Monitoring the socio-economic conditions in Argentina, L Gasparini

Tags: Argentina, EPH, FGT, 1992-2005, World Bank, increase, labor market, poverty, Universidad Nacional de La Plata, salaried workers, inequality, increased, unemployment rate, hourly wages, Encuesta Permanente de Hogares, macroeconomic crisis, economic crisis, social statistics, PJH, self-employed professionals, employment, men and women, unemployment, college education, labor force participation, primary education, employment rate, hourly wage, unskilled workers, labor income
Content: Working Paper N.1/05 This version: June, 2006 Monitoring the Socio-Economic Conditions in Argentina # Leonardo Gasparini * CEDLAS ** Universidad Nacional de La Plata Abstract This report documents the socio-economic situation in Argentina. The report is based on a wide range of distributional, labor and social statistics computed from microdata of the Encuesta Permanente de Hogares (EPH) from 1992 to 2005. We also draw from other data sources and the existing literature. Argentina has witnessed dramatic changes in its distributional, labor and social conditions in the last three decades. The country has experienced a sharp increase in poverty, inequality, unemployment, and labor informality. Argentina has had one of the most disappointing social performances in the region. The situation has significantly improved since the crisis of 2002, although poverty and inequality are still at higher levels than in the 1990s. Keywords: poverty, inequality, education, labor, wages, employment, Argentina # This document is an updated version of a paper with the same title published as working paper by the World Bank and CEDLAS. The study is part of the project "Monitoring the socio-economic conditions in Argentina, Chile, Paraguay and Uruguay", CEDLAS-The World Bank. The CEDLAS team is comprised by Leonardo Gasparini (director), Georgina Pizzolito, Leopoldo Tornarolli and Hernбn Winkler. We are grateful to the very helpful comments of Jesko Hentschel, Evelina Bertranou, Evelyn Vezza and seminar participants at the World Bank and UNLP. * E-mail: [email protected] ** CEDLAS is the Center for Distributional, Labor and Social Studies at Universidad Nacional de La Plata. www.depeco.econo.unlp.edu.ar/cedlas
1. Introduction Argentina was traditionally one of the Latin American countries with better social indicators. Poverty and inequality were very low compared to most countries in the region. Also, unemployment was low and social and labor protection was widespread. However, since the 1970s the socioeconomic situation has been deteriorating, being the sharp increase in poverty the most dramatic sign of this fall. Since the 1970s Argentina has experienced several major macroeconomic crisis and episodes of structural changes. A severe macroeconomic crisis in mid 1970s under the Peronist administration was followed by some structural reforms carried out by the military regime. The debt crisis of the early 1980s hit the Argentina's economy, which enter a phase of deep recession. The lost decade of the 1980s, characterized by poor economic performance, finished with a major macroeconomic crisis, including two episodes of hyperinflation in 1989 and 1990. The Peronist administration that took power in 1989 introduced in the early 1990s a wide range of macro and market-based reforms. Despite an impressive macroeconomic record, the social situation significantly deteriorated. The 1990s ended with another recession, which was followed by a major breakdown: the 2001/02 crisis implied a fall in the GDP of more than 15%. The economy has strongly recovered since then, reaching levels of activity similar to those in the 1990s. The social situation in the country has been worsening over the last three decades. Poverty and inequality have increased even in the periods of economic expansion. The labor market performance has also been extremely weak. Argentina, traditionally a country of nearly full employment and widespread social protection, became an economy with persistent high unemployment and informality rates. This document shows evidence on the socio-economic performance of Argentina in the last three decades. The report is mostly focused on the period 1992-2005, and especially draws from statistics constructed from microdata of the Encuesta Permanente de Hogares (EPH). All the statistics presented in this report and computed by our team can be shown and downloaded from www.depeco.econo.unlp.edu.ar/cedlas/monitoreo.htm. All the indicators are updated as new information is released. The rest of the document is organized as follows. In section 2 we present the main sources of information used in this report. The next nine sections show and analyze information on incomes, poverty, inequality, aggregate welfare, the labor market, education, housing and Social Services, demographics, and poverty-alleviation programs. Section 12 closes with an assessment of the results. 2
2. The data Distributional, labor and social conditions can be monitored with the help of the Encuesta Permanente de Hogares (EPH), the main household survey in Argentina. The EPH is carried out by the Instituto Nacional de Estadнstica y Censos (INDEC). It now covers 31 urban areas (all the urban areas with more than 100,000 inhabitants) which are home of 71% of the Argentine urban population. Since the share of urban areas in Argentina is 87.1% (one of the largest in the world), the sample of the EPH represents around 62% of the total population of the country.1 The EPH gathers information on individual sociodemographic characteristics, employment status, hours of work, wages, incomes, type of job, education, and migration status. The microdata of the EPH is available for the Greater Buenos Aires (GBA) since 1974. The rest of the urban areas have been added during the last three decades. The EPH has been traditionally carried out twice a year, in May and October. During 2003 a major methodological change was implemented by INDEC, including changes in the questionnaires and in the frequency of the survey visits. The number of observations (individuals) has changed from around 90,000 in the late 1990s to around 60,000 in the early 2000s, and back to 90,000 in the new EPHC. In the last decade Argentina has conducted two Living Standard Surveys. The first survey, known as Encuesta de Desarrollo Social (EDS), was carried out in 1996/7 and includes around 75,000 individuals (representing 83% of total population) living in urban areas. The second survey, Encuesta de Condiciones de Vida (ECV), with similar coverage and questionnaires, was conducted in 2001. Both surveys include questions on housing, some assets, demographics, labor variables, health status and services, and education. The EDS and ECV were sponsored by the World Bank and have questionnaires similar to those in other countries. However, they are not part of the Living Standard Measurement Surveys (LSMS) program, and they do not include questions on expenditures as the LSMS surveys do.2 Although with a richer questionnaire and a somewhat larger geographical coverage, the ECV is of lower quality than the EPH. The World Bank has also carried out other surveys to characterize the socio-economic situation of the country. In particular, during 2002 the Bank conducted the Encuesta de Impacto Social de la Crisis en Argentina (ISCA) to learn about the consequences of the 1Although the EPH does not meet one of the Deininger and Squire (1996) criteria since it is an urban survey, it represents a reasonably large share of Argentina's population. Additionally, the missing population does not seem to affect some results. For instance, using a recent survey conducted by the World Bank that include small towns in rural areas, we find only a negligible difference in all inequality measures when we include or ignore rural areas. 2 They are usually labeled as quasi-LSMS. 3
deep economic crisis of 2001-2002, and the strategies used by households to cope with that crisis.3 Expenditures are reported in the Encuesta Nacional de Gastos de los Hogares (ENGH) conducted every 10 years (1986, 1996/7). Although the last ENGH includes some questions on socio-economic issues, we do not use this survey, since social topics are better covered in the EPH and the ECV. The EPH does not allow the closely monitoring of labor statistics, as results are available every six months (every three months in the EPHC). To fill part of this gap the Labor Department carries out the Encuesta de Indicadores Laborales (EIL). It is a survey of firms in the private sector in Greater Buenos Aires, Cуrdoba, Mendoza and Rosario. The survey covers only large firms (more than 10 workers) in the formal sector (workers should be registered in the SIJP). The EIL has around 800 observations in the Greater Buenos Aires and less than 200 in each of the other three largest cities in the country. There are other two surveys to firms with some information on labor indicators. The Encuesta Mensual Industrial (EMI) records information from firms in the manufacturing sector and includes some labor statistics. The Encuesta Nacional de Grandes Empresas (ENGE) is a panel of the largest 500 firms in the formal sector surveyed since 1993. It also contains information on some labor variables. Argentina has a program of conducting census every ten years. The last available census are for 1980, 1991 and 2001. Besides basic demographic variables, the census includes information on housing, education and some basic labor variables. Given the main objective of this report -monitoring the socioeconomic situation on a yearly basis- we use the census only as a reference. Summarizing, the EPH is the best data source for monitoring the distributional, labor and social conditions in Argentina on a yearly basis. The ECV provides useful information on some issues not well-captured or not captured at all by the EPH (e.g. health and social programs), while the EIL helps monitoring the labor situation in the formal sector on a monthly basis. The ENGH is the only survey that records expenditures but has been carried out every ten years. Some specific surveys (e.g. ISCA of The World Bank) are useful to study particular questions or periods. Administrative information is especially helpful to portrait the educational, health, and security situation. This document is especially based on information computed from microdata of the EPH. Three panels are presented in most tables. The first one refers to the main 15 urban areas 3 See Fiszbein and Giovagnoli (2003). 4
with available microdata from the EPH since 1992 (Capital Federal and Conurbano Bonaerense (known as Greater Buenos Aires or GBA), Comodoro Rivadavia, Cуrdoba, Jujuy, La Plata, Neuquйn, Paranб, Rнo Gallegos, Salta, San Luis, San Juan, Santa Rosa, Santa Fe, Santiago del Estero and Tierra del Fuego). The second panel adds another 13 urban areas with microdata since 1998 (Bahнa Blanca, Catamarca, Concordia, Corrientes, Formosa, La Rioja, Mar del Plata, Mendoza, Posadas, Resistencia, Rнo Cuarto, Rosario and Tucumбn).4 To match both series we compute all statistics in 1998 with both samples of 15 and 28 cities. All statistics correspond to the October round of the EPH, with the exception of 2003 since the microdata of the October wave is not available for that year.5 From 2003 on we include a third panel with information from the new EPHC. In this update we add to the report information for the second half of 2005. Unfortunately, the change from the EPH to the EPHC introduces noise in all the series. INDEC has not released the microdata for the first quarter of the EPHC 2003, which could have allowed studying the impact on the statistics introduced by the methodological changes. However, INDEC has published statistics computed with the microdata of the first half of the EPHC, which are close to our estimates with the May 2003 EPH. For instance, we estimate a poverty headcount ratio of 54.7% using the official moderate poverty line in May 2003, while INDEC publishes a value of 54% using the EPHC, first half of 2003. Given this preliminary evidence, we interpret estimated changes between the EPH and the EPHC as mostly driven by real facts more than by methodological changes.6 Among the modifications in the EPH Continua, the INDEC now reports population weights that control for income non-response. Although this is clearly an improvement, the use of these weights introduces a comparability problem with previous surveys. To assess the impact of this change, we add a line to most tables, including statistics for the second half of 2003 using the "old" weights, i.e. those that do not take income non-response into account. 3. Incomes Real incomes are the arguments of all poverty, inequality, polarization and welfare measures. Thus, before computing indicators of these distributional dimensions in the next sections, we show some basic statistics on real incomes. All incomes are presented in real values by deflating nominal incomes by the Consumer Price Index of the month when incomes reported in the survey were earned. 4 We do not include in the analysis Alto Valle del Rнo Negro and Interior de Mendoza which were covered in some rounds of the EPH, and the recently (2002) incorporated areas of San Nicolбs-Villa Constituciуn, Rawson-Trelew and Viedma-Carmen de Patagones. 5 Given that surveys cover only urban areas, most statistics are not significantly affected by seasonality issues. 6 See a companion paper (Gasparini, 2004b) for further discussion on this issue. 5
Table 3.1 shows real incomes by deciles for the aggregate of 15 urban areas for some selected years from 1992 to 1998; and for the aggregate of 28 cities from 1998 to 2005. Real income reported in the EPH fell around 8% between 1992 and 1996. This change is in sharp contrast with national accounts: per capita GDP increased in that period 8.9%. This discrepancy may be due to increasing under-reporting in the EPH, or overestimation in the GDP. It could also be the consequence of an increase in the share of sources not well captured in the EPH: capital income, benefits, and rents. Between 1996 and 1998 the economy enjoyed a phase of expansion: per capita income grew 10%. A similar number is also reported by national accounts. The crisis 1998-2002 implied a fall of around 40% in mean income reported in the EPH, which is higher than the figure from national accounts. Since 2003 the economy is strongly recovering: real per capita income grew 26% between the second half of 2003 and the second half of 2005, which is significantly more than national accounts estimates (17%). The second panel of Table 3.1 shows that income changes were not uniform across deciles. Income changes between 1992 and 2000 were clearly unequalizing. The crisis 2000-2002 hit all the population, although the richest decile suffered somewhat less than the rest. The recovery 2003/05 appears as clearly pro-poor. The growth-incidence curves of Figure 3.1 present a more detailed picture of the income change patterns. Each curve shows the proportional income change of each percentile in a given time period. Ideally, we would like these curves to be (i) well above the horizontal axis, implying income growth, and (ii) decreasing, implying pro-poor growth. In the Argentina's case, however, most curves are below the horizontal axis and have a positive "slope". The solid line labeled 1992-2005 summarizes the disappointing performance of the last thirteen years: real incomes reported in the EPH have dramatically fallen, in a highly unequalizing way. The situation since the 2002 crisis is depicted in Figure 3.2. Growth was pro-poor in 2003 and 2004. During 2005 the economy remained strong, although the growth-incidence curve became nearly flat. The income changes shown in this section suggest clear patterns for poverty, inequality and welfare. The non-uniform fall in income since 1992 surely has implied a significant increase in poverty and inequality, and a fall in aggregate welfare. The next three sections provide more evidence on these issues. 6
