THE SOCIO-ECONOMIC CONDITIONS IN ARGENTINA*
PRELIMINARY
Leonardo Gasparini+ and Martín Cicowiez++
CEDLAS**
Uniersidad Nacional de La Plata
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.
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,
*
This paper is based on the document “Monitoring the Socio-Economic Conditions in Argentina” prepared for
the World Bank. This paper was prepared for presentation at the John Fogarty Seminar. Buenos Aires, April 2627, 2007.
+
[email protected]
++
[email protected]
**
CEDLAS is the Center for Distributional, Labor and Social Studies at Universidad Nacional de La Plata.
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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 levels of activity
similar to those in the 1990s.
The social situation in the country has been worsening over the last three decades. Poverty
and inequality have increased even in the 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 1992-2005, and especially draws
from statistics constructed from microdata of the Encuesta Permanente de Hogares (EPH).1
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 six sections show and analyze information on
incomes, poverty, inequality, the labor market, education, and poverty-alleviation programs.
Section 9 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.2 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
1
All
the
statistics
presented
in
this
report
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.
2
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 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-
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 back
to 90,000 in the new EPHC.
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).3 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.4 From 2003 on we include a third panel
with information from the new EPHC. In this update we add to the report information for the
second half of 2005.
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.5
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.
3
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, RawsonTrelew and Viedma-Carmen de Patagones.
4
Given that surveys cover only urban areas, most statistics are not significantly affected by seasonality issues.
5
See a companion paper (Gasparini, 2004b) for further discussion on this issue.
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3. INCOMES
Real incomes are the arguments of all poverty and inequality 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 2005. 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 26% between the second half of 2003 and
the second half of 2005, which is significantly more than national accounts estimates (17%).
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 hit
all the population, although the richest decile suffered somewhat less than the rest. The
recovery 2003/05 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 1992-2005 summarizes the disappointing performance of the last thirteen
years: real incomes reported in the EPH have dramatically fallen, 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 the economy remained strong, although the growth-incidence
curve became nearly flat.
The income changes shown in this section suggest clear patterns for poverty and inequality.
The non-uniform fall in income since 1992 surely has implied a significant increase in
poverty and inequality. The next three sections provide more evidence on these issues.
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4. POVERTY
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).6 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.7 Table 4.1
presents the value of these poverty lines in local currency units for the period 1992-2005. 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).8 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.
Tables 4.2 to 4.6 show various poverty measures with alternative poverty lines.9 Argentina
has witnessed a dramatic increase in income poverty in the last thirteen years. 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 3.9 in the second half of 2005.10
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
2005) suggests a significant reduction in poverty, which nonetheless remains at a very high
level (3.9). Between 1992 and 2005 around one million Argentineans (out of a population of
38 millions) crossed the USD1-a-day poverty line.11 The patterns for the other poverty
indicators (poverty gap and FGT(2)) are similar.
6
See the methodological document for details.
7
See the methodological document and INDEC (2003).
8
See Foster, Greer and Thornbecke (1984) for references.
9
See the web page for an analysis of statistical significance of poverty changes based on bootstrapping
techniques.
10
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 seen 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.
11
That value is the net increase in poverty, which is the consequence of people jumping out and into poverty.
-5-
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 11.6 in 2005, which means that
the estimated number of poor increased in around three millions. Poverty increased 4 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
2005.
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 12.2% in the second half of 2005.
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 thirteen years. The headcount ratio increased around 14
points between 1992 and 2005, which means more than 6 million “new poor” individuals.12
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 8.5 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
12
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.
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headcount ratio increased around 10 points between 1993 and 1999, and jumped 28 points
during the crisis. Since 2002 poverty went down around 23 points, being in 2005 at roughly
the same level as in 2000.
The dramatic 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 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.13
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.14 Table 4.7 and Figure 4.8 suggest that
13
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.
14
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
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poverty did not increase when defined in the space of those variables.15 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.
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.
5. INEQUALITY
Poverty, a concept that refers to the mass of the income distribution below a certain threshold,
can increase after a shifting of the entire distribution to the left, and/or after an increase in the
dispersion of the income distribution. Mean income has fluctuated around a constant trend in
the last 30 years in Argentina. With no changes in the income distribution that economic
performance would imply stable poverty. However, the income distribution became
substantially more unequal over the last 30 years, driving poverty up.
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 2005. In the other extreme, the income share of the richest decile increased
from 34.1 in 1992 to 37.6 in 2005. Notice the heterogeneous pattern of changes across deciles.
As mentioned above the income share of decile 1 fell over the last decade. That was also the
situation for deciles 2 to 7. The shares of deciles 8 and 9 stayed roughly unchanged, while the
share of decile 10 went up nearly 4 points in thirteen years. While income distribution
changes were unequalizing over the period 1992-2001/2, they turned equalizing during the
recovery 2002-2005.
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
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.
15
There is not enough information in the dataset released for the EPHC to present comparable data for the 2003-
2005 period.
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parameters. All measures of inequality suggest the same increasing pattern over the last
thirteen 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 2002.16 This change is not only statistically significant
but, according to historical records, very high.17
Table 5.2 reports a large drop in inequality between 2003 and 2005. Some explanations are in
order. First, the use of the new weights included in the EPHC that consider unit non-response
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. Third, the inequality drop seems to have slowed down in
2005. Actually, most indices are not significantly different from those of 2004. Inequality in
the second half of 2005 showed around the same levels as before the latest economic crisis
(2000).
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.18 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 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.
16
Notice that changing the sample in 1998 does not significantly modify the value of any inequality index.
17
An analysis of statistical significance of inequality changes based on bootstrapping techniques is presented in
the web page of this project. See also Sosa Escudero and Gasparini (2001).
18
See Deaton and Zaidi (2003) and the methodological appendix for details on the implementation for
Argentina.
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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 2005. This inequality measure climbed from 0.347 in 1974 to 0.508 in
2005. 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 pre-crisis 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 3.6 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 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.
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.
6. THE LABOR MARKET
This section summarizes the structure and changes of the labor market in Argentina in the last
decade. Tables 6.1 to 6.3 show hourly wages, hours of work and labor income for the working
population. Since 2003 we compute two panels: one that includes and one that ignores
payments for the Programa Jefes de Hogar.
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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.19 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 for the
negative effects of the crisis. Hours of work, and in particular real wages, are still far from the
levels of the 1990s.
Tables 6.1 to 6.3 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. In fact,
from data for the last EPHC women earn on average a little more than men. 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.20 The tables in this section show some basic evidence on this
fact. Workers with at least some superior education earned 2 times more than those with
incomplete high school or less in 1992. That gap increased to 2.9 by 1998, and remained
around that value during the recent economic crisis. The increase in the wage premium 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 high-educated
person, in the early 2000s that difference completely vanished (see Table 6.2).
Tables 6.4 to 6.6 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 in the new EPHC. The relative loss for the selfemployed has occurred especially in terms of hourly wages. The heterogeneity of this group
becomes apparent in the second panel of Tables 6.4 to 6.6: 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. Similar figures arise from the recent EPHC.
19
Notice that the change in geographical coverage implies a significant fall in average wage.
20
See Galiani and Sanguinetti (2003) and Gasparini (2003), among others.
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In Tables 6.7 to 6.9 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 or even negative, as in utilities and the
public sector.
Table 6.10 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 6.11 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.
Are the differences in hourly wages reinforced by differences in hours of work? Table 6.12
suggests the opposite. Correlations between hours worked and hourly wages are negative and
significant for all years.
In Table 6.13 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 fell in the early stage of 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.21 Table 6.14 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
21
See Wodon (2000) and Duryea and Pages (2002) for estimates of the returns to years of education in several
LAC countries.
-12-
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 6.1). That jump is also noticeable for working women, and
for urban salaried workers (both men and women). Returns seem to have fallen a bit during
the last 3 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 6.15 shows the standard deviation of the error
term of each Mincer equation. The returns to unobservable factors have increased in
Argentina.
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 6.15 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 6.16 shows basic
statistics by gender, age and education. Labor force participation has increased in the last
decade. This increase is mainly the consequence of a flow of low and semi-skilled 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%. This
increase was shared neither by men, nor by youngsters (15-24), nor by the skilled, who all
reduced their labor market participation, especially between 1998 and 2003. 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 decade.
Figure 6.2 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.
-13-
Despite economic growth, the employment rate fell during the 1990s. The drop, however, was
not large: 1 point between 1992 and 1998. 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.
Probably the most remarkable fact in the Argentina’s labor markets of the last decade is the
dramatic increase in unemployment (see Figure 6.2). 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 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 6.2, and from Tables 6.16 and 6.17, 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 6.18 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 6.18 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.
The social concern for unemployment increases when unemployment spells are large. Table
6.19 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 decreased
since 2003 to the first half of 2005. There is some increase in unemployment duration in the
latest available survey (second half of 2005).
INDEC has published quarterly results for the main labor variables since 2003. Table 6.20
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 6.21 to 6.27 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 2005 that share reached 42%.
-14-
Older people have also gained participation. The last three columns of Table 6.21 show a
sizeable change in the educational structure of the working population in favor of the skilled.
The Greater Buenos Aires area has lost participation in employment, in particular during the
last crisis (Table 6.22). Also, there was a loss of participation for the group of entrepreneurs
captured in the EPH (Table 6.23). Employment in the public sector has gone 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 selfemployed 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 6.24 and 6.25 are aimed at presenting the formal-informal structure of the labor
market. Unfortunately there is not a single definition of informality. Following Gasparini
(2003), 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 6.24). According to the second
definition, formal workers are those who have the right to receive pensions when they retire
(see Table 6.25). Unfortunately the EPH allows implementing this definition only for wage
earners. According to the first definition, formal employment has not significantly changed in
the last decade. In sharp contrast, formality in the labor market has dramatically fallen
according to the second definition. The share of salaried workers with social security rights
drop 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 6.26 and 6.27). 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 some
manufacturing industries, construction and skilled services, while has fallen especially in the
public sector.
The concern for child labor has recently been increasing in the world. Table 6.28 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 6.29 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 6.30). Table 6.31 shows that most people report their
employment as “permanent”. The share of permanent jobs has increased in the 1990s,
decreased during the crisis and has been recovering recently.
-15-
7. 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
decade in Argentina (Table 7.1).22 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 26.4 in the latest EPHC. That rise has been more intense for women than for men.
A remarkable fact from Table 7.2 is the reversion of the gap in years of education between
men and women. While men older than 50 have more years of education than women of the
same age, the difference has recently turned in favor of women for people in their 40s. For the
working-age population (25 to 65), years of education have become slightly greater for
women since 1999.
Information in Table 7.3 suggests that the absolute gap in terms of years of education between
the rich and the poor has widened during the last decade. 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 7.3 suggests.
In Table 7.4 people are divided according to age and household income quintiles. In 2005 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 4.8 for people in their twenties, and 5.4 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.23 According
to Table 7.5 educational Ginis have slightly fallen during the last thirteen 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.
