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Preliminary draft Comments welcome Monitoring Socio-Economic Conditions in Argentina, Chile, Paraguay and Uruguay ARGENTINA CEDLAS * Centro de Estudios Distributivos, Laborales y Sociales Universidad Nacional de La Plata January 20, 2005 Abstract This report documents the socio-economic situation in Argentina The study is mainly based on a wide range of distributional, labor and social statistics computed from microdata collected by the Permanent Household Survey (EPH) from 1992 to 2004. Data has also been drawn from other sources and existing literature. Argentina has witnessed dramatic distributional, labor and social changes in the last three decades. The country has experienced a sharp increase in poverty, inequality, unemployment, and informal labor. Argentina has had one of the most disappointing social performances in the region. Keywords: poverty, inequality, education, labor, wages, employment, Argentina * This document was prepared by the following team from CEDLAS/UNLP: Leonardo Gasparini (director), Victoria Fazio, Paula Giovagnoli, Federico Gutiérrez, Georgina Pizzolito, Leopoldo Tornarolli, Julieta Trías and Hernán Winkler. E-mails: [email protected] and [email protected]. 32949 v1 Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized
Transcript

Preliminary draft Comments welcome

Monitoring Socio-Economic Conditions in

Argentina, Chile, Paraguay and Uruguay

ARGENTINA

CEDLAS *

Centro de Estudios Distributivos, Laborales y Sociales Universidad Nacional de La Plata

January 20, 2005

Abstract

This report documents the socio-economic situation in Argentina The study is mainly based on a wide range of distributional, labor and social statistics computed from microdata collected by the Permanent Household Survey (EPH) from 1992 to 2004. Data has also been drawn from other sources and existing literature. Argentina has witnessed dramatic distributional, labor and social changes in the last three decades. The country has experienced a sharp increase in poverty, inequality, unemployment, and informal labor. Argentina has had one of the most disappointing social performances in the region.

Keywords: poverty, inequality, education, labor, wages, employment, Argentina

* This document was prepared by the following team from CEDLAS/UNLP: Leonardo Gasparini (director), Victoria Fazio, Paula Giovagnoli, Federico Gutiérrez, Georgina Pizzolito, Leopoldo Tornarolli, Julieta Trías and Hernán Winkler. E-mails: [email protected] and [email protected].

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1. Introduction

Argentina had traditionally been one of the Latin American countries with better social indicators. Poverty and inequality were very low compared to most countries in the region. Unemployment was also low and social and labor protection had a wide coverage. However, the socioeconomic situation has progressively deteriorated since the 1970s, and the sharp increase in poverty is the most dramatic sign of this fall. In fact, Argentina has experienced several major macroeconomic crises and structural change episodes. In the mid 1970s, a severe macroeconomic crisis under the Peronist administration was followed by structural reforms that were carried out by the military regime. The debt crisis of the early 1980s shook Argentina’s economy and led the country into a severe recession period. At the end of the 1980s, a decade characterized by a poor economic performance, there was a major macroeconomic crisis, including two episodes of hyperinflation in 1989 and 1990. In the early 1990s, the Peronist administration that had come to power in 1989 introduced a wide range of macro and market-based reforms. Despite an impressive macroeconomic record, the social situation deteriorated significantly. At the end of the 1990s, there was another recession episode, which was followed by a major breakdown. In fact, in 2001/02 the crisis led to a GDP fall of over 15%. In the last two years, Argentina’s economy showed signs of recovery, although per capita disposable income is still at lower levels than in the 1990s, and shows similar values to those recorded in the 1970s. The social situation of the country progressively worsened over the last three decades. Poverty and inequality grew even in periods of economic expansion. The performance of the labor market has also been extremely weak. Argentina, a country where there used to be almost full employment and wide social protection coverage, became an economy with persistent high unemployment and informality rates. This document shows evidence on Argentina’s socio-economic performance in the last three decades. The report is mostly focused on the 1992-2003 period, and is especially based on statistics drawn from microdata recorded by the Permanent Household Survey (EPH). All statistics presented in this report and computed by our team are available at and can be downloaded from www.depeco.econo.unlp.edu.ar/cedlas/monitoreo.htm. All indicators are updated as new information is released. The rest of the document is organized as follows. In section 2, the main sources of information used in this report are presented. The next ten sections show and analyze information on incomes, poverty, inequality, aggregate welfare, the labor market,

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education, housing and social services, demographics, and poverty alleviation programs. Section 12 presents a poverty profile, and section 13, an assessment of the results.

2. The Data

Distributional, labor and social conditions can be monitored with the help of the Permanent Household Survey (EPH), the main household survey in Argentina. The EPH is carried out by the National Institute of Statistics and Census (INDEC). At present, it covers 31 urban areas (all urban areas with more than 100,000 inhabitants) where 71% of Argentina’s urban population lives. Since in Argentina urban areas represent 87.1% of the country (one of the largest shares in the world), the EPH sample represents around 62% of Argentina’s total population.1 The EPH gathers information on individual socio-demographic characteristics, employment status, work hours, wages, income, type of job, education, and migration status. The microdata of the EPH is available for the Greater Buenos Aires (GBA) since 1974. The remaining urban areas were added during the last three decades. The EPH used to be carried out twice a year - in May and October. In 2003, a major methodological change was implemented by the INDEC, including modifications to questionnaires and the frequency of survey visits. So far, only a reduced version of the dataset of the new EPH-Continua (EPHC) is available to the public. The number of observations (individuals) changed from around 90,000 in the late 1990s and 60,000 in the last EPHs, to approximately 50,000 per quarter in the new EPHC. In the last decade, Argentina conducted two Living Standard Surveys. The first one, known as the Social Development Survey (EDS), was carried out in 1996/7 and included around 75,000 individuals who lived in urban areas, and represented 83% of the total population. The second survey, called Living Conditions Survey (ECV), had similar coverage and questionnaires, and was conducted in 2001. Both surveys included questions on housing, some assets, demographics, labor variables, health status and services, and education. The EDS and the ECV were sponsored by the World Bank and have questionnaires that are similar to those used in other countries. However, they are not part of the Living Standard Measurement Surveys (LSMS) program, and do not include questions on expenditures as the LSMS surveys do.2 Although it has a richer questionnaire and a somewhat larger geographical coverage, the ECV is of lower quality than the EPH. In this paper, the ECV was used to analyze some social services (e.g. health) that are not covered by the EPH.

1Although the EPH does not meet one of the Deininger and Squire (1996) criteria -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 survey which was recently conducted by the World Bank and included small towns in rural areas, we found only a negligible difference in all inequality measures when we included or ignored rural areas. 2 They are usually labeled as quasi-LSMS.

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The World Bank has also carried out other surveys to characterize the socio-economic situation of the country. In particular, during 2002 the Bank conducted a survey on the Social Impact of the Argentine Crisis (ISCA) to learn about the consequences of the strong 2001-2002 economic crisis, and the strategies used by households to cope with it.3 Expenditures are reported by the National Household Expenditures Survey (ENGH), which is conducted every 10 years (1986, 1996/7). Although the last ENGH includes some questions on socio-economic issues, we do not use this survey because social topics are better covered by the EPH and the ECV. As EPH results are available every six months (every three months in the case of the EPHC), it is impossible to monitor labor statistics closely by means of this survey. To fill part of this gap, the Labor Department carries out the Labor Indicators Survey (EIL), which covers large companies (more than 10 workers) in the private sector based in the Greater Buenos Aires, Córdoba, Mendoza and Rosario. Surveyed companies operate in the formal sector (workers are registered in the Social Security System - SIJP). The EIL has around 800 observations in the Greater Buenos Aires and less than 200 in each of the other three large cities in the country. There are another two surveys on companies that provide some information on labor indicators. The Monthly Industry Survey (EMI) gathers data from firms in the manufacturing sector, and includes some labor statistics. The National Survey on Large Enterprises (ENIGH) is a panel of the largest 500 companies in the formal sector, which have been surveyed since 1993. It also provides information on some labor variables. In Argentina, censuses are conducted every ten years. The latest censuses available were carried out in 1980, 1991 and 2001. Besides basic demographic variables, they include information on housing, education and basic labor variables. Given the main objective of this report -monitoring the socioeconomic situation on a yearly basis- we use these censuses only as a reference. In summary, the EPH is the best data source to monitor distributional, labor and social conditions in Argentina on a yearly basis. The ECV provides useful information on some issues that are not well captured or are not captured by the EPH (e.g. health and social programs), while the EIL is useful to monitor labor conditions in the formal sector on a monthly basis. The ENGH is the only survey that records expenditures but it has only been carried out every ten years. Some specific surveys (e.g., ISCA of The World Bank) are useful to study specific questions or periods. Administrative information is especially helpful to portray the educational, health, and security situation. 3 See Fiszbein and Giovagnoli (2003).

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This document is mainly based on information computed from EPH microdata. Most tables are divided into three panels. The first one shows data for the 16 major urban areas for which EPH microdata is available since 1992 (Buenos Aires City, the Greater Buenos Aires area, 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 for which microdata is available since 1998 (Bahía Blanca, Catamarca, Concordia, Corrientes, Formosa, La Rioja, Mar del Plata, Mendoza, Posadas, Resistencia, Río Cuarto, Rosario and Tucumán).4 To match both series we computed all statistics in 1998 with both samples of 16 and 29 cities. All statistics belong to the October round of the EPH, with the exception of 2003 since the microdata of the October wave is not available for that year.5 As from 2003 we include information from the new EPHC in the third panel. Unfortunately, the change from the EPH to the EPHC generates alterations in all the series. The INDEC has not released the microdata of the first quarter 2003 of the EPHC, which could have allowed us to study the impact of the methodological changes on the statistics. However, the INDEC has published statistics computed with the microdata of the first semester of the EPHC, which are pretty close to those we estimated with the May 2003 EPH. For instance, we estimated a poverty headcount ratio of 55% using the official moderate poverty line in May 2003, while the INDEC published a value of 54% using the EPHC, 1st semester. Given this preliminary evidence, we interpret estimated changes between the EPH and the EPHC as being mostly driven by real facts rather than by methodological changes.6

3. Incomes

Real incomes are the arguments of all poverty, inequality, polarization and welfare measures. Thus, before indicators for these distributional dimensions are computed, as it will be done in the next sections, some basic statistics on real incomes are shown. 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 (April or September). We also take geographical price variations into account, the Greater Buenos Aires being the base region.7

4 The analysis does not include Alto Valle del Río Negro and the interior of Mendoza province, which were covered in some rounds of the EPH, and the areas of San Nicolás-Villa Constitución, Rawson-Trelew and Viedma-Carmen de Patagones, which were recently (2002) included. 5 Given that surveys cover only urban areas, most statistics are not significantly affected by seasonality issues. 6 See a companion paper (Gasparini, 2004b) for further discussion on this issue. 7 INDEC (2001).

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Table 3.1 shows real incomes by deciles for an aggregate of 16 urban areas for some selected years from 1992 to 1998. It also shows real incomes for an aggregate of 29 cities from 1998 to 2003. The real income reported by the EPH fell around 7% between 1992 and 1996. This change is in sharp contrast with the national accounts, according to which per capita GDP increased 8.9% in that period. This discrepancy may be due to increasing under-reporting in the EPH, or overestimation of the GDP. It could also be the consequence of an increase in the share of sources that are not well captured by the EPH - capital income, benefits, and rents. Between 1996 and 1998 economy expanded, and per capita income grew 8.9%. A similar percentage is also reported by the national accounts. The 1998-2003 crisis implied a 37.5% fall in the mean income reported by the EPH, which was higher than the 19% decline recorded by the national accounts. The second panel of Table 3.1 shows that income changes were not uniform across deciles. All income changes between 1992 and 2001 were clearly unequalizing. In contrast, the impact of the last economic crisis (2001/03) was rather neutral, as all deciles lost almost 30% of their real income. The exception is the poorest decile, where the loss was 14%, probably as a consequence of the implementation of the Heads of Household Program (PJH) in the first semester of 2002. This program transfers $150 a month (around US$50) to (mostly) poor households. The growth-incidence curves of Figure 3.1 present a more detailed picture of income change patterns. Each curve shows the proportional income change in 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 Argentina’s case, however, most curves are below the horizontal axis and have a positive “slope”. The solid line labeled 1992-2003 summarizes the disappointing performance of the last ten years - real incomes reported by the EPH have dramatically fallen, in a highly unequalizing way. In figure 3.2, Pen’s parade curves present another view. Each curve indicates real income by percentiles. For clarity purposes, panels B to D show the curves for different percentile groups. In all cases, the order of the curves is the same - 1992, 1998, 2001 and 2003 - reflecting falling real incomes.8 The income changes shown in the figures included in this section suggest clear patterns for poverty, inequality and welfare. The non-uniform fall in income has certainly implied a significant increase in poverty and inequality, and a fall in aggregate welfare. The next three sections provide more evidence on these issues. 8 The only exception is for percentiles 90 to 100, where real incomes grew from 1992 to 1998.

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4. Poverty

This report shows poverty computed with the most widely used poverty lines and poverty indicators. The US$1 -a-day and US$2 -a-day at PPP prices are international poverty lines extensively used by the World Bank (see World Bank Indicators, 2004).9 Most LAC countries, including Argentina, calculate official moderate and extreme poverty lines using the cost of a basic food bundle and the Engel/Orshansky ratio of food expenditures.10 Table 4.1 presents the value of these poverty lines for the period 1992-2004 in local currency units. We also consider the line set at 50% of the median of the household per capita income distribution, which captures a relative rather than an absolute concept of poverty. For each poverty line, we compute three poverty indicators: the headcount ratio, the poverty gap, and the FGT (2).11 We also calculate the number of poor people by expanding the survey to both (i) the population represented by the EPH and (ii) the entire population. In the latter case, we assume 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 poverty measured with alternative poverty lines. Argentina has witnessed a dramatic increase in income poverty in the last decade. All indicators shown on Tables 4.2 to 4.6 and Figures 4.1 to 4.2 agree with this statement. According to the US$1 line, the headcount ratio increased from 1.4 in 1992 to 5.9 in the first half of 2004.12 Poverty substantially increased between 1992 and 1996, despite a significant growth in GDP reported by the National Accounts. After a temporary reduction recorded around 1998, poverty increased again, fueled by the economic recession that started in the second semester of 1998. In 2002, the headcount ratio reached the record level of 9.5. The last available value (second half of 2004 with data from the EPHC) suggests a significant reduction in poverty, which nonetheless remains at a very high level (5.9). Between 1992 and 2003, around 2 million Argentine citizens (out of a population of 38 millions) crossed the US$1 -a-day poverty line.13 The patterns of the other poverty indicators (poverty gap and FGT (2)) are similar. When using the US$2 line, results are also similar - poverty dramatically increased in the last decade. The headcount ratio rose from 4.1 in 1992 to 15.8 in 2004, which means that the estimated number of poor increased in around 4.5 millions. Poverty increased 4 points from 1992 to 1998, 7 points during the stagnation period of 1998-2001, around 9 points

9 See the methodological document for details. 10 See the methodological document and INDEC (2003). 11 See Foster, Greer and Thornbecke (1984) for references. 12 Notice that the difference from taking 29 instead of 16 cities in 1998 is small. In all cases when that difference is small, we will not mention the change in sample in our comments on the statistics. 13 In fact, that is the net increase in poverty, which is the consequence of people moving out of and into poverty.

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during the 2001-2002 crisis, and then substantially fell between October 2002 and the first half of 2004. The growth incidence curve for the period 2001-2003 (May) (see Figure 2.1) shows some growth in income for the very poor, but a decline for the rest of the population, suggesting that for this group the impact of the economic crisis was greater than the relief provided by PJH transfers. These different patterns between the very poor and the rest help to explain why although the headcount ratio on Table 4.3 increased between 2001 and 2003, the FGT (2) dropped. The latter poverty index puts more weight on the situation of the poorest. The index may decrease if income increases for this small fraction of the population, even in situations when the total number of poor people goes up. That seems to have been the situation in Argentina between 2001 and 2003. Argentina’s official poverty line is set at higher levels than US$2 -a-day at PPP, a fact that reveals that it is a middle-income country. Although the official poverty level 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 3.7% in 1992 to 8.3% in 1996. After a fall recorded around 1998, extreme poverty increased again to 13.7% in 2001 and reached 27.5% in 2002. In 2003, poverty dropped to 25.9% according to the EPH (May), and to 17.3% according to the EPHC (first half of 2004). 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 ten years. The headcount ratio increased nearly 33 points between 1992 and 2003, which means over 11 million “new poor” individuals.14 About 3.5 millions moved into poverty during the economic growth of the 90s, another 3.5 millions joined that group in the first phase of the recession (1998-2001), while about 7.5 millions crossed the poverty line during the crisis of 2001-2002. The economic recovery until May 2003 substantially reduced the number of poor in around 3.3 million individuals. Data from the EPHC suggests an additional reduction in the number of poor people in more than 1 million. It is interesting to note that the moderate official poverty line is close to the mode of income distribution (see Figure 4.3). When that occurs, poverty-growth elasticity is large - changes in income have a strong impact on the poverty rate. This fact implies that a relatively small improvement in economic conditions may lead to a large decrease in the

14 Notice that changing the sample from 16 to 29 cities in 1998 implies an increase in poverty of around 2 points. A simple extrapolation suggests an increase in poverty from 21.9 in 1992 to 54.6 in 2003.

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official poverty measure. 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 recorded in the recovery period. Figure 4.4 shows poverty for the Greater Buenos Aires area, calculated with the official moderate poverty line. Restricting the analysis to this area, where 1/3 of the Argentine population lives, gives us a more historical perspective, since the EPH was initially conducted only in there. Poverty slowly increased during the first half of the 1980s, and skyrocketed during the hyperinflation crisis. After a sharp fall in the early 1990s, the poverty headcount ratio increased 9 points between 1993 and 1999, and jumped more than 28 points during the last crisis. In the last two years, poverty went down nearly 12 points. Yet, it remains at higher levels than in 2001. The growing trend of poverty in Argentina is not well documented in the international literature. In some datasets Argentina is discarded for having household surveys that cover only urban areas (e.g. Chen and Ravallion, 2003, Sala-i-Martin, 2001), while in those where Argentina is included, the number of observations is small, and they generally belong to the second half of the 1990s, when poverty was quite stable (Székely, 2001, Wodon, 2001, WDI, various years). CEPAL (2003) reports poverty indicators for the Greater Buenos Aires area for 4 years. According to these estimates, the poverty headcount ratio was 21.2 in 1990, 17.8 in 1997, 19.7 in 1999 and 41.5 in 2002. Since 1990 is taken as the initial year, the increasing pattern of poverty in the 1990s does not show up in ECLAC data. Year 1990 was characterized by a macroeconomic crisis and a high inflation episode that kept poverty very high. When the economy stabilized poverty substantially fell, as shown in Figure 4.4. The dramatic increase in income poverty recorded in Argentina over the last 3 decades contrasts with the performance of most Latin American countries. Although the region has not been very successful in fighting poverty, most Latin American countries had a much better performance than Argentina. The contrast with Chile, Brazil and Uruguay, for instance, is notorious, as during the last decades poverty significantly decreased in Chile and Brazil, and remained roughly unchanged in Uruguay. Figure 4.5, which is based on data from ECLAC, shows that in the early 1990s Argentina was a low-poverty country. In fact, according to ECLAC estimates, it was the country with the lowest poverty headcount ratio. At present, as the graph shows, Argentina does not belong to the low-poverty group. It is likely that once the crisis is over, poverty will decrease several points. However, it is unlikely that in the short run Argentina will have lower poverty levels than those of other regional countries such as Chile or Uruguay, as it was the case some decades ago.

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Using data from 1998, Székely (2001) 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 (see Figure 4.6). However, even before the 1998-2002 economic crisis, Argentina had higher poverty levels than Chile and Uruguay. 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 to justify this kind of poverty measure does not apply to Argentina, since the economy has been stagnant since the 1970s. Anyway, on Table 4.6 and in Figure 4.7 we show 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 to consider poverty as a multidimensional issue.15 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 constructed a poverty indicator based on the characteristics of the dwelling, the access to water, sanitation, education (of the household head and children) and dependency rates.16 Table 4.7 and Figure 4.8 suggest that poverty did not increase when defined by those variables. 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 on 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. On column (ii) of Table 4.7 poverty is defined as a situation where an individual is poor according to both the endowment and the income criteria. To compute this column, we take the US$2 line. The time pattern of column (ii) follows the income poverty pattern on Table 4.3. The level, however, is substantially lower. While in 2003 the per capita income of 23.5% of the population was lower than US$2 -a-day, less than half of this group was also poor according to the endowment criterion. 15 Bourguignon (2003) discusses the need and the problem of going from income poverty to a multidimensional endowments approach. Attanasio and Székely (eds.) (2001) show evidence of poverty as lack of certain assets for LAC countries. 16 An individual is poor if she lives in a household that meets at least one of the following conditions: (i) 4 or more people per room, (ii) dwelling in a shantytown or other inadequate place, (iii) walls of tin, adobe, or cardboard, (iv) unavailability of water in lot, (v) unavailability of hygienic restroom, (vi) children aged 7 to 11 not attending school, (vii) household head without a primary education degree, (viii) household head with no more than a primary education degree, and more than 4 persons per income earner.

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INDEC computes a basic needs indicator of poverty (Necesidades Básicas Insatisfechas – NBI) with census data. An individual is poor if she lives in a household that meets at least one of the following conditions: (i) more than 3 people per room, (ii) dwelling in a shantytown or other inadequate place, (iii) unavailability of a hygienic restroom (without retrete), (iv) children aged 6 to 12 not attending school, (v) a household head who has not completed three years of primary school, and a household with more than 4 people per income earner. According to the Census of 1980, 27.7% of the individuals lived in households that met at least one of these criteria. In the Census of 1991, that proportion was 19.9%, while in 2001 it was 17.7%. The small fall in NBI in the 1990s leads to the same two conclusions stated above. The optimistic result is that basic-needs poverty fell although there was a dramatic increase in income poverty. People now have less income than a decade ago, but they are (slightly) better-off in terms of housing, sewerage and education. The pessimistic view underlines the very modest fall in the NBI indicator in one decade. In fact, according to this definition, the number of poor people was almost the same in 1991 (6,427,257) and in 2001 (6,343,589). Basic needs indicators of poverty usually decrease over time, even in stagnant economies. Argentina in the 1980s is an example.

5. Inequality and Polarization

As discussed in the previous section, poverty has substantially increased in Argentina. 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 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. Table 5.1 presents the most tangible measures of inequality - decile shares 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 1.9 in 1992 to 1 in 2001, and rose to 1.2 by 2004. On the other end, the income share of the richest decile increased from 33.8 in 1992 to 38.4 in 2004. A heterogeneous change pattern is observed across deciles. The income share of deciles 1 to 7 decreased over the last decade. The shares of deciles 8 and 9 remained roughly unchanged, while the share of decile 10 went up nearly 5 points in one decade. While income distribution changes were unequalizing over the period 1992-2001/2, they became equalizing in the last two years. However, income distribution is still more unequal than at the onset of recession (1998/9).

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Table 5.2 presents several inequality indices: the Gini coefficient, the Theil index, the variation coefficient, the Atkinson index, and the generalized entropy index with different parameters. All measures of inequality suggest the same growth pattern over the last decade. The Gini coefficient, for instance, rose from 0.445 in 1992 to 0.521 in May, 2003, with a peak of 0.525 in 2002.17 This change is not only statistically significant but also, very high from a historical viewpoint.18 Although all indices on Table 5.2 reflect a pattern of increasing inequality over the decade, they show differences for the 2001-2003 period. As the relative income of the very poor increased, indices that attach more weight to the bottom tail of the distribution report a significant fall in inequality (e.g. Atkinson with parameters 1 and 2, and entropy with parameter 0). Table 5.2 reports a large drop in inequality between 2003 and 2004. Although expected, the fall seems very large and should be monitored with the EPHC data of the second half of 2004. If these values are confirmed, inequality would have fallen to the pre-crisis levels. On 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.19 The introduction of these adjustments does not imply significant changes in the assessments of the results. On Tables 5.5 and 5.6 we consider the distribution of a more restricted income variable - the monetary income of equivalized household labor. Again, inequality patterns are similar to those previously presented, with the exception of the period 2001-2003. When we focus on labor income, capital income, transfers and particularly incomes from the PJH are excluded from statistics. As a result, between 2001 and 2003 incomes in the first deciles go down. This is in contrast with the results obtained when transfers are included in the analysis. Consequently, all indices on Table 5.6 show an increase in inequality between 2001 and 2003. Tables 5.7 and 5.8 are aimed at assessing the robustness of results by presenting the Gini coefficient over the distribution of several income variables. The different columns consider different adult equivalent scales, restrict income to labor sources, consider total household income without adjusting for family size, and restrict the analysis to people in

17 Notice that changing the sample in 1998 does not modify the value of any inequality index significantly. 18 An analysis of the statistical significance of inequality changes based on bootstrapping techniques will be introduced in the next report. See Sosa Escudero and Gasparini (2001) for an analysis applied to inequality in Argentina. 19 See Deaton and Zaidi (2003) and the methodological appendix for details on the implementation for Argentina.

