seminario arg aus3 - destacados · 2011. 2. 11. · section 9 closes with an assessment of the...

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 26-27, 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.

2Although 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.

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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, Rawson-Trelew 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.

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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 self-employed 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.

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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.

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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%.

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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 self-employed and employment in small firms during the 1990s, and (ii) employment in large firms during the latest economic crisis. The recovery of the economy and the reduction in the size of the PJH contributed to a reduction in the share of workers in the public sector since 2003.

Tables 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.

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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.

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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).

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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 poverty-alleviation program covering around 2 million household heads. The PJH transfers $150 to unemployed household heads with dependents (children aged less than 18 or incapacitated) and it has a counterpart work requirement, with the aim of helping to assure that the transfers reached those in greatest need.

Given the size of this program the EPH started to include questions on that program. This section is based on the specific questions included in the May 2003 EPH and the EPHC. According to expectations, Table 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).

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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.

REFERENCES Altimir, O. (1986). Estimaciones de la Distribución del Ingreso en la Argentina, 1953-1980.

Desarrollo Económico 25 (100).

Andersen, L. (2001). Social Mobility in Latin America: Links with Adolescent Schooling. IADB Research Network Working Paper #R-433.

Attanasio, O. and Székely, M. (eds.) (2001). Portrait of the Poor. An Assets-Based Approach. IADB.

Bourguignon, F.(2003). From Income to Endowments: The Difficult Task of Expanding the Income Poverty Paradigm. Delta WP 2003-03.

Cowell, F. (1995). Measuring Inequality. LSE Handbooks in Economic Series. Prentice Hall/Harvester Wheatsheaf.

Chen, S. and Ravallion., M. (2001). How Did the World's Poorest Fare in the 1990s? World Bank Working Paper.

Deaton, A. and Zaidi, S. (2002). Guidelines for Constructing Consumption Aggregates for Welfare Analysis. LSMS Working Paper 135.

Duryea, S. and Pagés, C. (2002). Human Capital Policies: What They Can and Cannot Do For Productivity and Poverty Reduction in Latin America. IADB Working Paper #468.

Fiszbein, A., Giovagnoli, P. and Aduriz, I. (2002). Argentina’s Crisis and its Impact on Household Welfare. Mimeo.

Foster, J., Greer, J. and Thorbecke, E. (1984). A Class of Decomposable Poverty Measures. Econometrica 52: 761-776.

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Galiani, S., and Sanguinetti, P. (2003). The Impact of Trade Liberalization on Wage Inequality: Evidence from Argentina. Journal of Development Economics 72 (2): 497-513.

Gasparini, L (2003). Empleo y Protección Social en América Latina. Un análisis sobre la Base de Encuestas de Hogares. OIT.

Gasparini, L. (2003). Different Lives: Inequality in Latin America and the Caribbean. Capítulo 2 de Inequality in Latin America and the Caribbean: Breaking with History?. The World Bank.

Gasparini, L. (2004). Argentina’s Distributional Failure. The Role of Integration and Public Policies. IADB Working Paper.

Gasparini, L. (2004b). Poverty and Inequality in Argentina: Methodological Issues and a Literature Review. CEDLAS-The World Bank. Mimeo.

Gasparini, L. and Sosa Escudero, W. (2004). Implicit Rents from Own-Housing and Income Distribution. Econometric Estimates for Greater Buenos Aires. Journal of Income Distribution. Forthcoming.

Gasparini, L., Gutierrez, F. and Tornarolli, L (2005). Growth and Income Poverty in Latin America and the Caribbean. Background Paper for the 2005 LAC Flagship Report.

Gasparini, L., Marchionni, M. and Sosa Escudero, W. (2001). La Distribución del Ingreso en la Argentina. Editorial Triunfar.

INDEC (2001). Informe de Prensa. Incidencia de la Pobreza y de la Indigencia en los Aglomerados Urbanos. Octubre.

Juhn, C, Murphy, K. and Pierce, B. (1993). Wage Inequality and the Rise in Returns to Skill. Journal of Political Economy 101 (3): 410-442.

Lambert, P. (1993). The Distribution and Redistribution of Income. Manchester University Press.

Londoño, J. and Székely, M. (2000). Persistent Poverty and Excess Inequality: Latin America, 1970-1995. Journal of Applied Economics 3 (1). 93-134.

Sala-i-Martin, X. (2002). The World Distribution of Income (Estimated from Individual Country Distributions). Mimeo.

Sosa Escudero, W. and Gasparini, L. (2000). A Note on the Statistical Significance of Changes in Inequality. Económica XLVI (1). Enero-Junio.

Székely, M. (2004). The 1990s in Latin America: Another Decade of Persistent Inequality, But with Somewhat Lower Poverty. Journal of Applied Economics..

Thomas, V., Wang, Y. and Fan X. (2002). A New Dataset on Inequality in Education: Gini and Theil Indices of Schooling for 140 countries, 1960-2000. Mimeo.

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Wodon, Q. et al. (2000). Poverty and Policy in Latin America and the Caribbean. World Bank Technical Paper 467.

Wolfson, M. (1994). When Inequalities Diverge. The American Economic Review. 84 (2): 353-358.

World Bank (2000). Poor People in a Rich Country. Poverty Report for Argentina. The World Bank. Washington D.C.

World Bank (2003). Poverty Update for Argentina. The World Bank. Washington D.C.

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Table 3.1 Real income Argentina, 1992-2005 Real income

EPH - 15 cities EPHC - 28 cities Deciles 1992 1994 1996 1998 1998 2000 2002 2003 2005

1 53.9 49.5 32.1 36.5 33.5 27.5 16.6 17.5 29.72 100.9 95.8 76.9 79.6 73.8 65.5 35.9 45.1 65.83 136.7 131.7 110.2 114.2 106.0 95.3 55.2 68.6 96.94 172.2 168.8 143.9 151.1 139.8 126.9 77.0 93.1 130.15 208.8 210.4 180.8 192.3 177.7 164.3 101.3 121.0 168.06 257.2 257.1 226.4 239.8 222.7 210.7 130.2 155.5 213.47 316.7 316.8 285.0 307.7 285.5 267.5 165.2 203.7 266.38 405.6 403.5 371.6 409.6 376.9 358.6 221.7 276.3 347.99 557.3 551.0 529.7 584.2 536.7 512.7 328.4 399.4 490.410 1152.9 1148.6 1143.3 1293.9 1199.6 1108.0 769.9 932.1 1097.1

average 336.3 333.4 310.0 340.9 315.3 293.7 190.2 231.3 290.6

Proportional changesDeciles 1992-1994 1994-1996 1996-1998 1992-1998 1998-2000 2000-2002 1998-2002 2003-2005

1 -8.1 -35.3 13.7 -32.3 -17.9 -39.7 -50.5 69.12 -5.0 -19.8 3.5 -21.1 -11.2 -45.3 -51.4 45.83 -3.7 -16.3 3.6 -16.4 -10.1 -42.1 -48.0 41.24 -2.0 -14.8 5.0 -12.2 -9.2 -39.4 -44.9 39.75 0.8 -14.1 6.3 -7.9 -7.5 -38.3 -43.0 38.86 0.0 -12.0 5.9 -6.8 -5.4 -38.2 -41.5 37.37 0.0 -10.0 8.0 -2.9 -6.3 -38.3 -42.1 30.78 -0.5 -7.9 10.2 1.0 -4.9 -38.2 -41.2 25.99 -1.1 -3.9 10.3 4.8 -4.5 -35.9 -38.8 22.810 -0.4 -0.5 13.2 12.2 -7.6 -30.5 -35.8 17.7

average -0.9 -7.0 10.0 1.4 -6.8 -35.3 -39.7 25.7

EPH - 28 cities

Source: Own calculations based on microdata from the EPH.

Table 4.1 Poverty lines Argentina, 1992-2005

International PL ($ per capita) Oficial PL ($ per adult equivalent) RatiosUSD 1 a day USD 2 a day Extreme Moderate

(i) (ii) (iii) (iv) (iv)/(ii) (iv)/(iii) (iii)/(ii)1992 23.8 47.6 57.9 129.2 2.7 2.2 1.21993 25.9 51.8 62.4 138.0 2.7 2.2 1.21994 26.9 53.7 62.8 146.4 2.7 2.3 1.21995 27.5 54.9 66.1 154.7 2.8 2.3 1.21996 27.5 55.0 67.4 156.3 2.8 2.3 1.21997 27.7 55.3 67.4 157.6 2.8 2.3 1.21998 28.0 55.9 69.8 161.2 2.9 2.3 1.21999 27.4 54.8 64.6 155.0 2.8 2.4 1.22000 27.2 54.4 62.4 151.1 2.8 2.4 1.12001 26.9 53.8 61.0 150.1 2.8 2.5 1.12002 37.3 74.5 104.8 231.8 3.1 2.2 1.4

2003 (May) 38.6 77.1 106.6 232.3 3.0 2.2 1.42003 (II half) 38.6 77.1 103.6 227.7 3.0 2.2 1.32004 (I half) 39.7 79.5 106.5 233.3 2.9 2.2 1.32004 (II half) 40.8 81.7 108.5 237.7 2.9 2.2 1.32005 (I half) 43.2 86.5 114.2 250.1 2.9 2.2 1.32005 (II half) 45.0 90.1 120.1 259.5 2.9 2.2 1.3

Source: INDEC, WDI and own calculations. Note 1: mean values for GBA Note 2: For the EPHC, first half: April, second half: September.

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Table 4.2 Poverty Argentina, 1992-2005 USD-1-a day poverty line

Number Headcount Poverty gappoor people FGT(0) FGT(1) FGT(2)

(i) (ii) (iii) (iv) EPH-15 cities

1992 478,378 1.4 1.0 0.91993 586,197 1.7 1.0 0.91994 585,528 1.7 1.3 1.21995 1,108,580 3.2 2.0 1.71996 1,309,616 3.7 2.6 2.31997 1,105,174 3.1 2.0 1.71998 1,149,506 3.2 1.7 1.3

EPH - 28 cities1998 1,233,383 3.4 1.8 1.51999 1,281,780 3.5 2.1 1.82000 1,557,903 4.2 2.5 2.02001 2,589,201 6.9 4.1 3.32002 3,737,657 9.9 3.9 2.42003 3,014,981 7.9 2.8 1.8

EPH-C2003-II * 3,021,707 7.9 3.9 2.92003-II 2,917,955 7.6 3.8 2.92004-I 2,221,433 5.7 2.8 2.12004-II 2,009,830 5.2 2.5 1.82005-I 1,809,907 4.6 2.2 1.62005-II 1,521,238 3.9 1.8 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



1992 1,407,829 4.2 1.9 1.31993 1,689,431 5.0 2.1 1.41994 1,549,582 4.5 2.2 1.61995 2,592,179 7.5 3.7 2.61996 3,141,943 8.9 4.2 3.11997 2,967,630 8.3 3.7 2.61998 3,007,165 8.3 3.4 2.3

EPH - 28 cities1998 3,405,663 9.4 3.9 2.51999 3,316,156 9.1 4.1 2.82000 4,039,454 10.9 5.0 3.32001 5,848,228 15.6 7.7 5.32002 9,376,508 24.7 10.8 6.42003 9,083,922 23.7 9.2 5.1

EPH-C2003-II * 7,650,293 19.9 8.8 5.62003-II 7,325,050 19.1 8.5 5.52004-I 6,049,215 15.6 6.7 4.22004-II 5,492,855 14.2 6.0 3.72005-I 5,194,536 13.2 5.5 3.32005-II 4,538,654 11.6 4.6 2.7


Table 4.4 Poverty

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Argentina, 1992-2005 Official extreme poverty line



1992 1,254,803 3.8 1.7 1.21993 1,462,793 4.3 1.8 1.21994 1,317,220 3.8 1.8 1.41995 2,384,765 6.9 3.2 2.31996 2,896,186 8.2 3.9 2.91997 2,584,205 7.2 3.4 2.41998 2,783,867 7.7 3.4 2.2

EPH - 28 cities1998 3,041,767 8.4 3.7 2.41999 3,041,205 8.3 3.7 2.52000 3,536,364 9.5 4.3 2.92001 5,151,064 13.7 6.7 4.72002 10,473,374 27.6 12.2 7.22003 10,128,517 26.4 10.5 5.8

EPH-C2003-II * 8,214,687 21.4 9.4 5.92003-II 7,869,947 20.5 9.1 5.82004-I 6,600,271 17.0 7.1 4.42004-II 5,830,357 15.0 6.2 3.82005-I 5,338,445 13.6 5.7 3.42005-II 4,771,484 12.2 4.9 2.9


Table 4.5 Poverty Argentina, 1992-2005 Official moderate poverty line



1992 6,592,328 19.7 6.5 3.41993 6,209,474 18.3 6.6 3.61994 6,914,999 20.1 7.3 3.91995 9,262,680 26.6 10.6 6.21996 10,366,003 29.4 12.1 7.11997 9,894,132 27.7 11.3 6.51998 10,202,583 28.2 11.6 6.7

EPH - 28 cities1998 10,865,081 30.1 12.4 7.21999 11,159,500 30.5 12.5 7.32000 12,079,764 32.6 14.1 8.42001 14,401,152 38.4 18.1 11.62002 21,795,194 57.5 29.2 18.92003 20,986,517 54.7 27.3 17.1

EPH-C2003-II * 19,002,076 49.5 23.8 15.02003-II 18,348,115 47.8 22.9 14.42004-I 17,210,630 44.4 20.2 12.22004-II 15,582,682 40.2 18.0 10.82005-I 15,082,930 38.4 16.6 9.92005-II 13,287,919 33.8 14.6 8.6


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Table 4.6 Poverty Argentina, 1992-2005 50 % median income poverty line



1992 6,546,566 19.6 6.6 3.51993 6,850,027 20.2 7.3 4.11994 7,124,105 20.8 7.3 4.01995 7,374,942 21.2 8.8 5.31996 7,943,542 22.6 9.3 5.81997 7,929,285 22.2 9.5 5.71998 7,944,343 22.0 9.3 5.4

EPH - 28 cities1998 8,320,577 23.0 9.2 5.41999 8,226,625 22.5 9.5 5.72000 9,072,879 24.5 10.4 6.32001 9,403,156 25.1 12.2 8.12002 9,832,693 25.9 11.5 6.82003 9,543,469 24.9 10.2 5.7

EPH-C2003-II * 9,413,501 24.5 11.1 7.02003-II 9,477,571 24.7 11.3 7.22004-I 9,020,631 23.2 10.2 6.22004-II 9,611,727 24.8 10.5 6.32005-I 9,104,836 23.2 9.9 5.92005-II 9,636,760 24.5 10.2 6.0


Table 4.7 Poverty Argentina, 1992-2003 Endowments

Endowments Endowmentsplus income

(i) (ii) EPH-15 cities

1992 0.392 0.0311993 0.387 0.0351994 0.389 0.0311995 0.385 0.0571996 0.387 0.0671997 0.384 0.0621998 0.392 0.067

EPH - 28 cities1998 0.402 0.0751999 0.401 0.0702000 0.393 0.0832001 0.400 0.1162002 0.385 0.1672003 0.381 0.163


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Table 5.1 Distribution of household per capita income Share of deciles and income ratios Argentina, 1992-2005

Share of deciles Income ratios1 2 3 4 5 6 7 8 9 10 10/1 90/10 95/80

EPH-15 cities1992 1.8 3.0 4.1 5.1 6.2 7.6 9.4 12.0 16.5 34.1 19.0 7.9 2.01993 1.7 3.0 4.1 5.2 6.4 7.9 9.6 12.3 16.6 33.1 19.9 8.1 1.91994 1.7 2.9 4.0 5.1 6.3 7.7 9.5 12.1 16.4 34.2 19.7 8.2 1.91995 1.4 2.7 3.7 4.8 5.9 7.3 9.1 11.6 16.7 36.7 25.8 9.6 2.11996 1.4 2.6 3.6 4.7 5.9 7.3 9.2 11.9 17.0 36.5 26.5 10.1 2.01997 1.4 2.6 3.6 4.7 6.0 7.3 9.2 12.0 17.2 36.1 26.7 10.5 2.11998 1.2 2.4 3.4 4.5 5.7 7.0 9.0 12.0 17.1 37.7 30.2 11.2 2.1

EPH - 28 cities1998 1.3 2.4 3.4 4.5 5.7 7.1 9.0 11.9 16.9 37.8 29.9 11.1 2.11999 1.3 2.5 3.5 4.6 5.8 7.3 9.2 12.0 17.0 36.8 28.0 10.9 2.12000 1.2 2.3 3.3 4.4 5.6 7.2 9.1 12.2 17.4 37.4 32.3 11.9 2.12001 1.0 2.1 3.1 4.1 5.4 6.9 9.0 12.0 17.4 39.0 40.0 13.9 2.22002 1.0 2.0 3.0 4.1 5.4 6.9 8.7 11.6 17.2 40.3 39.4 14.3 2.32003 1.1 2.1 3.0 4.0 5.2 6.8 8.8 11.9 17.3 39.8 34.8 13.5 2.2