Table 7.6 and 7.7 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: 99% for those aged 15 to 24 and 96% for those aged 25 to 65.
22
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.
23
For instance, Thomas, Wang and Fan (2002) calculate Ginis over the distribution of years of education for 140
countries in the period 1960-2000.
-16-
Guaranteeing equality of access to formal education is one of the goals of most societies.
Tables 7.8 and 7.9 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 60%. Attendance also increased for children in primary-school age, reaching almost 100%.
Again, 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 more than 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 20 points in the last decade, 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.24 Table 7.10 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.
8. 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
24
For technical details see Andersen (2001).
-17-
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 povertyalleviation 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 8.1 shows that coverage is decreasing in income. The
program seems to be far from universal in the poorest strata of the population. Around 30% of
those households in the first quintile of the equivalized income distribution receive transfers
from the PJH. That share falls to around 17% in quintile 2, and 6% in quintile 3. Around 9%
of Argentine households are covered by the program. Table 8.2 shows that around 14% of
households headed by a person with low education are beneficiaries of the PJH. The mean
transfer by household is $5.2. In quintile 1, the mean transfer is $22.8, while in quintile 5 is
basically zero (see Table 8.3).
The program seems to be reasonably targeted to the poor (see Tables 8.4 and 8.5).25 Around
80% of the beneficiaries of the PJH belong to the 40% poorest of the population. This degree
of targeting has increased over time.
9. AN ASSESSMENT
The social performance of Argentina in the last thirteen years have been very disappointing.
According to most indicators poverty dramatically 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 13 years. Social indicators
have significantly improved since 2003, but most of them are still around the values of year
2000, even when the current levels of economic activity are significantly higher than in that
year.
25
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).
-18-
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 over
the decade. 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 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.
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Household Welfare. Mimeo.
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Galiani, S., and Sanguinetti, P. (2003). The Impact of Trade Liberalization on Wage
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-21-
Table 3.1
Real income
Argentina, 1992-2005
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
17.5
45.1
68.6
93.1
121.0
155.5
203.7
276.3
399.4
932.1
231.3
EPHC - 28 cities
2005
29.7
65.8
96.9
130.1
168.0
213.4
266.3
347.9
490.4
1097.1
290.6
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
2003-2005
69.1
45.8
41.2
39.7
38.8
37.3
30.7
25.9
22.8
17.7
25.7
Source: Own calculations based on microdata from the EPH.
Table 4.1
Poverty lines
Argentina, 1992-2005
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)
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
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
Source: INDEC, WDI and own calculations.
Note 1: mean values for GBA
Note 2: For the EPHC, first half: April, second half: September.
-22-
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
(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
(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
Table 4.2
Poverty
Argentina, 1992-2005
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
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
3,021,707
2,917,955
2,221,433
2,009,830
1,809,907
1,521,238
7.9
7.6
5.7
5.2
4.6
3.9
3.9
3.8
2.8
2.5
2.2
1.8
2.9
2.9
2.1
1.8
1.6
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, 1992-2005
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 *
7,650,293
2003-II
7,325,050
2004-I
6,049,215
2004-II
5,492,855
2005-I
5,194,536
2005-II
4,538,654
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
19.9
19.1
15.6
14.2
13.2
11.6
8.8
8.5
6.7
6.0
5.5
4.6
5.6
5.5
4.2
3.7
3.3
2.7
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 4.4
Poverty
-23-
Argentina, 1992-2005
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 *
8,214,687
2003-II
7,869,947
2004-I
6,600,271
2004-II
5,830,357
2005-I
5,338,445
2005-II
4,771,484
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
21.4
20.5
17.0
15.0
13.6
12.2
9.4
9.1
7.1
6.2
5.7
4.9
5.9
5.8
4.4
3.8
3.4
2.9
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, 1992-2005
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 *
19,002,076
2003-II
18,348,115
2004-I
17,210,630
2004-II
15,582,682
2005-I
15,082,930
2005-II
13,287,919
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
49.5
47.8
44.4
40.2
38.4
33.8
23.8
22.9
20.2
18.0
16.6
14.6
15.0
14.4
12.2
10.8
9.9
8.6
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.
-24-
Table 4.6
Poverty
Argentina, 1992-2005
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 *
9,413,501
2003-II
9,477,571
2004-I
9,020,631
2004-II
9,611,727
2005-I
9,104,836
2005-II
9,636,760
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
24.5
24.7
23.2
24.8
23.2
24.5
11.1
11.3
10.2
10.5
9.9
10.2
7.0
7.2
6.2
6.3
5.9
6.0
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.
-25-
Table 5.1
Distribution of household per capita income
Share of deciles and income ratios
Argentina, 1992-2005
1
2
3
EPH-15 cities
1992
1.8
3.0
4.1
1993
1.7
3.0
4.1
1994
1.7
2.9
4.0
1995
1.4
2.7
3.7
1996
1.4
2.6
3.6
1997
1.4
2.6
3.6
1998
1.2
2.4
3.4
EPH - 28 cities
1998
1.3
2.4
3.4
1999
1.3
2.5
3.5
2000
1.2
2.3
3.3
2001
1.0
2.1
3.1
2002
1.0
2.0
3.0
2003
1.1
2.1
3.0
EPH-C
2003-II *
1.0
2.1
3.0
2003-II
1.0Gini 2.1 Theil
3.1
2004-I cities1.2
2.3
3.3
EPH-15
2004-II
1.1
3.3
1992
0.450 2.3 0.370
2005-I
1.2
2.4
3.4
1993
0.444
0.359
2005-II
1.2
2.3
3.4
1994
0.453
0.378
1998
1999
2000
2001
2002
2003
EPH-C
2003-II *
2003-II
2004-I
2004-II
2005-I
2005-II
0.502
0.491
0.504
0.522
0.533
0.528
0.472
0.443
0.464
0.497
0.530
0.519
4
Share of deciles
5
6
7
8
9
10
Income ratios
10/1
90/10
95/80
5.1
5.2
5.1
4.8
4.7
4.7
4.5
6.2
6.4
6.3
5.9
5.9
6.0
5.7
7.6
7.9
7.7
7.3
7.3
7.3
7.0
9.4
9.6
9.5
9.1
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
17.0
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
7.9
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
4.5
4.6
4.4
4.1
4.1
4.0
5.7
5.8
5.6
5.4
5.4
5.2
7.1
7.3
7.2
6.9
6.9
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
41.0
39.8
E(0)
38.6
37.9
0.355
37.8
0.352
37.6
39.3
38.1
E(2)
32.7
33.0
0.606
32.5
0.580
32.7
13.4
13.7
11.8
12.0
11.7
11.8
2.2
2.2
2.1
2.0
2.1
2.1
4.0
5.2
6.6
4.1CV 5.3 A(.5)
6.7
4.3
5.5
7.1
4.4
7.2
1.101 5.7 0.165
4.4
5.7
7.3
1.077
0.162
4.5
5.8
7.3
1.112
0.168
1.307
1.213
1.231
1.264
1.356
1.343
0.207
0.197
0.208
0.224
0.233
0.227
8.6
11.6 16.7
8.8
17.1
A(1) 11.9 A(2)
9.0
11.9 16.8
9.1
17.0
0.299 12.0 0.510
9.1
11.9 16.9
0.297
0.517
9.1
11.9 16.8
0.303
0.510
0.361
0.618
0.368
0.356
0.377
0.404
0.412
0.401
0.605
0.606
0.647
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
0.379
0.373
0.373
0.673
0.672
0.621
0.624
0.624
0.624
0.539
0.522
0.478
0.476
0.466
0.467
4.671
1.061
1.469
1.201
0.853
1.005
Sour1995
ce: Own 0.481
calculations
om
PH. 0.416
0.430based
1.2on
05 mic0rod
.190ata fr0.3
40the E
0.569
0.726
60en de
0.194
49 1; (090/1
.607 0)=i0.42
Note1996
1: (10/1)0.486
=income0.442
ratio be1.2
twe
ciles 100.3
and
nco9me ra0.793
tio between percentiles 90 and 10, and
0.484
46 entil0es
.190
46 .
0.586
0.424
0.656
(95/1997
80)=incom
e ratio b0.422
etween 1.1
perc
95 an0.3
d 80
1998
0.502
0.471
1.300
0.207
0.369
0.608
0.461
0.845
* co
mpu
using weights that ignore income non-response.
EPH
- 28ted
cities
Table 5.2
Distribution of household per capita income
0.537
0.625
3.056
0.244
0.417
Inequality indices
0.529
0.532
1.457
0.231
0.407
Argentina, 10.510
992-200.507
05 1.714 0.216 0.380
0.506
0.502
0.501
0.499
0.473
0.480
1.550
1.306
1.418
0.213
0.208
0.209
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.
-26-
Table 5.3
Distribution of equivalized household income
Share of deciles and income ratios
Argentina, 1992-2005
1
EPH-15 cities
1992
2.0
1993
1.9
1994
2.0
1995
1.6
1996
1.6
1997
1.6
1998
1.5
EPH - 28 cities
1998
1.5
1999
1.5
2000
1.3
2001
1.1
2002
1.2
2003
1.3
EPH-C
2003-II *
1.2
2003-II
1.2
2004-I
1.4
Gini
2004-II
1.3
EPH-15
2005-I cities 1.4
1992
0.430
2005-II
1.3
1993
0.424
1994
0.431
1995
0.460
1996
0.463
1997
0.461
1998
0.480
EPH - 28 cities
1998
0.478
1999
0.468
2000
0.483
2001
0.501
2002
0.512
2003
0.506
EPH-C
2003-II *
0.514
2003-II
0.507
2004-I
0.488
2004-II
0.483
2005-I
0.480
2005-II
0.480
Share of deciles
5
6
7
2
3
4
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
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
3.3
4.4
3.4
4.4
3.6
4.7
3.6 CV4.8
2.7
3.8
4.8
0.334 3.7 1.018
2.6
4.8
0.325
1.004
0.341
1.040
0.391
1.127
0.398
1.168
0.380
1.066
0.425
1.198
5.5
5.6
5.8
A(.5)
6.0
6.0
0.150
6.1
0.147
0.152
0.173
0.176
0.173
0.188
7.4
0.272
7.5
0.270
0.275
0.311
0.318
0.316
0.338
0.190
0.206
0.215
0.208
0.347
0.374
0.381
0.368
2.3
2.3
2.6
Theil
2.6
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
16.9
32.7
31.8
32.8
35.3
35.0
34.5
36.2
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
16.8
16.8
17.1
17.1
16.9
17.0
36.2
35.3
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
7.3
A(1)
7.4
8.8
11.6 16.5
8.9
11.8 16.9
9.2
11.9 16.6
9.2A(2) 12.0 E(0)
16.8
39.4
38.4
37.0
E(2)
36.2
9.3
11.9 16.7 36.1
0.46811.90.318
9.3
16.6 0.518
36.1
0.475
0.315
0.504
0.467
0.322
0.541
0.525
0.372
0.635
0.561
0.383
0.682
0.545
0.379
0.568
0.565
0.412
0.718
32.4
31.6
27.1
27.2
26.8
27.1
11.4
11.6
10.2
10.3
10.1
10.1
2.1
2.1
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.