4. Poverty 7 This report shows poverty computed with the most widely used poverty lines and poverty indicators. The USD 1 a day and USD 2 a day at PPP prices are international poverty lines extensively used by the World Bank (see World Bank Indicators, 2004).8 Most LAC countries, including Argentina, calculate official moderate and extreme poverty lines based on the cost of a basic food bundle and the Engel/Orshansky ratio of food expenditures.9 Table 4.1 presents the value of these poverty lines in local currency units for the period 1992-2005. We also consider the line set at 50% of the median of the household per capita income distribution, which captures a relative rather than an absolute concept of poverty. For each poverty line we compute three poverty indicators: the headcount ratio, the poverty gap, and the FGT (2).10 We also calculate the number of poor people expanding the survey to all the population by assuming that the income distribution of the areas not covered by the survey mimics the distribution computed from the EPH. Tables 4.2 to 4.6 show various poverty measures with alternative poverty lines.11 Argentina has witnessed a dramatic increase in income poverty in the last thirteen years. All indicators shown in Tables 4.2 to 4.6 and Figures 4.1 to 4.2 agree with this statement. According to the USD1 line, the headcount ratio increased from 1.4 in 1992 to 3.9 in the second half of 2005.12 Poverty substantially increased between 1992 and 1996, despite a significant growth in GDP reported by National Accounts. After a temporary reduction around 1998, poverty increased again fueled by the economic recession that started in the second half of 1998. In 2002 the headcount ratio reached the record level of 9.9. The latest available value (second half of 2005) suggests a significant reduction in poverty, which nonetheless remains at a very high level (3.9). Between 1992 and 2005 around one million Argentineans (out of a population of 38 millions) crossed the USD1-a-day poverty line.13 The patterns for the other poverty indicators (poverty gap and FGT(2)) are similar. When using the USD2 line results are also similar: poverty has dramatically increased during the last decade. The headcount ratio rose from 4.2 in 1992 to 11.6 in 2005, which means that the estimated number of poor increased in around three millions. Poverty increased 4 points from 1992 to 1998, 6.2 points during the stagnation of 1998-2001, 7 See the Appendix for a poverty profile for year 2003. 8 See the methodological document for details. 9 See the methodological document and INDEC (2003). 10 See Foster, Greer and Thornbecke (1984) for references. 11 See the web page for an analysis of statistical significance of poverty changes based on bootstrapping techniques. 12 Notice that the difference from taking 28 instead of 15 cities in 1998 is small. Also the change in the survey in 2003 does not seen to greatly affect the income statistics. In all cases when differences are small, we will not mention the change in sample or methodology when commenting on the statistics. 13 That value is the net increase in poverty, which is the consequence of people jumping out and into poverty. 7
around 9.1 points during the crisis 2001-2002, and then substantially fell between October 2002 and the second half of 2005. The official poverty line in Argentina is set at higher levels than USD 2 a day at PPP, a fact that reveals that Argentina is a middle-income country. Although the level of official poverty is higher than poverty computed with international lines, the patterns shown in Tables 4.4 and 4.5 for official poverty are similar to those commented above. The dramatic increase in poverty is captured by all indicators. According to the official line, extreme poverty increased from 3.8% in 1992 to 8.2% in 1996. After a fall around 1998, extreme poverty increased again to 13.7% in 2001 and reached 27.6% in 2002. Extreme poverty has been falling since then, reaching 12.2% in the second half of 2005. The headcount ratio computed using the moderate poverty line is the most extensively cited poverty measure in policy discussions and the media in Argentina. Table 4.5 shows a large increase in this indicator over the last thirteen years. The headcount ratio increased around 14 points between 1992 and 2005, which means more than 6 million "new poor" individuals.14 About 3.5 millions entered poverty during the economic growth period of the 90s, another 3.5 joined that group in the first phase of the recession (1998-2001), while about 7.5 millions crossed the poverty line during the crisis 2001-2002. The economic recovery substantially reduced the number of poor in around 8.5 million individuals. It is interesting to notice that the moderate official poverty line is close to the mode of the income distribution (see Figure 4.3). When that occurs, the poverty-growth elasticity is large: changes in income generate a large impact on the poverty rate. This fact implies that a relatively small improvement in economic conditions may lead to a large fall in the official measure of poverty. The particular location of the poverty line close to the mode partially explains the huge increase in official poverty during the crisis and the sharp fall during the recovery. Figure 4.4 shows poverty computed with the official moderate poverty line for the Greater Buenos Aires area. Restricting the analysis to this area, which is home of 1/3 of the Argentine population, allows a more historical perspective, since the EPH was initially conducted only in that metropolitan area. Poverty slowly increased during the first half of the 1980s, and skyrocketed during the hyperinflation crisis. After a sharp fall in the early 1990s, the poverty headcount ratio increased around 10 points between 1993 and 1999, and jumped 28 points during the crisis. Since 2002 poverty went down around 23 points, being in 2005 at roughly the same level as in 2000. 14 Notice that changing the sample from 15 to 28 cities in 1998 implies an increase in poverty of around 2 points. Also, by changing the weights to consider unit non response the new EPHC implies a fall in recorded poverty of around 2 points. 8
The increasing trend in poverty in Argentina is not well documented in the international literature. In some datasets Argentina is discarded for having household surveys covering only urban areas (e.g. Chen and Ravallion, 2003, Sala-i-Martin, 2001), while in those where Argentina is included, the number of observations is small, and generally belong to the second half of the 1990s, when poverty was rather stable (Szйkely, 2001, Wodon, 2001, WDI, various years). The dramatic increase in income poverty in Argentina during the last 3 decades contrasts with the performance of most Latin American countries. Although the region has not been very successful in fighting poverty, the record of most of the Latin American countries is much better than the Argentine performance. The contrast with Chile and Brazil, for instance, is notorious, as poverty significantly decreased in these two countries during the last decades. Figure 4.5, based on Gasparini et al. (2005), shows that the poverty increase in Argentina was particularly harsh. Argentina ranks third according to points of poverty increase, and first if the increase is measured in percentages. Figure 4.6, also taken from Gasparini et al. (2005), places Argentina as a low-poverty country compared to the rest of LAC, due to its relatively high per capita GDP and still relatively low inequality. Notice that Argentina had poverty levels similar to Uruguay and much lower than Chile, which it is not the case anymore. Some countries (e.g. those in the European Union) use a relative rather than an absolute measure of poverty. According to this view, since social perceptions of poverty change as the country develops and living standards go up, the poverty line should increase along with economic growth. Probably the most popular relative poverty line is 50% of median income. The relevant scenario for justifying this kind of poverty measure does not apply to Argentina, since the economy is stagnant since the 1970s. Anyway, we show in Table 4.6 and Figure 4.7 poverty indicators computed with the 50% median income line. Relative poverty increased in the 1990s, and was not greatly affected by the last economic crisis. The main reason behind this latter fact lies on the generalized income fall across income strata occurred during the crisis: in this scenario relative poverty does not go up. There are convincing arguments for considering poverty as a multidimensional issue.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 construct an indicator of poverty according to the characteristics of the dwelling, access to water, sanitation, 15 Bourguignon (2003) discusses the need and the problem of going from income poverty to a multidimensional approach of endowments. Attanasio and Szйkely (eds.) (2001) show evidence of poverty as lack of certain assets for LAC countries. 9
education (of household head and children) and dependency rates.16 Table 4.7 and Figure 4.8 suggests that poverty did not increase when defined in the space of those variables.17 However, there was not much improvement either. Indicators of endowments or basic needs usually fall, since over time people improve their dwellings and governments invest in water, sanitation and education, even in stagnant economies. The constant pattern for the poverty indicator in Table 4.7 should be interpreted more as a negative sign of sluggish social development, than as a positive sign of no increase in poverty. In column (ii) of Table 4.7 we define poverty as a situation where an individual is poor according to both the endowment and the income criteria. We take the USD2 line for the computation of this column. The pattern from column (ii) follows that of income poverty in Table 4.3. The level, however, is lower. While in May 2003 23.7% of the population had a per capita income lower than USD2-a-day, 16.3% were poor also according to the endowment criterion. INDEC computes a basic-needs indicator of poverty (Necesidades Bбsicas Insatisfechas ­ NBI) with census data. An individual is poor if she lives in a household meeting at least one of the following conditions: (i) more than 3 persons per room, (ii) dwelling in a shantytown or other inconvenient place, (iii) unavailability of hygienic restroom (without retrete), (iv) children aged 6 to 12 not attending school, (v) household head who has not completed three years of primary school, and household with more than 4 persons per income earner. According to the Census 1980, 27.7% of the individuals lived in households that met at least one of these criteria. In the Census of 1991 that proportion was 19.9%, while in 2001 was 17.7%. The small fall in NBI in the 1990s leads to the same two conclusions stated above. The optimistic result is the fall in basic-needs poverty, despite a dramatic increase in income poverty. People now have less income than a decade ago, but they are (slightly) better-off in terms of housing, sewerage and education. The pessimistic view underlines the very modest fall in the NBI indicator in one decade. In fact, the number of poor people according to this definition was almost the same in 1991 (6,427,257) and 2001 (6,343,589). Basic needs indicators of poverty are usually decreasing over time, even in stagnant economies: an example is Argentina in the 1980s. 16 An individual is poor if she lives in a household meeting at least one of the following conditions: (i) 4 or more persons per room, (ii) dwelling in a shantytown or other inconvenient place, (iii) walls of chapa, adobe, or cartуn, (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. 17 There is not enough information in the dataset released for the EPHC to present comparable data for the 2003-2005 period. 10
5. Inequality and polarization Poverty, a concept that refers to the mass of the income distribution below a certain threshold, can increase after a shifting of the entire distribution to the left, and/or after an increase in the dispersion of the income distribution. Mean income has fluctuated around a constant trend in the last 30 years in Argentina. With no changes in the income distribution that economic performance would imply stable poverty. However, the income distribution became substantially more unequal over the last 30 years, driving poverty up. In Table 5.1 we present the most tangible measures of inequality: the shares of each decile and some income ratios. These measures are computed over the distribution of household per capita income. The income share of the poorest decile fell from 1.8 in 1992 to 1 in 2001, and increased to 1.2 by 2005. In the other extreme, the income share of the richest decile increased from 34.1 in 1992 to 37.6 in 2005. Notice the heterogeneous pattern of changes across deciles. As mentioned above the income share of decile 1 fell over the last decade. That was also the situation for deciles 2 to 7. The shares of deciles 8 and 9 stayed roughly unchanged, while the share of decile 10 went up nearly 4 points in thirteen years. While income distribution changes were unequalizing over the period 1992-2001/2, they turned equalizing during the recovery 2002-2005. In Table 5.2 we compute several inequality indices: the Gini coefficient, the Theil index, the coefficient of variation, the Atkinson index, and the generalized entropy index with different parameters. All measures of inequality suggest the same increasing pattern over the last thirteen years. The Gini coefficient, for instance, increased from 0.450 in 1992 to 0.528 in May, 2003, with a peak of 0.533 in 2002.18 This change is not only statistically significant but, according to historical records, very high.19 Table 5.2 reports a large drop in inequality between 2003 and 2005. Some explanations are in order. First, the use of the new weights included in the EPHC that consider unit nonresponse implies a very substantial drop in recorded inequality measures. For instance, the change in the Gini in 2003 is about 1 point. Second, there was a very large drop in inequality between 2003 and 2004. Although expected, the fall seems very large and might be due in part to undetected methodological issues. Third, the inequality drop seems to have slowed down in 2005. Actually, most indices are not significantly different from those of 2004. Inequality is today (second half of 2005) around the same levels as before the latest economic crisis (2000). 18 Notice that changing the sample in 1998 does not significantly modify the value of any inequality index. 19 An analysis of statistical significance of inequality changes based on bootstrapping techniques is presented in the web page of this project. See also Sosa Escudero and Gasparini (2001). 11
In Tables 5.3 and 5.4 we extend the analysis to the distribution of equivalized household income. Equivalized income takes into account the fact that food needs are different across age groups ­ leading to adjustments for adult equivalent scales ­ and that there are household economies of scale.20 The introduction of these adjustments do not imply significant changes in the assessments of the results. In Tables 5.5 and 5.6 we consider the distribution of a more restricted income variable: the equivalized household labor monetary income. Again, the inequality patterns are similar than in previous tables. One exception is the period 2001-2003. By focusing on labor income, capital income and transfers are ignored. In particular, incomes from the Programa Jefes de Hogar are excluded from the statistics. When doing that, incomes in the first deciles go down between 2001 and 2003, in contrast to the situation when including transfers. Therefore, all indices in Table 5.6 show a sizeable increase in inequality between 2001 and 2003. Table 5.7 is aimed at assessing the robustness of the results by presenting the Gini coefficient over the distribution of several income variables. The different columns consider different adult equivalent scales, consider total household income without adjusting for family size, and restrict the analysis to people in the same age bracket to control for life-cycle factors. All the main results drawn from previous tables hold when making these adjustments. The increase in inequality was not a distinctive feature only of the 1990s. Figure 5.1 shows the Gini coefficient for the distribution of per capita household income in the Greater Buenos Aires from 1974 to 2005. This inequality measure climbed from 0.347 in 1974 to 0.508 in 2005. Inequality greatly increased in the second half of the 1970s, remained stable in the first half of the 1980s and substantially increased during the macroeconomic crisis of the late 1980s. After stabilization, inequality went down, although did not reach the precrisis levels. The 1990s were again times of increasing inequality: the Gini climbed 6 points from 1992 to 1998. The recent macroeconomic crisis pushed the Gini another 4 points up. This inequality indicator went down 3.6 points during the recent recovery. Argentina has traditionally been one of the most equal countries in Latin America, along with Costa Rica and Uruguay (Londoсo and Szйkely, 2000). The presence of a large middle-class was a distinctive feature of Argentina's economy. Figure 5.2 shows the Gini coefficient for the distribution of equivalized income for most Latin American economies. In the early 1990s and despite 15 years of increasing inequality, Argentina remained as one of the low-inequality countries in the region. The Argentina's distributional story in the last 20 See Deaton and Zaidi (2003) and the methodological appendix for details on the implementation for Argentina. 12