EPH-C2003-II * 1.0 2.1 3.0 4.0 5.2 6.6 8.6 11.6 16.7 41.0 39.3 13.4 2.22003-II 1.0 2.1 3.1 4.1 5.3 6.7 8.8 11.9 17.1 39.8 38.1 13.7 2.22004-I 1.2 2.3 3.3 4.3 5.5 7.1 9.0 11.9 16.8 38.6 32.7 11.8 2.12004-II 1.1 2.3 3.3 4.4 5.7 7.2 9.1 12.0 17.0 37.9 33.0 12.0 2.02005-I 1.2 2.4 3.4 4.4 5.7 7.3 9.1 11.9 16.9 37.8 32.5 11.7 2.12005-II 1.2 2.3 3.4 4.5 5.8 7.3 9.1 11.9 16.8 37.6 32.7 11.8 2.1

Sour Own c ulations based mic ata fr the E . Note (10/1) ome io be en de s 10 1; ( 0)=i me ra between percentiles 90 and 10, and (95/ =incom atio b een entil 5 an . * co ted u ing weights that ignore income non-response.

ce: alc on rod om PH 1: =inc rat twe cile and 90/1 nco tio

80) e r etw perc es 9 d 80mpu s

istrib

ent 99 05

Table 5.2 D ution of household per capita income Inequality indices Arg ina, 1 2-20

Gini Theil CV A(.5) A(1) A(2) E(0) E(2) EPH-15 cities

1992 0.450 0.370 1.101 0.165 0.299 0.510 0.355 0.6061993 0.444 0.359 1.077 0.162 0.297 0.517 0.352 0.580

12 .168 03 .510 0.361 0.61805 .190 40 .569 0.416 0.72660 .194 49 .607 9 .79346 .190 46 0.586 0.424 0.65600 .207 69 .608 0.461 0.845

EPH - 28 cities1998 0.502 0.472 1.307 0.207 0.368 0.605 0.458 0.8541999 0.491 0.443 1.213 0.197 0.356 0.606 0.440 0.7352000 0.504 0.464 1.231 0.208 0.377 0.647 0.474 0.7572001 0.522 0.497 1.264 0.224 0.404 0.675 0.517 0.798

0.533 0.530 1.356 0.233 0.412 0.657 0.530 0.92043 .227 01 0.637 0.512 0.902

EPH-C3-II * .537 3.056 0.244 0.417 0.673 0.539 4.671

1.457 0.231 0.407 0.672 0.522 1.061.510 1.714 0.216 0.380 0.621 0.478 1.469

2004-II 0.506 0.499 1.550 0.213 0.379 0.624 0.476 1.2012005-I 0.502 0.473 1.306 0.208 0.373 0.624 0.466 0.8532005-II 0.501 0.480 1.418 0.209 0.373 0.624 0.467 1.005

1994 0.453 0.378 1.1 0 0.3 01995 0.481 0.430 1.2 0 0.3 01996 0.486 0.442 1.2 0 0.3 0 0.42 01997 0.484 0.422 1.1 0 0.31998 0.502 0.471 1.3 0 0.3 0

20022003 0.528 0.519 1.3 0 0.4

200 0 0.6252003-II 0.529 0.5322004-I 0 0.507

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.

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Table 5.3 Distribution of equivalized household income Share of deciles and income ratios Argentina, 1992-2005


EPH-15 cities1992 2.0 3.4 4.3 5.3 6.5 7.9 9.5 12.0 16.4 32.7 16.3 7.1 2.01993 1.9 3.3 4.4 5.5 6.7 8.1 9.7 12.3 16.3 31.8 17.0 7.3 1.91994 2.0 3.3 4.3 5.4 6.5 7.9 9.6 12.0 16.2 32.8 16.7 7.1 1.91995 1.6 3.0 4.1 5.1 6.2 7.5 9.1 11.6 16.4 35.3 21.6 8.6 2.11996 1.6 2.9 3.9 5.0 6.1 7.5 9.2 11.9 16.7 35.0 21.8 8.6 2.01997 1.6 2.9 3.9 5.0 6.2 7.6 9.4 12.0 16.9 34.5 22.2 9.0 2.11998 1.5 2.7 3.7 4.8 5.9 7.3 9.1 11.9 16.9 36.2 24.7 9.6 2.1

EPH - 28 cities1998 1.5 2.7 3.7 4.8 5.9 7.3 9.1 11.9 16.8 36.2 24.4 9.3 2.11999 1.5 2.8 3.9 4.9 6.1 7.5 9.3 12.0 16.8 35.3 23.1 9.3 2.02000 1.3 2.6 3.6 4.7 5.9 7.4 9.2 12.1 17.1 36.1 26.9 10.3 2.12001 1.1 2.4 3.4 4.5 5.7 7.2 9.1 11.9 17.1 37.6 33.3 12.1 2.22002 1.2 2.2 3.3 4.4 5.7 7.1 8.8 11.5 16.9 39.0 32.5 12.3 2.32003 1.3 2.4 3.3 4.3 5.6 7.0 8.9 11.8 17.0 38.3 28.4 11.3 2.2

EPH-C2003-II * 1.2 2.3 3.3 4.4 5.5 6.9 8.8 11.6 16.5 39.4 32.4 11.4 2.12003-II 1.2 2.3 3.4 4.4 5.6 7.0 8.9 11.8 16.9 38.4 31.6 11.6 2.12004-I 1.4 2.6 3.6 4.7 5.8 7.3 9.2 11.9 16.6 37.0 27.1 10.2 2.12004-II 1.3 2.6 3.6 4.8 6.0 7.4 9.2 12.0 16.8 36.2 27.2 10.3 2.02005-I 1.4 2.7 3.8 4.8 6.0 7.4 9.3 11.9 16.7 36.1 26.8 10.1 2.02005-II 1.3 2.6 3.7 4.8 6.1 7.5 9.3 11.9 16.6 36.1 27.1 10.1 2.0

Source: Own calculations bas n icrodata from the EPH. Not 1: (1 1)= om ati etw n iles 10 and 1; (90/10)=income ratio between p 90 and 10, and (95/80)=income ratio between percentiles 95 and 80. * co puted sing eig tha no nco e non-response.

ed o me 0/ inc e r o b ee dec

ercentilesm u w hts t ig re i m

istriqua ind

Table 5.4 D bution of equivalized household income Ine lity icesArgentina, 1992-2005


1992 0.430 0.334 1.018 0.150 0.272 0.468 0.318 0.5181993 0.424 0.325 1.004 0.147 0.270 0.475 0.315 0.5041994 0.431 0.341 1.040 0.152 0.275 0.467 0.322 0.541

127 .525168 .561066 .545198 .565

PH - 28 cities203 .561127 .564

2000 0.483 0.422 1.149 0.190 0.347 0.606 0.426 0.6602001 0.501 0.456 1.194 0.206 0.374 0.635 0.467 0.713

0.512 0.488 1.287 0.215 0.381 0.617 0.479 0.829261 .595 0.460 0.795

PH-CII * .514 2.766 0.223 0.384 0.637 0.485 3.825

3-II 0.507 360 0.212 0.376 0.638 0.471 0.9254-I .488 539 0.197 0.349 0.580 0.429 1.184

2004-II 0.483 0.450 1.429 0.194 0.347 0.584 0.426 1.0212005-I 0.480 0.429 1.232 0.190 0.341 0.582 0.417 0.7592005-II 0.480 0.446 1.433 0.192 0.344 0.585 0.421 1.027

1995 0.460 0.391 1. 0.173 0.311 0 0.372 0.6351996 0.463 0.398 1. 0.176 0.318 0 0.383 0.6821997 0.461 0.380 1. 0.173 0.316 0 0.379 0.5681998 0.480 0.425 1. 0.188 0.338 0 0.412 0.718

E1998 0.478 0.424 1. 0.187 0.335 0 0.409 0.7241999 0.468 0.400 1. 0.179 0.325 0 0.393 0.635

20022003 0.506 0.473 1. 0.208 0.368 0

E2003- 0 0.566200 0.487 1.200 0 0.459 1.

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.

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Table 5.5 Distribution of equivalized household labor monetary income Share of deciles and income ratios Argentina, 1992-2005


EPH-15 cities1992 2.0 3.4 4.5 5.5 6.6 8.0 9.7 12.1 16.5 31.8 16.2 6.9 1.91993 1.8 3.2 4.3 5.4 6.7 8.1 9.8 12.3 16.6 31.7 17.9 7.6 1.91994 1.9 3.3 4.3 5.4 6.5 7.9 9.6 11.9 16.2 33.0 17.4 7.2 1.91995 1.5 2.9 4.0 5.0 6.1 7.4 9.0 11.5 16.4 36.2 24.4 9.0 2.21996 1.5 2.8 3.8 4.9 6.0 7.4 9.2 11.8 16.7 36.0 24.6 9.2 2.11997 1.4 2.7 3.8 4.9 6.1 7.6 9.4 12.0 17.0 35.0 24.8 9.7 2.11998 1.3 2.6 3.6 4.6 5.7 7.2 8.9 11.8 17.0 37.3 28.6 10.3 2.1

EPH - 28 cities1998 1.3 2.6 3.6 4.6 5.8 7.1 9.0 11.7 16.9 37.3 28.2 10.2 2.21999 1.4 2.7 3.7 4.8 5.9 7.3 9.1 11.9 17.0 36.2 26.0 9.9 2.02000 1.2 2.4 3.5 4.5 5.8 7.2 9.1 12.0 17.1 37.1 31.4 11.5 2.12001 1.0 2.2 3.3 4.3 5.4 7.0 8.8 11.8 17.1 39.2 38.5 13.5 2.32002 1.1 2.0 2.9 4.1 5.3 6.8 8.6 11.3 16.9 41.1 38.7 13.4 2.42003 0.9 2.1 2.9 4.0 5.2 6.8 8.7 11.8 17.3 40.2 43.7 14.0 2.2

EPH-C2004-II 1.0 2.2 3.3 4.6 5.9 7.4 9.4 12.3 17.4 36.5 37.7 13.5 2.02005-I 1.0 2.3 3.5 4.6 5.8 7.4 9.3 12.1 17.0 36.9 35.2 12.0 2.12005-II 1.0 2.3 3.4 4.5 5.8 7.4 9.2 12.0 17.0 37.4 37.6 12.7 2.1

Sour Own c lati base mi ata f the EP . Note (10/1) ome io be en d es 1 d 1; /10) ome o between percentiles 90 and 10, and (95 e ratio between percentiles 95 and 80.

ce: alcu ons d on crod rom H 1: =inc rat twe ecil 0 an (90 =inc rati

/80)=incom

rib of iva d h ehold labor monetary income

rgen

Table 5.6 Dist ution equ lize ousInequality indices A tina, 1992-2005


1992 0.422 0.317 0.959 0.145 0.268 0.633 0.313 0.4601993 0.427 0.325 0.984 0.149 0.278 0.506 0.325 0.4841994 0.433 0.346 1.060 0.154 0.280 0.502 0.330 0.5621995 0.470 0.410 1.164 0.182 0.328 0.577 0.397 0.677

0.7450.584

28 cities1998 0.492 0.450 1.249 0.198 0.355 0.597 0.437 0.780

0.481 0.425 1.178 0.190 0.343 0.596 0.420 0.6940.497 0.449 1.200 0.202 0.368 0.646 0.459 0.719

1.354 0.236 0.422 0.698 0.547 0.917C

II 1.414 0.209 0.384 0.676 0.485 1.0002005-I 0.497 0.453 1.211 0.205 0.377 0.677 0.473 0.7332005-II 0.503 0.490 1.546 0.213 0.386 0.675 0.487 1.196

1996 0.474 0.421 1.221 0.186 0.335 0.590 0.4071997 0.469 0.393 1.081 0.179 0.330 0.574 0.4001998 0.493 0.450 1.242 0.199 0.357 0.603 0.442 0.772

EPH -

199920002001 0.519 0.493 1.262 0.221 0.397 0.667 0.506 0.7962002 0.536 0.539 1.374 0.235 0.413 0.660 0.533 0.9442003 0.535 0.533

EPH-2004- 0.499 0.469

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 Equivalized Equivalized Equivalized Equivalized Equivalized Total Equivalized Equivalized Equivalized Equivalizedincome income income income income income household income A income A income A income A

A B C D E income Age 0-10 Age 20-30 Age 40-50 Age 60-70 EPH-15 cities

1992 0.450 0.430 0.421 0.423 0.416 0.434 0.445 0.435 0.403 0.439 0.4291993 0.444 0.424 0.415 0.416 0.409 0.428 0.435 0.439 0.399 0.435 0.3981994 0.453 0.431 0.422 0.423 0.415 0.436 0.438 0.444 0.412 0.430 0.4171995 0.481 0.460 0.450 0.452 0.444 0.464 0.458 0.468 0.437 0.469 0.4231996 0.486 0.463 0.452 0.454 0.446 0.468 0.457 0.461 0.440 0.481 0.4271997 0.483 0.461 0.451 0.453 0.445 0.466 0.458 0.461 0.434 0.457 0.4561998 0.502 0.480 0.470 0.471 0.463 0.484 0.471 0.480 0.454 0.474 0.469

EPH - 28 cities1998 0.502 0.478 0.468 0.470 0.461 0.483 0.470 0.476 0.456 0.472 0.4681999 0.491 0.468 0.458 0.460 0.451 0.473 0.459 0.469 0.448 0.469 0.4542000 0.504 0.483 0.472 0.475 0.466 0.488 0.467 0.500 0.452 0.485 0.4462001 0.522 0.501 0.491 0.494 0.485 0.506 0.480 0.517 0.466 0.504 0.4692002 0.533 0.512 0.502 0.504 0.496 0.517 0.488 0.531 0.483 0.519 0.4642003 0.528 0.506 0.494 0.497 0.487 0.510 0.481 0.510 0.478 0.519 0.455

EPH-C2003-II * 0.537 0.514 0.501 0.505 0.495 0.520 0.490 0.510 0.467 0.521 0.5332003-II 0.529 0.507 0.495 0.499 0.489 0.513 0.481 0.518 0.469 0.524 0.4802004-I 0.510 0.488 0.476 0.480 0.469 0.493 0.465 0.492 0.459 0.506 0.4692004-II 0.506 0.483 0.471 0.475 0.465 0.489 0.460 0.482 0.454 0.478 0.4642005-I 0.502 0.480 0.468 0.471 0.461 0.484 0.458 0.479 0.457 0.486 0.4522005-II 0.501 0.480 0.469 0.473 0.463 0.485 0.459 0.503 0.446 0.468 0.451

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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

Gender Age EducationTotal Female Male (15-24) (25-64) (65 +) Low Mid High

EPH-15 cities1992 4.0 3.8 4.1 2.7 4.3 4.8 2.9 3.8 6.41993 4.0 3.8 4.1 2.9 4.3 4.7 3.0 3.7 6.51994 4.5 4.5 4.5 3.1 4.9 5.0 3.2 4.1 7.41995 4.4 4.3 4.5 2.9 4.7 4.8 3.0 4.0 7.81996 4.3 4.2 4.3 2.7 4.6 5.0 3.0 3.7 7.01997 4.2 4.2 4.3 2.7 4.5 5.7 3.0 3.7 7.01998 4.4 4.2 4.6 2.7 4.7 6.2 2.9 3.8 7.7

EPH - 28 cities1998 4.2 4.0 4.3 2.6 4.5 5.7 2.7 3.6 7.31999 4.0 4.0 4.1 2.7 4.3 5.1 2.7 3.6 6.82000 4.0 4.0 4.1 2.5 4.3 4.4 2.6 3.5 6.72001 4.0 4.0 4.0 2.5 4.3 4.7 2.6 3.5 6.62003 2.9 2.8 3.0 1.7 3.1 3.4 1.9 2.4 4.7

EPH-C2003-II 3.2 3.2 3.2 1.8 3.4 3.9 2.0 2.6 5.02004-I 3.2 3.3 3.2 1.8 3.4 6.1 2.1 2.8 4.92004-II 3.2 3.1 3.3 1.9 3.3 6.5 2.1 2.8 5.42005-I 3.3 3.2 3.3 2.0 3.5 3.5 2.2 2.8 5.42005-II 3.7 3.8 3.6 2.1 3.8 5.9 2.2 2.9 6.3

with PJH2002 2.8 2.7 3.0 1.7 3.0 3.4 1.8 2.4 4.82003 2.8 2.6 2.9 1.7 3.0 3.4 1.8 2.4 4.6

EPH-C2003-II * 3.0 3.1 3.0 1.8 3.2 3.6 2.0 2.5 4.92003-II 3.1 3.2 3.1 1.8 3.3 3.8 2.0 2.6 5.02004-I 3.2 3.2 3.2 1.8 3.3 6.0 2.1 2.8 4.92004-II 3.2 3.0 3.3 1.9 3.3 6.4 2.0 2.7 5.42005-I 3.2 3.2 3.3 2.0 3.5 3.4 2.2 2.8 5.32005-II 3.6 3.7 3.6 2.1 3.8 5.9 2.2 2.9 6.3


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Table 6.2 Hours of work By gender, age and education Argentina, 1992-2005


EPH-15 cities1992 44.3 37.6 48.4 41.5 45.4 35.7 45.1 45.4 40.81993 44.6 37.8 48.8 42.2 45.6 36.8 44.8 46.2 41.61994 44.0 36.5 48.4 42.0 44.7 38.7 44.0 45.8 40.61995 43.5 36.2 48.0 41.2 44.3 37.0 43.2 45.0 41.61996 43.6 36.2 48.2 41.1 44.6 34.2 42.3 45.7 42.21997 43.8 37.3 47.8 41.3 44.7 35.2 43.0 45.9 41.61998 43.9 37.0 48.5 40.5 45.0 37.3 43.7 45.7 41.4