0.424
1.203
0.187
0.335
0.561
0.409
0.724
127 tha
0.179
0.393
0.635
* computed using0.400
weig1.hts
t igno0.325
re inco0.564
me non-response.
0.422
0.456
0.488
0.473
1.149
1.194
1.287
1.261
0.606
0.635
0.617
0.595
0.426
0.467
0.479
0.460
0.660
0.713
0.829
0.795
0.584
0.582
0.585
0.485
0.471
0.429
0.426
0.417
0.421
3.825
0.925
1.184
1.021
0.759
1.027
Table 5.4
Distribution of equivalized household income
2.766
0.223
0.384
0.637
Inequality indices0.566
0.487
1.360
0.212
0.376
0.638
0.459
1.539
0.197
0.349
0.580
Argentina, 1992-2005
0.450
0.429
0.446
1.429
1.232
1.433
0.194
0.190
0.192
0.347
0.341
0.344
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.
-27-
Table 5.5
Distribution of equivalized household labor monetary income
Share of deciles and income ratios
Argentina, 1992-2005
1
2
EPH-15 cities
1992
2.0
3.4
1993
1.8
3.2
1994
1.9
3.3
1995
1.5
2.9
1996
1.5
2.8
1997
1.4
2.7
1998
1.3
2.6
EPH - 28 cities
1998
1.3
2.6
1999
1.4
2.7
2000
1.2
2.4
Gini
2001
1.0
2.2
EPH-15
cities
2002
1.1
2.0
1992
0.422
2003
0.9
2.1
1993
0.427
EPH-C
2004-II
1.00.4332.2
1994
2005-I
1.00.4702.3
1995
2005-II
1.0
2.3
3
4
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
3.6
3.7
3.5
Theil
3.3
2.9
0.317
2.9
Share of deciles
5
6
7
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
4.6
5.8
7.1
4.8
5.9
7.3
4.5
5.8
7.2
CV
A(.5)
4.3
5.4
7.0
4.1
5.3
6.8
0.959
0.145
4.0
5.2
6.8
9.7
9.8
9.6
9.0
9.2
9.4
8.9
Income ratios
10/1
90/10 95/80
8
9
10
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
31.7
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
37.3
36.2
37.1
E(0)
39.2
41.1
0.313
40.2
28.2
26.0
31.4
E(2)
38.5
38.7
0.460
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
9.0
11.7 16.9
9.1
11.9 17.0
9.1
12.0 17.1
A(1)
A(2)
8.8
11.8 17.1
8.6
11.3 16.9
0.26811.8 0.633
8.7
17.3
0.325
0.984
0.149
0.278
0.506
0.325
0.484
3.3
4.6
7.4
9.4
17.4 36.5
37.7
2.0
0.346
1.0605.9 0.154
0.28012.3 0.502
0.330
0.562 13.5
3.5
4.6
7.4
9.3
17.0 36.9
35.2
2.1
0.410
1.1645.8 0.182
0.32812.1 0.577
0.397
0.677 12.0
3.4
4.5
7.4
9.2
17.0 37.4
37.6
2.1
1996
0.474
0.421
1.2215.8 0.186
0.33512.0 0.590
0.407
0.745 12.7
Sour1997
ce: Own c0.469
alculati0.393
ons base1.081
d on mi0.179
crodata 0.330
from the0.574
EPH. 0.400 0.584
Note1998
1: (10/1)0.493
=income0.450
ratio be1.242
tween d0.199
eciles 10.357
0 and 1;0.603
(90/10)0.442
=income0.772
ratio between percentiles
EPH
- 28 cities e ratio between percentiles 95 and 80.
(95
/80)=incom
1998
0.492
0.450
1.249
0.198
0.355
0.597
0.437
0.780
1999
0.481
0.425
1.178
0.190
0.343
0.596
0.420
0.694
Table
20005.6 0.497
0.449
1.200
0.202
0.368
0.646
0.459
0.719
0.519
0.493
1.262
0.221
0.397
0.667
0.506
0.796
Dist2001
rib
ution
of
equ
iva
lize
d
h
ous
ehold
labor
monetary
income
2002
0.536
0.539
1.374
0.235
0.413
0.660
0.533
0.944
Inequality
indices
2003
0.535
0.533
1.354
0.236
0.422
0.698
0.547
0.917
EPH-Ctina, 1992-2005
Argen
2004-II
0.499
0.469
1.414
0.209
0.384
0.676
0.485
1.000
2005-I
0.497
0.453
1.211
0.205
0.377
0.677
0.473
0.733
2005-II
0.503
0.490
1.546
0.213
0.386
0.675
0.487
1.196
90 and 10, and
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.
Table 5.7
Distribution of household income
Gini coefficient
Argentina, 1992-2005
Per capita
income
EPH-15 cities
1992
0.450
1993
0.444
1994
0.453
1995
0.481
1996
0.486
1997
0.483
1998
0.502
EPH - 28 cities
1998
0.502
1999
0.491
2000
0.504
2001
0.522
2002
0.533
2003
0.528
EPH-C
2003-II *
0.537
2003-II
0.529
2004-I
0.510
2004-II
0.506
2005-I
0.502
2005-II
0.501
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
0.430
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
0.478
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
0.514
0.507
0.488
0.483
0.480
0.480
0.501
0.495
0.476
0.471
0.468
0.469
0.505
0.499
0.480
0.475
0.471
0.473
0.495
0.489
0.469
0.465
0.461
0.463
0.520
0.513
0.493
0.489
0.484
0.485
0.490
0.481
0.465
0.460
0.458
0.459
0.510
0.518
0.492
0.482
0.479
0.503
0.467
0.469
0.459
0.454
0.457
0.446
0.521
0.524
0.506
0.478
0.486
0.468
0.533
0.480
0.469
0.464
0.452
0.451
-28-
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 6.1
Hourly wages
By gender, age and education
Argentina, 1992-2005
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
2004-I
3.2
2004-II
3.2
2005-I
3.3
2005-II
3.7
with PJH
2002
2.8
2003
2.8
EPH-C
2003-II *
3.0
2003-II
3.1
2004-I
3.2
2004-II
3.2
2005-I
3.2
2005-II
3.6
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
3.3
3.1
3.2
3.8
3.2
3.2
3.3
3.3
3.6
1.8
1.8
1.9
2.0
2.1
3.4
3.4
3.3
3.5
3.8
3.9
6.1
6.5
3.5
5.9
2.0
2.1
2.1
2.2
2.2
2.6
2.8
2.8
2.8
2.9
5.0
4.9
5.4
5.4
6.3
2.7
2.6
3.0
2.9
1.7
1.7
3.0
3.0
3.4
3.4
1.8
1.8
2.4
2.4
4.8
4.6
3.1
3.2
3.2
3.0
3.2
3.7
3.0
3.1
3.2
3.3
3.3
3.6
1.8
1.8
1.8
1.9
2.0
2.1
3.2
3.3
3.3
3.3
3.5
3.8
3.6
3.8
6.0
6.4
3.4
5.9
2.0
2.0
2.1
2.0
2.2
2.2
2.5
2.6
2.8
2.7
2.8
2.9
4.9
5.0
4.9
5.4
5.3
6.3
Source: Own calculations based on microdata from the EPH.
-29-
Table 6.2
Hours of work
By gender, age and education
Argentina, 1992-2005
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
EPH-C
2003-II
40.8
2004-I
41.7
2004-II
41.4
2005-I
42.1
2005-II
42.0
with PJH
2002
39.5
2003
39.6
2003-II *
39.1
2003-II
39.2
2004-I
40.3
2004-II
40.3
2005-I
41.0
2005-II
41.2
Gender
Female
Male
(15-24)
Age
(25-64)
(65 +)
Low
Education
Mid
High
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
34.4
35.3
35.0
35.5
35.3
45.4
45.9
45.7
46.4
46.6
38.1
37.9
38.1
39.5
38.7
41.7
42.9
42.7
43.0
43.2
34.1
35.1
33.0
35.3
34.0
40.4
41.6
41.2
42.0
41.5
42.8
43.3
42.6
43.2
43.7
38.7
39.6
40.0
40.6
40.2
33.6
33.0
32.5
32.7
33.6
33.5
34.1
34.3
44.0
44.7
44.2
44.4
45.1
45.2
45.9
46.2
36.3
35.5
36.5
36.7
36.8
37.2
38.7
38.1
40.3
40.4
39.9
40.1
41.3
41.4
41.8
42.2
35.6
38.0
33.4
33.6
34.7
32.8
35.1
33.6
37.6
37.7
37.5
37.7
39.0
39.3
39.9
39.9
41.4
41.6
40.8
41.0
41.9
41.5
42.3
43.0
39.4
39.2
38.3
38.4
39.5
39.8
40.4
40.1
Source: Own calculations based on microdata from the EPH.
Table 6.3
Labor income
By gender, age and education
Argentina, 1992-2005
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
467.2
2005-I
494.8
2005-II
525.7
with PJH
2002
403.6
2003
423.4
EPH-C
2004-II
454.6
2005-I
482.7
2005-II
513.4
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
399.3
410.8
464.2
549.5
582.7
617.0
252.8
287.2
301.6
531.1
563.1
604.2
597.4
412.9
527.3
303.5
329.2
328.2
432.5
446.6
479.0
807.6
840.3
919.8
321.6
343.5
463.3
477.8
212.6
219.9
439.9
458.2
361.7
440.8
226.6
245.8
352.6
370.1
715.7
699.5
386.4
398.3
449.3
538.2
572.4
608.0
249.5
284.0
298.4
516.2
548.8
589.2
589.9
405.2
524.1
292.5
317.1
318.0
424.1
440.6
470.5
802.8
835.8
915.9
Source: Own calculations based on microdata from the EPH.
-30-
Table 6.4
Hourly wages
By type of work
Argentina, 1992-2005
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
6.8
3.1
3.0
2004-I
5.5
3.2
3.0
2004-II
5.1
3.2
3.1
2005-I
6.3
3.2
3.1
2005-II
9.5
3.4
3.2
with PJH
2002
6.8
2.7
2.7
2003
5.9
2.7
2.8
EPH-C
2003-II *
6.3
3.0
2.8
2003-II
6.8
3.0
3.0
2004-I
5.4
3.2
2.9
2004-II
5.1
3.1
3.0
2005-I
6.3
3.1
3.0
2005-II
9.4
3.4
3.1
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
6.8
5.5
5.1
6.3
9.5
3.4
3.7
3.6
3.5
3.7
4.2
4.2
4.1
4.4
4.9
6.4
6.0
6.0
6.0
6.3
2.0
2.0
2.0
2.1
2.1
2.5
2.5
2.6
2.5
2.6
6.8
5.9
3.0
3.0
3.1
3.1
5.9
6.1
1.9
1.8
2.2
2.2
6.3
6.8
5.4
5.1
6.3
9.4
3.3
3.4
3.7
3.6
3.4
3.7
3.9
4.0
4.1
4.0
4.2
4.8
6.2
6.4
5.9
6.0
6.0
6.3
2.0
2.0
2.0
2.0
2.1
2.1
2.4
2.5
2.5
2.5
2.5
2.6
Source: Own calculations based on microdata from the EPH.