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the same age bracket to control life-cycle factors.20 All the main results drawn from previous tables hold when these adjustments are made. The increase in inequality was not a distinctive feature of the 1990s only. Figure 5.1 shows the Gini coefficient for the distribution of equivalized household income in the Greater Buenos Aires from 1974 to 2003. This inequality measure climbed from 0.324 in 1974 to 0.518 in 2003. Inequality grew considerably in the second half of the 1970s, remained stable in the first half of the 1980s and increased substantially during the macroeconomic crisis of the late 1980s. After stabilization, inequality went down, although it did not reach pre-crisis levels. The 1990s were again times of increasing inequality. In fact, the Gini coefficient climbed 6 points from 1992 to 1998. The recent macroeconomic crisis pushed the Gini up 4 more points. Other indices also show these results. For instance, the share of the poorest decile fell from 3% in 1974 to 1.2% in 2003, while the income ratio between the two extreme deciles rose from 8 to more than 30 in three decades. 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 growing inequality, Argentina remained one of the low-inequality countries in the region. Argentina’s distributional history in the last decade was substantially different from that of the rest of the region. Although inequality increased in many countries, especially in South America, changes have been small compared to those experienced by Argentina. The second panel in Figure 5.2 shows that Argentina is no longer in the low-inequality group of LAC. It is interesting to compare with Uruguay. The distributions of these two neighboring countries, which were once almost identical, are now clearly different after three decades of relative distributional stability in Uruguay and turbulence in Argentina. Figure 5.3 again shows the disappointing distributional performance of Argentina, compared to the rest of Latin America. The rise in the Gini coefficient recorded in Argentina was almost double the rise recorded in Venezuela, which ranks second in inequality increases. Polarization is a dimension of equity that has recently received attention in the literature. It refers to homogeneous clusters that antagonize each other. Table 5.9 shows the Wolfson (1994) and Esteban, Gradín and Ray (1999) bipolarization indices. Polarization and inequality can go in different directions. This was not the case in Argentina, where distribution became more unequal and more polarized at the same time. Horenstein and 20 Some columns on Table 5.8 are just presented for comparison with other countries.

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Olivieri (2004) compute the new generalized polarization index of Duclos, Esteban and Ray (2004), finding similar results. As it was mentioned above, all surveys conducted in Argentina cover only urban areas. The World Bank’s Survey on the Social Impact of the Argentine Crisis (ISCA) included some small towns located in rural areas. According to the information gathered by that survey, income distribution in rural areas is not significantly different from income distribution in urban areas. The Gini coefficient for household per capita income distribution is 0.474 in urban areas, 0.482 in rural areas, and 0.475 for the entire country. This fact suggests that urban inequality statistics can be taken as a good approximation to national figures.

6. Aggregate Welfare

Rather than maximizing mean income, or minimizing poverty or inequality, in principle societies seek the maximization of aggregate welfare. Welfare is usually analyzed with the help of growth-incidence curves, generalized Lorenz curves, Pen’s parade curves and aggregate welfare functions. In section 3 we presented growth-incidence curves and Pen’s parade curves that suggested a substantial fall in welfare over the last decade. The same conclusion arises from the generalized Lorenz curves of Figure 6.1: the curve for 2003 always lies below the corresponding curve for 1992. Therefore, any social welfare function would rank 2003 as a worse year than 1992. A welfare analysis was also performed using four abbreviated welfare functions (see Table 6.1 and Figure 6.2). The first one is represented by the average income of the population and according to this value judgment, inequality is irrelevant. The remaining the functions take inequality into account. These are the functions proposed by Sen (equal to the mean times 1 minus the Gini coefficient) and Atkinson (CES functions with two alternative parameters of inequality aversion).21 For this exercise, the real per capita GDP from the National Accounts was taken as the average income measure and combined with the inequality indices shown above.22 Given that most assessments of the performance of an economy are made by looking at per capita GDP, we use this variable and complement it with inequality indices from our study to obtain rough estimates of the value of aggregate welfare according to different value judgments.23 For various reasons, per capita income from household surveys differs from National Accounts estimates. Although the economy substantially grew between 1992 and 1998 (according to NA estimates), welfare assessments are not as positive. While per capita GDP grew 19%,

21 See Lambert (1993) for technical details. 22 The source for GDP figures is ECLAC (2004). 23 See Gasparini and Sosa Escudero (2001) for a more complete justification of this kind of study.

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welfare increased around 8% according to Sen and Atkinson (1) functions. The contrast with an Atkinson (2) function is even more striking. According to the (Rawlsian-like) value judgment implicit in this function, welfare actually dropped between 1992 and 1998. The fall in real income of the poorest households offsets the significant increase in mean income. From 1998 to 2002 all functions show a dramatic fall in welfare driven by both an increase in inequality and a fall in mean income.24 Between 2002 and 2004, the positive change in mean income and income distribution is captured by all welfare functions. Despite this increase, aggregate welfare is still significantly lower than one decade ago when considering inequality concerns: 7% according to Sen and Atkinson (1) functions, and 20% lower according to a more Rawlsian value judgment. These figures are clear evidence of the disappointing performance of the Argentine economy. For reasons not yet well understood changes in mean income from the EPH do not closely match changes in mean disposable income from National Accounts. In Table 6.2 and Figure 6.3 we repeat the welfare exercise using only information from the EPH. In this case the performance of the Argentine economy is even worse, since per capita income in the EPH substantially fell in the last 12 years.

7. The Labor Market

This section summarizes the structure and changes of the labor market in Argentina in the last decade. Table 7.1 shows hourly wages, hours of work and labor income for the working population. Real hourly wages (deflated by the CPI) increased in the first half of the decade and decreased thereafter. Real hourly wages were higher in 2001 than in 1992, even after 4 years of stagnation.25 During the latest crisis, the wage drop was dramatic. In fact, according to the EPH, real wages fell 32% between September 2001 and April 2003. Hours of work also declined, although less than wages - from 44.3 hours a week in 1992 and 43.8 in 1998, to 41.7 in 2001, and 39.6 in 2003. Argentine citizens work, on average, around 5 hours a week less than a decade ago. Labor income was dominated by the behavior of wages: earnings significantly increased between 1992 and 1998, and dramatically fell thereafter. In 2003, mean labor income was just 63% of the corresponding value in 1992. From information of the EPHC it appears that both hourly wages and hours of work have increased in 2004. Tables 7.2 to 7.4 report hourly wages, hours of work and earnings by gender, age and education. Men earn more than women, and work considerably more hours, which implies higher earnings. In May 2003, an average man earned 13% more than a typical woman, and

24 Notice that if we had used mean income from the EPH the exercise would have implied a more dramatic fall in aggregate welfare in the last decade. 25 Notice that the change in geographical coverage implies a significant fall in the average wage.

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worked 26% more in terms of weekly hours. That latter gap is also present in the EPHC. Instead, the hourly wage gap between men and women does not show up in the EPHC data. Change patterns in labor variables have been approximately the same for males and females. This is not the case across age groups. People in the 41-64 bracket have significantly improved in relative terms. While in 1992 mean hourly wages for people aged 41 to 64 were 4% higher than wages for people aged 26 to 40, that difference expanded to 18% in 1998 and 24% in 2003. Relative wages also increased for workers in the 41-64 bracket compared to those aged 16 to 25 and over 65 years. The changes in work hours were similar across age groups, with the exception of those older than 65 - over the decade, the hours worked by the elderly increased, even in the latest crisis. Many authors have highlighted the substantial increase in the gap between skilled and unskilled workers in Argentina.26 Table 7.4 shows some basic evidence on this fact. Workers with at least some higher 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 is the consequence of both a wider wage gap and a greater difference in work hours. While in 1992 an adult with little education on average worked 5 hours a week more than a high-educated person, by 2003 that difference had completely vanished. In fact, Table 7.4 shows a difference of more than 1 hour in favor of the high-educated workers. Table 7.5 divides the working population into entrepreneurs, wage earners, self-employed workers and workers with zero income. The self-employed have significantly lost compared to the other groups. While in 1992 the average earnings of that group were higher than the earnings of salaried workers, now they are just 80%. The relative loss for the self-employed has occurred in terms of both hourly wages and hours of work. The heterogeneity of this group becomes apparent on Table 7.6: while earnings significantly increased in the 1990s for self-employed professionals, labor income substantially dropped for self-employed workers of low education level. Furthermore, the relative earnings of workers employed by small firms compared to those in large firms or the public sector fell from 70% in 1992 to 52% in 1998 and to 47% in 2003. On table 7.7 the working population is divided by economic activity. During the 1990s (1992 to 1998) earnings significantly increased in three sectors: the high-tech industry, the skilled services sectors (business services, the financial sector, professionals) and the public administration. In contrast, earnings fell in low-skilled services such as construction,

26 See Galiani and Sanguinetti (2003) and Gasparini (2003), among others.

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commerce and domestic service. In the last 5 years, the fall in earnings was generalized across economic sectors. During the 1990s, the region of Cuyo experienced a better labor performance than the rest of the country. Mean earnings grew 15% there while in the Greater Buenos Aires area they increased 5%, and in the rest of the regions they remained unchanged (see Table 7.8). Again, the fall in earnings was generalized across regions during the latest crisis. Table 7.9 records the share of salaried workers, self-employed workers and entrepreneurs in total labor income. It is not possible to compute these statistics with the EPH questionnaires of the early 1990s. From 1996 to 2003 there were no significant changes in the share of these three groups. Inequality in labor outcomes is probably the main source of inequality in household income. Table 7.10 shows the Gini coefficient for the distribution of hourly wages for male workers aged 25 to 55. Inequality grew over the period. The increase, however, is significantly lower than the rise in household inequality reported in section 5. The Gini went up for all educational groups. Are the differences in hourly wages reinforced by differences in work hours? Table 7.11 suggests the opposite. Correlations between hours worked and hourly wages are negative and significant for all years. Over the period, negative correlations fell in absolute terms - a fact that has an unequalizing impact on the distribution of earnings. On Table 7.12 wage gaps for three educational groups are computed. In 1992 a skilled prime-age male worker earned 2.61 times more per hour in his primary job on average than a similar unskilled worker. That value increased to 3.04 by 1998 and to more than 3 in 2003. Instead, the wage gap between semi-skilled and unskilled workers (column (iii)) did not change significantly. Preliminary evidence from the EPHC indicates that the skill premium is substantially falling. In order to further analyze the relationship between education and hourly wages, we ran regressions of the logarithm of hourly wages in the primary job on educational dummies and other control variables (age, age squared, and regional dummies) for men and women separately.27 Table 7.13 shows the results of these Mincer equations. For instance, in 1992 a male worker between 25 and 55 years of age with a primary education degree earned on average nearly 29% more than a similar worker without that degree. Having secondary school complete implied a wage increase of 45% over the earnings of a worker with only

27 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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primary school. In other words, the marginal return of completing secondary school -versus completing primary school and not having started secondary school- was 45%. The wage premium for a college education is an additional 56%. The returns to primary and secondary school did not significantly change over the last decade. In contrast, there was a high jump in the returns to college education (Figure 7.1)-, which is also noticeable for working women, and for urban salaried workers (both men and women). 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 of factors that are unobservable in household surveys, such as natural ability, contacts and work ethics, on hourly wages. An increase in the dispersion of this error term may reflect an increase in the returns to these unobservable factors in terms of hourly wages (Juhn et al. (1993)). Table 7.14 shows the standard deviation of the error term of each Mincer equation. The returns to unobservable factors have clearly increased in Argentina. The coefficients in the Mincer regressions are different for men and women, indicating that they are paid differently even when they have the same observable characteristics (education, age, location). To further look into this point, we simulated the counterfactual wage that men would earn if they were paid like women. The last column on Table 7.14 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 observable characteristics are controlled. This result 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). It seems that the gender wage gap has somewhat shrunk during the last decade. Argentina has witnessed great changes in labor force participation. Table 7.15 shows basic statistics by gender, age and education. Labor force participation increased several points in the last decade. This large increase is mainly the consequence of an enormous flow of low and semi-skilled prime-age women into the labor market. While in 1992 around 46% of adult women were in the labor market (either employed or unemployed), ten years later that fraction was higher than 56%. This rise was not shared by men, youngsters (16-25), or skilled workers, 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 7.2 suggests that this phenomenon was particularly important in the 1990s. During the 1980s, labor market participation remained roughly constant. It was in the period 1991-1999 when this variable increased considerably.

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Despite a remarkable economic growth, in the 1990s the employment rate fell. The drop, however, was not large: 1 point between 1992 and 1998. The employment rate decreased 4 points between 1998 and 2001, but by 2003 it recovered nearly 3 points. Again, changes have been very different across gender and age groups. While female employment increased throughout the decade, the situation for men was the opposite. Employment increased for people above 40, and went down especially for those younger than 25. Throughout the decade, the fall in employment was quite homogeneous across educational groups. Probably the most remarkable fact in Argentina’s labor markets of the last decade is the dramatic increase in unemployment (Figure 7.2). Unemployment sharply increased until 1996, first in the context of an economic boom (1991-1994), and later during a recession (1995-1996). By the end of the 1990s the unemployment rate stabilized around 12%. But that situation did not last long, as the economic crisis pushed this variable up again to levels around 18%. The recent period of economic growth is lowering the unemployment rate. From Figure 7.2, and from Tables 7.15 and 7.16, it is clear that during the 1990s the increase in unemployment was the consequence of a sharp increase in labor market participation facing a constant employment rate. Conversely, the increase in unemployment in the 2000s is mainly the consequence of the fall in employment linked to the economic crisis. Table 7.17 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 raising unemployment. Table 7.17 also shows that during the 1990s the increase in unemployment was particularly harsh for the unskilled, while the recent crisis especially hit the semi-skilled. The social concern for unemployment increases when unemployment spells are long. Table 7.18 shows a large increase in these spells. While in 1992 a typical unemployed person would stay 4 months without employment, in 2003 that spell would last more than 8 months. The increase in duration was similar across educational groups. INDEC has published quarterly results for the main labor variables since 2003. Table 7.19 reproduces statistics for labor force, employment and unemployment rates under two alternatives. In the first one, people who report that the PJH is their main labor activity are considered 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

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increase in employment that drove the unemployment rate around 6 points down between 2003 and 2004. The Labor Department carries out the Labor Indicators Survey (EIL) to monitor formal employment in large private firms (more than 10 employees) established in large conglomerates (GBA, Córdoba, Rosario, and Mendoza). Although limited in scope, the EIL provides useful information to monitor the labor market on a monthly basis. Table 7.20 and Figure 7.3 show the pattern of employment in the last three years. After a significant fall in the first semester of 2002, employment in formal firms started to recover. The recovery was faster as from the second semester of 2003. In July 2004, the employment level was almost 10% higher than in the worst month of recession. However, employment was still 2% lower than in August 2001 and 6% lower than in June 1999. Tables 7.21 to 7.26 depict the employment structure of urban Argentina. There are more men than women employed, but the gap has dramatically shrunk over the last decade. While in 1992 37% of the working population was female, in 2003 working women represented 43%. The participation of older people has also increased. Finally, the last three columns of Table 7.21 show a sizeable change in the educational structure of the working population in favor of the skilled. The Greater Buenos Aires area lost share in employment, particularly during the last crisis (Table 7.22). The entrepreneurs group has also lost share, as captured by the EPH (Table 7.23). Employment in the public sector rose during the period. The share of public employment increased over the last decade. However, (i) in the 1990s both the share of unskilled self-employed workers and employment in small firms fell, and (ii) so did employment in large firms during the latest economic crisis. Table 7.24 presents the formal-informal structure of the labor market. Unfortunately, there is not a single definition of informality. Following Gasparini (2003), two definitions were implemented with the information available in the EPH. According to the first definition, entrepreneurs, salaried workers in large firms and in the public sector, and self-employed professionals are considered formal workers. According to the second definition, formal workers are those who have the right to receive pensions when they retire. Unfortunately, the EPH allows us to implement 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 dropped 6 points in the 1990s and 7 points since 1998.

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The sector structure of the economy has changed (see Tables 7.25 and 7.26). During the 1990s there was a large fall in the share of employment in the manufacturing industry and commerce. Employment grew significantly in construction, skilled services and the public sector. During the recent crisis, all sectors lost participation against the public sector. The fall was particularly harsh in the high-tech and construction industries. There is a growing concern for child labor all over the world. Table 7.27 shows the proportion of working children between 10 and 14 years of age. Child labor is less relevant than in most LAC countries and according to EPH data, it has been decreasing even during the recent economic crisis. The last three tables in this section assess different dimensions of the quality of employment. Table 7.28 shows that most people report their employment as “permanent”. The share of permanent jobs increased in the 1990s and decreased during the crisis. As mentioned above, the coverage of the pension system shrunk in the last ten years. Table 7.29 shows that this pattern was similar for men and women, and that it was especially severe for the unskilled in comparison to skilled workers. Similar results apply to health insurance provided by the employer (see Table 7.30).

8. Education

In this section we provide an assessment of changes in the educational structure of the population. In Argentina, the proportion of high-educated people grew significantly in the last decade (Table 8.1).28 While in 1992 17.8% of adults aged between 25 and 65 had more than 13 years of formal education, that share rose to 21.3% in 1998 and to 24.7% in 2003. This rise was more intense for women than for men. A remarkable fact derived from Table 8.2 is the reversion of the gap in years of education between men and women. While men aged over 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. Among the working-age population (25 to 65), years of education have become slightly higher for women since 2001. The information on Table 8.3 suggests that the absolute gap between the rich and the poor in terms of years of education has widened over the last decade. That difference increased in over one year of education. In addition, the EPH does not allow us to capture years of education in graduate programs, so the variable is truncated at 17 years. Presumably, if

28 Please note that some tables in this section have a line that separates the early 1990s (1992 to 1994) from the rest of the decade. The reason is that a methodological change in the EPH made in 1995 allowed a better estimation of years of education from that year on.

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years of graduate education had been reported, the gap between the rich and the poor would have increased even more than what Table 8.3 suggests. On Table 8.4 the population is divided by age and household income quintiles. The widest gap in years of education between top and bottom income quintiles corresponds to adults aged 41-50. The gap is somewhat narrower for younger and older people. For instance, in 2003 while the educational gap between the poor and the rich was 6.5 years for people aged 41 to 50, it was 6.2 for people in their thirties, and 5.1 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.29 According to Table 8.5 educational Ginis have slightly fallen over the last decade. 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 dropped significantly. Tables 8.6 and 8.7 show a rough measure of education - the self-reported literacy rate. Argentina has high literacy rates compared to the rest of Latin America. However, there was not much progress in the last decade. Literacy rates are still at 96% and 98% for quintiles 1 and 2, as one decade ago. Guaranteeing equality of access to formal education is one of the goals of most societies. Tables 8.8 and 8.9 show school enrollment rates by equivalized income quintiles. Attendance rates have sharply increased for children aged 3 to 5. While in 1992 one third of these children attended a kindergarten, in 2003 half of them did. Attendance also increased for children in primary-school age, reaching 100% in 2003. Again, it should be noted 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 a somewhat slower pace. The schooling gap in favor of women has also shrunk in this age group. 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

29 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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18 to 23 in the top quintile increased 23 points in the last decade, it actually decreased 1 point for those youngsters in the bottom quintile of the equivalized household income distribution. Educational Mobility We followed the methodology developed in Andersen (2001) to provide estimates of educational mobility, i.e. the degree to which parental education and income determines 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 if she had 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.30 Table 8.10 shows the EMI for teenagers (13 to 19) and young adults (20 to 25). It seems that there have not been improvements in educational mobility during the last decade.

9. Housing and Social Services

Housing is probably the main asset that most people own. The EPH reports whether the house is owned by the family who lives in it or not, but it does not contain information on the rental value of the dwelling. Table 9.1 shows the share of families owning a house (the building and the lot) for each income quintile. Housing ownership is widespread along the income distribution. Actually, the share of poor people who own a dwelling is not substantially smaller than the corresponding share for the rich. However, Table 9.1 suggests that housing ownership in rich households has grown in relation to poorer households over the last decade. In fact, while housing ownership increased 3 points for the top quintile, it fell for the rest of the income distribution. The evidence suggests that housing markets are increasingly excluding the poor. Poor families live in houses that are smaller in the number of rooms than the houses where richer households dwell. Since poor families are also larger in size, the number of individuals per room is significantly larger. In the last decade, this indicator increased 0.30 for poor households, while it fell 0.13 for rich families. We have constructed an indicator of poor dwellings. This variable takes a value of 1 if the family lives in a shantytown, inquilinato, pension (boarding house), or other space not

30 For technical details see Andersen (2001).

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meant to be used as a house. Today, around 2 percent of the population lives in poor dwellings. This proportion was lower in the 1990s, and remained roughly unchanged between 1998 and 2003. Anyway, the share of these dwellings captured by the EPH is so small that it is difficult to know when changes or differences across groups are statistically significant. That problem is even more serious when analyzing houses made of “low-quality” materials, i.e. houses with walls made of chapa (tin sheets), adobe or chorizo (a type of adobe). These houses are around 1.5% of total dwellings. According to the last panel of Table 9.1 the share of these dwellings fell in the last decade. Table 9.2 reports housing statistics by age groups. Housing ownership has increased for the elderly and has decreased for the rest of the population. The shares of poor dwellings and low-quality dwellings have also significantly decreased for all, except for those households whose head is young (16 to 25 years of age). On Table 9.3 it is interesting to notice that changes in housing ownership by education were not similar to changes by income. For instance, ownership increased in households with low-educated heads. Table 9.4 reports statistics on the access to water, hygienic restrooms, sewerage, and electricity in the house by income strata.31 These gaps tend to be larger for hygienic restrooms and sewerage than for electricity and water, where coverage is more widespread. Poor people have access to electricity and clean water in the house, but many of them do not have a hygienic restroom, and most of them do not have access to public sewerage. The access to clean water and sewerage has increased, especially for quintiles 2 to 4.

10. Demographics

Resources available to each individual depend on the number of household members among whom total household resources are shared. The size and composition of the household are key determinants of an individual’s economic well-being. Table 10.1 shows household size by income quintiles and by education of the household head. It is interesting to notice that the absence of significant changes in the average size of households is the consequence of two forces that compensate each other - household size increased for poor families and decreased for the rest. A similar phenomenon, although with fewer differences between the poor and the rest, is reflected on Table 10.2, which reports the number of children by quintile of parental income. That number has decreased especially for parental income quintiles 3 to 5. On average, dependency rates remained quite stable during the decade. However, this fact is again the result of decreasing dependency rates for quintiles 4 and 5, and increasing

31 See the methodological document for definitions.

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dependency rates for the poor, in particular for quintile 2. Table 10.3 shows this result by presenting the number of income earners over household size by quintiles and by education of the household head. As expected, the mean age of the population has not significantly changed in the decade (see Table 10.4). However, it is interesting to see heterogeneous changes across quintiles again. The average age in quintile 5 increased 4.3 years between 1992 and 2003, while the average age fell 4.3 years in quintile 1. These are big changes that surely have some impact on poverty and inequality. Inequality is reinforced if marriages take place between people of similar income potential. Table 10.5 presents some simple linear correlations that suggest the existence of assortative mating in urban Argentina.32 Men with more years of formal education tend to marry women with a similar educational background (column (i)). This is one of the factors that contribute to a positive correlation of hourly wages within couples shown on column (ii). There are no clear signs of changes in the degree of assortative mating in the last decade according to these simple statistics. Finally, columns (iii) and (iv) show positive - though small - correlations in hours of work, both considering and excluding people who do not work.

11. Poverty Alleviation Programs

Probably as a consequence of the traditionally low incidence of poverty, and the wide coverage of social benefits linked to the labor market, Argentina never had a large poverty alleviation program. Instead, there was a multiplicity of small programs at different government levels targeted to particular groups or areas. These programs were not usually recorded in household surveys. In the midst of the deep recession of 2002, Argentina introduced the Heads of Household Program (PJH), 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 below 18 or incapacitated) and it has a counterpart work requirement, to help ensure that transfers reached those in the greatest need. Given the size of this program, the EPH started to include questions about it. This section is based on the specific questions included in the May 2003 EPH and the EPHC. Unfortunately, the change in methodology does not allow us to say much on the variable changes. Nonetheless, useful information on the structure of this program can be derived from the microdata.

32 See also Fernández, Guner and Knowles (2001).

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According to expectations, Table 11.1 shows that coverage is decreasing in income. The program seems to be far from universal in the poorest strata of the population. Around one third of households in the first quintile of the equivalized income distribution receive transfers from the PJH. That share falls to slightly more than 20% in quintile 2, and 10% in quintile 3. Around 12% of Argentine households are covered by the program. Table 11.2 shows that around 17% of households led by a low-educated head are beneficiaries of the PJH. The mean transfer by household is $9. In quintile 1, the mean transfer is $39.3, while in quintile 5 it is $0.5 (see Table 11.3). The program seems to be reasonably targeted to the poor (see Tables 11.4 and 11.5).33 Around 75% of the beneficiaries of the PJH belong to the poorest 40% of the population. However, there is still scope for improving the degree of targeting - more than 8% of the total beneficiaries report household incomes that place them among the richest 40% of the population.