EPH - 28 cities1998 44.0 37.1 48.5 40.9 45.0 37.7 43.8 45.9 41.41999 43.6 36.8 48.3 40.2 44.7 36.9 43.4 45.5 41.22000 43.1 36.6 47.5 39.6 44.1 37.7 42.4 44.8 41.42001 41.9 35.3 46.5 37.2 43.0 38.6 40.8 44.1 40.22003 41.1 34.6 45.6 36.9 42.0 38.2 40.2 43.0 39.5

EPH-C2003-II 40.8 34.4 45.4 38.1 41.7 34.1 40.4 42.8 38.72004-I 41.7 35.3 45.9 37.9 42.9 35.1 41.6 43.3 39.62004-II 41.4 35.0 45.7 38.1 42.7 33.0 41.2 42.6 40.02005-I 42.1 35.5 46.4 39.5 43.0 35.3 42.0 43.2 40.62005-II 42.0 35.3 46.6 38.7 43.2 34.0 41.5 43.7 40.2

with PJH2002 39.5 33.6 44.0 36.3 40.3 35.6 37.6 41.4 39.42003 39.6 33.0 44.7 35.5 40.4 38.0 37.7 41.6 39.2

2003-II * 39.1 32.5 44.2 36.5 39.9 33.4 37.5 40.8 38.32003-II 39.2 32.7 44.4 36.7 40.1 33.6 37.7 41.0 38.42004-I 40.3 33.6 45.1 36.8 41.3 34.7 39.0 41.9 39.52004-II 40.3 33.5 45.2 37.2 41.4 32.8 39.3 41.5 39.82005-I 41.0 34.1 45.9 38.7 41.8 35.1 39.9 42.3 40.42005-II 41.2 34.3 46.2 38.1 42.2 33.6 39.9 43.0 40.1


Table 6.3 Labor income By gender, age and education Argentina, 1992-2005


EPH-15 cities1992 677.6 539.5 762.7 445.6 745.2 501.9 506.5 648.3 1053.11993 694.6 551.9 782.0 459.3 761.6 531.0 503.4 664.3 1093.51994 702.0 577.3 776.5 454.9 768.3 577.5 487.4 678.1 1124.41995 693.4 537.5 786.7 416.0 762.6 563.9 453.1 659.8 1255.11996 672.8 530.5 757.8 393.7 739.5 641.5 426.6 620.6 1136.51997 668.2 540.7 745.7 394.8 732.9 611.9 430.4 627.6 1117.41998 702.8 548.7 801.5 392.8 766.5 827.3 424.9 634.8 1230.9

EPH - 28 cities1998 664.0 519.1 754.4 370.0 725.2 767.9 405.7 609.4 1162.41999 633.8 510.8 714.9 358.6 695.4 609.7 393.5 578.9 1077.02000 620.8 504.9 698.0 340.3 680.9 543.6 378.7 565.9 1051.02001 598.2 495.5 667.4 308.8 657.0 538.0 350.8 542.9 1020.32003 428.1 348.4 482.3 220.3 464.4 441.3 249.2 373.7 701.7

EPH-C2004-II 467.2 399.3 549.5 252.8 531.1 597.4 303.5 432.5 807.62005-I 494.8 410.8 582.7 287.2 563.1 412.9 329.2 446.6 840.32005-II 525.7 464.2 617.0 301.6 604.2 527.3 328.2 479.0 919.8

with PJH2002 403.6 321.6 463.3 212.6 439.9 361.7 226.6 352.6 715.72003 423.4 343.5 477.8 219.9 458.2 440.8 245.8 370.1 699.5

EPH-C2004-II 454.6 386.4 538.2 249.5 516.2 589.9 292.5 424.1 802.82005-I 482.7 398.3 572.4 284.0 548.8 405.2 317.1 440.6 835.82005-II 513.4 449.3 608.0 298.4 589.2 524.1 318.0 470.5 915.9


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Table 6.4 Hourly wages By type of work Argentina, 1992-2005

Formal workers Informal workersType of work Salaried workers Self-employed Salaried Self-employedEntrepreneursWage earnersSelf-employed Entrepreneurs Large firms Public sector professionals Small firms Unskilled

EPH-15 cities1992 3.6 4.4 4.1 4.1 9.4 3.1 4.01993 3.8 4.4 4.1 4.4 8.8 2.9 3.91994 4.3 5.0 4.7 5.5 10.4 3.2 4.21995 9.2 4.1 4.5 9.2 4.3 5.0 9.8 3.0 3.71996 8.9 4.0 4.5 8.9 4.2 5.1 10.1 3.0 3.61997 8.0 4.0 4.4 8.0 4.1 5.2 10.3 3.0 3.51998 8.8 4.1 4.7 8.8 4.4 5.5 12.6 2.8 3.5

EPH - 28 cities1998 8.5 3.9 4.3 8.5 4.2 5.2 11.6 2.6 3.21999 7.7 3.9 4.1 7.7 4.1 5.2 10.1 2.6 3.32000 7.2 3.9 4.0 7.2 4.2 5.2 8.5 2.6 3.32001 7.9 3.9 3.7 7.9 4.2 5.1 8.7 2.7 3.12003 5.9 2.8 2.8 5.9 3.0 3.8 6.1 1.8 2.3

EPH-C2003-II 6.8 3.1 3.0 6.8 3.4 4.2 6.4 2.0 2.52004-I 5.5 3.2 3.0 5.5 3.7 4.2 6.0 2.0 2.52004-II 5.1 3.2 3.1 5.1 3.6 4.1 6.0 2.0 2.62005-I 6.3 3.2 3.1 6.3 3.5 4.4 6.0 2.1 2.52005-II 9.5 3.4 3.2 9.5 3.7 4.9 6.3 2.1 2.6

with PJH2002 6.8 2.7 2.7 6.8 3.0 3.1 5.9 1.9 2.22003 5.9 2.7 2.8 5.9 3.0 3.1 6.1 1.8 2.2

EPH-C2003-II * 6.3 3.0 2.8 6.3 3.3 3.9 6.2 2.0 2.42003-II 6.8 3.0 3.0 6.8 3.4 4.0 6.4 2.0 2.52004-I 5.4 3.2 2.9 5.4 3.7 4.1 5.9 2.0 2.52004-II 5.1 3.1 3.0 5.1 3.6 4.0 6.0 2.0 2.52005-I 6.3 3.1 3.0 6.3 3.4 4.2 6.0 2.1 2.52005-II 9.4 3.4 3.1 9.4 3.7 4.8 6.3 2.1 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

Formal workers Informal workersType of work Salaried workers Self-employed Salaried Self-employedEntrepreneursWage earnersSelf-employedZero-income Entrepreneurs Large firms Public sector professionals Small firms Unskilled Zero-income

EPH-15 cities1992 43.6 44.2 45.2 45.6 40.5 39.2 42.5 44.8 45.21993 44.5 44.3 41.8 46.1 41.6 42.8 42.5 44.4 41.81994 43.9 43.8 38.1 46.0 40.3 41.7 41.1 44.1 38.11995 56.0 43.0 42.9 40.9 56.0 46.6 38.7 40.9 39.7 43.2 40.91996 56.4 43.1 43.7 36.4 56.4 46.8 39.6 43.3 40.0 43.7 36.41997 57.6 43.5 42.6 39.1 57.6 47.1 39.1 41.5 40.5 42.8 39.11998 58.4 43.6 42.6 39.5 58.4 47.3 39.4 41.4 40.6 42.8 39.5

EPH - 28 cities1998 57.8 43.5 43.5 40.4 57.8 47.4 39.1 42.0 40.6 43.8 40.41999 56.0 43.3 42.6 42.1 56.0 47.2 38.2 42.3 41.1 42.6 42.12000 57.2 42.9 41.4 40.3 57.2 47.0 38.1 41.5 40.6 41.4 40.32001 56.1 41.8 39.7 41.2 56.1 46.3 37.4 40.5 38.9 39.6 41.22003 54.8 41.4 38.2 38.5 54.8 46.1 36.7 39.2 38.7 38.0 38.5

EPH-C2003-II 51.6 41.1 38.7 31.8 51.6 45.8 37.6 37.8 37.5 38.8 31.82004-I 53.0 42.1 38.9 30.1 53.0 46.2 39.1 39.2 38.5 38.9 30.12004-II 54.3 41.3 39.7 32.5 54.3 45.9 38.9 39.4 36.9 39.8 32.52005-I 53.2 42.2 39.9 35.5 53.2 46.6 38.7 41.0 38.3 39.7 35.52005-II 52.6 41.8 41.1 31.4 52.6 45.9 39.4 39.7 37.3 41.3 31.4

with PJH2002 54.2 39.0 39.3 36.3 54.2 44.9 32.6 40.0 38.1 39.2 36.32003 54.8 39.3 38.4 38.7 54.8 45.4 32.1 39.3 38.5 38.2 38.7

2003-II * 51.9 38.8 39.0 31.9 51.9 44.8 32.1 37.5 37.2 39.2 31.92003-II 51.7 39.0 39.0 32.0 51.7 44.8 32.3 37.8 37.4 39.2 32.02004-I 53.1 40.2 39.1 29.9 53.1 45.2 33.7 39.6 38.3 39.1 29.92004-II 54.5 39.8 39.9 32.5 54.5 45.1 34.4 39.4 36.9 40.0 32.52005-I 53.2 40.7 40.0 36.4 53.2 45.9 34.5 41.0 38.1 39.8 36.42005-II 52.7 40.7 41.0 31.6 52.7 45.3 36.3 39.6 37.0 41.3 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.

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Table 6.6 Labor income By type of work Argentina, 1992-2005

Formal workers Informal workersType of work Salaried workers Self-employed Salaried Self-employedEntrepreneursWage earnersSelf-employed Entrepreneurs Large firms Public sector professionals Small firms Unskilled

EPH-15 cities1992 662.5 751.8 774.3 775.3 1427.9 534.7 682.71993 701.1 699.8 788.7 834.9 1437.6 486.8 617.11994 701.4 735.6 798.8 837.3 1500.0 480.0 631.61995 1865.5 659.2 645.8 1865.5 754.7 753.9 1415.7 420.2 533.51996 1848.0 647.8 631.8 1848.0 750.5 776.4 1374.9 412.5 510.61997 1699.7 646.1 618.4 1699.7 727.4 806.2 1478.5 397.3 483.61998 1937.7 673.9 653.8 1937.7 775.6 828.2 1683.4 404.8 489.8

EPH - 28 cities1998 1833.4 638.1 610.3 1833.4 739.5 781.0 1578.9 383.4 468.61999 1619.6 625.2 550.6 1619.6 729.0 769.4 1472.3 373.2 429.32000 1458.3 624.5 530.4 1458.3 733.9 770.0 1319.1 378.7 425.22001 1453.7 611.5 478.5 1453.7 736.0 736.2 1257.9 358.7 378.32003 1131.7 436.6 346.3 1131.7 521.9 540.8 842.6 246.9 265.5

EPH-C2004-II 1004.3 498.0 413.8 1004.3 605.3 633.4 870.0 272.6 330.52005-I 1231.5 517.9 414.9 1231.5 625.2 653.3 901.7 295.3 320.12005-II 1507.6 548.0 435.2 1507.6 628.8 749.6 878.8 291.6 351.0

with PJH2002 1218.3 404.9 328.4 1218.3 519.2 416.4 833.4 247.9 255.02003 1129.9 431.9 342.6 1129.9 520.2 528.1 833.5 244.8 263.6

EPH-C2004-II 998.4 484.5 404.3 998.4 599.4 587.2 862.5 268.6 323.42005-I 1229.9 504.8 407.9 1229.9 618.0 608.9 901.0 291.6 315.22005-II 1501.1 536.0 424.9 1501.1 622.5 710.8 872.7 287.6 343.2

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 Industry Industry Utilities & Skilled Public Education & Domesticactivities low tech high tech Construction Commerce transportation services administration Health servants

EPH-15 cities1992 4.3 3.1 3.9 3.1 3.6 4.1 6.4 4.3 4.5 3.21993 7.5 3.1 4.1 3.6 3.3 4.0 6.3 4.4 4.6 3.31994 4.8 3.3 4.4 3.4 3.5 4.4 7.0 5.3 5.6 3.61995 6.6 3.2 4.9 3.3 3.3 4.2 6.9 5.1 5.1 3.51996 4.0 3.2 4.4 3.1 3.2 3.9 6.3 5.3 5.3 3.51997 4.0 3.0 4.3 3.3 3.0 4.2 6.3 5.1 5.1 3.61998 3.5 3.1 4.6 3.1 3.1 4.1 7.1 5.7 5.5 3.3

EPH - 28 cities1998 3.8 3.0 4.4 2.9 3.0 3.9 6.8 5.4 5.2 3.01999 3.6 2.9 4.2 3.0 2.9 3.5 5.8 5.3 5.4 3.02000 3.5 2.9 4.4 3.0 2.9 3.5 6.1 5.4 5.2 2.92001 5.1 3.0 4.2 3.2 2.8 3.8 6.3 5.1 5.1 2.92003 2.8 2.5 3.0 2.0 2.0 2.5 4.3 3.8 3.8 2.1

EPH-C2003-II 5.3 2.1 3.4 2.4 2.1 3.1 4.5 4.4 4.4 2.12004-I 3.9 2.2 3.7 2.4 2.2 3.6 4.5 4.4 4.4 2.12004-II 4.3 2.4 4.2 2.4 2.2 3.1 4.9 4.2 4.2 2.22005-I 4.3 2.6 3.9 2.4 2.4 3.0 4.8 4.3 4.3 2.12005-II 2.5 3.8 2.4 2.5 3.2 5.7 4.9 4.6 2.1

with PJH2002 2.6 2.6 2.8 2.2 1.9 2.8 4.7 2.9 3.5 2.12003 2.6 2.4 3.0 2.0 1.9 2.5 4.3 3.2 3.4 2.1

EPH-C2003-II * 4.6 2.0 3.3 2.3 2.1 2.9 4.3 4.0 4.2 2.12003-II 5.1 2.1 3.3 2.3 2.1 3.0 4.4 4.2 4.3 2.12004-I 3.9 2.2 3.7 2.4 2.2 3.5 4.5 4.2 4.4 2.12004-II 4.2 2.4 4.2 2.3 2.2 3.1 4.9 4.1 4.1 2.22005-I 4.1 2.5 3.9 2.3 2.4 2.9 4.8 4.2 4.2 2.12005-II 2.5 3.8 2.4 2.5 3.1 5.7 4.8 4.6 2.1


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Table 6.8 Hours of work By sector Argentina, 1992-2005


EPH-15 cities1992 57.1 46.6 47.4 46.6 48.9 50.5 42.4 42.3 38.6 31.31993 49.8 48.3 47.1 44.4 49.7 54.8 42.4 43.9 37.1 31.71994 52.6 48.0 46.0 45.3 49.4 52.4 44.0 42.9 36.8 27.91995 53.3 47.9 43.7 42.3 49.7 53.2 44.1 42.7 36.7 27.11996 50.5 45.5 45.3 42.7 49.9 54.3 43.9 42.0 36.5 26.51997 53.2 48.5 47.0 41.2 50.9 52.8 44.7 41.1 36.8 26.51998 52.7 48.3 46.5 42.9 49.2 55.1 45.5 42.5 37.3 26.2

EPH - 28 cities1998 52.9 48.0 46.6 43.4 49.7 54.8 45.5 42.1 37.0 26.91999 49.3 47.0 45.0 43.9 49.5 56.3 44.7 41.3 36.0 28.12000 55.2 47.9 44.3 41.8 47.8 53.4 45.2 41.4 36.5 28.72001 50.5 47.2 43.2 38.7 47.1 53.2 44.7 40.8 35.5 26.72003 49.1 43.3 45.0 38.5 46.6 52.5 42.8 40.2 34.8 25.7

EPH-C2003-II 47.4 42.7 43.1 38.6 45.8 50.4 42.9 42.0 33.9 28.52004-I 48.4 44.3 44.9 39.1 46.3 50.9 41.5 43.0 35.3 28.62004-II 50.4 42.0 44.6 39.8 46.6 50.0 41.7 43.3 35.7 25.72005-I 49.7 43.9 45.2 41.4 46.6 51.2 42.2 42.9 35.6 29.12005-II 45.3 44.2 45.0 41.4 46.3 51.8 42.6 43.7 36.2 26.6

with PJH2002 47.2 46.2 43.3 38.1 46.4 49.7 42.9 33.8 33.1 25.52003 43.8 42.6 44.7 37.9 46.3 52.6 42.7 35.1 32.5 26.0

2003-II * 38.7 41.9 42.8 38.3 45.5 50.5 42.8 36.5 31.0 28.02003-II 38.6 42.1 42.9 38.4 45.7 50.4 42.8 36.8 31.2 28.42004-I 42.6 42.7 44.7 39.2 46.2 50.9 41.4 37.7 32.8 28.42004-II 42.0 40.8 44.5 39.8 46.6 50.1 41.6 39.5 33.3 26.02005-I 43.5 42.8 45.1 41.2 46.4 51.2 42.1 39.7 33.4 29.12005-II 41.7 43.3 45.0 41.0 46.2 51.8 42.5 41.6 34.3 26.7