Note: in 1992 to 1994 public sector wages refer only to the GBA.
Table 6.5
Hours of work
By type of work
Argentina, 1992-2005
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
EPH-C
2003-II
51.6
41.1
38.7
31.8
2004-I
53.0
42.1
38.9
30.1
2004-II
54.3
41.3
39.7
32.5
2005-I
53.2
42.2
39.9
35.5
2005-II
52.6
41.8
41.1
31.4
with PJH
2002
54.2
39.0
39.3
36.3
2003
54.8
39.3
38.4
38.7
2003-II *
51.9
38.8
39.0
31.9
2003-II
51.7
39.0
39.0
32.0
2004-I
53.1
40.2
39.1
29.9
2004-II
54.5
39.8
39.9
32.5
2005-I
53.2
40.7
40.0
36.4
2005-II
52.7
40.7
41.0
31.6
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
51.6
53.0
54.3
53.2
52.6
45.8
46.2
45.9
46.6
45.9
37.6
39.1
38.9
38.7
39.4
37.8
39.2
39.4
41.0
39.7
37.5
38.5
36.9
38.3
37.3
38.8
38.9
39.8
39.7
41.3
31.8
30.1
32.5
35.5
31.4
54.2
54.8
51.9
51.7
53.1
54.5
53.2
52.7
44.9
45.4
44.8
44.8
45.2
45.1
45.9
45.3
32.6
32.1
32.1
32.3
33.7
34.4
34.5
36.3
40.0
39.3
37.5
37.8
39.6
39.4
41.0
39.6
38.1
38.5
37.2
37.4
38.3
36.9
38.1
37.0
39.2
38.2
39.2
39.2
39.1
40.0
39.8
41.3
36.3
38.7
31.9
32.0
29.9
32.5
36.4
31.6
Source: Own calculations based on microdata from the EPH.
Note: in 1992 to 1994 public sector hours of work refer only to the GBA.
-31-
Table 6.6
Labor income
By type of work
Argentina, 1992-2005
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
1004.3
498.0
413.8
2005-I
1231.5
517.9
414.9
2005-II
1507.6
548.0
435.2
with PJH
2002
1218.3
404.9
328.4
2003
1129.9
431.9
342.6
EPH-C
2004-II
998.4
484.5
404.3
2005-I
1229.9
504.8
407.9
2005-II
1501.1
536.0
424.9
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
1004.3
1231.5
1507.6
605.3
625.2
628.8
633.4
653.3
749.6
870.0
901.7
878.8
272.6
295.3
291.6
330.5
320.1
351.0
1218.3
1129.9
519.2
520.2
416.4
528.1
833.4
833.5
247.9
244.8
255.0
263.6
998.4
1229.9
1501.1
599.4
618.0
622.5
587.2
608.9
710.8
862.5
901.0
872.7
268.6
291.6
287.6
323.4
315.2
343.2
Source: Own calculations based on microdata from the EPH.
Note: in 1992 to 1994 public sector earnings refer only to the GBA.
Table 6.7
Hourly wages
By sector
Argentina, 1992-2005
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
5.3
2004-I
3.9
2004-II
4.3
2005-I
4.3
2005-II
with PJH
2002
2.6
2003
2.6
EPH-C
2003-II *
4.6
2003-II
5.1
2004-I
3.9
2004-II
4.2
2005-I
4.1
2005-II
Industry
low tech
Industry
high tech
Utilities &
Construction Commerce transportation
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
2.1
2.2
2.4
2.6
2.5
3.4
3.7
4.2
3.9
3.8
2.4
2.4
2.4
2.4
2.4
2.1
2.2
2.2
2.4
2.5
3.1
3.6
3.1
3.0
3.2
4.5
4.5
4.9
4.8
5.7
4.4
4.4
4.2
4.3
4.9
4.4
4.4
4.2
4.3
4.6
2.1
2.1
2.2
2.1
2.1
2.6
2.4
2.8
3.0
2.2
2.0
1.9
1.9
2.8
2.5
4.7
4.3
2.9
3.2
3.5
3.4
2.1
2.1
2.0
2.1
2.2
2.4
2.5
2.5
3.3
3.3
3.7
4.2
3.9
3.8
2.3
2.3
2.4
2.3
2.3
2.4
2.1
2.1
2.2
2.2
2.4
2.5
2.9
3.0
3.5
3.1
2.9
3.1
4.3
4.4
4.5
4.9
4.8
5.7
4.0
4.2
4.2
4.1
4.2
4.8
4.2
4.3
4.4
4.1
4.2
4.6
2.1
2.1
2.1
2.2
2.1
2.1
Source: Own calculations based on microdata from the EPH.
-32-
Skilled
services
Public
Education &
administration
Health
Domestic
servants
Table 6.8
Hours of work
By sector
Argentina, 1992-2005
Primary
Industry
Industry
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
EPH-C
2003-II
47.4
2004-I
48.4
2004-II
50.4
2005-I
49.7
2005-II
45.3
with PJH
2002
47.2
2003
43.8
2003-II *
38.7
2003-II
38.6
2004-I
42.6
2004-II
42.0
2005-I
43.5
2005-II
41.7
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
42.7
44.3
42.0
43.9
44.2
43.1
44.9
44.6
45.2
45.0
38.6
39.1
39.8
41.4
41.4
45.8
46.3
46.6
46.6
46.3
50.4
50.9
50.0
51.2
51.8
42.9
41.5
41.7
42.2
42.6
42.0
43.0
43.3
42.9
43.7
33.9
35.3
35.7
35.6
36.2
28.5
28.6
25.7
29.1
26.6
46.2
42.6
41.9
42.1
42.7
40.8
42.8
43.3
43.3
44.7
42.8
42.9
44.7
44.5
45.1
45.0
38.1
37.9
38.3
38.4
39.2
39.8
41.2
41.0
46.4
46.3
45.5
45.7
46.2
46.6
46.4
46.2
49.7
52.6
50.5
50.4
50.9
50.1
51.2
51.8
42.9
42.7
42.8
42.8
41.4
41.6
42.1
42.5
33.8
35.1
36.5
36.8
37.7
39.5
39.7
41.6
33.1
32.5
31.0
31.2
32.8
33.3
33.4
34.3
25.5
26.0
28.0
28.4
28.4
26.0
29.1
26.7
Construction Commerce transportation
Source: Own calculations based on microdata from the EPH.
Table 6.9
Labor income
By sector
Argentina, 1992-2005
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
856.3
2005-I
848.4
2005-II
with PJH
2002
527.1
2003
620.1
EPH-C
2004-II
804.8
2005-I
755.3
2005-II
1662.2
Industry
low tech
Industry
high tech
Utilities &
Construction Commerce transportation
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
411.6
460.3
448.4
668.1
682.8
674.3
344.9
364.6
368.6
395.1
425.2
445.3
568.8
571.4
615.3
734.7
778.9
852.0
711.5
718.5
839.6
568.1
575.0
626.6
175.8
188.4
180.9
433.2
395.5
484.8
520.7
245.1
263.2
312.7
321.7
465.1
473.2
770.6
696.3
404.6
598.5
423.7
459.6
151.3
152.1
402.6
447.2
441.0
665.2
678.2
670.1
338.6
358.9
362.4
388.5
420.8
438.1
562.8
565.3
611.1
730.7
774.6
848.1
667.2
681.5
802.0
544.0
553.6
606.3
174.0
186.6
180.2
Source: Own calculations based on microdata from the EPH.
Table 6.10
Distribution of labor income
-33-
Skilled
services
Public
Education &
Health
administration
Domestic
servants
Shares
Argentina, 1992-2005
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
71.9
18.8
9.3
1999
72.5
19.1
8.4
2000
72.6
18.8
8.6
2001
72.6
18.3
9.2
2002
72.6
18.3
9.1
2003
70.7
20.6
8.7
EPH-C
2004-II
72.8
18.0
9.2
2005-I
73.2
17.3
9.5
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
Source: Own calculations based on microdata from the EPH.
Table 6.11
Distribution of wages (primary activity)
Gini coefficient
Argentina, 1992-2005
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
EPH - 28 cities
1998
0.435
1999
0.424
2000
0.433
2001
0.445
2003
0.440
EPH-C
2003-II
0.453
2004-I
0.455
2004-II
0.442
2005-I
0.429
2005-II
0.458
with PJH
2002
0.453
2003
0.439
EPH-C
2003-II *
0.454
2003-II
0.458
2004-I
0.460
2004-II
0.443
2005-I
0.431
2005-II
0.459
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
0.440
0.408
0.412
0.413
0.349
0.366
0.338
0.336
0.342
0.385
0.381
0.364
0.355
0.346
0.428
0.435
0.386
0.403
0.393
0.465
0.460
0.364
0.326
0.398
0.391
0.441
0.458
0.438
0.443
0.442
0.411
0.416
0.415
0.349
0.349
0.367
0.343
0.343
0.345
0.391
0.390
0.382
0.364
0.357
0.348
0.425
0.430
0.434
0.386
0.406
0.395
Source: Own calculations based on microdata from the EPH.
-34-
Table 6.12
Correlations hours of work-hourly wages
Argentina, 1992-2005
All workers
EPH-15 cities
1992
-0.18
1993
-0.22
1994
-0.23
1995
-0.18
1996
-0.19
1997
-0.21
1998
-0.16
EPH - 28 cities
1998
-0.16
1999
-0.19
2000
-0.19
2001
-0.18
2003
-0.16
EPH-C
2004-II
-0.10
2005-I
-0.18
2005-II
-0.07
with PJH
2002
-0.13
2003
-0.16
EPH-C
2004-II
-0.09
2005-I
-0.17
2005-II
-0.07
Urban
salaried
workers
-0.17
-0.17
-0.20
-0.18
-0.17
-0.20
-0.17
-0.17
-0.18
-0.17
-0.18
-0.17
-0.08
-0.17
-0.10
-0.10
-0.16
-0.08
-0.16
-0.09
Source: Own calculations based on microdata from the EPH.
-35-
Table 6.13
Ratio of hourly wages by educational group
Prime-age males
Argentina, 1992-2005
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
EPH-C
2003-II
1.93
2004-I
1.88
2004-II
1.77
2005-I
1.87
2005-II
1.92
with PJH
2002
2.09
2003
2.14
2003-II *
1.93
2003-II
1.97
2004-I
1.91
2004-II
1.78
2005-I
1.89
2005-II
1.93
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
2.60
2.49
2.46
2.46
2.56
1.35
1.33
1.39
1.32
1.33
3.02
3.09
2.61
2.69
2.55
2.52
2.49
2.57
1.44
1.45
1.35
1.36
1.34
1.41
1.32
1.33
Source: Own calculations based on microdata from the EPH.
