prefer to show statistics computed over the clean dataset, but according to
the table that methodological step does not imply significant changes in the results.
The use or not of the new weights adjusted for non-response does have a substantial impact on the poverty
and inequality results. Non-response is more usual among the non-poor. The reweighting of the observations
implies lower poverty (around 1 point when using the USD2 line, and 2 points when using the moderate line),
and slightly lower inequality (0.03/0.04 Gini points). Given that difference, we present two sets of statistics
for 2003 (the year when the change was implemented): one with the new weights and one with the old
weights).
15
6. Aggregate welfare
Rather than maximizing mean income, or minimizing poverty or inequality, in principle
societies seek the maximization of aggregate welfare. Welfare is usually analyzed with the
help of growth-incidence curves, generalized Lorenz curves, Pen’s parade curves and
aggregate welfare functions. In section 3 we presented growth-incidence curves that
suggested an unequalizing pattern with a small change in mean income over the last
fourteen years. The same conclusion arises from the generalized Lorenz curves of Figure
6.1: the curve for 2006 lies below the corresponding curve for 1992 until around the
percentile 80th. Therefore, most social welfare functions (although not all) would rank 2006
as a worse year in terms of aggregate welfare than 1992.
Given the crossing in the generalized Lorenz curves, it is particularly useful to perform a
welfare analysis in terms of abbreviated welfare functions (see Tables 6.1 and 6.2 and
Figures 6.2 and 6.3). The first function considered for this analysis is represented by the
average income of the population: according to this value judgment inequality is irrelevant.
The rest of the functions take inequality into account. These are the ones proposed by Sen
(equal to the mean times 1 minus the Gini coefficient) and Atkinson (CES functions with
two alternative parameters of inequality aversion).24 For this exercise we take real per
capita GDP from National Accounts as the average income measure, and combine it with
the inequality indices shown above.25 Given that most assessments of the performance of an
economy are made by looking at per capita GDP, we use this variable and complement it
with inequality indices from our study to obtain rough estimates of the value of aggregate
welfare according to different value judgments.26 For various reasons per capita income
from household surveys differs from National Accounts estimates.
Although the economy substantially grew between 1992 and 1998 (according to NA
estimates), the welfare assessments are not as positive. While per capita GDP grew 20%,
welfare increased around 9% according to Sen and Atkinson(1) functions. The contrast
with an Atkinson(2) function is even more striking. According to the (Rawlsian-like) value
judgment implicit in this function, welfare actually dropped 3% between 1992 and 1998.
The fall in real income of the poorest households offset the significant increase in mean
income. From 1998 to 2002 all functions agree on showing a dramatic fall in welfare driven
by both an increase in inequality and a fall in mean income. The positive changes between
2002 and 2006 in mean income and income distribution is captured by all welfare
functions. However, despite this increase aggregate welfare today is just slightly above the
1992 levels according to Sen and Atkinson (1) functions, and still below (1.2%) according
24
See Lambert (1993) for technical details.
The source for GDP figures is IMF (2007).
26
See Gasparini and Sosa Escudero (2001) for a more complete justification of this kind of study.
25
16
to a more Rawlsian value judgment. These figures are clear evidence of the disappointing
performance of the Argentine economy.
For reasons not yet well understood changes in mean income from the EPH do not closely
match changes in mean disposable income from National Accounts. In Table 6.2 and
Figure 6.3 we repeat the welfare exercise using only information from the EPH. In this case
the performance of the Argentine economy is even worse, since per capita income in the
EPH is now about at the same level as 14 years ago. This fact combined with the
unequalizing distributional changes documented above account for the lower level of
aggregate welfare of 2006 compared to 1992.
7. The labor market
This section summarizes the structure and changes of the labor market in Argentina in the
last decade. Tables 7.1 to 7.3 show hourly wages, hours of work and labor income for the
working population. Real hourly wages (deflated by the CPI) have increased in the first half
of the 1990s and decreased thereafter. Real hourly wages were higher in 2001 than in 1992,
even after 4 years of stagnation.27 The wage drop during the latest crisis was dramatic:
according to the EPH real wages fell 28% between September 2001 and April 2003. Hours
of work have also declined, although less than wages: from 44.3 hours a week in 1992 to
43.9 in 1998, to 41.9 in 2001, and 41.1 in 2003. Labor incomes were dominated by the
behavior of wages: earnings increased between 1992 and 1998, and dramatically fell
thereafter. In 2003, mean labor income was just 62% of the corresponding value in 1992.
The information of the EPHC suggests that both hourly wages and hours of work have
increased since 2003, although the changes have not been enough to compensate
completely for the negative effects of the crisis. Hours of work and real wages are still
below the levels of the 1990s.
Tables 7.1 to 7.3 also report hourly wages, hours of work and earnings by gender, age and
education. It is interesting how the gender wage gap was closed in the last few years. The
gap in terms of hours of work remains constant in around 11 hours.
Many authors have highlighted the substantial increase in the gap between skilled and
unskilled workers in Argentina.28 The tables in this section show some basic evidence on
this fact. Workers with at least some superior education earned 2.2 times more than those
with incomplete high school or less in 1992. That earnings gap increased to 2.9 by 1998,
and remained around that value during the recent economic crisis, with some signs of
27
28
Notice that the change in geographical coverage implies a significant fall in average wage.
See Galiani and Sanguinetti (2003) and Gasparini (2003 b), among others.
17
reduction during the current expansion. The increase in the wage premium since 1992 was
the consequence of both a wider wage gap and a greater difference in hours of work. While
in 1992 a low-educated adult worked on average 4.3 hours a week more than a higheducated person, in the early 2000s that difference completely vanished. The recent
recovery has implied a new gap in favor of the unskilled, although of a smaller size than in
the early 1990s (see Table 7.2).
Tables 7.4 to 7.6 divide the working population by type of work. The self-employed have
significantly lost compared to the rest of the groups. While in they early 1990s average
earnings of that group were similar or even higher than earnings for salaried workers, in
2003 they were just 80%. That gap is also present today in the new EPHC. The relative loss
for the self-employed has occurred especially in terms of hourly wages. The heterogeneity
of this group becomes apparent in the second panel of Tables 7.4 to 7.6: while earnings
have significantly increased during the 1990s for the group of self-employed professionals,
labor income has substantially fallen for those self-employed with low education. Also, the
relative earnings of workers in small firms compared to those in large firms fell from 70%
in 1992 to 52% in 1998 and to 47% in 2003. The proportion today is even smaller (44% in
2006).
In Tables 7.7 to 7.9 we divide the working population by economic activity. During the
1990s (1992 to 1998) earnings significantly increased in three sectors: the high-tech
industry, the skilled service sectors (business services, finance sector, professionals) and the
public administration. In contrast, earnings fell in low-skilled services like construction,
commerce and domestic service. The fall in earnings during the crisis was generalized
across economic sectors. During the recent recovery hours of work went up in all sectors
(except skilled services), while real hourly wages substantially increased in the industry,
commerce and public administration. In other sectors gains were small, as for construction
workers and domestic servants.
Table 7.10 records the share of salaried workers, self-employed workers and entrepreneurs
in total labor income. The EPH questionnaires of the early 1990s do not allow computing
these statistics. From 1996 to the present there seems to be some increase in the share of
earnings from salaried work and a relative fall of income from self-employment.
Inequality in labor outcomes is probably the main source of inequality in household
income. Table 7.11 shows the Gini coefficient for the distribution of hourly wages for all
workers, and men workers aged 25 to 55. Inequality has increased over the period. The
increase, however, is significantly lower than the increase in household inequality reported
in section 5. When dividing the sample for education, it is interesting to notice that the Gini
went significantly up only for the unskilled.
18
Are the differences in hourly wages reinforced by differences in hours of work? Table 7.12
suggests the opposite. Correlations between hours worked and hourly wages are negative
and significant for all years.
In Table 7.13 we compute the wage gaps among three educational groups. In 1992 a skilled
prime-age male worker earned per hour in his primary job on average 2.61 times more than
a similar unskilled worker. That value increased to 3.04 by 1998 and to 3.02 in 2003.
Instead, the wage gap between semi-skilled and unskilled workers (column (iii)) did not
significantly change. Evidence from the EPHC indicates that the skill premium has
significantly fallen during the recovery.
In order to further investigate the relationship between education and hourly wages we run
regressions of the logarithm of hourly wage in the primary job on educational dummies and
other control variables (age, age squared, and regional dummies) for men and women
separately.29 Table 7.14 shows the results of these Mincer equations. For instance, in 1992 a
male worker between 25 and 55 years old with a primary education degree earned on
average nearly 29% more than a similar worker without that degree. Having secondary
school complete implied a wage increase of 45% over the earnings of a worker with only
primary school: the marginal return of completing secondary school -versus completing
primary school and not even starting secondary school- was 45%. The wage premium for a
college education was an additional 56%. The returns to primary school fell in the first half
of the 1990s and then increased. Overall, changes were negligible. The returns to secondary
school have somewhat fallen during the last decade. In contrast, there was a large jump in
the returns to college education (see Figure 7.1). That jump is also noticeable for working
women, and for urban salaried workers (both men and women). Returns seem to have
significantly fallen in the last four years.
The Mincer equation is also informative on two interesting factors: the role of unobservable
variables and the gender wage gap. The error term in the Mincer regression is usually
interpreted as capturing the effect on hourly wages of factors that are unobservable in
household surveys, like natural ability, contacts and work ethics. An increase in the
dispersion of this error term may reflect an increase in the returns to these unobservable
factors in terms of hourly wages (Juhn et al. (1993)). Table 7.15 shows the standard
deviation of the error term of each Mincer equation. The returns to unobservable factors
increased in Argentina, until the recent recovery when they started to shrink substantially.
29
See Wodon (2000) and Duryea and Pages (2002) for estimates of the returns to years of education in
several LAC countries.
19
The coefficients in the Mincer regressions are different for men and women, indicating that
they are paid differently even when having the same observable characteristics (education,
age, location). To further investigate this point we simulate the counterfactual wage that
men would earn if they were paid like women. The last column in Table 7.15 reports the
ratio between the average of this simulated wage and the actual average wage for men. In
all cases this ratio is less than one, reflecting the fact that women earn less than men even
when controlling for observable characteristics. This result has two main alternative
interpretations: it can be either the consequence of gender discrimination against women, or
the result of men having more valuable unobservable factors than women (e.g. be more
attached to work).
Argentina has witnessed large changes in labor force participation. Table 7.16 shows basic
statistics by gender, age and education.30 Labor force participation has increased in the last
decade and a half. This increase is mainly the consequence of a flow of low and semiskilled prime-age women into the labor market. While in 1992 around 48% of adult women
were in the labor market (either employed or unemployed), ten years later that fraction was
around 60%, and now it is 63%. This increase was shared neither by men, nor by
youngsters (15-24), who reduced their labor market participation. Only the elderly (aged 65
+) substantially increased their participation in the labor market. This massive entry of
women into the labor market is one of the most noticeable labor facts of the last decades.
Figure 7.2 suggests that this phenomenon was particularly important in the 1990s. During
the 1970s and 1980s labor market participation stayed roughly constant. It was in the period
1991-1999 when this variable went substantially up. Labor participation increased in 2002
with the implementation of the Program Jefes de Hogar and the inclusion of most of their
beneficiaries as part of the labor force.
Despite economic growth, the employment rate fell during the 1990s. The drop, however,
was not large: 1 point between 1992 and 1998 (see table 7.17). The employment rate
decreased 4 points between 1998 and 2001, and recovered about 1 point by 2003. The new
EPHC records a higher employment rate, that strongly increased since 2003. Again,
changes have been very different across gender and age groups. While women employment
increased throughout the period, the story for men was the opposite, except during the last
four years when employment increased strongly for both groups.
Probably the most remarkable fact in the Argentina’s labor markets of the last decade is the
dramatic increase in unemployment (see Figure 7.2). Unemployment sharply increased
until 1996, first in the framework of an economic boom (1991-1994), and then during a
recession (1995-1996). The unemployment rate stabilized around 12% by the end of the
30
Since 2003 we compute two panels: one that includes and one that ignores payments for the Programa Jefes
de Hogar.
20
1990s. But that situation did not last long: the economic crisis pushed this variable up again
to levels around 18%. The recent period of economic growth has consistently lowered the
unemployment rate. From Figure 7.2, and from Tables 7.16 and 7.17, it is clear that the
increase in unemployment during the 1990s was the consequence of a sharp increase in
labor market participation facing a constant employment rate. Instead, the increase in
unemployment in the early 2000s is mainly the consequence of the employment fall
associated to the economic crisis. The recent fall in unemployment comes from a strong
growth in the employment rate during the economic recovery.
Table 7.18 shows that the increase in unemployment was similar for women and men.
However, as we have seen above, the factors behind these behaviors are very different.
Employment increased for women, but not enough to absorb all women who entered the
labor market. In contrast, some men left the labor market, but male employment fell at a
higher rate, thus increasing unemployment. Table 7.18 also shows that during the 1990s the
increase in unemployment was particularly harsh for the unskilled, while the recent crisis
hit especially the semi-skilled. Unemployment has dramatically fallen since the crisis. In
the second half of 2006 the unemployment rate was about half that of 2003 for all skills
level.
The social concern for unemployment increases when unemployment spells are large. Table
7.19 shows a large increase of these spells. While in 1992 a typical unemployed person
stayed 4 months without employment, in 2003 that spell lasted more than 8 months. The
increase in duration was similar across educational groups. The length of unemployment
spells has decreased a bit since 2003.
INDEC has published quarterly results for the main labor variables since 2003. Table 7.20
reproduces statistics for labor force, employment and unemployment rates under two
alternatives. In the first one, people who report the PJH as the main labor activity is
considered as employed. In the second alternative those in that situation who are seeking a
job are considered unemployed. In any of the two alternatives, the Table shows a
significant increase in employment that strongly drove the unemployment rate down since
2003.
Tables 7.21 to 7.27 depict the employment structure of urban Argentina. There are more
males than females employed, but the gap has dramatically shrunk during the last decade.
While in 1992 37% of the working population were women, in 2006 that share reached
41%. Older people have also gained participation. The last three columns of Table 7.21
show a sizeable change in the educational structure of the working population in favor of
the skilled.
21
The Greater Buenos Aires area has lost participation in employment, in particular during
the last crisis (Table 7.22). Also, there was a loss of participation for the group of
entrepreneurs captured in the EPH (Table 7.23). Employment in the public sector went up
until 2003, even more if we consider the beneficiaries of the PJH as part of the public sector
(as it is done in the EPH). The counterpart of that increase is (i) the fall in the share of the
unskilled self-employed and employment in small firms during the 1990s, and (ii)
employment in large firms during the latest economic crisis. The recovery of the economy
and the reduction in the size of the PJH contributed to a reduction in the share of workers in
the public sector since 2003.
Tables 7.24 and 7.25 are aimed at presenting the formal-informal structure of the labor
market. Unfortunately there is not a single definition of informality. Following Gasparini
(2003 a), we implement two definitions with the information available in the EPH.
According to the first one formal workers are the entrepreneurs, salaried workers in large
firms and in the public sector, and self-employed professionals (see Table 7.24). According
to the second definition, formal workers are those who have the right to receive pensions
when they retire (see Table 7.25). Unfortunately the EPH allows implementing this
definition only for wage earners. According to the first definition, informal employment has
been somewhat reduced in the last decade and a half. In sharp contrast, informality in the
labor market has dramatically increased according to the second definition. The share of
salaried workers without social security rights increased 8 points in the period 1992-2003.
The EPHC records a significant fall in informality since 2003.
The sectoral structure of the economy has changed (see Tables 7.26 and 7.27). During the
1990s there was a large fall in the share of employment in the manufacturing industry and
commerce. Employment went significantly up in construction, skilled services and the
public sector. During the recent recovery relative employment has particularly increased in
construction and some skilled services.
The concern for child labor has recently been increasing in the world. Table 7.28 shows the
proportion of working children between 10 and 14 years old. Child labor is less relevant
than in most LAC countries and has been decreasing according to EPH data, even during
the recent economic crisis.
The last three tables in this section are aimed at assessing different dimensions of the
quality of employment. As commented above, the coverage of the pension system shrunk in
the last thirteen years. Table 7.29 shows that this pattern was similar for men and women,
and especially severe for the unskilled in comparison to the skilled workers. Similar results
apply to in-the-job health insurance (see Table 7.30). Table 7.31 shows that most people
report their employment as “permanent”.
22
8. Education
In this section we provide an assessment of changes in the educational structure of the
population. The proportion of high-educated people has significantly increased during the
last fourteen years in Argentina (Table 8.1).31 While in 1992 17.8% of adults aged 25 to 65
had more than 13 years of formal education, that share increased to 21.3% in 1998 to
24.7% in 2003, and to 27% in the latest EPHC. That rise has been more intense for women
than for men.
A remarkable fact from Table 8.2 is the reversion of the gap in years of education between
men and women. While men older than 60 have more years of education than women of the
same age, the difference has recently turned in favor of women for people in their 50s. For
the working-age population (25 to 65), years of education have become greater for women
since 1999.
Information in Table 8.3 suggests that the absolute gap in terms of years of education
between the rich and the poor has widened during the last decade and a half. In addition,
notice that the EPH does not allow capturing years of education in graduate programs, so
the variable is truncated at 17 years. Presumably, if years of graduate education had been
reported, the gap between the rich and the poor would have increased even more than what
Table 8.3 suggests.
In Table 8.4 people are divided according to age and household income quintiles. In 2006
the widest gap in years of education between top and bottom income quintiles corresponds
to adults aged 31-40. While the educational gap between the poor and the rich is 6.3 years
for people aged 31 to 40, it is 4.7 for people in their twenties, and 5.2 for individuals older
than 60.
Recently, there have been efforts to gather educational information from most countries in
the world and to compute measures of inequality in education access and outcomes.32
According to Table 8.5 educational Ginis have slightly fallen during the last fourteen years.
Notice that even when the ratio in years of education between the rich and the poor
increased between 1992 and 1998, the Gini did not significantly change. In contrast,
between 1998 and 2003, both the ratio and the Gini went significantly down.
31
Note that some tables in this section have a line that separate the early 1990s (1992 to 1994) from the rest.
The reason is that a methodological change in the EPH in 1995 allowed a better estimation of years of
education since that year on.
32
For instance, Thomas, Wang and Fan (2002) calculate Ginis over the distribution of years of education for
140 countries in the period 1960-2000.
23
Table 8.6 and 8.7 show a rough measure of education: the self-reported literacy rate.
Argentina has high literacy rates compared to the rest of Latin America. Even for the urban
poor literacy is very high: around 99% for those aged 15 to 24 and around 96% for those
aged 25 to 65.
Guaranteeing equality of access to formal education is one of the goals of most societies.
Tables 8.8 and 8.9 show school enrollment rates by equivalized income quintiles.
Attendance rates have sharply increased for children aged 3 to 5. While in 1992 one third of
these children attended a kindergarten, in 2003 half of them did it. The latest EPHC reports
a share of 62%. Attendance for children in primary-school age is almost 100%. Notice that
the recent economic crisis did not have a negative impact on schooling. Girls are more
likely to attend high school than boys. This gap has narrowed down over the last decade as
attendance has significantly increased, reaching around 90% in both gender groups. The
increase in school attendance has continued over the crisis period. The rise in attendance
for youngsters aged 18 to 23 is also noticeable, although it has taken place at somewhat
slower pace.
The increase in attendance rates has been similar across household income quintiles for
children aged 3 to 5, it has been larger in poor quintiles for children aged 6 to 17, and much
larger in rich quintiles for youth aged 18 to 23. Summarizing, it seems that educational
disparities in terms of school attendance have decreased in primary school and high school,
but have substantially increased for college. While the attendance rate for youngsters aged
18 to 23 in the top quintile increased around 15 to 20 points in the last decade and a half, it
actually decreased for those youngsters in the bottom quintile of the equivalized household
income distribution.
Educational mobility
We follow the methodology developed in Andersen (2001) to provide estimates of
educational mobility, i.e. the degree to which parental education and income determine a
child’s education. The dependent variable is the schooling gap, defined as the difference
between (i) years of education that a child would have completed had she entered school at
normal age and advanced one grade each year, and (ii) the actual years of education. In
other words, the schooling gap measures years of missing education. The Educational
Mobility Index (EMI) is defined as 1 minus the proportion of the variance of the school gap
that is explained by family background. In an economy with low mobility, family
background would be important and thus the index would be small.33 Table 8.10 shows the
EMI for teenagers (13 to 19) and young adults (20 to 25). It seems that there has not been
sizeable improvements in educational mobility during the last decade and a half.
33
For technical details see Andersen (2001).
24
9. Housing and social services
Housing is probably the main asset that most people own. The EPH reports whether the
house is owned by the family who lives in, but does not contain information on the rental
value of the dwelling. Table 9.1 shows for each income quintile the share of families
owning a house (the building and the lot). Housing ownership is widespread along the
income distribution, especially among the well-off. Table 9.1 suggests that housing
ownership in rich households has grown relative to poorer households in the last decade
and a half. In fact, while housing ownership increased 3 points for the top quintile between
1992 and 2003, it fell for the rest of the income distribution. The evidence suggests that
housing markets increasingly excluded the poor. There are not clear patterns since 2003 in
the EPHC.
Poor families live in houses smaller -in number of rooms- than richer households. Since
poor families are also larger in size, the number of persons per room is significantly greater.
We have constructed an indicator of poor dwellings. This variable takes a value 1 if the
family lives in a shantytown, inquilinato, pensión, or other space not meant to be used as a
house. Around 3 percent of the population live in poor dwellings. This proportion has been
reduced in the 1990s, and stayed roughly unchanged between 1998 and 2003. Anyway, the
share of these dwellings captured by the EPH is so small, that it is difficult to know when
changes or differences across groups are statistically significant. That problem is even more
serious when analyzing houses of “low-quality” materials, i.e. houses whose walls are
made of chapa, adobe or chorizo. These houses are around 2% of total dwellings.
Housing ownership has decreased for all age groups but the elderly (Table 9.2). The shares
of poor dwellings and low-quality dwellings have significantly decreased for all, except for
those households whose head is young (15 to 24).
Table 9.4 reports statistics by income strata on the access to some basic services in the
house: water, hygienic restrooms, sewerage, and electricity.34 These gaps tend to be larger
for hygienic restrooms and sewerage than for electricity and water, where coverage is more
widespread. Poor people have access to electricity and clean water in the house, but many
of them do not have a hygienic restroom, and most of them do not have access to public
sewerage.
34
See the methodological document for definitions.
25
10.
Demographics
Resources available to each person depend on the number of people among whom she has
to share household total resources with. The size and composition of the household are key
determinants of an individual’s economic well-being. Table 10.1 shows household size by
income quintiles and by education of the household head. It is interesting to notice that the
absence of significant changes for the average household size between 1992 and 2003 is the
consequence of two forces that compensate each other: household size increased for poor
families and decreased for the rest (see also Table 10.2). No significant pattern can be
identified in the new EPHC. Dependency rates have stayed quite stable during the last
decades. Table 10.3 shows this result by presenting household size over the number of
income earners by quintiles and by education of the household head. It is interesting to
notice a fall in dependency rates for the poor since 2003.
As expected, the mean age of the population has not significantly changed in the period
under analysis (see Table 10.4). However, it is interesting to see again heterogeneous
changes across quintiles. The average age in quintile 5 increased 4.1 years between 1992
and 2003, while the average age fell 4.0 years in quintile 1. These are large changes that
surely have some impact on poverty and inequality. It is interesting to also notice a fall in
the mean age of the richest quintile since 2003.
Inequality is reinforced if marriages take place between persons of similar income
potential. Table 10.5 presents some simple linear correlations that suggest the existence of
assortative mating in urban Argentina.35 Men with more years of formal education tend to
marry women with a similar educational background (column(i)). This is one of the factors
that contribute to a positive correlation of hourly wages within couples shown in column
(ii). There are not clear signs of changes in the degree of assortative mating in the last
fourteen years, according to these simple statistics. Finally, columns (iii) and (iv) show
positive, though small, correlations in hours of work, both considering and excluding
people who do not work.
11.
Poverty-alleviation programs
Probably as a consequence of the traditionally low incidence of poverty, and the wide
coverage of social benefits linked to the labor market, Argentina had never had a large
poverty-alleviation program. Instead, there were a multiplicity of small programs at
different government levels targeted to particular groups or areas. These programs were not
usually recorded in the household surveys. In the midst of the 2002 deep recession
Argentina introduced the Programa Jefes de Hogar, which soon became the largest national
35
See also Fernández, Guner and Knowles (2001).
26
poverty-alleviation program covering around 2 million household heads. The PJH transfers
$150 to unemployed household heads with dependents (children aged less than 18 or
incapacitated) and it has a counterpart work requirement, with the aim of helping to assure
that the transfers reached those in greatest need.
Given the size of this program the EPH started to include questions on that program. This
section is based on the specific questions included in the May 2003 EPH and the EPHC.
According to expectations, Table 11.1 shows that coverage is decreasing in income. The
program seems to be far from universal in the poorest strata of the population. Around 22%
of those households in the first quintile of the equivalized income distribution receive
transfers from the PJH. That share falls to around 13% in quintile 2, and 5% in quintile 3.
Around 7% of Argentine households are covered by the program. Table 11.2 shows that
around 11% of households headed by a person with low education are beneficiaries of the
PJH. The mean transfer by household is $3.4. In quintile 1, the mean transfer is $14.7,
while in quintile 5 is basically zero (see Table 11.3).
The program seems to be reasonably targeted to the poor (see Tables 11.4 and 11.5).36
Around 80% of the beneficiaries of the PJH belong to the 40% poorest of the population.
This degree of targeting has increased over time.
12.
An assessment
The social performance of Argentina in the last fourteen years has been overall very
disappointing. According to most indicators poverty increased in Argentina, in contrast to
the experience of most countries in the region. The rise in poverty was the consequence of
economic stagnation and a substantial increase in inequality, again more intense than in any
other LAC country. Inequality has increased measured by all indicators and computed over
the distribution of all income variables. The increase in inequality coupled with a stagnant
per capita income has implied a fall in aggregate welfare in the last 14 years, according to
most welfare functions. Social indicators have substantially improved since 2003, but most
of them are still around the values of year 1998/99, even when the current levels of
economic activity are significantly higher than in that year.
There has been a lot of action in the Argentina’s labor markets during the last decade.
Unemployment reached record levels, pushed by a massive entry of unskilled women into
the labor market, and a loss of employment for prime-age unskilled men. Wages have fallen
36
The target population of the PJH is a topic of debate. Although the Decree that creates the program limits
the benefits to households with certain characteristics (e.g. unemployed heads complying with the counterpart
work requirement), in practice the program has become a typical poverty-alleviation program targeted to all
the poor. The degree of targeting can then be evaluated in terms of all the poor population, instead of those
meeting the initial requirements (that include many non-poor).
27
over the last decade and a half. Changes have not been uniform across groups. In particular,
the wage premium to skilled labor has substantially increased. The weak labor market has
also implied less hours of work for the unskilled and a significant fall in the coverage of
social security. Since 2003 the country has experienced a recovery of the labor market. The
recovery has been very strong in terms of employment, and weaker in terms of real wages.
Attendance rates to pre-school, primary school and secondary school have increased,
particularly in poor income strata. This is not case for college, where the gap between the
rich and the poor has increased. That gap has also widened in the housing market. Finally,
changes in demographic variables have been heterogeneous, as well. While household size
fell in the upper income quintiles, the opposite happened in the poor income strata.
28
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31
Appendix: A poverty profile
This appendix presents a poverty profile based on information from the EPH, 2006 (second
half). A poverty profile is a characterization of the poor population, often compared to the
non-poor population. We take the USD 2 a day and the official moderate poverty lines as
the two criteria to define the poor. To make the text less cumbersome, in general we discuss
the results for the USD 2-a-day poverty line (columns (i) and (ii) in each table), except
when a significant difference justifies the discussion of the alternative poverty definition.
Table A.1 shows some basic demographic characterization of the poor and non-poor
population. According to the USD2 poverty line, 8.5% of the total population is poor. The
differences in this share across age groups are substantial: while 15% of the children under
15 are poor, that share is just 3% for the elderly. The share of the poor population is
monotonically decreasing in age. Nearly half of the poor population (47.1%) are children
aged less than 15, while only 3.7% are people above 65. The age structure is significantly
different between the poor and the non-poor. This is summarized in mean age: it is 33.2 for
the non-poor and only 23 for the poor.
These patterns illustrate the relevance of the impact of the demographic factors on poverty.