Table 6.9 Labor income By sector Argentina, 1992-2005


EPH-15 cities1992 929.5 562.7 705.4 578.2 639.9 802.3 1013.8 796.9 690.6 366.81993 884.6 586.7 772.8 609.3 614.5 863.5 1019.8 831.3 723.7 366.71994 1006.1 573.3 750.0 559.8 622.6 805.7 1130.6 848.6 730.8 333.61995 1226.1 583.3 815.9 506.0 586.0 793.7 1177.9 860.3 669.4 284.41996 770.7 559.1 769.1 478.9 588.6 765.4 1063.7 859.9 690.4 272.71997 833.8 558.2 759.0 473.4 581.2 759.0 1083.2 832.2 686.7 267.11998 649.8 572.4 820.4 476.3 556.4 772.8 1238.1 953.5 727.8 260.2

EPH - 28 cities1998 656.8 556.9 786.9 450.2 537.9 744.4 1191.4 881.7 693.6 243.81999 677.4 519.8 719.5 477.7 525.9 698.7 1036.0 850.7 674.7 249.82000 715.7 524.8 744.2 430.7 489.6 663.3 1045.3 848.0 680.1 250.82001 937.6 514.1 690.0 386.7 463.8 661.1 1046.0 804.7 663.9 231.62003 622.6 403.0 524.5 264.9 324.8 475.5 697.6 612.4 463.8 153.3

EPH-C2004-II 856.3 411.6 668.1 344.9 395.1 568.8 734.7 711.5 568.1 175.82005-I 848.4 460.3 682.8 364.6 425.2 571.4 778.9 718.5 575.0 188.42005-II 448.4 674.3 368.6 445.3 615.3 852.0 839.6 626.6 180.9

with PJH2002 527.1 433.2 484.8 245.1 312.7 465.1 770.6 404.6 423.7 151.32003 620.1 395.5 520.7 263.2 321.7 473.2 696.3 598.5 459.6 152.1

EPH-C2004-II 804.8 402.6 665.2 338.6 388.5 562.8 730.7 667.2 544.0 174.02005-I 755.3 447.2 678.2 358.9 420.8 565.3 774.6 681.5 553.6 186.62005-II 1662.2 441.0 670.1 362.4 438.1 611.1 848.1 802.0 606.3 180.2


Table 6.10 Distribution of labor income

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Shares Argentina, 1992-2005

Salaried Self- employed Entrepreneursworkers Total

EPH-15 cities1992 73.9 26.1 100.01993 73.7 26.3 100.01994 74.0 26.0 100.01995 68.1 21.1 10.8 100.01996 69.2 20.9 9.9 100.01997 70.0 20.3 9.7 100.01998 69.8 19.8 10.3 100.0

EPH - 28 cities1998 71.9 18.8 9.3 100.01999 72.5 19.1 8.4 100.02000 72.6 18.8 8.6 100.02001 72.6 18.3 9.2 100.02002 72.6 18.3 9.1 100.02003 70.7 20.6 8.7 100.0

EPH-C2004-II 72.8 18.0 9.2 100.02005-I 73.2 17.3 9.5 100.0


Table 6.11 Distribution of wages (primary activity) Gini coefficient Argentina, 1992-2005

Male workers aged 25-55All Education

All Low Mid High EPH-15 cities

1992 0.400 0.412 0.319 0.362 0.4191993 0.390 0.397 0.305 0.354 0.4021994 0.392 0.397 0.307 0.345 0.3961995 0.410 0.420 0.311 0.371 0.4171996 0.415 0.417 0.321 0.358 0.4121997 0.413 0.404 0.326 0.331 0.3971998 0.434 0.438 0.330 0.371 0.418

EPH - 28 cities1998 0.435 0.436 0.330 0.371 0.4191999 0.424 0.417 0.335 0.352 0.4012000 0.433 0.437 0.352 0.377 0.4032001 0.445 0.444 0.368 0.373 0.4272003 0.440 0.461 0.335 0.391 0.458

EPH-C2003-II 0.453 0.439 0.349 0.385 0.4282004-I 0.455 0.440 0.366 0.381 0.4352004-II 0.442 0.408 0.338 0.364 0.3862005-I 0.429 0.412 0.336 0.355 0.4032005-II 0.458 0.413 0.342 0.346 0.393

with PJH2002 0.453 0.465 0.364 0.398 0.4412003 0.439 0.460 0.326 0.391 0.458

EPH-C2003-II * 0.454 0.438 0.349 0.391 0.4252003-II 0.458 0.443 0.349 0.390 0.4302004-I 0.460 0.442 0.367 0.382 0.4342004-II 0.443 0.411 0.343 0.364 0.3862005-I 0.431 0.416 0.343 0.357 0.4062005-II 0.459 0.415 0.345 0.348 0.395


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Table 6.12 Correlations hours of work-hourly wages Argentina, 1992-2005

All workers Urban salariedworkers

EPH-15 cities1992 -0.18 -0.171993 -0.22 -0.171994 -0.23 -0.201995 -0.18 -0.181996 -0.19 -0.171997 -0.21 -0.201998 -0.16 -0.17

EPH - 28 cities1998 -0.16 -0.171999 -0.19 -0.182000 -0.19 -0.172001 -0.18 -0.182003 -0.16 -0.17

EPH-C2004-II -0.10 -0.082005-I -0.18 -0.172005-II -0.07 -0.10

with PJH2002 -0.13 -0.102003 -0.16 -0.16

EPH-C2004-II -0.09 -0.082005-I -0.17 -0.162005-II -0.07 -0.09


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Table 6.13 Ratio of hourly wages by educational group Prime-age males Argentina, 1992-2005

High/Medium High/Low Medium/Low EPH-15 cities

1992 1.86 2.61 1.411993 1.85 2.57 1.391994 1.84 2.64 1.431995 1.99 2.81 1.411996 1.93 2.60 1.341997 1.95 2.64 1.351998 2.09 3.04 1.45

EPH - 28 cities1998 2.05 2.97 1.451999 1.95 2.70 1.382000 1.97 2.84 1.442001 2.03 2.77 1.372003 2.11 3.02 1.43

EPH-C2003-II 1.93 2.60 1.352004-I 1.88 2.49 1.332004-II 1.77 2.46 1.392005-I 1.87 2.46 1.322005-II 1.92 2.56 1.33

with PJH2002 2.09 3.02 1.442003 2.14 3.09 1.45

2003-II * 1.93 2.61 1.352003-II 1.97 2.69 1.362004-I 1.91 2.55 1.342004-II 1.78 2.52 1.412005-I 1.89 2.49 1.322005-II 1.93 2.57 1.33


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Table 6.14 Mincer equation Estimated coefficients of educational dummies Argentina, 1992-2005 All workers Urban salaried workers

Men Women Men WomenPrimary Secondary College Primary Secondary College Primary Secondary College Primary Secondary College

EPH-15 cities1992 0.287 0.451 0.557 -0.095 0.462 0.454 0.153 0.432 0.560 0.007 0.383 0.2261993 0.090 0.453 0.606 0.005 0.322 0.496 0.113 0.419 0.657 -0.025 0.387 0.3511994 0.178 0.429 0.716 -0.013 0.431 0.404 0.187 0.362 0.687 0.003 0.416 0.4031995 0.140 0.510 0.707 -0.027 0.277 0.291 0.183 0.403 0.664 0.020 0.364 0.4801996 0.159 0.475 0.716 0.024 0.292 0.605 0.167 0.336 0.732 0.073 0.232 0.5031997 0.173 0.428 0.624 0.072 0.322 0.452 0.178 0.439 0.598 -0.053 0.363 0.4301998 0.253 0.476 0.779 0.011 0.350 0.208 0.162 0.456 0.679 0.000 0.469 0.450

EPH - 28 cities1998 0.227 0.457 0.757 0.042 0.450 0.523 0.161 0.442 0.668 0.025 0.457 0.4671999 0.175 0.381 0.753 0.032 0.340 0.341 0.183 0.326 0.670 -0.057 0.403 0.5122000 0.158 0.487 0.726 -0.023 0.304 0.331 0.118 0.421 0.697 -0.070 0.387 0.5772001 0.232 0.414 0.785 0.160 0.320 0.198 0.252 0.364 0.675 0.109 0.393 0.5662003 0.256 0.414 0.784 0.103 0.399 0.624 0.250 0.344 0.723 -0.182 0.380 0.552

EPH-C2003-II 0.104 0.408 0.680 0.159 0.420 0.682 0.094 0.403 0.601 0.196 0.303 0.6312004-I 0.174 0.394 0.678 0.125 0.297 0.374 0.194 0.318 0.671 0.166 0.269 0.6072004-II 0.183 0.390 0.548 0.133 0.408 0.588 0.280 0.328 0.588 0.157 0.314 0.5802005-I 0.115 0.358 0.669 0.159 0.247 0.393 0.140 0.321 0.612 0.204 0.302 0.5592005-II 0.129 0.390 0.653 0.126 0.377 0.667 0.106 0.371 0.626 0.012 0.336 0.603

with PJH2002 0.306 0.472 0.825 0.041 0.432 0.300 0.211 0.344 0.753 0.029 0.384 0.6022003 0.240 0.409 0.804 0.083 0.344 0.676 0.233 0.346 0.737 -0.136 0.349 0.585

EPH-C2003-II * 0.126 0.402 0.688 0.176 0.312 0.377 0.101 0.383 0.610 0.194 0.322 0.6282003-II 0.084 0.350 0.693 0.132 0.325 0.647 0.100 0.392 0.616 0.196 0.322 0.6322004-I 0.173 0.399 0.677 0.076 0.251 0.367 0.195 0.322 0.673 0.172 0.270 0.6102004-II 0.156 0.382 0.545 0.140 0.395 0.573 0.268 0.333 0.595 0.143 0.316 0.5822005-I 0.098 0.366 0.677 0.100 0.232 0.389 0.134 0.327 0.619 0.159 0.322 0.5582005-II 0.181 0.392 0.650 0.058 0.300 0.492 0.119 0.375 0.623 0.006 0.338 0.609


Table 6.15 Mincer equation Dispersion in unobservables and gender wage gap Argentina, 1992-2005

Dispersion in unobservables Gender wage gap All workers Urban salaried Urban salaried

Men Women Men Women workers EPH-15 cities

1992 0.640 0.655 0.528 0.503 0.8661993 0.605 0.617 0.538 0.509 0.8751994 0.616 0.613 0.533 0.517 0.9041995 0.631 0.817 0.540 0.517 0.9221996 0.626 0.640 0.550 0.520 0.9141997 0.632 0.644 0.561 0.536 0.8981998 0.619 0.833 0.560 0.543 0.857

EPH - 28 cities1998 0.617 0.649 0.563 0.542 0.8541999 0.612 0.778 0.569 0.545 0.8892000 0.640 0.825 0.567 0.558 0.8692001 0.665 0.900 0.596 0.570 0.8872003 0.666 0.637 0.585 0.536 0.890

EPH-C2003-II 0.680 0.757 0.617 0.587 0.9342004-I 0.680 0.942 0.599 0.577 0.8902004-II 0.695 0.679 0.584 0.562 0.9042005-I 0.638 0.890 0.586 0.581 0.8892005-II 0.629 0.688 0.570 0.574 0.882

with PJH2002 0.695 0.878 0.574 0.530 0.8682003 0.661 0.633 0.582 0.522 0.892

EPH-C2003-II * 0.683 0.885 0.617 0.591 0.9322003-II 0.720 0.769 0.617 0.591 0.9292004-I 0.689 0.958 0.602 0.577 0.8952004-II 0.712 0.689 0.586 0.566 0.9002005-I 0.643 0.889 0.590 0.585 0.8872005-II 0.637 0.842 0.572 0.580 0.877


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Table 6.16 Share of adults in the labor force Argentina, 1992-2005

Adults (25-64) Age Gender Education

Total (15-24) (25-64) (65 +) Female Male Low Medium High15 main cities

1992 0.560 0.492 0.685 0.113 0.483 0.914 0.608 0.708 0.8411993 0.568 0.494 0.698 0.116 0.507 0.912 0.618 0.714 0.8541994 0.565 0.504 0.698 0.095 0.508 0.910 0.618 0.714 0.8561995 0.573 0.502 0.712 0.095 0.533 0.913 0.643 0.730 0.8561996 0.575 0.506 0.714 0.103 0.530 0.916 0.638 0.729 0.8431997 0.582 0.494 0.726 0.119 0.550 0.919 0.649 0.739 0.8611998 0.584 0.463 0.735 0.137 0.563 0.929 0.659 0.742 0.873

28 main cities1998 0.571 0.450 0.722 0.129 0.545 0.923 0.652 0.729 0.8541999 0.577 0.444 0.730 0.138 0.563 0.919 0.648 0.745 0.8582000 0.581 0.439 0.736 0.138 0.571 0.920 0.663 0.752 0.8422001 0.569 0.417 0.733 0.119 0.565 0.921 0.660 0.739 0.8522003 0.564 0.401 0.735 0.134 0.575 0.908 0.641 0.750 0.846

EPH-C2003-II 0.599 0.466 0.767 0.149 0.620 0.927 0.685 0.773 0.8532004-I 0.602 0.469 0.765 0.161 0.619 0.926 0.690 0.768 0.8492004-II 0.605 0.462 0.771 0.159 0.625 0.929 0.698 0.782 0.8622005-I 0.599 0.448 0.764 0.168 0.613 0.927 0.690 0.773 0.8552005-II 0.608 0.450 0.772 0.176 0.629 0.926 0.702 0.769 0.870

with PJH2002 0.580 0.414 0.749 0.125 0.600 0.915 0.684 0.752 0.8492003 0.577 0.414 0.744 0.135 0.596 0.909 0.662 0.760 0.844

2003-II 0.614 0.482 0.776 0.152 0.641 0.929 0.708 0.781 0.8552004-I 0.616 0.479 0.774 0.164 0.641 0.927 0.714 0.776 0.8492004-II 0.618 0.471 0.780 0.161 0.646 0.930 0.720 0.789 0.8622005-I 0.610 0.456 0.769 0.172 0.629 0.928 0.707 0.776 0.8562005-II 0.617 0.457 0.776 0.179 0.643 0.926 0.713 0.774 0.870


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Table 6.17 Share of adults employed Argentina, 1992-2005



1992 0.522 0.424 0.651 0.107 0.459 0.869 0.572 0.672 0.8161993 0.516 0.397 0.651 0.113 0.466 0.861 0.567 0.669 0.8161994 0.495 0.389 0.632 0.083 0.451 0.835 0.550 0.640 0.8121995 0.476 0.353 0.618 0.080 0.451 0.806 0.538 0.632 0.7961996 0.473 0.341 0.617 0.090 0.446 0.804 0.529 0.632 0.7691997 0.500 0.368 0.645 0.106 0.476 0.830 0.563 0.651 0.8001998 0.509 0.354 0.660 0.122 0.496 0.846 0.570 0.670 0.823

28 main cities1998 0.500 0.344 0.652 0.115 0.484 0.842 0.568 0.662 0.8071999 0.497 0.332 0.649 0.123 0.493 0.825 0.561 0.662 0.7912000 0.495 0.318 0.649 0.120 0.498 0.819 0.564 0.662 0.7842001 0.464 0.283 0.621 0.103 0.485 0.774 0.532 0.622 0.7772003 0.470 0.259 0.643 0.121 0.508 0.789 0.543 0.645 0.780

EPH-C2003-II 0.501 0.307 0.674 0.128 0.530 0.831 0.576 0.678 0.7832004-I 0.509 0.317 0.679 0.141 0.534 0.837 0.598 0.674 0.7782004-II 0.525 0.330 0.696 0.144 0.549 0.853 0.615 0.700 0.8072005-I 0.521 0.321 0.690 0.150 0.540 0.853 0.612 0.694 0.7962005-II 0.542 0.339 0.711 0.161 0.566 0.868 0.633 0.702 0.827

with PJH2002 0.476 0.275 0.641 0.108 0.516 0.780 0.565 0.641 0.7622003 0.487 0.275 0.655 0.121 0.533 0.793 0.572 0.658 0.779

2003-II 0.519 0.327 0.688 0.131 0.558 0.836 0.608 0.690 0.7852004-I 0.526 0.331 0.691 0.145 0.561 0.840 0.630 0.685 0.7782004-II 0.540 0.341 0.707 0.147 0.575 0.857 0.643 0.709 0.8082005-I 0.533 0.331 0.698 0.154 0.559 0.855 0.635 0.699 0.7962005-II 0.552 0.346 0.716 0.164 0.581 0.868 0.647 0.708 0.827


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Table 6.18 Unemployment rates Argentina, 1992-2005



1992 0.068 0.137 0.050 0.056 0.051 0.049 0.058 0.051 0.0301993 0.092 0.197 0.066 0.029 0.081 0.057 0.082 0.062 0.0451994 0.123 0.227 0.094 0.130 0.112 0.083 0.110 0.104 0.0521995 0.169 0.297 0.132 0.159 0.154 0.118 0.163 0.133 0.0701996 0.177 0.326 0.137 0.122 0.158 0.123 0.171 0.133 0.0891997 0.141 0.256 0.112 0.113 0.134 0.097 0.132 0.118 0.0711998 0.128 0.236 0.101 0.108 0.119 0.089 0.135 0.097 0.057

28 main cities1998 0.125 0.235 0.097 0.108 0.111 0.087 0.129 0.092 0.0551999 0.139 0.253 0.111 0.107 0.124 0.102 0.134 0.112 0.0782000 0.148 0.275 0.117 0.131 0.129 0.110 0.149 0.120 0.0692001 0.184 0.322 0.153 0.138 0.141 0.160 0.194 0.159 0.0882003 0.166 0.355 0.125 0.101 0.116 0.131 0.152 0.140 0.078