-36-
Table 6.14
Mincer equation
Estimated coefficients of educational dummies
Argentina, 1992-2005
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
EPH-C
2003-II
0.104
0.408
0.680
2004-I
0.174
0.394
0.678
2004-II
0.183
0.390
0.548
2005-I
0.115
0.358
0.669
2005-II
0.129
0.390
0.653
with PJH
2002
0.306
0.472
0.825
2003
0.240
0.409
0.804
EPH-C
2003-II *
0.126
0.402
0.688
2003-II
0.084
0.350
0.693
2004-I
0.173
0.399
0.677
2004-II
0.156
0.382
0.545
2005-I
0.098
0.366
0.677
2005-II
0.181
0.392
0.650
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
0.159
0.125
0.133
0.159
0.126
0.420
0.297
0.408
0.247
0.377
0.682
0.374
0.588
0.393
0.667
0.094
0.194
0.280
0.140
0.106
0.403
0.318
0.328
0.321
0.371
0.601
0.671
0.588
0.612
0.626
0.196
0.166
0.157
0.204
0.012
0.303
0.269
0.314
0.302
0.336
0.631
0.607
0.580
0.559
0.603
0.041
0.083
0.432
0.344
0.300
0.676
0.211
0.233
0.344
0.346
0.753
0.737
0.029
-0.136
0.384
0.349
0.602
0.585
0.176
0.132
0.076
0.140
0.100
0.058
0.312
0.325
0.251
0.395
0.232
0.300
0.377
0.647
0.367
0.573
0.389
0.492
0.101
0.100
0.195
0.268
0.134
0.119
0.383
0.392
0.322
0.333
0.327
0.375
0.610
0.616
0.673
0.595
0.619
0.623
0.194
0.196
0.172
0.143
0.159
0.006
0.322
0.322
0.270
0.316
0.322
0.338
0.628
0.632
0.610
0.582
0.558
0.609
Source: Own calculations based on microdata from the EPH.
Table 6.15
Mincer equation
Dispersion in unobservables and gender wage gap
Argentina, 1992-2005
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
EPH-C
2003-II
0.680
0.757
0.617
0.587
2004-I
0.680
0.942
0.599
0.577
2004-II
0.695
0.679
0.584
0.562
2005-I
0.638
0.890
0.586
0.581
2005-II
0.629
0.688
0.570
0.574
with PJH
2002
0.695
0.878
0.574
0.530
2003
0.661
0.633
0.582
0.522
EPH-C
2003-II *
0.683
0.885
0.617
0.591
2003-II
0.720
0.769
0.617
0.591
2004-I
0.689
0.958
0.602
0.577
2004-II
0.712
0.689
0.586
0.566
2005-I
0.643
0.889
0.590
0.585
2005-II
0.637
0.842
0.572
0.580
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
0.934
0.890
0.904
0.889
0.882
0.868
0.892
0.932
0.929
0.895
0.900
0.887
0.877
Source: Own calculations based on microdata from the EPH.
-37-
Table 6.16
Share of adults in the labor force
Argentina, 1992-2005
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
EPH-C
2003-II
0.599
2004-I
0.602
2004-II
0.605
2005-I
0.599
2005-II
0.608
with PJH
2002
0.580
2003
0.577
2003-II
0.614
2004-I
0.616
2004-II
0.618
2005-I
0.610
2005-II
0.617
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
0.466
0.469
0.462
0.448
0.450
0.767
0.765
0.771
0.764
0.772
0.149
0.161
0.159
0.168
0.176
0.620
0.619
0.625
0.613
0.629
0.927
0.926
0.929
0.927
0.926
0.685
0.690
0.698
0.690
0.702
0.773
0.768
0.782
0.773
0.769
0.853
0.849
0.862
0.855
0.870
0.414
0.414
0.482
0.479
0.471
0.456
0.457
0.749
0.744
0.776
0.774
0.780
0.769
0.776
0.125
0.135
0.152
0.164
0.161
0.172
0.179
0.600
0.596
0.641
0.641
0.646
0.629
0.643
0.915
0.909
0.929
0.927
0.930
0.928
0.926
0.684
0.662
0.708
0.714
0.720
0.707
0.713
0.752
0.760
0.781
0.776
0.789
0.776
0.774
0.849
0.844
0.855
0.849
0.862
0.856
0.870
Source: Own calculations based on microdata from the EPH.
-38-
Table 6.17
Share of adults employed
Argentina, 1992-2005
Adults (25-64)
Total
15 main cities
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
EPH-C
2003-II
0.501
2004-I
0.509
2004-II
0.525
2005-I
0.521
2005-II
0.542
with PJH
2002
0.476
2003
0.487
2003-II
0.519
2004-I
0.526
2004-II
0.540
2005-I
0.533
2005-II
0.552
Age
(15-24) (25-64) (65 +)
Gender
Female Male
Low
Education
Medium High
0.424
0.397
0.389
0.353
0.341
0.368
0.354
0.651
0.651
0.632
0.618
0.617
0.645
0.660
0.107
0.113
0.083
0.080
0.090
0.106
0.122
0.459
0.466
0.451
0.451
0.446
0.476
0.496
0.869
0.861
0.835
0.806
0.804
0.830
0.846
0.572
0.567
0.550
0.538
0.529
0.563
0.570
0.672
0.669
0.640
0.632
0.632
0.651
0.670
0.816
0.816
0.812
0.796
0.769
0.800
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
0.307
0.317
0.330
0.321
0.339
0.674
0.679
0.696
0.690
0.711
0.128
0.141
0.144
0.150
0.161
0.530
0.534
0.549
0.540
0.566
0.831
0.837
0.853
0.853
0.868
0.576
0.598
0.615
0.612
0.633
0.678
0.674
0.700
0.694
0.702
0.783
0.778
0.807
0.796
0.827
0.275
0.275
0.327
0.331
0.341
0.331
0.346
0.641
0.655
0.688
0.691
0.707
0.698
0.716
0.108
0.121
0.131
0.145
0.147
0.154
0.164
0.516
0.533
0.558
0.561
0.575
0.559
0.581
0.780
0.793
0.836
0.840
0.857
0.855
0.868
0.565
0.572
0.608
0.630
0.643
0.635
0.647
0.641
0.658
0.690
0.685
0.709
0.699
0.708
0.762
0.779
0.785
0.778
0.808
0.796
0.827
Source: Own calculations based on microdata from the EPH.
-39-
Table 6.18
Unemployment rates
Argentina, 1992-2005
Adults (25-64)
Total
(15-24)
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
EPH-C
2003-II
0.164
2004-I
0.154
2004-II
0.133
2005-I
0.131
2005-II
0.109
with PJH
2002
0.179
2003
0.157
2003-II
0.154
2004-I
0.146
2004-II
0.126
2005-I
0.125
2005-II
0.106
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
0.342
0.324
0.285
0.283
0.248
0.121
0.113
0.098
0.096
0.079
0.137
0.121
0.092
0.107
0.084
0.144
0.136
0.121
0.118
0.100
0.104
0.096
0.081
0.080
0.063
0.159
0.133
0.119
0.113
0.098
0.124
0.122
0.106
0.103
0.087
0.082
0.083
0.063
0.070
0.049
0.335
0.335
0.322
0.309
0.276
0.274
0.242
0.145
0.119
0.114
0.107
0.093
0.092
0.077
0.136
0.100
0.138
0.118
0.090
0.104
0.084
0.140
0.106
0.130
0.124
0.111
0.110
0.096
0.148
0.128
0.101
0.094
0.079
0.078
0.062
0.174
0.136
0.141
0.118
0.107
0.103
0.093
0.148
0.134
0.116
0.117
0.102
0.100
0.085
0.102
0.078
0.082
0.084
0.063
0.070
0.049
Source: Own calculations based on microdata from the EPH.
Table 6.19
Duration of unemployment
Argentina, 1992-2005
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
EPH-C
2003-II
11.7
2004-I
10.6
2004-II
10.0
2005-I
9.8
2005-II
10.2
with PJH
2002
8.9
2003
8.5
2003-II
11.7
2004-I
10.7
2004-II
10.0
2005-I
9.8
2005-II
10.2
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
10.6
9.2
9.2
8.4
8.8
12.5
11.6
10.6
10.7
11.0
13.2
11.1
9.8
11.4
13.5
13.9
13.3
11.9
12.0
12.6
11.0
9.7
9.3
9.3
9.1
12.3
10.5
9.6
9.5
10.5
11.9
11.8
11.1
11.7
10.9
13.6
12.7
11.6
10.8
12.2
8.7
7.6
10.6
9.2
9.2
8.4
8.8
8.8
8.9
12.4
11.6
10.6
10.7
11.0
18.4
11.1
13.0
11.1
9.8
11.4
13.7
11.1
11.8
13.8
13.4
12.0
12.0
12.6
7.1
7.2
11.0
9.8
9.2
9.3
9.1
7.2
7.9
12.4
10.7
9.6
9.5
10.6
9.4
9.3
11.8
11.9
11.2
11.8
10.9
11.1
10.2
13.5
12.7
11.7
10.7
12.2
Source: Own calculations based on microdata from the EPH.
-40-
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
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.
Table 6.20
Labor force, employment rate and unemployment rate
Argentina, 2003-2005
Encuesta Permanente de Hogares Continua
Unemployment
24.3
21.0
19.6
17.7
17.4
17.4
15.7
14.5
14.9
13.9
12.5
Source: INDEC, boletines de prensa.
Table 6.21
Age, gender and educational structure of employment
Argentina, 1992-2005
Gender
Female
Male
15 main cities
1992
1993
1994
1995
1996
1997
1998
28 main cities
1998
1999
2000
2001
2003
EPH-C
2003-II
2004-I
2004-II
2005-I
2005-II
with PJH
2002
2003
2003-II
2004-I
2004-II
2005-I
2005-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
40.1
40.0
40.7
59.5
59.6
59.9
60.0
59.3
0.6
0.7
0.6
0.6
0.6
17.8
18.1
18.0
17.8
17.6
35.9
36.5
36.8
36.5
37.5
41.8
40.6
40.6
40.8
40.0
3.9
4.1
4.0
4.2
4.3
28.6
29.4
31.8
31.6
31.4
39.2
38.7
40.3
40.0
39.2
32.2
32.0
27.9
28.4
29.4
42.2
42.9
42.5
42.6
42.1
41.8
42.3
57.8
57.1
57.5
57.4
57.9
58.2
57.7
0.3
0.1
0.5
0.6
0.6
0.5
0.6
17.4
14.1
18.0
18.0
17.8
17.7
17.3
37.9
41.0
36.8
37.3
37.5
37.1
38.0
41.3
41.3
41.0
40.2
40.3
40.6
39.9
3.1
3.5
3.7
3.9
3.8
4.1
4.2
34.2
32.7
30.7
31.6
34.0
33.9
33.1
38.0
38.8
39.1
38.5
39.8
39.3
38.9
27.8
28.4
30.2
29.9
26.2
26.8
28.0
Source: Own calculations based on microdata from the EPH.