84% of the elderly in urban Argentina are heads or spouses of the household they belong.
More than 50% of them live in households with 1 or 2 members. By living alone the elderly
manage to escape income poverty, at least in the usual narrow definition of poverty.
The poor and the non-poor substantially differ in the household size. While on average a
non-poor household has 3.2 persons, a poor household has on average 4.6 members. That
difference is mostly explained by the difference in children under 12. There is on average
1.2 children in each non-poor family with the head aged 25 to 45, while there are 2.5
children on average in poor households with a prime-age head. This difference implies that
even with similar total income, an average poor family where both parents are present
would have a per capita income 40% less than a typical non-poor family. The dependency
rates (number of income earners per person) are also dramatically different: 0.26 in poor
household and more than double in non-poor households (0.65).
The share of female-headed households is somewhat larger for the poor when defining
poverty as USD2. However, the difference almost vanishes when using the official poverty
line. This change is the consequence of a significantly lower proportion of female-headed
households in the decile 3 of the income distribution.
32
Unfortunately, given that the EPH has only urban coverage, there are no estimates for rural
poverty. As mentioned in section 4, based on the Encuesta de Impacto Social de la Crisis en
Argentina (ISCA) Haimovich (2004) finds that rural poverty is around 15 points higher
than urban poverty. When assuming that prices in rural areas are 20% lower the difference
becomes smaller, but still significant (7 points). Table A.2 shows that poverty is
particularly high in the Northern regions of the country (18.2% in NEA and 13.7% in NOA,
compared to a country average of 8.5%), and particularly low in Patagonia (4.8%). Given
its size most of the poor live in the Greater Buenos Aires: 45% of the poor population lives
in this conglomerate (50.1% when using the official poverty line), while more than 20% of
the population lives in the Pampeana region.
Although housing ownership is less usual among the poor, the difference with the non-poor
is not very large: while 67% of the non-poor are owners, 50% of the poor report being
owners of both the lot and the dwelling where they live (Table A.3). The poor live in
smaller houses of a worse quality and with fewer services. In an average poor household
there are 2.4 persons per room. That number is 1.2 in non-poor households.
Around 11% of the poor population lives in shantytowns and other inconvenient places,
while around 10% had dwellings where the floors are of “ladrillo suelto/tierra”, or the roof
is made of “chapa de cartón, caña, tabla, paja con barro o paja sola”. The access of the
urban poor to water, although lower than for the non-poor, is relatively high: 95% of the
poor reported having access to water in their lots. The big difference with the non-poor
appears in the access to hygienic restrooms and to the public sewerage system. While 64%
of the urban non-poor were connected to that system, that share drops to 28% for the urban
poor.
The poor have fewer years of formal education than the rest of the population for any age
group. The educational gap is particularly wide for the [31,40] age group.37 These
differences show up in the second panel of Table A.4. While just a third of the non-poor
adults are unskilled, that share rises to 70% for the poor. 27.7% of the non-poor adults are
skilled, while just 4.4% of the poor are. That share is probably significantly smaller, given
that some professionals are recorded as poor if their monthly income was low in the month
of the survey, just for seasonality reasons, or temporary unemployment.
The literacy rate is high for the poor: 96% of those older than 10 report being able to read
and write. That share rises to 99% for the non-poor. The last panel of Table A.4 indicates
that school attendance is almost universal for those children aged 6 to 12. Attendance rates
significantly fall, especially for the poor, in the pre-primary, secondary and tertiary levels.
37
Naturally, the gap is smaller for the [10,20] age group, when the educational process is still not complete
for many individuals, especially the non-poor.
33
While the rate of attendance is 98% for the poor aged 6 to 12, it drops to 80% for those
aged 13 to 17 and to 25% for those in the (18,23) age group.
The rate of labor market participation of the poor is smaller than the rate of the non-poor,
especially for women. While 69% of the non-poor women are in the labor market, that
share drops to 53% for the poor women (see Table A.5). The only exception for which the
participation rate is not lower for the poor is the elderly.
Employment is significantly higher for the non-poor, while the unemployment rates are
substantially higher for the poor. The unemployment rate of the poor is around four times
the rate for the non-poor. That gap is wider for men than for women. The unemployment
spell of the poor, however, is not larger than for the non-poor. A typical poor unemployed
person spends 9.1 months without finding a job.
The poor are not only less likely to find a job, but when having one, they work less hours
and get lower wages (see Table A.6). On average, a non-poor employed person works 10.6
hours more a week than a poor person. That gap is smaller for the youth (9.4 hours) and for
men (8 hours). On average, the hourly wage of a poor person is just a third of a non-poor
worker. The difference is smaller for the youth and for women, and larger for the elderly
and prime-age males.
Table A.7 characterizes the employment structure of the population. Compared to the nonpoor the working poor are especially self-employed unskilled workers. According to a
definition of informality based on labor groups, 72.4% of the poor are informal, while 39%
of the non-poor are in that category. When defining informality based on the access to
social security the differences are dramatic: while 41% of the salaried non-poor are
informal, that share jumps to 94.9% for the poor.
The sectoral structure of employment is different between the poor and the rest. Compared
to the non-poor the poor are relatively concentrated in labor-intensive manufacturing
industries, and particularly in construction, commerce, and domestic servants. Commerce is
the main source of jobs for the poor: 27.7% of the poor find job in that sector, followed by
20.6% in construction, 17.1% domestic servants and 13.6% in education and health.
The last rows in Table A.7 show substantial differences in the access to stable jobs with
social security rights. The share of permanent jobs, and labor positions with rights to
pensions and health insurance is significantly lower for the poor. For instance, while 61%
of the working non-poor report having access to health insurance linked to their
employment, only 5% of the poor have health insurance.
34
Table A.8 reports statistics of the main poverty-alleviation program of Argentina: the
Programa Jefes de Hogar. Based on the USD2-a-day definition 23% of the poor households
receive transfers for the PJH, while 6% of the non-poor households are beneficiaries of that
program. When considering the official definition of moderate poverty, the shares change
to 22% and 3% for the poor and the non-poor, respectively. The household mean income
from the PJH is $18.8 for the poor and $2.4 for the non-poor. According to the USD2
definition, 23.8% of the beneficiaries of the PJH are poor, while 23.5% of the transfers go
to the poor. Targeting indicators are better when considering a wider definition of poverty:
66.4% of the beneficiaries of the PJH are poor, according to the official definition.
Table A.9 summarizes mean income, and the income structure of the poor and the rest of
the population. It also shows that inequality, as measured by the Gini coefficient for the
distribution of household per capita income, is much lower with the poor than within the
non-poor (0.223 and 0.451 respectively).
The last table in this poverty profile was built with census data. As commented above the
Argentina’s government computes a basic needs indicator (NBI) based on housing
characteristics, sanitation, primary school enrollment, household head education and
dependency rates. Table A.10 shows the geographical structure of this indicator. Basicneeds poverty is higher in NEA and NOA and lower in the city of Buenos Aires (excluding
the Greater Buenos Aires). According to this table, improvements in the living standards of
the poor were significant in the 1980s, and slowed down in the 1990s. While the NBI
indicator fell 7.8 points between 1980 and 1991, it fell 2.2 points between 1991 and 2001.
35
Table 3.1
Real income
Argentina, 1992-2006
Real income
Deciles
1
2
3
4
5
6
7
8
9
10
average
1992
53.9
100.9
136.7
172.2
208.8
257.2
316.7
405.6
557.3
1152.9
336.3
Proportional changes
Deciles
1992-1994
1
-8.1
2
-5.0
3
-3.7
4
-2.0
5
0.8
6
0.0
7
0.0
8
-0.5
9
-1.1
10
-0.4
average
-0.9
1994
49.5
95.8
131.7
168.8
210.4
257.1
316.8
403.5
551.0
1148.6
333.4
EPH - 15 cities
1996
32.1
76.9
110.2
143.9
180.8
226.4
285.0
371.6
529.7
1143.3
310.0
1998
36.5
79.6
114.2
151.1
192.3
239.8
307.7
409.6
584.2
1293.9
340.9
1998
33.5
73.8
106.0
139.8
177.7
222.7
285.5
376.9
536.7
1199.6
315.3
EPH - 28 cities
2000
27.5
65.5
95.3
126.9
164.3
210.7
267.5
358.6
512.7
1108.0
293.7
2002
16.6
35.9
55.2
77.0
101.3
130.2
165.2
221.7
328.4
769.9
190.2
2003
17.2
44.4
67.6
92.0
119.6
153.4
201.4
272.4
394.0
925.5
228.8
EPHC - 28 cities
2006
34.6
78.7
114.7
151.1
194.2
246.8
310.4
399.7
549.5
1160.1
324.0
1994-1996
-35.3
-19.8
-16.3
-14.8
-14.1
-12.0
-10.0
-7.9
-3.9
-0.5
-7.0
1996-1998
13.7
3.5
3.6
5.0
6.3
5.9
8.0
10.2
10.3
13.2
10.0
1992-1998
-32.3
-21.1
-16.4
-12.2
-7.9
-6.8
-2.9
1.0
4.8
12.2
1.4
1998-2000
-17.9
-11.2
-10.1
-9.2
-7.5
-5.4
-6.3
-4.9
-4.5
-7.6
-6.8
2000-2002
-39.7
-45.3
-42.1
-39.4
-38.3
-38.2
-38.3
-38.2
-35.9
-30.5
-35.3
1998-2002
-50.5
-51.4
-48.0
-44.9
-43.0
-41.5
-42.1
-41.2
-38.8
-35.8
-39.7
2003-2006
101.2
77.3
69.7
64.3
62.4
60.9
54.1
46.8
39.5
25.3
41.6
Source: Own calculations based on microdata from the EPH.
Table 4.1
Poverty lines
Argentina, 1992-2006
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003 (May)
2003 (II half)
2004 (I half)
2004 (II half)
2005 (I half)
2005 (II half)
2006 (I half)
2006 (II half)
International PL ($ per capita)
USD 1 a day
USD 2 a day
(i)
(ii)
23.8
47.6
25.9
51.8
26.9
53.7
27.5
54.9
27.5
55.0
27.7
55.3
28.0
55.9
27.4
54.8
27.2
54.4
26.9
53.8
37.3
74.5
38.6
77.1
38.6
77.1
39.7
79.5
40.8
81.7
43.2
86.5
45.0
90.1
48.3
96.5
49.74
99.5
Oficial PL ($ per adult equivalent)
Extreme
Moderate
(iii)
(iv)
57.9
129.2
62.4
138.0
62.8
146.4
66.1
154.7
67.4
156.3
67.4
157.6
69.8
161.2
64.6
155.0
62.4
151.1
61.0
150.1
104.8
231.8
106.6
232.3
103.6
227.7
106.5
233.3
108.5
237.7
114.2
250.1
120.1
259.5
127.2
276.0
126.8
279.0
Ratios
(iv)/(ii)
2.7
2.7
2.7
2.8
2.8
2.8
2.9
2.8
2.8
2.8
3.1
3.0
3.0
2.9
2.9
2.9
2.9
2.9
2.8
(iv)/(iii)
2.2
2.2
2.3
2.3
2.3
2.3
2.3
2.4
2.4
2.5
2.2
2.2
2.2
2.2
2.2
2.2
2.2
2.2
2.2
(iii)/(ii)
1.2
1.2
1.2
1.2
1.2
1.2
1.2
1.2
1.1
1.1
1.4
1.4
1.3
1.3
1.3
1.3
1.3
1.3
1.3
Source: INDEC, WDI and own calculations.
Note 1: mean values for GBA
Note 2: For the EPHC, first half: April, second half: September.
36
Table 4.2
Poverty
Argentina, 1992-2006
USD-1-a day poverty line
Number
poor people
(i)
EPH-15 cities
1992
1993
1994
1995
1996
1997
1998
EPH - 28 cities
1998
1999
2000
2001
2002
2003
EPH-C
2003-II *
2003-II
2004-I
2004-II
2005-I
2005-II
2006-I
2006-II
Headcount
FGT(0)
(ii)
Poverty gap
FGT(1)
(iii)
FGT(2)
(iv)
478,378
586,197
585,528
1,108,580
1,309,616
1,105,174
1,149,506
1.4
1.7
1.7
3.2
3.7
3.1
3.2
1.0
1.0
1.3
2.0
2.6
2.0
1.7
0.9
0.9
1.2
1.7
2.3
1.7
1.3
1,233,383
1,281,780
1,557,903
2,589,201
3,737,657
3,014,981
3.4
3.5
4.2
6.9
9.9
7.9
1.8
2.1
2.5
4.1
3.9
2.8
1.5
1.8
2.0
3.3
2.4
1.8
3,083,104
2,963,995
2,183,430
2,003,317
1,790,591
1,545,954
1,467,723
1,284,361
8.0
7.7
5.6
5.2
4.6
3.9
3.7
3.2
4.0
3.9
2.8
2.4
2.1
1.8
1.8
1.6
3.0
2.9
2.1
1.8
1.6
1.3
1.3
1.2
Source: Own calculations based on microdata from the EPH.
Note: FGT(0)=headcount ratio, FGT(1)=poverty gap, FGT(2)=Foster,
Greer and Thornbecke index with parameter 2.
* computed using weights that ignore income non-response.
Table 4.3
Poverty
Argentina, 1992-2006
USD-2-a day poverty line
Number
poor people
(i)
EPH-15 cities
1992
1,407,829
1993
1,689,431
1994
1,549,582
1995
2,592,179
1996
3,141,943
1997
2,967,630
1998
3,007,165
EPH - 28 cities
1998
3,405,663
1999
3,316,156
2000
4,039,454
2001
5,848,228
2002
9,376,508
2003
9,083,922
EPH-C
2003-II *
7,763,964
2003-II
7,425,991
2004-I
6,093,498
2004-II
5,380,228
2005-I
5,154,842
2005-II
4,579,515
2006-I
4,175,451
2006-II
3,368,274
Headcount
FGT(0)
(ii)
Poverty gap
FGT(1)
(iii)
FGT(2)
(iv)
4.2
5.0
4.5
7.5
8.9
8.3
8.3
1.9
2.1
2.2
3.7
4.2
3.7
3.4
1.3
1.4
1.6
2.6
3.1
2.6
2.3
9.4
9.1
10.9
15.6
24.7
23.7
3.9
4.1
5.0
7.7
10.8
9.2
2.5
2.8
3.3
5.3
6.4
5.1
20.2
19.4
15.7
13.9
13.1
11.7
10.5
8.5
9.0
8.7
6.7
5.9
5.4
4.7
4.4
3.7
5.8
5.6
4.1
3.6
3.3
2.8
2.7
2.3
Source: Own calculations based on microdata from the EPH.
Note: FGT(0)=headcount ratio, FGT(1)=poverty gap, FGT(2)=Foster,
Greer and Thornbecke index with parameter 2.
* computed using weights that ignore income non-response.
37
Table 4.4
Poverty
Argentina, 1992-2006
Official extreme poverty line
Number
poor people
(i)
EPH-15 cities
1992
1,254,803
1993
1,462,793
1994
1,317,220
1995
2,384,765
1996
2,896,186
1997
2,584,205
1998
2,783,867
EPH - 28 cities
1998
3,041,767
1999
3,041,205
2000
3,536,364
2001
5,151,064
2002
10,473,374
2003
10,128,517
EPH-C
2003-II *
8,370,107
2003-II
8,005,978
2004-I
6,578,791
2004-II
5,699,318
2005-I
5,493,745
2005-II
4,782,424
2006-I
4,414,403
2006-II
3,420,196
Headcount
FGT(0)
(ii)
Poverty gap
FGT(1)
(iii)
FGT(2)
(iv)
3.8
4.3
3.8
6.9
8.2
7.2
7.7
1.7
1.8
1.8
3.2
3.9
3.4
3.4
1.2
1.2
1.4
2.3
2.9
2.4
2.2
8.4
8.3
9.5
13.7
27.6
26.4
3.7
3.7
4.3
6.7
12.2
10.5
2.4
2.5
2.9
4.7
7.2
5.8
21.8
20.9
17.0
14.7
14.0
12.2
11.1
8.6
9.6
9.2
7.2
6.2
5.9
4.9
4.6
3.7
6.0
5.8
4.4
3.8
3.6
2.9
2.8
2.3
Source: Own calculations based on microdata from the EPH.
Note: FGT(0)=headcount ratio, FGT(1)=poverty gap, FGT(2)=Foster,
Greer and Thornbecke index with parameter 2.
* computed using weights that ignore income non-response.
Table 4.5
Poverty
Argentina, 1992-2006
Official moderate poverty line
Number
poor people
(i)
EPH-15 cities
1992
6,592,328
1993
6,209,474
1994
6,914,999
1995
9,262,680
1996
10,366,003
1997
9,894,132
1998
10,202,583
EPH - 28 cities
1998
10,865,081
1999
11,159,500
2000
12,079,764
2001
14,401,152
2002
21,795,194
2003
20,986,517
EPH-C
2003-II *
19,136,565
2003-II
18,465,922
2004-I
17,154,260
2004-II
15,471,888
2005-I
15,265,700
2005-II
13,335,905
2006-I
12,363,642
2006-II
10,615,236
Headcount
FGT(0)
(ii)
Poverty gap
FGT(1)
(iii)
FGT(2)
(iv)
19.7
18.3
20.1
26.6
29.4
27.7
28.2
6.5
6.6
7.3
10.6
12.1
11.3
11.6
3.4
3.6
3.9
6.2
7.1
6.5
6.7
30.1
30.5
32.6
38.4
57.5
54.7
12.4
12.5
14.1
18.1
29.2
27.3
7.2
7.3
8.4
11.6
18.9
17.1
49.9
48.1
44.2
39.9
38.9
34.0
31.1
26.7
24.0
23.1
20.1
17.9
16.9
14.7
13.3
11.2
15.2
14.6
12.2
10.7
10.1
8.7
7.9
6.5
Source: Own calculations based on microdata from the EPH.
Note: FGT(0)=headcount ratio, FGT(1)=poverty gap, FGT(2)=Foster,
Greer and Thornbecke index with parameter 2.
* computed using weights that ignore income non-response.
38
Table 4.6
Poverty
Argentina, 1992-2006
50 % median income poverty line
Number
poor people
(i)
EPH-15 cities
1992
6,546,566
1993
6,850,027
1994
7,124,105
1995
7,374,942
1996
7,943,542
1997
7,929,285
1998
7,944,343
EPH - 28 cities
1998
8,320,577
1999
8,226,625
2000
9,072,879
2001
9,403,156
2002
9,832,693
2003
9,543,469
EPH-C
2003-II *
9,434,103
2003-II
9,588,306
2004-I
9,025,898
2004-II
9,714,616
2005-I
9,331,070
2005-II
9,774,979
2006-I
9,408,851
2006-II
9,607,600
Headcount
FGT(0)
(ii)
Poverty gap
FGT(1)
(iii)
FGT(2)
(iv)
19.6
20.2
20.8
21.2
22.6
22.2
22.0
6.6
7.3
7.3
8.8
9.3
9.5
9.3
3.5
4.1
4.0
5.3
5.8
5.7
5.4
23.0
22.5
24.5
25.1
25.9
24.9
9.2
9.5
10.4
12.2
11.5
10.2
5.4
5.7
6.3
8.1
6.8
5.7
24.6
25.0
23.3
25.0
23.8
24.9
23.7
24.2
11.1
11.4
10.2
10.5
10.1
10.4
10.1
9.9
7.0
7.2
6.2
6.3
6.0
6.1
6.0
5.8
Source: Own calculations based on microdata from the EPH.
Note: FGT(0)=headcount ratio, FGT(1)=poverty gap, FGT(2)=Foster,
Greer and Thornbecke index with parameter 2.
* computed using weights that ignore income non-response.
Table 4.7
Poverty
Argentina, 1992-2003
Endowments
Endowments Endowments
plus income
(i)
(ii)
EPH-15 cities
1992
0.392
0.031
1993
0.387
0.035
1994
0.389
0.031
1995
0.385
0.057
1996
0.387
0.067
1997
0.384
0.062
1998
0.392
0.067
EPH - 28 cities
1998
0.402
0.075
1999
0.401
0.070
2000
0.393
0.083
2001
0.400
0.116
2002
0.385
0.167
2003
0.381
0.163
Source: Own calculations based on microdata from the EPH.
39
Table 5.1
Distribution of household per capita income
Share of deciles and income ratios
Argentina, 1992-2006
15 main cities
1992
1993
1994
1995
1996
1997
1998
28 main cities
1998
1999
2000
2001
2002
2003
EPHC
2003-II *
2003-II
2004-I
2004-II
2005-I
2005-II
2006-I
2006-II
Share of deciles
5
6
7
1
2
3
4
1.8
1.7
1.7
1.4
1.4
1.4
1.2
3.0
3.0
2.9
2.7
2.6
2.6
2.4
4.1
4.1
4.0
3.7
3.6
3.6
3.4
5.1
5.2
5.1
4.8
4.7
4.7
4.5
6.2
6.4
6.3
6.0
5.9
6.0
5.7
7.6
7.9
7.7
7.3
7.3
7.3
7.0
1.3
1.3
1.2
1.0
1.0
1.1
2.4
2.5
2.3
2.1
2.0
2.1
3.4
3.5
3.3
3.1
3.0
3.0
4.5
4.6
4.4
4.1
4.1
4.0
5.6
5.8
5.6
5.4
5.4
5.2
1.0
1.0
1.2
1.1
1.1
1.1
1.2
1.2
2.0
2.1
2.3
2.3
2.3
2.3
2.4
2.5
3.0
3.0
3.3
3.3
3.3
3.3
3.5
3.6
4.0
4.1
4.2
4.4
4.4
4.5
4.6
4.7
5.2
5.3
5.4
5.7
5.6
5.8
6.0
6.0
Income ratios
10/1
90/10
8
9
10
95/80
9.4
9.6
9.5
9.0
9.2
9.2
9.0
12.0
12.3
12.1
11.6
11.9
12.0
12.0
16.5
16.6
16.4
16.7
16.9
17.2
17.1
34.1
33.1
34.2
36.7
36.5
36.1
37.7
19.0
19.9
19.7
25.8
26.5
26.7
30.2
7.8
8.1
8.2
9.6
10.1
10.5
11.2
2.0
1.9
1.9
2.1
2.0
2.1
2.1
7.1
7.3
7.2
6.9
6.8
6.8
9.0
9.2
9.1
9.0
8.7
8.8
11.9
12.0
12.2
12.0
11.6
11.9
16.9
17.0
17.4
17.4
17.2
17.3
37.8
36.8
37.4
39.0
40.3
39.8
29.9
28.0
32.3
40.0
39.4
34.8
11.1
10.9
11.9
13.9
14.3
13.5
2.1
2.1
2.1
2.2
2.3
2.2
6.6
6.7
7.0
7.2
7.1
7.3
7.5
7.6
8.5
8.8
8.9
9.1
9.0
9.1
9.4
9.6
11.5
11.8
11.8
12.0
11.7
11.8
12.2
12.3
16.6
17.1
16.8
17.1
16.7
16.7
17.0
16.9
41.6
40.0
38.0
37.6
38.7
37.7
36.1
35.7
40.4
38.7
32.7
32.7
34.1
32.8
30.9
29.9
13.3
13.8
12.2
12.0
12.1
11.8
11.4
11.0
2.2
2.2
2.2
2.0
2.1
2.1
2.0
2.0
Source: Own calculations based on microdata from the EPH.
Note 1: (10/1)=income ratio between deciles 10 and 1; (90/10)=income ratio between percentiles 90 and 10,
and (95/80)=income ratio between percentiles 95 and 80.
* computed using weights that ignore income non-response.
Table 5.2
Distribution of household per capita income
Inequality indices
Argentina, 1992-2006
15 main cities
1992
1993
1994
1995
1996
1997
1998
28 main cities
1998
1999
2000
2001
2002
2003
EPHC
2003-II *
2003-II
2004-I
2004-II
2005-I
2005-II
2006-I
2006-II
Gini
Theil
CV
A(.5)
A(1)
A(2)
E(0)
E(2)
0.450
0.444
0.453
0.481
0.486
0.483
0.502
0.370
0.359
0.378
0.430
0.442
0.422
0.471
1.101
1.077
1.112
1.205
1.260
1.145
1.300
0.165
0.162
0.168
0.190
0.194
0.190
0.207
0.299
0.297
0.303
0.340
0.349
0.346
0.369
0.510
0.517
0.510
0.569
0.607
0.586
0.608
0.355
0.352
0.361
0.416
0.429
0.424
0.461
0.606
0.580
0.618
0.726
0.793
0.656
0.845
0.502
0.491
0.504
0.522
0.533
0.528
0.472
0.442
0.464
0.497
0.530
0.518
1.307
1.213
1.231
1.263
1.356
1.343
0.207
0.197
0.208
0.224
0.233
0.227
0.368
0.356
0.377
0.404
0.412
0.401
0.605
0.606
0.646
0.675
0.657
0.637
0.458
0.440
0.474
0.517
0.530
0.512
0.854
0.735
0.757
0.798
0.920
0.902
0.541
0.530
0.515
0.504
0.511
0.504
0.488
0.483
0.656
0.539
0.522
0.478
0.497
0.492
0.442
0.431
3.355
1.488
1.800
1.360
1.384
1.469
1.249
1.308
0.250
0.232
0.220
0.210
0.216
0.212
0.197
0.193
0.422
0.409
0.386
0.376
0.383
0.377
0.359
0.354
0.668
0.664
0.625
0.624
0.635
0.627
0.614
0.617
0.548
0.525
0.488
0.471
0.483
0.473
0.445
0.437
5.628
1.107
1.621
0.925
0.957
1.080
0.780
0.855
Source: Own calculations based on microdata from the EPH.
CV=coefficient of variation. A(e) refers to the Atkinson index with a CES
function with parameter e. E(e) refers to the generalized entropy index with parameter e. E(1)=Theil.
* computed using weights that ignore income non-response.
40
Table 5.3
Distribution of equivalized household income
Share of deciles and income ratios
Argentina, 1992-2006
15 main cities
1992
1993
1994
1995
1996
1997
1998
28 main cities
1998
1999
2000
2001
2002
2003
EPHC
2003-II *
2003-II
2004-I
2004-II
2005-I
2005-II
2006-I
2006-II
Share of deciles
5
6
7
1
2
3
4
2.0
1.9
2.0
1.6
1.6
1.6
1.5
3.4
3.3
3.3
3.0
2.9
2.9
2.7
4.3
4.4
4.3
4.1
3.9
3.9
3.7
5.3
5.5
5.4
5.1
5.0
5.0
4.8
6.5
6.7
6.5
6.2
6.1
6.2
5.9
7.9
8.1
7.9
7.5
7.5
7.6
7.3
1.5
1.5
1.3
1.1
1.2
1.3
2.7
2.8
2.6
2.4
2.2
2.4
3.7
3.9
3.6
3.4
3.3
3.3
4.8
4.9
4.7
4.5
4.4
4.3
5.9
6.1
5.9
5.7
5.7
5.6
1.2
1.2
1.3
1.3
1.3
1.3
1.3
1.4
2.3
2.3
2.5
2.6
2.6
2.6
2.7
2.8
3.3
3.4
3.6
3.6
3.6
3.6
3.8
3.9
4.4
4.4
4.6
4.8
4.7
4.8
5.0
5.0
5.5
5.6
5.7
6.0
5.9
6.0
6.3
6.3
Income ratios
10/1
90/10 95/80
8
9
10
9.5
9.7
9.6
9.1
9.2
9.4
9.1
12.0
12.3
12.0
11.6
11.9
12.0
11.9
16.4
16.3
16.2
16.4
16.7
16.9
16.8
32.7
31.8
32.8
35.3
35.0
34.5
36.3
16.3
17.0
16.7
21.6
21.8
22.2
24.7
7.1
7.3
7.1
8.6
8.6
9.0
9.6
2.0
1.9
1.9
2.1
2.0
2.1
2.1
7.3
7.5
7.4
7.2
7.1
7.0
9.1
9.3
9.2
9.1
8.8
8.9
11.9
12.0
12.1
11.9
11.5
11.8
16.7
16.8
17.1
17.1
16.9
17.0
36.2
35.2
36.1
37.6
39.0
38.3
24.4
23.1
26.9
33.3
32.5
28.4
9.3
9.3
10.3
12.1
12.3
11.3
2.1
2.0
2.1
2.2
2.3
2.2
6.9
7.0
7.1
7.5
7.3
7.5
7.7
7.8
8.7
8.9
9.1
9.3
9.2
9.2
9.6
9.7
11.5
11.8
11.8
12.1
11.8
11.9
12.2
12.4
16.5
16.9
16.7
16.9
16.5
16.5
16.8
16.7
39.8
38.4
37.5
35.7
37.0
36.5
34.6
34.0
33.3
31.9
27.8
26.9
28.2
27.5
25.6
24.8
11.5
11.6
10.5
10.2
10.2
10.1
9.8
9.3
2.1
2.1
2.1
2.0
2.1
2.0
2.0
2.0
Source: Own calculations based on microdata from the EPH.