EPH-C2003-II 0.164 0.342 0.121 0.137 0.144 0.104 0.159 0.124 0.0822004-I 0.154 0.324 0.113 0.121 0.136 0.096 0.133 0.122 0.0832004-II 0.133 0.285 0.098 0.092 0.121 0.081 0.119 0.106 0.0632005-I 0.131 0.283 0.096 0.107 0.118 0.080 0.113 0.103 0.0702005-II 0.109 0.248 0.079 0.084 0.100 0.063 0.098 0.087 0.049

with PJH2002 0.179 0.335 0.145 0.136 0.140 0.148 0.174 0.148 0.1022003 0.157 0.335 0.119 0.100 0.106 0.128 0.136 0.134 0.078

2003-II 0.154 0.322 0.114 0.138 0.130 0.101 0.141 0.116 0.0822004-I 0.146 0.309 0.107 0.118 0.124 0.094 0.118 0.117 0.0842004-II 0.126 0.276 0.093 0.090 0.111 0.079 0.107 0.102 0.0632005-I 0.125 0.274 0.092 0.104 0.110 0.078 0.103 0.100 0.0702005-II 0.106 0.242 0.077 0.084 0.096 0.062 0.093 0.085 0.049


Table 6.19 Duration of unemployment Argentina, 1992-2005



1992 3.8 4.0 3.7 4.3 4.2 3.3 3.1 4.0 4.81993 5.1 5.2 5.1 3.4 5.4 4.9 4.3 5.9 5.81994 5.4 5.2 5.5 8.8 6.6 4.5 4.9 5.8 6.51995 6.8 6.3 7.0 11.6 8.2 5.9 6.1 7.4 10.01996 8.1 7.9 8.1 13.2 10.6 6.1 7.2 8.3 10.31997 6.6 6.6 6.3 13.0 7.8 5.0 5.3 7.1 7.51998 6.2 5.7 6.7 4.0 9.0 4.6 5.9 6.8 9.0

28 main cities1998 6.1 5.5 6.5 4.7 8.7 4.7 5.8 6.6 8.41999 6.4 5.9 6.6 8.7 8.6 4.9 5.5 6.5 9.32000 6.6 6.3 6.8 6.0 8.9 5.1 6.1 7.1 8.52001 6.8 7.0 6.7 9.5 8.3 5.7 6.1 6.8 8.42003 8.5 7.6 9.0 11.1 12.0 7.2 8.0 9.3 10.2

EPH-C2003-II 11.7 10.6 12.5 13.2 13.9 11.0 12.3 11.9 13.62004-I 10.6 9.2 11.6 11.1 13.3 9.7 10.5 11.8 12.72004-II 10.0 9.2 10.6 9.8 11.9 9.3 9.6 11.1 11.62005-I 9.8 8.4 10.7 11.4 12.0 9.3 9.5 11.7 10.82005-II 10.2 8.8 11.0 13.5 12.6 9.1 10.5 10.9 12.2

with PJH2002 8.9 8.7 8.8 18.4 11.1 7.1 7.2 9.4 11.12003 8.5 7.6 8.9 11.1 11.8 7.2 7.9 9.3 10.2

2003-II 11.7 10.6 12.4 13.0 13.8 11.0 12.4 11.8 13.52004-I 10.7 9.2 11.6 11.1 13.4 9.8 10.7 11.9 12.72004-II 10.0 9.2 10.6 9.8 12.0 9.2 9.6 11.2 11.72005-I 9.8 8.4 10.7 11.4 12.0 9.3 9.5 11.8 10.72005-II 10.2 8.8 11.0 13.7 12.6 9.1 10.6 10.9 12.2


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Table 6.20 La orce, e loyment te and unemployment rate Argentina, 2003-2005 Encuesta Permanente de Hogares Continua

bor f mp ra

Alternative 1 Alternative 2Labor force Employment Unemployment Labor force Employment Unemployment

I-2003 45.6 36.3 20.4 44.2 33.5 24.3II-2003 45.6 37.4 17.8 44.4 35.1 21.0III-2003 45.7 38.2 16.3 44.7 35.9 19.6IV-2003 45.7 39.1 14.5 44.6 36.7 17.7

2004 45.4 38.9 14.4 44.3 36.6 17.42004 46.2 39.4 14.8 45.3 37.4 17.4-2004 0.1 .1 38.0 15.7

V-2004 5.9 40.4 12.1 45.0 38.5 14.52005 39.4 13.0 44.4 37.7 14.92005 0.1 44.7 38.4 13.9-2005 1.1 45.3 39.7 12.5

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.

I-II-III 46.2 4 13.2 45I 4I- 45.2II- 45.6 4 12.1III 46.2 4 11.1

Source: INDEC, boletines de prensa.

Table 6.21 Age, gender and educational structure of employment Argentina, 1992-2005

Gender Age EducationFemale Male (0-14) (15-24) (25-40) (41-64) (65 +) Low Medium High

15 main cities1992 37.2 62.8 0.5 19.1 39.0 38.6 2.8 39.8 38.8 21.41993 37.6 62.4 0.5 18.3 38.4 39.8 3.0 38.0 39.1 22.91994 37.2 62.8 0.3 18.8 39.7 38.7 2.4 37.7 39.2 23.21995 38.1 61.9 0.3 18.3 40.9 38.2 2.4 40.9 37.7 21.51996 38.0 62.0 0.6 20.4 37.4 38.9 2.7 36.4 38.9 24.71997 38.3 61.7 0.2 17.8 39.3 39.6 3.1 37.7 37.7 24.61998 39.5 60.5 0.2 17.4 39.3 39.7 3.3 36.3 38.1 25.5

28 main cities1998 39.0 61.0 0.3 17.3 39.8 39.4 3.2 37.4 37.7 24.91999 40.0 60.0 0.4 19.6 36.8 39.8 3.3 35.8 38.5 25.72000 40.2 59.8 0.3 19.0 37.7 39.8 3.2 35.6 38.2 26.22001 40.9 59.1 0.2 15.5 39.8 41.5 3.0 34.8 37.5 27.82003 40.8 59.2 0.1 14.0 40.3 41.9 3.7 30.6 39.0 30.4

EPH-C2003-II 40.5 59.5 0.6 17.8 35.9 41.8 3.9 28.6 39.2 32.22004-I 40.4 59.6 0.7 18.1 36.5 40.6 4.1 29.4 38.7 32.02004-II 40.1 59.9 0.6 18.0 36.8 40.6 4.0 31.8 40.3 27.92005-I 40.0 60.0 0.6 17.8 36.5 40.8 4.2 31.6 40.0 28.42005-II 40.7 59.3 0.6 17.6 37.5 40.0 4.3 31.4 39.2 29.4

with PJH2002 42.2 57.8 0.3 17.4 37.9 41.3 3.1 34.2 38.0 27.82003 42.9 57.1 0.1 14.1 41.0 41.3 3.5 32.7 38.8 28.4

2003-II 42.5 57.5 0.5 18.0 36.8 41.0 3.7 30.7 39.1 30.22004-I 42.6 57.4 0.6 18.0 37.3 40.2 3.9 31.6 38.5 29.92004-II 42.1 57.9 0.6 17.8 37.5 40.3 3.8 34.0 39.8 26.22005-I 41.8 58.2 0.5 17.7 37.1 40.6 4.1 33.9 39.3 26.82005-II 42.3 57.7 0.6 17.3 38.0 39.9 4.2 33.1 38.9 28.0


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Table 6.22 Regional structure of employment Argentina, 1992-2005

GBA Pampeana Cuyo NOA Patagonia NEA15 main cities

1992 74.5 15.7 2.6 4.6 2.71993 75.6 15.0 2.6 4.2 2.61994 74.2 15.7 2.8 4.5 2.81995 73.4 15.4 2.9 5.1 3.21996 73.3 15.3 3.0 4.9 3.51997 73.1 15.6 2.9 5.1 3.31998 73.5 15.3 2.9 5.0 3.3

28 main cities1998 57.2 21.8 6.1 8.0 2.6 4.31999 57.0 21.7 6.2 8.2 2.6 4.32000 55.9 22.6 6.2 8.3 2.7 4.32001 55.1 22.6 6.3 8.6 2.9 4.52003 55.7 22.6 6.3 8.2 3.0 4.2

EPH-C2003-II 56.2 22.5 6.3 8.6 2.4 4.12004-I 56.5 22.3 6.4 8.3 2.5 4.12004-II 56.6 22.3 6.1 8.5 2.5 4.02005-I 55.8 22.7 6.3 8.6 2.5 4.22005-II 56.5 22.4 6.0 8.5 2.5 4.1

with PJH2002 55.4 22.6 6.3 8.4 2.8 4.62003 55.2 22.6 6.1 8.6 2.9 4.6

2003-II 55.7 22.3 6.3 9.0 2.4 4.42004-I 55.9 22.1 6.4 8.7 2.5 4.42004-II 56.2 22.1 6.1 8.8 2.5 4.32005-I 55.5 22.5 6.3 8.9 2.5 4.42005-II 56.0 22.4 6.1 8.8 2.4 4.3


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Table 6.23 Structure of employment By type of work Argentina, 1992-2005 Labor relationship Type of firm

Entrepreneurs Wage earners Self-employed Zero income Large Small Public(i) (ii) (iii) (iv) (v) (vi) (vii)

GBA1992 5.2 70.0 23.6 1.21993 5.5 68.7 24.6 1.31994 4.6 70.1 23.9 1.5

15 main cities1995 4.9 71.0 22.7 1.4 36.1 48.3 15.61996 4.5 72.2 21.7 1.6 35.1 49.4 15.61997 4.8 72.7 21.1 1.4 36.7 47.7 15.61998 4.7 73.4 20.7 1.2 37.0 47.5 15.5

28 main cities1998 4.6 72.5 21.6 1.3 35.1 48.7 16.31999 4.5 72.5 21.6 1.4 34.7 49.0 16.32000 4.6 72.1 22.1 1.2 33.2 49.8 16.92001 4.4 71.3 23.4 0.9 31.8 50.5 17.62003 4.2 69.9 24.7 1.2 30.9 51.4 17.7

EPH-C2003-II 4.1 71.9 22.2 1.8 31.2 52.3 16.52004-I 4.1 73.0 21.2 1.7 33.3 51.3 15.42004-II 4.4 72.9 21.3 1.4 33.1 51.1 15.82005-I 4.0 73.7 21.2 1.1 34.0 50.5 15.52005-II 4.3 73.9 20.5 1.3 35.5 48.9 15.6

with PJH2002 4.0 72.0 23.0 1.0 28.8 48.8 22.42003 3.8 71.8 23.2 1.1 29.3 48.5 22.2

2003-II 3.8 73.7 20.9 1.7 29.8 49.1 21.12004-I 3.8 74.5 20.2 1.6 31.9 48.8 19.32004-II 4.1 74.1 20.4 1.4 31.8 49.0 19.22005-I 3.8 74.7 20.4 1.1 33.0 48.7 18.42005-II 4.1 74.5 20.2 1.2 34.5 47.9 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 cities1995 5.2 34.1 15.3 3.3 20.3 20.5 1.51996 4.6 33.4 15.2 3.2 22.6 19.2 1.71997 4.9 35.0 15.3 3.0 21.8 18.5 1.41998 4.8 35.4 15.2 3.0 22.2 18.1 1.3

28 main cities1998 4.7 33.5 16.0 3.0 22.3 19.1 1.41999 4.6 33.2 16.1 2.9 22.4 19.3 1.42000 4.8 31.6 16.5 2.9 22.9 20.1 1.22001 4.5 30.5 17.3 3.1 22.6 21.0 1.02003 4.3 29.8 17.2 3.8 22.0 21.6 1.3

EPH-C2003-II 4.3 29.5 16.5 3.3 24.5 20.0 1.92004-I 4.3 31.7 15.4 3.3 24.6 19.0 1.72004-II 4.6 31.5 15.7 3.5 24.2 18.9 1.52005-I 4.2 32.4 15.4 3.8 24.4 18.5 1.22005-II 4.5 33.8 15.5 3.6 23.3 18.0 1.3

with PJH2002 4.1 27.5 22.2 3.3 21.7 20.3 1.12003 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 1.72004-I 4.0 30.4 19.3 3.1 23.6 18.1 1.62004-II 4.3 30.3 19.2 3.3 23.3 18.1 1.42005-I 4.0 31.4 18.3 3.6 23.7 17.9 1.12005-II 4.3 32.9 17.6 3.4 22.8 17.7 1.3


Table 6.24 Structure of employment Share of informal workers (productive definition)

Argentina, 1992-2005

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Adults (25-64) Youths (15-24) Age Gender Education Gender

Total (15-24) (25-64) (65 +) Female Male Low Medium High Female Male15 main cities

1995 0.422 0.496 0.397 0.634 0.440 0.370 0.546 0.410 0.123 0.506 0.4891996 0.435 0.517 0.408 0.654 0.440 0.388 0.584 0.422 0.139 0.518 0.5161997 0.417 0.482 0.395 0.596 0.423 0.377 0.567 0.401 0.139 0.482 0.4821998 0.416 0.481 0.393 0.586 0.424 0.371 0.567 0.409 0.134 0.491 0.475

28 main cities1998 0.428 0.504 0.402 0.613 0.432 0.383 0.571 0.420 0.141 0.523 0.4921999 0.432 0.510 0.405 0.643 0.438 0.383 0.587 0.424 0.143 0.499 0.5182000 0.442 0.515 0.419 0.650 0.439 0.405 0.592 0.450 0.154 0.512 0.5172001 0.446 0.542 0.420 0.626 0.431 0.413 0.606 0.449 0.162 0.563 0.5282003 0.448 0.581 0.419 0.580 0.408 0.427 0.634 0.467 0.155 0.572 0.587

EPH-C2003-II 0.464 0.578 0.433 0.614 0.455 0.418 0.646 0.507 0.170 0.588 0.5722004-I 0.453 0.582 0.416 0.645 0.440 0.400 0.615 0.482 0.168 0.616 0.5612004-II 0.445 0.547 0.414 0.647 0.438 0.397 0.616 0.463 0.147 0.608 0.5102005-I 0.441 0.520 0.415 0.635 0.450 0.390 0.616 0.462 0.155 0.556 0.4992005-II 0.426 0.513 0.396 0.625 0.415 0.383 0.603 0.447 0.143 0.553 0.490

with PJH2002 0.430 0.553 0.400 0.632 0.370 0.422 0.563 0.438 0.159 0.506 0.5882003 0.425 0.536 0.399 0.579 0.375 0.417 0.564 0.444 0.156 0.498 0.568

2003-II 0.437 0.532 0.409 0.598 0.413 0.407 0.570 0.477 0.171 0.511 0.5472004-I 0.433 0.548 0.398 0.635 0.403 0.395 0.549 0.461 0.168 0.547 0.5492004-II 0.429 0.524 0.399 0.638 0.406 0.394 0.559 0.448 0.147 0.562 0.4992005-I 0.427 0.499 0.403 0.623 0.422 0.388 0.563 0.451 0.155 0.511 0.4922005-II 0.418 0.500 0.391 0.617 0.400 0.384 0.572 0.437 0.144 0.526 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

Adults (25-64) Youths (15-24) Age Gender Education Gender

Total (15-24) (25-64) (65 +) Female Male Low Medium High Female Male15 main cities

1992 0.312 0.507 0.241 0.548 0.290 0.210 0.347 0.211 0.111 0.504 0.5091993 0.319 0.487 0.259 0.554 0.320 0.219 0.361 0.234 0.141 0.454 0.5081994 0.291 0.475 0.231 0.404 0.282 0.198 0.339 0.209 0.098 0.436 0.4991995 0.331 0.532 0.269 0.517 0.325 0.230 0.372 0.237 0.141 0.531 0.5331996 0.351 0.530 0.295 0.624 0.343 0.264 0.440 0.253 0.154 0.546 0.5191997 0.362 0.537 0.307 0.570 0.356 0.275 0.432 0.293 0.155 0.519 0.5491998 0.371 0.562 0.311 0.582 0.353 0.281 0.465 0.269 0.168 0.553 0.568

28 main cities1998 0.379 0.590 0.315 0.573 0.359 0.284 0.469 0.272 0.169 0.590 0.5911999 0.383 0.583 0.325 0.565 0.367 0.294 0.474 0.300 0.179 0.585 0.5822000 0.385 0.598 0.331 0.446 0.383 0.292 0.478 0.318 0.176 0.581 0.6092001 0.387 0.604 0.333 0.490 0.375 0.301 0.516 0.306 0.174 0.638 0.5802003 0.388 0.656 0.330 0.531 0.340 0.323 0.503 0.332 0.184 0.656 0.656

EPH-C2003-II 0.437 0.708 0.374 0.621 0.420 0.338 0.566 0.390 0.205 0.703 0.7112004-I 0.433 0.676 0.371 0.594 0.408 0.343 0.540 0.388 0.212 0.698 0.6632004-II 0.435 0.690 0.374 0.592 0.408 0.348 0.571 0.353 0.203 0.712 0.6762005-I 0.430 0.657 0.376 0.535 0.429 0.336 0.551 0.359 0.221 0.659 0.6552005-II 0.423 0.642 0.368 0.588 0.402 0.341 0.561 0.360 0.196 0.643 0.642

with PJH2002 0.441 0.692 0.387 0.498 0.430 0.349 0.585 0.383 0.178 0.700 0.6852003 0.449 0.691 0.398 0.538 0.436 0.363 0.598 0.393 0.197 0.713 0.672