decade was substantially different from the rest of the region. Although inequality increased in many countries, especially in South America, changes have been small compared to the ones experienced by Argentina. The second panel of Figure 5.2 suggests that Argentina no longer belongs to the low-inequality group of LAC. It is interesting the comparison with Uruguay: once almost identical, the distributions of these two neighbor countries are now clearly different, after three decades of relative distributional stability in Uruguay and turbulence in Argentina. Figure 5.3 shows again the disappointing distributional performance of Argentina, compared to the rest of Latin America. The raise in the Gini in Argentina was almost double the one in Venezuela, which ranks second according to inequality increases. Polarization is a dimension of equity that has recently received attention in the literature. It refers to homogeneous clusters that antagonize each other. Table 5.8 shows the Wolfson (1994) and Esteban, Gradнn and Ray (1999) indices of bipolarization. Polarization and inequality can go in different directions. This was not the case in Argentina, where the distribution became more unequal and more polarized at the same time. Horenstein and Olivieri (2004) compute the new generalized polarization index of Duclos, Esteban and Ray (2004), finding similar results. As commented above all the surveys in Argentina have only urban coverage. The World Bank's Encuesta de Impacto Social de la Crisis en Argentina (ISCA) included some small towns in rural areas. From the information of that survey the income distribution in rural areas turns out to be not significantly different from the income distribution in urban areas. The Gini coefficient for the distribution of household per capita income is 0.474 in urban areas, 0.482 in rural areas, and 0.475 for the whole country. This fact suggests that the urban inequality statistics can be taken as a good approximation for the national figures. 6. Aggregate welfare Rather than maximizing mean income, or minimizing poverty or inequality, in principle societies seek the maximization of aggregate welfare. Welfare is usually analyzed with the help of growth-incidence curves, generalized Lorenz curves, Pen's parade curves and aggregate welfare functions. In section 3 we presented growth-incidence curves that suggested a substantial fall in welfare over the last thirteen years. The same conclusion arises from the generalized Lorenz curves of Figure 6.1: the curve for 2005 lies always below the corresponding curve for 1992. Therefore, any social welfare function would rank 2005 as a worse year than 1992. 13
We also perform a welfare analysis in terms of four abbreviated welfare functions (see Tables 6.1 and 6.2 and Figures 6.2 and 6.3). The first function is represented by the average income of the population: according to this value judgment inequality is irrelevant. The rest of the functions take inequality into account. These are the ones proposed by Sen (equal to the mean times 1 minus the Gini coefficient) and Atkinson (CES functions with two alternative parameters of inequality aversion).21 For this exercise we take real per capita GDP from National Accounts as the average income measure, and combine it with the inequality indices shown above.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), the welfare assessments are not as positive. While per capita GDP grew 20%, 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 4% between 1992 and 1998. The fall in real income of the poorest households offset the significant increase in mean income. From 1998 to 2002 all functions agree on showing a dramatic fall in welfare driven by both an increase in inequality and a fall in mean income. The positive changes between 2002 and 2005 in mean income and income distribution is captured by all welfare functions. However, despite this increase aggregate welfare today is just slightly above the 1992 levels according to Sen and Atkinson (1) functions, and significantly below (10%) 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 13 years. 7. The labor market This section summarizes the structure and changes of the labor market in Argentina in the last decade. Tables 7.1 to 7.3 show hourly wages, hours of work and labor income for the 21 See Lambert (1993) for technical details. 22 The source for GDP figures is ECLAC (2004) and estimates for 2005. 23 See Gasparini and Sosa Escudero (2001) for a more complete justification of this kind of study. 14
working population. Since 2003 we compute two panels: one that includes and one that ignores payments for the Programa Jefes de Hogar. Real hourly wages (deflated by the CPI) have increased in the first half of the 1990s and decreased thereafter. Real hourly wages were higher in 2001 than in 1992, even after 4 years of stagnation.24 The wage drop during the latest crisis was dramatic: according to the EPH real wages fell 28% between September 2001 and April 2003. Hours of work have also declined, although less than wages: from 44.3 hours a week in 1992 to 43.9 in 1998, to 41.9 in 2001, and 41.1 in 2003. Labor incomes were dominated by the behavior of wages: earnings increased between 1992 and 1998, and dramatically fell thereafter. In 2003, mean labor income was just 62% of the corresponding value in 1992. The information of the EPHC suggests that both hourly wages and hours of work have increased since 2003, although the changes have not been enough to compensate for the negative effects of the crisis. Hours of work, and in particular real wages, are still far from the levels of the 1990s. Tables 7.1 to 7.3 also report hourly wages, hours of work and earnings by gender, age and education. It is interesting how the gender wage gap was closed in the last few years. In fact, from data for the last EPH-C women earn on average a little more than men. The gap in terms of hours of work remains constant in around 11 hours. Many authors have highlighted the substantial increase in the gap between skilled and unskilled workers in Argentina.25 The tables in this section show some basic evidence on this fact. Workers with at least some superior education earned 2 times more than those with incomplete high school or less in 1992. That gap increased to 2.9 by 1998, and remained around that value during the recent economic crisis. The increase in the wage premium was the consequence of both a wider wage gap and a greater difference in hours of work. While in 1992 a low-educated adult worked on average 4.3 hours a week more than a high-educated person, in the early 2000s that difference completely vanished (see Table 7.2). Tables 7.4 to 7.6 divide the working population by type of work. The self-employed have significantly lost compared to the rest of the groups. While in they early 1990s average earnings of that group were similar or even higher than earnings for salaried workers, in 2003 they were just 80%. That gap is also present in the new EPHC. The relative loss for the self-employed has occurred especially in terms of hourly wages. The heterogeneity of this group becomes apparent in the second panel of Tables 7.4 to 7.6: while earnings have 24 Notice that the change in geographical coverage implies a significant fall in average wage. 25 See Galiani and Sanguinetti (2003) and Gasparini (2003), among others. 15
significantly increased during the 1990s for the group of self-employed professionals, labor income has substantially fallen for those self-employed with low education. Also, the relative earnings of workers in small firms compared to those in large firms fell from 70% in 1992 to 52% in 1998 and to 47% in 2003. Similar figures arise from the recent EPHC. In Tables 7.7 to 7.9 we divide the working population by economic activity. During the 1990s (1992 to 1998) earnings significantly increased in three sectors: the high-tech industry, the skilled service sectors (business services, finance sector, professionals) and the public administration. In contrast, earnings fell in low-skilled services like construction, commerce and domestic service. The fall in earnings during the crisis was generalized across economic sectors. During the recent recovery hours of work went up in all sectors (except skilled services), while real hourly wages substantially increased in the industry, commerce and public administration. In other sectors gains were small or even negative, as in utilities and the public sector. Table 7.10 records the share of salaried workers, self-employed workers and entrepreneurs in total labor income. The EPH questionnaires of the early 1990s do not allow computing these statistics. From 1996 to the present there seems to be some increase in the share of earnings from salaried work and a relative fall of income from self-employment. Inequality in labor outcomes is probably the main source of inequality in household income. Table 7.11 shows the Gini coefficient for the distribution of hourly wages for all workers, and men workers aged 25 to 55. Inequality has increased over the period. The increase, however, is significantly lower than the increase in household inequality reported in section 5. When dividing the sample for education, it is interesting to notice that the Gini went significantly up only for the unskilled. Are the differences in hourly wages reinforced by differences in hours of work? Table 7.12 suggests the opposite. Correlations between hours worked and hourly wages are negative and significant for all years. In Table 7.13 we compute the wage gaps among three educational groups. In 1992 a skilled prime-age male worker earned per hour in his primary job on average 2.61 times more than a similar unskilled worker. That value increased to 3.04 by 1998 and to 3.02 in 2003. Instead, the wage gap between semi-skilled and unskilled workers (column (iii)) did not significantly change. Evidence from the EPHC indicates that the skill premium fell in the early stage of the recovery. In order to further investigate the relationship between education and hourly wages we run regressions of the logarithm of hourly wage in the primary job on educational dummies and 16
other control variables (age, age squared, and regional dummies) for men and women separately.26 Table 7.14 shows the results of these Mincer equations. For instance, in 1992 a male worker between 25 and 55 years old with a primary education degree earned on average nearly 29% more than a similar worker without that degree. Having secondary school complete implied a wage increase of 45% over the earnings of a worker with only primary school: the marginal return of completing secondary school -versus completing primary school and not even starting secondary school- was 45%. The wage premium for a college education was an additional 56%. The returns to primary school fell in the first half of the 1990s and then increased. Overall, changes were negligible. The returns to secondary school have somewhat fallen during the last decade. In contrast, there was a large jump in the returns to college education (see Figure 7.1). That jump is also noticeable for working women, and for urban salaried workers (both men and women). Returns seem to have fallen a bit during the last 3 years. The Mincer equation is also informative on two interesting factors: the role of unobservable variables and the gender wage gap. The error term in the Mincer regression is usually interpreted as capturing the effect on hourly wages of factors that are unobservable in household surveys, like natural ability, contacts and work ethics. An increase in the dispersion of this error term may reflect an increase in the returns to these unobservable factors in terms of hourly wages (Juhn et al. (1993)). Table 7.15 shows the standard deviation of the error term of each Mincer equation. The returns to unobservable factors have increased in Argentina. The coefficients in the Mincer regressions are different for men and women, indicating that they are paid differently even when having the same observable characteristics (education, age, location). To further investigate this point we simulate the counterfactual wage that men would earn if they were paid like women. The last column in Table 7.15 reports the ratio between the average of this simulated wage and the actual average wage for men. In all cases this ratio is less than one, reflecting the fact that women earn less than men even when controlling for observable characteristics. This result has two main alternative interpretations: it can be either the consequence of gender discrimination against women, or the result of men having more valuable unobservable factors than women (e.g. be more attached to work). Argentina has witnessed large changes in labor force participation. Table 7.16 shows basic statistics by gender, age and education. Labor force participation has increased in the last decade. This increase is mainly the consequence of a flow of low and semi-skilled primeage women into the labor market. While in 1992 around 48% of adult women were in the 26 See Wodon (2000) and Duryea and Pages (2002) for estimates of the returns to years of education in several LAC countries. 17
labor market (either employed or unemployed), ten years later that fraction was around 60%. This increase was shared neither by men, nor by youngsters (15-24), nor by the skilled, who all reduced their labor market participation, especially between 1998 and 2003. Only the elderly (aged 65 +) substantially increased their participation in the labor market. This massive entry of women into the labor market is one of the most noticeable labor facts of the last decade. Figure 7.2 suggests that this phenomenon was particularly important in the 1990s. During the 1970s and 1980s labor market participation stayed roughly constant. It was in the period 1991-1999 when this variable went substantially up. Labor participation increased in 2002 with the implementation of the Program Jefes de Hogar and the inclusion of most of their beneficiaries as part of the labor force. Despite economic growth, the employment rate fell during the 1990s. The drop, however, was not large: 1 point between 1992 and 1998. The employment rate decreased 4 points between 1998 and 2001, and recovered about 1 point by 2003. The new EPHC records a higher employment rate, that strongly increased since 2003. Again, changes have been very different across gender and age groups. While women employment increased throughout the period, the story for men was the opposite. Probably the most remarkable fact in the Argentina's labor markets of the last decade is the dramatic increase in unemployment (see Figure 7.2). Unemployment sharply increased until 1996, first in the framework of an economic boom (1991-1994), and then during a recession (1995-1996). The unemployment rate stabilized around 12% by the end of the 1990s. But that situation did not last long: the economic crisis pushed this variable up again to levels around 18%. The recent period of economic growth has consistently lowered the unemployment rate. From Figure 7.2, and from Tables 7.16 and 7.17, it is clear that the increase in unemployment during the 1990s was the consequence of a sharp increase in labor market participation facing a constant employment rate. Instead, the increase in unemployment in the early 2000s is mainly the consequence of the employment fall associated to the economic crisis. The recent fall in unemployment comes from a strong growth in the employment rate during the economic recovery. Table 7.18 shows that the increase in unemployment was similar for women and men. However, as we have seen above, the factors behind these behaviors are very different. Employment increased for women, but not enough to absorb all women who entered the labor market. In contrast, some men left the labor market, but male employment fell at a higher rate, thus increasing unemployment. Table 7.18 also shows that during the 1990s the increase in unemployment was particularly harsh for the unskilled, while the recent crisis hit especially the semi-skilled. 18