Note 1: (10/1)=income ratio between deciles 10 and 1; (90/10)=income ratio between percentiles 90 and 10,
and (95/80)=income ratio between percentiles 95 and 80.
* computed using weights that ignore income non-response.
Table 5.4
Distribution of equivalized household income
Inequality indices
Argentina, 1992-2006
15 main cities
1992
1993
1994
1995
1996
1997
1998
28 main cities
1998
1999
2000
2001
2002
2003
EPHC
2003-II *
2003-II
2004-I
2004-II
2005-I
2005-II
2006-I
2006-II
Gini
Theil
CV
A(.5)
A(1)
A(2)
E(0)
E(2)
0.430
0.424
0.431
0.460
0.463
0.461
0.480
0.334
0.325
0.341
0.390
0.398
0.380
0.425
1.017
1.004
1.040
1.127
1.168
1.066
1.198
0.150
0.147
0.152
0.173
0.176
0.173
0.188
0.272
0.270
0.275
0.311
0.318
0.316
0.338
0.468
0.475
0.467
0.525
0.561
0.545
0.565
0.318
0.315
0.322
0.372
0.383
0.379
0.412
0.518
0.504
0.541
0.635
0.682
0.568
0.718
0.478
0.468
0.483
0.501
0.512
0.506
0.424
0.400
0.422
0.456
0.488
0.473
1.203
1.127
1.149
1.194
1.287
1.261
0.187
0.179
0.190
0.206
0.215
0.208
0.335
0.325
0.347
0.373
0.381
0.368
0.561
0.564
0.606
0.635
0.617
0.595
0.409
0.393
0.426
0.467
0.479
0.460
0.724
0.635
0.660
0.713
0.829
0.795
0.518
0.508
0.493
0.481
0.488
0.483
0.466
0.461
0.591
0.490
0.473
0.432
0.451
0.459
0.402
0.388
3.028
1.378
1.612
1.266
1.299
1.506
1.187
1.196
0.228
0.213
0.201
0.191
0.197
0.196
0.180
0.175
0.389
0.377
0.355
0.344
0.352
0.348
0.330
0.324
0.630
0.627
0.584
0.584
0.597
0.588
0.573
0.577
0.492
0.473
0.439
0.422
0.433
0.427
0.400
0.391
4.584
0.950
1.300
0.801
0.844
1.134
0.704
0.716
Source: Own calculations based on microdata from the EPH.
CV=coefficient of variation. A(e) refers to the Atkinson index with a CES
function with parameter e. E(e) refers to the generalized entropy index with parameter e. E(1)=Theil.
* computed using weights that ignore income non-response.
41
Table 5.5
Distribution of equivalized household labor monetary income
Share of deciles and income ratios
Argentina, 1992-2006
15 main cities
1992
1993
1994
1995
1996
1997
1998
28 main cities
1998
1999
2000
2001
2002
2003
EPHC
2004-II
2005-I
2005-II
2006-I
2006-II
Share of deciles
5
6
7
1
2
3
4
2.0
1.8
1.9
1.5
1.5
1.4
1.3
3.4
3.2
3.3
2.9
2.8
2.7
2.6
4.5
4.3
4.3
4.0
3.8
3.8
3.6
5.5
5.4
5.4
5.0
4.9
4.9
4.6
6.6
6.7
6.5
6.1
6.0
6.1
5.7
8.0
8.1
7.9
7.4
7.4
7.6
7.2
1.3
1.4
1.2
1.0
1.1
0.9
2.6
2.7
2.4
2.2
2.0
2.1
3.6
3.7
3.5
3.3
2.9
2.9
4.6
4.8
4.5
4.3
4.1
4.0
5.8
5.9
5.8
5.4
5.3
5.2
1.0
1.0
1.0
1.1
1.1
2.2
2.3
2.2
2.5
2.5
3.4
3.4
3.4
3.6
3.7
4.6
4.5
4.5
4.9
4.8
6.0
5.8
5.8
6.2
6.1
Income ratios
10/1
90/10 95/80
8
9
10
9.7
9.8
9.6
9.0
9.2
9.4
8.9
12.1
12.3
11.9
11.5
11.8
12.0
11.8
16.5
16.6
16.2
16.4
16.7
17.0
17.0
31.8
31.8
33.0
36.2
36.0
35.0
37.3
16.2
17.9
17.4
24.4
24.6
24.8
28.6
6.9
7.6
7.2
9.0
9.2
9.7
10.3
1.9
1.9
1.9
2.2
2.1
2.1
2.1
7.1
7.3
7.2
7.0
6.8
6.8
9.0
9.1
9.1
8.8
8.6
8.7
11.7
11.9
12.0
11.8
11.3
11.8
16.9
17.0
17.2
17.1
16.9
17.3
37.3
36.2
37.1
39.2
41.1
40.3
28.2
26.0
31.4
38.5
38.7
43.7
10.2
9.9
11.5
13.5
13.4
14.0
2.2
2.0
2.1
2.3
2.4
2.2
7.5
7.3
7.3
7.7
7.7
9.5
9.3
9.2
9.6
9.7
12.4
12.0
11.9
12.4
12.6
17.5
16.9
16.9
17.1
17.2
35.9
37.6
37.7
35.0
34.6
36.3
36.2
38.0
32.7
32.2
13.4
12.0
12.7
11.6
11.9
2.0
2.1
2.1
2.0
2.0
Source: Own calculations based on microdata from the EPH.
Note 1: (10/1)=income ratio between deciles 10 and 1; (90/10)=income ratio between percentiles 90 and 10,
and (95/80)=income ratio between percentiles 95 and 80.
Table 5.6
Distribution of equivalized household labor monetary income
Inequality indices
Argentina, 1992-2006
15 main cities
1992
1993
1994
1995
1996
1997
1998
28 main cities
1998
1999
2000
2001
2002
2003
EPHC
2004-II
2005-I
2005-II
2006-I
2006-II
Gini
Theil
CV
A(.5)
A(1)
A(2)
E(0)
E(2)
0.422
0.427
0.433
0.470
0.474
0.469
0.493
0.317
0.325
0.346
0.410
0.421
0.393
0.450
0.959
0.984
1.060
1.163
1.221
1.081
1.242
0.145
0.149
0.154
0.182
0.186
0.179
0.199
0.268
0.278
0.280
0.328
0.335
0.329
0.357
0.633
0.506
0.502
0.576
0.590
0.574
0.603
0.313
0.325
0.329
0.397
0.407
0.400
0.442
0.460
0.484
0.562
0.677
0.745
0.584
0.772
0.491
0.481
0.497
0.519
0.536
0.535
0.450
0.425
0.449
0.493
0.539
0.533
1.249
1.178
1.200
1.262
1.374
1.354
0.198
0.190
0.202
0.221
0.235
0.236
0.355
0.343
0.368
0.397
0.413
0.422
0.597
0.596
0.646
0.667
0.660
0.698
0.437
0.420
0.459
0.506
0.533
0.547
0.780
0.694
0.719
0.796
0.944
0.917
0.495
0.503
0.506
0.479
0.476
0.444
0.469
0.505
0.420
0.402
1.208
1.257
1.641
1.213
1.116
0.203
0.210
0.216
0.191
0.187
0.378
0.384
0.389
0.358
0.354
0.668
0.689
0.673
0.666
0.652
0.475
0.484
0.492
0.444
0.436
0.730
0.790
1.347
0.736
0.623
Source: Own calculations based on microdata from the EPH.
CV=coefficient of variation. A(e) refers to the Atkinson index with a CES
function with parameter e. E(e) refers to the generalized entropy index with parameter e. E(1)=Theil.
42
Table 5.7
Distribution of household income
Gini coefficient
Argentina, 1992-2006
Per capita
income
15 main cities
1992
1993
1994
1995
1996
1997
1998
28 main cities
1998
1999
2000
2001
2002
2003
EPHC
2003-II *
2003-II
2004-I
2004-II
2005-I
2005-II
2006-I
2006-II
Equivalized
income
A
Equivalized
income
B
Equivalized
income
C
Equivalized
income
D
Equivalized
income
E
Total
household
income
Equivalized
income A
Age 0-10
Equivalized
income A
Age 20-30
Equivalized
income A
Age 40-50
Equivalized
income A
Age 60-70
0.450
0.444
0.453
0.481
0.486
0.483
0.502
0.430
0.424
0.431
0.460
0.463
0.461
0.480
0.421
0.415
0.422
0.450
0.452
0.451
0.470
0.423
0.416
0.423
0.452
0.454
0.453
0.471
0.416
0.409
0.415
0.444
0.446
0.445
0.463
0.434
0.428
0.436
0.464
0.468
0.466
0.484
0.445
0.435
0.438
0.458
0.457
0.458
0.471
0.435
0.439
0.444
0.468
0.461
0.461
0.480
0.403
0.399
0.412
0.437
0.440
0.434
0.454
0.439
0.435
0.430
0.469
0.481
0.457
0.474
0.429
0.398
0.417
0.423
0.427
0.456
0.469
0.502
0.491
0.504
0.522
0.533
0.528
0.478
0.468
0.483
0.501
0.512
0.506
0.468
0.458
0.472
0.491
0.502
0.494
0.470
0.460
0.475
0.494
0.504
0.497
0.461
0.451
0.466
0.485
0.496
0.487
0.483
0.473
0.488
0.506
0.517
0.510
0.470
0.459
0.467
0.480
0.488
0.481
0.476
0.469
0.500
0.517
0.531
0.510
0.456
0.448
0.452
0.466
0.483
0.478
0.472
0.469
0.485
0.504
0.519
0.519
0.468
0.454
0.446
0.469
0.464
0.455
0.541
0.530
0.515
0.504
0.511
0.504
0.488
0.483
0.518
0.508
0.493
0.481
0.488
0.483
0.466
0.461
0.505
0.496
0.481
0.469
0.477
0.473
0.456
0.450
0.509
0.500
0.485
0.472
0.479
0.476
0.458
0.453
0.498
0.489
0.475
0.462
0.470
0.467
0.449
0.444
0.523
0.514
0.498
0.486
0.492
0.489
0.471
0.466
0.493
0.482
0.470
0.456
0.468
0.462
0.455
0.446
0.507
0.513
0.496
0.481
0.477
0.509
0.470
0.463
0.468
0.470
0.464
0.458
0.468
0.451
0.439
0.435
0.522
0.525
0.520
0.475
0.494
0.469
0.461
0.458
0.555
0.492
0.461
0.469
0.472
0.455
0.477
0.464
Source: Own calculations based on microdata from the EPH.
Note: Equivalized income A: theta=0.9, alpha1=0.5 and alpha2=0.75; B: theta=0.75, alpha1=0.5 and
alpha2=0.75; C: theta=0.9, alpha1=0.3 and alpha2=0.5, D: theta=0.75, alpha1=0.3 and alpha2=0.5; E:
Amsterdam scale. Adult equivalent equal to 0.98 for men between 14 and 17, 0.9 for women over 14, 0.52 for
children under 14, and 1 for the rest.
* computed using weights that ignore income non-response.
Table 5.8
Polarization
EGR and Wolfson indices of bipolarization
Argentina, 1992-2006
Household per capita income
EGR
Wolfson
15 main cities
1992
1993
1994
1995
1996
1997
1998
28 main cities
1998
1999
2000
2001
2002
2003
EPHC
2003-II *
2003-II
2004-I
2004-II
2005-I
2005-II
2006-I
2006-II
Equivalized income
EGR
Wolfson
0.151
0.145
0.151
0.158
0.160
0.162
0.165
0.401
0.407
0.414
0.430
0.434
0.447
0.454
0.140
0.137
0.140
0.149
0.149
0.154
0.156
0.377
0.368
0.381
0.394
0.404
0.420
0.424
0.169
0.165
0.170
0.177
0.179
0.176
0.468
0.462
0.477
0.503
0.505
0.493
0.158
0.152
0.160
0.163
0.164
0.167
0.428
0.421
0.441
0.456
0.462
0.459
0.182
0.182
0.173
0.171
0.169
0.163
0.163
0.161
0.517
0.517
0.503
0.491
0.491
0.490
0.479
0.471
0.169
0.169
0.163
0.159
0.157
0.153
0.151
0.151
0.478
0.478
0.459
0.457
0.456
0.451
0.445
0.443
Source: Own calculations based on microdata from the EPH.
Note: EGR=Esteban, Gradin and Ray.
* computed using weights that ignore income non-response.
43
Table 6.1
Aggregate welfare
Mean income from National Accounts
Argentina, 1992-2006
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
Mean income
(i)
100.0
104.7
109.4
105.0
109.5
117.0
120.1
114.8
112.7
106.7
94.1
101.5
109.6
118.6
127.3
Sen
(ii)
100.0
105.8
108.8
99.2
102.6
110.1
109.1
106.5
101.9
93.0
80.3
85.2
97.2
105.0
117.6
Atk(1)
(iii)
100.0
105.0
108.7
98.9
101.7
109.2
108.5
105.6
100.3
90.9
79.2
84.2
96.1
103.8
115.5
Atk(2)
(iv)
100.0
103.3
109.5
92.4
88.1
99.1
97.1
92.6
81.6
71.2
66.1
69.0
83.4
89.6
98.8
Table 6.2
Aggregate welfare
Mean income from EPH
Argentina, 1992-2006
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
Mean income
(i)
100.0
100.4
99.1
93.6
91.5
96.3
101.5
96.6
94.6
87.1
61.2
73.7
82.5
93.7
104.3
Sen
(ii)
100.0
101.5
98.5
88.4
85.8
90.6
92.2
89.6
85.5
75.9
52.2
61.8
73.2
83.0
96.4
Atk(1)
(iii)
100.0
100.7
98.5
88.1
85.1
89.9
91.7
88.8
84.1
74.3
51.5
61.1
72.3
82.0
94.6
Atk(2)
(iv)
100.0
99.1
99.2
82.4
73.7
81.6
82.0
77.9
68.5
58.1
43.0
50.1
62.8
70.8
80.9
44
Table 7.1
Hourly wages
By gender, age and education
Argentina, 1992-2006
Total
EPH-15 cities
1992
4.0
1993
4.0
1994
4.5
1995
4.4
1996
4.3
1997
4.2
1998
4.4
EPH - 28 cities
1998
4.2
1999
4.0
2000
4.0
2001
4.0
2003
2.9
EPH-C
2003-II *
3.2
2003-II
3.2
2004-II
3.2
2005-I
3.3
2005-II
3.6
2006-I
3.7
2006-II
3.8
Gender
Female
Male
(15-24)
Age
(25-64)
(65 +)
Low
Education
Mid
High
3.8
3.8
4.5
4.3
4.2
4.2
4.2
4.1
4.1
4.5
4.5
4.3
4.3
4.6
2.7
2.9
3.1
2.9
2.7
2.7
2.7
4.3
4.3
4.9
4.7
4.6
4.5
4.7
4.8
4.7
5.0
4.8
5.0
5.7
6.2
2.9
3.0
3.2
3.0
3.0
3.0
2.9
3.8
3.7
4.1
4.0
3.7
3.7
3.8
6.4
6.5
7.4
7.8
7.0
7.0
7.7
4.0
4.0
4.0
4.0
2.8
4.3
4.1
4.1
4.0
3.0
2.6
2.7
2.5
2.5
1.7
4.5
4.3
4.3
4.3
3.1
5.7
5.1
4.4
4.7
3.4
2.7
2.7
2.6
2.6
1.9
3.6
3.6
3.5
3.5
2.4
7.3
6.8
6.7
6.6
4.7
3.2
3.1
3.1
3.2
3.8
3.5
3.7
3.1
3.3
3.3
3.4
3.5
3.8
3.8
1.8
1.9
1.9
1.9
2.1
2.3
2.3
3.4
3.4
3.4
3.6
3.8
4.0
4.0
3.6
4.1
5.2
3.4
4.8
3.6
4.6
2.0
2.1
2.1
2.2
2.2
2.4
2.4
2.6
2.7
2.8
2.8
2.9
3.1
3.2
5.0
5.0
5.3
5.6
6.2
6.3
6.1
Education
Mid
High
Source: Own calculations based on microdata from the EPH.
Table 7.2
Hours of work
By gender, age and education
Argentina, 1992-2006
Total
EPH-15 cities
1992
44.3
1993
44.6
1994
44.0
1995
43.5
1996
43.6
1997
43.8
1998
43.9
EPH - 28 cities
1998
44.0
1999
43.6
2000
43.1
2001
41.9
2003
41.1
EPHC
2003-II
40.7
2004-I
41.7
2004-II
41.4
2005-I
42.1
2005-II
42.0
2006-I
42.1
2006-II
41.8
Gender
Female
Male
(15-24)
Age
(25-64)
(65 +)
Low
37.6
37.8
36.5
36.2
36.2
37.3
37.0
48.4
48.8
48.4
48.0
48.2
47.8
48.5
41.5
42.2
42.0
41.2
41.1
41.3
40.5
45.4
45.6
44.7
44.3
44.6
44.7
45.0
35.7
36.8
38.7
37.0
34.2
35.2
37.3
45.1
44.8
44.0
43.2
42.3
43.0
43.7
45.4
46.2
45.8
45.0
45.7
45.9
45.7
40.8
41.6
40.6
41.6
42.2
41.6
41.4
37.1
36.8
36.6
35.3
34.6
48.5
48.3
47.5
46.5
45.6
40.9
40.2
39.6
37.2
36.9
45.0
44.7
44.1
43.0
42.0
37.7
36.9
37.7
38.6
38.2
43.8
43.4
42.4
40.8
40.2
45.9
45.5
44.8
44.1
43.0
41.4
41.2
41.4
40.2
39.5
34.1
35.4
34.9
35.7
35.3
35.1
35.2
45.4
45.8
45.6
46.3
46.5
46.8
46.3
38.1
38.1
37.8
39.7
38.7
39.0
38.1
41.7
42.9
42.7
43.1
43.3
43.2
43.0
33.8
35.1
33.0
34.5
33.5
34.1
34.3
40.3
41.4
41.1
41.7
41.5
41.6
41.5
42.7
43.5
42.6
43.4
43.7
43.9
43.5
38.6
39.7
39.8
40.8
40.3
39.9
39.7
Source: Own calculations based on microdata from the EPH.
45
Table 7.3
Labor income
By gender, age and education
Argentina, 1992-2006
Total
EPH-15 cities
1992
677.6
1993
694.6
1994
702.0
1995
693.4
1996
672.8
1997
668.2
1998
702.8
EPH - 28 cities
1998
664.0
1999
633.8
2000
620.8
2001
598.2
2003
428.1
EPH-C
2004-II
465.6
2005-I
502.5
2005-II
524.6
2006-I
549.1
2006-II
570.1
Gender
Female
Male
(15-24)
Age
(25-64)
(65 +)
Low
Education
Mid
High
539.5
551.9
577.3
537.5
530.5
540.7
548.7
762.7
782.0
776.5
786.7
757.8
745.7
801.5
445.6
459.3
454.9
416.0
393.7
394.8
392.8
745.2
761.6
768.3
762.6
739.5
732.9
766.5
501.9
531.0
577.5
563.9
641.5
611.9
827.3
506.5
503.4
487.4
453.1
426.6
430.4
424.9
648.3
664.3
678.1
659.8
620.6
627.6
634.8
1053.1
1093.5
1124.4
1255.1
1136.5
1117.4
1230.9
519.1
510.8
504.9
495.5
348.4
754.4
714.9
698.0
667.4
482.3
370.0
358.6
340.3
308.8
220.3
725.2
695.4
680.9
657.0
464.4
767.9
609.7
543.6
538.0
441.3
405.7
393.5
378.7
350.8
249.2
609.4
578.9
565.9
542.9
373.7
1162.4
1077.0
1051.0
1020.3
701.7
398.3
411.3
468.2
457.2
485.4
544.3
596.4
613.6
641.6
659.8
253.6
278.2
301.3
322.8
323.2
532.5
574.9
603.7
623.6
642.2
475.7
406.9
539.9
393.3
522.9
302.7
325.9
325.8
365.2
366.9
431.4
447.0
480.8
505.3
531.7
798.1
877.7
924.0
889.5
909.8
Source: Own calculations based on microdata from the EPH.
Table 7.4
Hourly wages
By type of work
Argentina, 1992-2006
Type of work
EntrepreneursWage earnersSelf-employed
EPH-15 cities
1992
3.6
4.4
1993
3.8
4.4
1994
4.3
5.0
1995
9.2
4.1
4.5
1996
8.9
4.0
4.5
1997
8.0
4.0
4.4
1998
8.8
4.1
4.7
EPH - 28 cities
1998
8.5
3.9
4.3
1999
7.7
3.9
4.1
2000
7.2
3.9
4.0
2001
7.9
3.9
3.7
2003
5.9
2.8
2.8
EPH-C
2003-II
6.2
3.1
3.0
2004-I
5.7
3.1
3.0
2004-II
5.2
3.1
3.1
2005-I
6.7
3.2
3.2
2005-II
10.3
3.3
3.2
2006-I
7.8
3.5
3.6
2006-II
6.8
3.7
3.3
Formal workers
Informal workers
Salaried workers Self-employed Salaried Self-employed
Entrepreneurs Large firms Public sector professionals Small firms
Unskilled
9.2
8.9
8.0
8.8
4.1
4.1
4.7
4.3
4.2
4.1
4.4
4.1
4.4
5.5
5.0
5.1
5.2
5.5
9.4
8.8
10.4
9.8
10.1
10.3
12.6
3.1
2.9
3.2
3.0
3.0
3.0
2.8
4.0
3.9
4.2
3.7
3.6
3.5
3.5
8.5
7.7
7.2
7.9
5.9
4.2
4.1
4.2
4.2
3.0
5.2
5.2
5.2
5.1
3.8
11.6
10.1
8.5
8.7
6.1
2.6
2.6
2.6
2.7
1.8
3.2
3.3
3.3
3.1
2.3
6.2
5.7
5.2
6.7
10.3
7.8
6.8
3.4
3.6
3.5
3.5
3.5
3.8
4.0
4.1
4.2
4.0
4.4
4.9
5.1
5.6
6.7
5.7
6.2
6.4
6.2
9.0
6.5
2.0
2.0
2.1
2.1
2.1
2.2
2.2
2.5
2.6
2.6
2.6
2.6
2.7
2.7
Source: Own calculations based on microdata from the EPH.
Note: in 1992 to 1994 public sector wages refer only to the GBA.
46
Table 7.5
Hours of work
By type of work
Argentina, 1992-2006
Type of work
EntrepreneursWage earnersSelf-employedZero-income
EPH-15 cities
1992
43.6
44.2
45.2
1993
44.5
44.3
41.8
1994
43.9
43.8
38.1
1995
56.0
43.0
42.9
40.9
1996
56.4
43.1
43.7
36.4
1997
57.6
43.5
42.6
39.1
1998
58.4
43.6
42.6
39.5
EPH - 28 cities
1998
57.8
43.5
43.5
40.4
1999
56.0
43.3
42.6
42.1
2000
57.2
42.9
41.4
40.3
2001
56.1
41.8
39.7
41.2
2003
54.8
41.4
38.2
38.5
EPHC
2003-II
51.3
41.1
38.3
31.0
2004-I
52.9
42.2
38.8
29.1
2004-II
54.3
41.3
39.5
32.4
2005-I
53.7
42.4
39.5
35.1
2005-II
52.4
41.8
41.3
31.2
2006-I
53.6
42.0
40.8
29.4
2006-II
51.2
41.6
41.0
31.2
Formal workers
Informal workers
Salaried workers Self-employed Salaried Self-employed
Entrepreneurs Large firms Public sector professionals Small firms
Unskilled Zero-income
56.0
56.4
57.6
58.4
45.6
46.1
46.0
46.6
46.8
47.1
47.3
40.5
41.6
40.3
38.7
39.6
39.1
39.4
39.2
42.8
41.7
40.9
43.3
41.5
41.4
42.5
42.5
41.1
39.7
40.0
40.5
40.6
44.8
44.4
44.1
43.2
43.7
42.8
42.8
45.2
41.8
38.1
40.9
36.4
39.1
39.5
57.8
56.0
57.2
56.1
54.8
47.4
47.2
47.0
46.3
46.1
39.1
38.2
38.1
37.4
36.7
42.0
42.3
41.5
40.5
39.2
40.6
41.1
40.6
38.9
38.7
43.8
42.6
41.4
39.6
38.0
40.4
42.1
40.3
41.2
38.5
51.3
52.9
54.3
53.7
52.4
53.6
51.2
45.8
46.3
45.9
46.7
45.9
46.1
45.5
37.6
38.9
39.0
39.0
39.5
39.0
38.6
37.2
39.7
38.8
40.6
40.5
39.5
39.2
37.4
38.7
36.6
38.4
37.0
37.6
37.4
38.5
38.6
39.7
39.3
41.5
41.0
41.3
31.0
29.1
32.4
35.1
31.2
29.4
31.2
Source: Own calculations based on microdata from the EPH.
Note: in 1992 to 1994 public sector hours of work refer only to the GBA.
Table 7.6
Labor income
By type of work
Argentina, 1992-2006
Type of work
EntrepreneursWage earnersSelf-employed
EPH-15 cities
1992
662.5
751.8
1993
701.1
699.8
1994
701.4
735.6
1995
1865.5
659.2
645.8
1996
1848.0
647.8
631.8
1997
1699.7
646.1
618.4
1998
1937.7
673.9
653.8
EPH - 28 cities
1998
1833.4
638.1
610.3
1999
1619.6
625.2
550.6
2000
1458.3
624.5
530.4
2001
1453.7
611.5
478.5
2003
1131.7
436.6
346.3
EPH-C
2004-II
1030.3
491.3
418.6
2005-I
1310.4
524.2
418.9
2005-II
1563.1
543.0
441.0
2006-I
1290.1
570.3
469.3
2006-II
1238.0
596.4
465.2
Formal workers
Informal workers
Salaried workers Self-employed Salaried Self-employed
Entrepreneurs Large firms Public sector professionals Small firms
Unskilled
1865.5
1848.0
1699.7
1937.7
774.3
788.7
798.8
754.7
750.5
727.4
775.6
775.3
834.9
837.3
753.9
776.4
806.2
828.2
1427.9
1437.6
1500.0
1415.7
1374.9
1478.5
1683.4
534.7
486.8
480.0
420.2
412.5
397.3
404.8
682.7
617.1
631.6
533.5
510.6
483.6
489.8
1833.4
1619.6
1458.3
1453.7
1131.7
739.5
729.0
733.9
736.0
521.9
781.0
769.4
770.0
736.2
540.8
1578.9
1472.3
1319.1
1257.9
842.6
383.4
373.2
378.7
358.7
246.9
468.6
429.3
425.2
378.3
265.5
1030.3
1310.4
1563.1
1290.1
1238.0
588.8
641.9
620.5
669.6
700.2
629.5
656.9
751.0
755.6
797.4
875.9
941.3
887.9
1080.0
943.0
273.6
298.1
289.8
301.6
308.4
335.1
314.3
354.0
361.4
376.9
Source: Own calculations based on microdata from the EPH.
Note: in 1992 to 1994 public sector earnings refer only to the GBA.