2003-II 0.494 0.738 0.438 0.644 0.506 0.379 0.647 0.453 0.222 0.751 0.7282004-I 0.486 0.702 0.433 0.613 0.497 0.378 0.626 0.444 0.223 0.741 0.6742004-II 0.483 0.708 0.431 0.612 0.493 0.377 0.645 0.403 0.212 0.742 0.6872005-I 0.474 0.676 0.427 0.560 0.502 0.364 0.625 0.396 0.230 0.695 0.6632005-II 0.458 0.658 0.410 0.603 0.469 0.359 0.616 0.398 0.203 0.670 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

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By sector Argentina, 1992-2005


15 main cities1992 0.9 8.7 12.3 6.4 25.0 7.5 7.9 6.2 17.6 7.61993 0.8 8.1 11.7 7.1 25.2 7.8 7.8 6.4 17.1 8.01994 0.8 6.7 12.1 7.5 23.5 8.9 8.7 6.7 17.2 7.81995 0.9 7.1 11.1 7.0 22.5 9.0 9.9 7.1 17.8 7.81996 0.7 6.9 10.3 7.3 23.0 9.2 10.0 7.6 17.3 7.51997 0.8 6.5 10.6 7.5 21.8 9.0 10.2 7.1 18.5 8.01998 0.7 5.6 10.4 8.1 22.2 8.5 10.5 7.2 19.2 7.6

28 main cities1998 1.0 5.8 9.6 8.5 23.3 8.2 9.5 7.7 18.9 7.61999 1.0 5.6 9.0 8.4 22.8 9.0 9.7 7.6 19.1 7.72000 0.8 5.8 8.2 7.9 24.0 8.7 9.6 7.8 19.3 7.92001 1.1 5.4 8.3 7.2 24.0 8.5 9.0 8.4 20.2 7.92003 1.3 6.3 7.2 6.7 23.2 8.2 10.2 7.5 22.2 7.3

EPH-C2003-II 1.4 7.9 6.6 7.3 24.8 7.7 9.8 7.6 18.9 8.02004-I 1.5 7.8 6.9 7.9 25.0 7.4 9.8 7.3 18.7 7.72004-II 1.3 7.9 7.1 8.0 25.4 7.8 9.3 7.2 18.4 7.72005-I 1.1 7.7 7.3 8.1 24.0 7.6 10.5 7.1 18.9 7.82005-II 1.3 7.6 6.9 8.7 24.3 7.6 9.9 7.2 18.9 7.6

with PJH2002 1.4 5.5 7.3 6.7 21.8 7.6 9.1 10.3 23.3 6.92003 1.4 6.1 6.7 6.5 22.0 7.6 9.4 9.1 24.1 7.0

2003-II 1.7 7.7 6.1 7.1 23.3 7.1 9.0 8.9 21.3 7.62004-I 1.7 7.8 6.4 7.6 23.7 6.9 9.1 8.7 20.5 7.52004-II 1.5 7.9 6.6 7.8 24.2 7.4 8.7 8.1 20.2 7.52005-I 1.4 7.7 6.9 8.0 23.1 7.2 9.9 7.7 20.4 7.72005-II 1.4 7.6 6.6 8.6 23.6 7.3 9.4 7.6 20.3 7.7


Table 6.27 Structure of employment By sector (CIIU -1 digit) Argentina, 1992-2005

Restauranansportation Business Public Healt & Other Domestic ForeignAgro Fishing MiningManufacturing Utilities Construction Commerce & hotels& commu Finance services administrationTeachingcial servic services servants rganization

15 main cities1992 0.9 21.4 0.9 6.5 22.5 2.9 6.7 2.4 5.7 6.3 6.3 5.6 4.3 7.71993 0.8 20.1 0.6 7.2 22.6 3.0 7.3 2.2 5.8 6.5 7.1 5.3 3.4 8.11994 0.8 19.0 0.8 7.6 20.7 3.1 8.3 2.7 6.2 6.7 7.1 5.5 3.7 7.91995 0.9 18.5 0.7 7.1 19.5 3.3 8.3 2.8 7.3 7.2 7.0 5.6 4.0 7.91996 0.7 17.5 0.9 7.4 20.4 3.0 8.5 2.6 7.5 7.8 6.2 6.1 3.8 7.71997 0.8 17.4 0.7 7.7 19.4 2.8 8.4 2.9 7.5 7.2 6.7 6.8 3.6 8.21998 0.7 16.3 0.6 8.2 19.7 2.9 8.0 2.8 7.9 7.4 7.5 6.2 4.1 7.7

28 main cities1998 1.0 15.6 0.6 8.7 20.8 2.9 7.7 2.5 7.2 7.8 7.6 6.1 3.8 7.71999 1.0 14.9 0.6 8.5 20.2 3.0 8.6 2.4 7.5 7.8 7.7 5.8 4.1 7.92000 0.8 14.3 0.6 8.0 21.1 3.4 8.3 2.5 7.4 8.0 7.7 5.8 4.3 8.02001 1.1 14.1 0.6 7.3 21.1 3.3 8.1 2.5 6.7 8.6 8.5 5.6 4.5 8.12003 1.3 13.7 0.6 6.8 20.9 2.8 7.8 2.4 8.0 7.6 9.6 6.1 4.9 7.5

EPH-C2003-II 0.9 0.1 0.3 14.5 0.6 7.3 21.9 2.9 7.1 1.8 8.0 7.5 8.2 5.5 5.3 8.0 0.12004-I 1.0 0.1 0.4 14.7 0.5 7.9 21.4 3.6 6.9 1.8 8.0 7.2 7.6 5.2 5.8 7.7 0.02004-II 0.8 0.1 0.4 15.0 0.5 8.0 21.9 3.5 7.3 1.6 7.7 7.2 7.4 5.6 5.4 7.7 0.02005-I 0.7 0.1 0.3 14.9 0.5 8.1 20.4 3.5 7.1 1.8 8.7 7.1 7.4 5.6 5.9 7.8 0.02005-II 0.9 0.1 0.3 14.5 0.5 8.7 20.9 3.4 7.1 1.8 8.1 7.2 7.7 5.5 5.7 7.6 0.0

with PJH2002 1.4 13.1 0.5 6.8 19.3 2.9 7.3 2.3 6.9 10.5 9.5 6.6 5.9 7.02003 1.5 13.1 0.5 6.6 19.8 2.7 7.2 2.2 7.4 9.2 9.8 6.9 5.9 7.1

2003-II 1.3 0.1 0.3 13.8 0.5 7.1 20.6 2.8 6.6 1.7 7.4 8.9 8.4 7.3 5.6 7.6 0.02004-I 1.2 0.1 0.4 14.3 0.5 7.6 20.3 3.4 6.4 1.7 7.4 8.7 7.7 6.7 6.1 7.5 0.02004-II 1.0 0.1 0.4 14.5 0.5 7.8 20.8 3.4 6.9 1.5 7.2 8.1 7.6 7.0 5.7 7.5 0.02005-I 1.0 0.1 0.3 14.6 0.5 8.0 19.6 3.5 6.7 1.7 8.2 7.7 7.4 6.7 6.4 7.7 0.02005-II 1.0 0.1 0.3 14.2 0.5 8.6 20.3 3.3 6.8 1.7 7.7 7.5 7.7 6.5 6.0 7.7 0.0


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Table 6.28 Child labor By equivalized household income quintiles Argentina, 1992-2005

Gender Equivalized income quintileTotal Female Male 1 2 3 4 5

15 main cities1992 0.021 0.012 0.030 0.016 0.015 0.032 0.022 0.0021993 0.020 0.010 0.029 0.019 0.019 0.041 0.005 0.0071994 0.013 0.005 0.022 0.015 0.013 0.017 0.010 0.0071995 0.010 0.007 0.013 0.011 0.004 0.007 0.021 0.0101996 0.012 0.009 0.015 0.010 0.016 0.011 0.020 0.0001997 0.009 0.004 0.014 0.010 0.014 0.002 0.003 0.0001998 0.010 0.007 0.012 0.020 0.011 0.009 0.001 0.000

28 main cities1998 0.011 0.008 0.014 0.020 0.011 0.011 0.007 0.0001999 0.008 0.003 0.012 0.014 0.004 0.007 0.004 0.0062000 0.005 0.003 0.007 0.008 0.006 0.001 0.000 0.0012001 0.007 0.003 0.010 0.009 0.009 0.005 0.001 0.0002003 0.003 0.001 0.005 0.004 0.002 0.002 0.000 0.000

EPH-C2003-II 0.016 0.012 0.019 0.024 0.011 0.023 0.013 0.0012004-I 0.020 0.012 0.028 0.030 0.022 0.021 0.013 0.0032004-II 0.015 0.012 0.018 0.026 0.012 0.008 0.007 0.0012005-I 0.015 0.012 0.018 0.022 0.007 0.016 0.002 0.0022005-II 0.019 0.012 0.025 0.033 0.013 0.004 0.008 0.012

with PJH2002 0.007 0.004 0.009 0.009 0.008 0.002 0.001 0.0042003 0.003 0.001 0.005 0.004 0.002 0.002 0.000 0.000

2003-II 0.016 0.012 0.019 0.024 0.011 0.023 0.014 0.0012004-I 0.020 0.012 0.028 0.030 0.022 0.021 0.013 0.0032004-II 0.015 0.012 0.019 0.026 0.012 0.008 0.007 0.0012005-I 0.015 0.012 0.018 0.022 0.007 0.016 0.002 0.0022005-II 0.019 0.012 0.025 0.033 0.013 0.004 0.008 0.012


Table 6.29 Right to receive social security (pensions) By gender and education Argentina, 1992-2005



1992 0.692 0.493 0.759 0.452 0.710 0.790 0.653 0.789 0.8891993 0.685 0.513 0.741 0.446 0.680 0.781 0.639 0.766 0.8591994 0.711 0.525 0.769 0.596 0.718 0.802 0.661 0.791 0.9021995 0.671 0.468 0.731 0.483 0.675 0.770 0.628 0.763 0.8591996 0.650 0.470 0.705 0.376 0.657 0.736 0.560 0.747 0.8461997 0.639 0.463 0.693 0.430 0.644 0.725 0.568 0.707 0.8451998 0.630 0.438 0.689 0.418 0.647 0.719 0.535 0.731 0.832

28 main cities1998 0.622 0.410 0.685 0.427 0.641 0.716 0.531 0.728 0.8311999 0.618 0.417 0.675 0.435 0.633 0.706 0.526 0.700 0.8212000 0.616 0.402 0.669 0.554 0.617 0.708 0.522 0.682 0.8242001 0.614 0.396 0.667 0.510 0.625 0.699 0.484 0.694 0.8262003 0.612 0.344 0.670 0.469 0.660 0.677 0.497 0.668 0.816

EPH-C2003-II 0.564 0.292 0.626 0.379 0.580 0.662 0.434 0.610 0.7952004-I 0.569 0.324 0.629 0.406 0.592 0.657 0.460 0.612 0.7882004-II 0.566 0.310 0.626 0.408 0.592 0.652 0.429 0.647 0.7972005-I 0.571 0.343 0.624 0.465 0.571 0.664 0.449 0.641 0.7792005-II 0.579 0.358 0.632 0.412 0.598 0.659 0.439 0.640 0.804

with PJH2002 0.560 0.308 0.613 0.502 0.570 0.651 0.415 0.617 0.8222003 0.552 0.309 0.602 0.462 0.564 0.637 0.402 0.607 0.803

2003-II 0.507 0.262 0.562 0.356 0.494 0.621 0.353 0.547 0.7782004-I 0.516 0.298 0.567 0.387 0.503 0.622 0.374 0.556 0.7772004-II 0.518 0.292 0.569 0.388 0.507 0.623 0.355 0.597 0.7882005-I 0.527 0.324 0.573 0.440 0.498 0.636 0.375 0.604 0.7702005-II 0.543 0.342 0.590 0.397 0.531 0.641 0.384 0.602 0.797


Table 6.30 Access to labor health insurance

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By gender and education Argentina, 1992-2004



1992 0.664 0.478 0.726 0.448 0.670 0.761 0.616 0.764 0.8521993 0.653 0.474 0.712 0.397 0.648 0.753 0.599 0.745 0.8331994 0.689 0.510 0.746 0.531 0.695 0.779 0.634 0.768 0.8861995 0.655 0.452 0.714 0.482 0.657 0.754 0.606 0.746 0.8521996 0.620 0.450 0.671 0.375 0.619 0.705 0.537 0.714 0.7971997 0.623 0.453 0.675 0.417 0.620 0.711 0.550 0.696 0.8181998 0.620 0.434 0.678 0.405 0.636 0.706 0.522 0.719 0.822

28 main cities1998 0.610 0.404 0.672 0.412 0.629 0.702 0.517 0.715 0.8191999 0.606 0.405 0.663 0.421 0.617 0.696 0.515 0.690 0.8052000 0.585 0.384 0.635 0.510 0.583 0.674 0.492 0.652 0.7802001 0.584 0.380 0.633 0.472 0.596 0.663 0.457 0.661 0.7872003 0.605 0.331 0.664 0.465 0.656 0.670 0.491 0.661 0.812

EPH-C2003-II 0.560 0.291 0.620 0.406 0.578 0.653 0.424 0.603 0.7922004-I 0.568 0.321 0.626 0.426 0.592 0.652 0.446 0.611 0.7942004-II 0.569 0.319 0.629 0.419 0.593 0.656 0.427 0.647 0.8042005-I 0.573 0.345 0.627 0.461 0.574 0.667 0.444 0.640 0.7932005-II 0.579 0.358 0.632 0.419 0.601 0.657 0.437 0.640 0.807

with PJH2002 0.549 0.309 0.600 0.492 0.558 0.637 0.410 0.602 0.8032003 0.546 0.297 0.598 0.458 0.561 0.631 0.397 0.601 0.799

2003-II 0.505 0.267 0.558 0.384 0.493 0.614 0.348 0.543 0.7762004-I 0.516 0.300 0.566 0.408 0.504 0.619 0.365 0.556 0.7832004-II 0.522 0.302 0.572 0.401 0.508 0.627 0.355 0.598 0.7952005-I 0.532 0.328 0.577 0.440 0.502 0.641 0.374 0.604 0.7852005-II 0.545 0.345 0.592 0.407 0.536 0.640 0.385 0.603 0.800


Table 6.31 Labor benefits Argentina, 1992-2005

Permanent job 13th month Holidays Employment pro15 main cities

1992 0.709 0.7091993 0.706 0.7031994 0.725 0.7241995 0.694 0.6901996 0.827 0.661 0.6581997 0.827 0.634 0.6301998 0.840 0.633 0.632

28 main cities1998 0.834 0.628 0.6261999 0.853 0.625 0.6232000 0.849 0.624 0.6252001 0.838 0.622 0.6212003 0.844 0.618 0.620

EPH-C2003-II 0.837 0.592 0.5922004-I 0.846 0.599 0.6022004-II 0.835 0.589 0.5882005-I 0.841 0.596 0.6012005-II 0.848 0.600 0.599

with PJH2002 0.791 0.563 0.5632003 0.798 0.557 0.558

2003-II 0.745 0.532 0.533 0.0682004-I 0.763 0.544 0.548 0.0612004-II 0.760 0.540 0.541 0.0552005-I 0.776 0.554 0.560 0.0472005-II 0.797 0.564 0.566 0.037


Table 7.1 Educational structure

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Adults 25-65 Argentina, 1992-2005

All Males Females Working males Working femalesLow Medium High Low Medium High Low Medium High Low Medium High Low Medium High

EPH - 15 cities (1)1992 47.7 34.5 17.8 46.1 35.5 18.5 49.2 33.6 17.2 44.7 36.7 18.6 38.7 33.2 28.21993 45.4 35.7 18.8 44.8 35.7 19.5 46.0 35.8 18.2 44.3 36.9 18.8 35.0 36.6 28.41994 45.6 35.6 18.8 45.0 36.2 18.8 46.2 35.1 18.7 44.5 37.0 18.5 36.2 33.8 30.1

EPH- 15 cities (2)1995 47.5 34.5 18.0 47.2 34.7 18.2 47.8 34.3 17.9 45.9 35.5 18.6 37.6 34.5 27.81996 43.4 35.8 20.8 42.6 36.6 20.7 44.1 35.1 20.8 40.8 37.5 21.6 34.2 34.3 31.61997 43.7 35.4 20.9 43.6 36.2 20.2 43.9 34.7 21.5 41.9 37.7 20.4 34.4 32.7 32.91998 42.9 35.8 21.3 42.6 37.2 20.3 43.2 34.6 22.2 41.5 38.0 20.6 33.6 33.1 33.3

EPH - 28 cities1998 43.4 35.5 21.1 43.4 36.5 20.1 43.4 34.7 21.9 42.4 37.4 20.3 33.8 33.2 33.01999 41.9 35.9 22.2 42.0 37.4 20.6 41.9 34.5 23.6 41.1 38.4 20.5 31.7 34.0 34.42000 41.9 35.4 22.7 41.9 36.7 21.4 41.9 34.3 23.9 40.6 38.1 21.4 32.2 33.3 34.52001 41.1 35.7 23.2 41.4 37.0 21.6 40.8 34.5 24.7 39.9 38.4 21.7 31.5 32.8 35.82002 39.5 36.4 24.2 40.3 37.1 22.7 38.7 35.7 25.5 40.0 37.4 22.6 31.8 34.3 33.92003 38.4 37.0 24.7 39.1 37.6 23.3 37.7 36.4 25.9 37.7 38.8 23.4 26.4 35.5 38.2