12. A Poverty Profile

This section presents a poverty profile based on information from the EPH, May 2003. A poverty profile is a characterization of the poor population, often compared to the non-poor population. We take the US$2 -a-day and the official moderate poverty lines as the criteria to define the poor. To make the text less cumbersome, in general we discuss the results for the US$2 -a-day poverty line (columns (i) and (ii) on each table), except when a significant difference justifies the discussion of the alternative poverty definition. Table 12.1 shows some basic demographic characterization of the poor and non-poor population. According to the US$2 poverty line, 23.5% of the total population is poor. The differences in this share across age groups are substantial - while 37.9% of the children under 15 are poor, that share is just 6.3% for the elderly. The share of the poor population is monotonically decreasing in age. Nearly half of the poor population (44.3%) is children aged below 15, while only 2.7% is people above 65. The age structure is significantly different between the poor and the non-poor. This is summarized in mean age, which is 34.5 for the non-poor and only 22.3 for the poor. These patterns illustrate the relevance of the impact of demographic factors on poverty. In urban Argentina, 85% of the elderly are heads or spouses of the household they belong to. 33 The target population of the PJH is a topic of debate. Although the Decree that created 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 the entire poor population, instead of those meeting the initial requirements, which included many non-poor.

26

More than 55% of them live in households with 1 or 2 members. By living alone the elderly manage to escape income poverty, at least in the usual narrow definition of poverty. The poor and the non-poor substantially differ in household size. While a typical non-poor household has 3 persons, a typical poor household has 5 members. That difference is mostly explained by the difference in children under 12. On average, there is 1.1 child in each non-poor family with the head aged 25 to 45, while on average there are 2.5 children in poor households with a prime-age head. This difference implies that even with similar total income, an average poor family where both parents are present would have a per capita income that is 40% lower than that of a typical non-poor family. The dependency rates (number of income earners per person) are also dramatically different - 0.29 in poor households and more than double in non-poor households (0.65). It is interesting to note that the share of households headed by women is the same for the poor and the non-poor (30%) when poverty is defined as US$2. However, this share increases for the non-poor when the official poverty line is used. This change is the consequence of a significantly lower proportion of female-headed households in deciles 3 to 6 of the income distribution. Unfortunately, there are no estimates for rural poverty because the EPH has only urban coverage. As it was mentioned in section 4, based on the Survey on the Social Impact of the Argentine Crisis (ISCA) Haimovich (2004) finds that rural poverty is around 15 points higher than urban poverty. When it is assumed that prices in rural areas are 20% lower, the difference becomes smaller, but it is still significant (7 points). Table 12.2 shows that poverty is particularly high in the Northern regions of the country (32.7% in NEA and 27% in NOA, compared to a country average of 22.6%), and particularly low in Patagonia (11.6%). Given the size of the Greater Buenos Aires, most of the poor live in this area. In fact half of the poor population lives in this conglomerate, while more than 20% lives in the Pampeana region. Although housing ownership is less usual among the poor, the difference with the non-poor is not large. In fact, while 71% of the non-poor are owners, 61% of the poor report that they are owners of both the lot and the dwelling where they live (Table 12.4). The poor live in smaller houses of a worse quality and with fewer services. In an average poor household there are 2.43 people per room. That number is 1.21 in non-poor households. It is interesting to note that less than 5% of the poor population lives in shantytowns and other inconvenient places, while just 3% have dwellings with walls made of chapa (tin sheets), chorizo (adobe), or cardboard. The access of the urban poor to water and electricity, although lower than for the non-poor, is relatively high - 94% of the poor report having access to water in their lots, and 98% of them have electricity. The big difference with the

27

non-poor appears in the access to hygienic restrooms and to the public sewerage system. While 66% of the urban non-poor are connected to that system, that share drops to 30% for the urban poor. The poor have fewer years of formal education than the rest of the population for any age group. The educational gap is particularly wide for the [31, 50] age group.34 These differences are shown in the second panel of Table 12.4. While just a third of non-poor adults are unskilled, that share rises to nearly 70% for the poor. Among non-poor adults, 27% are skilled, while among the poor this proportion is of just 3.8%. Moreover, this share might be significantly smaller as some professionals were recorded as poor if their monthly income was low in April, 2003 just for seasonality reasons, or due to temporary unemployment. The literacy rate is high for the poor. In fact, 97% of poor people aged over 10 report that they can read and write. That share rises to 99% for the non-poor. The last panel of Table 12.4 indicates that school attendance is almost universal for children aged from 6 to 12. Attendance rates fall significantly, especially in the case of the poor, in the pre-primary, secondary and tertiary levels. While the rate of attendance is 99% for the poor aged from 6 to 12, it drops to 87% for ages 13 to 17, and to 32% for the 18 -23 age group. The rate of labor market participation of the poor is smaller than the rate of the non-poor, especially for women. While 65% of non-poor women are in the labor market, that share drops to 54% for poor women. The only exception for which the participation rate is higher for the poor is the elderly. Employment is significantly higher for the non-poor, while unemployment is substantially higher for the poor. The unemployment rate of the poor is more than double the rate for the non-poor. That gap is wider for the elderly, and smaller - although still substantial - for adult women. The unemployment spell of the poor, however, is on average slightly smaller than for the non-poor. In May 2003, a typical unemployed poor person had spent 8 months out of work. Finally, Table 12.5 reports that child labor is significantly higher among the poor. In May 2003, around 4 out of 1000 poor children had worked at least one hour. The poor are not only less likely to find a job but also work fewer hours and get lower wages when they are employed, (see Table 12.6). On average, an employed non-poor person works 11.5 hours more a week than a poor person. That gap is smaller for the youth (8.6 hours) and larger for prime age women (13 hours). On average, the hourly wage of a poor person is half that of a non-poor worker. The difference is smaller for youth and women, and larger for the elderly and prime age men.

34 Naturally, the gap is smaller for the [10, 20] age group, when the educational process is still not complete for many individuals, especially the non-poor.

28

Table 12.7 characterizes the employment structure of the population. Compared to the non-poor, the working poor are especially self-employed unskilled workers. However, it is interesting to notice that 30% of the poor work for the public sector. According to a definition of informality based on labor groups, 55% of the poor are informal workers, while 40% of the non-poor are in that category. When defining informality based on the access to social security, differences are dramatic - while 40% of non-poor workers are informal, that share jumps to 87% for the poor. The sector structure of employment is different between the poor and the rest. Compared to the non-poor, the poor are relatively concentrated on labor-intensive manufacturing industries, and particularly on construction and domestic service. However, commerce is the main source of jobs for the poor. In fact, 22% of the poor find jobs in that sector, followed by 17% who work in construction, education and health, and 13% who are employed as domestic servants. The last rows on Table 12.7 show substantial differences in the access to stable jobs with social security rights. The share of permanent jobs and labor positions with rights to pensions and health insurance is significantly lower for the poor. For instance, while 60% of the working non-poor report that they have access to health insurance linked to their employment, only 12% of the poor have this benefit. Table 12.8 reports statistics of the main poverty alleviation program in Argentina - the Heads of Household Program (PJH). According to the US$2 -a-day definition, 36.2% of the poor receive transfers for the PJH, while 7.4% of non-poor households are beneficiaries of that program. When considering the official definition of moderate poverty, the shares change to 24.1% and 2.1% for the poor and the non-poor respectively. Households mean income from the PJH is $36.5 for the poor (column (ii)) and $4.1 for the non-poor. According to the US$2 definition, 55.6% of the beneficiaries of the PJH are poor, while just 49.9% of the transfers go to the poor. Targeting indicators are better when a wider definition of poverty is considered - 92.5% of the beneficiaries of the PJH are poor, according to the official definition. Table 12.9 summarizes mean income as well as the income structure of the poor and the rest of the population. It also shows that inequality, as measured by the Gini coefficient for the distribution of household per capita income, is much lower for the poor than for the non-poor (0.203 and 0.448 respectively). The remainder of the table shows that, compared to the non-poor, the poor rely on transfers and income from self-employment relatively more.

29

Table 12.10 summarizes the value of household income and size, and performs a simple simulation to characterize the difference in per capita income between a typical poor person and the rest (panel B). The table indicates a typical poor per capita income if a particular variable (e.g. household size) took the mean value for the non-poor. The actual per capita income of a typical poor person is $46.9 a month. If household size for the poor were the same as for the non-poor, keeping the rest constant, per capita income would be $77.1. Of course, this exercise is helpful just as a preliminary characterization of the differences between the poor and the non-poor. The poor have less per capita income than the rest because they have fewer income earners in the household, lower non-labor income, and larger household size, but especially because they earn substantially less in the labor market. Table 12.11 shows that, according to our indicator, while 22% of the non-poor have deficiencies in at least one variable (water, education, housing, etc.), that share rises to 68% in the case of the poor. The last table in this poverty profile was built with census data. As commented above, Argentina’s government computes a basic needs indicator (NBI) based on housing characteristics, sanitation, primary school enrollment, education of the household head and dependency rates. Table 12.12 shows the geographical structure of this indicator. Basic needs poverty is higher in NEA and NOA and lower in the city of Buenos Aires (excluding the Greater Buenos Aires). According to this table, improvements in the living standards of the poor were significant in the 1980s, and decreased in the 1990s. While the NBI indicator fell 7.8 points between 1980 and 1991, it dropped 2.2 points between 1991 and 2001.

13. An Assessment

The last decade can be divided into two clearly different periods, according to economic performance. From 1992 to 1998, the government implemented an ambitious structural reforms program and the economy experienced high growth with macroeconomic stability. Since mid 1998, the economy suffered first a mild recession and then a deep crisis, which the country is currently overcoming. Although very different, both periods have been very disappointing from a social perspective. According to most indicators, poverty dramatically increased in Argentina, in contrast to the experience of most countries in the region. The rise in poverty is the consequence of economic stagnation and a substantial increase in inequality, again much more intense than in any other LAC countries. Inequality has increased measured by all indicators and computed over the distribution of all income variables. According to all value judgments, in the last 10 years the increase in inequality coupled with a stagnant per capita income implied a fall in aggregate welfare. Social

30

indicators have significantly improved during 2003 and 2004, but they are still around the levels of 2001. There was a lot of action in 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 fewer hours of work for the unskilled and a significant fall in social security coverage. Attendance rates in 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 and average age fell in the upper income quintiles, the opposite happened in the poor income strata.

31

References

Altimir, O. (1986). Estimaciones de la distribución del ingreso en la Argentina, 1953-1980. Desarrollo Económico 25 (100), January-March.

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. CEPAL (2003). BADEINSO. Santiago de Chile. Chen, S. and Ravallion, M. (2001). How Did the World's Poorest Fare in the 1990s?

World Bank Working Paper. Cowell, F. (1995). Measuring Inequality. LSE Handbooks in Economic Series, Prentice

Hall/Harvester Wheatsheaf. 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.

Esteban, J., Gradin, C. and Ray, D. (1999). Extension of a Measure of Polarization, with an

Application to the Income Distribution of Five OECD Countries. Instituto de Estudios Económicos de Galicia Pedro Barrie de la Maza Working Papers Series 24.

Fernández, R., Guner, N. and Knowles, J. (2001). Love and Money: a Theoretical and

Empirical Analysis of Household Sorting and Inequality. Mimeo. Fiszbein, A., Giovagnoli, P. and Aduriz, I. (2002). Argentina’s Crisis and its Impact on

Household Welfare. Mimeo.

32

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

Galiani, S., and Sanguinetti, P. (2003). The Impact of Trade Liberalization on Wage

Inequality: Evidence from Argentina. Journal of Development Economics, Volume 72, Issue 2, 497-513.

Gasparini, L (2003). Empleo y protección social en América Latina. Un análisis sobre la

base de encuestas de hogares. ILO. Gasparini, L. (2003). Different Lives: Inequality in Latin America and the Caribbean.

Chapter 2 of 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, forthcoming.. Gasparini, L. (2004b). Poverty and inequality in Argentina: methodological issues and a

literature review. CEDLAS-The World Bank, mimeo. Gasparini, L., Marchionni, M. and Sosa Escudero, W. (2001). La distribución del ingreso

en la Argentina. Editorial Triunfar. Gasparini, L. and Sosa Escudero, W. (2001). Assessing Aggregate Welfare: Growth and

Inequality in Argentina. Cuadernos de Economía 38 (113), Santiago de Chile. 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. (2004). Argentina’s Distributional Failure. The Role of Integration and Public

Policies. IADB Working Paper, forthcoming. 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. INDEC (2001). Press Release. Incidencia de la pobreza y de la indigencia en los

aglomerados urbanos. October.

33

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). January-June. 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. 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.

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

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Table 3.1 Real Income Argentina, 1992-2003 Real income

16 citiesDeciles 1992 1994 1996 1998 1998 1999 2000 2001 2002 2003

1 60.9 57.7 43.3 42.8 41.0 40.7 35.0 27.4 19.9 23.62 101.4 96.9 81.1 81.3 77.9 77.2 69.5 58.7 37.7 42.93 134.9 131.9 111.5 114.3 109.2 107.2 98.1 86.5 57.3 61.04 167.0 165.2 143.2 148.8 141.5 138.1 129.3 114.0 78.3 80.55 204.8 204.7 180.0 187.0 177.9 175.8 166.0 148.3 101.8 104.46 248.7 248.7 220.7 234.5 221.8 218.2 210.3 189.6 129.5 134.27 303.8 305.2 277.2 299.3 282.5 274.1 266.2 244.7 164.0 172.38 391.1 388.4 360.1 393.3 371.5 357.5 353.6 325.2 218.7 231.39 535.0 526.4 510.3 559.4 523.2 503.1 501.0 468.0 322.4 333.510 1096.0 1092.4 1085.8 1222.2 1150.6 1069.8 1066.7 1032.3 738.0 752.1

average 324.4 321.8 301.4 328.3 309.7 296.2 289.6 269.5 186.8 193.6

Proportional changesDeciles 1992-1994 1994-1996 1996-1998 1992-1998 1998-2000 2000-2001 2001-2002 2002-2003 2001-2003 1998-2003

1 -5.2 -25.0 -1.0 -29.6 -14.7 -21.7 -27.5 18.7 -14.0 -42.62 -4.5 -16.3 0.3 -19.9 -10.7 -15.6 -35.7 13.7 -26.8 -44.93 -2.3 -15.4 2.5 -15.3 -10.1 -11.8 -33.8 6.5 -29.5 -44.24 -1.1 -13.3 3.9 -10.9 -8.7 -11.8 -31.3 2.8 -29.4 -43.15 0.0 -12.1 3.9 -8.7 -6.7 -10.6 -31.4 2.6 -29.6 -41.36 0.0 -11.2 6.2 -5.7 -5.2 -9.8 -31.7 3.6 -29.3 -39.57 0.4 -9.2 8.0 -1.5 -5.8 -8.1 -32.9 5.1 -29.6 -39.08 -0.7 -7.3 9.2 0.6 -4.8 -8.0 -32.7 5.7 -28.9 -37.79 -1.6 -3.1 9.6 4.6 -4.2 -6.6 -31.1 3.5 -28.7 -36.310 -0.3 -0.6 12.6 11.5 -7.3 -3.2 -28.5 1.9 -27.1 -34.6

average -0.8 -6.3 8.9 1.2 -6.5 -6.9 -30.7 3.7 -28.1 -37.5

29 cities

Source: Calculations by CEDLAS based on EPH microdata. Table 4.1 Poverty Lines Argentina, 1992-2004

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 74.5 104.9 231.8 3.1 2.2 1.4

2003 (May) 38.6 77.1 106.6 232.3 3.0 2.2 1.42003 (IV quarter) 38.9 77.8 105.2 229.4 2.9 2.2 1.4

2003 (II semester) 38.7 77.4 103.6 227.7 2.9 2.2 1.32004 (I semester) 39.6 79.2 106.3 232.9 2.9 2.2 1.3

Source: INDEC, WDI and CEDLAS calculations.

Table 4.2 Poverty Argentina, 1992-2004 US$1 -a-Day Poverty Line

Number of poor people Headcount Poverty gapAll Survey FGT(0) FGT(1) FGT(2)(i) (ii) (iii) (iv) (v)

16 main cities1992 473,101 175,283 1.4 1.0 0.91993 554,535 217,448 1.6 1.0 0.91994 579,247 227,784 1.7 1.3 1.21995 1,122,377 464,553 3.2 2.0 1.71996 1,214,250 491,404 3.4 2.5 2.31997 1,111,226 462,820 3.1 2.0 1.71998 1,120,680 481,338 3.1 1.7 1.3

29 main cities1998 1,166,536 665,776 3.2 1.8 1.51999 1,250,082 524,793 3.4 2.1 1.82000 1,533,511 864,073 4.1 2.4 1.92001 2,605,449 1,447,723 7.0 4.0 3.22002 3,598,312 1,989,040 9.5 3.9 2.42003 3,051,808 1,663,049 8.0 2.8 1.8

EPH-C2003-IV 2,726,708 1,218,203 7.1 3.7 2.92003-II 3,105,080 1,380,482 8.1 4.0 3.02004-I 2,277,948 1,039,215 5.9 2.9 2.2

Source: Calculations by CEDLAS based on EPH microdata. Note: FGT (0) =headcount ratio, FGT (1) =poverty gap, FGT (2) =Foster, Greer and Thornbecke index with parameter 2. Table 4.3 Poverty Argentina, 1992-2004 US$2 -a-Day Poverty Line

Number of poor people Headcount Poverty gapAll Survey FGT(0) FGT(1) FGT(2)(i) (ii) (iii) (iv) (v)

16 main cities1992 1,376,532 510,002 4.1 1.8 1.31993 1,765,876 692,447 5.2 2.1 1.41994 1,686,753 663,301 4.9 2.2 1.61995 2,650,296 1,096,961 7.6 3.7 2.61996 3,066,280 1,240,916 8.7 4.2 3.11997 2,876,050 1,197,859 8.1 3.7 2.61998 2,921,913 1,254,978 8.1 3.5 2.3

29 main cities1998 3,224,817 1,840,497 8.9 3.8 2.51999 3,354,513 1,408,249 9.2 4.0 2.72000 4,041,197 2,277,055 10.9 4.9 3.32001 5,929,654 3,294,826 15.8 7.6 5.32002 9,221,116 5,097,161 24.3 10.7 6.32003 8,997,666 4,903,179 23.5 9.2 5.1

EPH-C2003-IV 7,087,085 3,166,274 18.5 8.0 5.22003-II 7,502,901 3,335,701 19.6 8.8 5.62004-I 6,147,085 2,804,341 15.8 6.9 4.3

Source: Calculations by CEDLAS based on EPH microdata. Note: FGT(0)=headcount ratio, FGT(1)=poverty gap, FGT(2)=Foster, Greer and Thornbecke index with parameter 2.

2

Table 4.4 Poverty Argentina, 1992-2004 Official Extreme Poverty Line

Number of poor people Headcount Poverty gapAll Survey FGT(0) FGT(1) FGT(2)(i) (ii) (iii) (iv) (v)

16 main cities1992 1,233,394 456,969 3.7 1.6 1.21993 1,509,263 591,822 4.5 1.9 1.31994 1,362,723 535,879 4.0 1.9 1.41995 2,447,791 1,013,144 7.0 3.4 2.41996 2,920,252 1,181,818 8.3 4.0 3.01997 2,628,030 1,094,560 7.4 3.5 2.41998 2,749,975 1,181,130 7.6 3.3 2.2

29 main cities1998 2,999,062 1,711,652 8.3 3.6 2.31999 2,963,905 1,244,269 8.1 3.6 2.52000 3,503,868 1,974,291 9.5 4.2 2.92001 5,117,133 2,843,346 13.7 6.7 4.72002 10,411,719 5,755,291 27.5 12.2 7.22003 9,946,808 5,420,403 25.9 10.3 5.7

EPH-C2003-IV 7,570,857 3,382,408 19.7 8.5 5.52003-II 7,903,199 3,513,669 20.6 9.2 5.92004-I 6,705,996 3,059,320 17.3 7.3 4.5

Source: Calculations by CEDLAS based on EPH microdata. Note: FGT(0)=headcount ratio, FGT(1)=poverty gap, FGT(2)=Foster, Greer and Thornbecke index with parameter 2. Table 4.5 Poverty Argentina, 1992-2004 Official Moderate Poverty Line

Number of poor people Headcount Poverty gapAll Survey FGT(0) FGT(1) FGT(2)(i) (ii) (iii) (iv) (v)

16 main cities1992 6,635,891 2,458,582 19.9 6.5 3.41993 6,459,724 2,533,030 19.1 6.8 3.71994 7,205,746 2,833,597 21.0 7.6 4.11995 9,457,991 3,914,674 27.2 10.9 6.41996 10,493,800 4,246,814 29.8 12.3 7.31997 10,056,957 4,188,668 28.2 11.5 6.71998 10,287,132 4,418,382 28.5 11.7 6.7

29 main cities1998 10,989,221 6,271,869 30.4 12.5 7.21999 11,272,294 4,732,190 30.8 12.6 7.42000 12,239,562 6,896,510 33.0 14.3 8.52001 14,532,827 8,075,198 38.8 18.3 11.82002 21,984,983 12,152,649 58.0 29.4 19.12003 21,085,772 11,490,459 55.0 27.4 17.2

EPH-C2003-IV 18,641,189 8,328,264 48.6 22.6 14.12003-II 18,514,492 8,231,324 48.3 23.3 14.82004-I 17,395,804 7,936,081 44.8 20.6 12.6

Source: Calculations by CEDLAS based on EPH microdata. Note: FGT(0)=headcount ratio, FGT(1)=poverty gap, FGT(2)=Foster, Greer and Thornbecke index with parameter 2.

3

Table 4.6 Poverty Argentina, 1992-2004 50 % Median Income Poverty Line

Number of poor people Headcount Poverty gapAll Survey FGT(0) FGT(1) FGT(2)(i) (ii) (iii) (iv) (v)

16 main cities1992 6,200,670 2,297,334 18.6 6.5 3.51993 6,518,671 2,556,145 19.2 7.2 4.01994 6,817,646 2,680,980 19.9 7.3 3.91995 6,730,626 2,785,814 19.4 8.2 5.01996 7,541,614 3,052,072 21.4 9.2 5.71997 8,071,596 3,361,776 22.6 9.0 5.31998 8,193,288 3,519,064 22.7 8.9 5.2

29 main cities1998 7,802,096 4,452,884 21.6 8.9 5.21999 8,172,215 3,430,754 22.3 9.3 5.52000 8,875,302 5,000,883 24.0 10.0 6.12001 9,493,292 5,274,969 25.3 11.7 7.72002 9,686,713 5,354,529 25.5 11.1 6.62003 9,476,700 5,164,223 24.7 9.9 5.5

EPH-C2003-IV 9,019,497 4,029,612 23.5 10.3 6.52003-II 9,502,078 4,224,511 24.8 11.2 7.12004-I 9,049,021 4,176,407 23.6 10.1 6.1

Source: Calculations by CEDLAS based on EPH microdata. Note: FGT(0)=headcount ratio, FGT(1)=poverty gap, FGT(2)=Foster, Greer and Thornbecke index with parameter 2. Table 4.7 Poverty Argentina, 1992-2004 Endowments

Endowments Endowmentsplus income

(i) (ii) 16 main cities

1992 0.354 0.0271993 0.342 0.0331994 0.345 0.0301995 0.338 0.0511996 0.344 0.0581997 0.350 0.0561998 0.355 0.058

29 main cities1998 0.363 0.0651999 0.364 0.0652000 0.354 0.0752001 0.364 0.1072002 0.341 0.1462003 0.341 0.097

Source: Calculations by CEDLAS based on EPH microdata.