47
Table 7.7
Hourly wages
By sector
Argentina, 1992-2006
Primary
activities
EPH-15 cities
1992
4.3
1993
7.5
1994
4.8
1995
6.6
1996
4.0
1997
4.0
1998
3.5
EPH - 28 cities
1998
3.8
1999
3.6
2000
3.5
2001
5.1
2003
2.8
EPH-C
2003-II
4.7
2004-I
4.2
2004-II
4.0
2005-I
4.3
2005-II
2006-I
4.8
2006-II
5.3
Industry
low tech
Industry
high tech
Utilities &
Construction Commerce transportation
Skilled
services
Public
Education &
administration
Health
Domestic
servants
3.1
3.1
3.3
3.2
3.2
3.0
3.1
3.9
4.1
4.4
4.9
4.4
4.3
4.6
3.1
3.6
3.4
3.3
3.1
3.3
3.1
3.6
3.3
3.5
3.3
3.2
3.0
3.1
4.1
4.0
4.4
4.2
3.9
4.2
4.1
6.4
6.3
7.0
6.9
6.3
6.3
7.1
4.3
4.4
5.3
5.1
5.3
5.1
5.7
4.5
4.6
5.6
5.1
5.3
5.1
5.5
3.2
3.3
3.6
3.5
3.5
3.6
3.3
3.0
2.9
2.9
3.0
2.5
4.4
4.2
4.4
4.2
3.0
2.9
3.0
3.0
3.2
2.0
3.0
2.9
2.9
2.8
2.0
3.9
3.5
3.5
3.8
2.5
6.8
5.8
6.1
6.3
4.3
5.4
5.3
5.4
5.1
3.8
5.2
5.4
5.2
5.1
3.8
3.0
3.0
2.9
2.9
2.1
2.2
2.2
2.4
2.6
2.5
2.5
2.6
3.2
3.7
3.4
3.7
3.9
4.7
4.3
2.4
2.4
2.4
2.4
2.4
2.5
2.7
2.1
2.2
2.2
2.4
2.4
2.6
2.7
3.0
3.6
3.1
3.0
3.2
3.5
3.7
4.5
4.5
5.1
5.1
5.0
5.0
5.1
4.4
4.2
4.1
4.2
4.9
4.9
5.4
4.3
4.1
4.2
4.3
4.5
5.3
5.1
2.2
2.1
2.1
2.0
2.1
2.1
2.1
Source: Own calculations based on microdata from the EPH.
Table 7.8
Hours of work
By sector
Argentina, 1992-2006
Primary
activities
EPH-15 cities
1992
57.1
1993
49.8
1994
52.6
1995
53.3
1996
50.5
1997
53.2
1998
52.7
EPH - 28 cities
1998
52.9
1999
49.3
2000
55.2
2001
50.5
2003
49.1
EPHC
2003-II
47.9
2004-I
47.5
2004-II
49.6
2005-I
49.9
2005-II
46.0
2006-I
50.3
2006-II
50.4
Industry
Industry
low tech
high tech
Utilities &
Skilled
Public
Education &
Domestic
services
administration
Health
servants
46.6
48.3
48.0
47.9
45.5
48.5
48.3
47.4
47.1
46.0
43.7
45.3
47.0
46.5
46.6
44.4
45.3
42.3
42.7
41.2
42.9
48.9
49.7
49.4
49.7
49.9
50.9
49.2
50.5
54.8
52.4
53.2
54.3
52.8
55.1
42.4
42.4
44.0
44.1
43.9
44.7
45.5
42.3
43.9
42.9
42.7
42.0
41.1
42.5
38.6
37.1
36.8
36.7
36.5
36.8
37.3
31.3
31.7
27.9
27.1
26.5
26.5
26.2
48.0
47.0
47.9
47.2
43.3
46.6
45.0
44.3
43.2
45.0
43.4
43.9
41.8
38.7
38.5
49.7
49.5
47.8
47.1
46.6
54.8
56.3
53.4
53.2
52.5
45.5
44.7
45.2
44.7
42.8
42.1
41.3
41.4
40.8
40.2
37.0
36.0
36.5
35.5
34.8
26.9
28.1
28.7
26.7
25.7
42.2
44.0
42.2
43.8
43.7
45.2
44.1
43.0
45.0
44.9
45.3
44.9
45.4
44.9
38.6
39.1
39.4
41.3
40.8
42.4
42.7
45.6
46.2
46.3
46.6
46.5
46.1
46.1
50.2
51.0
50.1
51.4
51.7
52.0
51.4
43.1
41.4
41.7
42.2
42.9
41.1
41.5
41.8
42.8
43.4
42.9
43.5
43.0
42.4
33.9
35.5
35.7
35.7
36.3
35.9
35.7
28.3
29.0
25.8
29.7
26.5
27.7
27.6
Construction Commerce transportation
Source: Own calculations based on microdata from the EPH.
48
Table 7.9
Labor income
By sector
Argentina, 1992-2006
Primary
activities
EPH-15 cities
1992
929.5
1993
884.6
1994
1006.1
1995
1226.1
1996
770.7
1997
833.8
1998
649.8
EPH - 28 cities
1998
656.8
1999
677.4
2000
715.7
2001
937.6
2003
622.6
EPH-C
2004-II
806.8
2005-I
908.9
2005-II
2006-I
979.8
2006-II
1134.5
Industry
low tech
Industry
high tech
Utilities &
Construction Commerce transportation
Skilled
services
Public
Education &
Health
administration
Domestic
servants
562.7
586.7
573.3
583.3
559.1
558.2
572.4
705.4
772.8
750.0
815.9
769.1
759.0
820.4
578.2
609.3
559.8
506.0
478.9
473.4
476.3
639.9
614.5
622.6
586.0
588.6
581.2
556.4
802.3
863.5
805.7
793.7
765.4
759.0
772.8
1013.8
1019.8
1130.6
1177.9
1063.7
1083.2
1238.1
796.9
831.3
848.6
860.3
859.9
832.2
953.5
690.6
723.7
730.8
669.4
690.4
686.7
727.8
366.8
366.7
333.6
284.4
272.7
267.1
260.2
556.9
519.8
524.8
514.1
403.0
786.9
719.5
744.2
690.0
524.5
450.2
477.7
430.7
386.7
264.9
537.9
525.9
489.6
463.8
324.8
744.4
698.7
663.3
661.1
475.5
1191.4
1036.0
1045.3
1046.0
697.6
881.7
850.7
848.0
804.7
612.4
693.6
674.7
680.1
663.9
463.8
243.8
249.8
250.8
231.6
153.3
421.8
472.2
436.4
453.2
472.7
590.1
678.8
673.0
691.0
748.5
344.9
370.4
364.2
401.1
448.8
396.1
429.3
441.9
475.7
486.2
577.8
573.2
614.0
683.5
681.6
740.3
834.7
836.9
804.9
843.6
711.5
724.9
853.3
828.0
847.3
570.1
588.2
623.6
673.1
684.2
177.0
193.0
182.1
195.5
193.4
Source: Own calculations based on microdata from the EPH.
Table 7.10
Distribution of labor income
Shares
Argentina, 1992-2006
Salaried Self- employed Entrepreneurs
workers
EPH-15 cities
1992
73.9
26.1
1993
73.7
26.3
1994
74.0
26.0
1995
68.1
21.1
10.8
1996
69.2
20.9
9.9
1997
70.0
20.3
9.7
1998
69.8
19.8
10.3
EPH - 28 cities
1998
69.2
20.4
10.4
1999
71.9
18.8
9.3
2000
72.5
19.1
8.4
2001
72.6
18.8
8.6
2002
72.6
18.3
9.2
2003
70.7
20.6
8.7
EPHC
2004-II
71.3
19.3
9.5
2005-I
71.0
17.9
11.1
2005-II
70.4
17.2
12.4
2006-I
73.8
16.6
9.6
2006-II
75.4
15.3
9.4
Total
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
Source: Own calculations based on microdata from the EPH.
49
Table 7.11
Distribution of wages (primary activity)
Gini coefficient
Argentina, 1992-2006
All
All
EPH-15 cities
1992
0.400
1993
0.390
1994
0.392
1995
0.410
1996
0.415
1997
0.413
1998
0.434
28 main cities
1998
0.435
1999
0.424
2000
0.433
2001
0.445
2003
0.440
EPHC
2003-II
0.455
2004-I
0.451
2004-II
0.435
2005-I
0.444
2005-II
0.455
2006-I
0.441
2006-II
0.420
Male workers aged 25-55
Education
Low
Mid
High
0.412
0.397
0.397
0.420
0.417
0.404
0.438
0.319
0.305
0.307
0.311
0.321
0.326
0.330
0.362
0.354
0.345
0.371
0.358
0.331
0.371
0.419
0.402
0.396
0.417
0.412
0.397
0.418
0.436
0.417
0.437
0.444
0.461
0.330
0.335
0.352
0.368
0.335
0.371
0.352
0.377
0.373
0.391
0.419
0.401
0.403
0.427
0.458
0.439
0.447
0.413
0.435
0.412
0.406
0.397
0.344
0.369
0.345
0.351
0.343
0.337
0.328
0.389
0.387
0.366
0.367
0.344
0.352
0.349
0.425
0.443
0.389
0.430
0.392
0.391
0.379
Source: Own calculations based on microdata from the EPH.
50
Table 7.12
Correlations hours of work-hourly wages
Argentina, 1992-2006
All workers
EPH-15 cities
1992
-0.181
1993
-0.224
1994
-0.230
1995
-0.176
1996
-0.188
1997
-0.205
1998
-0.161
28 main cities
1998
-0.163
1999
-0.194
2000
-0.187
2001
-0.181
2003
-0.163
EPHC
2004-II
-0.108
2005-I
-0.175
2005-II
-0.066
2006-I
-0.108
2006-II
-0.149
Urban
salaried
workers
-0.166
-0.173
-0.196
-0.180
-0.173
-0.203
-0.171
-0.174
-0.184
-0.173
-0.176
-0.164
-0.091
-0.157
-0.152
-0.174
-0.139
Source: Own calculations based on microdata from the EPH.
Table 7.13
Ratio of hourly wages by educational group
Prime-age males
Argentina, 1992-2006
High/Medium
EPH-15 cities
1992
1.86
1993
1.85
1994
1.84
1995
1.99
1996
1.93
1997
1.95
1998
2.09
EPH - 28 cities
1998
2.05
1999
1.95
2000
1.97
2001
2.03
2003
2.11
EPHC
2003-II
1.94
2004-I
1.90
2004-II
1.77
2005-I
2.02
2005-II
1.93
2006-I
1.81
2006-II
1.73
High/Low
Medium/Low
2.61
2.57
2.64
2.81
2.60
2.64
3.04
1.41
1.39
1.43
1.41
1.34
1.35
1.45
2.97
2.70
2.84
2.77
3.02
1.45
1.38
1.44
1.37
1.43
2.65
2.54
2.49
2.63
2.56
2.40
2.39
1.37
1.34
1.41
1.30
1.33
1.33
1.38
Source: Own calculations based on microdata from the EPH.
51
Table 7.14
Mincer equation
Estimated coefficients of educational dummies
Argentina, 1992-2006
All workers
Men
Primary Secondary College
EPH-15 cities
1992
0.287
0.451
0.557
1993
0.090
0.453
0.606
1994
0.178
0.429
0.716
1995
0.140
0.510
0.707
1996
0.159
0.475
0.716
1997
0.173
0.428
0.624
1998
0.253
0.476
0.779
EPH - 28 cities
1998
0.227
0.457
0.757
1999
0.175
0.381
0.753
2000
0.158
0.487
0.726
2001
0.232
0.414
0.785
2003
0.256
0.414
0.784
EPHC
2003-II
0.092
0.402
0.700
2004-I
0.158
0.412
0.652
2004-II
0.198
0.421
0.575
2005-I
0.152
0.357
0.742
2005-II
0.129
0.387
0.662
2006-I
0.210
0.378
0.637
2006-II
0.257
0.356
0.620
Women
Primary Secondary College
Urban salaried workers
Men
Women
Primary Secondary College
Primary Secondary College
-0.095
0.005
-0.013
-0.027
0.024
0.072
0.011
0.462
0.322
0.431
0.277
0.292
0.322
0.350
0.454
0.496
0.404
0.291
0.605
0.452
0.208
0.153
0.113
0.187
0.183
0.167
0.178
0.162
0.432
0.419
0.362
0.403
0.336
0.439
0.456
0.560
0.657
0.687
0.664
0.732
0.598
0.679
0.007
-0.025
0.003
0.020
0.073
-0.053
0.000
0.383
0.387
0.416
0.364
0.232
0.363
0.469
0.226
0.351
0.403
0.480
0.503
0.430
0.450
0.042
0.032
-0.023
0.160
0.103
0.450
0.340
0.304
0.320
0.399
0.523
0.341
0.331
0.198
0.624
0.161
0.183
0.118
0.252
0.250
0.442
0.326
0.421
0.364
0.344
0.668
0.670
0.697
0.675
0.723
0.025
-0.057
-0.070
0.109
-0.182
0.457
0.403
0.387
0.393
0.380
0.467
0.512
0.577
0.566
0.552
0.207
0.084
0.195
0.224
0.151
0.343
0.111
0.448
0.302
0.256
0.163
0.284
0.262
0.362
0.719
0.408
0.365
0.472
0.521
0.504
0.689
0.094
0.185
0.282
0.190
0.128
0.183
0.174
0.402
0.331
0.314
0.311
0.354
0.320
0.351
0.595
0.665
0.590
0.628
0.628
0.608
0.554
0.256
0.141
0.153
0.103
0.049
0.264
0.009
0.309
0.287
0.295
0.288
0.325
0.359
0.420
0.636
0.610
0.592
0.600
0.594
0.596
0.604
Source: Own calculations based on microdata from the EPH.
Table 7.15
Mincer equation
Dispersion in unobservables and gender wage gap
Argentina, 1992-2006
Dispersion in unobservables
All workers
Urban salaried
Men
Women
Men
Women
EPH-15 cities
1992
0.640
0.655
0.528
0.503
1993
0.605
0.617
0.538
0.509
1994
0.616
0.613
0.533
0.517
1995
0.631
0.817
0.540
0.517
1996
0.626
0.640
0.550
0.520
1997
0.632
0.644
0.561
0.536
1998
0.619
0.833
0.560
0.543
EPH - 28 cities
1998
0.617
0.649
0.563
0.542
1999
0.612
0.778
0.569
0.545
2000
0.640
0.825
0.567
0.558
2001
0.665
0.900
0.596
0.570
2003
0.666
0.637
0.585
0.536
EPHC
2003-II
0.676
0.786
0.615
0.598
2004-I
0.683
0.989
0.605
0.578
2004-II
0.647
0.868
0.587
0.566
2005-I
0.658
1.021
0.593
0.590
2005-II
0.629
0.817
0.570
0.576
2006-I
0.625
0.873
0.569
0.587
2006-II
0.623
0.635
0.571
0.581
Gender wage gap
Urban salaried
workers
0.866
0.875
0.904
0.922
0.914
0.898
0.857
0.854
0.889
0.869
0.887
0.890
0.932
0.899
0.900
0.865
0.879
0.858
0.853
Source: Own calculations based on microdata from the EPH.
52
Table 7.16
Share of adults in the labor force
Argentina, 1992-2006
Adults (25-64)
Total
15 main cities
1992
0.560
1993
0.568
1994
0.565
1995
0.573
1996
0.575
1997
0.582
1998
0.584
28 main cities
1998
0.571
1999
0.577
2000
0.581
2001
0.569
2003
0.564
EPHC
2003-II
0.602
2004-I
0.604
2004-II
0.607
2005-I
0.601
2005-II
0.607
2006-I
0.613
2006-II
0.611
with PJH
2002
0.580
2003
0.577
2003-II
0.617
2004-I
0.617
2004-II
0.619
2005-I
0.611
2005-II
0.616
2006-I
0.620
2006-II
0.616
Age
(15-24) (25-64) (65 +)
Gender
Female Male
Low
Education
Medium High
0.492
0.494
0.504
0.502
0.506
0.494
0.463
0.685
0.698
0.698
0.712
0.714
0.726
0.735
0.113
0.116
0.095
0.095
0.103
0.119
0.137
0.483
0.507
0.508
0.533
0.530
0.550
0.563
0.914
0.912
0.910
0.913
0.916
0.919
0.929
0.608
0.618
0.618
0.643
0.638
0.649
0.659
0.708
0.714
0.714
0.730
0.729
0.739
0.742
0.841
0.854
0.856
0.856
0.843
0.861
0.873
0.450
0.444
0.439
0.417
0.401
0.722
0.730
0.736
0.733
0.735
0.129
0.138
0.138
0.119
0.134
0.545
0.563
0.571
0.565
0.575
0.923
0.919
0.920
0.921
0.908
0.652
0.648
0.663
0.660
0.641
0.729
0.745
0.752
0.739
0.750
0.854
0.858
0.842
0.852
0.846
0.469
0.468
0.461
0.450
0.449
0.458
0.452
0.768
0.767
0.773
0.764
0.771
0.776
0.772
0.146
0.162
0.165
0.172
0.175
0.174
0.172
0.621
0.621
0.625
0.614
0.628
0.636
0.630
0.929
0.927
0.931
0.928
0.925
0.925
0.928
0.690
0.691
0.700
0.690
0.699
0.703
0.701
0.773
0.769
0.782
0.774
0.770
0.778
0.769
0.855
0.853
0.864
0.856
0.869
0.871
0.867
0.414
0.414
0.486
0.479
0.470
0.457
0.456
0.463
0.456
0.749
0.744
0.778
0.775
0.781
0.770
0.775
0.778
0.773
0.125
0.135
0.151
0.165
0.167
0.176
0.177
0.176
0.173
0.600
0.596
0.643
0.641
0.645
0.630
0.642
0.645
0.637
0.915
0.909
0.931
0.929
0.932
0.928
0.925
0.926
0.928
0.684
0.662
0.713
0.713
0.721
0.707
0.712
0.711
0.709
0.752
0.760
0.781
0.777
0.789
0.777
0.773
0.780
0.770
0.849
0.844
0.856
0.853
0.865
0.857
0.869
0.871
0.867
Source: Own calculations based on microdata from the EPH.
53
Table 7.17
Share of adults employed
Argentina, 1992-2006
Adults (25-64)
Total
1992
0.522
1993
0.516
1994
0.495
1995
0.476
1996
0.473
1997
0.500
1998
0.509
28 main cities
1998
0.500
1999
0.497
2000
0.495
2001
0.464
2003
0.470
EPHC
2003-II
0.502
2004-I
0.512
2004-II
0.526
2005-I
0.522
2005-II
0.542
2006-I
0.544
2006-II
0.552
with PJH
2002
0.476
2003
0.487
2003-II
0.521
2004-I
0.528
2004-II
0.541
2005-I
0.534
2005-II
0.551
2006-I
0.552
2006-II
0.558
Age
(15-24) (25-64) (65 +)
0.424 0.651 0.107
0.397 0.651 0.113
0.389 0.632 0.083
0.353 0.618 0.080
0.341 0.617 0.090
0.368 0.645 0.106
0.354 0.660 0.122
Gender
Female Male
0.459 0.869
0.466 0.861
0.451 0.835
0.451 0.806
0.446 0.804
0.476 0.830
0.496 0.846
Education
Low Medium High
0.572 0.672 0.816
0.567 0.669 0.816
0.550 0.640 0.812
0.538 0.632 0.796
0.529 0.632 0.769
0.563 0.651 0.800
0.570 0.670 0.823
0.344
0.332
0.318
0.283
0.259
0.652
0.649
0.649
0.621
0.643
0.115
0.123
0.120
0.103
0.121
0.484
0.493
0.498
0.485
0.508
0.842
0.825
0.819
0.774
0.789
0.568
0.561
0.564
0.532
0.543
0.662
0.662
0.662
0.622
0.645
0.807
0.791
0.784
0.777
0.780
0.308
0.320
0.330
0.319
0.340
0.347
0.346
0.673
0.681
0.696
0.691
0.711
0.711
0.720
0.127
0.141
0.149
0.153
0.160
0.159
0.162
0.529
0.537
0.548
0.540
0.566
0.568
0.577
0.830
0.839
0.855
0.854
0.867
0.863
0.877
0.576
0.598
0.615
0.613
0.633
0.633
0.642
0.675
0.677
0.698
0.693
0.703
0.704
0.711
0.785
0.783
0.810
0.795
0.826
0.824
0.830
0.275
0.275
0.329
0.334
0.341
0.328
0.348
0.353
0.350
0.641
0.655
0.687
0.693
0.707
0.698
0.715
0.714
0.722
0.108
0.121
0.130
0.144
0.151
0.157
0.163
0.161
0.163
0.516
0.533
0.557
0.562
0.572
0.559
0.581
0.580
0.585
0.780
0.793
0.835
0.843
0.858
0.856
0.867
0.864
0.878
0.565
0.572
0.609
0.628
0.642
0.635
0.647
0.644
0.652
0.641
0.658
0.688
0.688
0.707
0.699
0.707
0.706
0.713
0.762
0.779
0.787
0.783
0.811
0.796
0.827
0.824
0.830
Source: Own calculations based on microdata from the EPH.
54
Table 7.18
Unemployment rates
Argentina, 1992-2006
Adults (25-64)
Total
15 main cities
1992
0.068
1993
0.092
1994
0.123
1995
0.169
1996
0.177
1997
0.141
1998
0.128
28 main cities
1998
0.125
1999
0.139
2000
0.148
2001
0.184
2003
0.166
EPHC
2003-II
0.166
2004-I
0.152
2004-II
0.133
2005-I
0.132
2005-II
0.108
2006-I
0.112
2006-II
0.097
with PJH
2002
0.179
2003
0.157
2003-II
0.156
2004-I
0.144
2004-II
0.127
2005-I
0.127
2005-II
0.105
2006-I
0.110
2006-II
0.095
(15-24)
Age
(25-64) (65 +)
Gender
Female
Male
Low
Education
Medium High
0.137
0.197
0.227
0.297
0.326
0.256
0.236
0.050
0.066
0.094
0.132
0.137
0.112
0.101
0.056
0.029
0.130
0.159
0.122
0.113
0.108
0.051
0.081
0.112
0.154
0.158
0.134
0.119
0.049
0.057
0.083
0.118
0.123
0.097
0.089
0.058
0.082
0.110
0.163
0.171
0.132
0.135
0.051
0.062
0.104
0.133
0.133
0.118
0.097
0.030
0.045
0.052
0.070
0.089
0.071
0.057
0.235
0.253
0.275
0.322
0.355
0.097
0.111
0.117
0.153
0.125
0.108
0.107
0.131
0.138
0.101
0.111
0.124
0.129
0.141
0.116
0.087
0.102
0.110
0.160
0.131
0.129
0.134
0.149
0.194
0.152
0.092
0.112
0.120
0.159
0.140
0.055
0.078
0.069
0.088
0.078
0.343
0.318
0.283
0.291
0.243
0.241
0.235
0.124
0.112
0.099
0.096
0.078
0.084
0.067
0.133
0.131
0.094
0.110
0.082
0.087
0.057
0.148
0.135
0.123
0.120
0.099
0.107
0.084
0.107
0.095
0.082
0.079
0.063
0.068
0.055
0.165
0.135
0.122
0.111
0.095
0.101
0.084
0.126
0.120
0.108
0.104
0.087
0.095
0.075
0.082
0.082
0.063
0.071
0.050
0.054
0.042
0.335
0.335
0.322
0.303
0.274
0.282
0.237
0.238
0.232
0.145
0.119
0.116
0.106
0.094
0.093
0.077
0.082
0.066
0.136
0.100
0.134
0.127
0.093
0.107
0.083
0.086
0.057
0.140
0.106
0.134
0.123
0.113
0.112
0.095
0.102
0.081
0.148
0.128
0.103
0.093
0.080
0.078
0.063
0.067
0.055
0.174
0.136
0.146
0.119
0.109
0.102
0.091
0.094
0.080
0.148
0.134
0.119
0.114
0.104
0.101
0.086
0.095
0.074
0.102
0.078
0.081
0.082
0.063
0.071
0.049
0.054
0.042
Source: Own calculations based on microdata from the EPH.
55
Table 7.19
Duration of unemployment
Argentina, 1992-2006
Total
15 main cities
1992
3.8
1993
5.1
1994
5.4
1995
6.8
1996
8.1
1997
6.6
1998
6.2
28 main cities
1998
6.1
1999
6.4
2000
6.6
2001
6.8
2003
8.5
EPHC
2003-II
11.8
2004-I
10.5
2004-II
10.1
2005-I
9.8
2005-II
10.0
2006-I
8.9
2006-II
9.2
Age
(15-24) (25-64) (65 +)
Gender
Female
Male
Adults (25-64)
Education
Low
Medium
High
4.0
5.2
5.2
6.3
7.9
6.6
5.7
3.7
5.1
5.5
7.0
8.1
6.3
6.7
4.3
3.4
8.8
11.6
13.2
13.0
4.0
4.2
5.4
6.6
8.2
10.6
7.8
9.0
3.3
4.9
4.5
5.9
6.1
5.0
4.6
3.1
4.3
4.9
6.1
7.2
5.3
5.9
4.0
5.9
5.8
7.4
8.3
7.1
6.8
4.8
5.8
6.5
10.0
10.3
7.5
9.0
5.5
5.9
6.3
7.0
7.6
6.5
6.6
6.8
6.7
9.0
4.7
8.7
6.0
9.5
11.1
8.7
8.6
8.9
8.3
12.0
4.7
4.9
5.1
5.7
7.2
5.8
5.5
6.1
6.1
8.0
6.6
6.5
7.1
6.8
9.3
8.4
9.3
8.5
8.4
10.2
10.9
8.9
9.3
8.4
8.5
7.0
7.7
12.4
11.5
10.7
10.7
10.8
10.3
10.2
12.6
11.1
9.9
11.0
14.4
8.9
11.8
13.9
13.4
11.9
11.9
12.6
11.7
11.8
11.0
9.6
9.4
9.3
8.8
8.6
8.4
12.4
10.3
9.7
9.5
10.4
9.4
8.8
11.7
12.0
11.2
11.9
10.7
11.1
10.8
13.8
12.9
11.8
10.4
11.9
10.1
11.9
Source: Own calculations based on microdata from the EPH.
Table 7.20
Labor force, employment rate and unemployment rate
Argentina, 2003-2006
Encuesta Permanente de Hogares Continua
Alternative 1
Alternative 2
Labor force Employment Unemployment
Labor force Employment
I-2003
45.6
36.3
20.4
44.2
33.5
II-2003
45.6
37.4
17.8
44.4
35.1
III-2003
45.7
38.2
16.3
44.7
35.9
IV-2003
45.7
39.1
14.5
44.6
36.7
I-2004
45.4
38.9
14.4
44.3
36.6
II-2004
46.2
39.4
14.8
45.3
37.4
III-2004
46.2
40.1
13.2
45.1
38.0
IV-2004
45.9
40.4
12.1
45.0
38.5
I-2005
45.2
39.4
13.0
44.4
37.7
II-2005
45.6
40.1
12.1
44.7
38.4
III-2005
46.2
41.1
11.1
45.3
39.7
IV-2005
45.9
41.3
10.1
45.2
40.1
I-2006
46.0
40.7
11.4
45.3
39.5
II-2006
46.7
41.8
10.4
46.1
40.7
III-2006
46.3
41.6
10.2
45.8
40.7
IV-2006
46.1
42.1
8.7
45.7
41.4
Alternative 1: People who report working for the PJH as main activity are employed.
Alternative 2: People with PJH as main activity and seeking employment are unemployed.
Unemployment
24.3
21.0
19.6
17.7
17.4
17.4
15.7
14.5
14.9
13.9
12.5
11.4
12.8
11.6
11.2
9.3
56
Table 7.21
Age, gender and educational structure of employment
Argentina, 1992-2006
Gender
Female
Male
15 main cities
1992
1993
1994
1995
1996
1997
1998
28 main cities
1998
1999
2000
2001
2003
EPHC
2003-II
2004-I
2004-II
2005-I
2005-II
2006-I
2006-II
with PJH
2002
2003
2003-II
2004-I
2004-II
2005-I
2005-II
2006-I
2006-II
(0-14)
(15-24)
Age
(25-40)
(41-64)
(65 +)
Low
Education
Medium
High
37.2
37.6
37.2
38.1
38.0
38.3
39.5
62.8
62.4
62.8
61.9
62.0
61.7
60.5
0.5
0.5
0.3
0.3
0.6
0.2
0.2
19.1
18.3
18.8
18.3
20.4
17.8
17.4
39.0
38.4
39.7
40.9
37.4
39.3
39.3
38.6
39.8
38.7
38.2
38.9
39.6
39.7
2.8
3.0
2.4
2.4
2.7
3.1
3.3
39.8
38.0
37.7
40.9
36.4
37.7
36.3
38.8
39.1
39.2
37.7
38.9
37.7
38.1
21.4
22.9
23.2
21.5
24.7
24.6
25.5
39.0
40.0
40.2
40.9
40.8
61.0
60.0
59.8
59.1
59.2
0.3
0.4
0.3
0.2
0.1
17.3
19.6
19.0
15.5
14.0
39.8
36.8
37.7
39.8
40.3
39.4
39.8
39.8
41.5
41.9
3.2
3.3
3.2
3.0
3.7
37.4
35.8
35.6
34.8
30.6
37.7
38.5
38.2
37.5
39.0
24.9
25.7
26.2
27.8
30.4
40.5
40.4
40.0
39.9
40.6
40.8
41.3
59.5
59.6
60.0
60.1
59.4
59.2
58.7
0.6
0.7
0.6
0.6
0.6
0.6
0.6
18.0
18.1
17.9
17.6
17.7
17.8
17.4
36.5
36.9
37.0
37.1
37.6
37.4
39.0
41.2
40.3
40.3
40.4
39.9
40.1
38.9
3.7
4.0
4.2
4.3
4.3
4.2
4.1
29.0
29.5
32.0
31.6
31.5
30.8
30.1
39.0
38.4
39.9
40.0
39.3
39.9
40.3
32.0
32.1
28.1
28.4
29.2
29.2
29.5
42.2
42.9
42.5
42.4
42.0
41.6
42.3
42.2
42.3
57.8
57.1
57.5
57.6
58.0
58.4
57.7
57.8
57.7
0.3
0.1
0.6
0.6
0.6
0.6
0.6
0.6
0.6
17.4
14.1
18.2
18.0
17.7
17.5
17.5
17.6
17.2
37.9
41.0
37.3
37.6
37.8
37.6
38.1
37.9
39.2
41.3
41.3
40.3
39.9
40.0
40.2
39.7
39.9
39.0
3.1
3.5
3.5
3.8
3.9
4.1
4.1
4.0
4.0
34.2
32.7
31.2
31.6
34.2
33.9
33.2
32.5
31.5
38.0
38.8
39.0
38.4
39.4
39.3
38.9
39.5
40.0
27.8
28.4
29.9
30.0
26.4
26.9
27.9
28.0
28.5
Source: Own calculations based on microdata from the EPH.