EPH-C2004-II 38.3 36.6 25.1 38.8 38.2 23.0 37.9 35.2 26.9 37.4 38.9 23.8 30.9 33.9 35.22005-I 38.4 36.1 25.5 38.9 37.2 23.9 37.9 35.2 27.0 37.3 37.9 24.8 31.1 33.8 35.22005-II 37.2 36.4 26.4 38.6 37.0 24.4 36.1 35.9 28.0 37.2 37.5 25.3 28.7 33.9 37.5


Table 7.2 Years of education By age and gender Argentina, 1992-2005

(25-65) (10-20) (21-30) (31-40) (41-50) (51-60) (61+)Female Male All Female Male All Female Male All Female Male All Female Male All Female Male All Female Male All

EPH - 15 cities (1)1992 9.3 9.5 9.4 7.8 7.6 7.7 10.9 10.8 10.8 10.1 10.0 10.0 9.2 9.3 9.2 8.0 8.8 8.4 6.7 7.6 7.11993 9.4 9.6 9.5 7.9 7.7 7.8 11.0 10.9 11.0 10.1 10.0 10.1 9.4 9.4 9.4 8.2 8.7 8.5 7.0 7.8 7.31994 9.5 9.6 9.5 7.8 7.7 7.8 11.2 10.9 11.0 10.2 10.0 10.1 9.4 9.6 9.5 8.2 8.9 8.6 6.9 7.9 7.3

EPH- 15 cities (2)1995 9.6 9.6 9.6 7.8 7.5 7.7 11.1 10.6 10.9 10.4 10.2 10.3 9.3 9.3 9.3 8.4 8.8 8.6 6.8 8.0 7.31996 9.6 9.7 9.7 7.6 7.3 7.5 11.2 10.6 10.9 10.4 10.1 10.3 9.5 9.5 9.5 8.4 8.7 8.6 6.9 7.9 7.31997 9.9 9.9 9.9 8.1 7.8 7.9 11.2 10.7 11.0 10.6 10.4 10.5 9.8 9.6 9.7 8.6 8.9 8.7 7.1 8.0 7.51998 9.9 9.9 9.9 8.2 7.8 8.0 11.4 10.8 11.1 10.6 10.3 10.5 9.9 9.9 9.9 8.7 9.1 8.9 7.0 8.0 7.4

EPH - 28 cities1998 9.9 9.9 9.9 8.1 7.7 7.9 11.4 10.7 11.1 10.5 10.3 10.4 9.8 9.8 9.8 8.6 9.0 8.8 6.9 7.9 7.31999 10.1 10.0 10.0 8.2 7.8 8.0 11.6 10.8 11.2 10.7 10.5 10.6 10.1 10.0 10.1 8.7 9.0 8.8 7.0 7.9 7.32000 10.1 10.0 10.1 8.2 7.8 8.0 11.5 10.7 11.1 10.9 10.6 10.7 10.1 9.8 10.0 8.6 9.3 8.9 7.1 8.0 7.52001 10.2 10.1 10.2 8.3 7.8 8.0 11.6 11.0 11.3 10.9 10.5 10.7 10.2 9.8 10.0 9.0 9.4 9.2 7.2 8.1 7.52002 10.4 10.1 10.2 8.3 7.8 8.0 11.7 11.1 11.4 11.1 10.6 10.8 10.4 9.9 10.2 9.0 9.3 9.2 7.4 8.3 7.72003 10.4 10.2 10.3 8.6 8.2 8.4 11.9 11.2 11.6 11.1 10.7 10.9 10.4 10.2 10.3 9.1 9.3 9.2 7.3 8.3 7.7

EPH-C2003 10.8 10.4 10.6 8.1 7.9 8.0 11.9 11.1 11.6 11.5 11.0 11.3 10.6 10.1 10.4 9.6 9.6 9.6 7.7 8.5 8.1

2004-I 10.7 10.4 10.6 8.3 8.0 8.2 11.9 11.3 11.6 11.3 10.8 11.1 10.7 10.2 10.5 9.6 9.6 9.6 7.7 8.4 8.02004-II 10.5 10.3 10.5 8.2 7.7 7.9 11.8 11.2 11.5 11.2 10.8 11.0 10.4 10.1 10.2 9.4 9.5 9.5 7.6 8.5 8.02005-I 10.6 10.4 10.5 8.4 7.9 8.2 11.7 11.2 11.5 11.2 11.0 11.1 10.4 10.1 10.3 9.7 9.7 9.7 7.6 8.5 8.02005-II 10.7 10.4 10.6 8.1 7.6 7.9 11.9 11.2 11.6 11.4 10.9 11.2 10.8 10.5 10.6 9.6 9.5 9.5 7.6 8.5 8.0


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Table 7.3 Years of education By household equivalized income quintiles Adults 25-65 Argentina, 1992-2005

1 2 3 4 5 Mea EPH - 15 cities (1)

1992 7.2 7.8 8.5 9.7 12.2 9.41993 7.0 8.0 8.5 9.6 12.1 9.31994 7.1 8.0 8.4 9.6 12.3 9.3

EPH- 15 cities (2)1995 7.2 7.9 8.8 9.6 12.5 9.51996 7.0 7.9 8.8 9.8 12.7 9.61997 7.2 8.0 8.9 10.2 12.8 9.81998 7.0 8.1 8.8 10.2 13.1 9.8

EPH - 28 cities1998 7.0 8.1 8.9 10.1 13.0 9.81999 7.2 8.3 9.1 10.3 13.0 9.92000 7.2 8.2 9.2 10.3 13.2 10.02001 7.2 8.2 9.2 10.5 13.2 10.02002 7.4 8.3 9.3 10.4 13.4 10.12003 7.4 8.4 9.4 10.7 13.3 10.2

EPH-C2003 * 7.8 8.7 9.5 10.8 13.4 10.32003 7.9 8.7 9.5 10.9 13.5 10.4

2004-I 7.8 8.7 9.8 11.0 13.5 10.52004-II 7.8 8.7 9.6 10.9 13.4 10.42005-I 7.9 8.6 9.7 11.0 13.3 10.42005-II 7.7 8.7 9.6 11.2 13.6 10.5

n


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Table 7.4 Years of education By age and income Argentina, 1992-2005

(10-20) (21-30) (31-40)1 2 3 4 5 Mean 1 2 3 4 5 Mean 1 2 3 4 5 Mea

EPH - 15 cities (1)1992 6.8 7.4 7.6 8.2 8.5 7.6 8.3 9.2 10.4 11.2 13.3 10.8 7.8 8.4 9.4 10.6 13.1 10.11993 6.8 7.4 7.8 8.2 8.6 7.7 8.2 9.6 10.1 11.5 13.0 10.8 7.2 8.7 9.2 10.7 13.0 9.91994 6.8 7.4 7.7 8.1 8.7 7.6 8.4 9.4 10.3 11.1 13.1 10.8 7.3 8.5 9.2 10.6 13.4 10.0

EPH- 15 cities (2)1995 6.7 7.2 7.9 8.1 8.7 7.6 8.5 9.0 10.2 11.3 13.1 10.8 7.6 8.4 9.6 10.7 13.7 10.21996 6.3 7.0 7.6 8.2 8.5 7.4 8.1 9.3 10.4 11.4 13.3 10.8 7.4 8.4 9.5 11.0 13.4 10.21997 6.9 7.4 8.0 8.6 9.0 7.9 8.6 9.4 10.3 11.5 13.5 10.9 7.8 8.7 9.7 11.3 13.9 10.41998 6.8 7.6 8.0 8.7 9.0 7.8 8.3 9.4 10.2 11.9 13.8 11.0 7.5 8.7 9.6 10.9 14.4 10.4

EPH - 28 cities1998 6.8 7.5 8.1 8.6 9.0 7.8 8.3 9.4 10.4 11.7 13.7 10.9 7.4 8.6 9.7 10.9 14.1 10.41999 7.0 7.6 8.1 8.6 9.1 7.9 8.6 9.5 10.8 11.8 13.5 11.0 7.6 8.8 9.6 11.5 13.8 10.52000 6.9 7.6 8.2 8.8 9.0 7.9 8.5 9.7 10.6 11.7 13.7 11.1 7.5 8.8 10.0 11.2 14.2 10.62001 6.9 7.7 8.1 8.6 9.2 7.9 8.6 9.7 10.6 12.0 13.7 11.1 7.7 8.7 10.1 11.3 14.2 10.62002 7.1 7.6 8.0 8.7 9.1 7.9 8.8 9.9 10.9 11.9 13.9 11.2 7.8 8.8 10.1 11.3 14.4 10.72003 7.5 7.9 8.6 9.0 9.2 8.3 9.1 10.1 11.2 12.1 14.0 11.4 7.8 9.2 9.8 11.8 14.1 10.8

EPH-C2003 * 7.2 7.6 8.0 8.4 8.8 7.8 9.2 9.9 11.0 12.0 13.8 11.3 8.4 9.1 10.3 11.9 14.4 11.02003 7.3 7.6 8.0 8.5 8.8 7.9 9.2 10.0 11.1 12.1 13.9 11.4 8.5 9.2 10.4 12.0 14.5 11.2

2004-I 7.4 7.9 8.5 8.8 8.9 8.1 9.3 10.3 11.3 12.4 13.8 11.6 8.1 9.2 10.6 12.1 14.3 11.12004-II 7.1 7.7 8.2 8.5 9.1 7.9 9.1 10.1 11.2 12.3 13.7 11.4 8.2 9.3 10.3 11.9 14.2 11.02005-I 7.3 8.0 8.4 8.9 9.3 8.2 9.1 10.1 11.2 12.2 14.0 11.4 8.3 9.1 10.5 11.8 14.2 11.02005-II 6.9 7.7 8.2 8.8 8.9 7.9 9.2 9.9 11.2 12.4 14.0 11.5 8.0 9.4 10.4 12.0 14.3 11.1

(41-50) (51-60) (61+)1 2 3 4 5 Mean 1 2 3 4 5 Mean 1 2 3 4 5 Mea

EPH - 15 cities (1)1992 7.1 7.6 8.0 9.5 12.0 9.1 6.3 6.7 7.5 8.2 11.0 8.3 5.9 6.1 6.6 7.7 9.5 7.11993 7.0 7.6 8.2 9.2 12.1 9.2 6.4 6.7 7.3 8.4 10.9 8.4 5.8 6.2 6.8 7.4 9.9 7.21994 6.9 7.7 8.3 9.4 12.2 9.2 6.7 6.7 6.9 8.2 10.9 8.3 5.6 6.4 6.8 7.5 9.6 7.2

EPH- 15 cities (2)1995 7.0 7.7 8.8 9.4 12.5 9.3 6.3 6.9 7.3 8.2 11.1 8.4 5.5 6.0 6.6 7.2 9.7 7.11996 7.1 7.5 8.5 9.4 13.1 9.4 6.1 6.8 7.4 8.4 11.5 8.4 5.4 6.2 6.5 7.1 10.0 7.31997 6.9 7.6 8.7 10.1 12.8 9.6 5.8 6.9 7.4 8.8 11.6 8.6 5.6 6.0 6.4 7.5 10.2 7.31998 6.9 8.1 8.6 10.0 13.1 9.7 6.0 6.8 7.5 8.6 11.8 8.7 5.5 5.8 6.3 7.2 10.6 7.3

EPH - 28 cities1998 6.9 8.0 8.6 10.0 13.0 9.6 5.9 6.8 7.4 8.6 11.7 8.7 5.3 5.7 6.2 7.1 10.3 7.21999 7.2 8.2 9.0 10.1 13.1 9.9 6.2 7.0 7.5 8.6 11.6 8.6 5.0 6.0 6.3 7.4 9.8 7.22000 7.1 7.9 9.1 10.3 13.4 9.9 6.2 7.0 7.4 8.8 11.9 8.8 5.0 6.0 6.4 7.4 9.9 7.32001 7.0 8.0 8.9 10.5 13.3 9.8 6.2 6.9 7.9 9.0 12.1 9.0 4.9 5.8 6.4 7.3 10.3 7.42002 7.1 8.0 9.4 10.3 13.8 10.0 6.4 7.2 7.5 9.1 12.1 9.0 4.8 5.9 6.4 7.6 10.0 7.52003 7.1 8.0 9.5 10.5 13.7 10.1 6.0 6.6 7.9 9.3 12.2 9.1 5.2 6.1 6.3 7.7 10.2 7.6

EPH-C2003 * 7.4 8.5 8.9 10.8 13.5 9.9 6.7 7.4 8.0 9.3 12.5 9.2 6.0 6.3 6.5 7.7 10.3 7.72003 7.5 8.6 9.0 10.9 13.6 10.1 6.8 7.5 8.1 9.4 12.6 9.4 6.2 6.4 6.6 7.8 10.4 7.9

2004-I 7.7 8.7 9.6 10.7 13.6 10.4 7.0 7.4 8.4 9.6 12.5 9.4 5.8 6.0 7.0 7.9 10.8 7.92004-II 7.6 8.7 9.4 10.6 13.4 10.2 6.5 7.2 8.1 9.6 12.5 9.3 5.2 6.0 7.0 8.0 10.6 7.92005-I 7.7 8.7 9.3 10.9 13.3 10.2 6.9 7.4 8.6 9.8 12.5 9.6 5.6 5.9 6.8 8.0 10.7 7.92005-II 7.8 8.8 9.7 11.3 13.6 10.5 6.4 7.5 8.0 9.7 12.6 9.4 5.6 6.2 6.9 8.2 11.0 7.9

n

n


-50-

Table 7.5 Gini coefficient Years of education By age Argentina, 1992-2003

(25-65) (10-20) (21-30) (31-40) (41-50) (51-60) (61+) EPH - 15 cities (1)

1992 0.237 0.214 0.195 0.216 0.242 0.250 0.2801993 0.237 0.212 0.190 0.217 0.243 0.254 0.2761994 0.233 0.213 0.185 0.212 0.236 0.254 0.276

EPH- 15 cities (2)1995 0.235 0.209 0.181 0.214 0.234 0.264 0.2921996 0.236 0.243 0.180 0.212 0.241 0.263 0.2861997 0.234 0.215 0.182 0.209 0.236 0.266 0.2901998 0.231 0.216 0.177 0.208 0.233 0.259 0.297

EPH - 28 cities1998 0.233 0.219 0.180 0.211 0.237 0.262 0.3001999 0.229 0.219 0.175 0.207 0.230 0.261 0.2922000 0.229 0.218 0.177 0.207 0.233 0.263 0.2942001 0.225 0.220 0.172 0.205 0.228 0.255 0.2902002 0.225 0.223 0.169 0.200 0.228 0.261 0.2812003 0.222 0.209 0.162 0.197 0.226 0.258 0.283

EPH-C2003 0.224 0.219 0.168 0.198 0.235 0.256 0.286

2004-I 0.224 0.213 0.166 0.205 0.228 0.256 0.2842004-II 0.220 0.215 0.166 0.197 0.222 0.260 0.2872005-I 0.219 0.206 0.169 0.195 0.220 0.251 0.2962005-II 0.218 0.214 0.165 0.197 0.216 0.253 0.288


Table 7.6 Literacy By age and gender Adults aged 25 to 65 Argentina, 1992-2005

(15-24) (25-65) (65 +)Female Male Mean Female Male Mean Female Male Mean

EPH-15 cities1992 0.99 0.99 0.99 0.98 0.99 0.99 0.94 0.97 0.951993 0.99 0.99 0.99 0.98 0.99 0.99 0.95 0.97 0.961994 1.00 0.99 0.99 0.99 0.99 0.99 0.96 0.97 0.961995 1.00 1.00 1.00 0.99 0.99 0.99 0.96 0.98 0.971996 1.00 0.99 1.00 0.99 0.99 0.99 0.95 0.98 0.961997 1.00 0.99 0.99 0.99 0.99 0.99 0.97 0.98 0.971998 0.99 0.99 0.99 0.99 0.99 0.99 0.97 0.97 0.97

EPH - 28 cities1998 0.99 0.99 0.99 0.98 0.99 0.98 0.96 0.97 0.971999 0.99 0.99 0.99 0.98 0.99 0.99 0.96 0.97 0.972000 0.99 0.99 0.99 0.99 0.99 0.99 0.96 0.97 0.962001 0.99 0.99 0.99 0.99 0.99 0.99 0.96 0.97 0.962002 1.00 0.99 0.99 0.99 0.98 0.99 0.96 0.98 0.972003 1.00 0.99 0.99 0.99 0.98 0.99 0.97 0.97 0.97

EPH-C2004-II 0.99 0.99 0.99 0.99 0.99 0.99 0.96 0.98 0.972005-I 1.00 0.99 0.99 0.99 0.99 0.99 0.96 0.98 0.972005-II 1.00 0.99 0.99 0.99 0.99 0.99 0.96 0.98 0.97


-51-

Table 7.7 Literacy By household equivalized income quintiles Argentina, 1992-2005

Age 15 to 24 Age 25 to 651 2 3 4 5 Mean 1 2 3 4 5 Mean

EPH-15 cities1992 0.98 0.98 0.99 0.99 1.00 0.99 0.96 0.97 0.98 0.99 1.00 0.981993 0.99 0.99 0.99 1.00 1.00 0.99 0.96 0.98 0.99 0.99 1.00 0.991994 0.99 0.99 0.99 1.00 1.00 0.99 0.97 0.98 0.99 0.99 1.00 0.991995 0.98 0.99 1.00 1.00 1.00 0.99 0.97 0.99 0.99 0.99 1.00 0.991996 0.99 0.99 1.00 1.00 1.00 1.00 0.96 0.98 0.99 0.99 1.00 0.991997 0.98 0.99 1.00 1.00 1.00 0.99 0.96 0.98 0.99 0.99 1.00 0.991998 0.98 0.99 0.99 1.00 1.00 0.99 0.95 0.98 0.99 0.99 1.00 0.99