The social concern for unemployment increases when unemployment spells are large. Table 7.19 shows a large increase of these spells. While in 1992 a typical unemployed person stayed 4 months without employment, in 2003 that spell lasted more than 8 months. The increase in duration was similar across educational groups. The length of unemployment spells decreased since 2003 to the first half of 2005. There is some increase in unemployment duration in the latest available survey (second half of 2005). INDEC has published quarterly results for the main labor variables since 2003. Table 7.20 reproduces statistics for labor force, employment and unemployment rates under two alternatives. In the first one, people who report the PJH as the main labor activity is considered as employed. In the second alternative those in that situation who are seeking a job are considered unemployed. In any of the two alternatives, the Table shows a significant increase in employment that strongly drove the unemployment rate down since 2003. Tables 7.21 to 7.27 depict the employment structure of urban Argentina. There are more males than females employed, but the gap has dramatically shrunk during the last decade. While in 1992 37% of the working population were women, in 2005 that share reached 42%. Older people have also gained participation. The last three columns of Table 7.21 show a sizeable change in the educational structure of the working population in favor of the skilled. The Greater Buenos Aires area has lost participation in employment, in particular during the last crisis (Table 7.22). Also, there was a loss of participation for the group of entrepreneurs captured in the EPH (Table 7.23). Employment in the public sector has gone up until 2003, even more if we consider the beneficiaries of the PJH as part of the public sector (as it is done in the EPH). The counterpart of that increase is (i) the fall in the share of the unskilled self-employed and employment in small firms during the 1990s, and (ii) employment in large firms during the latest economic crisis. The recovery of the economy and the reduction in the size of the PJH contributed to a reduction in the share of workers in the public sector since 2003. Tables 7.24 and 7.25 are aimed at presenting the formal-informal structure of the labor market. Unfortunately there is not a single definition of informality. Following Gasparini (2003), we implement two definitions with the information available in the EPH. According to the first one formal workers are the entrepreneurs, salaried workers in large firms and in the public sector, and self-employed professionals (see Table 7.24). According to the second definition, formal workers are those who have the right to receive pensions when they retire (see Table 7.25). Unfortunately the EPH allows implementing this definition only for wage earners. According to the first definition, formal employment has 19
not significantly changed in the last decade. In sharp contrast, formality in the labor market has dramatically fallen according to the second definition. The share of salaried workers with social security rights drop 8 points in the period 1992-2003. The EPHC records a significant fall in informality since 2003. The sectoral structure of the economy has changed (see Tables 7.26 and 7.27). During the 1990s there was a large fall in the share of employment in the manufacturing industry and commerce. Employment went significantly up in construction, skilled services and the public sector. During the recent recovery relative employment has particularly increased in some manufacturing industries, construction and skilled services, while has fallen especially in the public sector. The concern for child labor has recently been increasing in the world. Table 7.28 shows the proportion of working children between 10 and 14 years old. Child labor is less relevant than in most LAC countries and has been decreasing according to EPH data, even during the recent economic crisis. The last three tables in this section are aimed at assessing different dimensions of the quality of employment. As commented above, the coverage of the pension system shrunk in the last thirteen years. Table 7.29 shows that this pattern was similar for men and women, and especially severe for the unskilled in comparison to the skilled workers. Similar results apply to in-the-job health insurance (see Table 7.30). Table 7.31 shows that most people report their employment as "permanent". The share of permanent jobs has increased in the 1990s, decreased during the crisis and has been recovering recently. 8. Education In this section we provide an assessment of changes in the educational structure of the population. The proportion of high-educated people has significantly increased during the last decade in Argentina (Table 8.1).27 While in 1992 17.8% of adults aged 25 to 65 had more than 13 years of formal education, that share increased to 21.3% in 1998 to 24.7% in 2003, and to 26.4 in the latest EPHC. That rise has been more intense for women than for men. A remarkable fact from Table 8.2 is the reversion of the gap in years of education between men and women. While men older than 50 have more years of education than women of the same age, the difference has recently turned in favor of women for people in their 40s. For 27 Note that some tables in this section have a line that separate the early 1990s (1992 to 1994) from the rest. The reason is that a methodological change in the EPH in 1995 allowed a better estimation of years of education since that year on. 20
the working-age population (25 to 65), years of education have become slightly greater for women since 1999. Information in Table 8.3 suggests that the absolute gap in terms of years of education between the rich and the poor has widened during the last decade. In addition, notice that the EPH does not allow capturing years of education in graduate programs, so the variable is truncated at 17 years. Presumably, if years of graduate education had been reported, the gap between the rich and the poor would have increased even more than what Table 8.3 suggests. In Table 8.4 people are divided according to age and household income quintiles. In 2005 the widest gap in years of education between top and bottom income quintiles corresponds to adults aged 31-40. While the educational gap between the poor and the rich is 6.3 years for people aged 31 to 40, it is 4.8 for people in their twenties, and 5.4 for individuals older than 60. Recently, there have been efforts to gather educational information from most countries in the world and to compute measures of inequality in education access and outcomes.28 According to Table 8.5 educational Ginis have slightly fallen during the last thirteen years. Notice that even when the ratio in years of education between the rich and the poor increased between 1992 and 1998, the Gini did not significantly change. In contrast, between 1998 and 2003, both the ratio and the Gini went significantly down. Table 8.6 and 8.7 show a rough measure of education: the self-reported literacy rate. Argentina has high literacy rates compared to the rest of Latin America. Even for the urban poor literacy is very high: 99% for those aged 15 to 24 and 96% for those aged 25 to 65. Guaranteeing equality of access to formal education is one of the goals of most societies. Tables 8.8 and 8.9 show school enrollment rates by equivalized income quintiles. Attendance rates have sharply increased for children aged 3 to 5. While in 1992 one third of these children attended a kindergarten, in 2003 half of them did it. The latest EPHC reports a share of 60%. Attendance also increased for children in primary-school age, reaching almost 100%. Again, notice that the recent economic crisis did not have a negative impact on schooling. Girls are more likely to attend high school than boys. This gap has narrowed down over the last decade as attendance has significantly increased, reaching more than 90% in both gender groups. The increase in school attendance has continued over the crisis period. The rise in attendance for youngsters aged 18 to 23 is also noticeable, although it has taken place at somewhat slower pace. 28 For instance, Thomas, Wang and Fan (2002) calculate Ginis over the distribution of years of education for 140 countries in the period 1960-2000. 21
The increase in attendance rates has been similar across household income quintiles for children aged 3 to 5, it has been larger in poor quintiles for children aged 6 to 17, and much larger in rich quintiles for youth aged 18 to 23. Summarizing, it seems that educational disparities in terms of school attendance have decreased in primary school and high school, but have substantially increased for college. While the attendance rate for youngsters aged 18 to 23 in the top quintile increased 20 points in the last decade, it actually decreased for those youngsters in the bottom quintile of the equivalized household income distribution. Educational mobility We follow the methodology developed in Andersen (2001) to provide estimates of educational mobility, i.e. the degree to which parental education and income determine a child's education. The dependent variable is the schooling gap, defined as the difference between (i) years of education that a child would have completed had she entered school at normal age and advanced one grade each year, and (ii) the actual years of education. In other words, the schooling gap measures years of missing education. The Educational Mobility Index (EMI) is defined as 1 minus the proportion of the variance of the school gap that is explained by family background. In an economy with low mobility, family background would be important and thus the index would be small.29 Table 8.10 shows the EMI for teenagers (13 to 19) and young adults (20 to 25). It seems that there has not been sizeable improvements in educational mobility during the last decade. 9. Housing and social services Housing is probably the main asset that most people own. The EPH reports whether the house is owned by the family who lives in, but does not contain information on the rental value of the dwelling. Table 9.1 shows for each income quintile the share of families owning a house (the building and the lot). Housing ownership is widespread along the income distribution, especially among the well-off. Table 9.1 suggests that housing ownership in rich households has grown relative to poorer households in the last decade. In fact, while housing ownership increased 3 points for the top quintile between 1992 and 2003, it fell for the rest of the income distribution. The evidence suggests that housing markets increasingly excluded the poor. There are not clear patterns since 2003 in the EPHC. Poor families live in houses smaller -in number of rooms- than richer households. Since poor families are also larger in size, the number of persons per room is significantly greater. 29 For technical details see Andersen (2001). 22
This indicator has increased 0.2 for poor households during the last decade, while it fell 0.1 for rich families. We have constructed an indicator of poor dwellings. This variable takes a value 1 if the family lives in a shantytown, inquilinato, pensiуn, or other space not meant to be used as a house. Around 2 percent of the population live in poor dwellings. This proportion has been reduced in the 1990s, and stayed roughly unchanged between 1998 and 2003. Anyway, the share of these dwellings captured by the EPH is so small, that it is difficult to know when changes or differences across groups are statistically significant. That problem is even more serious when analyzing houses of "low-quality" materials, i.e. houses whose walls are made of chapa, adobe or chorizo. These houses are around 1.5% of total dwellings. According to the last panel of Table 9.1 the share of these dwellings fell during the last decade. Table 9.2 reports housing statistics by age groups. Housing ownership has increased for the elderly and decreased for the rest of the population. Also, the shares of poor dwellings and low-quality dwellings have significantly decreased for all, except for those households whose head is young (16 to 25). Table 9.4 reports statistics by income strata on the access to some basic services in the house: water, hygienic restrooms, sewerage, and electricity.30 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 person depend on the number of people among whom she has to share household total resources with. The size and composition of the household are key determinants of an individual's economic well-being. Table 10.1 shows household size by income quintiles and by education of the household head. It is interesting to notice that the absence of significant changes for the average household size is the consequence of two forces that compensate each other: household size increased for poor families and decreased for the rest. Dependency rates have stayed quite stable during the decade. Table 10.3 shows this result by presenting household size over the number of income earners by quintiles and by education of the household head. 30 See the methodological document for definitions. 23
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 again heterogeneous changes across quintiles. The average age in quintile 5 increased 4.1 years between 1992 and 2003, while the average age fell 4.0 years in quintile 1. These are large changes that surely have some impact on poverty and inequality. Inequality is reinforced if marriages take place between persons of similar income potential. Table 10.5 presents some simple linear correlations that suggest the existence of assortative mating in urban Argentina.31 Men with more years of formal education tend to marry women with a similar educational background (column(i)). This is one of the factors that contribute to a positive correlation of hourly wages within couples shown in column (ii). There are not clear signs of changes in the degree of assortative mating in the last thirteen years, according to these simple statistics. Finally, columns (iii) and (iv) show positive, though small, correlations in hours of work, both considering and excluding people who do not work. 11. Poverty-alleviation programs Probably as a consequence of the traditionally low incidence of poverty, and the wide coverage of social benefits linked to the labor market, Argentina had never had a large poverty-alleviation program. Instead, there were a multiplicity of small programs at different government levels targeted to particular groups or areas. These programs were not usually recorded in the household surveys. In the midst of the 2002 deep recession Argentina introduced the Programa Jefes de Hogar, which soon became the largest national poverty-alleviation program covering around 2 million household heads. The PJH transfers $150 to unemployed household heads with dependents (children aged less than 18 or incapacitated) and it has a counterpart work requirement, with the aim of helping to assure that the transfers reached those in greatest need. Given the size of this program the EPH started to include questions on that program. This section is based on the specific questions included in the May 2003 EPH and the EPHC. According to expectations, Table 11.1 shows that coverage is decreasing in income. The program seems to be far from universal in the poorest strata of the population. Around 30% of those households in the first quintile of the equivalized income distribution receive transfers from the PJH. That share falls to around 17% in quintile 2, and 6% in quintile 3. Around 9% of Argentine households are covered by the program. Table 11.2 shows that around 14% of households headed by a person with low education are beneficiaries of the 31 See also Fernбndez, Guner and Knowles (2001). 24