4

Table 5.1 Distribution of Household per Capita Income Share of Deciles and Income Ratios Argentina, 1992-2004

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

(i) (ii) (iii) (iv) (v) (vi) (vii) (viii) (ix) (x) (xi) (xii) (xiii) 16 main cities

1992 1.9 3.1 4.2 5.1 6.3 7.7 9.4 12.1 16.5 33.8 18.0 7.5 2.01993 1.7 3.1 4.2 5.3 6.5 7.9 9.7 12.3 16.6 32.8 19.0 8.0 1.81994 1.8 3.0 4.1 5.1 6.4 7.7 9.5 12.1 16.4 34.0 18.9 8.1 1.91995 1.5 2.8 3.8 4.8 6.0 7.4 9.1 11.7 16.7 36.2 24.4 9.3 2.11996 1.4 2.7 3.7 4.8 6.0 7.3 9.2 11.9 16.9 36.1 25.1 9.5 2.01997 1.4 2.7 3.7 4.8 6.0 7.4 9.3 12.1 17.1 35.6 25.2 10.2 2.11998 1.3 2.5 3.5 4.5 5.7 7.1 9.1 12.0 17.0 37.2 28.5 10.7 2.1

29 main cities1998 1.3 2.5 3.5 4.6 5.7 7.2 9.1 12.0 16.9 37.2 28.0 10.5 2.11999 1.4 2.6 3.6 4.7 5.9 7.4 9.3 12.1 17.0 36.1 26.3 10.3 2.02000 1.2 2.4 3.4 4.5 5.7 7.3 9.2 12.2 17.3 36.8 30.5 11.4 2.02001 1.0 2.2 3.2 4.2 5.5 7.0 9.1 12.1 17.4 38.3 37.7 13.5 2.22002 1.1 2.0 3.1 4.2 5.4 6.9 8.8 11.7 17.3 39.5 37.1 14.0 2.32003 1.2 2.1 3.1 4.1 5.4 6.9 8.9 12.0 17.3 39.0 33.0 12.9 2.2

EPH-C2003-IV 1.2 2.3 3.3 4.3 5.4 6.9 8.9 11.9 17.0 38.6 32.6 11.8 2.12003-II 1.1 2.1 3.1 4.2 5.3 6.8 9.0 12.0 17.2 39.1 36.8 13.5 2.22004-I 1.2 2.4 3.4 4.4 5.6 7.1 9.1 11.9 16.7 38.4 31.8 11.4 2.1

Source: Calculations by CEDLAS based on EPH microdata. Table 5.2 Distribution of Household per Capita Income Inequality Indices Argentina, 1992-2004

Gini Theil CV A(.5) A(1) A(2) E(0) E(2)(i) (ii) (iii) (iv) (v) (vi) (vii) (viii)

16 main cities1992 0.445 0.363 1.091 0.162 0.292 0.498 0.345 0.5951993 0.439 0.352 1.071 0.158 0.290 0.505 0.342 0.5741994 0.449 0.370 1.102 0.165 0.296 0.499 0.352 0.6081995 0.476 0.419 1.183 0.185 0.332 0.557 0.404 0.7001996 0.480 0.430 1.235 0.189 0.341 0.596 0.417 0.7621997 0.477 0.410 1.125 0.185 0.337 0.573 0.411 0.6321998 0.496 0.458 1.272 0.202 0.361 0.597 0.447 0.809

29 main cities1998 0.493 0.455 1.271 0.200 0.357 0.592 0.441 0.8081999 0.483 0.427 1.183 0.191 0.345 0.592 0.424 0.7002000 0.497 0.450 1.211 0.202 0.368 0.634 0.459 0.7342001 0.515 0.482 1.235 0.217 0.394 0.665 0.501 0.7632002 0.525 0.511 1.318 0.226 0.401 0.646 0.514 0.8682003 0.521 0.502 1.314 0.221 0.391 0.628 0.497 0.864

EPH-C2003-IV 0.512 0.495 1.389 0.215 0.381 0.622 0.480 0.9652003-II 0.522 0.514 1.402 0.225 0.399 0.666 0.510 0.9832004-I 0.507 0.504 1.769 0.213 0.375 0.614 0.470 1.564

Source: Calculations by CEDLAS based on EPH microdata. 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.

5

Table 5.3 Distribution of Equivalized Household Income Share of Deciles and Income Ratios Argentina, 1992-2004

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

(i) (ii) (iii) (iv) (v) (vi) (vii) (viii) (ix) (x) (xi) (xii) (xiii) 16 main cities

1992 2.1 3.5 4.4 5.4 6.6 7.9 9.5 12.0 16.4 32.4 15.5 6.8 1.91993 1.9 3.4 4.5 5.6 6.8 8.1 9.7 12.3 16.2 31.5 16.2 6.9 1.81994 2.0 3.3 4.4 5.5 6.6 7.9 9.6 12.0 16.2 32.5 16.1 7.0 1.91995 1.7 3.1 4.2 5.1 6.3 7.6 9.2 11.7 16.4 34.8 20.6 8.2 2.11996 1.7 3.0 4.0 5.1 6.2 7.6 9.3 11.9 16.7 34.5 20.7 8.2 2.01997 1.6 3.0 4.0 5.1 6.3 7.7 9.4 12.0 16.9 34.0 21.0 8.7 2.01998 1.5 2.8 3.8 4.8 6.0 7.4 9.2 12.0 16.8 35.7 23.4 9.2 2.0

29 main cities1998 1.5 2.8 3.9 4.9 6.0 7.4 9.2 12.0 16.7 35.6 22.9 8.9 2.01999 1.6 2.9 4.0 5.0 6.2 7.6 9.4 12.0 16.8 34.6 21.7 8.8 2.02000 1.4 2.7 3.7 4.8 6.0 7.5 9.3 12.1 17.0 35.4 25.5 9.9 2.12001 1.2 2.5 3.5 4.6 5.8 7.3 9.2 12.0 17.1 36.9 31.3 11.6 2.12002 1.2 2.3 3.4 4.5 5.8 7.1 8.9 11.6 17.0 38.2 30.7 11.6 2.32003 1.4 2.5 3.4 4.4 5.7 7.1 9.0 12.0 17.0 37.6 27.0 10.8 2.1

EPH-C2003-IV 1.4 2.6 3.6 4.7 5.8 7.2 9.1 11.9 16.7 37.1 27.1 10.2 2.12003-II 1.2 2.4 3.4 4.5 5.7 7.1 9.1 12.0 17.0 37.5 30.5 11.6 2.12004-I 1.4 2.6 3.7 4.7 5.9 7.4 9.3 12.0 16.7 36.2 26.3 10.0 2.1

Source: Calculations by CEDLAS based on EPH microdata. Note 1: Column (xi)=income ratio between deciles 10 and 1; column (xii)=income ratio between percentiles 90 and 10, and column (xiii)=income ratio between percentiles 95 and 80. Table 5.4 Distribution of Equivalized Household Income Inequality Indices Argentina, 1992-2004

Gini Theil CV A(.5) A(1) A(2) E(0) E(2)(i) (ii) (iii) (iv) (v) (vi) (vii) (viii)

16 main cities1992 0.425 0.326 1.007 0.146 0.266 0.457 0.309 0.5071993 0.418 0.317 0.994 0.143 0.263 0.463 0.306 0.4941994 0.426 0.333 1.028 0.149 0.269 0.457 0.313 0.5291995 0.454 0.379 1.105 0.169 0.303 0.513 0.361 0.6101996 0.456 0.386 1.143 0.171 0.310 0.551 0.371 0.6531997 0.454 0.368 1.044 0.167 0.307 0.532 0.366 0.5451998 0.473 0.412 1.172 0.183 0.329 0.554 0.399 0.686

29 main cities1998 0.470 0.407 1.169 0.181 0.325 0.548 0.393 0.6831999 0.460 0.384 1.097 0.173 0.314 0.550 0.378 0.6022000 0.475 0.408 1.128 0.184 0.337 0.593 0.411 0.6362001 0.494 0.440 1.164 0.199 0.364 0.625 0.452 0.6772002 0.504 0.469 1.248 0.208 0.371 0.606 0.463 0.7792003 0.498 0.456 1.230 0.202 0.359 0.585 0.444 0.757

EPH-C2003-IV 0.489 0.450 1.300 0.197 0.350 0.584 0.431 0.8452003-II 0.500 0.468 1.311 0.206 0.368 0.632 0.459 0.8592004-I 0.481 0.433 1.287 0.190 0.341 0.572 0.418 0.828

Source: Calculations by CEDLAS based on EPH microdata. 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.

6

Table 5.5 Distribution of Equivalized Household Labor Monetary Income Share of Deciles and Income Ratios Argentina, 1992-2003

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

(i) (ii) (iii) (iv) (v) (vi) (vii) (viii) (ix) (x) (xi) (xii) (xiii) 16 main cities

1992 2.0 3.5 4.5 5.5 6.7 8.0 9.7 12.1 16.4 31.6 15.6 6.8 1.91993 1.8 3.3 4.4 5.5 6.8 8.1 9.8 12.3 16.5 31.5 17.2 7.4 1.91994 1.9 3.3 4.4 5.4 6.5 7.9 9.5 12.0 16.2 32.8 17.0 7.0 1.91995 1.5 3.0 4.0 5.0 6.1 7.5 9.0 11.5 16.4 35.9 23.4 8.7 2.21996 1.5 2.9 3.9 4.9 6.1 7.5 9.2 11.8 16.7 35.7 23.9 9.0 2.11997 1.5 2.8 3.9 5.0 6.2 7.6 9.4 12.0 17.0 34.7 23.9 9.4 2.11998 1.3 2.6 3.6 4.7 5.8 7.2 9.0 11.9 16.9 36.9 27.7 10.1 2.1

29 main cities1998 1.4 2.7 3.7 4.7 5.8 7.2 9.0 11.8 16.8 36.8 26.9 9.8 2.11999 1.4 2.7 3.8 4.9 5.9 7.4 9.2 12.0 17.0 35.7 25.0 9.7 2.02000 1.2 2.5 3.5 4.6 5.8 7.3 9.2 12.0 17.1 36.7 30.3 11.2 2.12001 1.0 2.2 3.3 4.4 5.5 7.0 8.9 11.8 17.1 38.7 36.9 13.1 2.22002 1.1 2.0 3.0 4.1 5.4 6.9 8.6 11.4 17.0 40.5 37.5 13.3 2.42003 0.9 2.1 3.0 4.0 5.3 6.9 8.8 11.9 17.3 39.7 42.0 13.4 2.2

0

Source: Calculations by CEDLAS based on EPH microdata. Note 1: Column (xi)=income ratio between deciles 10 and 1; column (xii)=income ratio between percentiles 90 and 10, and column (xiii)=income ratio between percentiles 95 and 80. Table 5.6 Distribution of Equivalized Household Labor Monetary Income Inequality Indices Argentina, 1992-2003

Country Gini Theil CV A(.5) A(1) A(2) E(0) E(2)(i) (ii) (iii) (iv) (v) (vi) (vii) (viii)

16 main cities1992 0.418 0.311 0.951 0.142 0.263 0.632 0.305 0.4521993 0.423 0.319 0.973 0.146 0.272 0.499 0.318 0.4731994 0.430 0.340 1.049 0.152 0.277 0.496 0.324 0.5501995 0.465 0.402 1.146 0.178 0.322 0.571 0.389 0.6571996 0.470 0.414 1.206 0.183 0.330 0.585 0.400 0.7281997 0.465 0.386 1.069 0.176 0.324 0.568 0.392 0.5711998 0.489 0.442 1.226 0.196 0.352 0.598 0.434 0.752

29 main cities1998 0.485 0.438 1.223 0.193 0.347 0.589 0.426 0.7481999 0.476 0.414 1.156 0.185 0.336 0.588 0.410 0.6682000 0.491 0.438 1.178 0.198 0.361 0.639 0.448 0.6942001 0.513 0.480 1.236 0.216 0.390 0.660 0.494 0.7642002 0.530 0.525 1.347 0.230 0.407 0.653 0.522 0.9072003 0.530 0.520 1.326 0.231 0.415 0.694 0.537 0.880

Source: Calculations by CEDLAS based on EPH microdata. 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.

7

Table 5.7 Distribution of Household Income Gini Coefficient Argentina, 1992-2004

Country 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

(i) (ii) (iii) (iv) (v) (vi) (vii) (viii) (ix) (x) (xi) 16 main cities

1992 0.445 0.425 0.416 0.417 0.411 0.429 0.440 0.429 0.396 0.434 0.4251993 0.439 0.418 0.409 0.411 0.404 0.423 0.430 0.430 0.393 0.431 0.3971994 0.449 0.426 0.416 0.418 0.410 0.431 0.433 0.437 0.407 0.426 0.4151995 0.475 0.454 0.444 0.446 0.437 0.458 0.452 0.460 0.430 0.464 0.4201996 0.480 0.456 0.446 0.448 0.439 0.462 0.450 0.453 0.433 0.475 0.4241997 0.477 0.454 0.444 0.446 0.437 0.459 0.451 0.452 0.426 0.452 0.4521998 0.496 0.473 0.463 0.465 0.456 0.478 0.465 0.472 0.446 0.470 0.465

29 main cities1998 0.493 0.470 0.459 0.461 0.452 0.474 0.461 0.467 0.446 0.465 0.4631999 0.483 0.460 0.449 0.452 0.443 0.464 0.451 0.460 0.439 0.462 0.4482000 0.497 0.475 0.464 0.468 0.458 0.481 0.459 0.491 0.443 0.478 0.4432001 0.515 0.494 0.483 0.486 0.477 0.499 0.472 0.508 0.458 0.498 0.4652002 0.525 0.504 0.493 0.496 0.487 0.509 0.480 0.521 0.474 0.511 0.4612003 0.520 0.498 0.486 0.489 0.479 0.503 0.473 0.502 0.471 0.512 0.451

EPH-C2003-IV 0.512 0.489 0.476 0.481 0.470 0.494 0.461 0.498 0.455 0.505 0.4732003-II 0.522 0.500 0.488 0.493 0.482 0.506 0.471 0.509 0.467 0.521 0.4792004-I 0.503 0.481 0.469 0.473 0.463 0.486 0.453 0.482 0.468 0.476 0.468

Source: Calculations by CEDLAS based on EPH microdata. 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.

8

Table 5.8 Distribution of Household Income Gini Coefficient Argentina, 1992-2004

Country Per capita Equivalized Per capita Per capita Per capita Per capitaincome income income income income incomeOnly urban Only urban Only labor Only monetary Only labor Urban labor

monetary monetary(i) (ii) (iii) (iv) (v) (vi)

16 main cities1992 0.445 0.425 0.438 0.445 0.438 0.4381993 0.439 0.418 0.443 0.439 0.443 0.4431994 0.449 0.426 0.452 0.449 0.452 0.4521995 0.475 0.454 0.484 0.475 0.484 0.4841996 0.480 0.456 0.491 0.480 0.491 0.4911997 0.477 0.454 0.484 0.477 0.484 0.4841998 0.496 0.473 0.509 0.496 0.509 0.509

29 main cities1998 0.493 0.470 0.504 0.493 0.504 0.5041999 0.483 0.460 0.494 0.483 0.494 0.4942000 0.497 0.475 0.508 0.497 0.508 0.5082001 0.515 0.494 0.529 0.515 0.529 0.5292002 0.525 0.504 0.546 0.525 0.546 0.5462003 0.520 0.498 0.550 0.520 0.550 0.550

EPH-C2003-IV 0.512 0.489 0.5122003-II 0.522 0.500 0.5222004-I 0.503 0.481 0.503

Source: Calculations by CEDLAS based on EPH microdata. 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.

9

Table 5.9 Polarization EGR and Wolfson Indices of Bipolarization Argentina, 1992-2004

Household p/c income Equivalized incomeEGR Wolfson EGR Wolfson

(i) (ii) (iii) (iv) 16 main cities

1992 0.148 0.393 0.138 0.3631993 0.142 0.393 0.132 0.3591994 0.150 0.404 0.139 0.3741995 0.154 0.423 0.144 0.3851996 0.156 0.427 0.147 0.3961997 0.158 0.433 0.151 0.4081998 0.162 0.445 0.152 0.412

29 main cities1998 0.165 0.452 0.154 0.4161999 0.161 0.452 0.149 0.4112000 0.167 0.468 0.157 0.4282001 0.173 0.492 0.159 0.4462002 0.175 0.500 0.161 0.4532003 0.173 0.484 0.163 0.448

EPH-C2003-IV 0.170 0.489 0.160 0.4372003-II 0.180 0.519 0.168 0.4802004-I 0.167 0.482 0.157 0.450

Source: Calculations by CEDLAS based on EPH microdata. Note: EGR=Esteban, Gradin and Ray.

10

Table 6.1 Aggregate Welfare Mean income from National Accounts Argentina, 1992-2004

Mean income Sen Atk(1) Atk(2)(i) (ii) (iii) (iv)

1992 100.0 100.0 100.0 100.01993 104.5 105.6 104.8 103.11994 109.0 108.4 108.3 109.01995 104.5 98.9 98.6 92.41996 108.9 102.1 101.3 87.61997 116.1 109.5 108.7 98.81998 119.0 108.7 108.1 96.91999 113.6 105.9 105.0 92.42000 111.3 100.9 99.3 81.22001 105.0 91.8 89.8 70.22002 90.1 77.2 76.2 63.52003 96.4 83.3 82.9 71.52004 104.1 93.3 92.4 80.3

Table 6.2 Aggregate Welfare Mean income from EPH Argentina, 1992-2004

Mean income Sen Atk(1) Atk(2)(i) (ii) (iii) (iv)

1992 100.0 100.0 100.0 100.01993 100.2 101.2 100.4 98.91994 99.1 98.6 98.5 99.11995 94.1 89.0 88.7 83.11996 92.5 86.7 86.1 74.41997 96.6 91.2 90.5 82.21998 101.3 92.5 92.0 82.51999 96.8 90.3 89.6 78.82000 94.7 85.9 84.6 69.12001 88.1 77.0 75.4 58.92002 61.1 52.3 51.6 43.12003 73.6 63.6 63.3 54.62004 78.0 69.9 69.2 60.1

11

Table 7.1 Wages, Hours and Labor Income Argentina, 1992-2004

Wages Hours Labor income(i) (ii) (iii)

16 main cities1992 4.1 44.3 695.41993 4.1 44.6 711.61994 4.6 44.0 719.71995 4.5 43.6 711.21996 4.4 43.6 689.71997 4.3 43.8 684.91998 4.5 43.9 719.8

29 main cities1998 4.4 44.0 692.11999 4.2 43.6 661.72000 4.2 43.1 648.12001 4.2 41.9 624.62002 3.0 39.5 421.62003 2.9 39.6 442.5

EPH-C2003-IV 3.3 39.6 .2003-II 3.2 39.2 .2004-I 3.3 39.9 .

Source: Calculations by CEDLAS based on EPH microdata. Table 7.2 Wages, Hours and Labor Income By Gender Argentina, 1992-2004

Wages Hours of work Labor income Female Male Female Male Female Male

(i) (ii) (iii) (iv) (v) (vi) 16 main cities

1992 3.9 4.2 37.6 48.4 554.0 782.51993 3.9 4.2 37.7 48.9 566.4 800.41994 4.7 4.6 36.5 48.5 592.5 795.61995 4.4 4.6 36.2 48.1 551.6 806.71996 4.4 4.4 36.2 47.9 544.4 776.41997 4.3 4.4 37.3 47.8 554.6 764.11998 4.3 4.7 37.0 47.4 562.6 820.4

29 main cities1998 4.2 4.5 37.1 48.5 541.2 786.41999 4.2 4.3 36.8 48.1 532.5 746.52000 4.2 4.2 36.6 47.4 527.7 728.22001 4.2 4.2 35.3 46.5 517.5 696.82002 2.8 3.1 33.6 44.0 336.5 483.42003 2.7 3.1 33.0 44.7 358.9 499.4

EPH-C2003-IV 3.3 3.3 32.7 44.82003-II 3.3 3.2 32.7 44.42004-I 3.3 3.3 33.2 44.9

Source: Calculations by CEDLAS based on EPH microdata.

12

Table 7.3 Wages, Hours and Labor Income By Age Argentina, 1992-2004

Wages Hours of work Labor income (15-24) (25-40) (41-64) (65 +) (15-24) (25-40) (41-64) (65 +) (15-24) (25-40) (41-64) (65 +)

(i) (ii) (iii) (iv) (v) (vi) (vii) (viii) (ix) (x) (xi) (xii) 16 main cities

1992 2.9 4.3 4.5 4.9 41.9 45.1 45.7 35.6 476.9 758.8 782.9 513.91993 2.9 4.1 4.7 4.8 42.3 45.9 45.2 36.7 469.0 749.4 812.1 543.41994 3.2 4.8 5.2 5.0 42.1 44.5 44.8 39.1 465.1 761.1 817.6 588.71995 2.9 4.6 5.1 4.9 41.2 44.1 44.6 36.9 425.5 740.8 828.6 578.91996 2.8 4.4 5.1 5.1 41.0 44.3 44.9 34.2 399.1 704.6 815.6 654.51997 2.8 4.2 5.1 5.8 41.3 44.7 44.6 35.2 403.8 686.7 816.7 624.11998 2.8 4.3 5.4 6.2 40.5 45.3 44.7 37.3 402.2 716.4 856.4 837.6

29 main cities1998 2.7 4.2 5.2 5.9 40.9 45.2 44.8 37.7 385.3 691.0 825.1 788.41999 2.8 4.1 4.9 5.3 40.1 44.7 44.8 36.9 370.5 656.4 798.2 628.62000 2.6 4.2 4.9 4.6 39.4 44.2 43.9 37.7 352.6 657.4 768.9 564.92001 2.6 4.1 4.9 4.8 37.2 43.5 42.4 38.6 322.1 638.3 733.3 560.22002 1.8 2.8 3.5 3.5 36.2 40.7 39.9 35.6 220.7 406.5 514.7 378.42003 1.8 2.7 3.5 3.5 36.3 40.6 40.3 38.0 249.0 432.3 531.2 455.9

EPH-C2003-IV 2.0 3.3 3.8 3.9 37.5 40.7 39.9 34.82003-II 2.0 3.2 3.8 3.9 37.1 40.2 40.1 33.72004-I 2.0 3.2 3.8 6.2 37.5 41.2 40.6 34.2

Source: Calculations by CEDLAS based on EPH microdata. Table 7.4 Wages, Hours and Labor Income By Education Argentina, 1992-2004

Wages Hours of work Labor income Low Mid High Low Mid High Low Mid High(i) (ii) (iii) (iv) (v) (vi) (vii) (viii) (ix)

16 main cities1992 2.9 3.9 6.6 45.1 45.4 40.8 518.3 665.7 1082.41993 3.1 3.8 6.6 44.8 46.2 41.6 514.7 680.4 1122.41994 3.3 4.2 7.6 44.8 46.6 41.4 498.7 695.5 1154.31995 3.1 4.1 8.0 43.2 45.0 41.6 464.5 677.6 1285.91996 3.1 3.8 7.2 42.3 45.7 42.2 438.5 637.2 1161.51997 3.1 3.8 7.2 43.0 45.9 41.6 441.1 644.3 1143.91998 2.9 3.9 7.9 43.7 45.7 41.4 435.4 651.0 1258.9

29 main cities1998 2.8 3.8 7.6 43.8 45.9 41.4 424.3 636.1 1208.01999 2.8 3.7 7.0 43.4 45.5 41.2 412.1 605.1 1121.72000 2.8 3.7 7.0 42.4 44.8 41.4 396.4 591.7 1094.12001 2.7 3.7 6.9 40.8 44.1 40.2 367.7 568.6 1060.62002 1.9 2.5 5.0 37.6 41.4 39.4 237.9 370.1 743.22003 1.9 2.5 4.8 37.7 41.6 39.2 258.2 388.3 727.3

EPH-C2003-IV 2.1 2.7 5.2 38.0 41.4 38.82003-II 2.1 2.7 5.1 37.7 41.0 38.52004-I 2.2 2.9 5.0 38.6 41.5 39.1

Source: Calculations by CEDLAS based on EPH microdata.