57
Table 7.22
Regional structure of employment
Argentina, 1992-2006
15 main cities
1992
1993
1994
1995
1996
1997
1998
28 main cities
1998
1999
2000
2001
2003
EPHC
2003-II
2004-I
2004-II
2005-I
2005-II
2006-I
2006-II
with PJH
2002
2003
2003-II
2004-I
2004-II
2005-I
2005-II
2006-I
2006-II
GBA
Pampeana
Cuyo
NOA
Patagonia
NEA
74.5
75.6
74.2
73.4
73.3
73.1
73.5
15.7
15.0
15.7
15.4
15.3
15.6
15.3
2.6
2.6
2.8
2.9
3.0
2.9
2.9
4.6
4.2
4.5
5.1
4.9
5.1
5.0
2.7
2.6
2.8
3.2
3.5
3.3
3.3
57.2
57.0
55.9
55.1
55.7
21.8
21.7
22.6
22.6
22.6
6.1
6.2
6.2
6.3
6.3
8.0
8.2
8.3
8.6
8.2
2.6
2.6
2.7
2.9
3.0
4.3
4.3
4.3
4.5
4.2
57.5
57.8
57.7
56.3
57.2
57.0
55.1
21.9
21.6
21.8
22.5
22.0
22.1
23.0
6.0
6.2
5.9
6.1
5.9
6.2
6.0
8.3
8.1
8.2
8.5
8.3
8.2
8.3
2.4
2.4
2.5
2.5
2.5
2.5
3.5
4.0
3.9
3.9
4.1
4.0
4.0
4.2
55.4
55.2
57.2
57.2
57.3
55.9
56.7
56.6
54.9
22.6
22.6
21.6
21.5
21.6
22.3
22.0
22.0
22.8
6.3
6.1
6.0
6.2
6.0
6.1
6.0
6.2
6.1
8.4
8.6
8.6
8.4
8.6
8.8
8.6
8.5
8.5
2.8
2.9
2.3
2.4
2.4
2.5
2.4
2.5
3.4
4.6
4.6
4.3
4.2
4.2
4.3
4.2
4.2
4.3
Source: Own calculations based on microdata from the EPH.
58
Table 7.23
Structure of employment
By type of work
Argentina, 1992-2006
Labor relationship
Entrepreneurs Wage earners Self-employed Zero income
(i)
(ii)
(iii)
(iv)
GBA
1992
5.2
70.0
23.6
1.2
1993
5.5
68.7
24.6
1.3
1994
4.6
70.1
23.9
1.5
15 main cities
1995
4.9
71.0
22.7
1.4
1996
4.5
72.2
21.7
1.6
1997
4.8
72.7
21.1
1.4
1998
4.7
73.4
20.7
1.2
28 main cities
1998
4.6
72.5
21.6
1.3
1999
4.5
72.5
21.6
1.4
2000
4.6
72.1
22.1
1.2
2001
4.4
71.3
23.4
0.9
2003
4.2
69.9
24.7
1.2
EPHC
2003-II
4.0
72.0
22.2
1.8
2004-I
4.1
73.1
21.1
1.7
2004-II
4.3
72.5
21.7
1.5
2005-I
4.2
73.7
21.0
1.2
2005-II
4.4
73.5
20.8
1.3
2006-I
4.1
75.1
19.7
1.1
2006-II
4.3
75.5
19.1
1.1
with PJH
2002
4.0
72.0
23.0
1.0
2003
3.8
71.8
23.2
1.1
2003-II
3.7
73.8
20.9
1.6
2004-I
3.8
74.5
20.1
1.6
2004-II
4.0
73.7
20.9
1.4
2005-I
3.9
74.7
20.2
1.1
2005-II
4.1
74.2
20.4
1.3
2006-I
3.9
75.7
19.3
1.1
2006-II
4.1
75.8
18.9
1.1
Type of firm
Large
(v)
Small
(vi)
Public
(vii)
36.1
35.1
36.7
37.0
48.3
49.4
47.7
47.5
15.6
15.6
15.6
15.5
35.1
34.7
33.2
31.8
30.9
48.7
49.0
49.8
50.5
51.4
16.3
16.3
16.9
17.6
17.7
31.2
33.3
33.2
34.2
35.7
36.4
36.8
52.6
51.3
51.1
50.5
48.9
48.0
47.4
16.2
15.5
15.7
15.3
15.4
15.6
15.8
28.8
29.3
29.8
31.9
31.9
33.1
34.7
35.3
35.9
48.8
48.5
49.3
48.8
49.2
48.7
47.8
47.1
47.1
22.4
22.2
20.9
19.3
19.0
18.2
17.5
17.6
17.0
Source: Own calculations based on microdata from the EPH.
59
Table 7.24
Structure of employment
Share of informal workers (productive definition)
Argentina, 1992-2006
Total
15 main cities
1995
0.422
1996
0.435
1997
0.417
1998
0.416
28 main cities
1998
0.428
1999
0.432
2000
0.442
2001
0.446
2003
0.448
EPHC
2003-II
0.467
2004-I
0.451
2004-II
0.446
2005-I
0.441
2005-II
0.425
2006-I
0.417
2006-II
0.413
with PJH
2002
0.430
2003
0.425
2003-II
0.439
2004-I
0.431
2004-II
0.430
2005-I
0.427
2005-II
0.418
2006-I
0.412
2006-II
0.412
Age
(15-24) (25-64) (65 +)
Gender
Female Male
Low
Adults (25-64)
Youths (15-24)
Education
Gender
Medium High
Female Male
0.496
0.517
0.482
0.481
0.397
0.408
0.395
0.393
0.634
0.654
0.596
0.586
0.440
0.440
0.423
0.424
0.370
0.388
0.377
0.371
0.546
0.584
0.567
0.567
0.410
0.422
0.401
0.409
0.123
0.139
0.139
0.134
0.506
0.518
0.482
0.491
0.489
0.516
0.482
0.475
0.504
0.510
0.515
0.542
0.581
0.402
0.405
0.419
0.420
0.419
0.613
0.643
0.650
0.626
0.580
0.432
0.438
0.439
0.431
0.408
0.383
0.383
0.405
0.413
0.427
0.571
0.587
0.592
0.606
0.634
0.420
0.424
0.450
0.449
0.467
0.141
0.143
0.154
0.162
0.155
0.523
0.499
0.512
0.563
0.572
0.492
0.518
0.517
0.528
0.587
0.570
0.571
0.539
0.526
0.511
0.498
0.496
0.437
0.416
0.415
0.413
0.396
0.390
0.384
0.627
0.646
0.653
0.636
0.624
0.626
0.618
0.461
0.437
0.440
0.450
0.418
0.423
0.419
0.420
0.401
0.398
0.386
0.380
0.365
0.359
0.649
0.619
0.620
0.613
0.602
0.596
0.593
0.505
0.481
0.464
0.462
0.446
0.433
0.433
0.177
0.168
0.150
0.152
0.141
0.141
0.137
0.582
0.605
0.609
0.561
0.555
0.537
0.544
0.563
0.551
0.497
0.506
0.485
0.472
0.465
0.553
0.536
0.524
0.537
0.518
0.506
0.498
0.489
0.490
0.400
0.399
0.412
0.398
0.402
0.400
0.390
0.385
0.385
0.632
0.579
0.607
0.637
0.645
0.626
0.616
0.623
0.619
0.370
0.375
0.418
0.402
0.409
0.421
0.402
0.410
0.417
0.422
0.417
0.408
0.396
0.396
0.384
0.381
0.366
0.361
0.563
0.564
0.572
0.554
0.565
0.561
0.571
0.564
0.576
0.438
0.444
0.476
0.460
0.451
0.449
0.438
0.428
0.432
0.159
0.156
0.178
0.169
0.149
0.152
0.141
0.143
0.137
0.506
0.498
0.505
0.536
0.565
0.519
0.530
0.518
0.533
0.588
0.568
0.538
0.538
0.487
0.498
0.479
0.469
0.462
Source: Own calculations based on microdata from the EPH.
Norte: Informal=salaried workers in small firms, non-professional self-employed and zero-income workers
60
Table 7.25
Structure of employment
Share of informal workers (social-protection definition)
Argentina, 1992-2006
15 main cities
1992
1993
1994
1995
1996
1997
1998
28 main cities
1998
1999
2000
2001
2003
EPHC
2003-II
2004-I
2004-II
2005-I
2005-II
2006-I
2006-II
with PJH
2002
2003
2003-II
2004-I
2004-II
2005-I
2005-II
2006-I
2006-II
Age
(25-64)
(65 +)
Gender
Female
Male
Adults (25-64)
Education
Low
Medium
High
Youths (15-24)
Gender
Female
Male
Total
(15-24)
0.312
0.319
0.291
0.331
0.351
0.362
0.371
0.507
0.487
0.475
0.532
0.530
0.537
0.562
0.241
0.259
0.231
0.269
0.295
0.307
0.311
0.548
0.554
0.404
0.517
0.624
0.570
0.582
0.290
0.320
0.282
0.325
0.343
0.356
0.353
0.210
0.219
0.198
0.230
0.264
0.275
0.281
0.347
0.361
0.339
0.372
0.440
0.432
0.465
0.211
0.234
0.209
0.237
0.253
0.293
0.269
0.111
0.141
0.098
0.141
0.154
0.155
0.168
0.504
0.454
0.436
0.531
0.546
0.519
0.553
0.509
0.508
0.499
0.533
0.519
0.549
0.568
0.379
0.383
0.385
0.387
0.388
0.590
0.583
0.598
0.604
0.656
0.315
0.325
0.331
0.333
0.330
0.573
0.565
0.446
0.490
0.531
0.359
0.367
0.383
0.375
0.340
0.284
0.294
0.292
0.301
0.323
0.469
0.474
0.478
0.516
0.503
0.272
0.300
0.318
0.306
0.332
0.169
0.179
0.176
0.174
0.184
0.590
0.585
0.581
0.638
0.656
0.591
0.582
0.609
0.580
0.656
0.442
0.436
0.433
0.432
0.421
0.411
0.407
0.715
0.678
0.686
0.650
0.642
0.642
0.644
0.378
0.375
0.372
0.380
0.365
0.352
0.348
0.632
0.602
0.591
0.548
0.585
0.559
0.604
0.427
0.409
0.405
0.431
0.399
0.400
0.391
0.340
0.349
0.346
0.342
0.339
0.315
0.314
0.572
0.550
0.572
0.562
0.555
0.532
0.537
0.389
0.388
0.350
0.361
0.359
0.341
0.333
0.211
0.216
0.201
0.221
0.193
0.194
0.195
0.707
0.701
0.706
0.659
0.639
0.650
0.674
0.720
0.663
0.674
0.645
0.644
0.637
0.625
0.441
0.449
0.500
0.488
0.480
0.475
0.457
0.443
0.432
0.692
0.691
0.746
0.704
0.704
0.670
0.659
0.654
0.653
0.387
0.398
0.442
0.435
0.428
0.430
0.407
0.391
0.379
0.498
0.538
0.658
0.621
0.606
0.570
0.598
0.569
0.607
0.430
0.436
0.513
0.496
0.490
0.502
0.466
0.460
0.439
0.349
0.363
0.382
0.382
0.376
0.369
0.357
0.334
0.328
0.585
0.598
0.653
0.629
0.645
0.632
0.612
0.590
0.584
0.383
0.393
0.452
0.445
0.399
0.397
0.396
0.373
0.358
0.178
0.197
0.227
0.227
0.210
0.230
0.201
0.201
0.199
0.700
0.713
0.756
0.743
0.736
0.693
0.668
0.670
0.687
0.685
0.672
0.738
0.676
0.684
0.654
0.653
0.644
0.630
Source: Own calculations based on microdata from the EPH.
Norte: Informal= Absence of right to have a pension when retired.
61
Table 7.26
Structure of employment
By sector
Argentina, 1992-2006
Primary
activities
Industry
low tech
Industry
high tech
0.9
0.8
0.8
0.9
0.7
0.8
0.7
8.7
8.1
6.7
7.1
6.9
6.5
5.6
12.3
11.7
12.1
11.1
10.3
10.6
10.4
6.4
7.1
7.5
7.0
7.3
7.5
8.1
25.0
25.2
23.5
22.5
23.0
21.8
22.2
7.5
7.8
8.9
9.0
9.2
9.0
8.5
7.9
7.8
8.7
9.9
10.0
10.2
10.5
6.2
6.4
6.7
7.1
7.6
7.1
7.2
17.6
17.1
17.2
17.8
17.3
18.5
19.2
7.6
8.0
7.8
7.8
7.5
8.0
7.6
1.0
1.0
0.8
1.1
1.3
5.8
5.6
5.8
5.4
6.3
9.6
9.0
8.2
8.3
7.2
8.5
8.4
7.9
7.2
6.7
23.3
22.8
24.0
24.0
23.2
8.2
9.0
8.7
8.5
8.2
9.5
9.7
9.6
9.0
10.2
7.7
7.6
7.8
8.4
7.5
18.9
19.1
19.3
20.2
22.2
7.6
7.7
7.9
7.9
7.3
1.3
1.4
1.2
1.1
1.2
1.2
1.2
7.9
7.8
7.8
7.6
7.6
7.6
7.1
6.5
6.9
7.0
7.2
6.9
7.1
7.0
7.6
8.1
8.0
8.2
8.7
8.4
9.0
24.9
24.9
25.4
24.0
24.3
24.2
24.5
7.7
7.4
7.8
7.8
7.7
7.3
7.1
9.8
9.9
9.5
10.4
10.1
10.4
10.2
7.4
7.3
7.2
7.0
7.3
7.5
7.1
18.7
18.6
18.4
18.9
18.6
18.7
18.7
8.1
7.7
7.7
7.8
7.6
7.7
7.9
1.4
1.4
1.7
1.6
1.5
1.3
1.4
1.3
1.2
5.5
6.1
7.7
7.8
7.8
7.7
7.5
7.7
7.2
7.3
6.7
6.0
6.5
6.6
6.8
6.6
6.8
6.9
6.7
6.5
7.3
7.8
7.8
8.1
8.6
8.2
9.0
21.8
22.0
23.4
23.6
24.2
23.1
23.6
23.6
24.0
7.6
7.6
7.2
6.9
7.4
7.4
7.3
7.0
6.9
9.1
9.4
9.1
9.2
8.9
9.8
9.6
10.0
9.9
10.3
9.1
8.8
8.6
8.1
7.7
7.6
7.9
7.5
23.3
24.1
21.0
20.5
20.3
20.4
20.0
19.7
19.4
6.9
7.0
7.8
7.5
7.6
7.7
7.7
7.8
8.1
15 main cities
1992
1993
1994
1995
1996
1997
1998
28 main cities
1998
1999
2000
2001
2003
EPHC
2003-II
2004-I
2004-II
2005-I
2005-II
2006-I
2006-II
with PJH
2002
2003
2003-II
2004-I
2004-II
2005-I
2005-II
2006-I
2006-II
Utilities &
Construction Commerce transportation
Skilled
services
Public
Education &
Health
administration
Domestic
servants
Source: Own calculations based on microdata from the EPH.
Table 7.27
Structure of employment
By sector (CIIU -1 digit)
Argentina, 1992-2006
Agro
15 main cities
1992
1993
1994
1995
1996
1997
1998
28 main cities
1998
1999
2000
2001
2003
EPHC
2003-II
2004-I
2004-II
2005-I
2005-II
2006-I
2006-II
with PJH
2002
2003
2003-II
2004-I
2004-II
2005-I
2005-II
2006-I
2006-II
Fishing
MiningManufacturing Utilities Construction
Commerce
Restauranansportation
& hotels& commu Finance
Business
Public
Healt &
Other Domestic Foreign
services administrationTeachingcial servic services servants rganization
0.9
0.8
0.8
0.9
0.7
0.8
0.7
21.4
20.1
19.0
18.5
17.5
17.4
16.3
0.9
0.6
0.8
0.7
0.9
0.7
0.6
6.5
7.2
7.6
7.1
7.4
7.7
8.2
22.5
22.6
20.7
19.5
20.4
19.4
19.7
2.9
3.0
3.1
3.3
3.0
2.8
2.9
6.7
7.3
8.3
8.3
8.5
8.4
8.0
2.4
2.2
2.7
2.8
2.6
2.9
2.8
5.7
5.8
6.2
7.3
7.5
7.5
7.9
6.3
6.5
6.7
7.2
7.8
7.2
7.4
6.3
7.1
7.1
7.0
6.2
6.7
7.5
5.6
5.3
5.5
5.6
6.1
6.8
6.2
4.3
3.4
3.7
4.0
3.8
3.6
4.1
7.7
8.1
7.9
7.9
7.7
8.2
7.7
1.0
1.0
0.8
1.1
1.3
15.6
14.9
14.3
14.1
13.7
0.6
0.6
0.6
0.6
0.6
8.7
8.5
8.0
7.3
6.8
20.8
20.2
21.1
21.1
20.9
2.9
3.0
3.4
3.3
2.8
7.7
8.6
8.3
8.1
7.8
2.5
2.4
2.5
2.5
2.4
7.2
7.5
7.4
6.7
8.0
7.8
7.8
8.0
8.6
7.6
7.6
7.7
7.7
8.5
9.6
6.1
5.8
5.8
5.6
6.1
3.8
4.1
4.3
4.5
4.9
7.7
7.9
8.0
8.1
7.5
0.9
0.9
0.7
0.7
0.8
0.7
0.7
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.4
0.4
0.4
0.3
0.3
0.4
0.4
14.4
14.7
14.8
14.8
14.5
14.8
14.2
0.6
0.5
0.5
0.5
0.5
0.5
0.5
7.6
8.1
8.0
8.2
8.7
8.4
9.0
22.0
21.4
21.9
20.4
21.0
20.5
20.6
2.9
3.5
3.5
3.6
3.4
3.6
3.9
7.2
6.9
7.3
7.2
7.1
6.8
6.6
1.8
1.8
1.6
1.8
1.8
1.9
2.0
8.0
8.0
7.8
8.6
8.3
8.5
8.3
7.4
7.3
7.2
7.0
7.2
7.4
7.1
8.0
7.6
7.4
7.3
7.5
7.5
8.1
5.3
5.4
5.6
5.7
5.4
5.5
5.3
5.3
5.7
5.4
6.0
5.6
5.7
5.4
8.1
7.7
7.7
7.8
7.6
7.7
7.9
0.1
0.0
0.0
0.0
0.0
0.0
0.0
1.4
1.5
1.3
1.2
1.0
0.9
1.0
0.8
0.7
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.3
0.4
0.4
0.3
0.3
0.3
0.4
13.1
13.1
13.7
14.2
14.4
14.5
14.2
14.5
14.0
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.4
0.5
6.8
6.6
7.3
7.8
7.8
8.1
8.6
8.2
9.0
19.3
19.8
20.6
20.3
20.8
19.6
20.3
20.0
20.2
2.9
2.7
2.8
3.4
3.3
3.5
3.3
3.6
3.8
7.3
7.2
6.6
6.4
6.9
6.9
6.8
6.6
6.4
2.3
2.2
1.7
1.7
1.5
1.7
1.7
1.8
1.9
6.9
7.4
7.4
7.5
7.3
8.1
7.9
8.1
8.0
10.5
9.2
8.7
8.6
8.1
7.7
7.6
7.9
7.5
9.5
9.8
8.2
7.7
7.6
7.3
7.5
7.5
8.0
6.6
6.9
7.2
6.9
7.0
6.7
6.4
6.4
5.9
5.9
5.9
5.6
5.9
5.6
6.4
6.0
5.8
5.5
7.0
7.1
7.8
7.5
7.6
7.7
7.7
7.8
8.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Source: Own calculations based on microdata from the EPH.
62
Table 7.28
Child labor
By equivalized household income quintiles
Argentina, 1992-2006
Total
15 main cities
1992
1993
1994
1995
1996
1997
1998
28 main cities
1998
1999
2000
2001
2003
EPHC
2003-II
2004-I
2004-II
2005-I
2005-II
2006-I
2006-II
Gender
Female
Male
1
2
Equivalized income quintile
3
4
5
0.021
0.020
0.013
0.010
0.012
0.009
0.010
0.012
0.010
0.005
0.007
0.009
0.004
0.007
0.030
0.029
0.022
0.013
0.015
0.014
0.012
0.016
0.019
0.015
0.011
0.010
0.010
0.020
0.015
0.019
0.013
0.004
0.016
0.014
0.011
0.032
0.041
0.017
0.007
0.011
0.002
0.009
0.022
0.005
0.010
0.021
0.020
0.003
0.001
0.002
0.007
0.007
0.010
0.000
0.000
0.000
0.011
0.008
0.005
0.007
0.003
0.008
0.003
0.003
0.003
0.001
0.014
0.012
0.007
0.010
0.005
0.020
0.014
0.008
0.009
0.004
0.011
0.004
0.006
0.009
0.002
0.011
0.007
0.001
0.005
0.002
0.007
0.004
0.000
0.001
0.000
0.000
0.006
0.001
0.000
0.000
0.017
0.021
0.016
0.016
0.018
0.015
0.017
0.014
0.013
0.012
0.012
0.012
0.010
0.012
0.020
0.029
0.019
0.019
0.025
0.020
0.022
0.027
0.030
0.027
0.022
0.037
0.023
0.024
0.013
0.024
0.013
0.008
0.015
0.012
0.009
0.025
0.026
0.009
0.012
0.002
0.025
0.024
0.014
0.015
0.003
0.001
0.007
0.001
0.005
0.001
0.004
0.001
0.003
0.015
0.001
0.007
Source: Own calculations based on microdata from the EPH.
63
Table 7.29
Right to receive social security (pensions)
By gender and education
Argentina, 1992-2006
Total
15 main cities
1992
1993
1994
1995
1996
1997
1998
28 main cities
1998
1999
2000
2001
2003
EPHC
2003-II
2004-I
2004-II
2005-I
2005-II
2006-I
2006-II
with PJH
2002
2003
2003-II
2004-I
2004-II
2005-I
2005-II
2006-I
2006-II
Age
(15-24)
(25-64)
(65 +)
Gender
Female
Male
Adults (25-64)
Education
Low
Medium
High
0.692
0.685
0.711
0.671
0.650
0.639
0.630
0.493
0.513
0.525
0.468
0.470
0.463
0.438
0.759
0.741
0.769
0.731
0.705
0.693
0.689
0.452
0.446
0.596
0.483
0.376
0.430
0.418
0.710
0.680
0.718
0.675
0.657
0.644
0.647
0.790
0.781
0.802
0.770
0.736
0.725
0.719
0.653
0.639
0.661
0.628
0.560
0.568
0.535
0.789
0.766
0.791
0.763
0.747
0.707
0.731
0.889
0.859
0.902
0.859
0.846
0.845
0.832
0.622
0.618
0.616
0.614
0.612
0.410
0.417
0.402
0.396
0.344
0.685
0.675
0.669
0.667
0.670
0.427
0.435
0.554
0.510
0.469
0.641
0.633
0.617
0.625
0.660
0.716
0.706
0.708
0.699
0.677
0.531
0.526
0.522
0.484
0.497
0.728
0.700
0.682
0.694
0.668
0.831
0.821
0.824
0.826
0.816
0.559
0.566
0.568
0.569
0.581
0.591
0.594
0.285
0.322
0.314
0.350
0.358
0.358
0.356
0.622
0.625
0.628
0.620
0.635
0.648
0.652
0.368
0.398
0.409
0.452
0.415
0.441
0.396
0.573
0.591
0.595
0.569
0.601
0.600
0.609
0.660
0.651
0.654
0.658
0.661
0.685
0.686
0.428
0.450
0.428
0.438
0.445
0.468
0.463
0.611
0.612
0.650
0.639
0.641
0.659
0.667
0.789
0.784
0.799
0.779
0.807
0.806
0.805
0.560
0.552
0.501
0.514
0.521
0.526
0.545
0.559
0.569
0.308
0.309
0.254
0.296
0.296
0.330
0.341
0.346
0.347
0.613
0.602
0.558
0.565
0.572
0.570
0.593
0.609
0.621
0.502
0.462
0.342
0.379
0.394
0.430
0.402
0.431
0.393
0.570
0.564
0.487
0.504
0.510
0.498
0.534
0.540
0.561
0.651
0.637
0.618
0.618
0.624
0.631
0.643
0.666
0.672
0.415
0.402
0.347
0.371
0.355
0.368
0.388
0.410
0.416
0.617
0.607
0.548
0.555
0.601
0.603
0.604
0.627
0.642
0.822
0.803
0.773
0.773
0.790
0.770
0.799
0.799
0.801
Source: Own calculations based on microdata from the EPH.
64
Table 7.30
Access to labor health insurance
By gender and education
Argentina, 1992-2004
Total
15 main cities
1992
1993
1994
1995
1996
1997
1998
28 main cities
1998
1999
2000
2001
2003
EPHC
2003-II
2004-I
2004-II
2005-I
2005-II
2006-I
2006-II
with PJH
2002
2003
2003-II
2004-I
2004-II
2005-I
2005-II
2006-I
2006-II
Age
(15-24)
(25-64)
(65 +)
Gender
Female
Male
Adults (25-64)
Education
Low
Medium
High
0.664
0.653
0.689
0.655
0.620
0.623
0.620
0.478
0.474
0.510
0.452
0.450
0.453
0.434
0.726
0.712
0.746
0.714
0.671
0.675
0.678
0.448
0.397
0.531
0.482
0.375
0.417
0.405
0.670
0.648
0.695
0.657
0.619
0.620
0.636
0.761
0.753
0.779
0.754
0.705
0.711
0.706
0.616
0.599
0.634
0.606
0.537
0.550
0.522
0.764
0.745
0.768
0.746
0.714
0.696
0.719
0.852
0.833
0.886
0.852
0.797
0.818
0.822
0.610
0.606
0.585
0.584
0.605
0.404
0.405
0.384
0.380
0.331
0.672
0.663
0.635
0.633
0.664
0.412
0.421
0.510
0.472
0.465
0.629
0.617
0.583
0.596
0.656
0.702
0.696
0.674
0.663
0.670
0.517
0.515
0.492
0.457
0.491
0.715
0.690
0.652
0.661
0.661
0.819
0.805
0.780
0.787
0.812
0.554
0.565
0.571
0.572
0.581
0.592
0.598
0.281
0.321
0.317
0.351
0.358
0.354
0.360
0.616
0.623
0.631
0.623
0.635
0.650
0.655
0.386
0.410
0.421
0.452
0.419
0.437
0.425
0.571
0.590
0.595
0.570
0.604
0.602
0.613
0.651
0.649
0.658
0.663
0.660
0.687
0.689
0.420
0.438
0.426
0.434
0.444
0.466
0.460
0.606
0.612
0.653
0.637
0.641
0.660
0.666
0.784
0.789
0.805
0.796
0.809
0.809
0.819
0.549
0.546
0.499
0.514
0.525
0.531
0.547
0.561
0.575
0.309
0.297
0.257
0.299
0.302
0.335
0.343
0.343
0.352
0.600
0.598
0.554
0.564
0.576
0.575
0.595
0.612
0.626
0.492
0.458
0.360
0.394
0.408
0.435
0.409
0.429
0.423
0.558
0.561
0.487
0.504
0.512
0.500
0.538
0.544
0.567
0.637
0.631
0.611
0.616
0.629
0.637
0.644
0.668
0.676
0.410
0.397
0.343
0.361
0.355
0.367
0.390
0.412
0.417
0.602
0.601
0.545
0.555
0.605
0.601
0.606
0.629
0.642
0.803
0.799
0.768
0.779
0.796
0.788
0.802
0.803
0.816
Source: Own calculations based on microdata from the EPH.