PJH. The mean transfer by household is $5.2. In quintile 1, the mean transfer is $22.8, while in quintile 5 is basically zero (see Table 11.3). The program seems to be reasonably targeted to the poor (see Tables 11.4 and 11.5).32 Around 80% of the beneficiaries of the PJH belong to the 40% poorest of the population. This degree of targeting has increased over time. 12. An assessment The social performance of Argentina in the last thirteen years have been very disappointing. According to most indicators poverty dramatically increased in Argentina, in contrast to the experience of most countries in the region. The rise in poverty was the consequence of economic stagnation and a substantial increase in inequality, again more intense than in any other LAC country. Inequality has increased measured by all indicators and computed over the distribution of all income variables. The increase in inequality coupled with a stagnant per capita income has implied a fall in aggregate welfare in the last 13 years. Social indicators have significantly improved since 2003, but most of them are still around the values of year 2000, even when the current levels of economic activity are significantly higher than in that year. There has been a lot of action in the Argentina's labor markets during the last decade. Unemployment reached record levels, pushed by a massive entry of unskilled women into the labor market, and a loss of employment for prime-age unskilled men. Wages have fallen over the decade. Changes have not been uniform across groups. In particular, the wage premium to skilled labor has substantially increased. The weak labor market has also implied less hours of work for the unskilled and a significant fall in the coverage of social security. Since 2003 the country has experienced a recovery of the labor market. The recovery has been strong in terms of employment, and weaker in terms of real wages. Attendance rates to pre-school, primary school and secondary school have increased, particularly in poor income strata. This is not case for college, where the gap between the rich and the poor has increased. That gap has also widened in the housing market. Finally, changes in demographic variables have been heterogeneous, as well. While household size fell in the upper income quintiles, the opposite happened in the poor income strata. 32 The target population of the PJH is a topic of debate. Although the Decree that creates the program limits the benefits to households with certain characteristics (e.g. unemployed heads complying with the counterpart work requirement), in practice the program has become a typical poverty-alleviation program targeted to all the poor. The degree of targeting can then be evaluated in terms of all the poor population, instead of those meeting the initial requirements (that include many non-poor). 25
References Altimir, O. (1986). Estimaciones de la distribuciуn del ingreso en la Argentina, 1953-1980. Desarrollo Econуmico 25 (100), enero-marzo. Andersen, L. (2001). Social mobility in Latin America: links with adolescent schooling. IADB Research Network Working Paper #R-433. Attanasio, O. and Szйkely, 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 Pagйs, 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 Economicos de Galicia Pedro Barrie de la Maza Working Papers Series 24. Fernбndez, 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. Foster, J., Greer, J. and Thorbecke, E. (1984). A class of decomposable poverty measures. Econometrica 52, 761-776. 26
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 protecciуn social en Amйrica Latina. Un anбlisis sobre la base de encuestas de hogares. OIT. Gasparini, L. (2003). Different lives: inequality in Latin America and the Caribbean. Capнtulo 2 de Inequality in Latin America and the Caribbean: Breaking with History?, The World Bank. Gasparini, L. (2004). Argentina's Distributional Failure. The role of Integration and Public Policies. IADB Working Paper, 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 distribuciуn del ingreso en la Argentina. Editorial Triunfar. Gasparini, L. and Sosa Escudero, W. (2001). Assessing aggregate welfare: growth and inequality in Argentina. Cuadernos de Economнa 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., Gutierrez, F. and Tornarolli, L (2005). Growth and income poverty in Latin America and the Caribbean. Background paper for the 2005 LAC Flagship Report. 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). Informe de Prensa. Incidencia de la pobreza y de la indigencia en los aglomerados urbanos. Octubre. Lambert, P. (1993). The distribution and redistribution of income. Manchester University Press. 27
Londoсo, J. and Szйkely, 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. Econуmica XLVI (1). Enero-Junio. Szйkely, 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. 28
Appendix: A poverty profile This appendix presents a poverty profile based on information from the EPH, 2005 (second half). A poverty profile is a characterization of the poor population, often compared to the non-poor population. We take the USD 2 a day and the official moderate poverty lines as the two criteria to define the poor. To make the text less cumbersome, in general we discuss the results for the USD 2-a-day poverty line (columns (i) and (ii) in each table), except when a significant difference justifies the discussion of the alternative poverty definition. Table A.1 shows some basic demographic characterization of the poor and non-poor population. According to the USD2 poverty line, 11.6% of the total population is poor. The differences in this share across age groups are substantial: while 20% of the children under 15 are poor, that share is just 3.6% for the elderly. The share of the poor population is monotonically decreasing in age. Nearly half of the poor population (47.4%) are children aged less than 15, while only 3.2% are people above 65. The age structure is significantly different between the poor and the non-poor. This is summarized in mean age: it is 33.8 for the non-poor and only 22.8 for the poor. These patterns illustrate the relevance of the impact of the demographic factors on poverty. 84% of the elderly in urban Argentina are heads or spouses of the household they belong. More than 50% of them live in households with 1 or 2 members. By living alone the elderly manage to escape income poverty, at least in the usual narrow definition of poverty. The poor and the non-poor substantially differ in the household size. While a typical nonpoor household has 3 persons, a typical poor household has 5 members. That difference is mostly explained by the difference in children under 12. There is on average 1.2 children in each non-poor family with the head aged 25 to 45, while there are 2.5 children on average in poor households with a prime-age head. This difference implies that even with similar total income, an average poor family where both parents are present would have a per capita income 40% less than a typical non-poor family. The dependency rates (number of income earners per person) are also dramatically different: 0.28 in poor household and more than double in non-poor households (0.66). The share of female-headed households is somewhat larger for the poor when defining poverty as USD2. However, the difference vanishes when using the official poverty line. This change is the consequence of a significantly lower proportion of female-headed households in the decile 3 of the income distribution. 29
Unfortunately, given that the EPH has only urban coverage, there are no estimates for rural poverty. As mentioned in section 4, based on the Encuesta de Impacto Social de la Crisis en Argentina (ISCA) Haimovich (2004) finds that rural poverty is around 15 points higher than urban poverty. When assuming that prices in rural areas are 20% lower the difference becomes smaller, but still significant (7 points). Table A.2 shows that poverty is particularly high in the Northern regions of the country (22.9% in NEA and 18.9% in NOA, compared to a country average of 11.6%), and particularly low in Patagonia (5.1%). Given its size most of the poor live in the Greater Buenos Aires. In fact 43% of the poor population lives in this conglomerate (49.1% when using the official poverty line), while more than 20% of the population lives in the Pampeana region. Although housing ownership is less usual among the poor, the difference with the non-poor is not very large: while 67.3% of the non-poor are owners, 50.6% of the poor report being owners of both the lot and the dwelling where they live (Table A.3). The poor live in smaller houses of a worse quality and with fewer services. In an average poor household there are 2.39 persons per room. That number is 1.21 in non-poor households. The second panel in Table A.3 extends the housing statistics by showing information for 2003, given that some housing variables have not been released by INDEC yet. It is interesting to notice that less than 5% of the poor population in 2003 lived in shantytowns and other inconvenient places, while just 3% had dwellings with walls of chapa, chorizo, or cartуn. The access of the urban poor to water and electricity, although lower than for the non-poor, are relatively high: in 2003 94% of the poor reported having access to water in their lots, and 98% of them had electricity. The big difference with the non-poor appears in the access to hygienic restrooms and to the public sewerage system. While 66% of the urban non-poor were 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 [31,40] age group.33 These differences show up in the second panel of Table A.4. While just a third of the non-poor adults are unskilled, that share rises to nearly 70% for the poor. 27.6% of the non-poor adults are skilled, while just 4.7% of the poor are. That share is probably significantly smaller, given that some professionals are recorded as poor if their monthly income was low in the month of the survey, just for seasonality reasons, or temporary unemployment. The literacy rate is high for the poor: 97% of those older than 10 report being able to read and write. That share rises to 99% for the non-poor. The last panel of Table A.4 indicates that school attendance is almost universal for those children aged 6 to 12. Attendance rates 33 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. 30
significantly fall, especially for the poor, in the pre-primary, secondary and tertiary levels. While the rate of attendance is 98% for the poor aged 6 to 12, it drops to 83% for those aged 13 to 17 and to 21% for those in the (18,23) age group. The rate of labor market participation of the poor is smaller than the rate of the non-poor, especially for women. While 69% of the non-poor women are in the labor market, that share drops to 57% for the 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. A typical poor unemployed person spends 9.8 months without finding a job. Finally, Table A.5 reports that child labor is significantly higher for the poor. Around 4 out of 1000 poor children had worked at least one hour in the second half of 2005. The poor are not only less likely to find a job, but when having one, they work less hours and get lower wages (see Table A.6). On average a non-poor employed person works 9.6 hours more a week than a poor person. That gap is smaller for the youth (5.5 hours) and larger for the elderly (12.3 hours). On average the hourly wage of a poor person is just a third of a non-poor worker. The difference is smaller for the youth and for women, and larger for the elderly and prime-age males. Table A.7 characterizes the employment structure of the population. Compared to the nonpoor the working poor are especially self-employed unskilled workers. According to a definition of informality based on labor groups, 65.8% of the poor are informal, while 39.3% of the non-poor are in that category. When defining informality based on the access to social security the differences are dramatic: while 42.4% of the salaried non-poor are informal, that share jumps to 94.1% for the poor. The sectoral structure of employment is different between the poor and the rest. Compared to the non-poor the poor are relatively concentrated in labor-intensive manufacturing industries, and particularly in construction, commerce, and domestic servants. Commerce is the main source of jobs for the poor: 27.6% of the poor find job in that sector, followed by 18.7% in construction, 15.4% in education and health, and 13% domestic servants. The last rows in Table A.7 show substantial differences in the access to stable jobs with social security rights. The share of permanent jobs, and labor positions with rights to pensions and health insurance is significantly lower for the poor. For instance, while 59% 31
of the working non-poor report having access to health insurance linked to their employment, only 6% of the poor have health insurance. Table A.8 reports statistics of the main poverty-alleviation program of Argentina: the Programa Jefes de Hogar. Based on the USD2-a-day definition 33.7% of the poor households receive transfers for the PJH, while 6.9% of the non-poor households are beneficiaries of that program. When considering the official definition of moderate poverty, the shares change to 26.4% and 3.3% for the poor and the non-poor, respectively. The household mean income from the PJH is $28 for the poor (column (ii)) and $3.2 for the non-poor. According to the USD2 definition, 66.4% of the beneficiaries of the PJH are poor, while 66.9% of the transfers go to the poor. Targeting indicators are better when considering a wider definition of poverty: 76.4% of the beneficiaries of the PJH are poor, according to the official definition. Table A.9 summarizes mean income, and the income structure of the poor and the rest of the population. It also shows that inequality, as measured by the Gini coefficient for the distribution of household per capita income, is much lower with the poor than within the non-poor (0.202 and 0.461 respectively). Table A.10 summarizes the value of household income and size, and performs in panel B a simple simulation to characterize the difference in per capita income between a typical poor person and the rest. The table indicates typical poor's 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 $53.9 a month. If household size for the poor were the same than for the non-poor, keeping the rest constant, per capita income would be $81.3. 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. The last table in this poverty profile was built with census data. As commented above the Argentina's government computes a basic needs indicator (NBI) based on housing characteristics, sanitation, primary school enrollment, household head education and dependency rates. Table A.11 shows the geographical structure of this indicator. Basicneeds poverty is higher in NEA and NOA and lower in the city of Buenos Aires (excluding the Greater Buenos Aires). According to this table, improvements in the living standards of the poor were significant in the 1980s, and slowed down in the 1990s. While the NBI indicator fell 7.8 points between 1980 and 1991, it fell 2.2 points between 1991 and 2001. 32
Table 3.1 Real income Argentina, 1992-2005
Real income Deciles 1 2 3 4 5 6 7 8 9 10 average
1992 53.9 100.9 136.7 172.2 208.8 257.2 316.7 405.6 557.3 1152.9 336.3
1994 49.5 95.8 131.7 168.8 210.4 257.1 316.8 403.5 551.0 1148.6 333.4
EPH - 15 cities 1996 32.1 76.9 110.2 143.9 180.8 226.4 285.0 371.6 529.7 1143.3 310.0
1998 36.5 79.6 114.2 151.1 192.3 239.8 307.7 409.6 584.2 1293.9 340.9
1998 33.5 73.8 106.0 139.8 177.7 222.7 285.5 376.9 536.7 1199.6 315.3
EPH - 28 cities 2000 27.5 65.5 95.3 126.9 164.3 210.7 267.5 358.6 512.7 1108.0 293.7
2002 16.6 35.9 55.2 77.0 101.3 130.2 165.2 221.7 328.4 769.9 190.2
Proportional changes
Deciles 1
1992-1994 -8.1
2
-5.0
3
-3.7
4
-2.0
5
0.8
6
0.0
7
0.0
8
-0.5
9
-1.1
10
-0.4
average
-0.9
1994-1996 -35.3 -19.8 -16.3 -14.8 -14.1 -12.0 -10.0 -7.9 -3.9 -0.5 -7.0
1996-1998 13.7 3.5 3.6 5.0 6.3 5.9 8.0 10.2 10.3 13.2 10.0
1992-1998 -32.3 -21.1 -16.4 -12.2 -7.9 -6.8 -2.9 1.0 4.8 12.2 1.4
1998-2000 -17.9 -11.2 -10.1 -9.2 -7.5 -5.4 -6.3 -4.9 -4.5 -7.6 -6.8
2000-2002 -39.7 -45.3 -42.1 -39.4 -38.3 -38.2 -38.3 -38.2 -35.9 -30.5 -35.3
1998-2002 -50.5 -51.4 -48.0 -44.9 -43.0 -41.5 -42.1 -41.2 -38.8 -35.8 -39.7
Source: Own calculations based on microdata from the EPH.
EPHC - 28 cities
2003
2005
17.5
29.7
45.1
65.8
68.6
96.9
93.1
130.1
121.0
168.0
155.5
213.4
203.7
266.3
276.3
347.9
399.4
490.4
932.1
1097.1
231.3
290.6
2003-2005 69.1 45.8 41.2 39.7 38.8 37.3 30.7 25.9 22.8 17.7 25.7
Table 4.1 Poverty lines Argentina, 1992-2005
International PL ($ per capita)
Oficial PL ($ per adult equivalent)
USD 1 a day USD 2 a day
Extreme
Moderate
(i)
(ii)
(iii)
(iv)
1992 1993
23.8
47.6
25.9
51.8
57.9
129.2
62.4
138.0
1994
26.9
53.7
62.8
146.4
1995
27.5
54.9
66.1
154.7
1996
27.5
55.0
67.4
156.3
1997
27.7
55.3
67.4
157.6
1998 1999
28.0
55.9
27.4
54.8
69.8
161.2
64.6
155.0
2000
27.2
54.4
62.4
151.1
2001
26.9
53.8
61.0
150.1
2002
37.3
74.5
104.8
231.8
2003 (May)
38.6
77.1
106.6
232.3
2003 (II half)
38.6
77.1
2004 (I half)
39.7
79.5
103.6 106.5
227.7 233.3
2004 (II half)
40.8
81.7
108.5
237.7
2005 (I half)
43.2
86.5
114.2
250.1
2005 (II half)
45.0
90.1
120.1
259.5
Source: INDEC, WDI and own calculations.