-41-
Table 6.22
Regional structure of employment
Argentina, 1992-2005
15 main cities
1992
1993
1994
1995
1996
1997
1998
28 main cities
1998
1999
2000
2001
2003
EPH-C
2003-II
2004-I
2004-II
2005-I
2005-II
with PJH
2002
2003
2003-II
2004-I
2004-II
2005-I
2005-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
56.2
56.5
56.6
55.8
56.5
22.5
22.3
22.3
22.7
22.4
6.3
6.4
6.1
6.3
6.0
8.6
8.3
8.5
8.6
8.5
2.4
2.5
2.5
2.5
2.5
4.1
4.1
4.0
4.2
4.1
55.4
55.2
55.7
55.9
56.2
55.5
56.0
22.6
22.6
22.3
22.1
22.1
22.5
22.4
6.3
6.1
6.3
6.4
6.1
6.3
6.1
8.4
8.6
9.0
8.7
8.8
8.9
8.8
2.8
2.9
2.4
2.5
2.5
2.5
2.4
4.6
4.6
4.4
4.4
4.3
4.4
4.3
Source: Own calculations based on microdata from the EPH.
-42-
Table 6.23
Structure of employment
By type of work
Argentina, 1992-2005
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
EPH-C
2003-II
4.1
71.9
22.2
1.8
2004-I
4.1
73.0
21.2
1.7
2004-II
4.4
72.9
21.3
1.4
2005-I
4.0
73.7
21.2
1.1
2005-II
4.3
73.9
20.5
1.3
with PJH
2002
4.0
72.0
23.0
1.0
2003
3.8
71.8
23.2
1.1
2003-II
3.8
73.7
20.9
1.7
2004-I
3.8
74.5
20.2
1.6
2004-II
4.1
74.1
20.4
1.4
2005-I
3.8
74.7
20.4
1.1
2005-II
4.1
74.5
20.2
1.2
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
33.1
34.0
35.5
52.3
51.3
51.1
50.5
48.9
16.5
15.4
15.8
15.5
15.6
28.8
29.3
29.8
31.9
31.8
33.0
34.5
48.8
48.5
49.1
48.8
49.0
48.7
47.9
22.4
22.2
21.1
19.3
19.2
18.4
17.6
Labor category
Salaried workers
Self-employed SalariedSelf-employeWorkers with
Entrepreneurs Large firms Public sector professionals Small firms Unskilled zero income
(i)
(ii)
(iii)
(iv)
(v)
(vi)
(vii)
15 main cities
1995
5.2
34.1
15.3
3.3
20.3
20.5
1.5
1996
4.6
33.4
15.2
3.2
22.6
19.2
1.7
1997
4.9
35.0
15.3
3.0
21.8
18.5
1.4
1998
4.8
35.4
15.2
3.0
22.2
18.1
1.3
28 main cities
1998
4.7
33.5
16.0
3.0
22.3
19.1
1.4
1999
4.6
33.2
16.1
2.9
22.4
19.3
1.4
2000
4.8
31.6
16.5
2.9
22.9
20.1
1.2
2001
4.5
30.5
17.3
3.1
22.6
21.0
1.0
2003
4.3
29.8
17.2
3.8
22.0
21.6
1.3
EPH-C
2003-II
4.3
29.5
16.5
3.3
24.5
20.0
1.9
2004-I
4.3
31.7
15.4
3.3
24.6
19.0
1.7
2004-II
4.6
31.5
15.7
3.5
24.2
18.9
1.5
2005-I
4.2
32.4
15.4
3.8
24.4
18.5
1.2
2005-II
4.5
33.8
15.5
3.6
23.3
18.0
1.3
with PJH
2002
4.1
27.5
22.2
3.3
21.7
20.3
1.1
2003
4.0
28.3
21.7
3.6
21.0
20.3
1.2
2003-II
4.0
28.2
21.0
3.1
23.1
18.9
1.7
2004-I
4.0
30.4
19.3
3.1
23.6
18.1
1.6
2004-II
4.3
30.3
19.2
3.3
23.3
18.1
1.4
2005-I
4.0
31.4
18.3
3.6
23.7
17.9
1.1
2005-II
4.3
32.9
17.6
3.4
22.8
17.7
1.3
Source: Own calculations based on microdata from the EPH.
Table 6.24
Structure of employment
Share of informal workers (productive definition)
Argentina, 1992-2005
-43-
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
EPH-C
2003-II
0.464
2004-I
0.453
2004-II
0.445
2005-I
0.441
2005-II
0.426
with PJH
2002
0.430
2003
0.425
2003-II
0.437
2004-I
0.433
2004-II
0.429
2005-I
0.427
2005-II
0.418
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
0.578
0.582
0.547
0.520
0.513
0.433
0.416
0.414
0.415
0.396
0.614
0.645
0.647
0.635
0.625
0.455
0.440
0.438
0.450
0.415
0.418
0.400
0.397
0.390
0.383
0.646
0.615
0.616
0.616
0.603
0.507
0.482
0.463
0.462
0.447
0.170
0.168
0.147
0.155
0.143
0.588
0.616
0.608
0.556
0.553
0.572
0.561
0.510
0.499
0.490
0.553
0.536
0.532
0.548
0.524
0.499
0.500
0.400
0.399
0.409
0.398
0.399
0.403
0.391
0.632
0.579
0.598
0.635
0.638
0.623
0.617
0.370
0.375
0.413
0.403
0.406
0.422
0.400
0.422
0.417
0.407
0.395
0.394
0.388
0.384
0.563
0.564
0.570
0.549
0.559
0.563
0.572
0.438
0.444
0.477
0.461
0.448
0.451
0.437
0.159
0.156
0.171
0.168
0.147
0.155
0.144
0.506
0.498
0.511
0.547
0.562
0.511
0.526
0.588
0.568
0.547
0.549
0.499
0.492
0.483
Source: Own calculations based on microdata from the EPH.
Norte: Informal=salaried workers in small firms, non-professional self-employed and zero-income workers
Table 6.25
Structure of employment
Share of informal workers (social-protection definition)
Argentina, 1992-2005
15 main cities
1992
1993
1994
1995
1996
1997
1998
28 main cities
1998
1999
2000
2001
2003
EPH-C
2003-II
2004-I
2004-II
2005-I
2005-II
with PJH
2002
2003
2003-II
2004-I
2004-II
2005-I
2005-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
0.437
0.433
0.435
0.430
0.423
0.708
0.676
0.690
0.657
0.642
0.374
0.371
0.374
0.376
0.368
0.621
0.594
0.592
0.535
0.588
0.420
0.408
0.408
0.429
0.402
0.338
0.343
0.348
0.336
0.341
0.566
0.540
0.571
0.551
0.561
0.390
0.388
0.353
0.359
0.360
0.205
0.212
0.203
0.221
0.196
0.703
0.698
0.712
0.659
0.643
0.711
0.663
0.676
0.655
0.642
0.441
0.449
0.494
0.486
0.483
0.474
0.458
0.692
0.691
0.738
0.702
0.708
0.676
0.658
0.387
0.398
0.438
0.433
0.431
0.427
0.410
0.498
0.538
0.644
0.613
0.612
0.560
0.603
0.430
0.436
0.506
0.497
0.493
0.502
0.469
0.349
0.363
0.379
0.378
0.377
0.364
0.359
0.585
0.598
0.647
0.626
0.645
0.625
0.616
0.383
0.393
0.453
0.444
0.403
0.396
0.398
0.178
0.197
0.222
0.223
0.212
0.230
0.203
0.700
0.713
0.751
0.741
0.742
0.695
0.670
0.685
0.672
0.728
0.674
0.687
0.663
0.650
Source: Own calculations based on microdata from the EPH.
Norte: Informal= Absence of right to have a pension when retired.
Table 6.26
Structure of employment
-44-
By sector
Argentina, 1992-2005
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
1.4
1.5
1.3
1.1
1.3
7.9
7.8
7.9
7.7
7.6
6.6
6.9
7.1
7.3
6.9
7.3
7.9
8.0
8.1
8.7
24.8
25.0
25.4
24.0
24.3
7.7
7.4
7.8
7.6
7.6
9.8
9.8
9.3
10.5
9.9
7.6
7.3
7.2
7.1
7.2
18.9
18.7
18.4
18.9
18.9
8.0
7.7
7.7
7.8
7.6
1.4
1.4
1.7
1.7
1.5
1.4
1.4
5.5
6.1
7.7
7.8
7.9
7.7
7.6
7.3
6.7
6.1
6.4
6.6
6.9
6.6
6.7
6.5
7.1
7.6
7.8
8.0
8.6
21.8
22.0
23.3
23.7
24.2
23.1
23.6
7.6
7.6
7.1
6.9
7.4
7.2
7.3
9.1
9.4
9.0
9.1
8.7
9.9
9.4
10.3
9.1
8.9
8.7
8.1
7.7
7.6
23.3
24.1
21.3
20.5
20.2
20.4
20.3
6.9
7.0
7.6
7.5
7.5
7.7
7.7
15 main cities
1992
1993
1994
1995
1996
1997
1998
28 main cities
1998
1999
2000
2001
2003
EPH-C
2003-II
2004-I
2004-II
2005-I
2005-II
with PJH
2002
2003
2003-II
2004-I
2004-II
2005-I
2005-II
Utilities &
Construction Commerce transportation
Skilled
services
Public
Education &
administration
Health
Domestic
servants
Source: Own calculations based on microdata from the EPH.