65
Table 7.31
Labor benefits
Argentina, 1992-2006
Permanent job 13th month
15 main cities
1992
1993
1994
1995
1996
1997
1998
28 main cities
1998
1999
2000
2001
2003
EPHC
2003-II
2004-I
2004-II
2005-I
2005-II
2006-I
2006-II
with PJH
2002
2003
2003-II
2004-I
2004-II
2005-I
2005-II
2006-I
2006-II
Holidays
0.827
0.827
0.840
0.709
0.706
0.725
0.694
0.661
0.634
0.633
0.709
0.703
0.724
0.690
0.658
0.630
0.632
0.834
0.853
0.849
0.838
0.844
0.628
0.625
0.624
0.622
0.618
0.626
0.623
0.625
0.621
0.620
0.835
0.845
0.837
0.845
0.848
0.852
0.851
0.586
0.596
0.592
0.595
0.602
0.619
0.622
0.588
0.601
0.590
0.601
0.601
0.621
0.619
0.791
0.798
0.742
0.764
0.764
0.780
0.797
0.806
0.820
0.563
0.557
0.526
0.542
0.543
0.553
0.566
0.585
0.596
0.563
0.558
0.527
0.548
0.544
0.561
0.568
0.591
0.596
Employment
program
0.069
0.060
0.053
0.046
0.037
0.032
0.022
Source: Own calculations based on microdata from the EPH.
66
Table 8.1
Educational structure
Adults 25-65
Argentina, 1992-2006
Low
EPH - 15 cities (1)
1992
47.7
1993
45.4
1994
45.6
EPH- 15 cities (2)
1995
47.5
1996
43.4
1997
43.7
1998
42.9
EPH - 28 cities
1998
43.4
1999
41.9
2000
41.9
2001
41.1
2002
39.5
2003
38.4
EPH-C
2004-II
38.3
2005-I
38.1
2005-II
37.2
2006-I
36.9
2006-II
35.5
All
Medium
High
Low
Males
Medium
High
Low
34.5
35.7
35.6
17.8
18.8
18.8
46.1
44.8
45.0
35.5
35.7
36.2
18.5
19.5
18.8
49.2
46.0
46.2
34.5
35.8
35.4
35.8
18.0
20.8
20.9
21.3
47.2
42.6
43.6
42.6
34.7
36.6
36.2
37.2
18.2
20.7
20.2
20.3
35.5
35.9
35.4
35.7
36.4
37.0
21.1
22.2
22.7
23.2
24.2
24.7
43.4
42.0
41.9
41.4
40.3
39.1
36.5
37.4
36.7
37.0
37.1
37.6
36.4
36.3
36.6
36.8
37.6
25.2
25.6
26.2
26.3
27.0
38.7
38.7
38.3
37.4
36.9
37.9
37.4
37.3
38.2
38.5
Females
Medium
Working males
Medium
High
Low
Working females
Medium
High
High
Low
33.6
35.8
35.1
17.2
18.2
18.7
44.7
44.3
44.5
36.7
36.9
37.0
18.6
18.8
18.5
38.7
35.0
36.2
33.2
36.6
33.8
28.2
28.4
30.1
47.8
44.1
43.9
43.2
34.3
35.1
34.7
34.6
17.9
20.8
21.5
22.2
45.9
40.8
41.9
41.5
35.5
37.5
37.7
38.0
18.6
21.6
20.4
20.6
37.6
34.2
34.4
33.6
34.5
34.3
32.7
33.1
27.8
31.6
32.9
33.3
20.1
20.6
21.4
21.6
22.7
23.3
43.4
41.9
41.9
40.8
38.7
37.7
34.7
34.5
34.3
34.5
35.7
36.4
21.9
23.6
23.9
24.7
25.5
25.9
42.4
41.1
40.6
39.9
40.0
37.7
37.4
38.4
38.1
38.4
37.4
38.8
20.3
20.5
21.4
21.7
22.6
23.4
33.8
31.7
32.2
31.5
31.8
26.4
33.2
34.0
33.3
32.8
34.3
35.5
33.0
34.4
34.5
35.8
33.9
38.2
23.3
24.0
24.4
24.4
24.5
38.0
37.7
36.2
36.4
34.2
35.0
35.3
35.9
35.6
36.7
27.0
27.0
27.9
28.0
29.1
37.3
37.1
37.1
36.1
35.6
38.6
38.1
37.7
38.5
39.1
24.1
24.7
25.2
25.4
25.2
30.9
30.9
28.8
29.2
26.9
33.5
33.8
33.8
33.6
34.4
35.6
35.3
37.4
37.2
38.7
Source: Own calculations based on microdata from the EPH.
Table 8.2
Years of education
By age and gender
Argentina, 1992-2006
(25-65)
Female Male
EPH - 15 cities (1)
1992
9.3
9.5
1993
9.4
9.6
1994
9.5
9.6
EPH- 15 cities (2)
1995
9.6
9.6
1996
9.6
9.7
1997
9.9
9.9
1998
9.9
9.9
EPH - 28 cities
1998
9.9
9.9
1999
10.1 10.0
2000
10.1 10.0
2001
10.2 10.1
2002
10.4 10.1
2003
10.4 10.2
EPH-C
2003
10.9 10.6
2004-I
10.9 10.6
2004-II
10.5 10.4
2005-I
10.6 10.4
2005-II
10.7 10.5
2006-I
10.7 10.5
2006-II
10.9 10.5
All
(10-20)
Female Male
All
(21-30)
Female Male
All
(31-40)
Female Male
All
(41-50)
Female Male
All
(51-60)
Female Male
All
(61+)
Female Male
All
9.4
9.5
9.5
7.8
7.9
7.8
7.6
7.7
7.7
7.7
7.8
7.8
10.9
11.0
11.2
10.8
10.9
10.9
10.8
11.0
11.0
10.1
10.1
10.2
10.0
10.0
10.0
10.0
10.1
10.1
9.2
9.4
9.4
9.3
9.4
9.6
9.2
9.4
9.5
8.0
8.2
8.2
8.8
8.7
8.9
8.4
8.5
8.6
6.7
7.0
6.9
7.6
7.8
7.9
7.1
7.3
7.3
9.6
9.7
9.9
9.9
7.8
7.6
8.1
8.2
7.5
7.3
7.8
7.8
7.7
7.5
7.9
8.0
11.1
11.2
11.2
11.4
10.6
10.6
10.7
10.8
10.9
10.9
11.0
11.1
10.4
10.4
10.6
10.6
10.2
10.1
10.4
10.3
10.3
10.3
10.5
10.5
9.3
9.5
9.8
9.9
9.3
9.5
9.6
9.9
9.3
9.5
9.7
9.9
8.4
8.4
8.6
8.7
8.8
8.7
8.9
9.1
8.6
8.6
8.7
8.9
6.8
6.9
7.1
7.0
8.0
7.9
8.0
8.0
7.3
7.3
7.5
7.4
9.9
10.0
10.1
10.2
10.2
10.3
8.1
8.2
8.2
8.3
8.3
8.6
7.7
7.8
7.8
7.8
7.8
8.2
7.9
8.0
8.0
8.0
8.0
8.4
11.4
11.6
11.5
11.6
11.7
11.9
10.7
10.8
10.7
11.0
11.1
11.2
11.1
11.2
11.1
11.3
11.4
11.6
10.5
10.7
10.9
10.9
11.1
11.1
10.3
10.5
10.6
10.5
10.6
10.7
10.4
10.6
10.7
10.7
10.8
10.9
9.8
10.1
10.1
10.2
10.4
10.4
9.8
10.0
9.8
9.8
9.9
10.2
9.8
10.1
10.0
10.0
10.2
10.3
8.6
8.7
8.6
9.0
9.0
9.1
9.0
9.0
9.3
9.4
9.3
9.3
8.8
8.8
8.9
9.2
9.2
9.2
6.9
7.0
7.1
7.2
7.4
7.3
7.9
7.9
8.0
8.1
8.3
8.3
7.3
7.3
7.5
7.5
7.7
7.7
10.8
10.8
10.5
10.5
10.6
10.6
10.7
8.2
8.4
8.2
8.4
8.2
8.4
8.2
8.0
8.1
7.7
8.0
7.7
8.0
7.8
8.1
8.3
7.9
8.2
7.9
8.2
8.0
12.1
12.0
11.7
11.7
11.8
11.8
11.9
11.3
11.6
11.2
11.2
11.2
11.2
11.4
11.7
11.8
11.5
11.5
11.5
11.5
11.7
11.7
11.5
11.2
11.2
11.3
11.4
11.5
11.1
11.0
10.8
11.0
10.9
11.0
10.9
11.4
11.3
11.0
11.1
11.1
11.2
11.2
10.8
10.9
10.4
10.4
10.8
10.7
10.9
10.3
10.4
10.1
10.2
10.5
10.5
10.3
10.5
10.7
10.3
10.3
10.6
10.6
10.6
9.8
9.8
9.5
9.6
9.6
9.7
9.9
9.8
9.8
9.5
9.6
9.5
9.7
9.8
9.8
9.8
9.5
9.6
9.5
9.7
9.9
8.0
8.0
7.6
7.6
7.6
7.7
7.8
8.7
8.7
8.5
8.5
8.5
8.5
8.6
8.3
8.3
8.0
7.9
8.0
8.0
8.1
Source: Own calculations based on microdata from the EPH.
67
Table 8.3
Years of education
By household equivalized income quintiles
Adults 25-65
Argentina, 1992-2006
1
EPH - 15 cities (1)
1992
7.2
1993
7.0
1994
7.1
EPH- 15 cities (2)
1995
7.2
1996
7.0
1997
7.2
1998
7.0
EPH - 28 cities
1998
7.0
1999
7.2
2000
7.2
2001
7.2
2002
7.4
2003
7.4
EPH-C
2003
8.1
2004-I
8.0
2004-II
7.7
2005-I
8.0
2005-II
7.7
2006-I
7.9
2006-II
7.8
2
3
4
5
Mean
7.8
8.0
8.0
8.5
8.5
8.4
9.7
9.6
9.6
12.2
12.1
12.3
9.4
9.3
9.3
7.9
7.9
8.0
8.1
8.8
8.8
8.9
8.8
9.6
9.8
10.2
10.2
12.5
12.7
12.8
13.1
9.5
9.6
9.8
9.8
8.1
8.3
8.2
8.2
8.3
8.4
8.9
9.1
9.2
9.2
9.3
9.4
10.1
10.3
10.3
10.5
10.4
10.7
13.0
13.0
13.2
13.2
13.4
13.3
9.8
9.9
10.0
10.0
10.1
10.2
9.0
9.0
8.6
8.6
8.7
8.8
8.8
9.8
10.0
9.6
9.8
9.6
9.7
9.8
11.0
11.2
10.9
10.9
11.2
11.1
11.3
13.7
13.7
13.4
13.5
13.5
13.6
13.7
10.6
10.7
10.4
10.5
10.5
10.5
10.6
Source: Own calculations based on microdata from the EPH.
68
Table 8.4
Years of education
By age and income
Argentina, 1992-2006
1
2
EPH - 15 cities (1)
1992
6.8 7.4
1993
6.8 7.4
1994
6.8 7.4
EPH- 15 cities (2)
1995
6.7 7.2
1996
6.3 7.0
1997
6.9 7.4
1998
6.8 7.6
EPH - 28 cities
1998
6.8 7.5
1999
7.0 7.6
2000
6.9 7.6
2001
6.9 7.7
2002
7.1 7.6
2003
7.5 7.9
EPH-C
2003
7.4 7.8
2004-I
7.5 8.0
2004-II
7.1 7.7
2005-I
7.4 8.0
2005-II
7.0 7.8
2006-I
7.3 8.0
2006-II
7.1 7.9
1
2
EPH - 15 cities (1)
1992
7.1 7.6
1993
7.0 7.6
1994
6.9 7.7
EPH- 15 cities (2)
1995
7.0 7.7
1996
7.1 7.5
1997
6.9 7.6
1998
6.9 8.1
EPH - 28 cities
1998
6.9 8.0
1999
7.2 8.2
2000
7.1 7.9
2001
7.0 8.0
2002
7.1 8.0
2003
7.1 8.0
EPH-C
2003
7.6 8.8
2004-I
7.9 9.0
2004-II
7.7 8.7
2005-I
7.9 8.7
2005-II
7.7 8.8
2006-I
7.9 8.6
2006-II
7.8 8.8
(10-20)
3
4
5
Mean
1
2
(21-30)
3
4
5
Mean
1
2
(31-40)
3
4
5
Mean
7.6
7.8
7.7
8.2
8.2
8.1
8.5
8.6
8.7
7.6
7.7
7.6
8.3
8.2
8.4
9.2
9.6
9.4
10.4 11.2 13.3 10.8
10.1 11.5 13.0 10.8
10.3 11.1 13.1 10.8
7.8
7.2
7.3
8.4
8.7
8.5
9.4
9.2
9.2
10.6 13.1 10.1
10.7 13.0 9.9
10.6 13.4 10.0
7.9
7.6
8.0
8.0
8.1
8.2
8.6
8.7
8.7
8.5
9.0
9.0
7.6
7.4
7.9
7.8
8.5
8.1
8.6
8.3
9.0
9.3
9.4
9.4
10.2
10.4
10.3
10.2
11.3
11.4
11.5
11.9
13.1
13.3
13.5
13.8
10.8
10.8
10.9
11.0
7.6
7.4
7.8
7.5
8.4
8.4
8.7
8.7
9.6
9.5
9.7
9.6
10.7
11.0
11.3
10.9
13.7
13.4
13.9
14.4
10.2
10.2
10.4
10.4
8.1
8.1
8.2
8.1
8.0
8.6
8.6
8.6
8.8
8.6
8.7
9.0
9.0
9.1
9.0
9.2
9.1
9.2
7.8
7.9
7.9
7.9
7.9
8.3
8.3
8.6
8.5
8.6
8.8
9.1
9.4
9.5
9.7
9.7
9.9
10.1
10.4
10.8
10.6
10.6
10.9
11.2
11.7
11.8
11.7
12.0
11.9
12.1
13.7
13.5
13.7
13.7
13.9
14.0
10.9
11.0
11.1
11.1
11.2
11.4
7.4
7.6
7.5
7.7
7.8
7.8
8.6
8.8
8.8
8.7
8.8
9.2
9.7
9.6
10.0
10.1
10.1
9.8
10.9
11.5
11.2
11.3
11.3
11.8
14.1
13.8
14.2
14.2
14.4
14.1
10.4
10.5
10.6
10.6
10.7
10.8
8.1
8.6
8.1
8.6
8.2
8.4
8.2
8.6
8.9
8.5
8.9
8.7
8.9
8.7
8.8
9.0
9.0
9.4
9.0
9.2
9.1
8.0
8.2
7.9
8.2
7.9
8.2
8.0
9.3
9.5
9.1
9.3
9.2
9.2
9.1
10.2
10.6
10.1
10.1
9.9
10.2
10.2
11.3
11.6
11.2
11.3
11.3
11.1
11.3
12.2
12.6
12.2
12.1
12.4
12.2
12.5
14.0
14.0
13.8
14.2
14.0
14.0
13.8
11.6
11.8
11.4
11.5
11.5
11.5
11.6
8.6
8.3
8.0
8.3
8.0
8.3
8.2
9.4
9.4
9.2
9.0
9.4
9.4
9.1
10.6
10.8
10.3
10.6
10.5
10.5
10.4
12.1
12.2
11.9
11.7
11.9
12.0
12.2
14.6
14.4
14.2
14.3
14.2
14.2
14.5
11.3
11.2
11.0
11.0
11.1
11.2
11.2
5
Mean
1
2
5
Mean
1
2
(61+)
3
4
5
Mean
(41-50)
3
4
(51-60)
3
4
8.0
8.2
8.3
9.5
9.2
9.4
12.0
12.1
12.2
9.1
9.2
9.2
6.3
6.4
6.7
6.7
6.7
6.7
7.5
7.3
6.9
8.2
8.4
8.2
11.0
10.9
10.9
8.3
8.4
8.3
5.9
5.8
5.6
6.1
6.2
6.4
6.6
6.8
6.8
7.7
7.4
7.5
9.5
9.9
9.6
7.1
7.2
7.2
8.8
8.5
8.7
8.6
9.4
9.4
10.1
10.0
12.5
13.1
12.8
13.1
9.3
9.4
9.6
9.7
6.3
6.1
5.8
6.0
6.9
6.8
6.9
6.8
7.3
7.4
7.4
7.5
8.2
8.4
8.8
8.6
11.1
11.5
11.6
11.8
8.4
8.4
8.6
8.7
5.5
5.4
5.6
5.5
6.0
6.2
6.0
5.8
6.6
6.5
6.4
6.3
7.2
7.1
7.5
7.2
9.7
10.0
10.2
10.6
7.1
7.3
7.3
7.3
8.6
9.0
9.1
8.9
9.4
9.5
10.0
10.1
10.3
10.5
10.3
10.5
13.0 9.6
13.1 9.9
13.4 9.9
13.3 9.8
13.8 10.0
13.7 10.1
5.9
6.2
6.2
6.2
6.4
6.0
6.8
7.0
7.0
6.9
7.2
6.6
7.4
7.5
7.4
7.9
7.5
7.9
8.6
8.6
8.8
9.0
9.1
9.3
11.7
11.6
11.9
12.1
12.1
12.2
8.7
8.6
8.8
9.0
9.0
9.1
5.3
5.0
5.0
4.9
4.8
5.2
5.7
6.0
6.0
5.8
5.9
6.1
6.2
6.3
6.4
6.4
6.4
6.3
7.1
7.4
7.4
7.3
7.6
7.7
10.3
9.8
9.9
10.3
10.0
10.2
7.2
7.2
7.3
7.4
7.5
7.6
9.2
9.8
9.3
9.5
9.7
9.6
9.7
11.0
11.0
10.8
11.0
11.3
11.1
11.2
13.8
13.8
13.5
13.6
13.6
13.7
13.7
7.0
7.2
6.3
6.8
6.3
6.9
6.7
7.8
7.6
7.2
7.4
7.4
7.6
7.8
8.4
8.6
8.2
8.5
7.9
8.2
8.6
9.6
9.8
9.5
9.5
9.7
9.6
10.2
12.8
12.8
12.6
12.5
12.6
12.8
12.9
9.6
9.7
9.3
9.5
9.4
9.6
9.7
6.5
6.1
5.2
5.7
5.5
5.6
5.6
6.6
6.4
5.9
5.9
6.2
6.4
6.1
6.8
7.2
6.9
6.6
7.0
7.3
7.4
7.9
8.1
8.0
7.9
8.2
8.0
8.0
10.6
11.0
10.8
10.7
11.0
11.0
10.8
8.1
8.2
7.9
7.8
7.9
8.0
7.9
10.3
10.6
10.2
10.3
10.5
10.5
10.6
Source: Own calculations based on microdata from the EPH.
69
Table 8.5
Gini coefficient
Years of education
By age
Argentina, 1992-2006
(25-65)
EPH - 15 cities (1)
1992
0.237
1993
0.237
1994
0.233
EPH- 15 cities (2)
1995
0.235
1996
0.236
1997
0.234
1998
0.231
EPH - 28 cities
1998
0.233
1999
0.229
2000
0.229
2001
0.225
2002
0.225
2003
0.222
EPH-C
2003
0.219
2004-I
0.218
2004-II
0.220
2005-I
0.219
2005-II
0.218
2006-I
0.217
2006-II
0.215
(10-20)
(21-30)
(31-40)
(41-50)
(51-60)
(61+)
0.214
0.212
0.213
0.195
0.190
0.185
0.216
0.217
0.212
0.242
0.243
0.236
0.250
0.254
0.254
0.280
0.276
0.276
0.209
0.243
0.215
0.216
0.181
0.180
0.182
0.177
0.214
0.212
0.209
0.208
0.234
0.241
0.236
0.233
0.264
0.263
0.266
0.259
0.292
0.286
0.290
0.297
0.219
0.219
0.218
0.220
0.223
0.209
0.180
0.175
0.177
0.172
0.169
0.162
0.211
0.207
0.207
0.205
0.200
0.197
0.237
0.230
0.233
0.228
0.228
0.226
0.262
0.261
0.263
0.255
0.261
0.258
0.300
0.292
0.294
0.290
0.281
0.283
0.216
0.210
0.215
0.206
0.214
0.203
0.212
0.163
0.159
0.166
0.169
0.165
0.165
0.160
0.193
0.200
0.198
0.195
0.196
0.192
0.197
0.231
0.223
0.222
0.220
0.216
0.218
0.214
0.250
0.251
0.258
0.254
0.253
0.251
0.247
0.276
0.274
0.289
0.295
0.289
0.288
0.288
Source: Own calculations based on microdata from the EPH.
Table 8.6
Literacy
By age and gender
Adults aged 25 to 65
Argentina, 1992-2006
Female
EPH-15 cities
1992
0.99
1993
0.99
1994
1.00
1995
1.00
1996
1.00
1997
1.00
1998
0.99
EPH - 28 cities
1998
0.99
1999
0.99
2000
0.99
2001
0.99
2002
1.00
2003
1.00
EPHC
2004-II
0.99
2005-I
1.00
2005-II
1.00
2006-I
1.00
2006-II
1.00
(15-24)
Male
Mean
Female
0.99
0.99
0.99
1.00
0.99
0.99
0.99
0.99
0.99
0.99
1.00
1.00
0.99
0.99
0.98
0.98
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
(25-65)
Male
(65 +)
Male
Mean
0.94
0.95
0.96
0.96
0.95
0.97
0.97
0.97
0.97
0.97
0.98
0.98
0.98
0.97
0.95
0.96
0.96
0.97
0.96
0.97
0.97
0.98
0.99
0.99
0.99
0.99
0.99
0.96
0.96
0.96
0.96
0.96
0.97
0.97
0.97
0.97
0.97
0.98
0.97
0.97
0.97
0.96
0.96
0.97
0.97
0.99
0.99
0.99
0.99
0.99
0.96
0.96
0.96
0.96
0.96
0.98
0.98
0.98
0.98
0.97
0.97
0.97
0.97
0.97
0.97
Mean
Female
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.98
0.98
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.98
0.98
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
Source: Own calculations based on microdata from the EPH.
70
Table 8.7
Literacy
By household equivalized income quintiles
Argentina, 1992-2006
1
EPH-15 cities
1992
0.98
1993
0.99
1994
0.99
1995
0.98
1996
0.99
1997
0.98
1998
0.98
EPH - 28 cities
1998
0.98
1999
0.98
2000
0.98
2001
0.99
2002
0.99
2003
0.99
EPHC
2004-II
0.99
2005-I
0.98
2005-II
0.99
2006-I
0.99
2006-II
0.99
Age 15 to 24
4
2
3
0.98
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
1.00
1.00
1.00
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
Age 25 to 65
3
4
5
Mean
1
2
5
Mean
0.99
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
0.99
0.99
0.99
0.99
1.00
0.99
0.99
0.96
0.96
0.97
0.97
0.96
0.96
0.95
0.97
0.98
0.98
0.99
0.98
0.98
0.98
0.98
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
1.00
1.00
1.00
1.00
1.00
1.00
1.00
0.98
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
1.00
0.99
1.00
1.00
1.00
0.99
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
0.99
0.99
0.99
0.99
0.99
0.99
0.95
0.97
0.96
0.96
0.96
0.96
0.98
0.98
0.98
0.98
0.98
0.98
0.99
0.99
0.99
0.99
0.98
0.98
0.99
0.99
0.99
1.00
0.99
0.99
1.00
1.00
1.00
1.00
1.00
1.00
0.98
0.99
0.99
0.99
0.99
0.98
1.00
0.99
0.99
1.00
1.00
0.99
1.00
1.00
1.00
1.00
0.99
1.00
1.00
1.00
1.00
0.99
0.99
0.99
0.99
0.99
0.96
0.97
0.96
0.97
0.96
0.98
0.98
0.99
0.99
0.98
0.99
0.99
0.99
0.99
0.99
0.99
0.99
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
0.99
0.99
0.99
0.99
0.99
Source: Own calculations based on microdata from the EPH.
Table 8.8
Enrollment rates
By age and gender
Argentina, 1992-2006
3 to 5 years-old
Female Male Mean
EPH-15 cities
1992
0.35
0.34
0.34
1993
0.32
0.36
0.34
1994
0.30
0.30
0.30
1995
0.27
0.32
0.29
1996
0.33
0.34
0.34
1997
0.35
0.34
0.34
1998
0.44
0.40
0.42
EPH - 28 cities
1998
0.38
0.36
0.37
1999
0.41
0.41
0.41
2000
0.43
0.43
0.43
2001
0.41
0.38
0.40
2002
0.43
0.40
0.42
2003
0.50
0.51
0.51
EPH-C
2003-II
0.55
0.56
0.56
2004-I
0.61
0.63
0.62
2004-II
0.59
0.58
0.58
2005-I
0.63
0.64
0.64
2005-II
0.61
0.59
0.60
2006-I
0.66
0.63
0.65
2006-II
0.62
0.62
0.62
6 to 12 years-old
Female Male Mean
13 to 17 years-old
Female Male Mean
18 to 23 years old
Female Male Mean
0.98
0.98
0.98
0.99
0.99
0.99
0.99
0.98
0.99
0.98
0.99
0.98
0.99
0.99
0.98
0.98
0.98
0.99
0.99
0.99
0.99
0.83
0.81
0.83
0.81
0.81
0.85
0.89
0.74
0.76
0.77
0.77
0.78
0.82
0.85
0.78
0.78
0.80
0.79
0.79
0.83
0.87
0.45
0.45
0.46
0.47
0.47
0.47
0.49
0.38
0.39
0.37
0.38
0.38
0.41
0.43
0.41
0.42
0.42
0.43
0.42
0.44
0.46
0.99
0.99
0.99
0.99
0.99
1.00
0.99
0.99
0.99
0.98
0.99
1.00
0.99
0.99
0.99
0.99
0.99
1.00
0.88
0.90
0.91
0.93
0.93
0.94
0.84
0.86
0.90
0.90
0.90
0.91
0.86
0.88
0.90
0.91
0.91
0.93
0.49
0.53
0.53
0.53
0.52
0.53
0.42
0.44
0.45
0.46
0.50
0.49
0.45
0.49
0.49
0.49
0.51
0.51
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.98
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.91
0.92
0.90
0.92
0.92
0.93
0.92
0.87
0.90
0.89
0.90
0.91
0.90
0.89
0.89
0.91
0.89
0.91
0.91
0.91
0.90
0.49
0.51
0.51
0.51
0.51
0.51
0.52
0.44
0.46
0.43
0.45
0.43
0.45
0.44
0.46
0.49
0.47
0.48
0.47
0.48
0.48
Source: Own calculations based on microdata from the EPH.