Note 1: mean values for GBA
Note 2: For the EPHC, first half: April, second half: September.
(iv)/(ii) 2.7 2.7 2.7 2.8 2.8 2.8 2.9 2.8 2.8 2.8 3.1 3.0 3.0 2.9 2.9 2.9 2.9
Ratios (iv)/(iii) 2.2 2.2 2.3 2.3 2.3 2.3 2.3 2.4 2.4 2.5 2.2 2.2 2.2 2.2 2.2 2.2 2.2
(iii)/(ii) 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.1 1.1 1.4 1.4 1.3 1.3 1.3 1.3 1.3
33
Table 4.2 Poverty Argentina, 1992-2005 USD-1-a day poverty line
Number
Headcount Poverty gap
poor people
FGT(0)
FGT(1)
FGT(2)
(i)
(ii)
(iii)
(iv)
EPH-15 cities
1992
478,378
1.4
1.0
0.9
1993
586,197
1.7
1.0
0.9
1994
585,528
1.7
1.3
1.2
1995
1,108,580
3.2
2.0
1.7
1996
1,309,616
3.7
2.6
2.3
1997
1,105,174
3.1
2.0
1.7
1998
1,149,506
3.2
1.7
1.3
EPH - 28 cities
1998
1,233,383
3.4
1.8
1.5
1999
1,281,780
3.5
2.1
1.8
2000
1,557,903
4.2
2.5
2.0
2001
2,589,201
6.9
4.1
3.3
2002
3,737,657
9.9
3.9
2.4
2003
3,014,981
7.9
2.8
1.8
EPH-C
2003-II *
3,021,707
7.9
3.9
2.9
2003-II
2,917,955
7.6
3.8
2.9
2004-I
2,221,433
5.7
2.8
2.1
2004-II
2,009,830
5.2
2.5
1.8
2005-I
1,809,907
4.6
2.2
1.6
2005-II
1,521,238
3.9
1.8
1.2
Source: Own calculations based on microdata from the EPH.
Note: FGT(0)=headcount ratio, FGT(1)=poverty gap, FGT(2)=Foster,
Greer and Thornbecke index with parameter 2.
* computed using weights that ignore income non-response.
Table 4.3 Poverty Argentina, 1992-2005 USD-2-a day poverty line
Number
poor people
(i)
EPH-15 cities
1992
1,407,829
1993
1,689,431
1994
1,549,582
1995
2,592,179
1996
3,141,943
1997
2,967,630
1998
3,007,165
EPH - 28 cities
1998
3,405,663
1999
3,316,156
2000
4,039,454
2001
5,848,228
2002
9,376,508
2003
9,083,922
EPH-C
2003-II *
7,650,293
2003-II
7,325,050
2004-I
6,049,215
2004-II
5,492,855
2005-I
5,194,536
2005-II
4,538,654
Headcount FGT(0) (ii) 4.2 5.0 4.5 7.5 8.9 8.3 8.3 9.4 9.1 10.9 15.6 24.7 23.7 19.9 19.1 15.6 14.2 13.2 11.6
Poverty gap FGT(1) (iii) 1.9 2.1 2.2 3.7 4.2 3.7 3.4 3.9 4.1 5.0 7.7 10.8 9.2 8.8 8.5 6.7 6.0 5.5 4.6
FGT(2) (iv) 1.3 1.4 1.6 2.6 3.1 2.6 2.3 2.5 2.8 3.3 5.3 6.4 5.1 5.6 5.5 4.2 3.7 3.3 2.7
Source: Own calculations based on microdata from the EPH.
Note: FGT(0)=headcount ratio, FGT(1)=poverty gap, FGT(2)=Foster,
Greer and Thornbecke index with parameter 2.
* computed using weights that ignore income non-response.
34
Table 4.4 Poverty Argentina, 1992-2005 Official extreme poverty line
Number Headcount Poverty gap
poor people FGT(0)
FGT(1)
FGT(2)
(i)
(ii)
(iii)
(iv)
EPH-15 cities
1992
1,254,803
3.8
1.7
1.2
1993
1,462,793
4.3
1.8
1.2
1994
1,317,220
3.8
1.8
1.4
1995
2,384,765
6.9
3.2
2.3
1996
2,896,186
8.2
3.9
2.9
1997
2,584,205
7.2
3.4
2.4
1998
2,783,867
7.7
3.4
2.2
EPH - 28 cities
1998
3,041,767
8.4
3.7
2.4
1999
3,041,205
8.3
3.7
2.5
2000
3,536,364
9.5
4.3
2.9
2001
5,151,064
13.7
6.7
4.7
2002
10,473,374
27.6
12.2
7.2
2003
10,128,517
26.4
10.5
5.8
EPH-C
2003-II *
8,214,687
21.4
9.4
5.9
2003-II
7,869,947
20.5
9.1
5.8
2004-I
6,600,271
17.0
7.1
4.4
2004-II
5,830,357
15.0
6.2
3.8
2005-I
5,338,445
13.6
5.7
3.4
2005-II
4,771,484
12.2
4.9
2.9
Source: Own calculations based on microdata from the EPH.
Note: FGT(0)=headcount ratio, FGT(1)=poverty gap, FGT(2)=Foster,
Greer and Thornbecke index with parameter 2.
* computed using weights that ignore income non-response.
Table 4.5 Poverty Argentina, 1992-2005 Official moderate poverty line
Number
poor people
(i)
EPH-15 cities
1992
6,592,328
1993
6,209,474
1994
6,914,999
1995
9,262,680
1996
10,366,003
1997
9,894,132
1998
10,202,583
EPH - 28 cities
1998
10,865,081
1999
11,159,500
2000
12,079,764
2001
14,401,152
2002
21,795,194
2003
20,986,517
EPH-C
2003-II * 19,002,076
2003-II
18,348,115
2004-I
17,210,630
2004-II 15,582,682
2005-I
15,082,930
2005-II 13,287,919
Headcount FGT(0) (ii) 19.7 18.3 20.1 26.6 29.4 27.7 28.2 30.1 30.5 32.6 38.4 57.5 54.7 49.5 47.8 44.4 40.2 38.4 33.8
Poverty gap FGT(1) (iii) 6.5 6.6 7.3 10.6 12.1 11.3 11.6 12.4 12.5 14.1 18.1 29.2 27.3 23.8 22.9 20.2 18.0 16.6 14.6
FGT(2) (iv) 3.4 3.6 3.9 6.2 7.1 6.5 6.7 7.2 7.3 8.4 11.6 18.9 17.1 15.0 14.4 12.2 10.8 9.9 8.6
Source: Own calculations based on microdata from the EPH.
Note: FGT(0)=headcount ratio, FGT(1)=poverty gap, FGT(2)=Foster,
Greer and Thornbecke index with parameter 2.
* computed using weights that ignore income non-response.
35
Table 4.6 Poverty Argentina, 1992-2005 50 % median income poverty line
Number
poor people
(i)
EPH-15 cities
1992
6,546,566
1993
6,850,027
1994
7,124,105
1995
7,374,942
1996
7,943,542
1997
7,929,285
1998
7,944,343
EPH - 28 cities
1998
8,320,577
1999
8,226,625
2000
9,072,879
2001
9,403,156
2002
9,832,693
2003
9,543,469
EPH-C
2003-II *
9,413,501
2003-II
9,477,571
2004-I
9,020,631
2004-II
9,611,727
2005-I
9,104,836
2005-II
9,636,760
Headcount FGT(0) (ii) 19.6 20.2 20.8 21.2 22.6 22.2 22.0 23.0 22.5 24.5 25.1 25.9 24.9 24.5 24.7 23.2 24.8 23.2 24.5
Poverty gap FGT(1) (iii) 6.6 7.3 7.3 8.8 9.3 9.5 9.3 9.2 9.5 10.4 12.2 11.5 10.2 11.1 11.3 10.2 10.5 9.9 10.2
FGT(2) (iv) 3.5 4.1 4.0 5.3 5.8 5.7 5.4 5.4 5.7 6.3 8.1 6.8 5.7 7.0 7.2 6.2 6.3 5.9 6.0
Source: Own calculations based on microdata from the EPH.
Note: FGT(0)=headcount ratio, FGT(1)=poverty gap, FGT(2)=Foster,
Greer and Thornbecke index with parameter 2.
* computed using weights that ignore income non-response.
Table 4.7 Poverty Argentina, 1992-2003 Endowments
Endowments Endowments
plus income
(i)
(ii)
EPH-15 cities
1992
0.392
0.031
1993
0.387
0.035
1994
0.389
0.031
1995
0.385
0.057
1996
0.387
0.067
1997
0.384
0.062
1998
0.392
0.067
EPH - 28 cities
1998
0.402
0.075
1999
0.401
0.070
2000
0.393
0.083
2001
0.400
0.116
2002
0.385
0.167
2003
0.381
0.163
Source: Own calculations based on microdata from the EPH.
36
Table 5.1 Distribution of household per capita income Share of deciles and income ratios Argentina, 1992-2005
Share of deciles
Income ratios
1
2
3
4
5
6
7
8
9 10
10/1 90/10 95/80
EPH-15 cities
1992
1.8 3.0 4.1 5.1 6.2 7.6 9.4 12.0 16.5 34.1
19.0 7.9
2.0
1993
1.7 3.0 4.1 5.2 6.4 7.9 9.6 12.3 16.6 33.1
19.9 8.1
1.9
1994
1.7 2.9 4.0 5.1 6.3 7.7 9.5 12.1 16.4 34.2
19.7 8.2
1.9
1995
1.4 2.7 3.7 4.8 5.9 7.3 9.1 11.6 16.7 36.7
25.8 9.6
2.1
1996
1.4 2.6 3.6 4.7 5.9 7.3 9.2 11.9 17.0 36.5
26.5 10.1 2.0
1997
1.4 2.6 3.6 4.7 6.0 7.3 9.2 12.0 17.2 36.1
26.7 10.5 2.1
1998
1.2 2.4 3.4 4.5 5.7 7.0 9.0 12.0 17.1 37.7
30.2 11.2 2.1
EPH - 28 cities
1998
1.3 2.4 3.4 4.5 5.7 7.1 9.0 11.9 16.9 37.8
29.9 11.1 2.1
1999
1.3 2.5 3.5 4.6 5.8 7.3 9.2 12.0 17.0 36.8
28.0 10.9 2.1
2000
1.2 2.3 3.3 4.4 5.6 7.2 9.1 12.2 17.4 37.4
32.3 11.9 2.1
2001
1.0 2.1 3.1 4.1 5.4 6.9 9.0 12.0 17.4 39.0
40.0 13.9 2.2
2002
1.0 2.0 3.0 4.1 5.4 6.9 8.7 11.6 17.2 40.3
39.4 14.3 2.3
2003
1.1 2.1 3.0 4.0 5.2 6.8 8.8 11.9 17.3 39.8
34.8 13.5 2.2
EPH-C
2003-II * 1.0 2.1 3.0 4.0 5.2 6.6 8.6 11.6 16.7 41.0
39.3 13.4 2.2
2003-II
1.0 2.1 3.1 4.1 5.3 6.7 8.8 11.9 17.1 39.8
38.1 13.7 2.2
2004-I
1.2 2.3 3.3 4.3 5.5 7.1 9.0 11.9 16.8 38.6
32.7 11.8 2.1
2004-II
1.1 2.3 3.3 4.4 5.7 7.2 9.1 12.0 17.0 37.9
33.0 12.0 2.0
2005-I 2005-II
1.2 2.4 3.4 4.4 5.7 7.3 9.1 11.9 16.9 37.8 1.2 2.3 3.4 4.5 5.8 7.3 9.1 11.9 16.8 37.6
32.5 11.7 2.1 32.7 11.8 2.1
Source: Own calculations based on microdata from the EPH.
Note 1: (10/1)=income ratio between deciles 10 and 1; (90/10)=income ratio between percentiles 90 and 10,
and (95/80)=income ratio between percentiles 95 and 80.
* computed using weights that ignore income non-response.