Table 6.27
Structure of employment
By sector (CIIU -1 digit)
Argentina, 1992-2005
Agro
15 main cities
1992
1993
1994
1995
1996
1997
1998
28 main cities
1998
1999
2000
2001
2003
EPH-C
2003-II
2004-I
2004-II
2005-I
2005-II
with PJH
2002
2003
2003-II
2004-I
2004-II
2005-I
2005-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
1.0
0.8
0.7
0.9
0.1
0.1
0.1
0.1
0.1
0.3
0.4
0.4
0.3
0.3
14.5
14.7
15.0
14.9
14.5
0.6
0.5
0.5
0.5
0.5
7.3
7.9
8.0
8.1
8.7
21.9
21.4
21.9
20.4
20.9
2.9
3.6
3.5
3.5
3.4
7.1
6.9
7.3
7.1
7.1
1.8
1.8
1.6
1.8
1.8
8.0
8.0
7.7
8.7
8.1
7.5
7.2
7.2
7.1
7.2
8.2
7.6
7.4
7.4
7.7
5.5
5.2
5.6
5.6
5.5
5.3
5.8
5.4
5.9
5.7
8.0
7.7
7.7
7.8
7.6
0.1
0.0
0.0
0.0
0.0
1.4
1.5
1.3
1.2
1.0
1.0
1.0
0.1
0.1
0.1
0.1
0.1
0.3
0.4
0.4
0.3
0.3
13.1
13.1
13.8
14.3
14.5
14.6
14.2
0.5
0.5
0.5
0.5
0.5
0.5
0.5
6.8
6.6
7.1
7.6
7.8
8.0
8.6
19.3
19.8
20.6
20.3
20.8
19.6
20.3
2.9
2.7
2.8
3.4
3.4
3.5
3.3
7.3
7.2
6.6
6.4
6.9
6.7
6.8
2.3
2.2
1.7
1.7
1.5
1.7
1.7
6.9
7.4
7.4
7.4
7.2
8.2
7.7
10.5
9.2
8.9
8.7
8.1
7.7
7.5
9.5
9.8
8.4
7.7
7.6
7.4
7.7
6.6
6.9
7.3
6.7
7.0
6.7
6.5
5.9
5.9
5.6
6.1
5.7
6.4
6.0
7.0
7.1
7.6
7.5
7.5
7.7
7.7
0.0
0.0
0.0
0.0
0.0
Source: Own calculations based on microdata from the EPH.
-45-
Table 6.28
Child labor
By equivalized household income quintiles
Argentina, 1992-2005
Total
15 main cities
1992
1993
1994
1995
1996
1997
1998
28 main cities
1998
1999
2000
2001
2003
EPH-C
2003-II
2004-I
2004-II
2005-I
2005-II
with PJH
2002
2003
2003-II
2004-I
2004-II
2005-I
2005-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
0.016
0.020
0.015
0.015
0.019
0.012
0.012
0.012
0.012
0.012
0.019
0.028
0.018
0.018
0.025
0.024
0.030
0.026
0.022
0.033
0.011
0.022
0.012
0.007
0.013
0.023
0.021
0.008
0.016
0.004
0.013
0.013
0.007
0.002
0.008
0.001
0.003
0.001
0.002
0.012
0.007
0.003
0.016
0.020
0.015
0.015
0.019
0.004
0.001
0.012
0.012
0.012
0.012
0.012
0.009
0.005
0.019
0.028
0.019
0.018
0.025
0.009
0.004
0.024
0.030
0.026
0.022
0.033
0.008
0.002
0.011
0.022
0.012
0.007
0.013
0.002
0.002
0.023
0.021
0.008
0.016
0.004
0.001
0.000
0.014
0.013
0.007
0.002
0.008
0.004
0.000
0.001
0.003
0.001
0.002
0.012
Source: Own calculations based on microdata from the EPH.
Table 6.29
Right to receive social security (pensions)
By gender and education
Argentina, 1992-2005
Total
15 main cities
1992
1993
1994
1995
1996
1997
1998
28 main cities
1998
1999
2000
2001
2003
EPH-C
2003-II
2004-I
2004-II
2005-I
2005-II
with PJH
2002
2003
2003-II
2004-I
2004-II
2005-I
2005-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
0.564
0.569
0.566
0.571
0.579
0.292
0.324
0.310
0.343
0.358
0.626
0.629
0.626
0.624
0.632
0.379
0.406
0.408
0.465
0.412
0.580
0.592
0.592
0.571
0.598
0.662
0.657
0.652
0.664
0.659
0.434
0.460
0.429
0.449
0.439
0.610
0.612
0.647
0.641
0.640
0.795
0.788
0.797
0.779
0.804
0.560
0.552
0.507
0.516
0.518
0.527
0.543
0.308
0.309
0.262
0.298
0.292
0.324
0.342
0.613
0.602
0.562
0.567
0.569
0.573
0.590
0.502
0.462
0.356
0.387
0.388
0.440
0.397
0.570
0.564
0.494
0.503
0.507
0.498
0.531
0.651
0.637
0.621
0.622
0.623
0.636
0.641
0.415
0.402
0.353
0.374
0.355
0.375
0.384
0.617
0.607
0.547
0.556
0.597
0.604
0.602
0.822
0.803
0.778
0.777
0.788
0.770
0.797
Source: Own calculations based on microdata from the EPH.
Table 6.30
Access to labor health insurance
-46-
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
EPH-C
2003-II
2004-I
2004-II
2005-I
2005-II
with PJH
2002
2003
2003-II
2004-I
2004-II
2005-I
2005-II
Age
(15-24)
(25-64)
(65 +)
Gender
Female
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
0.560
0.568
0.569
0.573
0.579
0.291
0.321
0.319
0.345
0.358
0.620
0.626
0.629
0.627
0.632
0.406
0.426
0.419
0.461
0.419
0.578
0.592
0.593
0.574
0.601
0.653
0.652
0.656
0.667
0.657
0.424
0.446
0.427
0.444
0.437
0.603
0.611
0.647
0.640
0.640
0.792
0.794
0.804
0.793
0.807
0.549
0.546
0.505
0.516
0.522
0.532
0.545
0.309
0.297
0.267
0.300
0.302
0.328
0.345
0.600
0.598
0.558
0.566
0.572
0.577
0.592
0.492
0.458
0.384
0.408
0.401
0.440
0.407
0.558
0.561
0.493
0.504
0.508
0.502
0.536
0.637
0.631
0.614
0.619
0.627
0.641
0.640
0.410
0.397
0.348
0.365
0.355
0.374
0.385
0.602
0.601
0.543
0.556
0.598
0.604
0.603
0.803
0.799
0.776
0.783
0.795
0.785
0.800
Source: Own calculations based on microdata from the EPH.
Table 6.31
Labor benefits
Argentina, 1992-2005
Permanent job 13th month
15 main cities
1992
1993
1994
1995
1996
1997
1998
28 main cities
1998
1999
2000
2001
2003
EPH-C
2003-II
2004-I
2004-II
2005-I
2005-II
with PJH
2002
2003
2003-II
2004-I
2004-II
2005-I
2005-II
Male
Adults (25-64)
Education
Low
Medium
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
0.837
0.846
0.835
0.841
0.848
0.592
0.599
0.589
0.596
0.600
0.592
0.602
0.588
0.601
0.599
0.791
0.798
0.745
0.763
0.760
0.776
0.797
0.563
0.557
0.532
0.544
0.540
0.554
0.564
0.563
0.558
0.533
0.548
0.541
0.560
0.566
Employment pro
0.068
0.061
0.055
0.047
0.037
Source: Own calculations based on microdata from the EPH.
Table 7.1
Educational structure
-47-
Adults 25-65
Argentina, 1992-2005
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
38.4
2005-II
37.2
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
36.6
36.1
36.4
25.1
25.5
26.4
38.8
38.9
38.6
38.2
37.2
37.0
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
23.0
23.9
24.4
37.9
37.9
36.1
35.2
35.2
35.9
26.9
27.0
28.0
37.4
37.3
37.2
38.9
37.9
37.5
23.8
24.8
25.3
30.9
31.1
28.7
33.9
33.8
33.9
35.2
35.2
37.5
Source: Own calculations based on microdata from the EPH.
Table 7.2
Years of education
By age and gender
Argentina, 1992-2005
(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
10.8 10.4
2004-I
10.7 10.4
2004-II
10.5 10.3
2005-I
10.6 10.4
2005-II
10.7 10.4
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
10.6
10.6
10.5
10.5
10.6
8.1
8.3
8.2
8.4
8.1
7.9
8.0
7.7
7.9
7.6
8.0
8.2
7.9
8.2
7.9
11.9
11.9
11.8
11.7
11.9
11.1
11.3
11.2
11.2
11.2
11.6
11.6
11.5
11.5
11.6
11.5
11.3
11.2
11.2
11.4
11.0
10.8
10.8
11.0
10.9
11.3
11.1
11.0
11.1
11.2
10.6
10.7
10.4
10.4
10.8
10.1
10.2
10.1
10.1
10.5
10.4
10.5
10.2
10.3
10.6
9.6
9.6
9.4
9.7
9.6
9.6
9.6
9.5
9.7
9.5
9.6
9.6
9.5
9.7
9.5
7.7
7.7
7.6
7.6
7.6
8.5
8.4
8.5
8.5
8.5
8.1
8.0
8.0
8.0
8.0
Source: Own calculations based on microdata from the EPH.
-48-
Table 7.3
Years of education
By household equivalized income quintiles
Adults 25-65
Argentina, 1992-2005
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 *
7.8
2003
7.9
2004-I
7.8
2004-II
7.8
2005-I
7.9
2005-II
7.7
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
8.7
8.7
8.7
8.7
8.6
8.7
9.5
9.5
9.8
9.6
9.7
9.6
10.8
10.9
11.0
10.9
11.0
11.2
13.4
13.5
13.5
13.4
13.3
13.6
10.3
10.4
10.5
10.4
10.4
10.5
Source: Own calculations based on microdata from the EPH.
-49-
Table 7.4
Years of education
By age and income
Argentina, 1992-2005
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 *
7.2 7.6
2003
7.3 7.6
2004-I
7.4 7.9
2004-II
7.1 7.7
2005-I
7.3 8.0
2005-II
6.9 7.7
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 *
7.4 8.5
2003
7.5 8.6
2004-I
7.7 8.7
2004-II
7.6 8.7
2005-I
7.7 8.7
2005-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
8.0
8.0
8.5
8.2
8.4
8.2
8.4
8.5
8.8
8.5
8.9
8.8
8.8
8.8
8.9
9.1
9.3
8.9
7.8
7.9
8.1
7.9
8.2
7.9
9.2
9.2
9.3
9.1
9.1
9.2
9.9
10.0
10.3
10.1
10.1
9.9
11.0
11.1
11.3
11.2
11.2
11.2
12.0
12.1
12.4
12.3
12.2
12.4
13.8
13.9
13.8
13.7
14.0
14.0
11.3
11.4
11.6
11.4
11.4
11.5
8.4
8.5
8.1
8.2
8.3
8.0
9.1
9.2
9.2
9.3
9.1
9.4
10.3
10.4
10.6
10.3
10.5
10.4
11.9
12.0
12.1
11.9
11.8
12.0
14.4
14.5
14.3
14.2
14.2
14.3
11.0
11.2
11.1
11.0
11.0
11.1
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 8.7
11.6 8.6
11.9 8.8
12.1 9.0
12.1 9.0
12.2 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
8.9
9.0
9.6
9.4
9.3
9.7
10.8
10.9
10.7
10.6
10.9
11.3
13.5
13.6
13.6
13.4
13.3
13.6
6.7
6.8
7.0
6.5
6.9
6.4
7.4
7.5
7.4
7.2
7.4
7.5
8.0
8.1
8.4
8.1
8.6
8.0
9.3
9.4
9.6
9.6
9.8
9.7
12.5
12.6
12.5
12.5
12.5
12.6
6.0
6.2
5.8
5.2
5.6
5.6
6.3
6.4
6.0
6.0
5.9
6.2
6.5
6.6
7.0
7.0
6.8
6.9
7.7
7.8
7.9
8.0
8.0
8.2
10.3
10.4
10.8
10.6
10.7
11.0
7.7
7.9
7.9
7.9
7.9
7.9
9.9
10.1
10.4
10.2
10.2
10.5
Source: Own calculations based on microdata from the EPH.