13

Table 7.5 Wages, Hours and Labor Income By Type of Work Argentina, 1992-2004 Wages Hours of work Labor income

Entrepreneurs Wage earners Self-employed EntrepreneurWage earnerSelf-employeZero income EntrepreneurWage earnerSelf-employed(i) (ii) (iii) (iv) (v) (vi) (vii) (viii) (ix) (xi)

16 main cities1992 3.7 4.6 55.5 43.6 44.2 46.0 679.6 773.11993 3.9 4.5 60.6 44.5 46.7 41.1 718.1 717.31994 4.4 5.1 58.7 43.9 44.6 39.5 719.0 754.81995 9.4 4.2 4.6 55.7 43.0 42.8 43.7 1902.6 676.4 662.51996 9.1 4.1 4.6 56.4 43.1 43.7 36.4 1885.6 664.2 648.71997 8.1 4.1 4.6 56.9 43.5 42.1 39.1 1737.7 662.5 634.31998 9.0 4.2 4.8 58.4 43.6 42.6 39.5 1975.5 690.7 670.0

29 main cities1998 8.9 4.1 4.5 57.8 43.5 43.5 40.4 1904.9 665.1 637.91999 8.1 4.0 4.2 56.0 43.3 42.6 42.1 1692.7 652.5 576.22000 7.5 4.1 4.1 57.2 42.9 41.4 40.3 1526.3 651.4 555.22001 8.2 4.1 3.9 56.1 41.8 39.7 41.2 1515.0 638.5 500.32002 7.1 2.8 2.8 54.2 39.0 39.3 36.3 1266.1 423.1 343.42003 6.2 2.8 2.9 54.8 39.3 38.4 38.7 1178.3 451.3 358.8

EPH-C2003-IV 6.9 3.2 2.9 50.4 39.3 39.1 32.72003-II 6.8 3.2 3.1 51.7 39.0 39.0 32.02004-I 5.7 3.3 3.0 53.0 39.8 39.0 30.1

Source: Calculations by CEDLAS based on EPH microdata. Table 7.6 Wages, Hours and Labor Income By Labor Group Argentina, 1992-2004 Wages Hours of work

Formal workers Informal workers Formal workers Informal workers Salaried workersSelf-employed Salaried Self-employed Salaried workersSelf-employed Salaried Self-employeWorkers with

Entrepreneur Large firmsPublic sectorprofessionals Small firms Unskilled Entrepreneur Large firmsPublic sectorprofessionals Small firms Unskilled zero income(i) (ii) (iii) (iv) (v) (vi) (vii) (viii) (ix) (x) (xi) (xii) (xiii)

16 main cities1992 9.7 4.1 55.5 40.5 39.2 44.8 46.01993 4.2 4.4 9.1 3.0 4.0 57.4 46.1 41.6 42.8 42.5 44.4 41.11994 4.8 5.5 10.6 3.3 4.3 57.7 46.0 40.3 41.7 41.1 44.1 39.51995 9.4 4.4 5.2 10.0 3.1 3.8 56.0 46.6 38.7 40.9 39.7 43.2 43.71996 9.1 4.3 5.3 10.3 3.1 3.7 56.4 46.8 39.6 43.3 40.0 43.7 36.41997 8.1 4.2 5.4 10.5 3.1 3.6 57.6 47.1 39.1 41.5 40.5 42.8 39.11998 9.0 4.5 5.7 12.9 2.9 3.5 58.4 47.3 39.4 41.4 40.6 42.8 39.5

29 main cities1998 8.9 4.3 5.5 12.0 2.8 3.4 57.8 47.4 39.1 42.0 40.6 43.8 40.41999 8.1 4.3 5.5 10.5 2.7 3.4 56.0 47.2 38.2 42.3 41.1 42.6 42.12000 7.5 4.4 5.5 8.9 2.7 3.5 57.2 47.0 38.1 41.5 40.6 41.4 40.32001 8.2 4.4 5.4 8.9 2.8 3.2 56.1 46.3 37.4 40.5 38.9 39.6 41.22002 7.1 3.1 3.3 6.2 2.0 2.3 54.2 44.9 32.6 40.0 38.1 39.2 36.32003 6.2 3.1 3.3 6.3 1.9 2.3 54.8 45.4 32.1 39.3 38.5 38.2 38.7

EPH-C2003-IV 6.9 3.6 4.3 6.8 2.1 2.4 50.4 45.2 32.3 36.6 37.7 39.4 32.72003-II 6.8 3.5 4.2 6.5 2.1 2.6 51.7 44.8 32.3 37.8 37.5 39.2 32.02004-I 5.7 3.7 4.3 6.1 2.1 2.6 53.0 45.1 33.2 39.4 37.9 38.9 30.1

Labor income Formal workers Informal workers Salaried workersSelf-employed Salaried Self-employed

Entrepreneur Large firmsPublic sectorprofessionals Small firms Unskilled(xiv) (xv) (xvi) (xvii) (xvii) (xix)

16 main cities1992 1483.6 700.51993 808.7 836.2 1475.9 498.8 632.31994 819.2 838.6 1540.9 492.8 647.81995 1902.6 768.0 787.5 1452.6 431.1 547.31996 1885.6 764.2 808.2 1407.2 421.9 525.01997 1737.7 740.1 838.8 1506.5 406.9 497.61998 1975.5 788.6 861.6 1718.8 414.7 503.0

29 main cities1998 1904.9 762.8 827.7 1635.9 399.9 491.81999 1692.7 753.0 816.1 1533.7 390.1 450.12000 1526.3 757.1 816.8 1375.7 395.4 445.82001 1515.0 759.0 781.5 1301.5 376.2 397.32002 1266.1 536.0 442.0 865.0 259.1 267.72003 1178.3 538.1 559.3 871.3 256.2 276.4

Source: Calculations by CEDLAS based on EPH microdata.

14

Table 7.7 Wages, Hours and Labor Income By Sector Argentina, 1992-2004 Wages

Primary Industry Industry Utilities & Skilled Public Education & Domesticactivities low tech high tech ConstructionCommerceransportatio services administratio Health servants

(i) (ii) (iii) (iv) (v) (vi) (vii) (viii) (ix) (x) 16 main cities

1992 4.5 3.2 4.0 3.2 3.7 4.2 6.5 4.5 4.6 3.21993 7.7 3.2 4.1 3.7 3.4 4.1 6.4 4.7 4.8 3.41994 5.0 3.3 4.4 3.5 3.6 4.5 7.2 5.6 5.8 3.71995 6.9 3.3 4.9 3.4 3.4 4.3 7.0 5.4 5.3 3.61996 4.3 3.3 4.5 3.2 3.3 4.0 6.4 5.5 5.5 3.61997 4.3 3.1 4.3 3.4 3.1 4.3 6.4 5.4 5.3 3.71998 3.7 3.1 4.7 3.2 3.2 4.2 7.2 6.0 5.7 3.3

29 main cities1998 4.1 3.1 4.6 3.1 3.1 4.1 7.0 5.7 5.5 3.11999 3.9 3.0 4.3 3.2 3.1 3.6 6.0 5.6 5.7 3.12000 3.8 3.1 4.5 3.2 3.0 3.6 6.3 5.7 5.4 3.02001 5.4 3.2 4.3 3.3 2.9 3.9 6.4 5.4 5.3 3.12002 2.8 2.7 2.9 2.3 2.0 2.9 4.8 3.1 3.7 2.22003 2.8 2.5 3.1 2.1 2.0 2.6 4.4 3.4 3.6 2.2

EPH-C2003-IV 5.3 2.2 3.4 2.5 2.2 3.3 4.7 4.6 4.4 2.22003-II 5.3 2.2 3.5 2.4 2.2 3.2 4.5 4.4 4.4 2.22004-I 3.9 2.2 3.8 2.5 2.3 3.5 4.4 4.4 4.5 2.2

Hours of workPrimary Industry Industry Utilities & Skilled Public Education & Domesticactivities low tech high tech ConstructionCommerceransportatio services administratio Health servants

(i) (ii) (iii) (iv) (v) (vi) (vii) (viii) (ix) (x) 16 main cities

1992 57.2 46.6 47.4 46.7 49.0 50.5 42.4 42.3 38.6 31.31993 49.7 48.4 47.2 44.4 50.0 54.7 42.4 43.9 36.9 31.71994 52.6 48.1 46.0 45.5 49.6 52.4 44.1 42.9 36.8 27.91995 54.1 48.0 43.9 42.3 49.7 53.2 44.2 42.7 36.6 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

29 main 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 26.72002 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

EPH-C2003-IV 37.9 44.0 43.5 39.0 46.5 50.6 42.6 36.5 31.3 29.02003-II 38.6 42.1 42.9 38.4 45.7 50.4 42.8 36.8 31.2 28.72004-I 41.4 42.6 44.5 39.3 45.8 51.0 41.3 37.2 32.4 28.1

Labor incomePrimary Industry Industry Utilities & Skilled Public Education & Domesticactivities low tech high tech ConstructionCommerceransportatio services administratio Health servants

(i) (ii) (iii) (iv) (v) (vi) (vii) (viii) (ix) (x) 16 main cities

1992 970.5 572.1 717.6 595.9 656.1 821.3 1034.0 838.7 710.5 376.01993 926.6 595.1 785.4 625.1 628.5 881.9 1039.3 871.3 744.4 375.01994 1047.4 583.7 762.7 576.3 638.6 821.1 1153.2 887.5 751.9 341.91995 1277.2 595.3 830.7 520.8 601.5 810.9 1196.3 899.0 689.8 293.01996 825.0 568.8 783.1 492.7 603.6 782.3 1080.6 893.4 711.0 279.71997 883.9 568.0 772.0 488.8 596.1 775.3 1100.1 867.1 706.3 274.51998 690.5 582.1 833.8 490.8 570.9 788.8 1256.7 992.9 747.0 267.7

29 main cities1998 715.3 579.9 809.4 473.4 564.7 773.1 1223.5 934.3 724.1 254.91999 738.3 544.2 741.8 502.7 551.5 726.6 1067.3 904.1 704.6 260.72000 778.8 543.6 765.7 452.8 513.2 691.0 1075.9 900.9 712.2 261.92001 997.3 535.1 710.3 408.2 487.0 686.2 1077.3 853.0 694.2 242.72002 566.2 447.2 500.8 258.5 329.2 484.3 790.7 432.9 442.3 158.22003 659.5 410.8 538.0 277.0 338.8 492.6 720.0 638.3 479.0 158.5

Source: Calculations by CEDLAS based on EPH microdata.

15

Table 7.8 Wages, Hours and Labor Income By Region Argentina, 1992-2004 Wages

GBA Pampeana Cuyo NOA Patagonia NEA(i) (ii) (iii) (iv) (v) (vi)

16 main cities1992 4.2 3.7 3.0 3.0 4.61993 4.2 3.7 3.5 3.2 4.61994 4.8 4.3 3.7 3.4 5.11995 4.7 4.2 3.4 3.3 5.21996 4.6 4.1 3.4 3.1 4.91997 4.5 4.0 3.5 3.2 5.01998 4.8 4.0 3.6 3.3 5.0

29 main cities1998 4.8 4.0 3.6 3.5 5.0 3.31999 4.6 4.0 3.6 3.5 5.1 3.12000 4.6 4.0 3.5 3.2 4.8 3.12001 4.7 3.8 3.4 3.2 4.7 3.12002 3.3 2.7 2.6 2.3 3.7 2.22003 3.2 2.7 2.5 2.3 3.6 2.1

EPH-C2003-IV 3.6 3.1 2.9 2.5 4.1 2.32003-II 3.5 3.2 2.8 2.5 4.0 2.42004-I 3.5 3.2 2.8 2.6 4.3 2.3

Hours of workGBA Pampeana Cuyo NOA Patagonia NEA(i) (ii) (iii) (iv) (v) (vi)

16 main cities1992 44.5 43.7 46.0 42.7 45.21993 45.0 43.5 45.4 42.7 45.11994 44.2 43.2 45.4 42.8 46.01995 43.9 42.1 43.2 42.8 44.81996 43.9 41.9 43.1 43.7 45.11997 44.1 42.1 44.6 42.7 44.21998 44.0 43.5 44.3 43.6 44.1

29 main cities1998 44.0 43.7 45.2 43.6 44.1 44.61999 43.5 43.7 45.0 43.0 42.7 44.92000 43.5 41.7 44.1 43.1 42.8 43.02001 42.0 41.4 43.2 41.7 42.8 40.92002 39.9 38.5 41.4 38.8 41.6 37.42003 39.2 40.1 41.6 39.2 42.4 38.9

EPH-C2003-IV 39.7 39.0 40.6 38.5 42.1 39.42003-II 39.5 38.7 39.5 38.0 42.2 38.72004-I 40.2 39.2 41.5 38.4 42.0 38.8

Labor incomeGBA Pampeana Cuyo NOA Patagonia NEA(i) (ii) (iii) (iv) (v) (vi)

16 main cities1992 724.8 631.0 509.3 511.4 831.21993 734.3 652.8 589.7 521.8 843.11994 741.8 673.3 572.4 526.3 857.61995 741.2 643.1 526.5 515.3 845.71996 717.9 624.6 528.5 488.6 806.11997 713.8 601.6 577.0 484.3 831.61998 754.8 617.6 587.8 518.2 822.7

29 main cities1998 754.8 627.2 582.0 552.2 822.7 525.61999 711.5 618.9 582.1 537.0 797.8 497.62000 711.5 596.4 548.4 490.3 788.8 474.42001 694.9 561.8 512.2 482.2 750.6 451.52002 461.8 381.0 369.2 325.9 560.6 307.42003 476.8 416.0 382.2 344.2 578.1 332.4

Source: Calculations by CEDLAS based on EPH microdata.

16

Table 7.9 Distribution of Labor Income Shares Argentina, 1992-2003

Salaried Self- employed Entrepreneursworkers

(i) (ii) (iii) 16 main cities

1992 73.9 26.11993 73.7 26.31994 74.0 26.01995 68.2 21.1 10.61996 69.3 20.9 9.71997 70.0 20.3 9.51998 69.9 19.8 10.3

29 main cities1998 69.2 20.4 10.31999 71.9 18.8 9.32000 72.4 19.1 8.52001 72.5 18.9 8.62002 72.6 18.3 9.12003 70.7 20.6 8.7

Source: Calculations by CEDLAS based on EPH microdata. Table 7.10 Distribution of Wages (Primary Activity) Gini Coefficient Argentina, 1992-2004

All Low edu Mid edu High edu Monetary Monetary salaried worker

16 main cities1992 0.409 0.314 0.358 0.416 0.409 0.3811993 0.393 0.302 0.350 0.396 0.393 0.3781994 0.394 0.305 0.341 0.391 0.394 0.3741995 0.417 0.308 0.366 0.412 0.417 0.3811996 0.413 0.318 0.356 0.409 0.413 0.3871997 0.401 0.323 0.329 0.392 0.401 0.3801998 0.435 0.328 0.367 0.413 0.435 0.399

29 main cities1998 0.430 0.325 0.366 0.412 0.430 0.3961999 0.412 0.331 0.347 0.393 0.412 0.3792000 0.432 0.347 0.373 0.396 0.432 0.4032001 0.438 0.364 0.368 0.419 0.438 0.4082002 0.459 0.360 0.392 0.433 0.459 0.4162003 0.454 0.323 0.386 0.449 0.454 0.416

EPH-C2003-IV 0.436 0.367 0.364 0.429 0.436 0.3952003-II 0.436 0.348 0.383 0.422 0.436 0.3932004-I 0.427 0.378 0.389 0.410 0.427 0.394

Source: Calculations by CEDLAS based on EPH microdata.

17

Table 7.11 Hours of Work – Correlations of Hourly Wages Argentina, 1992-2003

All workers Urban salariedworkers

(i) (ii) 16 main cities

1992 -0.1838* -0.1696*1993 -0.2264* -0.1773*1994 -0.2323* -0.2000*1995 -0.1801* -0.1859*1996 -0.1909* -0.1784*1997 -0.2094* -0.2089*1998 -0.1637* -0.1747*

29 main cities1998 -0.1665* -0.1793*1999 -0.1964* -0.1900*2000 -0.1913* -0.1808*2001 -0.1847* -0.1815*2002 -0.1314* -0.1037*2003 -0.1645* -0.1689*

Source: Calculations by CEDLAS based on EPH microdata. Table 7.12 Ratio of Hourly Wages by Educational Group Prime-age Males Argentina, 1992-2004

High/Medium High/Low Medium/Low(i) (ii) (iii)

16 main cities1992 1.85 2.61 1.411993 1.84 2.57 1.391994 1.84 2.64 1.441995 1.98 2.81 1.421996 1.92 2.58 1.341997 1.95 2.63 1.351998 2.09 3.04 1.45

29 main cities1998 2.04 2.96 1.451999 1.95 2.69 1.382000 1.96 2.83 1.442001 2.01 2.75 1.372002 2.07 2.99 1.442003 2.12 3.06 1.45

EPH-C2003-IV 2.02 2.58 1.282003-II 1.93 2.62 1.352004-I 1.88 2.46 1.31

Source: Calculations by CEDLAS based on EPH microdata.

18

Table 7.13 Mincer Equation Estimated Coefficients of Educational Dummies Argentina, 1992-2004 All workers Urban salaried workers

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

(i) (ii) (iii) (iv) (v) (vi) (vii) (viii) (ix) (x) (xi) (xii) 16 main cities

1992 0.287 0.451 0.557 -0.095 0.463 0.454 0.153 0.432 0.560 0.007 0.383 0.2261993 0.089 0.454 0.605 0.005 0.323 0.498 0.113 0.419 0.657 -0.025 0.387 0.3511994 0.176 0.430 0.719 -0.014 0.431 0.406 0.187 0.362 0.687 0.003 0.416 0.4031995 0.141 0.510 0.707 -0.027 0.277 0.290 0.183 0.403 0.664 0.020 0.364 0.4801996 0.159 0.476 0.716 0.024 0.292 0.605 0.167 0.336 0.732 0.073 0.232 0.5031997 0.173 0.428 0.724 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

29 main cities1998 0.227 0.457 0.757 0.042 0.450 0.522 0.168 0.440 0.670 0.037 0.454 0.4691999 0.175 0.381 0.753 0.033 0.340 0.341 0.195 0.323 0.673 -0.043 0.404 0.5092000 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 0.162 0.320 0.198 0.260 0.360 0.678 0.121 0.389 0.5712002 0.307 0.472 0.826 0.044 0.431 0.300 0.216 0.339 0.758 0.037 0.382 0.6062003 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-IV 0.093 0.340 0.720 0.065 0.283 0.686 0.134 0.367 0.629 -0.015 0.343 0.6162003-II 0.092 0.345 0.697 0.137 0.314 0.645 0.102 0.388 0.622 0.199 0.315 0.6332004-I 0.177 0.385 0.677 0.058 0.238 0.351 0.208 0.315 0.665 0.175 0.256 0.610

Source: Calculations by CEDLAS based on EPH microdata. Table 7.14 Mincer Equation Dispersion in Unobservables and Gender Wage Gap Argentina, 1992-2004

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

Men Women Men Women workers(i) (ii) (iii) (iv) (v)

16 main cities1992 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 0.9031995 0.632 0.817 0.540 0.517 0.9211996 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

29 main cities1998 0.617 0.649 0.569 0.551 0.8531999 0.612 0.778 0.576 0.554 0.8882000 0.640 0.825 0.567 0.558 0.8692001 0.665 0.900 0.601 0.577 0.8842002 0.696 0.878 0.577 0.534 0.8672003 0.661 0.633 0.582 0.522 0.892

EPH-C2003-IV 0.710 0.716 0.614 0.583 0.9422003-II 0.725 0.781 0.623 0.599 0.9262004-I 0.681 0.602 0.577 0.900

Source: Calculations by CEDLAS based on EPH microdata.

19

Table 7.15 Share of Adults in the Labor Force Argentina, 1992-2004

Gender Age EducationTotal Female Male (16-25) (26-40) (41-64) (65 +) Low Medium High(i) (ii) (iii) (iv) (v) (vi) (vii) (viii) (ix) (x)

16 main cities1992 0.666 0.483 0.868 0.637 0.739 0.637 0.230 0.608 0.677 0.7781993 0.676 0.504 0.866 0.621 0.749 0.654 0.230 0.618 0.685 0.7771994 0.679 0.502 0.873 0.641 0.761 0.643 0.218 0.618 0.687 0.7871995 0.687 0.523 0.866 0.628 0.786 0.648 0.213 0.644 0.682 0.8071996 0.691 0.525 0.871 0.635 0.776 0.660 0.278 0.640 0.703 0.7601997 0.701 0.540 0.875 0.639 0.781 0.677 0.283 0.652 0.709 0.7801998 0.700 0.543 0.874 0.599 0.779 0.695 0.322 0.656 0.700 0.779

29 main cities1998 0.686 0.523 0.866 0.578 0.767 0.683 0.299 0.649 0.685 0.7531999 0.689 0.538 0.856 0.568 0.774 0.691 0.321 0.646 0.694 0.7502000 0.693 0.542 0.861 0.566 0.781 0.694 0.344 0.654 0.699 0.7472001 0.686 0.536 0.853 0.546 0.773 0.698 0.291 0.653 0.681 0.7452002 0.699 0.564 0.849 0.550 0.793 0.709 0.293 0.672 0.696 0.7382003 0.698 0.567 0.845 0.581 0.789 0.706 0.328 0.655 0.714 0.731

EPH-C2003-IV 0.737 0.610 0.880 0.644 0.822 0.733 0.404 0.700 0.734 0.7852003-II 0.739 0.618 0.874 0.643 0.820 0.740 0.405 0.702 0.733 0.7882004-I 0.736 0.614 0.874 0.635 0.821 0.735 0.438 0.708 0.729 0.779

Source: Calculations by CEDLAS based on EPH microdata. Table 7.16 Share of Adults Employed Argentina, 1992-2004

Gender Age EducationTotal Female Male (15-24) (25-40) (41-64) (65 +) Low Medium High(i) (ii) (iii) (iv) (v) (vi) (vii) (viii) (ix) (x)

16 main cities1992 0.624 0.450 0.815 0.562 0.704 0.605 0.228 0.566 0.630 0.7431993 0.617 0.449 0.802 0.510 0.696 0.613 0.208 0.558 0.619 0.7331994 0.601 0.433 0.785 0.511 0.689 0.583 0.179 0.543 0.594 0.7311995 0.577 0.425 0.743 0.457 0.685 0.559 0.184 0.527 0.563 0.7351996 0.573 0.420 0.740 0.441 0.672 0.568 0.258 0.519 0.578 0.6621997 0.606 0.451 0.775 0.491 0.688 0.607 0.236 0.551 0.606 0.7091998 0.614 0.466 0.779 0.468 0.701 0.624 0.308 0.555 0.611 0.723

29 main cities1998 0.604 0.452 0.772 0.451 0.693 0.616 0.285 0.554 0.600 0.7001999 0.595 0.456 0.748 0.429 0.687 0.615 0.270 0.548 0.595 0.6722000 0.592 0.454 0.746 0.413 0.686 0.616 0.286 0.543 0.589 0.6762001 0.561 0.440 0.694 0.371 0.647 0.599 0.250 0.516 0.541 0.6602002 0.574 0.463 0.699 0.368 0.672 0.613 0.239 0.545 0.557 0.6422003 0.589 0.483 0.709 0.402 0.702 0.621 0.266 0.554 0.580 0.650

EPH-C2003-IV 0.633 0.507 0.774 0.464 0.733 0.661 0.348 0.601 0.613 0.7002003-II 0.628 0.511 0.758 0.457 0.724 0.660 0.342 0.592 0.609 0.6962004-I 0.631 0.511 0.767 0.456 0.726 0.665 0.380 0.612 0.603 0.692

Source: Calculations by CEDLAS based on EPH microdata.

20

Table 7.17 Unemployment Rates Argentina, 1992-2004

Gender Age EducationTotal Female Male (16-25) (26-40) (41-64) (65 +) Low Medium High(i) (ii) (iii) (iv) (v) (vi) (vii) (viii) (ix) (x)

16 main cities1992 6.3 6.7 6.1 11.7 4.8 5.0 0.9 6.9 6.8 4.51993 8.7 10.8 7.4 17.9 7.1 6.2 9.8 9.6 9.7 5.71994 11.5 13.9 10.0 20.2 9.5 9.3 17.7 12.1 13.5 7.21995 16.0 18.7 14.2 27.3 12.8 13.7 13.4 18.2 17.4 8.91996 17.0 20.0 15.1 30.6 13.4 13.9 7.4 19.0 17.8 12.91997 13.5 16.5 11.5 23.2 11.9 10.4 16.7 15.4 14.5 9.01998 12.3 14.3 10.9 21.9 10.0 10.2 4.3 15.4 12.7 7.2

29 main cities1998 11.9 13.6 10.8 21.9 9.6 9.7 4.7 14.7 12.4 7.01999 13.7 15.1 12.6 24.5 11.2 11.1 15.9 15.2 14.3 10.52000 14.6 16.3 13.4 27.1 12.2 11.3 16.8 17.0 15.7 9.42001 18.3 17.8 18.6 32.0 16.3 14.2 14.2 21.0 20.5 11.42002 17.8 17.9 17.7 33.1 15.4 13.6 18.5 18.9 20.1 13.02003 15.6 14.9 16.1 30.9 11.0 12.0 18.8 15.4 18.7 11.1

EPH-C2003-IV 14.1 16.8 12.0 28.0 10.8 9.8 13.8 14.1 16.4 10.92003-II 15.0 17.2 13.3 28.9 11.7 10.7 15.4 15.7 16.9 11.72004-I 14.3 16.8 12.3 28.2 11.5 9.6 13.1 13.5 17.2 11.1

Source: Calculations by CEDLAS based on EPH microdata. Table 7.18 Duration of Unemployment Argentina, 1992-2004

EducationLow Medium High Total(i) (ii) (iii) (iv)

16 main cities1992 3.1 4.4 4.7 3.91993 4.4 5.5 6.1 5.21994 4.9 5.8 6.0 5.51995 5.7 7.3 9.6 6.81996 7.0 8.3 10.2 8.11997 5.2 7.4 8.2 6.61998 5.5 6.6 8.5 6.4

29 main cities1998 5.4 6.3 8.1 6.21999 5.7 6.5 8.0 6.52000 5.9 7.1 7.8 6.72001 6.0 7.0 8.3 6.82002 6.9 9.4 10.7 8.82003 8.0 8.5 9.2 8.5

EPH-C2003-IV 11.0 11.0 12.2 11.32003-II 12.2 11.0 13.0 11.82004-I 10.2 10.4 12.3 10.8

Source: Calculations by CEDLAS based on EPH microdata.