71
Table 8.9
Enrollment rates
By household equivalized income quintiles
Argentina, 1992-2006
1
EPH-15 cities
1992
0.22
1993
0.26
1994
0.21
1995
0.20
1996
0.23
1997
0.28
1998
0.32
EPH - 28 cities
1998
0.28
1999
0.31
2000
0.33
2001
0.31
2002
0.30
2003
0.43
EPH-C
2003-II
0.48
2004-I
0.54
2004-II
0.49
2005-I
0.52
2005-II
0.47
2006-I
0.52
2006-II
0.47
2
3 to 5 years-old
3
4
5
Mean
1
2
6 to 12 years-old
3
4
5 Mean
1
2
13 to 17 years-old
3
4
5 Mean
1
18 to 23 years old
2
3
4
5
Mean
0.33
0.33
0.30
0.26
0.28
0.33
0.37
0.30
0.29
0.29
0.35
0.40
0.37
0.46
0.43
0.41
0.39
0.38
0.41
0.39
0.58
0.51
0.47
0.33
0.34
0.45
0.41
0.60
0.34
0.33
0.29
0.29
0.33
0.34
0.42
0.97
0.97
0.97
0.98
0.98
0.98
0.98
0.98
0.99
0.98
0.98
0.98
0.99
0.99
0.98
0.98
0.98
1.00
0.99
0.99
1.00
0.99
0.99
1.00
0.99
1.00
0.99
1.00
0.99
1.00
1.00
1.00
1.00
1.00
1.00
0.98
0.98
0.98
0.99
0.99
0.99
0.99
0.72
0.72
0.71
0.67
0.65
0.73
0.78
0.76
0.78
0.77
0.75
0.76
0.82
0.84
0.75
0.76
0.79
0.84
0.84
0.82
0.89
0.82
0.80
0.85
0.86
0.85
0.90
0.94
0.94
0.90
0.93
0.95
0.98
0.96
0.98
0.79
0.78
0.79
0.79
0.79
0.83
0.87
0.34
0.30
0.27
0.29
0.23
0.25
0.24
0.34
0.35
0.35
0.28
0.29
0.29
0.34
0.32
0.36
0.38
0.37
0.38
0.42
0.39
0.43
0.44
0.36
0.44
0.47
0.45
0.49
0.55
0.53
0.58
0.68
0.65
0.69
0.69
0.40
0.41
0.39
0.42
0.41
0.43
0.43
0.33
0.36
0.39
0.36
0.37
0.47
0.41
0.43
0.45
0.40
0.40
0.53
0.48
0.48
0.55
0.48
0.55
0.60
0.53
0.59
0.62
0.52
0.60
0.63
0.37
0.40
0.44
0.39
0.41
0.50
0.98
0.99
0.98
0.97
0.99
0.99
0.99
1.00
1.00
0.98
0.99
1.00
1.00
0.99
1.00
1.00
1.00
0.99
1.00
0.99
1.00
1.00
0.99
1.00
1.00
1.00
1.00
1.00
1.00
1.00
0.99
0.99
0.99
0.99
0.99
1.00
0.77
0.82
0.84
0.86
0.85
0.87
0.82
0.86
0.87
0.89
0.88
0.91
0.88
0.89
0.94
0.94
0.96
0.94
0.93
0.92
0.96
0.96
0.97
0.98
0.98
0.98
0.98
0.99
0.99
0.99
0.86
0.88
0.90
0.91
0.91
0.92
0.26
0.33
0.31
0.32
0.31
0.33
0.33
0.35
0.37
0.37
0.38
0.41
0.41
0.46
0.47
0.45
0.45
0.45
0.49
0.53
0.53
0.54
0.57
0.56
0.67
0.67
0.74
0.72
0.79
0.75
0.43
0.47
0.48
0.48
0.49
0.49
0.47
0.62
0.55
0.54
0.54
0.63
0.55
0.58
0.60
0.59
0.67
0.65
0.70
0.65
0.61
0.68
0.69
0.68
0.70
0.72
0.75
0.76
0.72
0.77
0.78
0.83
0.80
0.79
0.56
0.62
0.59
0.62
0.60
0.65
0.62
0.98
0.99
0.98
0.98
0.99
0.99
0.99
0.99
0.99
0.99
1.00
0.98
0.99
0.99
0.99
0.99
0.99
0.99
1.00
1.00
0.99
0.99
0.99
0.99
0.99
1.00
0.99
1.00
0.98
1.00
1.00
0.98
1.00
1.00
0.99
0.99
0.99
0.99
0.99
0.99
1.00
0.99
0.83
0.84
0.83
0.87
0.85
0.88
0.84
0.89
0.88
0.89
0.90
0.92
0.91
0.89
0.90
0.93
0.92
0.92
0.94
0.91
0.93
0.93
0.98
0.96
0.96
0.95
0.96
0.97
0.98
0.98
0.97
0.98
0.98
0.98
0.98
0.89
0.91
0.90
0.91
0.91
0.92
0.91
0.29
0.31
0.27
0.31
0.27
0.31
0.30
0.36
0.38
0.39
0.43
0.37
0.38
0.36
0.45
0.43
0.45
0.47
0.43
0.43
0.48
0.53
0.59
0.54
0.54
0.55
0.53
0.55
0.69
0.73
0.71
0.76
0.73
0.75
0.70
0.45
0.47
0.46
0.49
0.46
0.47
0.47
Source: Own calculations based on microdata from the EPH.
Table 8.10
Educational mobility
By age group
Argentina, 1992-2006
13-19
EPH-15 cities
1992
0.89
1993
0.88
1994
0.88
1995
0.87
1996
0.89
1997
0.87
1998
0.87
EPH - 28 cities
1998
0.86
1999
0.87
2000
0.87
2001
0.87
2002
0.89
2003
0.89
EPHC
2004-II
0.87
2005-I
0.85
2005-II
0.87
2006-I
0.88
2006-II
0.87
20-25
0.81
0.81
0.80
0.79
0.80
0.80
0.78
0.77
0.78
0.77
0.77
0.78
0.80
0.78
0.75
0.78
0.77
0.80
Source: Own calculations based
on microdata from the EPH.
72
Table 9.1
Housing
By household equivalized income quintiles
1
EPH-15 cities
1992
0.62
1993
0.63
1994
0.61
1995
0.62
1996
0.61
1997
0.58
1998
0.58
EPH - 28 cities
1998
0.56
1999
0.58
2000
0.57
2001
0.57
2002
0.56
2003
0.57
EPH-C
2003-II
0.54
2004-I
0.56
2004-II
0.56
2005-I
0.53
2005-II
0.53
2006-I
0.54
2006-II
0.55
1
EPH-15 cities
1992
0.06
1993
0.07
1994
0.09
1995
0.08
1996
0.08
1997
0.07
1998
0.04
EPH - 28 cities
1998
0.07
1999
0.07
2000
0.08
2001
0.08
2002
0.06
2003
0.04
EPHC
2006-II
0.08
Share of housing owners
2
3
4
5
Mean
1
2
Number of rooms
3
4
5
Mean
1
2
Persons per room
3
4
5
Mean
0.72
0.74
0.71
0.68
0.70
0.65
0.66
0.72
0.74
0.71
0.72
0.69
0.72
0.72
0.76
0.73
0.73
0.73
0.72
0.72
0.72
0.74
0.76
0.77
0.77
0.76
0.77
0.76
0.72
0.72
0.71
0.71
0.71
0.70
0.70
2.6
2.6
2.6
2.6
2.6
2.5
2.5
2.6
2.7
2.7
2.7
2.7
2.7
2.7
2.8
2.9
2.8
2.9
2.8
2.9
2.8
2.9
3.0
3.0
3.0
3.0
3.0
3.0
3.3
3.3
3.2
3.3
3.3
3.4
3.4
2.9
2.9
2.9
2.9
2.9
2.9
2.9
2.0
2.0
2.0
2.1
2.0
2.1
2.2
1.4
1.5
1.5
1.6
1.6
1.6
1.6
1.4
1.4
1.3
1.3
1.4
1.3
1.4
1.2
1.2
1.2
1.2
1.1
1.1
1.1
1.0
0.9
0.9
0.9
0.9
0.8
0.8
1.4
1.3
1.3
1.3
1.3
1.3
1.3
0.65
0.66
0.67
0.68
0.67
0.67
0.71
0.70
0.70
0.70
0.71
0.69
0.72
0.72
0.72
0.74
0.73
0.71
0.76
0.79
0.77
0.77
0.78
0.77
0.69
0.70
0.70
0.71
0.71
0.70
2.4
2.4
2.5
2.4
2.4
2.4
2.7
2.7
2.7
2.7
2.6
2.6
2.8
2.7
2.8
2.7
2.8
2.7
3.0
2.9
2.9
2.9
2.9
2.9
3.4
3.3
3.3
3.3
3.3
3.3
2.9
2.9
2.9
2.9
2.8
2.8
2.3
2.2
2.2
2.3
2.4
2.3
1.7
1.7
1.7
1.8
1.8
1.8
1.4
1.3
1.4
1.4
1.4
1.4
1.1
1.1
1.1
1.1
1.2
1.1
0.9
0.9
0.9
0.9
0.8
0.8
1.4
1.4
1.4
1.4
1.4
1.4
0.62
0.62
0.64
0.65
0.63
0.61
0.62
0.66
0.67
0.68
0.66
0.67
0.67
0.67
0.68
0.70
0.70
0.68
0.70
0.69
0.70
0.75
0.75
0.73
0.73
0.71
0.73
0.71
0.66
0.67
0.67
0.66
0.66
0.66
0.66
2.47
2.47
2.49
2.56
2.53
2.49
2.48
2.66
2.72
2.79
2.80
2.73
2.78
2.77
2.76
2.84
2.87
2.86
2.90
2.89
2.86
2.90
2.98
3.00
3.02
3.07
2.98
3.10
3.26
3.31
3.27
3.35
3.27
3.37
3.29
2.86
2.92
2.94
2.97
2.95
2.95
2.95
2.09
2.18
2.19
2.10
2.12
2.14
2.16
1.77
1.78
1.68
1.68
1.69
1.64
1.64
1.38
1.32
1.32
1.34
1.26
1.28
1.25
1.13
1.12
1.10
1.10
1.07
1.11
1.08
0.81
0.84
0.83
0.83
0.82
0.83
0.84
1.34
1.35
1.33
1.32
1.30
1.32
1.31
Mean
1
2
Share of "poor" dwellings
3
4
5
Share of dwellings of low-quality materials
2
3
4
5
Mean
0.04
0.05
0.04
0.06
0.04
0.04
0.03
0.03
0.03
0.05
0.02
0.04
0.03
0.01
0.03
0.03
0.02
0.03
0.03
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.00
0.03
0.04
0.04
0.04
0.03
0.03
0.02
0.04
0.05
0.04
0.04
0.04
0.04
0.03
0.03
0.03
0.03
0.03
0.02
0.03
0.02
0.02
0.02
0.02
0.02
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.00
0.00
0.00
0.01
0.00
0.00
0.00
0.02
0.02
0.02
0.02
0.01
0.02
0.01
0.03
0.04
0.04
0.05
0.04
0.04
0.02
0.03
0.03
0.03
0.02
0.03
0.01
0.02
0.02
0.02
0.02
0.01
0.00
0.01
0.00
0.00
0.01
0.01
0.02
0.03
0.03
0.03
0.02
0.02
0.04
0.04
0.04
0.04
0.04
0.03
0.02
0.02
0.02
0.03
0.02
0.03
0.02
0.02
0.01
0.02
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.02
0.02
0.01
0.02
0.02
0.01
0.05
0.03
0.01
0.01
0.03
0.07
0.03
0.01
0.01
0.00
0.02
Source: Own calculations based on microdata from the EPH.
73
Table 9.2
Housing
By age
Share of housing owners
[15,24] [25,40] [41,64] [65+) Mean
EPH-15 cities
1992
0.34
0.58
0.80
0.85
0.73
1993
0.31
0.57
0.81
0.86
0.74
1994
0.30
0.54
0.80
0.85
0.72
1995
0.29
0.54
0.79
0.85
0.72
1996
0.26
0.55
0.79
0.86
0.72
1997
0.27
0.53
0.78
0.84
0.70
1998
0.27
0.52
0.78
0.85
0.71
EPH - 28 cities
1998
0.25
0.52
0.78
0.84
0.70
1999
0.30
0.56
0.79
0.85
0.71
2000
0.25
0.57
0.78
0.84
0.71
2001
0.25
0.55
0.78
0.86
0.71
2002
0.31
0.57
0.79
0.85
0.72
2003
0.25
0.53
0.77
0.86
0.70
EPH-C
2003-II
0.26
0.52
0.76
0.85
0.69
2004-I
0.23
0.48
0.75
0.85
0.67
2004-II
0.24
0.49
0.75
0.84
0.67
2005-I
0.25
0.48
0.74
0.83
0.66
2005-II
0.24
0.47
0.73
0.83
0.66
2006-I
0.25
0.47
0.74
0.83
0.66
2006-II
0.24
0.46
0.75
0.84
0.66
Share of "poor" dwellings
[15,24] [25,40] [41,64] [65+) Mean
EPH-15 cities
1992
0.07
0.04
0.03
0.02
0.03
1993
0.08
0.05
0.03
0.01
0.03
1994
0.09
0.06
0.03
0.01
0.04
1995
0.09
0.06
0.03
0.01
0.03
1996
0.09
0.06
0.02
0.01
0.03
1997
0.08
0.05
0.02
0.01
0.03
1998
0.05
0.03
0.01
0.01
0.02
EPH - 28 cities
1998
0.06
0.04
0.02
0.01
0.02
1999
0.06
0.04
0.02
0.01
0.03
2000
0.08
0.04
0.02
0.01
0.03
2001
0.09
0.05
0.02
0.01
0.03
2002
0.06
0.04
0.01
0.01
0.02
2003
0.10
0.03
0.01
0.00
0.02
EPHC
2006-II
0.10
0.04
0.02
0.01
0.03
Number of rooms
[15,24] [25,40] [41,64] [65+)
Mean
Persons per room
[15,24] [25,40] [41,64] [65+)
Mean
2.0
2.1
2.1
2.1
2.1
2.1
2.1
2.7
2.8
2.7
2.7
2.7
2.6
2.6
3.1
3.2
3.2
3.2
3.2
3.2
3.2
2.9
3.0
2.9
2.9
3.0
3.0
3.0
2.9
3.0
2.9
2.9
3.0
3.0
2.9
1.7
1.5
1.5
1.5
1.4
1.5
1.3
1.8
1.7
1.8
1.7
1.7
1.7
1.8
1.4
1.3
1.3
1.3
1.3
1.3
1.3
0.8
0.8
0.8
0.8
0.8
0.8
0.8
1.4
1.3
1.3
1.3
1.3
1.3
1.3
2.1
2.2
2.0
2.0
2.1
2.0
2.6
2.6
2.6
2.6
2.5
2.5
3.2
3.2
3.2
3.1
3.1
3.1
3.0
2.9
2.9
3.0
3.0
3.0
2.9
2.9
2.9
2.9
2.9
2.9
1.4
1.5
1.5
1.5
1.5
1.4
1.8
1.7
1.7
1.8
1.8
1.8
1.3
1.4
1.4
1.4
1.4
1.4
0.8
0.8
0.8
0.8
0.8
0.8
1.4
1.4
1.4
1.4
1.4
1.4
2.05
2.16
2.11
2.11
2.10
2.07
2.03
2.59
2.57
2.62
2.64
2.62
2.61
2.61
3.23
3.23
3.25
3.26
3.25
3.26
3.27
3.04
3.00
3.04
3.04
3.06
3.06
3.08
2.95
2.94
2.97
2.99
2.98
2.97
2.97
1.70
1.59
1.65
1.69
1.61
1.58
1.59
1.75
1.79
1.72
1.71
1.69
1.70
1.67
1.32
1.31
1.28
1.28
1.26
1.28
1.29
0.81
0.81
0.80
0.82
0.80
0.82
0.80
1.33
1.34
1.31
1.31
1.30
1.31
1.30
Share of dwellings of low-quality materials
[15,24] [25,40] [41,64] [65+) Mean
0.02
0.03
0.02
0.04
0.03
0.02
0.01
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.02
0.02
0.02
0.02
0.01
0.02
0.01
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.01
0.02
0.02
0.02
0.01
0.01
0.01
0.01
0.01
0.01
0.02
0.02
0.01
0.01
0.01
0.01
0.02
0.02
0.01
0.02
0.01
0.01
0.04
0.03
0.02
0.01
0.02
Source: Own calculations based on microdata from the EPH.
74
Table 9.3
Housing
By education of the household head
Share of housing owners
Low Medium High
Mean
EPH-15 cities
1992
0.73
0.71
0.76
0.73
1993
0.73
0.74
0.74
0.74
1994
0.72
0.73
0.73
0.72
1995
0.72
0.70
0.73
0.72
1996
0.74
0.69
0.70
0.72
1997
0.71
0.68
0.72
0.70
1998
0.72
0.69
0.73
0.71
EPH - 28 cities
1998
0.71
0.68
0.71
0.70
1999
0.72
0.70
0.72
0.71
2000
0.72
0.70
0.70
0.71
2001
0.73
0.69
0.71
0.71
2002
0.74
0.71
0.69
0.72
2003
0.73
0.69
0.67
0.70
EPH-C
2003-II
0.72
0.65
0.68
0.69
2004-I
0.70
0.64
0.67
0.67
2004-II
0.70
0.63
0.69
0.67
2005-I
0.69
0.62
0.68
0.66
2005-II
0.68
0.62
0.67
0.66
2006-I
0.68
0.62
0.67
0.66
2006-II
0.68
0.63
0.66
0.66
Low
Low
Share of "poor" dwellings
Medium High
Mean
EPH-15 cities
1992
0.04
1993
0.05
1994
0.05
1995
0.05
1996
0.05
1997
0.04
1998
0.03
EPH - 28 cities
1998
0.03
1999
0.04
2000
0.04
2001
0.04
2002
0.03
2003
0.03
EPHC
2006-II
0.04
Number of rooms
Medium High
Mean
Low
Persons per room
Medium High
Mean
2.7
2.7
2.7
2.7
2.8
2.7
2.7
3.0
3.1
3.1
3.0
3.0
3.1
3.0
3.6
3.5
3.4
3.5
3.4
3.4
3.5
2.9
3.0
2.9
2.9
3.0
3.0
2.9
1.5
1.5
1.5
1.5
1.5
1.5
1.5
1.3
1.3
1.2
1.2
1.3
1.2
1.3
1.0
1.0
0.9
1.0
0.9
0.9
0.9
1.4
1.3
1.3
1.3
1.3
1.3
1.3
2.7
2.7
2.7
2.7
2.7
2.7
3.0
3.0
3.0
3.0
2.9
2.9
3.5
3.4
3.4
3.4
3.4
3.4
2.9
2.9
2.9
2.9
2.9
2.9
1.6
1.5
1.6
1.6
1.6
1.6
1.3
1.3
1.3
1.3
1.4
1.4
0.9
1.0
1.0
1.0
1.0
1.0
1.4
1.4
1.4
1.4
1.4
1.4
2.72
2.71
2.73
2.74
2.72
2.74
2.73
2.98
2.95
3.02
3.01
3.00
2.98
2.98
3.37
3.37
3.41
3.45
3.43
3.43
3.43
2.95
2.94
2.97
2.99
2.98
2.97
2.97
1.52
1.54
1.50
1.52
1.49
1.52
1.51
1.35
1.36
1.30
1.30
1.29
1.29
1.29
0.95
0.95
0.91
0.91
0.91
0.92
0.92
1.33
1.34
1.31
1.31
1.30
1.31
1.30
Share of dwellings of low-quality materials
Low Medium High
Mean
0.02
0.02
0.02
0.02
0.02
0.02
0.01
0.01
0.01
0.01
0.01
0.00
0.01
0.00
0.03
0.03
0.04
0.03
0.03
0.03
0.02
0.03
0.03
0.03
0.03
0.02
0.02
0.02
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.02
0.02
0.02
0.02
0.01
0.02
0.01
0.01
0.02
0.02
0.02
0.02
0.02
0.00
0.01
0.01
0.01
0.01
0.01
0.02
0.03
0.03
0.03
0.02
0.02
0.02
0.03
0.02
0.02
0.02
0.02
0.01
0.01
0.01
0.01
0.01
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.02
0.02
0.01
0.02
0.01
0.01
0.03
0.01
0.03
0.04
0.01
0.00
0.02
Source: Own calculations based on microdata from the EPH.
Table 9.4
Services
By household equivalized income quintiles
1
EPH-15 cities
1992
0.93
1993
0.93
1994
0.94
1995
0.93
1996
0.93
1997
0.93
1998
0.94
EPH - 28 cities
1998
0.93
1999
0.96
2000
0.94
2001
0.95
2002
0.95
2003
0.93
EPHC
2006-I
0.95
2
Water
3
4
5
Mean
1
2
Hygienic restrooms
3
4
5
Mean
1
2
Sewerage
3
4
5
Mean
1
2
Electricity
3
0.97
0.97
0.97
0.96
0.96
0.98
0.98
0.96
0.98
0.98
0.98
0.99
0.98
0.99
0.98
0.99
0.99
0.99
0.99
0.99
1.00
1.00
1.00
0.99
1.00
1.00
1.00
1.00
0.97
0.98
0.97
0.98
0.98
0.98
0.98
0.73
0.71
0.74
0.77
0.74
0.64
0.58
0.84
0.84
0.83
0.86
0.87
0.82
0.79
0.88
0.89
0.88
0.92
0.91
0.88
0.87
0.91
0.92
0.93
0.96
0.96
0.95
0.95
0.98
0.97
0.98
0.99
0.99
0.98
0.99
0.88
0.88
0.88
0.91
0.91
0.87
0.86
0.25
0.40
0.47
0.62
0.83
0.55
0.99
0.99
0.99
0.99
1.00
1.00
0.97
0.98
0.98
0.98
0.97
0.98
0.99
0.99
0.99
0.99
0.99
0.99
1.00
0.99
1.00
1.00
0.99
1.00
1.00
1.00
1.00
1.00
1.00
1.00
0.98
0.99
0.98
0.99
0.99
0.98
0.58
0.62
0.61
0.60
0.61
0.60
0.79
0.79
0.81
0.80
0.75
0.79
0.87
0.88
0.87
0.87
0.87
0.88
0.94
0.93
0.95
0.95
0.95
0.96
0.99
0.98
0.98
0.99
0.99
0.99
0.86
0.86
0.87
0.87
0.86
0.87
0.29
0.31
0.31
0.28
0.30
0.31
0.42
0.42
0.44
0.40
0.42
0.44
0.50
0.52
0.53
0.54
0.53
0.52
0.62
0.61
0.65
0.67
0.65
0.70
0.82
0.82
0.82
0.83
0.83
0.84
0.56
0.57
0.58
0.58
0.58
0.60
0.98
0.98
0.98
0.98
0.98
0.98
1.00
1.00
0.99
0.99
1.00
1.00
0.99
0.99
1.00
1.00
0.99
0.64
0.80
0.90
0.96
0.99
0.88
0.33
0.45
0.58
0.70
0.84
0.62
4
5
Mean
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
0.99
Source: Own calculations based on microdata from the EPH.
75
Table 10.1
Household size
EPH-15 cities
1992
1993
1994
1995
1996
1997
1998
EPH - 28 cities
1998
1999
2000
2001
2002
2003
EPH-C
2003-II
2004-I
2004-II
2005-I
2005-II
2006-I
2006-II
Equivalized income quintile
4
5
Mean
Education of household head
Low
Medium
High
1
2
3
Mean
4.3
4.2
4.3
4.5
4.6
4.4
4.6
3.3
3.5
3.5
3.7
3.8
3.7
3.7
3.6
3.6
3.4
3.5
3.4
3.4
3.4
3.4
3.2
3.3
3.2
3.1
3.2
3.1
2.8
2.8
2.7
2.7
2.7
2.7
2.7
3.4
3.4
3.4
3.4
3.4
3.4
3.4
3.6
3.5
3.5
3.6
3.6
3.6
3.6
3.6
3.5
3.4
3.3
3.4
3.3
3.3
3.2
3.2
3.0
3.1
3.0
3.0
3.0
3.5
3.5
3.4
3.4
3.4
3.4
3.4
4.6
4.6
4.6
4.8
4.8
4.8
3.9
4.0
4.0
4.1
4.1
4.2
3.4
3.3
3.5
3.4
3.4
3.4
3.1
3.1
3.1
3.0
3.0
3.0
2.7
2.7
2.7
2.6
2.6
2.6
3.4
3.4
3.4
3.4
3.4
3.4
3.6
3.6
3.7
3.7
3.6
3.7
3.4
3.5
3.4
3.4
3.4
3.4
3.0
3.0
3.0
3.0
3.0
2.9
3.4
3.4
3.5
3.5
3.4
3.4
4.3
4.5
4.5
4.5
4.5
4.4
4.4
4.0
4.1
4.0
4.0
3.9
3.9
3.9
3.4
3.3
3.4
3.4
3.3
3.3
3.2
2.9
3.0
3.0
3.0
3.0
3.0
3.1
2.5
2.6
2.5
2.6
2.5
2.6
2.6
3.3
3.4
3.3
3.4
3.3
3.3
3.3
3.6
3.6
3.5
3.6
3.5
3.5
3.5
3.4
3.4
3.4
3.3
3.3
3.3
3.3
2.9
2.9
2.9
2.9
2.9
2.9
2.9
3.4
3.4
3.3
3.4
3.3
3.3
3.3
Low
Medium
Source: Own calculations based on microdata from the EPH.
Table 10.2
Number of children
EPH-15 cities
1992
1993
1994
1995
1996
1997
1998
EPH - 28 cities
1998
1999
2000
2001
2002
2003
EPH-C
2003-II
2004-I
2004-II
2005-I
2005-II
2006-I
2006-II
Parental income quintile
3
4
5
Mean
Parental education
High
Mean
1
2
1.8
1.7
1.6
1.7
1.7
1.7
1.8
1.7
1.7
1.8
1.6
1.7
1.6
1.6
1.7
1.6
1.5
1.6
1.5
1.5
1.4
1.4
1.4
1.3
1.3
1.2
1.2
1.3
1.3
1.2
1.2
1.2
1.2
1.1
1.1
1.6
1.5
1.5
1.5
1.5
1.4
1.4
1.9
1.7
1.8
1.8
1.9
1.8
1.8
1.5
1.5
1.3
1.3
1.3
1.3
1.4
1.1
1.1
1.1
1.1
1.0
1.0
0.9
1.6
1.5
1.4
1.5
1.4
1.4
1.4
1.8
1.7
1.8
1.9
1.8
1.7
1.7
1.6
1.7
1.6
1.7
1.7
1.5
1.6
1.4
1.5
1.5
1.5
1.3
1.3
1.3
1.3
1.3
1.2
1.1
1.2
1.1
1.1
1.1
1.1
1.5
1.5
1.5
1.5
1.4
1.4
1.9
1.8
1.8
1.9
1.9
1.8
1.4
1.4
1.4
1.3
1.4
1.4
1.0
1.0
1.0
1.0
0.9
0.9
1.5
1.5
1.5
1.5
1.4
1.4
1.7
1.6
1.6
1.2
1.6
1.6
1.5
1.5
1.5
1.4
1.5
1.5
1.4
1.4
1.4
1.4
1.3
1.5
1.2
1.2
1.3
1.3
1.2
1.2
1.3
1.2
1.1
1.2
1.1
1.0
1.0
1.1
1.0
1.1
1.0
1.4
1.3
1.3
1.3
1.3
1.3
1.3
1.8
1.8
1.7
1.7
1.7
1.7
1.7
1.4
1.4
1.3
1.3
1.3
1.3
1.3
0.9
0.9
0.9
0.9
0.9
0.9
0.8
1.4
1.3
1.3
1.3
1.3
1.3
1.3
Source: Own calculations based on microdata from the EPH.
76
Table 10.3
Dependency rates
Household size over income earners
Argentina
EPH-15 cities
1992
1993
1994
1995
1996
1997
1998
EPH - 28 cities
1998
1999
2000
2001
2002
2003
EPH-C
2003-II
2004-I
2004-II
2005-I
2005-II
2006-I
2006-II
Equivalized income quintile
4
5
1
2
3
3.0
3.0
3.1
3.3
3.4
3.2
3.0
1.7
1.8
1.9
2.0
2.0
1.9
2.0
1.7
1.7
1.7
1.7
1.7
1.7
1.7
1.5
1.5
1.5
1.5
1.5
1.5
1.4
3.1
3.3
3.3
3.7
3.5
3.3
2.1
2.2
2.2
2.3
2.3
2.2
1.7
1.7
1.7
1.8
1.7
1.7
3.4
3.1
3.0
2.9
2.7
2.8
2.7
2.1
2.2
2.0
2.1
2.0
2.0
2.0
1.7
1.7
1.7
1.7
1.6
1.6
1.6
Education of household head
High
Mean
Mean
Low
Medium
1.4
1.4
1.3
1.3
1.4
1.3
1.3
1.7
1.7
1.7
1.7
1.7
1.7
1.7
1.8
1.8
1.8
1.8
1.9
1.8
1.8
2.0
1.9
1.9
1.8
1.9
1.8
1.8
1.8
1.7
1.7
1.8
1.7
1.7
1.6
1.9
1.8
1.8
1.8
1.9
1.8
1.8
1.5
1.5
1.5
1.5
1.5
1.5
1.3
1.3
1.3
1.3
1.3
1.3
1.7
1.7
1.7
1.7
1.7
1.7
1.8
1.8
1.9
2.0
1.9
1.9
1.8
1.9
1.9
1.9
2.0
1.9
1.6
1.7
1.7
1.7
1.7
1.7
1.8
1.8
1.8
1.9
1.9
1.9
1.5
1.5
1.5
1.5
1.4
1.4
1.4
1.3
1.3
1.3
1.3
1.3
1.3
1.3
1.7
1.7
1.6
1.7
1.6
1.6
1.6
2.1
2.0
1.9
1.9
1.8
1.8
1.8
2.3
2.1
2.1
2.0
2.0
1.9
1.9
2.0
1.9
1.8
1.8
1.7
1.7
1.7
2.1
2.0
1.9
1.9
1.8
1.8
1.8
Source: Own calculations based on microdata from the EPH.