-50-
9.2
9.4
9.4
9.3
9.6
9.4
Table 7.5
Gini coefficient
Years of education
By age
Argentina, 1992-2003
(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
0.224
2004-I
0.224
2004-II
0.220
2005-I
0.219
2005-II
0.218
(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
0.219
0.213
0.215
0.206
0.214
0.168
0.166
0.166
0.169
0.165
0.198
0.205
0.197
0.195
0.197
0.235
0.228
0.222
0.220
0.216
0.256
0.256
0.260
0.251
0.253
0.286
0.284
0.287
0.296
0.288
Source: Own calculations based on microdata from the EPH.
Table 7.6
Literacy
By age and gender
Adults aged 25 to 65
Argentina, 1992-2005
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
EPH-C
2004-II
0.99
2005-I
1.00
2005-II
1.00
(15-24)
Male
Mean
Female
(25-65)
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
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
(65 +)
Male
Mean
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
0.94
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
0.99
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
0.98
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
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.96
0.96
0.96
0.98
0.98
0.98
0.97
0.97
0.97
Source: Own calculations based on microdata from the EPH.
-51-
Table 7.7
Literacy
By household equivalized income quintiles
Argentina, 1992-2005
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
EPH-C
2004-II
0.99
2005-I
0.99
2005-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
1.00
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
0.99
0.99
0.99
1.00
1.00
1.00
0.99
1.00
1.00
0.99
0.99
0.99
0.97
0.97
0.96
0.98
0.98
0.98
0.99
0.99
0.99
1.00
0.99
1.00
1.00
1.00
1.00
0.99
0.99
0.99
Source: Own calculations based on microdata from the EPH.
Table 7.8
Enrollment rates
By age and gender
Argentina, 1992-2005
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
0.55
0.55
2004-I
0.63
0.62
0.63
2004-II
0.58
0.58
0.58
2005-I
0.64
0.64
0.64
2005-II
0.61
0.58
0.60
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
0.99
0.99
0.99
0.99
0.99
0.99
0.91
0.92
0.89
0.92
0.91
0.88
0.90
0.89
0.90
0.91
0.89
0.91
0.89
0.91
0.91
0.49
0.52
0.51
0.52
0.51
0.44
0.46
0.42
0.46
0.43
0.47
0.49
0.47
0.49
0.47
Source: Own calculations based on microdata from the EPH.
-52-
Table 7.9
Enrollment rates
By household equivalized income quintiles
Argentina, 1992-2005
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 * 0.46
2003-II
0.46
2004-I
0.54
2004-II
0.48
2005-I
0.51
2005-II
0.48
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
0.46
0.47
0.63
0.55
0.61
0.53
0.57
0.57
0.64
0.59
0.70
0.63
0.62
0.63
0.72
0.67
0.71
0.69
0.77
0.78
0.71
0.75
0.78
0.81
0.54
0.55
0.63
0.58
0.64
0.60
0.98
0.98
0.99
0.98
0.98
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
1.00
0.99
0.99
1.00
0.99
0.99
1.00
1.00
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.82
0.83
0.86
0.82
0.86
0.85
0.89
0.89
0.89
0.89
0.89
0.91
0.89
0.90
0.93
0.93
0.93
0.93
0.93
0.93
0.98
0.94
0.96
0.95
0.98
0.97
0.99
0.98
0.99
0.98
0.89
0.89
0.92
0.90
0.91
0.91
0.30
0.30
0.32
0.26
0.31
0.27
0.35
0.36
0.37
0.38
0.41
0.34
0.45
0.46
0.44
0.45
0.46
0.43
0.53
0.54
0.60
0.54
0.54
0.56
0.67
0.68
0.73
0.71
0.75
0.71
0.45
0.46
0.48
0.46
0.48
0.46
Source: Own calculations based on microdata from the EPH.
Table 7.10
Educational mobility
By age group
Argentina, 1992-2005
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
EPH-C
2004-II
0.87
2005-I
0.86
2005-II
0.86
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
0.76
0.77
Quintiles
Source: Own calculations
based
on microdata
from
the EPH.
1
2
3
4
5
EPH, 2003
0.34
EPH-C
Table
8.1 0.32
2003 *
Coverage
of PJH
2003
0.32
2004-I
0.32
Share
of households
2004-II
0.34
2005-I
0.34
2005-II
0.30
0.25
0.24
0.23
0.22
with
0.21
0.19
0.17
0.09
0.10
0.10
.10
PJH00.08
by
0.08
0.06
0.03
0.05
0.04
0.03
equivalized
0.03
0.02
0.03
0.01
Mean
0.11
0.01
0.01
0.01
income
0.01
0.01
0.00
0.12
0.12
0.11
quintiles
0.11
0.10
0.09
Source: Own calculations based on microdata from the EPH.
-53-
Table 8.2
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
Low
0.16
Medium
0.09
High
0.02
Mean
0.11
0.17
0.17
0.16
0.16
0.15
0.14
0.09
0.09
0.10
0.08
0.07
0.07
0.02
0.02
0.02
0.01
0.01
0.01
0.11
0.11
0.11
0.10
0.10
0.09
Source: Own calculations based on microdata from the EPH.
Table 8.3
Households
C
overage of PJH
1
2
3
Benefits
(in pe
sos) of PJH
by household
42.2
35.1
15.8
EPH, 2003
EPH-C
2003 *
41.3
2003
EPH, 200341.4
2004-I
42.8
EPH-C
2004-II
45.3
2004-II
2005-I
47.8
2005-I
2005-II
49.2
2005-II
Individuals
1
35.4
25.9
24.0
22.8
32.3
32.0
32.2
31.5
30.3
30.6
2
13.2
11.8
9.2
7.2
16.7
16.7
17.5
15.0
15.3
13.0
3
4.3
3.7
2.8
1.9
4
5.6
8.5
8.6
6.3
6.5
5.2
6.2
5
1.2
4
1.1
0.9
0.4
1.0
Source: Own calculations based on microdata from the EPH.
EPH, 2003
EPH-C
2003 *
2003
2004-I
2004-II
2005-I
2005-II
1
42.5
2
35.2
3
15.9
4
5.5
1.2
1.3
1.3
1.7
1.5
1.0
Total
100.0
5
0.5
0.2
0.0
0.0
5
1.1
100.0Mean
100.0 8.4
100.0
100.0
100.0 6.9
100.0 5.8
5.2
Total
100.0
Table 8.4
40.7
33.5
16.3
8.4
1.2
100.0
Incidence of PJH
40.8
33.2
16.2
8.5
1.2
100.0
6.2
1.2
100.0
Distribution o42.1
f PJH ben33.3
eficiaries17.1
by equivalized
income
quintile
45.2
48.3
49.1
31.7
30.0
31.3
14.9
15.1
12.6
6.6
5.4
6.1
1.6
1.4
1.0
100.0
100.0
100.0
Source: Own calculations based on microdata from the EPH.
Table 8.5
Incidence of PJH
Distribution of PJH benefits by equivalized income quintile
EPH, 2003
EPH-C
2004-II
2005-I
2005-II
1
42.5
2
35.2
3
15.9
4
5.5
5
1.1
Total
100.0
47.6
51.0
52.8
31.7
29.6
30.1
13.9
14.5
11.4
5.7
4.0
5.0
1.1
0.9
0.7
100.0
100.0
100.0
Source: Own calculations based on microdata from the EPH.
-54-
Figure 3.1
Growth-incidence curves
Household per capita income
Proportional changes by percentile
Argentina, 1992-2005
80
60
2003-2005
40
20
1992-1998
0
0
10
20
30
40
50
60
70
80
90
100
1992-2005
-20
-40
1998-2003
-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, 2002-2005
50
40
30
2003-2004
20
2004-2005
10
2002-2003
0
0
10
20
30
40
50
60
70
-10
Source: Own calculations based on microdata from the EPH.
-55-
80
90
100
Figure 4.1
Poverty
Argentina, 1992-2005
USD 1 and USD 2 lines
H
PG
FGT(2)
H
PG
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1992
2005
2004
2003
2002
0
2001
0
2000
5
1999
10
2
1998
4
1997
15
1996
20
6
1995
8
1994
25
1993
30
10
1992
12
1994
USD 2 a day
1993
USD 1 a day
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, 1992-2005
Official poverty lines
Official extreme poverty line
Official moderate poverty line
30
70
25
60
50
20
40
15
30
10
20
5
10
H
PG
FGT(2)
H
PG
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
0
1992
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.
-56-
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
2
4
6
log equivalized income
8
10
2005
0
0
.1
.1
Density
.2
Density
.2
.3
.3
.4
.4
2002
0
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 poverty line
Greater Buenos Aires, 1974-2005
60
50
40
30
20
10
0
74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05
Source: Own calculations based on the EPH.
-57-
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).
-58-
Figure 4.7
Poverty
Argentina, 1992-2005
50% median poverty line
30
25
20
15
10
5
H
PG
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
-59-
Figure 5.1
Gini coefficient
Distribution of household per capita income
Greater Buenos Aires, 1974-2005
0.55
0.50
0.45
0.40
0.35
Source: Author’s calculations based on the EPH.
Figure 5.2
Gini coefficient
Distribution of household per capita income
Around 1990 and around 2000
Early 1990s
60
55
50
45
Argentina
Costa Rica
Peru
Jamaica
El Salvador
Mexico
Nicaragua
Bolivia
Panama
Chile
Honduras
Colombia
Venezuela
Peru
Jamaica
Argentina
El Salvador
Mexico
Honduras
Nicaragua
Panama
Colombia
Bolivia
Chile
Brazil
Venezuela
Costa Rica
Uruguay
40
Early 2000s
60
55
50
45
Brazil
Uruguay
40
Source: Own estimates from Gasparini (2003).
Figure 5.3
Change in the Gini coefficient
-60-
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
Between early 1990s and early 2000s
Distribution of household per capita income
8
6
4
2
0
Brazil
Honduras
Mexico
Panama
Jamaica
Colombia
Nicaragua
Ecuador
Costa Rica
Chile
El Salvador
Bolivia
Peru
Uruguay
Argentina
Venezuela
-4
Paraguay
-2
Source: Own estimates from Gasparini (2003).
Figure 6.1
Marginal return to a college education
All working males, 1992-2003
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
Source: Own estimates from microdata of the EPH.
-61-
2003
Figure 6.2
Labor force, employment and unemployment
Greater Buenos Aires, 1974-2005
60
50
40
30
20
10
unemployment
activity
employment
Source: Own estimates from microdata of the EPH.
* estimate for second half of 2005
-62-
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