21

Table 7.19 Labor Force, Employment Rate and Unemployment Rate Argentina, 2003-2004 Encuesta Permanente de Hogares Continua (EPHC)

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 17.7I-2004 45.4 38.9 14.4 44.3 36.6 17.4II-2004 46.2 39.4 14.8 45.3 37.4 17.4

Alternative 1: People who report working for the PJH as main activity are employed.Alternative 2: People with PJH as main activity and seeking employment are unemployed. Table 7.20 Employment Level (August 2001=100) and Hours Worked Formal Sector, firms with more than 10 employees Greater Buenos Aires, 2002-2004

Month Employment level Worked hours August 2001=100

Ene-02 95.3 156.3Feb-02 94.3 146.8Mar-02 93.3 151.6Abr-02 92.5 158.9May-02 91.7 161.5Jun-02 91.1 159.0Jul-02 90.7 163.8Ago-02 90.5 157.5Sep-02 90.1 156.4Oct-02 90.4 157.6Nov-02 90.5 161.5Dic-02 90.8 157.3Ene-03 91.0 163.7Feb-03 91.1 157.1Mar-03 91.4 161.5Abr-03 91.5 160.3May-03 91.6 163.3Jun-03 91.7 168.8Jul-03 92.6 171.4Ago-03 92.9 169.7Sep-03 93.8 174.5Oct-03 94.3 171.1Nov-03 95.1 170.2Dic-03 95.9 173.6Ene-04 96.1 167.8Feb-04 96.7 166.3Mar-04 97.2 172.9Abr-04 97.3 163.1May-04 97.5 164.0Jun-04 98.1 165.6Jul-04 98.5 165.7

Source: Encuesta de Indicadores Laborales, GBA.

22

Table 7.21 Age, Gender and Educational Structure of Employment Argentina, 1992-2004

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

(i) (ii) (iii) (iv) (v) (vi) (vii) (viii) (ix) (x) 16 main cities

1992 37.2 62.8 1.1 21.0 36.5 38.6 2.9 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 0.3 17.8 40.3 38.9 2.7 36.4 39.0 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

29 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 0.2 17.1 39.6 39.8 3.3 35.8 38.5 25.72000 40.2 59.8 0.1 16.1 40.7 39.8 3.2 35.6 38.2 26.22001 40.9 59.2 0.2 15.5 39.8 41.5 3.0 34.8 37.5 27.82002 42.2 57.8 0.2 14.7 40.8 41.3 3.1 34.2 38.0 27.82003 42.9 57.2 0.2 16.9 38.2 41.3 3.5 32.7 38.8 28.4

EPH-C2003-IV 41.8 58.2 0.6 17.9 37.3 40.2 4.0 31.2 39.0 29.92003-II 42.5 57.5 0.6 18.0 36.8 41.0 3.7 30.7 39.1 30.22004-I 42.5 57.5 0.6 17.9 37.3 40.3 3.9 31.7 38.5 29.9

Source: Calculations by CEDLAS based on EPH microdata. Table 7.22 Regional Structure of Employment Argentina, 1992-2004

GBA Pampeana Cuyo NOA Patagonia NEA(i) (ii) (iv) (v) (vi)

16 main cities1992 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 15.4 3.0 4.9 3.51997 73.1 15.7 2.9 5.1 3.31998 73.6 15.3 2.9 5.0 3.3

29 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 4.52002 55.4 22.6 6.3 8.4 2.8 4.62003 55.2 22.6 6.1 8.6 2.9 4.6

EPH-C2003-IV 55.5 22.5 6.2 9.0 2.3 4.42003-II 55.7 22.3 6.3 9.0 2.4 4.42004-I 55.7 22.2 6.4 8.7 2.5 4.4

Source: Calculations by CEDLAS based on EPH microdata.

23

Table 7.23 Structure of Employment By Type of Work Argentina, 1992-2004 Labor relationship Type of firm

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

16 main cities1992 5.2 70.0 23.6 1.21993 5.5 68.7 24.6 1.3 40.9 51.7 7.51994 4.6 70.1 23.9 1.5 42.2 50.6 7.31995 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 1.3 37.0 47.6 15.5

29 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 49.9 16.92001 4.4 71.3 23.4 1.0 31.9 50.5 17.62002 4.0 72.0 23.0 1.0 28.8 48.8 22.42003 3.9 71.8 23.2 1.2 29.3 48.5 22.2

EPH-C2003-IV 3.9 73.8 20.7 1.6 30.2 49.4 20.42003-II 3.8 73.7 20.9 1.7 29.8 49.1 21.12004-I 3.8 74.4 20.2 1.6 31.9 48.8 19.4

Labor categor

y 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)

16 main cities19921993 5.9 38.7 7.3 2.8 20.3 23.6 1.41994 4.9 40.1 7.0 3.3 21.0 22.2 1.61995 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 3.1 22.2 18.1 1.3

29 main cities1998 4.7 33.5 16.0 3.0 22.3 19.2 1.41999 4.6 33.2 16.1 2.9 22.4 19.3 1.52000 4.8 31.6 16.5 2.9 22.9 20.1 1.32001 4.5 30.5 17.3 3.1 22.6 21.0 1.02002 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

EPH-C2003-IV 4.1 28.7 20.4 2.8 23.4 18.9 1.72003-II 4.0 28.2 21.1 3.1 23.1 18.9 1.72004-I 4.0 30.4 19.3 3.1 23.5 18.1 1.6

Source: Calculations by CEDLAS based on EPH microdata.

24

Table 7.24 Structure of Employment By Formality Argentina, 1992-2004

Definition 1 ( all workers) Definition 2 (salaried workers)Formal Informal Formal Informal

(i) (ii) (iii) (iv) 16 main cities

1992 68.9 31.21993 54.7 45.3 68.2 31.91994 55.3 44.8 70.9 29.11995 57.8 42.2 66.9 33.11996 56.5 43.5 64.9 35.11997 58.3 41.7 63.8 36.21998 58.5 41.6 62.9 37.2

29 main cities1998 57.2 42.8 62.1 37.91999 56.8 43.2 61.7 38.32000 55.8 44.2 61.5 38.52001 55.4 44.6 61.3 38.72002 57.0 43.0 55.9 44.12003 57.5 42.5 55.1 44.9

EPH-C2003-IV 56.0 44.0 50.7 49.32003-II 56.3 43.7 50.6 49.42004-I 56.8 43.2 48.4 51.6

Source: Calculations by CEDLAS based on EPH microdata.

25

Table 7.25 Structure of Employment By Sector Argentina, 1992-2004

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

(i) (ii) (iii) (iv) (v) (vi) (vii) (viii) (ix) (x) 16 main cities

1992 0.9 8.7 12.4 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

29 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 7.7 19.1 7.72000 0.8 5.8 8.2 7.9 24.0 8.7 9.7 7.8 19.3 7.92001 1.1 5.4 8.4 7.2 24.0 8.5 9.0 8.4 20.2 7.92002 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

EPH-C2003-IV 1.7 7.5 6.1 7.3 23.7 7.1 9.3 9.2 20.7 7.42003-II 1.7 7.7 6.1 7.1 23.3 7.2 9.1 8.9 21.3 7.62004-I 1.7 7.8 6.4 7.6 23.7 7.0 9.1 8.7 20.6 7.5

Source: Calculations by CEDLAS based on EPH microdata. Table 7.26 Structure of Employment By Sector (CIIU -1 digit) Argentina, 1992-2004

Restaurants Transportation Business Public Healt & Other DomesticAgricultureManufacturingUtilitiesConstructionCommerce& hotels & communicationFinance servicesadministrationTeachingsocial servicesservices servants

(i) (ii) (iii) (iv) (v) (vi) (vii) (viii) (ix) (x) (xi) (xii) (xiii) (xiv 16 main cities

1992 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 22.7 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 6.8 7.1 5.5 3.7 7.91995 0.9 18.5 0.8 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 0.9 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

29 main cities1998 1.0 15.6 0.7 8.7 20.8 2.9 7.7 2.5 7.2 7.8 7.6 6.1 3.9 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 0.9 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 8.12002 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

EPH-C2003-IV 1.4 0.1 0.3 13.6 20.7 3.0 6.7 1.9 7.4 9.2 8.0 7.1 5.6 7.42003-II 1.3 0.1 0.3 13.8 20.6 2.8 6.6 1.7 7.4 8.9 8.4 7.3 5.6 7.62004-I 1.2 0.1 0.4 14.2 20.3 3.4 6.5 1.7 7.4 8.7 7.8 6.7 6.07 7.5

)

Source: Calculations by CEDLAS based on EPH microdata.

26

Table 7.27 Child Labor By Equivalized Household Income Quintiles Argentina, 1992-2004

Equivalized household income quintile1 2 3 4 5 Avera

16 main cities1992 0.020 0.008 0.035 0.021 0.002 0.0181993 0.019 0.022 0.034 0.015 0.001 0.0201994 0.017 0.009 0.017 0.011 0.007 0.0131995 0.011 0.004 0.012 0.022 0.003 0.0111996 0.010 0.016 0.015 0.016 0.000 0.0121997 0.010 0.014 0.002 0.003 0.000 0.0071998 0.020 0.012 0.005 0.006 0.000 0.011

29 main cities1998 0.022 0.013 0.006 0.007 0.000 0.0121999 0.013 0.005 0.007 0.005 0.007 0.0082000 0.008 0.006 0.001 0.000 0.001 0.0042001 0.011 0.006 0.006 0.000 0.001 0.0062002 0.008 0.009 0.002 0.001 0.004 0.0062003 0.004 0.007 0.001 0.000 0.000 0.003

EPH-C2003-IV 0.027 0.023 0.019 0.015 0.004 0.0212003-II 0.024 0.016 0.017 0.015 0.001 0.0172004-I 0.030 0.023 0.025 0.007 0.003 0.021

ge

Source: Calculations by CEDLAS based on EPH microdata. Table 7.28 Permanent Jobs By Gender and Education Argentina, 1992-2004

Gender EducationFemale Male All Low Mid High All

(i) (ii) (iii) (iv) (v) (vi) (vii) 16 main cities

1996 0.837 0.829 0.832 0.775 0.839 0.901 0.8321997 0.842 0.823 0.830 0.773 0.844 0.890 0.8301998 0.846 0.842 0.843 0.799 0.851 0.891 0.843

29 main cities1998 0.840 0.836 0.837 0.795 0.847 0.882 0.8371999 0.863 0.851 0.856 0.810 0.870 0.895 0.8562000 0.852 0.849 0.850 0.817 0.851 0.893 0.8502001 0.836 0.841 0.839 0.777 0.854 0.894 0.8392002 0.770 0.809 0.792 0.694 0.797 0.901 0.7922003 0.782 0.807 0.796 0.707 0.793 0.897 0.796

EPH-C2003-IV 0.691 0.770 0.738 0.594 0.743 0.840 0.7382003-II 0.704 0.777 0.747 0.594 0.755 0.848 0.7472004-I 0.726 0.792 0.765 0.628 0.769 0.868 0.765

Source: Calculations by CEDLAS based on EPH microdata.

27

Table 7.29 Right to Receive Social Security (Pensions) By Gender and Education Argentina, 1992-2004

Gender EducationFemale Male All Low Mid High All

(i) (ii) (iii) (iv) (v) (vi) (vii) 16 main cities

1992 0.670 0.743 0.714 0.618 0.720 0.854 0.7141993 0.659 0.736 0.706 0.606 0.717 0.828 0.7061994 0.694 0.749 0.728 0.629 0.729 0.867 0.7281995 0.641 0.716 0.686 0.593 0.702 0.822 0.6861996 0.619 0.693 0.663 0.531 0.682 0.812 0.6631997 0.617 0.675 0.652 0.526 0.660 0.809 0.6521998 0.608 0.667 0.643 0.499 0.665 0.791 0.643

29 main cities1998 0.598 0.661 0.635 0.492 0.659 0.790 0.6351999 0.594 0.657 0.630 0.489 0.643 0.781 0.6312000 0.585 0.654 0.624 0.485 0.621 0.790 0.6242001 0.583 0.654 0.623 0.448 0.635 0.790 0.6232002 0.529 0.596 0.565 0.381 0.557 0.771 0.5652003 0.518 0.590 0.556 0.369 0.549 0.749 0.556

EPH-C2003-IV 0.453 0.569 0.517 0.340 0.476 0.731 0.5162003-II 0.456 0.565 0.515 0.328 0.481 0.730 0.5152004-I 0.469 0.573 0.526 0.359 0.492 0.727 0.526

Source: Calculations by CEDLAS based on EPH microdata. Table 7.30 Access to Labor Health Insurance By Gender and Education Argentina, 1992-2004

Gender EducationFemale Male All Low Mid High All

(i) (ii) (iii) (iv) (v) (vi) (vii) 16 main cities

1992 0.634 0.717 0.684 0.585 0.698 0.821 0.6851993 0.624 0.706 0.673 0.567 0.691 0.796 0.6731994 0.672 0.728 0.706 0.604 0.709 0.848 0.7061995 0.624 0.700 0.669 0.570 0.685 0.814 0.6691996 0.584 0.664 0.632 0.511 0.650 0.768 0.6321997 0.596 0.662 0.636 0.510 0.650 0.783 0.6361998 0.600 0.656 0.633 0.489 0.654 0.782 0.633

29 main cities1998 0.588 0.648 0.623 0.479 0.647 0.778 0.6231999 0.578 0.647 0.618 0.478 0.633 0.764 0.6182000 0.552 0.623 0.593 0.459 0.591 0.750 0.5932001 0.558 0.619 0.592 0.423 0.605 0.754 0.5922002 0.519 0.584 0.554 0.377 0.543 0.756 0.5542003 0.514 0.582 0.550 0.365 0.541 0.744 0.550

EPH-C2003-IV 0.451 0.565 0.514 0.339 0.471 0.731 0.5132003-II 0.457 0.561 0.513 0.326 0.478 0.729 0.5132004-I 0.470 0.572 0.526 0.350 0.492 0.733 0.526

Source: Calculations by CEDLAS based on EPH microdata.

28

Table 8.1 Educational Structure Adults 25-65 Argentina, 1992-2003

All Males Females Working malesLow Medium High Low Medium High Low Medium High Low Medium High(i) (ii) (iii) (iv) (v) (vi) (vii) (viii) (ix) (x) (xi) (xii)

16 main cities1992 47.7 34.5 17.8 46.1 35.5 18.5 49.2 33.6 17.2 44.7 36.7 18.61993 45.4 35.7 18.8 44.8 35.7 19.5 46.0 35.8 18.2 44.3 36.9 18.81994 45.6 35.6 18.8 45.0 36.2 18.8 46.2 35.1 18.7 44.5 37.0 18.51995 47.5 34.5 18.0 47.2 34.7 18.2 47.8 34.3 17.9 45.9 35.5 18.61996 43.4 35.8 20.8 42.6 36.6 20.7 44.1 35.1 20.8 40.8 37.5 21.61997 43.7 35.4 20.9 43.6 36.2 20.2 43.9 34.7 21.5 41.9 37.7 20.41998 42.9 35.8 21.3 42.6 37.2 20.3 43.2 34.6 22.2 41.5 38.0 20.6

29 main cities1998 43.4 35.5 21.1 43.4 36.5 20.1 43.4 34.7 21.9 42.4 37.4 20.31999 41.9 35.9 22.2 42.0 37.4 20.6 41.9 34.5 23.6 41.1 38.4 20.52000 41.9 35.4 22.7 41.9 36.7 21.4 41.9 34.3 23.9 40.6 38.1 21.42001 41.1 35.7 23.2 41.4 37.0 21.6 40.8 34.5 24.7 39.9 38.4 21.72002 39.5 36.4 24.2 40.3 37.1 22.7 38.7 35.7 25.5 40.0 37.4 22.62003 38.4 37.0 24.7 39.1 37.6 23.3 37.7 36.4 25.9 37.7 38.8 23.4

Source: Calculations by CEDLAS based on EPH microdata. Table 8.2 Years of Education By Age and Gender Argentina, 1992-2003

(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

16 main cities1992 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 7.31995 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

29 main 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

Source: Calculations by CEDLAS based on EPH microdata.

29

Table 8.3 Years of Education By Household Equivalized Income Quintiles Adults 25-65 Argentina, 1992-2003

1 2 3 4 5 Avera 16 main cities

1992 7.1 7.9 8.5 9.7 12.2 9.41993 7.0 8.0 8.6 9.6 12.2 9.41994 7.1 7.9 8.5 9.6 12.4 9.41995 7.1 7.9 8.7 9.7 12.6 9.51996 7.0 8.0 8.7 9.9 12.8 9.61997 7.1 8.0 8.8 10.3 12.9 9.81998 7.0 8.1 8.8 10.2 13.2 9.8

29 main cities1998 7.0 8.1 8.9 10.1 13.1 9.81999 7.2 8.3 9.1 10.4 13.1 9.92000 7.2 8.2 9.2 10.4 13.3 10.02001 7.2 8.2 9.2 10.6 13.3 10.12002 7.3 8.4 9.2 10.5 13.4 10.12003 7.3 8.3 9.4 10.8 13.3 10.2

ge

Source: Calculations by CEDLAS based on EPH microdata.

30

Table 8.4 Years of Education By Age and Income Argentina, 1992-2003

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

16 main cities1992 6.8 7.2 7.7 8.3 8.4 7.6 8.1 9.3 10.3 11.2 13.3 10.8 7.6 8.6 9.3 10.7 13.1 10.11993 6.8 7.5 7.7 8.2 8.6 7.7 8.2 9.6 10.2 11.3 13.0 10.8 7.2 8.6 9.3 10.6 13.0 9.91994 6.8 7.4 7.7 8.1 8.7 7.6 8.3 9.5 10.2 11.2 13.1 10.8 7.5 8.4 9.2 10.6 13.4 10.01995 6.6 7.3 7.8 8.2 8.8 7.6 8.5 9.0 10.2 11.2 13.2 10.8 7.6 8.3 9.6 10.7 13.8 10.21996 6.4 7.0 7.7 8.1 8.6 7.4 8.2 9.4 10.4 11.4 13.4 10.9 7.3 8.5 9.4 11.2 13.4 10.21997 7.0 7.5 8.1 8.6 9.1 7.9 8.6 9.4 10.2 11.6 13.5 10.9 7.7 8.7 9.6 11.5 14.0 10.51998 6.9 7.6 8.1 8.5 9.1 7.9 8.3 9.3 10.3 11.8 13.8 11.0 7.5 8.6 9.6 11.1 14.3 10.4

29 main cities1998 6.8 7.5 8.0 8.7 9.0 7.8 8.3 9.4 10.3 11.8 13.7 11.0 7.4 8.6 9.6 10.9 14.2 10.41999 7.0 7.6 8.2 8.6 9.1 7.9 8.6 9.6 10.7 11.8 13.5 11.1 7.5 8.8 9.7 11.6 13.9 10.52000 7.0 7.6 8.2 8.9 9.0 7.9 8.5 9.7 10.4 11.8 13.8 11.1 7.6 8.8 9.9 11.3 14.2 10.72001 7.0 7.6 8.1 8.8 9.1 7.9 8.5 9.7 10.7 12.0 13.7 11.2 7.6 8.8 9.9 11.4 14.2 10.72002 7.1 7.6 8.0 8.7 9.2 7.9 8.8 9.9 10.9 11.9 13.9 11.2 7.8 8.8 10.1 11.4 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

n

(41-50) (51-60) (61+)

1 2 3 4 5 Mean 1 2 3 4 5 Mean 1 2 3 4 5 Mea 16 main cities

1992 7.1 7.5 8.1 9.4 12.1 9.1 6.2 6.8 7.6 8.3 10.9 8.3 5.8 6.2 6.5 7.8 9.5 7.11993 6.9 7.7 8.3 9.1 12.1 9.2 6.2 6.8 7.3 8.4 11.0 8.4 5.8 6.2 6.9 7.6 9.9 7.21994 7.0 7.6 8.4 9.4 12.3 9.2 6.5 6.7 7.0 8.1 11.1 8.3 5.7 6.4 6.9 7.4 9.7 7.21995 6.9 7.8 8.6 9.5 12.5 9.3 6.1 7.0 7.3 8.3 11.2 8.4 5.6 5.9 6.7 7.2 9.9 7.11996 6.9 7.5 8.5 9.5 13.1 9.5 6.0 6.9 7.4 8.5 11.6 8.4 5.5 6.2 6.5 7.3 10.1 7.31997 6.8 7.6 8.8 10.3 12.8 9.6 5.8 6.7 7.5 8.9 11.6 8.6 5.4 6.1 6.4 7.5 10.3 7.31998 7.0 8.0 8.6 10.1 13.2 9.7 5.9 6.8 7.6 8.6 11.9 8.7 5.5 5.9 6.3 7.3 10.7 7.3

29 main cities1998 6.9 8.0 8.6 10.1 13.0 9.7 5.8 6.8 7.6 8.5 11.8 8.7 5.4 5.7 6.2 7.1 10.5 7.21999 7.1 8.1 9.0 10.3 13.2 9.9 6.0 7.0 7.5 8.7 11.8 8.7 5.1 6.1 6.3 7.4 9.9 7.22000 7.2 7.8 9.1 10.4 13.5 9.9 6.1 6.9 7.4 8.9 12.0 8.8 5.0 6.0 6.4 7.5 10.0 7.32001 7.1 8.0 9.0 10.6 13.4 9.9 6.0 6.9 8.1 9.0 12.1 9.0 5.1 5.8 6.4 7.4 10.3 7.42002 6.9 8.1 9.4 10.4 13.8 10.0 6.2 7.2 7.4 9.2 12.2 9.0 4.7 6.0 6.4 7.6 10.2 7.62003 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

n

Source: Calculations by CEDLAS based on EPH microdata.

31

Table 8.5 Gini Coefficient Years of Education By Age Argentina, 1992-2003

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

16 main cities1992 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 0.2761995 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

29 main 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

Source: Calculations by CEDLAS based on EPH microdata. Table 8.6 Literacy By Age and Gender Adults Aged 25 to 65 Argentina, 1992-2003

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

16 main 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

29 main 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

Source: Calculations by CEDLAS based on EPH microdata.

32

Table 8.7 Literacy By Household Equivalized Income Quintiles Adults Aged 25 to 65 Argentina, 1992-2003

1 2 3 4 5 Mea 16 main cities

1992 0.96 0.98 0.98 0.99 1.00 0.981993 0.97 0.98 0.99 0.99 1.00 0.991994 0.97 0.98 0.99 0.99 1.00 0.991995 0.97 0.99 0.99 0.99 1.00 0.991996 0.96 0.99 0.99 0.99 1.00 0.991997 0.97 0.98 0.99 0.99 1.00 0.991998 0.95 0.98 0.99 0.99 1.00 0.99

29 main cities1998 0.95 0.98 0.99 0.99 1.00 0.981999 0.97 0.98 0.98 0.99 1.00 0.992000 0.96 0.98 0.99 0.99 1.00 0.992001 0.97 0.97 0.99 1.00 1.00 0.992002 0.96 0.98 0.98 0.99 1.00 0.992003 0.96 0.97 0.99 0.99 1.00 0.99

n

Source: Calculations by CEDLAS based on EPH microdata. Table 8.8 Enrollment Rates By Age and Gender Argentina, 1992-2003

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

16 main 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

29 main 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

Source: Calculations by CEDLAS based on EPH microdata.

33

Table 8.9 Enrollment Rates By Household Equivalized Income Quintiles Argentina, 1992-2003

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

16 main cities1992 0.22 0.34 0.31 0.41 0.51 0.34 0.97 0.98 0.98 0.99 0.99 0.98 0.70 0.78 0.77 0.82 0.94 0.79 0.33 0.33 0.33 0.42 0.57 0.401993 0.29 0.29 0.33 0.37 0.47 0.34 0.97 0.99 0.97 0.99 1.00 0.98 0.74 0.78 0.75 0.77 0.95 0.78 0.31 0.35 0.38 0.41 0.55 0.411994 0.21 0.29 0.27 0.38 0.36 0.29 0.97 0.98 0.98 1.00 1.00 0.98 0.71 0.77 0.79 0.86 0.92 0.79 0.27 0.34 0.39 0.35 0.58 0.391995 0.20 0.26 0.37 0.35 0.35 0.29 0.98 0.98 0.99 0.99 1.00 0.99 0.67 0.76 0.83 0.86 0.97 0.79 0.28 0.28 0.37 0.45 0.69 0.421996 0.22 0.30 0.37 0.43 0.45 0.33 0.98 0.99 0.99 1.00 1.00 0.99 0.66 0.78 0.85 0.83 0.97 0.79 0.24 0.30 0.39 0.45 0.67 0.421997 0.28 0.32 0.37 0.38 0.44 0.34 0.98 0.99 1.00 0.99 1.00 0.99 0.73 0.82 0.83 0.91 0.95 0.83 0.24 0.29 0.42 0.47 0.70 0.431998 0.31 0.38 0.46 0.56 0.59 0.42 0.98 0.99 1.00 1.00 1.00 0.99 0.79 0.83 0.90 0.94 0.98 0.87 0.24 0.33 0.40 0.51 0.70 0.43

29 main cities1998 0.28 0.34 0.40 0.49 0.51 0.37 0.99 0.98 1.00 1.00 1.00 0.99 0.78 0.81 0.88 0.93 0.98 0.86 0.26 0.33 0.40 0.50 0.68 0.431999 0.33 0.34 0.44 0.48 0.57 0.41 0.99 1.00 0.99 0.99 1.00 0.99 0.82 0.86 0.90 0.92 0.98 0.88 0.32 0.34 0.47 0.54 0.67 0.472000 0.34 0.42 0.42 0.54 0.60 0.44 0.98 1.00 1.00 1.00 1.00 0.99 0.84 0.88 0.93 0.96 0.98 0.90 0.31 0.40 0.43 0.55 0.74 0.482001 0.31 0.39 0.39 0.47 0.51 0.40 0.97 0.98 1.00 1.00 1.00 0.99 0.86 0.89 0.94 0.96 0.99 0.91 0.34 0.36 0.45 0.56 0.72 0.482002 0.29 0.38 0.39 0.55 0.59 0.41 0.99 0.99 0.99 1.00 1.00 0.99 0.85 0.89 0.96 0.96 0.99 0.91 0.30 0.38 0.45 0.57 0.79 0.492003 0.43 0.50 0.50 0.59 0.63 0.51 0.99 1.00 0.99 1.00 1.00 1.00 0.88 0.89 0.94 0.97 0.99 0.92 0.34 0.39 0.45 0.54 0.78 0.49

n

Source: Calculations by CEDLAS based on EPH microdata. Table 8.10 Educational Mobility By Age Group Argentina, 1992-2003

13-19 20-25(i) (ii)

16 main cities1992 0.89 0.811993 0.89 0.811994 0.89 0.811995 0.87 0.791996 0.89 0.801997 0.88 0.801998 0.87 0.79

29 main cities1998 0.86 0.771999 0.87 0.782000 0.87 0.792001 0.87 0.772002 0.89 0.782003 0.89 0.80

Source: Calculations by CEDLAS based on EPH microdata.