Table 10.4
Mean age
EPH-15 cities
1992
1993
1994
1995
1996
1997
1998
EPH - 28 cities
1998
1999
2000
2001
2002
2003
EPH-C
2003-II
2004-I
2004-II
2005-I
2005-II
2006-I
2006-II
Equivalized income quintile
4
5
1
2
3
Mean
27.4
27.2
27.0
25.7
25.1
25.8
24.6
32.4
31.8
31.4
30.4
30.9
30.3
29.5
31.8
32.1
33.6
33.1
32.7
33.6
33.1
33.4
34.2
33.6
34.3
35.3
35.1
35.5
34.1
35.2
35.8
36.2
36.3
37.3
36.7
31.8
32.1
32.3
31.9
32.1
32.4
31.9
24.2
24.4
24.2
23.7
22.9
23.3
28.9
28.1
28.8
28.3
28.1
28.0
32.7
33.1
32.1
32.8
32.5
32.7
35.4
35.0
35.0
35.5
35.6
36.3
36.5
36.3
36.7
37.4
37.7
38.3
31.5
31.4
31.4
31.5
31.3
31.7
25.0
25.1
24.7
25.0
24.9
25.2
25.0
28.7
28.6
29.3
29.2
29.5
29.7
29.6
32.4
33.7
33.1
33.4
34.7
34.3
34.6
35.7
35.8
36.4
35.9
36.0
35.5
35.8
38.3
38.1
38.0
38.5
37.6
37.4
36.7
32.0
32.2
32.3
32.4
32.5
32.4
32.3
Source: Own calculations based on microdata from the EPH.
77
Table 10.5
Correlation between couples
Years of
education
Argentina
EPH-15 cities
1992
1993
1994
1995
1996
1997
1998
EPH - 28 cities
1998
1999
2000
2001
2002
2003
EPH-C
2003-II
2004-I
2004-II
2005-I
2005-II
2006-I
2006-II
Hourly
wages
Hours of work
All couples
Workers
0.651
0.658
0.657
0.665
0.660
0.661
0.665
0.475
0.463
0.482
0.482
0.462
0.435
0.456
0.133
0.112
0.116
0.112
0.122
0.120
0.113
0.190
0.153
0.209
0.193
0.183
0.213
0.176
0.667
0.662
0.659
0.662
0.660
0.664
0.473
0.457
0.512
0.418
0.372
0.461
0.115
0.114
0.139
0.123
0.108
0.127
0.191
0.201
0.220
0.213
0.190
0.210
0.671
0.660
0.690
0.673
0.685
0.682
0.675
0.388
0.436
0.314
0.405
0.199
0.187
0.334
0.117
0.109
0.119
0.103
0.106
0.122
0.096
0.140
0.145
0.148
0.128
0.149
0.115
0.108
Source: Own calculations based on microdata from the EPH.
78
Table 11.1
Coverage of PJH
Share of households with PJH by equivalized income quintiles
EPH, 2003
EPH-C
2003 *
2003
2004-I
2004-II
2005-I
2005-II
2006-I
2006-II
1
0.34
2
0.25
0.33
0.32
0.32
0.33
0.33
0.31
0.28
0.22
0.24
0.23
0.22
0.21
0.19
0.16
0.15
0.13
Quintiles
3
0.09
0.11
0.10
0.10
0.08
0.07
0.06
0.06
0.05
4
0.03
5
0.01
Mean
0.11
0.05
0.04
0.03
0.03
0.03
0.03
0.02
0.01
0.01
0.01
0.01
0.01
0.01
0.00
0.00
0.00
0.13
0.12
0.11
0.11
0.10
0.09
0.09
0.07
Source: Own calculations based on microdata from the EPH.
Table 11.2
Coverage of PJH
Share of households with PJH by education of household head
EPH, 2003
EPH-C
2003 *
2003
2004-I
2004-II
2005-I
2005-II
2006-I
2006-II
Low
0.16
Medium
0.09
High
0.02
Mean
0.11
0.17
0.18
0.17
0.17
0.16
0.14
0.14
0.11
0.09
0.10
0.10
0.08
0.07
0.07
0.06
0.05
0.02
0.03
0.02
0.02
0.02
0.01
0.01
0.01
0.11
0.12
0.11
0.11
0.10
0.09
0.09
0.07
Source: Own calculations based on microdata from the EPH.
Table 11.3
Coverage of PJH
Benefits (in pesos) of PJH by household
EPH, 2003
EPH-C
2004-II
2005-I
2005-II
2006-I
2006-II
1
35.4
2
13.2
3
4.3
4
1.1
5
0.5
Mean
8.4
25.5
25.6
23.5
21.8
14.7
12.1
8.9
6.2
6.4
4.6
3.6
2.3
2.1
2.0
1.4
0.8
0.2
1.1
0.6
0.6
0.3
0.0
0.1
0.0
0.0
6.9
5.9
5.2
5.1
3.4
Source: Own calculations based on microdata from the EPH.
79
Table 11.4
Incidence of PJH
Distribution of PJH beneficiaries by equivalized income quintile
Households
EPH, 2003
EPH-C
2003 *
2003
2004-I
2004-II
2005-I
2005-II
2006-I
2006-II
1
42.2
2
35.1
3
15.8
4
5.6
5
1.2
Total
100.0
41.3
41.3
43.5
45.4
48.5
49.8
49.0
48.4
32.1
31.9
31.6
31.7
31.1
29.6
29.0
31.2
17.1
17.1
17.6
14.9
13.4
12.9
14.8
15.0
8.2
8.3
6.1
6.2
5.6
6.5
6.4
4.7
1.3
1.4
1.2
1.8
1.4
1.2
0.8
0.8
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
1
42.4
2
35.2
3
15.9
4
5.5
5
1.1
Total
100.0
40.4
40.5
43.0
45.2
48.6
49.6
48.0
48.7
33.5
33.3
32.9
31.9
30.8
30.5
29.3
30.9
16.6
16.6
16.8
14.9
13.4
12.3
15.7
14.9
8.2
8.3
6.1
6.2
5.9
6.5
6.2
4.7
1.2
1.3
1.1
1.7
1.3
1.1
0.8
0.7
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
Individuals
EPH, 2003
EPH-C
2003 *
2003
2004-I
2004-II
2005-I
2005-II
2006-I
2006-II
Source: Own calculations based on microdata from the EPH.
Table 11.5
Incidence of PJH
Distribution of PJH benefits by equivalized income quintile
EPH, 2003
EPH-C
2004-II
2005-I
2005-II
2006-I
2006-II
1
42.4
2
35.1
3
15.9
4
5.5
5
1.1
Total
100.0
47.5
51.5
53.5
52.4
49.8
31.6
30.9
28.8
29.0
31.4
14.2
12.2
11.4
12.9
13.6
5.3
4.5
5.4
5.2
4.6
1.3
0.8
0.9
0.4
0.5
100.0
100.0
100.0
100.0
100.0
Source: Own calculations based on microdata from the EPH.
80
Table A.1
Poverty profile
Argentina, 2006
Demographic variables
USD 2 poverty line
Population share
National moderate line
Poor
Non-poor
Poor
Non-poor
8.5
91.5
26.7
73.3
Population share by age
[0,15]
[16,25]
[26,40]
[41,64]
[65+]
Age distribution
[0,15]
[16,25]
[26,40]
[41,64]
[65+]
Total
14.8
8.3
6.3
5.7
3.1
85.2
91.7
93.7
94.3
96.9
40.9
28.6
22.2
20.1
10.9
59.1
71.4
77.8
79.9
89.1
47.1
17.1
16.3
15.7
3.7
100.0
25.1
17.5
22.4
24.1
10.8
100.0
41.2
18.8
18.2
17.6
4.1
100.0
21.7
17.1
23.3
25.5
12.4
100.0
Mean age
23.0
33.2
24.9
35.1
0.48
0.48
0.47
Gender
Share males
0.47
Household size and structure
Family size
4.6
3.2
4.6
3.0
Children (<12)
2.5
1.2
2.1
1.0
Dependency rate
0.26
0.65
0.37
0.69
Female-headed hh.
41.1
30.8
33.1
31.5
Source: Own calculations based on microdata from the EPH.
Table A.2
Poverty profile
Argentina, 2006
Regions
USD 2 poverty line
Population share
GBA
Pampeana
Cuyo
NOA
Patagonia
NEA
Distribution
GBA
Pampeana
Cuyo
NOA
Patagonia
NEA
Total
National moderate line
Poor
Non-poor
Poor
Non-poor
7.3
7.8
7.5
13.7
4.8
18.2
92.7
92.2
92.5
86.3
95.2
81.8
25.3
22.2
27.0
39.3
16.4
45.3
74.7
77.8
73.0
60.7
83.6
54.7
45.2
21.3
5.4
15.3
1.9
10.8
100.0
53.6
23.2
6.2
8.9
3.5
4.5
100.0
50.1
19.2
6.2
13.9
2.1
8.5
100.0
53.9
24.5
6.1
7.8
3.9
3.8
100.0
Source: Own calculations based on microdata from the EPH.
81
Table A.3
Poverty profile
Argentina, 2006
Housing
USD 2 poverty line
National moderate line
Poor
Non-poor
Poor
Non-poor
House ownership
0.50
0.67
0.59
0.68
Number of rooms
2.3
3.0
2.6
3.0
Persons per room
2.4
1.2
2.2
1.1
Poor housing
0.11
0.02
0.07
0.02
Low-quality materials
0.10
0.01
0.06
0.01
Water
0.95
0.99
0.96
1.00
Hygienic restrooms
0.55
0.90
0.66
0.93
Sewerage
0.28
0.64
0.34
0.68
Source: Own calculations based on microdata from the EPH.
Table A.4
Poverty profile
Argentina, 2006
Education
USD 2 poverty line
National moderate line
Poor
Non-poor
Poor
Non-poor
5.2
6.6
8.4
7.6
7.5
6.1
5.5
8.5
8.2
11.8
11.5
10.8
9.9
8.0
5.9
7.2
9.1
8.3
8.0
6.7
5.6
9.1
8.5
12.2
12.1
11.3
10.4
8.2
70.0
25.6
4.4
100.0
33.9
38.3
27.7
100.0
64.6
30.1
5.3
100.0
28.5
39.6
31.9
100.0
69.7
25.8
4.4
100.0
35.8
39.3
24.9
100.0
66.2
28.5
5.4
100.0
30.2
41.2
28.5
100.0
70.3
25.4
4.4
100.0
32.3
37.5
30.3
100.0
63.3
31.5
5.2
100.0
27.0
38.1
34.8
100.0
70.2
24.9
4.9
100.0
41.2
35.1
23.7
100.0
67.8
27.3
4.9
100.0
37.2
36.2
26.6
100.0
Literacy rate
0.96
0.99
0.97
0.99
School attendance
[3,5]
[6,12]
[13,17]
[18,23]
0.44
0.98
0.80
0.25
0.64
0.99
0.91
0.47
0.49
0.99
0.84
0.29
0.69
0.99
0.94
0.51
Years of education
Total
[10,20]
[21,30]
[31,40]
[41,50]
[51,60]
[61+]
Educational groups
Adults
Low
Medium
High
Total
Male adults
Low
Medium
High
Total
Female adults
Low
Medium
High
Total
Household heads
Low
Medium
High
Total
Source: Own calculations based on microdata from the EPH.
82
Table A.5
Poverty profile
Argentina, 2006
Employment
USD 2 poverty line
In the labor force
Total
[16,24]
[25,55]
[56+]
Men [25,55]
Women [25,55]
Employed
Total
[16,24]
[25,55]
[56+]
Men [25,55]
Women [25,55]
Unemployment rate
Total
[16,24]
[25,55]
[56+]
Men [25,55]
Women [25,55]
Unemployment spell
(months)
Child labor
National moderate line
Poor
Non-poor
Poor
Non-poor
0.43
0.48
0.68
0.35
0.89
0.53
0.57
0.51
0.81
0.35
0.95
0.69
0.46
0.46
0.71
0.36
0.92
0.54
0.59
0.52
0.83
0.35
0.96
0.72
0.29
0.26
0.50
0.25
0.65
0.39
0.52
0.40
0.77
0.33
0.91
0.64
0.37
0.30
0.61
0.30
0.80
0.45
0.55
0.42
0.79
0.34
0.93
0.67
0.31
0.46
0.26
0.30
0.27
0.25
0.08
0.21
0.06
0.05
0.04
0.07
0.20
0.36
0.15
0.17
0.13
0.17
0.07
0.19
0.05
0.05
0.03
0.06
9.1
9.2
9.0
9.3
0.02
0.02
0.02
0.01
Source: Own calculations based on microdata from the EPH.
Table A.6
Poverty profile
Argentina, 2006
Wages, hours and earnings
USD 2 poverty line
Worked hours
Total
[16,24]
[25,55]
[56+]
Men [25,55]
Women [25,55]
Hourly wages
Total
[16,24]
[25,55]
[56+]
Men [25,55]
Women [25,55]
Earnings
Total
[16,24]
[25,55]
[56+]
Men [25,55]
Women [25,55]
National moderate line
Poor
Non-poor
Poor
Non-poor
31.1
29.0
32.2
29.5
40.6
22.6
41.7
38.4
43.1
39.1
48.6
35.7
35.9
33.4
37.3
32.8
44.7
26.5
42.5
39.3
43.8
39.9
49.1
37.1
2.5
2.0
2.6
2.5
2.2
3.2
7.0
4.4
7.3
8.2
7.3
7.3
3.1
2.4
3.3
3.3
3.2
3.5
7.6
4.7
7.9
8.6
8.0
7.7
198.8
153.3
220.3
156.9
263.8
157.8
1087.2
614.1
1181.6
1113.1
1336.2
972.1
366.7
272.0
407.6
288.6
489.8
263.8
1190.0
675.8
1290.6
1197.2
1477.6
1051.4
Source: Own calculations based on microdata from the EPH.
83
Table A.7
Poverty profile
Argentina, 2006
Employment structure
USD 2 poverty line
Poor
National moderate line
Non-poor
Poor
Non-poor
3.4
71.2
16.2
0.9
8.2
100.0
1.2
56.2
21.2
1.6
19.8
100.0
3.8
73.3
15.1
0.8
6.9
100.0
3.9
36.3
18.0
2.9
22.5
15.4
1.1
100.0
1.5
20.6
10.5
0.7
37.5
27.1
2.1
100.0
4.2
38.4
19.0
3.3
20.6
13.5
0.9
100.0
61.0
39.0
100.0
33.3
66.7
100.0
64.9
35.1
100.0
59.0
41.0
100.0
19.6
80.4
100.0
64.0
36.0
100.0
1.2
7.2
6.9
8.8
23.0
7.2
10.0
8.2
19.9
7.7
100.0
1.4
8.6
4.6
18.2
26.3
4.7
5.0
4.2
11.5
15.4
100.0
1.2
6.8
7.1
7.3
22.4
7.4
10.8
8.7
21.3
6.9
100.0
Labor relationship
Entrepreneur
0.9
Salaried worker
46.5
Self-employed
19.6
Zero income
1.9
Unemployed
31.1
Total
100.0
Labor group
Entrepreneurs
1.4
Salaried-large firms
15.8
Salaried-public sector
10.0
Self-employed professionals
0.5
Salaried-small firms
39.7
Self-employed unskilled
29.7
Zero income
2.9
Total
100.0
Formality (based on labor group)
Formal
27.6
Informal
72.4
Total
100.0
Formality (based on social security rights)
Formal
5.1
Informal
94.9
Total
100.0
Sectors
Primary activities
1.5
Industry-labor intensive
5.8
Industry-capital intensive
2.4
Construction
20.6
Commerce
27.7
Utilities & transportation
3.1
Skilled services
5.6
Public administration
2.7
Education & Health
13.6
Domestic servants
17.1
Total
100.0
Permanent job
0.31
0.83
0.53
0.86
Right to pensions
0.05
0.60
0.21
0.65
Labor health insurance
0.05
0.61
0.20
0.66
Source: Own calculations based on microdata from the EPH.
Table A.8
Poverty profile
Argentina, 2006
Poverty-alleviation programs (Programa Jefes)
USD 2 poverty line
National moderate line
Poor
Non-poor
Poor
Non-poor
Households with PJH
0.23
0.06
0.22
0.03
Mean income from PJH
18.8
2.4
13.0
1.1
Distribution
Beneficiaries
Transfers
23.8
23.5
76.2
76.5
66.4
62.7
33.6
37.3
Source: Own calculations based on microdata from the EPH.
84
Table A.9
Poverty profile
Argentina, 2006
Incomes
USD 2 poverty line
National moderate line
Poor
Non-poor
Poor
Non-poor
Household per capita income
Household total income
56.1
256.8
643.0
2073.2
129.7
598.0
762.6
2273.0
Gini per capita income
0.223
0.451
0.253
0.396
64.8
35.2
100.0
81.0
19.0
100.0
77.0
23.0
100.0
81.2
18.8
100.0
55.3
30.4
1.4
12.8
100.0
72.2
13.5
7.4
6.9
100.0
67.9
23.6
1.7
6.8
100.0
72.4
13.0
7.6
6.9
100.0
0.2
18.0
81.2
0.6
100.0
8.9
64.9
20.1
6.2
100.1
1.2
36.3
61.4
1.1
100.0
9.4
66.4
17.8
6.5
100.1
Individual income
Labor
Non-labor
Total
Labor income
Salaried work
Self-employment
Own firm
Others
Total
Non-labor income
Capital
Pensions
Transfers
Others
Total
Source: Own calculations based on microdata from the EPH.
85
Table A.10
Basic needs indicator (NBI)
Argentina, 2001
Total
Ciudad de Buenos Aires
Pampeana
Buenos Aires (includes GBA)
Córdoba
Entre Ríos
La Pampa
Santa Fe
Cuyo
Mendoza
San Juan
San Luis
Patagonia
Chubut
Neuquén
Río Negro
Santa Cruz
Tierra del Fuego
NOA
Catamarca
Jujuy
La Rioja
Salta
Santiago del Estero
Tucumán
NEA
Corrientes
Chaco
Formosa
Misiones
1980
Persons
(i)
%
(ii)
1991
Persons
(iii)
%
(iv)
2001
Persons
(v)
%
(vi)
Changes
1980-1991
(vii)
1991-2001
(viii)
1980-2001
(ix)
7,603,332
27.7
6,427,257
19.9
6,343,589
17.7
-7.8
-2.2
-10.0
231,872
8.3
232,203
8.1
212,489
7.8
-0.2
-0.3
-0.5
2,607,922
24.3
2,128,736
17.2
2,161,064
15.8
529,753
22.4
413,573
15.1
393,708
13.0
292,979
32.6
207,794
20.6
202,578
17.6
-7.1
-7.3
-12.0
-8.4
-6.9
-1.4
-2.1
-3.0
-3.2
-2.8
-8.5
-9.4
-15.0
-11.6
-9.7
-6.8
-11.0
-10.4
-2.2
-2.4
-5.9
-9.0
-13.4
-16.3
-12.9
-18.8
-15.7
-11.6
-5.1
-6.4
-4.4
-5.3
-4.3
-8.3
-19.3
-23.2
-21.0
-15.9
-13.4
-14.4
-13.3
-9.6
-9.7
-13.5
-14.7
-6.7
-6.7
-6.6
-5.5
-6.9
-3.8
-21.1
-20.0
-16.2
-15.2
-20.4
-18.5
-15.5
-12.6
-15.3
-11.8
-2.9
-6.5
-5.5
-6.5
-18.4
-19.1
-20.8
-18.3
44,379
21.9
34,705
13.5
30,587
10.3
595,239
24.5
489,854
17.6
440,346
14.8
287,076
24.4
246,789
17.6
241,053
15.4
142,404
30.8
103,865
19.8
107,372
17.4
67,019
31.9
61,057
21.5
57,072
15.6
87,343
34.8
76,608
21.9
62,872
15.5
93,507
40.2
81,391
21.4
79,547
17.0
145,707
38.9
116,323
23.2
97,486
17.9
27,245
26.3
22,860
14.7
19,985
10.4
6,356
27.5
14,862
22.4
14,033
14.1
87,039
42.6
73,944
28.2
71,145
21.5
196,892
48.8
180,025
35.5
175,179
28.8
59,224
36.6
59,311
27.0
58,869
20.4
305,776
46.8
318,532
37.1
338,484
31.6
302,681
51.7
254,830
38.2
250,747
31.3
406,748
42.4
314,828
27.7
318,209
23.9
303,818
46.9
248,144
31.4
264,277
28.5
359,857
52.1
329,139
39.5
323,354
33.0
159,072
54.4
155,072
39.1
162,862
33.6
263,424
45.4
262,812
33.6
260,271
27.1
Source: Census data
86
Figure 3.1
Growth-incidence curves
Household per capita income
Proportional changes by percentile
Argentina, 1992-2006
50
40
2004-2006
30
20
1998-2006
10
1992-2006
0
0
10
20
30
40
50
60
70
80
90
100
-10
1992-1998
-20
-30
-40
1998-2003
-50
-60
Source: Own calculations based on microdata from the EPH.
Figure 3.2
Growth-incidence curves
Household per capita income
Proportional changes by percentile
Argentina, 2002-2006
50
45
2003-2004
40
35
30
25
20
2005-2006
15
2004-2005
10
5
2002-2003
0
0
10
20
30
40
50
60
70
80
90
100
Source: Own calculations based on microdata from the EPH.
87
Figure 4.1
Poverty
Argentina, 1992-2006
USD 1 and USD 2 lines
USD 1 a day
USD 2 a day
10
25
9
8
20
7
6
15
5
4
10
3
2
5
1
H
PG
FGT(2)
H
PG
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
0
1992
0
FGT(2)
Source: Own calculations based on microdata from the EPH.
Note: H=headcount ratio, PG=poverty gap, FGT(2)=Foster, Greer and Thornbecke index with parameter 2.
Figure 4.2
Poverty
Argentina, 1992-2006
Official poverty lines
H
PG
FGT(2)
H
PG
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
2006
2005
2004
2003
2002
2001
0
2000
10
0
1999
20
5
1998
10
1997
30
1996
15
1995
40
1994
50
20
1993
60
25
1992
30
1993
Official moderate poverty line
1992
Official extreme poverty line
FGT(2)
Source: Own calculations based on microdata from the EPH.
Note: H=headcount ratio, PG=poverty gap, FGT(2)=Foster, Greer and Thornbecke index with parameter 2.
88
Figure 4.3
Density of the (log) income used to compute poverty with official lines
Non parametric estimation
Argentina
1998
0
0
.1
.1
Density
.2
Density
.2
.3
.3
.4
.4
.5
1992
0
2
4
6
log equivalized income
8
10
0
4
6
log equivalized incom e
8
10
2005
0
0
.1
.1
Density
.2
Density
.2
.3
.3
.4
.4
2002
2
0
2
4
6
log equivalized incom e
8
10
2
4
6
log equivalized income
8
10
Source: Own calculations based on microdata from the EPH.
Note: first vertical line corresponds to the official extreme poverty line of each year,
second vertical line corresponds to the official moderate poverty line of each year.
Figure 4.4
Poverty headcount ratio
Official moderate poverty line
Greater Buenos Aires, 1974-2006
Argentina, 1992-2006
60
50
40
30
20
10
GBA
06
05
04
03
02
01
00
99
98
97
96
95
94
93
92
91
90
89
88
87
86
85
84
83
82
81
80
79
78
77
76
75
74
0
Argentina
Source: Own calculations based on the EPH.
89
Hai
Nic
Bol
Par
Ecu
Els
Jam
Hon
Sur
Arg, 92-04
Col, 92-00
DR, 00-04
Ven, 89-00
Uru, 89-03
Par, 97-02
Hon, 97-03
Ecu, 94-98
Mex, 92-02
Per, 97-02
Pan, 95-02
Els, 91-03
Nic, 93-01
Jam, 90-02
Bol, 93-02
Bra, 90-03
Cri, 92-03
Chi, 90-03
-100
Gua
Per
Ven
Mex
Col
Bra
Pan
Dom
Arg
Cri
Chi
Uru
Ven, 89-00
Col, 92-00
Arg, 92-04
Par, 97-02
DR, 00-04
Hon, 97-03
Ecu, 94-98
Uru, 89-03
Mex, 92-02
Per, 97-02
Pan, 95-02
Cri, 92-03
Bra, 90-03
Bol, 93-02
Chi, 90-03
Els, 91-03
Nic, 93-01
Jam, 90-02
Figure 4.5
Change in poverty headcount ratio
LAC countries
Change in poverty (points)
15.0
10.0
5.0
0.0
-5.0
-10.0
-15.0
Change in poverty (%)
300
250
200
150
100
50
-50
0
Source: Gasparini et al. (2005).
Figure 4.6
Poverty headcount ratio
LAC countries
Early 2000s
70
60
50
40
30
20
10
0
Source: Gasparini et al. (2005).
90
Figure 4.7
Poverty
Argentina, 1992-2006
50% median poverty line
30
25
20
15
10
5
H
PG
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
0
FGT(2)
Source: Own calculations based on microdata from the EPH.
Note: H=headcount ratio, PG=poverty gap, FGT(2)=Foster,
Greer and Thornbecke index with parameter 2.
Figure 4.8
Poverty indicator
Endowments
Argentina, 1992-2003
0.5
0.45
0.4
0.35
0.3
0.25
0.2
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
91
Figure 5.1
Gini coefficient
Distribution of household per capita income
Greater Buenos Aires, 1974-2006
0.55
0.50
0.45
0.40
0.35
GBA
06
05
04
03
02
01
00
99
98
97
96
95
94
93
92
91
90
89
88
87
86
85
84
83
82
81
80
79
78
77
76
75
74
0.30
Arg
Source: Author’s calculations based on the EPH.
92
Figure 5.2
Gini coefficient
Distribution of household per capita income
Around 1990 and around 2000
Early 1990s
60
55
50
45
Brazil
Colombia
Honduras
Chile
Panama
Bolivia
Nicaragua
Mexico
El Salvador
El Salvador
Peru
Jamaica
Argentina
Argentina
Costa Rica
Venezuela
Uruguay
40
Early 2000s
60
55
50
45
Brazil
Chile
Bolivia
Colombia
Panama
Nicaragua
Honduras
Mexico
Jamaica
Peru
Venezuela
Costa Rica
Uruguay
40
Source: Own estimates from Gasparini (2003 b).
Figure 5.3
Change in the Gini coefficient
Between early 1990s and early 2000s
Distribution of household per capita income
8
6
4
2
0
Brazil
Honduras
Mexico
Panama
Jamaica
Colombia
Nicaragua
Costa Rica
Ecuador
Chile
El Salvador
Bolivia
Peru
Uruguay
Argentina
Venezuela
-4
Paraguay
-2
Source: Own estimates from Gasparini (2003 b).
93
Figure 6.1
Generalized Lorenz curves
Distribution of household per capita income
1992 and 2006
350
300
250
200
150
1992
100
2006
50
0
0
10
20
30
40
50
60
70
80
90
100
Source: Author’s calculations based on the EPH.
94
Figure 6.2
Aggregate welfare, 1992-2006
Inequality from EPH and mean income from national accounts
140
130
120
110
100
90
80
70
Per capita income
Sen
Atk(1)
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
60
Atk(2)
Source: Own estimates from EPH and National Accounts.
Note: Atk(e): CES welfare function with parameter e.
Figure 6.3
Aggregate welfare, 1992-2006
Inequality and mean income from EPH
110
100
90
80
70
60
50
Per capita income
Sen
Atk(1)
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
40
Atk(2)
Source: Own estimates from EPH.
Note: Atk(e): CES welfare function with parameter e.
95
Figure 7.1
Marginal return to a college education
All working males
0.80
0.75
0.70
0.65
0.60
0.55
0.50
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
Source: Own estimates from microdata of the EPH.
Figure 7.2
Labor force, employment and unemployment
Greater Buenos Aires, 1974-2006
60
50
40
30
20
10
unemployment
activity
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
1989
1988
1987
1986
1985
1984
1983
1982
1981
1980
1979
1978
1977
1976
1975
1974
0
employment
Source: Own estimates from microdata of the EPH.
96