EPH - 28 cities1998 0.98 0.99 0.99 1.00 1.00 0.99 0.95 0.98 0.99 0.99 1.00 0.981999 0.98 0.99 0.99 0.99 1.00 0.99 0.97 0.98 0.99 0.99 1.00 0.992000 0.98 0.99 1.00 1.00 1.00 0.99 0.96 0.98 0.99 0.99 1.00 0.992001 0.99 0.99 0.99 1.00 1.00 0.99 0.96 0.98 0.99 1.00 1.00 0.992002 0.99 0.99 1.00 1.00 1.00 0.99 0.96 0.98 0.98 0.99 1.00 0.992003 0.99 0.99 1.00 1.00 1.00 0.99 0.96 0.98 0.98 0.99 1.00 0.98

EPH-C2004-II 0.99 0.99 0.99 1.00 0.99 0.99 0.97 0.98 0.99 1.00 1.00 0.992005-I 0.99 1.00 0.99 1.00 1.00 0.99 0.97 0.98 0.99 0.99 1.00 0.992005-II 0.99 0.99 0.99 1.00 1.00 0.99 0.96 0.98 0.99 1.00 1.00 0.99


Table 7.8 Enrollment rates By age and gender Argentina, 1992-2005

3 to 5 years-old 6 to 12 years-old 13 to 17 years-old 18 to 23 years oldFemale Male Mean Female Male Mean Female Male Mean Female Male Mean

EPH-15 cities1992 0.35 0.34 0.34 0.98 0.98 0.98 0.83 0.74 0.78 0.45 0.38 0.411993 0.32 0.36 0.34 0.98 0.99 0.98 0.81 0.76 0.78 0.45 0.39 0.421994 0.30 0.30 0.30 0.98 0.98 0.98 0.83 0.77 0.80 0.46 0.37 0.421995 0.27 0.32 0.29 0.99 0.99 0.99 0.81 0.77 0.79 0.47 0.38 0.431996 0.33 0.34 0.34 0.99 0.98 0.99 0.81 0.78 0.79 0.47 0.38 0.421997 0.35 0.34 0.34 0.99 0.99 0.99 0.85 0.82 0.83 0.47 0.41 0.441998 0.44 0.40 0.42 0.99 0.99 0.99 0.89 0.85 0.87 0.49 0.43 0.46

EPH - 28 cities1998 0.38 0.36 0.37 0.99 0.99 0.99 0.88 0.84 0.86 0.49 0.42 0.451999 0.41 0.41 0.41 0.99 0.99 0.99 0.90 0.86 0.88 0.53 0.44 0.492000 0.43 0.43 0.43 0.99 0.99 0.99 0.91 0.90 0.90 0.53 0.45 0.492001 0.41 0.38 0.40 0.99 0.98 0.99 0.93 0.90 0.91 0.53 0.46 0.492002 0.43 0.40 0.42 0.99 0.99 0.99 0.93 0.90 0.91 0.52 0.50 0.512003 0.50 0.51 0.51 1.00 1.00 1.00 0.94 0.91 0.93 0.53 0.49 0.51

EPH-C2003-II 0.55 0.55 0.55 0.99 0.99 0.99 0.91 0.88 0.89 0.49 0.44 0.472004-I 0.63 0.62 0.63 0.99 0.99 0.99 0.92 0.90 0.91 0.52 0.46 0.492004-II 0.58 0.58 0.58 0.99 0.99 0.99 0.89 0.89 0.89 0.51 0.42 0.472005-I 0.64 0.64 0.64 0.99 0.99 0.99 0.92 0.90 0.91 0.52 0.46 0.492005-II 0.61 0.58 0.60 0.99 0.99 0.99 0.91 0.91 0.91 0.51 0.43 0.47


-52-

Table 7.9 Enrollment rates By household equivalized income quintiles Argentina, 1992-2005

3 to 5 years-old 6 to 12 years-old 13 to 17 years-old 18 to 23 years old1 2 3 4 5 Mean 1 2 3 4 5 Mean 1 2 3 4 5 Mean 1 2 3 4 5 Mea

EPH-15 cities1992 0.22 0.33 0.30 0.43 0.51 0.34 0.97 0.98 0.98 0.99 0.99 0.98 0.72 0.76 0.75 0.82 0.94 0.79 0.34 0.34 0.32 0.43 0.55 0.401993 0.26 0.33 0.29 0.41 0.47 0.33 0.97 0.99 0.98 0.99 1.00 0.98 0.72 0.78 0.76 0.80 0.90 0.78 0.30 0.35 0.36 0.44 0.53 0.411994 0.21 0.30 0.29 0.39 0.33 0.29 0.97 0.98 0.98 1.00 1.00 0.98 0.71 0.77 0.79 0.85 0.93 0.79 0.27 0.35 0.38 0.36 0.58 0.391995 0.20 0.26 0.35 0.38 0.34 0.29 0.98 0.98 1.00 0.99 1.00 0.99 0.67 0.75 0.84 0.86 0.95 0.79 0.29 0.28 0.37 0.44 0.68 0.421996 0.23 0.28 0.40 0.41 0.45 0.33 0.98 0.98 0.99 1.00 1.00 0.99 0.65 0.76 0.84 0.85 0.98 0.79 0.23 0.29 0.38 0.47 0.65 0.411997 0.28 0.33 0.37 0.39 0.41 0.34 0.98 0.99 0.99 0.99 1.00 0.99 0.73 0.82 0.82 0.90 0.96 0.83 0.25 0.29 0.42 0.45 0.69 0.431998 0.32 0.37 0.46 0.58 0.60 0.42 0.98 0.99 1.00 1.00 1.00 0.99 0.78 0.84 0.89 0.94 0.98 0.87 0.24 0.34 0.39 0.49 0.69 0.43

EPH - 28 cities1998 0.28 0.33 0.41 0.48 0.53 0.37 0.98 0.99 1.00 1.00 1.00 0.99 0.77 0.82 0.88 0.93 0.98 0.86 0.26 0.33 0.41 0.49 0.67 0.431999 0.31 0.36 0.43 0.48 0.59 0.40 0.99 1.00 0.99 0.99 1.00 0.99 0.82 0.86 0.89 0.92 0.98 0.88 0.33 0.35 0.46 0.53 0.67 0.472000 0.33 0.39 0.45 0.55 0.62 0.44 0.98 1.00 1.00 1.00 1.00 0.99 0.84 0.87 0.94 0.96 0.98 0.90 0.31 0.37 0.47 0.53 0.74 0.482001 0.31 0.36 0.40 0.48 0.52 0.39 0.97 0.98 1.00 1.00 1.00 0.99 0.86 0.89 0.94 0.96 0.99 0.91 0.32 0.37 0.45 0.54 0.72 0.482002 0.30 0.37 0.40 0.55 0.60 0.41 0.99 0.99 1.00 0.99 1.00 0.99 0.85 0.88 0.96 0.97 0.99 0.91 0.31 0.38 0.45 0.57 0.79 0.492003 0.43 0.47 0.53 0.60 0.63 0.50 0.99 1.00 0.99 1.00 1.00 1.00 0.87 0.91 0.94 0.98 0.99 0.92 0.33 0.41 0.45 0.56 0.75 0.49

EPH-C2003-II * 0.46 0.46 0.57 0.62 0.77 0.54 0.98 0.99 0.99 0.99 0.99 0.99 0.82 0.89 0.89 0.93 0.98 0.89 0.30 0.35 0.45 0.53 0.67 0.452003-II 0.46 0.47 0.57 0.63 0.78 0.55 0.98 0.99 0.99 0.99 0.99 0.99 0.83 0.89 0.90 0.93 0.97 0.89 0.30 0.36 0.46 0.54 0.68 0.462004-I 0.54 0.63 0.64 0.72 0.71 0.63 0.99 0.99 0.99 1.00 1.00 0.99 0.86 0.89 0.93 0.98 0.99 0.92 0.32 0.37 0.44 0.60 0.73 0.482004-II 0.48 0.55 0.59 0.67 0.75 0.58 0.98 0.99 0.99 0.99 1.00 0.99 0.82 0.89 0.93 0.94 0.98 0.90 0.26 0.38 0.45 0.54 0.71 0.462005-I 0.51 0.61 0.70 0.71 0.78 0.64 0.98 0.99 0.99 0.99 0.99 0.99 0.86 0.89 0.93 0.96 0.99 0.91 0.31 0.41 0.46 0.54 0.75 0.482005-II 0.48 0.53 0.63 0.69 0.81 0.60 0.99 0.98 0.99 1.00 0.99 0.99 0.85 0.91 0.93 0.95 0.98 0.91 0.27 0.34 0.43 0.56 0.71 0.46

n


Table 7.10 Educational mobility By age group Argentina, 1992-2005

13-19 20-25 EPH-15 cities

1992 0.89 0.811993 0.88 0.811994 0.88 0.801995 0.87 0.791996 0.89 0.801997 0.87 0.801998 0.87 0.78

EPH - 28 cities1998 0.86 0.771999 0.87 0.782000 0.87 0.772001 0.87 0.772002 0.89 0.782003 0.89 0.80

EPH-C2004-II 0.87 0.782005-I 0.86 0.762005-II 0.86 0.77


Table 8.1 Coverage of PJH Share of households with PJH by equivalized income quintiles

Quintiles2 3 4 5 Mean

EPH, 2001

3 0.34 0.25 0.09 0.03 0.01 0.11H-C03 * .32 0.24 0.10 0.05 0.01 0.12003 .32 0.23 0.10 0.04 0.01 0.12

4-I .32 .22 .10 .03 .01 .1104-II .34 .21 .08 .03 .01 .11

2005-I 0.34 0.19 0.08 0.02 0.01 0.102005-II 0.30 0.17 0.06 0.03 0.00 0.09

EP20 02 0

200 0 0 0 0 0 020 0 0 0 0 0 0


-53-

Table 8.2 Coverage of PJH Share of households with PJH by education of household head

Low Medium High Mean EPH, 2003 0.16 0.09 0.02 0.11

EPH-C2003 * 0.17 0.09 0.02 0.112003 0.17 0.09 0.02 0.11

2004-I 0.16 0.10 0.02 0.112004-II 0.16 0.08 0.01 0.102005-I 0.15 0.07 0.01 0.102005-II 0.14 0.07 0.01 0.09


Table 8.3 C e of PJH Benefits (in pe

overagsos) of PJH by household

1 2 3 4 5 Mea EPH, 2003 35.4 13.2 4.3 1.1 0.5 8.4

EPH-C2004-II 25.9 11.8 3.7 0.9 0.2 6.92005-I 24.0 9.2 2.8 0.4 0.0 5.82005-II 22.8 7.2 1.9 1.0 0.0 5.2

n


Table 8.4 Inc ce of PJH Dis ution o JH ben iciaries

identrib f P ef by equivalized income quintile

Households1 2 3 4 5

Total

EPH, 2003 42.2 35.1 15.8 5.6 1.2 100.0EPH-C2003 * 41.3 32.3 16.7 8.5 1.2 100.02003 41.4 32.0 16.7 8.6 1.3 100.0

2004-I 42.8 32.2 17.5 6.3 1.3 100.02004-II 45.3 31.5 15.0 6.5 1.7 100.02005-I 47.8 30.3 15.3 5.2 1.5 100.02005-II 49.2 30.6 13.0 6.2 1.0 100.0

ndividualsTotal

EPH, 200

I1 2 3 4 5

3 42.5 35.2 15.9 5.5 1.1 100.0-C

003 * 33.5 16.3 8.4 1.2 100.003 33.2 16.2 8.5 1.2 100.0

.2 00.02004-II 45.2 31.7 14.9 6.6 1.6 100.02005-I 48.3 30.0 15.1 5.4 1.4 100.02005-II 49.1 31.3 12.6 6.1 1.0 100.0

EPH2 40.720 40.8

2004-I 42.1 33.3 17.1 6 1.2 1


Table 8.5 Incidence of PJH Distribution of PJH benefits by equivalized income quintile

1 2 3 4 5 Tota EPH, 2003 42.5 35.2 15.9 5.5 1.1 100.0

EPH-C2004-II 47.6 31.7 13.9 5.7 1.1 100.02005-I 51.0 29.6 14.5 4.0 0.9 100.02005-II 52.8 30.1 11.4 5.0 0.7 100.0

l


-54-

Figure 3.1 Growth-incidence curves Household per capita income Proportional changes by percentile Argentina, 1992-2005

-60

-40

-20

0

20

40

60

80

0 10 20 30 40 50 60 70 80 90 100

2003-2005

1992-1998

1998-2003

1992-2005


Figure 3.2 Growth-incidence curves Household per capita income Proportional changes by percentile Argentina, 2002-2005

-10

0

10

20

30

40

50

0 10 20 30 40 50 60 70 80 90 100

2003-2004

2002-2003

2004-2005


-55-

Figure 4.1 Poverty Argentina, 1992-2005 USD 1 and USD 2 lines USD 1 a day USD 2 a day

0

2

4

6

8

10

12

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

H PG FGT(2)

0

5

10

15

20

25

30

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

H PG 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

0

10

20

30

40

50

60

70

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

H PG FGT(2)

0

5

10

15

20

25

30

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

H PG 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

1992 1998

2002 2005

0.1

.2.3

.4.5

Den

sity

0 2 4 6 8 10log equivalized income

0.1

.2.3

.4D

ensi

ty


0.1

.2.3

.4D

ensi

ty

2 4 6 8 10log equivalized income

0.1

.2.3

.4D

ensi

ty


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

0

10

20

30

40

50

60

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-

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

Jam

, 90-

02

Nic

, 93-

01

Els

, 91-

03

Chi

, 90-

03

Bol

, 93-

02

Bra

, 90-

03

Cri,

92-

03

Pan

, 95-

02

Per

, 97-

02

Mex

, 92-

02

Uru

, 89-

03

Ecu,

94-

98

Hon

, 97-

03

DR

, 00-

04

Par

, 97-

02

Arg

, 92-

04

Col

, 92-

00

Ven

, 89-

00

Change in poverty (%)

-100

-50

0

50

100

150

200

250

300

Chi

, 90-

03

Cri,

92-

03

Bra,

90-

03

Bol

, 93-

02

Jam

, 90-

02

Nic

, 93-

01

Els

, 91-

03

Pan

, 95-

02

Per,

97-0

2

Mex

, 92-

02

Ecu

, 94-

98

Hon

, 97-

03

Par,

97-0

2

Uru

, 89-

03

Ven

, 89-

00

DR

, 00-

04

Col

, 92-

00

Arg,

92-

04

Source: Gasparini et al. (2005).

Figure 4.6 Poverty headcount ratio LAC countries Early 2000s

0

10

20

30

40

50

60

70

Uru Chi Cri

Arg

Dom Pan Bra Col

Mex

Ven Per

Gua Sur

Hon

Jam Els

Ecu Par Bol

Nic

Hai

Source: Gasparini et al. (2005).

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Figure 4.7 Poverty Argentina, 1992-2005 50% median poverty line

0

5

10

15

20

25

30

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

H PG 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

0.2

0.25

0.3

0.35

0.4

0.45

0.5

1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

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Figure 5.1 Gini coefficient Distribution of household per capita income Greater Buenos Aires, 1974-2005

0.30

0.35

0.40

0.45

0.50

0.55

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: 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

Early 2000s

40

45

50

55

60

Uru

guay

Ven

ezue

la

Arg

entin

a

Cos

ta R

ica

Peru

Jam

aica

El S

alva

dor

Mex

ico

Nic

arag

ua

Boliv

ia

Pana

ma

Chi

le

Hon

dura

s

Col

ombi

a

Braz

il

40

45

50

55

60

Uru

guay

Cos

ta R

ica

Ven

ezue

la

Peru

Jam

aica

Arg

entin

a

El S

alva

dor

Mex

ico

Hon

dura

s

Nic

arag

ua

Pana

ma

Col

ombi

a

Boliv

ia

Chi

le

Braz

il

Source: Own estimates from Gasparini (2003).

Figure 5.3 Change in the Gini coefficient

-60-

Between early 1990s and early 2000s Distribution of household per capita income

-4

-2

0

2

4

6

8A

rgen

tina

Ven

ezue

la

Par

agua

y

Per

u

Uru

guay

Bol

ivia

Chi

le

El S

alva

dor

Ecu

ador

Cos

ta R

ica

Nic

arag

ua

Col

ombi

a

Pan

ama

Jam

aica

Mex

ico

Bra

zil

Hon

dura

s

Source: Own estimates from Gasparini (2003).

Figure 6.1 Marginal return to a college education All working males, 1992-2003

0.50

0.55

0.60

0.65

0.70

0.75

0.80

1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

Source: Own estimates from microdata of the EPH.

-61-

Figure 6.2 Labor force, employment and unemployment Greater Buenos Aires, 1974-2005

0

10

20

30

40

50

60

1974

1975

1976

1977

1978

1979

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

unemployment activity employment Source: Own estimates from microdata of the EPH. * estimate for second half of 2005

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seminario arg aus3 - destacados · 2011. 2. 11. · section 9 closes with an assessment of the...

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