34

Table 9.1 Housing By Household Equivalized Income Quintiles

Ownership of housing Number of rooms Persons per room1 2 3 4 5 Mean 1 2 3 4 5 Mean 1 2 3 4 5 Mea

16 main cities1992 0.678 0.728 0.733 0.739 0.711 0.719 2.601 2.641 2.807 2.956 3.264 2.882 2.011 1.427 1.428 1.235 0.948 1.3641993 0.688 0.735 0.748 0.724 0.724 0.725 2.609 2.707 2.913 2.998 3.233 2.920 1.898 1.521 1.359 1.155 0.945 1.3351994 0.678 0.700 0.719 0.726 0.739 0.715 2.600 2.695 2.848 2.966 3.166 2.884 1.945 1.562 1.351 1.204 0.932 1.3501995 0.666 0.710 0.716 0.711 0.738 0.712 2.607 2.726 2.842 2.972 3.265 2.920 2.044 1.557 1.319 1.180 0.876 1.3301996 0.658 0.712 0.711 0.706 0.737 0.709 2.696 2.704 2.815 2.981 3.276 2.932 2.047 1.568 1.356 1.125 0.863 1.3181997 0.623 0.680 0.712 0.711 0.740 0.700 2.569 2.690 2.902 2.981 3.358 2.947 2.095 1.565 1.333 1.126 0.831 1.3171998 0.630 0.692 0.703 0.711 0.735 0.701 2.515 2.672 2.774 2.989 3.353 2.914 2.171 1.594 1.363 1.103 0.838 1.3301999 0.637 0.683 0.723 0.715 0.781 0.716 2.553 2.666 2.816 2.875 3.352 2.902 2.175 1.637 1.307 1.144 0.850 1.337

29 main cities1998 0.615 0.672 0.704 0.713 0.728 0.694 2.480 2.694 2.760 2.972 3.348 2.905 2.237 1.655 1.373 1.127 0.854 1.3611999 0.624 0.669 0.721 0.709 0.758 0.705 2.523 2.690 2.767 2.933 3.302 2.893 2.231 1.729 1.300 1.168 0.863 1.3662000 0.613 0.682 0.710 0.711 0.751 0.702 2.501 2.724 2.788 2.944 3.271 2.896 2.240 1.691 1.360 1.132 0.861 1.3632001 0.630 0.700 0.702 0.725 0.750 0.710 2.492 2.652 2.747 2.891 3.290 2.871 2.370 1.711 1.402 1.142 0.853 1.3842002 0.601 0.703 0.708 0.719 0.756 0.708 2.452 2.627 2.749 2.852 3.322 2.862 2.399 1.813 1.409 1.180 0.849 1.4142003 0.618 0.682 0.704 0.691 0.744 0.696 2.423 2.645 2.689 2.880 3.298 2.851 2.387 1.775 1.409 1.126 0.849 1.390

EPH-C2003- IV 0.578 0.646 0.691 0.662 0.724 0.670 2.484 2.788 2.753 2.892 3.224 2.878 2.178 1.703 1.366 1.128 0.817 1.3362003-II 0.583 0.638 0.678 0.681 0.725 0.671 2.472 2.767 2.787 2.928 3.274 2.900 2.345 1.773 1.369 1.105 0.822 1.3702004-I 0.571 0.623 0.665 0.684 0.708 0.658 2.463 2.707 2.825 2.951 3.266 2.884 2.516 1.807 1.332 1.116 0.840 1.431

Poor dwellings Low-quality materials1 2 3 4 5 Mean 1 2 3 4 5 Mean

16 main cities1992 0.056 0.040 0.030 0.032 0.014 0.033 0.039 0.026 0.020 0.013 0.003 0.0191993 0.056 0.053 0.032 0.034 0.015 0.036 0.046 0.025 0.023 0.013 0.004 0.0211994 0.079 0.046 0.047 0.028 0.011 0.039 0.032 0.031 0.021 0.009 0.004 0.0181995 0.074 0.049 0.026 0.037 0.010 0.036 0.037 0.027 0.018 0.010 0.006 0.0181996 0.074 0.041 0.031 0.031 0.009 0.033 0.037 0.020 0.012 0.008 0.005 0.0141997 0.063 0.040 0.026 0.014 0.009 0.027 0.033 0.033 0.013 0.008 0.002 0.0161998 0.034 0.029 0.014 0.012 0.004 0.017 0.025 0.018 0.017 0.009 0.002 0.0131999 0.055 0.032 0.030 0.024 0.006 0.027 0.037 0.016 0.013 0.009 0.003 0.014

29 main cities1998 0.058 0.035 0.018 0.012 0.005 0.022 0.034 0.024 0.019 0.011 0.003 0.0161999 0.068 0.039 0.026 0.024 0.007 0.029 0.041 0.022 0.017 0.013 0.004 0.0172000 0.077 0.035 0.026 0.018 0.005 0.028 0.032 0.022 0.014 0.008 0.004 0.0142001 0.064 0.047 0.031 0.016 0.004 0.028 0.035 0.025 0.019 0.010 0.004 0.0162002 0.051 0.034 0.021 0.017 0.009 0.023 0.037 0.022 0.015 0.011 0.005 0.0162003 0.039 0.037 0.024 0.014 0.007 0.021 0.030 0.027 0.014 0.011 0.004 0.015

n

Source: Calculations by CEDLAS based on EPH microdata.

35

Table 9.2 Housing By Age

Ownership of housing Number of rooms Persons per room[16,25] [26,40] [41,64] [65+) Mean [16,25] [26,40] [41,64] [65+) Mean [16,25] [26,40] [41,64] [65+) Mean

16 main cities1992 0.328 0.588 0.795 0.853 0.731 1.976 2.748 3.117 2.948 2.931 1.705 1.780 1.351 0.845 1.3731993 0.314 0.571 0.807 0.865 0.737 2.143 2.757 3.198 2.962 2.989 1.527 1.709 1.302 0.830 1.3221994 0.302 0.541 0.805 0.848 0.722 2.072 2.655 3.197 2.949 2.950 1.496 1.764 1.304 0.802 1.3291995 0.291 0.540 0.794 0.848 0.716 2.135 2.669 3.163 2.931 2.933 1.511 1.718 1.326 0.814 1.3271996 0.238 0.540 0.790 0.863 0.715 2.081 2.689 3.173 3.007 2.958 1.444 1.685 1.321 0.809 1.3101997 0.269 0.531 0.777 0.837 0.705 2.110 2.644 3.208 3.007 2.967 1.457 1.692 1.321 0.805 1.3091998 0.269 0.522 0.784 0.854 0.708 2.129 2.565 3.207 3.004 2.945 1.338 1.775 1.306 0.793 1.3251999 0.329 0.568 0.794 0.860 0.725 2.250 2.631 3.175 2.916 2.929 1.429 1.689 1.337 0.826 1.326

29 main cities1998 0.251 0.523 0.778 0.842 0.699 2.097 2.583 3.192 2.986 2.932 1.418 1.802 1.336 0.816 1.3561999 0.281 0.554 0.785 0.847 0.713 2.159 2.614 3.164 2.914 2.915 1.443 1.733 1.357 0.841 1.3532000 0.239 0.557 0.781 0.844 0.709 2.019 2.619 3.175 2.950 2.921 1.462 1.713 1.370 0.835 1.3572001 0.249 0.552 0.782 0.857 0.713 1.970 2.584 3.149 2.958 2.904 1.480 1.765 1.396 0.827 1.3772002 0.288 0.562 0.786 0.855 0.717 2.086 2.508 3.144 3.026 2.896 1.502 1.815 1.381 0.830 1.3832003 0.240 0.549 0.768 0.859 0.704 1.968 2.544 3.111 2.970 2.875 1.520 1.773 1.382 0.836 1.366

EPH-C2003-IV 0.245 0.512 0.754 0.842 0.686 2.061 2.608 3.232 3.017 2.959 1.648 1.751 1.312 0.803 1.3252003-II 0.275 0.522 0.757 0.842 0.692 2.113 2.618 3.240 3.041 2.977 1.837 1.796 1.351 0.835 1.3692004-I 0.247 0.495 0.749 0.848 0.677 2.202 2.610 3.225 3.016 2.960 1.693 1.831 1.362 0.852 1.389

Poor dwellings Low-quality materials[16,25] [26,40] [41,64] [65+) Mean [16,25] [26,40] [41,64] [65+) Mean

16 main cities1992 0.080 0.042 0.027 0.015 0.031 0.022 0.023 0.016 0.012 0.0171993 0.078 0.051 0.033 0.008 0.034 0.027 0.023 0.019 0.015 0.0191994 0.091 0.055 0.031 0.015 0.036 0.021 0.019 0.019 0.012 0.0171995 0.089 0.058 0.026 0.010 0.034 0.036 0.023 0.015 0.011 0.0171996 0.080 0.064 0.023 0.009 0.033 0.028 0.016 0.013 0.010 0.0141997 0.076 0.049 0.020 0.009 0.027 0.023 0.021 0.014 0.013 0.0161998 0.053 0.026 0.012 0.007 0.017 0.014 0.016 0.010 0.012 0.0121999 0.059 0.042 0.018 0.012 0.025 0.012 0.018 0.012 0.011 0.013

29 main cities1998 0.062 0.036 0.017 0.009 0.022 0.019 0.019 0.013 0.017 0.0161999 0.067 0.045 0.021 0.014 0.028 0.015 0.019 0.014 0.017 0.0162000 0.083 0.039 0.023 0.011 0.027 0.019 0.015 0.012 0.015 0.0142001 0.089 0.048 0.021 0.007 0.028 0.023 0.018 0.014 0.012 0.0152002 0.062 0.042 0.013 0.005 0.021 0.010 0.017 0.013 0.014 0.0142003 0.089 0.034 0.013 0.004 0.020 0.026 0.015 0.012 0.012 0.013

Source: Calculations by CEDLAS based on EPH microdata.

36

Table 9.3 Housing By Education of the Household Head

Ownership of housing Number of rooms Persons per roomLow Middle High Mean Low Middle High Mean Low Middle High Mean

16 main cities1992 0.734 0.709 0.764 0.731 2.682 3.022 3.555 2.930 1.521 1.335 0.971 1.3741993 0.734 0.739 0.741 0.737 2.730 3.095 3.548 2.988 1.477 1.270 0.966 1.3231994 0.717 0.729 0.726 0.722 2.714 3.089 3.401 2.950 1.507 1.247 0.943 1.3291995 0.724 0.695 0.728 0.716 2.722 3.019 3.469 2.933 1.497 1.230 0.953 1.3281996 0.739 0.687 0.702 0.715 2.753 3.010 3.414 2.958 1.480 1.271 0.925 1.3101997 0.711 0.684 0.723 0.705 2.736 3.074 3.417 2.967 1.498 1.224 0.935 1.3091998 0.715 0.685 0.728 0.708 2.696 3.005 3.510 2.945 1.528 1.252 0.907 1.3251999 0.730 0.705 0.745 0.725 2.668 3.003 3.450 2.928 1.510 1.293 0.928 1.326

29 main cities1998 0.706 0.682 0.712 0.699 2.683 3.007 3.490 2.932 1.560 1.276 0.931 1.3561999 0.720 0.699 0.720 0.713 2.662 2.997 3.425 2.915 1.535 1.317 0.953 1.3542000 0.717 0.701 0.700 0.709 2.697 2.979 3.377 2.921 1.557 1.297 0.967 1.3572001 0.727 0.694 0.711 0.713 2.680 2.958 3.360 2.904 1.583 1.320 0.971 1.3772002 0.735 0.706 0.693 0.717 2.674 2.901 3.389 2.896 1.581 1.367 0.962 1.3832003 0.733 0.685 0.674 0.705 2.669 2.857 3.353 2.875 1.554 1.350 0.983 1.366

EPH-C2003- IV 0.711 0.647 0.692 0.685 2.738 2.964 3.366 2.960 1.508 1.359 0.941 1.3262003-II 0.718 0.660 0.688 0.692 2.739 3.001 3.390 2.977 1.561 1.394 0.970 1.3692004-I 0.700 0.647 0.677 0.677 2.729 2.977 3.371 2.960 1.597 1.404 0.975 1.389

Poor dwellings Low-quality materialsLow Middle High Mean Low Middle High Mean

16 main cities1992 0.043 0.022 0.008 0.031 0.025 0.011 0.005 0.0171993 0.052 0.021 0.007 0.034 0.031 0.011 0.002 0.0191994 0.055 0.023 0.006 0.036 0.025 0.013 0.003 0.0171995 0.050 0.019 0.009 0.034 0.025 0.012 0.002 0.0171996 0.050 0.024 0.003 0.033 0.023 0.006 0.003 0.0141997 0.042 0.017 0.006 0.027 0.025 0.010 0.001 0.0161998 0.025 0.011 0.002 0.017 0.020 0.006 0.002 0.0121999 0.036 0.020 0.010 0.025 0.023 0.007 0.002 0.013

29 main cities1998 0.035 0.012 0.004 0.022 0.025 0.010 0.003 0.0161999 0.041 0.019 0.010 0.028 0.026 0.010 0.003 0.0162000 0.042 0.018 0.006 0.027 0.022 0.008 0.002 0.0132001 0.040 0.022 0.008 0.028 0.024 0.009 0.003 0.0152002 0.030 0.018 0.009 0.022 0.021 0.012 0.002 0.0142003 0.027 0.016 0.013 0.020 0.020 0.010 0.004 0.013

Source: Calculations by CEDLAS based on EPH microdata.

37

Table 9.4 Social Services By Household Equivalized Income Quintiles

Water Restrooms Sewerage Electricity1 2 3 4 5 Mean 1 2 3 4 5 Mean 1 2 3 4 5 Mean 1 2 3 4 5 Mea

16 main cities1992 0.934 0.973 0.960 0.978 0.995 0.971 0.733 0.854 0.869 0.909 0.974 0.8781993 0.940 0.970 0.984 0.988 0.998 0.979 0.740 0.826 0.897 0.914 0.969 0.8791994 0.942 0.970 0.978 0.983 0.993 0.976 0.748 0.835 0.884 0.925 0.979 0.8851995 0.940 0.969 0.976 0.987 0.996 0.977 0.783 0.865 0.917 0.959 0.988 0.9141996 0.943 0.965 0.987 0.993 0.998 0.981 0.767 0.878 0.915 0.958 0.989 0.915 0.989 0.993 0.997 0.999 1.000 0.9961997 0.937 0.981 0.985 0.992 0.997 0.982 0.663 0.819 0.882 0.951 0.982 0.878 0.988 0.998 0.999 0.999 1.000 0.9971998 0.944 0.977 0.991 0.998 0.998 0.985 0.592 0.796 0.866 0.950 0.987 0.863 0.249 0.395 0.472 0.621 0.838 0.552 0.988 0.998 0.999 0.999 1.000 0.9971999 0.973 0.986 0.995 0.995 0.998 0.991 0.629 0.776 0.885 0.933 0.985 0.865 0.269 0.394 0.506 0.597 0.830 0.555 0.989 0.998 0.998 0.998 1.000 0.997

29 main cities1998 0.935 0.975 0.986 0.996 0.999 0.982 0.593 0.788 0.876 0.942 0.987 0.862 0.287 0.409 0.509 0.630 0.830 0.568 0.984 0.997 0.997 0.999 1.000 0.9961999 0.961 0.984 0.992 0.994 0.998 0.988 0.638 0.777 0.880 0.935 0.981 0.866 0.308 0.416 0.527 0.620 0.821 0.573 0.986 0.997 0.997 0.997 0.999 0.9962000 0.944 0.983 0.987 0.995 0.999 0.985 0.619 0.803 0.869 0.953 0.985 0.870 0.299 0.433 0.519 0.665 0.822 0.584 0.982 0.997 0.996 0.999 0.999 0.9962001 0.951 0.981 0.991 0.996 1.000 0.987 0.601 0.804 0.879 0.953 0.992 0.875 0.269 0.411 0.547 0.675 0.834 0.590 0.982 0.992 0.998 0.999 1.000 0.9952002 0.956 0.975 0.990 0.994 0.996 0.985 0.608 0.761 0.867 0.955 0.986 0.865 0.284 0.408 0.529 0.669 0.824 0.585 0.985 0.996 0.998 0.999 0.998 0.9962003 0.949 0.977 0.989 0.996 0.998 0.986 0.622 0.782 0.892 0.957 0.988 0.877 0.305 0.430 0.541 0.695 0.845 0.607 0.979 0.996 0.998 0.998 0.998 0.995

n

Source: Calculations by CEDLAS based on EPH microdata.

38

Table 10.1 Household Size

Equivalized income quintile Education of household head1 2 3 4 5 Mean Low Medium High Mean

16 main cities1992 4.35 3.34 3.62 3.38 2.82 3.43 3.56 3.59 3.17 3.511993 4.13 3.64 3.58 3.23 2.82 3.42 3.53 3.54 3.16 3.471994 4.25 3.65 3.45 3.25 2.73 3.39 3.54 3.42 2.99 3.411995 4.56 3.72 3.40 3.21 2.71 3.41 3.57 3.33 3.09 3.421996 4.72 3.77 3.39 3.11 2.67 3.41 3.59 3.40 2.99 3.421997 4.49 3.68 3.49 3.13 2.64 3.38 3.57 3.33 2.96 3.381998 4.65 3.73 3.36 3.05 2.63 3.36 3.56 3.33 2.97 3.38

29 main cities1998 4.69 3.89 3.38 3.09 2.67 3.41 3.60 3.39 3.02 3.431999 4.75 4.05 3.28 3.15 2.66 3.43 3.59 3.47 3.00 3.442000 4.76 4.04 3.45 3.08 2.65 3.45 3.66 3.41 3.01 3.452001 5.01 4.00 3.42 3.01 2.62 3.43 3.68 3.40 2.99 3.452002 5.00 4.11 3.44 3.02 2.62 3.46 3.64 3.42 2.97 3.432003 5.01 4.13 3.41 2.97 2.59 3.43 3.67 3.36 2.93 3.41

EPH-C2003-IV 4.53 4.06 3.28 2.85 2.42 3.25 3.55 3.40 2.88 3.352003-II 4.78 4.12 3.35 2.91 2.48 3.35 3.64 3.52 2.97 3.442004-I 4.96 4.16 3.31 2.96 2.51 3.45 3.68 3.51 2.98 3.46

Source: Calculations by CEDLAS based on EPH microdata. Table 10.2 Number of Children

Parental income quintile Parental education1 2 3 4 5 Mean Low Medium High Mean

16 main cities1992 1.80 1.81 1.61 1.45 1.31 1.60 1.86 1.53 1.15 1.571993 1.74 1.67 1.59 1.40 1.23 1.53 1.75 1.49 1.10 1.501994 1.59 1.75 1.48 1.28 1.20 1.46 1.76 1.34 1.06 1.441995 1.68 1.70 1.55 1.25 1.19 1.47 1.81 1.30 1.11 1.471996 1.77 1.63 1.48 1.22 1.18 1.46 1.87 1.32 1.03 1.441997 1.64 1.62 1.44 1.21 1.15 1.41 1.80 1.25 1.03 1.401998 1.80 1.58 1.38 1.26 1.11 1.43 1.80 1.38 0.92 1.43

29 main cities1998 1.80 1.64 1.49 1.31 1.16 1.48 1.86 1.41 0.98 1.481999 1.72 1.61 1.57 1.31 1.21 1.48 1.81 1.43 1.00 1.472000 1.76 1.67 1.38 1.32 1.16 1.46 1.84 1.37 1.02 1.452001 1.91 1.60 1.44 1.29 1.11 1.47 1.94 1.35 0.97 1.462002 1.85 1.65 1.40 1.27 1.08 1.45 1.85 1.36 0.89 1.412003 1.73 1.69 1.45 1.20 1.10 1.43 1.84 1.38 0.87 1.39

EPH-C2003-IV 1.75 1.44 1.44 1.23 1.08 1.39 1.83 1.41 0.89 1.382003-II 1.78 1.51 1.44 1.29 1.15 1.43 1.83 1.45 0.95 1.422004-I 1.77 1.56 1.44 1.23 1.13 1.42 1.82 1.36 0.94 1.38

Source: Calculations by CEDLAS based on EPH microdata.

39

Table 10.3 Dependency Rates Income Earners over Household Size

Equivalized income quintile Education of household head1 2 3 4 5 Mean Low Medium High Mean

16 main cities1992 0.353 0.592 0.579 0.649 0.734 0.598 0.543 0.499 0.563 0.5331993 0.398 0.540 0.578 0.666 0.740 0.599 0.557 0.520 0.575 0.5491994 0.397 0.520 0.593 0.655 0.754 0.601 0.547 0.531 0.586 0.5491995 0.337 0.520 0.591 0.652 0.746 0.592 0.546 0.541 0.552 0.5451996 0.344 0.509 0.581 0.661 0.745 0.593 0.532 0.523 0.578 0.5381997 0.359 0.517 0.588 0.677 0.778 0.608 0.552 0.552 0.602 0.5611998 0.355 0.500 0.596 0.689 0.772 0.609 0.557 0.556 0.620 0.568

29 main cities1998 0.355 0.478 0.590 0.681 0.761 0.600 0.552 0.544 0.612 0.5601999 0.350 0.454 0.605 0.667 0.764 0.597 0.543 0.526 0.588 0.5462000 0.350 0.457 0.585 0.670 0.761 0.594 0.535 0.528 0.597 0.5452001 0.327 0.446 0.569 0.658 0.764 0.586 0.508 0.523 0.584 0.5282002 0.325 0.461 0.569 0.654 0.758 0.586 0.526 0.498 0.577 0.5272003 0.335 0.462 0.582 0.680 0.767 0.600 0.526 0.515 0.583 0.534

EPH-C2003-IV 0.381 0.466 0.610 0.686 0.779 0.617 0.496 0.452 0.504 0.4832003-II 0.377 0.472 0.601 0.677 0.771 0.610 0.488 0.444 0.502 0.4762004-I 0.382 0.468 0.600 0.673 0.778 0.603 0.499 0.472 0.521 0.495

Source: Calculations by CEDLAS based on EPH microdata. Table 10.4 Mean Age

Equivalized income quintile1 2 3 4 5 Mean

16 main cities1992 27.7 32.7 31.9 32.8 34.1 31.81993 27.8 31.4 32.5 33.8 34.9 32.11994 27.6 31.1 33.5 33.8 35.3 32.31995 26.0 30.7 33.4 34.1 35.9 32.01996 25.7 31.0 33.2 34.9 36.2 32.21997 26.2 30.6 33.1 35.3 37.3 32.51998 25.3 29.9 32.9 35.4 36.5 32.0

29 main cities1998 25.0 29.0 32.6 35.3 36.3 31.61999 24.8 28.0 33.5 34.6 36.3 31.52000 24.3 28.7 32.4 35.1 36.5 31.42001 23.8 29.0 32.9 35.3 37.3 31.62002 22.9 28.4 32.4 35.7 37.4 31.42003 23.0 28.6 33.0 35.7 38.3 31.7

EPH-C2003-IV 24.7 29.7 33.0 35.7 38.3 32.32003-II 24.6 29.4 33.0 36.0 38.1 32.22004-I 24.7 28.6 33.4 35.7 37.3 31.7

Source: Calculations by CEDLAS based on EPH microdata.