Table 5.2 Distribution of household per capita income Inequality indices Argentina, 1992-2005
Gini Theil
CV
A(.5)
A(1)
A(2)
E(0)
E(2)
EPH-15 cities
1992
0.450 0.370 1.101 0.165 0.299 0.510 0.355 0.606
1993
0.444 0.359 1.077 0.162 0.297 0.517 0.352 0.580
1994
0.453 0.378 1.112 0.168 0.303 0.510 0.361 0.618
1995
0.481 0.430 1.205 0.190 0.340 0.569 0.416 0.726
1996
0.486 0.442 1.260 0.194 0.349 0.607 0.429 0.793
1997
0.484 0.422 1.146 0.190 0.346 0.586 0.424 0.656
1998
0.502 0.471 1.300 0.207 0.369 0.608 0.461 0.845
EPH - 28 cities
1998
0.502 0.472 1.307 0.207 0.368 0.605 0.458 0.854
1999
0.491 0.443 1.213 0.197 0.356 0.606 0.440 0.735
2000
0.504 0.464 1.231 0.208 0.377 0.647 0.474 0.757
2001
0.522 0.497 1.264 0.224 0.404 0.675 0.517 0.798
2002
0.533 0.530 1.356 0.233 0.412 0.657 0.530 0.920
2003
0.528 0.519 1.343 0.227 0.401 0.637 0.512 0.902
EPH-C
2003-II * 0.537 0.625 3.056 0.244 0.417 0.673 0.539 4.671
2003-II
0.529 0.532 1.457 0.231 0.407 0.672 0.522 1.061
2004-I
0.510 0.507 1.714 0.216 0.380 0.621 0.478 1.469
2004-II
0.506 0.499 1.550 0.213 0.379 0.624 0.476 1.201
2005-I
0.502 0.473 1.306 0.208 0.373 0.624 0.466 0.853
2005-II
0.501 0.480 1.418 0.209 0.373 0.624 0.467 1.005
Source: Own calculations based on microdata from the EPH.
CV=coefficient of variation. A(e) refers to the Atkinson index with a CES
function with parameter e. E(e) refers to the generalized entropy index with parameter e. E(1)=Theil.
* computed using weights that ignore income non-response.
37
Table 5.3 Distribution of equivalized household income Share of deciles and income ratios Argentina, 1992-2005
Share of deciles
Income ratios
1
2
3
4
5
6
7
8
9
10
10/1 90/10 95/80
EPH-15 cities
1992
2.0 3.4 4.3 5.3 6.5 7.9 9.5 12.0 16.4 32.7
16.3 7.1 2.0
1993
1.9 3.3 4.4 5.5 6.7 8.1 9.7 12.3 16.3 31.8
17.0 7.3 1.9
1994
2.0 3.3 4.3 5.4 6.5 7.9 9.6 12.0 16.2 32.8
16.7 7.1 1.9
1995
1.6 3.0 4.1 5.1 6.2 7.5 9.1 11.6 16.4 35.3
21.6 8.6 2.1
1996
1.6 2.9 3.9 5.0 6.1 7.5 9.2 11.9 16.7 35.0
21.8 8.6 2.0
1997
1.6 2.9 3.9 5.0 6.2 7.6 9.4 12.0 16.9 34.5
22.2 9.0 2.1
1998
1.5 2.7 3.7 4.8 5.9 7.3 9.1 11.9 16.9 36.2
24.7 9.6 2.1
EPH - 28 cities
1998
1.5 2.7 3.7 4.8 5.9 7.3 9.1 11.9 16.8 36.2
24.4 9.3 2.1
1999
1.5 2.8 3.9 4.9 6.1 7.5 9.3 12.0 16.8 35.3
23.1 9.3 2.0
2000
1.3 2.6 3.6 4.7 5.9 7.4 9.2 12.1 17.1 36.1
26.9 10.3 2.1
2001
1.1 2.4 3.4 4.5 5.7 7.2 9.1 11.9 17.1 37.6
33.3 12.1 2.2
2002
1.2 2.2 3.3 4.4 5.7 7.1 8.8 11.5 16.9 39.0
32.5 12.3 2.3
2003
1.3 2.4 3.3 4.3 5.6 7.0 8.9 11.8 17.0 38.3
28.4 11.3 2.2
EPH-C
2003-II * 1.2 2.3 3.3 4.4 5.5 6.9 8.8 11.6 16.5 39.4
32.4 11.4 2.1
2003-II
1.2 2.3 3.4 4.4 5.6 7.0 8.9 11.8 16.9 38.4
31.6 11.6 2.1
2004-I
1.4 2.6 3.6 4.7 5.8 7.3 9.2 11.9 16.6 37.0
27.1 10.2 2.1
2004-II
1.3 2.6 3.6 4.8 6.0 7.4 9.2 12.0 16.8 36.2
27.2 10.3 2.0
2005-I
1.4 2.7 3.8 4.8 6.0 7.4 9.3 11.9 16.7 36.1
26.8 10.1 2.0
2005-II
1.3 2.6 3.7 4.8 6.1 7.5 9.3 11.9 16.6 36.1
27.1 10.1 2.0
Source: Own calculations based on microdata from the EPH.
Note 1: (10/1)=income ratio between deciles 10 and 1; (90/10)=income ratio between percentiles 90 and 10,
and (95/80)=income ratio between percentiles 95 and 80.
* computed using weights that ignore income non-response.
Table 5.4 Distribution of equivalized household income Inequality indices Argentina, 1992-2005
Gini Theil CV A(.5) A(1) A(2) E(0) E(2)
EPH-15 cities
1992
0.430 0.334 1.018 0.150 0.272 0.468 0.318 0.518
1993
0.424 0.325 1.004 0.147 0.270 0.475 0.315 0.504
1994
0.431 0.341 1.040 0.152 0.275 0.467 0.322 0.541
1995
0.460 0.391 1.127 0.173 0.311 0.525 0.372 0.635
1996 1997
0.463 0.398 1.168 0.176 0.318 0.561 0.383 0.682 0.461 0.380 1.066 0.173 0.316 0.545 0.379 0.568
1998
0.480 0.425 1.198 0.188 0.338 0.565 0.412 0.718
EPH - 28 cities
1998
0.478 0.424 1.203 0.187 0.335 0.561 0.409 0.724
1999
0.468 0.400 1.127 0.179 0.325 0.564 0.393 0.635
2000 2001
0.483 0.422 1.149 0.190 0.347 0.606 0.426 0.660 0.501 0.456 1.194 0.206 0.374 0.635 0.467 0.713
2002
0.512 0.488 1.287 0.215 0.381 0.617 0.479 0.829
2003
0.506 0.473 1.261 0.208 0.368 0.595 0.460 0.795
EPH-C
2003-II * 0.514 0.566 2.766 0.223 0.384 0.637 0.485 3.825
2003-II 2004-I
0.507 0.487 1.360 0.212 0.376 0.638 0.471 0.925 0.488 0.459 1.539 0.197 0.349 0.580 0.429 1.184
2004-II 0.483 0.450 1.429 0.194 0.347 0.584 0.426 1.021
2005-I
0.480 0.429 1.232 0.190 0.341 0.582 0.417 0.759
2005-II 0.480 0.446 1.433 0.192 0.344 0.585 0.421 1.027
Source: Own calculations based on microdata from the EPH.
CV=coefficient of variation. A(e) refers to the Atkinson index with a CES
function with parameter e. E(e) refers to the generalized entropy index with parameter e. E(1)=Theil.
* computed using weights that ignore income non-response.
38
Table 5.5 Distribution of equivalized household labor monetary income Share of deciles and income ratios Argentina, 1992-2005
Share of deciles
1
2
3
4
5
6
7
8
9 10
Income ratios 10/1 90/10 95/80
EPH-15 cities
1992
2.0 3.4 4.5 5.5 6.6 8.0 9.7 12.1 16.5 31.8
16.2 6.9 1.9
1993
1.8 3.2 4.3 5.4 6.7 8.1 9.8 12.3 16.6 31.7
17.9 7.6 1.9
1994
1.9 3.3 4.3 5.4 6.5 7.9 9.6 11.9 16.2 33.0
17.4 7.2 1.9
1995 1996
1.5 2.9 4.0 5.0 6.1 7.4 9.0 11.5 16.4 36.2 1.5 2.8 3.8 4.9 6.0 7.4 9.2 11.8 16.7 36.0
24.4 9.0 2.2 24.6 9.2 2.1
1997
1.4 2.7 3.8 4.9 6.1 7.6 9.4 12.0 17.0 35.0
24.8 9.7 2.1
1998
1.3 2.6 3.6 4.6 5.7 7.2 8.9 11.8 17.0 37.3
28.6 10.3 2.1
EPH - 28 cities
1998 1999
1.3 2.6 3.6 4.6 5.8 7.1 9.0 11.7 16.9 37.3 1.4 2.7 3.7 4.8 5.9 7.3 9.1 11.9 17.0 36.2
28.2 10.2 2.2 26.0 9.9 2.0
2000
1.2 2.4 3.5 4.5 5.8 7.2 9.1 12.0 17.1 37.1
31.4 11.5 2.1
2001
1.0 2.2 3.3 4.3 5.4 7.0 8.8 11.8 17.1 39.2
38.5 13.5 2.3
2002
1.1 2.0 2.9 4.1 5.3 6.8 8.6 11.3 16.9 41.1
38.7 13.4 2.4
2003 EPH-C 2004-II
0.9 2.1 2.9 4.0 5.2 6.8 8.7 11.8 17.3 40.2 1.0 2.2 3.3 4.6 5.9 7.4 9.4 12.3 17.4 36.5
43.7 14.0 2.2 37.7 13.5 2.0
2005-I
1.0 2.3 3.5 4.6 5.8 7.4 9.3 12.1 17.0 36.9
35.2 12.0 2.1
2005-II
1.0 2.3 3.4 4.5 5.8 7.4 9.2 12.0 17.0 37.4
37.6 12.7 2.1
Source: Own calculations based on microdata from the EPH.
Note 1: (10/1)=income ratio between deciles 10 and 1; (90/10)=income ratio between percentiles 90 and 10,
and (95/80)=income ratio between percentiles 95 and 80.
Table 5.6 Distribution of equivalized household labor monetary income Inequality indices Argentina, 1992-2005
Gini Theil CV A(.5) A(1) A(2) E(0) E(2)
EPH-15 cities
1992
0.422 0.317 0.959 0.145 0.268 0.633 0.313 0.460
1993
0.427 0.325 0.984 0.149 0.278 0.506 0.325 0.484
1994
0.433 0.346 1.060 0.154 0.280 0.502 0.330 0.562
1995
0.470 0.410 1.164 0.182 0.328 0.577 0.397 0.677
1996
0.474 0.421 1.221 0.186 0.335 0.590 0.407 0.745
1997
0.469 0.393 1.081 0.179 0.330 0.574 0.400 0.584
1998
0.493 0.450 1.242 0.199 0.357 0.603 0.442 0.772
EPH - 28 cities
1998
0.492 0.450 1.249 0.198 0.355 0.597 0.437 0.780
1999
0.481 0.425 1.178 0.190 0.343 0.596 0.420 0.694
2000
0.497 0.449 1.200 0.202 0.368 0.646 0.459 0.719
2001
0.519 0.493 1.262 0.221 0.397 0.667 0.506 0.796
2002
0.536 0.539 1.374 0.235 0.413 0.660 0.533 0.944
2003
0.535 0.533 1.354 0.236 0.422 0.698 0.547 0.917
EPH-C
2004-II 0.499 0.469 1.414 0.209 0.384 0.676 0.485 1.000
2005-I
0.497 0.453 1.211 0.205 0.377 0.677 0.473 0.733
2005-II 0.503 0.490 1.546 0.213 0.386 0.675 0.487 1.196
Source: Own calculations based on microdata from the EPH.
CV=coefficient of variation. A(e) refers to the Atkinson index with a CES
function with parameter e. E(e) refers to the generalized entropy index with parameter e. E(1)=Theil.
39
Table 5.7 Distribution of household income Gini coefficient Argentina, 1992-2005
Per capita Equivalized Equivalized Equivalized Equivalized Equivalized Total Equivalized Equivalized Equivalized Equivalized
income
income
income
income
income
income household income A income A income A income A
EPH-15 cities
1992
0.450
1993
0.444
1994
0.453
1995
0.481
A 0.430 0.424 0.431 0.460
B 0.421 0.415 0.422 0.450
C 0.423 0.416 0.423 0.452
D 0.416 0.409 0.415 0.444
E 0.434 0.428 0.436 0.464
income 0.445 0.435 0.438 0.458
Age 0-10 Age 20-30 Age 40-50 Age 60-70
0.435 0.439 0.444 0.468
0.403 0.399 0.412 0.437
0.439 0.435 0.430 0.469
0.429 0.398 0.417 0.423
1996 1997
0.486 0.483
0.463 0.461
0.452 0.451
0.454 0.453
0.446 0.445
0.468 0.466
0.457 0.458
0.461 0.461
0.440 0.434
0.481 0.457
0.427 0.456
1998
0.502
EPH - 28 cities
0.480
0.470
0.471
0.463
0.484
0.471
0.480
0.454
0.474
0.469
1998 1999
0.502 0.491
0.478 0.468
0.468 0.458
0.470 0.460
0.461 0.451
0.483 0.473
0.470 0.459
0.476 0.469
0.456 0.448
0.472 0.469
0.468 0.454
2000 2001
0.504 0.522
0.483 0.501
0.472 0.491
0.475 0.494
0.466 0.485
0.488 0.506
0.467 0.480
0.500 0.517
0.452 0.466
0.485 0.504
0.446 0.469
2002 2003 EPH-C 2003-II * 2003-II 2004-I 2004-II 2005-I 2005-II
0.533 0.528 0.537 0.529 0.510 0.506 0.502 0.501
0.512 0.506 0.514 0.507 0.488 0.483 0.480 0.480
0.502 0.494 0.501 0.495 0.476 0.471 0.468 0.469
0.504 0.497 0.505 0.499 0.480 0.475 0.471 0.473
0.496 0.487 0.495 0.489 0.469 0.465 0.461 0.463
0.517 0.510 0.520 0.513 0.493 0.489 0.484 0.485
0.488 0.481 0.490 0.481 0.465 0.460 0.458 0.459
0.531 0.510 0.510 0.518 0.492 0.482 0.479 0.503
0.483 0.478 0.467 0.469 0.459 0.454 0.457 0.446
0.519 0.519 0.521 0.524 0.506 0.478 0.486 0.468
0.464 0.455 0.533 0.480 0.469 0.464 0.452 0.451
Source: Own calculations based on microdata from the EPH.