40

Table 10.5 Correlation between Couples

Years of Hourly Hourseducation wages All Workers

(i) (ii) (iii) (iv) 16 main cities

1992 0.651 0.470 0.132 0.1921993 0.657 0.453 0.111 0.1501994 0.656 0.471 0.116 0.2091995 0.665 0.472 0.111 0.1921996 0.660 0.455 0.122 0.1831997 0.661 0.424 0.120 0.2131998 0.665 0.451 0.113 0.177

29 main cities1998 0.667 0.468 0.115 0.1911999 0.662 0.445 0.114 0.2012000 0.659 0.501 0.139 0.2202001 0.662 0.406 0.124 0.2132002 0.660 0.369 0.108 0.1902003 0.664 0.451 0.127 0.210

EPH-C2003-IV 0.672 0.323 0.130 0.1382003-II 0.661 0.346 0.124 0.1542004-I 0.650 0.433 0.117 0.143

Source: Calculations by CEDLAS based on EPH microdata.

41

Table 11.1 Coverage of PJH Share of Households with PJH by Equivalized Income Quintiles

1 2 3 4 5 MeaEPH2003 0.368 0.237 0.090 0.031 0.005 0.116

EPH-C2003-IV 0.362 0.213 0.104 0.044 0.005 0.1182003-II 0.372 0.224 0.099 0.041 0.006 0.1222004-I 0.356 0.219 0.100 0.035 0.005 0.118

n

Source: Calculations by CEDLAS based on EPH microdata. Table 11.2 Coverage of PJH Share of Households with PJH by Education of Household Head

Low Medium High Mean(i) (ii) (iii) (iv)

EPH2003 0.158 0.087 0.021 0.106

EPH-C2003-IV 0.171 0.095 0.021 0.1112003-II 0.174 0.091 0.024 0.1122004-I 0.168 0.097 0.018 0.110

Source: Calculations by CEDLAS based on EPH microdata. Table 11.3 Coverage of PJH Benefits (in pesos) of PJH by Household

1 2 3 4 5 MeaEPH2003 39.3 13.8 4.7 1.2 0.5 9.0

n

Source: Calculations by CEDLAS based on EPH microdata.

42

Table 11.4 Incidence of PJH Distribution of PJH Beneficiaries by Equivalized Income Quintile Households

1 2 3 4 5 TotaEPH2003 43.2 33.9 15.7 6.1 1.2 100.0

EPH-C2003-IV 44.0 29.0 17.4 8.5 1.1 100.02003-II 44.6 30.1 16.3 7.7 1.4 100.02004-I 44.5 30.3 17.2 6.8 1.2 100.0

Individuals1 2 3 4 5 Tota

EPH2003 43.3 34.0 15.7 6.0 1.1 100.0

EPH-C2003-IV 43.1 29.9 17.6 8.4 1.0 100.02003-II 43.7 31.0 16.2 7.8 1.3 100.02004-I 43.1 31.8 17.1 6.9 1.1 100.0

l

l

Source: Calculations by CEDLAS based on EPH microdata. Table 11.5 Incidence of PJH Distribution of PJH Benefits by Equivalized Income Quintile

1 2 3 4 5 TotEPH2003 43.1 33.9 15.9 6.0 1.1 100.0

al

Source: Calculations by CEDLAS based on EPH microdata.

43

Table 12.1 Poverty Profile Argentina, 2003 Demographic Variables

USD 2 Official moderateNon-poor Poor Non-poor Poor

(i) (ii) (iii) (iv)

Population share 76.5 23.5 45.0 55.0

Population share by age [0,15] 62.1 37.9 28.7 71.3 [16,25] 74.4 25.6 39.5 60.5 [26,40] 80.3 19.7 49.0 51.0 [41,64] 84.4 15.6 53.9 46.1 [65+] 93.7 6.3 72.2 27.8Age distribution [0,15] 23.9 44.3 17.4 35.6 [16,25] 18.0 19.8 15.8 20.0 [26,40] 20.4 17.8 23.0 19.8 [41,64] 25.4 15.4 27.6 19.5 [65+] 12.4 2.7 16.3 5.2 Total 100.0 100.0 100.0 100.0

Mean age 34.5 22.3 38.0 26.4

GenderShare males 0.474 0.475 0.460 0.486Household size and structureFamily size 3.1 5.1 2.7 4.4

Children (<12) 1.1 2.5 0.9 1.9

Dependency rate 0.65 0.29 0.73 0.41

Female-headed hh. 0.30 0.30 0.34 0.26 Source: Calculations by CEDLAS based on EPH microdata. Table 12.2 Poverty Profile Argentina, 2003 Regions

USD 2 Official moderateNon-poor Poor Non-poor Poor

(i) (ii) (iii) (iv)Population share GBA 78.3 21.7 48.7 51.3 Pampeana 79.1 20.9 46.7 53.3 Cuyo 76.2 23.8 41.5 58.5 NOA 73.0 27.0 33.6 66.4 Patagonia 88.4 11.6 62.0 38.0 NEA 67.3 32.7 29.6 70.4Distribution GBA 52.0 49.5 55.1 48.3 Pampeana 22.6 20.4 22.7 21.6 Cuyo 7.0 7.5 6.5 7.6 NOA 10.0 12.7 7.9 12.9 Patagonia 3.5 1.6 4.2 2.1 NEA 5.0 8.3 3.8 7.4Total 100.0 100.0 100.0 100.0 Source: Calculations by CEDLAS based on EPH microdata.

44

Table 12.3 Poverty Profile Argentina, 2003 Housing

USD 2 Official moderateNon-poor Poor Non-poor Poor

(i) (ii) (iii) (iv)

Home ownership 0.711 0.607 0.725 0.654

Number of rooms 2.917 2.411 3.028 2.585

Persons per room 1.206 2.429 0.981 1.950

Poor housing 0.018 0.043 0.012 0.035

Low-quality materials 0.012 0.029 0.008 0.024

Water 0.992 0.940 0.997 0.967

Hygienic restrooms 0.920 0.617 0.970 0.744

Sewerage 0.659 0.298 0.756 0.397

Electricity 0.998 0.980 0.998 0.991 Source: Calculations by CEDLAS based on EPH microdata. Table 12.4 Poverty Profile Argentina, 2003 Education

USD 2 Official moderateNon-poor Poor Non-poor Poor

(i) (ii) (iii) (iv)Years of education Total 8.7 5.6 9.7 6.5 [10,20] 8.6 7.5 9.2 7.9 [21,30] 12.0 9.0 12.8 10.0 [31,40] 11.5 7.9 12.9 8.9 [41,50] 10.7 7.1 12.0 8.1 [51,60] 9.4 6.1 10.6 6.9 [61+] 7.8 5.1 8.4 5.9Educational groups Adults Low 33.8 69.5 23.1 57.9 Medium 39.2 26.7 39.7 34.2 High 27.0 3.8 37.2 7.9 Total 100.0 100.0 100.0 100.0 Male adults Low 34.7 72.4 23.4 59.8 Medium 40.1 24.7 41.0 33.9 High 25.2 2.9 35.7 6.2 Total 100.0 100.0 100.0 100.0 Female adults Low 33.0 67.1 22.9 56.2 Medium 38.3 28.4 38.6 34.5 High 28.7 4.5 38.5 9.4 Total 100.0 100.0 100.0 100.0 Household heads Low 42.3 71.3 34.4 63.6 Medium 34.5 26.1 35.4 30.4 High 23.1 2.6 30.3 6.0 Total 100.0 100.0 100.0 100.0

Literacy rate 0.99 0.97 0.99 0.98

School attendance [3,5] 0.55 0.43 0.60 0.47 [6,12] 1.00 0.99 1.00 0.99 [13,17] 0.94 0.87 0.98 0.89 [18,23] 0.51 0.32 0.61 0.37 Source: Calculations by CEDLAS based on EPH microdata.

45

Table 12.5 Poverty Profile Argentina, 2003 Employment

USD 2 Official moderateNon-poor Poor Non-poor Poor

(i) (ii) (iii) (iv)In the labor force Total 0.449 0.305 0.485 0.360 [16,24] 0.475 0.417 0.487 0.444 [25,55] 0.789 0.705 0.827 0.722 [56+] 0.282 0.357 0.278 0.309 Men [25,55] 0.946 0.909 0.953 0.926 Women [25,55] 0.647 0.539 0.713 0.544Employed Total 0.388 0.215 0.441 0.273 [16,24] 0.341 0.192 0.381 0.255 [25,55] 0.707 0.567 0.768 0.595 [56+] 0.252 0.208 0.261 0.222 Men [25,55] 0.847 0.707 0.892 0.752 Women [25,55] 0.581 0.453 0.657 0.458Unemployment rate Total 0.135 0.293 0.091 0.241 [16,24] 0.282 0.540 0.218 0.426 [25,55] 0.104 0.196 0.071 0.176 [56+] 0.106 0.418 0.063 0.281 Men [25,55] 0.104 0.222 0.064 0.188 Women [25,55] 0.103 0.160 0.078 0.157

Unemployment spell 8.6 8.0 8.8 8.2(months)

Child labor 0.001 0.004 0.001 0.003 Source: Calculations by CEDLAS based on EPH microdata. Table 12.6 Poverty Profile Argentina, 2003 Wages, Hours and Earnings

USD 2 Official moderateNon-poor Poor Non-poor Poor

(i) (ii) (iii) (iv)Worked hours Total 41.4 29.9 43.0 35.5 [16,24] 37.2 28.6 39.2 32.6 [25,55] 42.5 30.2 44.2 36.1 [56+] 40.1 29.8 40.9 35.7 Men [25,55] 47.9 36.3 49.6 42.0 Women [25,55] 35.3 22.4 37.3 27.8Hourly wages Total 4.5 2.2 5.4 2.4 [16,24] 2.6 1.8 3.0 1.9 [25,55] 4.7 2.2 5.7 2.5 [56+] 5.2 2.1 6.0 2.4 Men [25,55] 4.9 2.0 6.1 2.5 Women [25,55] 4.4 2.5 5.2 2.6Earnings Total 687.5 182.1 852.1 289.3 [16,24] 351.4 136.5 417.3 208.2 [25,55] 739.3 195.4 918.4 311.9 [56+] 742.2 162.1 887.4 255.8 Men [25,55] 837.3 223.6 1077.1 353.6 Women [25,55] 597.1 143.3 716.3 232.1 Source: Calculations by CEDLAS based on EPH microdata.

46

Table 12.7 Poverty Profile Argentina, 2003 Employment Structure

USD 2 Official moderateNon-poor Poor Non-poor Poor

(i) (ii) (iii) (iv)Labor relationship Entrepreneur 3.1 0.5 4.5 0.6 Salaried worker 64.1 48.4 67.9 54.3 Self-employed 18.5 20.1 17.7 19.8 Zero income 0.8 1.7 0.7 1.2 Unemployed 13.6 29.3 9.1 24.1 Total 100.0 100.0 100.0 100.0Labor group Entrepreneurs 3.7 0.7 5.1 0.8 Salaried-large firms 31.1 14.2 33.5 22.3 Salaried-public sector 21.7 30.0 22.2 23.7 Self-employed professionals 3.6 0.2 4.9 0.7 Salaried-small firms 20.8 24.0 18.5 25.0 Self-employed unskilled 18.2 28.5 15.1 25.9 Zero income 0.9 2.5 0.8 1.6 Total 100.0 100.0 100.0 100.0Formality (based on labor group) Formal 60.0 45.1 65.6 47.5 Informal 40.0 55.0 34.4 52.5 Total 100.0 100.0 100.0 100.0Formality (based on social security rights) Formal 60.2 12.7 69.4 32.3 Informal 39.9 87.3 30.6 67.7 Total 100.0 100.0 100.0 100.0Sectors Primary activities 1.2 2.6 1.2 1.7 Industry-labor intensive 6.3 7.1 5.9 7.1 Industry-capital intensive 7.6 4.1 7.6 6.4 Construction 7.1 17.0 4.1 13.9 Commerce 21.8 22.0 20.9 22.9 Utilities & transportation 8.4 4.0 8.4 6.9 Skilled services 9.4 2.7 12.3 3.8 Public administration 8.3 10.3 9.0 8.2 Education & Health 23.2 17.1 25.8 18.1 Domestic servants 6.7 13.1 4.8 11.1 Total 100.0 100.0 100.0 100.0

Contract n.a n.a n.a n.a

Permanent job 0.83 0.52 0.90 0.65

Right to pensions 0.61 0.13 0.70 0.33

Labor health insurance 0.60 0.12 0.69 0.32

Unionized n.a n.a n.a n.a Source: Calculations by CEDLAS based on EPH microdata.

47

Table 12.8 Poverty Profile Argentina, 2003 Poverty Alleviation Programs (Programa Jefes)

USD 2 Official moderateNon-poor Poor Non-poor Poor

(i) (ii) (iii) (iv)

Households with PJH 0.074 0.362 0.021 0.241

Mean income from PJH 4.1 36.5 1.3 19.0

Distribution Beneficiaries 44.4 55.6 7.5 92.5 Transfers 50.1 49.9 10.0 90.0 Source: Calculations by CEDLAS based on EPH microdata. Table 12.9 Poverty Profile Argentina, 2003 Incomes

USD 2 Official moderateNon-poor Poor Non-poor Poor

(i) (ii) (iii) (iv)

Household per capita income 337.8 46.9 483.9 92.7Household total income 1049.0 239.4 1302.9 407.8

Gini per capita income 0.448 0.203 0.366 0.287

Individual income Labor 77.6 61.2 77.8 73.3 Non-labor 22.4 38.8 22.2 26.7 Total 100.0 100.0 100.0 100.0Labor income Salaried work 70.5 61.0 69.6 72.7 Self-employment 20.4 38.0 19.8 26.2 Own firm 9.1 1.0 10.6 1.2 Total 100.0 100.0 100.0 100.0Non-labor income Capital 7.7 0.2 8.9 1.0 Pensions 68.7 14.5 71.8 40.7 Transfers 23.6 85.3 19.4 58.3 Total 100.0 100.0 100.0 100.0 Source: Calculations by CEDLAS based on EPH microdata.

48

Table 12.10 Poverty Profile Argentina, 2003 Decomposition of Household per Capita Income A. Household incomes and size

Non-poor Poor (i) (ii)

Household per capita income 337.8 46.9

Household total income 1049.0 239.4

Household size 3.1 5.1

Individual labor income 690.0 180.7

Number of labor income earners 1.2 0.8

Household non-labor income 234.6 92.8

B. Simulations$

Poor's per capita income 46.9Poor's per capita income with the non-poor's 1. Household size 77.1

2.Individual labor income 127.9

3.Number of labor income earners 60.0

4.Household non-labor income 74.7

5.Household total income 205.5

6.Household total income and size 337.8 Source: Calculations by CEDLAS based on EPH microdata. Table 12.11 Poverty Profile Argentina, 2003 Endowments

USD 2 Official moderateNon-poor Poor Non-poor Poor

(i) (ii) (iii) (iv)Poor as

Lack of endowments 0.22 0.68 0.13 0.51

Lack of endowments and 0.00 0.68 0.00 0.24income less than 2USD Source: Calculations by CEDLAS based on EPH microdata.

49

Table 12.12 Basic Needs Indicator (NBI) Argentina, 2001

1980 1991 2001 ChangesPersons % Persons % Persons % 1980-1991 1991-2001 1980-2001

(i) (ii) (iii) (iv) (v) (vi) (vii) (viii) (ix)

Total 7,603,332 27.7 6,427,257 19.9 6,343,589 17.7 -7.8 -2.2 -10.0

Ciudad de Buenos Aires 231,872 8.3 232,203 8.1 212,489 7.8 -0.2 -0.3 -0.5

PampeanaBuenos Aires (includes GBA) 2,607,922 24.3 2,128,736 17.2 2,161,064 15.8 -7.1 -1.4 -8.5Córdoba 529,753 22.4 413,573 15.1 393,708 13.0 -7.3 -2.1 -9.4Entre Ríos 292,979 32.6 207,794 20.6 202,578 17.6 -12.0 -3.0 -15.0La Pampa 44,379 21.9 34,705 13.5 30,587 10.3 -8.4 -3.2 -11.6Santa Fe 595,239 24.5 489,854 17.6 440,346 14.8 -6.9 -2.8 -9.7

CuyoMendoza 287,076 24.4 246,789 17.6 241,053 15.4 -6.8 -2.2 -9.0San Juan 142,404 30.8 103,865 19.8 107,372 17.4 -11.0 -2.4 -13.4San Luis 67,019 31.9 61,057 21.5 57,072 15.6 -10.4 -5.9 -16.3

PatagoniaChubut 87,343 34.8 76,608 21.9 62,872 15.5 -12.9 -6.4 -19.3Neuquén 93,507 40.2 81,391 21.4 79,547 17.0 -18.8 -4.4 -23.2Río Negro 145,707 38.9 116,323 23.2 97,486 17.9 -15.7 -5.3 -21.0Santa Cruz 27,245 26.3 22,860 14.7 19,985 10.4 -11.6 -4.3 -15.9Tierra del Fuego 6,356 27.5 14,862 22.4 14,033 14.1 -5.1 -8.3 -13.4

NOACatamarca 87,039 42.6 73,944 28.2 71,145 21.5 -14.4 -6.7 -21.1Jujuy 196,892 48.8 180,025 35.5 175,179 28.8 -13.3 -6.7 -20.0La Rioja 59,224 36.6 59,311 27.0 58,869 20.4 -9.6 -6.6 -16.2Salta 305,776 46.8 318,532 37.1 338,484 31.6 -9.7 -5.5 -15.2Santiago del Estero 302,681 51.7 254,830 38.2 250,747 31.3 -13.5 -6.9 -20.4Tucumán 406,748 42.4 314,828 27.7 318,209 23.9 -14.7 -3.8 -18.5

NEACorrientes 303,818 46.9 248,144 31.4 264,277 28.5 -15.5 -2.9 -18.4Chaco 359,857 52.1 329,139 39.5 323,354 33.0 -12.6 -6.5 -19.1Formosa 159,072 54.4 155,072 39.1 162,862 33.6 -15.3 -5.5 -20.8Misiones 263,424 45.4 262,812 33.6 260,271 27.1 -11.8 -6.5 -18.3 Source: Census data

50

Figure 3.1 Growth Incidence Curves Household per Capita Income Proportional Changes by Percentile Argentina, 1992-2003

-70

-60

-50

-40

-30

-20

-10

0

10

20

30

0 10 20 30 40 50 60 70 80 90 100

1992-2003

2001-2003

1992-1998

1998-2001

Source: Calculations by CEDLAS based on EPH microdata.

51

Figure 3.2 Pen Parade’s Curves Argentina, 1992-2003 A. All the distribution

0

500

1000

1500

2000

2500

3000

0 20 40 60 80 100

1992 1996 1998 2001 2003 B. Percentiles 1 to 40

0

20

40

60

80

100

120

140

160

180

200

0 5 10 15 20 25 30 35 40

1992 1998

2001

2003

C. Percentiles 40 to 80

0

50

100

150

200

250

300

350

400

450

500

40 45 50 55 60 65 70 75 80

19921998

2001

2003

D. Percentiles 80 to 100

0

200

400

600

800

1000

1200

1400

1600

1800

2000

80 85 90 95 100

1992

1998

20012003

52

Figure 4.1 Poverty Argentina, 1992-2004 US$1 and US$2 Lines USD 1 a day USD 2 a day

0123456789

10

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

H PG FGT(2)

0

5

10

15

20

25

30

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

H PG FGT(2) Source: Calculations by CEDLAS based on EPH microdata. Note: H=headcount ratio, PG=poverty gap, FGT(2)=Foster, Greer and Thornbecke index with parameter 2. Figure 4.2 Poverty Argentina, 1992-2004 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

H PG FGT(2)

0

5

10

15

20

25

30

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

H PG FGT(2) Source: Calculations by CEDLAS based on EPH microdata. Note: H=headcount ratio, PG=poverty gap, FGT(2)=Foster, Greer and Thornbecke index with parameter 2.

53

Figure 4.3 Density of the Log Household Equivalized Income Non-Parametric Estimation Argentina, 2003

0.1

.2.3

.4D

ensi

ty

0 2 4 6 8 10log household equivalized income

Extreme PL

Moderate PL

Source: Calculations by CEDLAS based on EPH microdata. Figure 4.4 Poverty Headcount Ratio Official Poverty Line Greater Buenos Aires, 1980-2004

0

10

20

30

40

50

60

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

+ 4.3

+ 31.3

-24.8

+ 9.1

+ 28.4

- 11.6

Source: Calculations by CEDLAS based on EPH microdata.

54

Figure 4.5 Poverty Headcount Ratio LAC Countries Around 2002 and 1990 ECLAC Estimates Around 2002

Around 1990

01020304050607080

Uru

guay

Cos

ta R

ica

Chi

le

Pana

Bra

sil

Méx

ico

R.D

omin

ican

a

Arg

entin

a

Vene

zuel

a

El S

alva

dor

Col

ombi

a

Perú

Gua

tem

ala

Par

agua

y

Bol

ivia

Nic

arag

ua

Hon

dura

s

0

20

40

60

80

100

Arge

ntin

a

Uru

guay

Cos

ta R

ica

R.D

omin

ican

a

Chi

le

Ven

ezue

la

Pan

amá

Méx

ico

Per

ú

Bras

il

El S

alva

dor

Col

ombi

a

Par

agua

y

Bol

ivia

Gua

tem

ala

Nic

arag

ua

Hon

dura

s

Figure 4.6 Poverty Headcount Ratio LAC Countries Late 1990s, Early 2000s

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

Uru

guay

Chi

le

Arg

entin

a

Vene

zuel

a

Mex

ico

Cos

ta R

ica

Dom

inic

an R

.

Pan

ama

Col

ombi

a

Braz

il

Per

u

Ecu

ador

Par

agua

y

Boliv

ia

El S

alva

dor

Nic

arag

ua

Hon

dura

s

Source: Székely (2001).

55

Figure 4.7 Poverty Argentina, 1992-2004 50% Median Poverty Line

0

5

10

15

20

25

30

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

H PG FGT(2) Source: Calculations by CEDLAS based on EPH microdata. 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

56

Figure 5.1 Gini Coefficient Distribution of Equivalized Household Income Greater Buenos Aires, 1974-2003

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

Source: Calculations by CEDLAS based on EPH microdata.

57

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

Per

u

Jam

aica

El S

alva

dor

Mex

ico

Nic

arag

ua

Bol

ivia

Pan

ama

Chi

le

Hon

dura

s

Col

ombi

a

Bra

zil

40

45

50

55

60

Uru

guay

Cos

ta R

ica

Ven

ezue

la

Per

u

Jam

aica

Arg

entin

a

El S

alva

dor

Mex

ico

Hon

dura

s

Nic

arag

ua

Pan

ama

Col

ombi

a

Bol

ivia

Chi

le

Bra

zil

Source: Estimates by CEDLAS based on Gasparini (2003). Figure 5.3 Change in the Gini Coefficient Between Early 1990s and Early 2000s Distribution of Household per Capita Income

-4

-2

0

2

4

6

8

Arg

entin

a

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: Estimates by CEDLAS based on Gasparini (2003).

58

Figure 6.1 Generalized Lorenz Curves Distribution of Household per Capita Income 1992 and 2003

0

50

100

150

200

250

300

350

0 10 20 30 40 50 60 70 80 90 100

1992

2003

Source: Calculations by CEDLAS based on EPH microdata.

59

Figure 6.2 Aggregate Welfare, 1992-2004 Inequality from the EPH and Mean Income from the National Accounts

60

70

80

90

100

110

120

13019

92

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

Per capita income Sen Atk(1) Atk(2)

Source: Estimates by CEDLAS based on EPH microdata and the National Accounts. Note: Atk (e): CES welfare function with parameter e. Figure 6.3 Aggregate Welfare, 1992-2004 Inequality and Mean Income from the EPH

40.0

50.0

60.0

70.0

80.0

90.0

100.0

110.0

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

Per capita income Sen Atk(1) Atk(2)

Source: Estimates by CEDLAS based on EPH microdata. Note: Atk (e): CES welfare function with parameter e.

60

Figure 7.1 Marginal Return to a College Education All Working Males

0.4

0.45

0.5

0.55

0.6

0.65

0.7

0.75

0.8

0.85

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

Source: Estimates by CEDLAS based on EPH microdata. Figure 7.2 Labor Force, Employment and Unemployment Greater Buenos Aires, 1980-2002

0

5

10

15

20

25

30

35

40

45

50

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

desempleo actividad empleo

Source: Estimates by CEDLAS based on EPH microdata.

61

Figure 7.3 Employment Level (August 2001=100) Formal Sector, firms with more than 10 employees Greater Buenos Aires, 2002 -2004

85

90

95

100

105

Ene-02 Abr-02 Ago-02 Nov-02 Feb-03 May-03 Sep-03 Dic-03 Mar-04 Jul-04

62


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