Note: Equivalized income A: theta=0.9, alpha1=0.5 and alpha2=0.75; B: theta=0.75, alpha1=0.5 and
alpha2=0.75; C: theta=0.9, alpha1=0.3 and alpha2=0.5, D: theta=0.75, alpha1=0.3 and alpha2=0.5; E:
Amsterdam scale. Adult equivalent equal to 0.98 for men between 14 and 17, 0.9 for women over 14, 0.52 for
children under 14, and 1 for the rest.
* computed using weights that ignore income non-response.
Table 5.8 Polarization EGR and Wolfson indices of bipolarization Argentina, 1992-2005
Household per capita incom
Equivalized income
EGR
EPH-15 cities
1992
0.151
Wolfson 0.401
EGR 0.140
Wolfson 0.377
1993
0.145
0.407
0.137
0.368
1994
0.151
0.414
0.140
0.381
1995
0.158
0.430
0.149
0.394
1996 1997
0.160 0.162
0.434 0.447
0.149 0.154
0.404 0.420
1998
0.165
0.454
0.156
0.424
EPH - 28 cities
1998
0.169
0.468
0.158
0.428
1999
0.165
0.462
0.152
0.421
2000 2001
0.170 0.177
0.477 0.503
0.160 0.163
0.441 0.456
2002
0.179
0.505
0.164
0.462
2003
0.176
0.493
0.167
0.459
EPH-C
2003-II * 2003-II 2004-I
0.183 0.183 0.172
0.528 0.528 0.503
0.169 0.169 0.161
0.482 0.482 0.459
2004-II
0.169
0.487
0.158
0.455
2005-I
0.164
0.486
0.155
0.445
2005-II
0.164
0.490
0.153
0.451
Source: Own calculations based on microdata from the EPH.
Note: EGR=Esteban, Gradin and Ray.
* computed using weights that ignore income non-response.
40
Table 6.1 Aggregate welfare Mean income from National Accounts Argentina, 1992-2005
Mean income (i) 1992 100.0 1993 104.8 1994 109.5 1995 105.0 1996 109.4 1997 116.8 1998 119.8 1999 114.3 2000 112.0 2001 105.8 2002 93.1 2003 100.2 2004 107.9 2005 115.4
Sen (ii) 100.0 106.0 108.9 99.1 102.4 109.8 108.5 105.9 101.0 92.0 79.2 85.9 96.9 104.7
Atk(1) (iii) 100.0 105.1 108.9 98.8 101.6 108.9 107.7 105.0 99.4 89.9 78.1 84.7 95.6 103.2
Atk(2) (iv) 100.0 103.4 109.7 92.4 87.9 98.8 95.9 92.0 80.8 70.3 65.2 67.0 82.8 88.5
Table 6.2 Aggregate welfare Mean income from EPH Argentina, 1992-2005
Mean income (i) 1992 100.0 1993 100.4 1994 99.1 1995 93.6 1996 91.5 1997 96.3 1998 101.5 1999 96.6 2000 94.6 2001 87.1 2002 61.2 2003 74.5 2004 82.6 2005 93.6
Sen (ii) 100.0 101.5 98.5 88.3 85.7 90.5 91.9 89.4 85.3 75.7 52.1 63.8 74.2 84.9
Atk(1) (iii) 100.0 100.7 98.5 88.1 85.0 89.8 91.3 88.7 84.0 74.1 51.4 63.0 73.2 83.6
Atk(2) (iv) 100.0 99.1 99.2 82.3 73.5 81.4 81.2 77.7 68.2 57.9 42.8 49.8 63.4 71.8
41
Table 7.1 Hourly wages By gender, age and education Argentina, 1992-2005
Total
EPH-15 cities
1992
4.0
1993
4.0
1994
4.5
1995
4.4
1996
4.3
1997
4.2
1998
4.4
EPH - 28 cities
1998
4.2
1999
4.0
2000
4.0
2001
4.0
2003
2.9
EPH-C
2003-II
3.2
2004-I
3.2
2004-II
3.2
2005-I
3.3
2005-II
3.7
with PJH
2002
2.8
2003
2.8
EPH-C
2003-II *
3.0
2003-II
3.1
2004-I
3.2
2004-II
3.2
2005-I
3.2
2005-II
3.6
Gender
Age
Female Male
(15-24) (25-64) (65 +)
Low
3.8
4.1
2.7
4.3
4.8
2.9
3.8
4.1
2.9
4.3
4.7
3.0
4.5
4.5
3.1
4.9
5.0
3.2
4.3
4.5
2.9
4.7
4.8
3.0
4.2
4.3
2.7
4.6
5.0
3.0
4.2
4.3
2.7
4.5
5.7
3.0
4.2
4.6
2.7
4.7
6.2
2.9
4.0
4.3
2.6
4.5
5.7
2.7
4.0
4.1
2.7
4.3
5.1
2.7
4.0
4.1
2.5
4.3
4.4
2.6
4.0
4.0
2.5
4.3
4.7
2.6
2.8
3.0
1.7
3.1
3.4
1.9
3.2
3.2
1.8
3.4
3.9
2.0
3.3
3.2
1.8
3.4
6.1
2.1
3.1
3.3
1.9
3.3
6.5
2.1
3.2
3.3
2.0
3.5
3.5
2.2
3.8
3.6
2.1
3.8
5.9
2.2
2.7
3.0
1.7
3.0
3.4
1.8
2.6
2.9
1.7
3.0
3.4
1.8
3.1
3.0
1.8
3.2
3.6
2.0
3.2
3.1
1.8
3.3
3.8
2.0
3.2
3.2
1.8
3.3
6.0
2.1
3.0
3.3
1.9
3.3
6.4
2.0
3.2
3.3
2.0
3.5
3.4
2.2
3.7
3.6
2.1
3.8
5.9
2.2
Source: Own calculations based on microdata from the EPH.
Education
Mid
High
3.8
6.4
3.7
6.5
4.1
7.4
4.0
7.8
3.7
7.0
3.7
7.0
3.8
7.7
3.6
7.3
3.6
6.8
3.5
6.7
3.5
6.6
2.4
4.7
2.6
5.0
2.8
4.9
2.8
5.4
2.8
5.4
2.9
6.3
2.4
4.8
2.4
4.6
2.5
4.9
2.6
5.0
2.8
4.9
2.7
5.4
2.8
5.3
2.9
6.3
Table 7.2 Hours of work By gender, age and education Argentina, 1992-2005
Gender
Age
Total
Female Male
(15-24) (25-64) (65 +)
Low
EPH-15 cities
1992
44.3
37.6
48.4
41.5
45.4
35.7
45.1
1993
44.6
37.8
48.8
42.2
45.6
36.8
44.8
1994
44.0
36.5
48.4
42.0
44.7
38.7
44.0
1995
43.5
36.2
48.0
41.2
44.3
37.0
43.2
1996
43.6
36.2
48.2
41.1
44.6
34.2
42.3
1997
43.8
37.3
47.8
41.3
44.7
35.2
43.0
1998
43.9
37.0
48.5
40.5
45.0
37.3
43.7
EPH - 28 cities
1998
44.0
37.1
48.5
40.9
45.0
37.7
43.8
1999
43.6
36.8
48.3
40.2
44.7
36.9
43.4
2000
43.1
36.6
47.5
39.6
44.1
37.7
42.4
2001
41.9
35.3
46.5
37.2
43.0
38.6
40.8
2003
41.1
34.6
45.6
36.9
42.0
38.2
40.2
EPH-C
2003-II
40.8
34.4
45.4
38.1
41.7
34.1
40.4
2004-I
41.7
35.3
45.9
37.9
42.9
35.1
41.6
2004-II
41.4
35.0
45.7
38.1
42.7
33.0
41.2
2005-I
42.1
35.5
46.4
39.5
43.0
35.3
42.0
2005-II
42.0
35.3
46.6
38.7
43.2
34.0
41.5
with PJH
2002
39.5
33.6
44.0
36.3
40.3
35.6
37.6
2003
39.6
33.0
44.7
35.5
40.4
38.0
37.7
2003-II * 39.1
32.5
44.2
36.5
39.9
33.4
37.5
2003-II
39.2
32.7
44.4
36.7
40.1
33.6
37.7
2004-I
40.3
33.6
45.1
36.8
41.3
34.7
39.0
2004-II
40.3
33.5
45.2
37.2
41.4
32.8
39.3
2005-I
41.0
34.1
45.9
38.7
41.8
35.1
39.9
2005-II
41.2
34.3
46.2
38.1
42.2
33.6
39.9
Source: Own calculations based on microdata from the EPH.
Education
Mid
High
45.4
40.8
46.2
41.6
45.8
40.6
45.0
41.6
45.7
42.2
45.9
41.6
45.7
41.4
45.9
41.4
45.5
41.2
44.8
41.4
44.1
40.2
43.0
39.5
42.8
38.7
43.3
39.6
42.6
40.0
43.2
40.6
43.7
40.2
41.4
39.4
41.6
39.2
40.8
38.3
41.0
38.4
41.9
39.5
41.5
39.8
42.3
40.4
43.0
40.1
42
Table 7.3 Labor income By gender, age and education Argentina, 1992-2005
Gender
Age
Total
Female Male
(15-24) (25-64) (65 +)
EPH-15 cities
1992
677.6
539.5 762.7
445.6 745.2 501.9
1993
694.6
551.9 782.0
459.3 761.6 531.0
1994
702.0
577.3 776.5
454.9 768.3 577.5
1995
693.4
537.5 786.7
416.0 762.6 563.9
1996
672.8
530.5 757.8
393.7 739.5 641.5
1997
668.2
540.7 745.7
394.8 732.9 611.9
1998
702.8
548.7 801.5
392.8 766.5 827.3
EPH - 28 cities
1998
664.0
519.1 754.4
370.0 725.2 767.9
1999
633.8
510.8 714.9
358.6 695.4 609.7
2000
620.8
504.9 698.0
340.3 680.9 543.6
2001
598.2
495.5 667.4
308.8 657.0 538.0
2003
428.1
348.4 482.3
220.3 464.4 441.3
EPH-C
2004-II 467.2
399.3 549.5
252.8 531.1 597.4
2005-I 494.8
410.8 582.7
287.2 563.1 412.9
2005-II 525.7
464.2 617.0
301.6 604.2 527.3
with PJH
2002
403.6
321.6 463.3
212.6 439.9 361.7
2003
423.4
343.5 477.8
219.9 458.2 440.8
EPH-C
2004-II 454.6
386.4 538.2
249.5 516.2 589.9
2005-I 482.7
398.3 572.4
284.0 548.8 405.2
2005-II 513.4
449.3 608.0
298.4 589.2 524.1
Source: Own calculations based on microdata from the EPH.
Education
Low
Mid
High
506.5 503.4 487.4 453.1 426.6 430.4 424.9
648.3 664.3 678.1 659.8 620.6 627.6 634.8
1053.1 1093.5 1124.4 1255.1 1136.5 1117.4 1230.9
405.7 393.5 378.7 350.8 249.2
609.4 578.9 565.9 542.9 373.7
1162.4 1077.0 1051.0 1020.3 701.7
303.5 329.2 328.2
432.5 446.6 479.0
807.6 840.3 919.8
226.6 245.8
352.6 370.1
715.7 699.5
292.5 317.1 318.0
424.1 440.6 470.5
802.8 835.8 915.9
Table 7.4 Hourly wages By type of work Argentina, 1992-2005
Type of work
EntrepreneursWage earnersSelf-employed
EPH-15 cities
1992
3.6
4.4
1993
3.8
4.4
1994
4.3
5.0
1995
9.2
4.1
4.5
1996
8.9
4.0
4.5
1997
8.0
4.0
4.4
1998
8.8
4.1
4.7
EPH - 28 cities
1998
8.5
3.9
4.3
1999
7.7
3.9
4.1
2000
7.2
3.9
4.0
2001
7.9
3.9
3.7
2003
5.9
2.8
2.8
EPH-C
2003-II
6.8
3.1
3.0
2004-I
5.5
3.2
3.0
2004-II
5.1
3.2
3.1
2005-I
6.3
3.2
3.1
2005-II
9.5
3.4
3.2
with PJH
2002
6.8
2.7
2.7
2003
5.9
2.7
2.8
EPH-C
2003-II *
6.3
3.0
2.8
2003-II
6.8
3.0
3.0
2004-I
5.4
3.2
2.9
2004-II
5.1
3.1
3.0
2005-I
6.3
3.1
3.0
2005-II
9.4
3.4
3.1
Formal workers
Informal workers
Salaried workers Self-employed Salaried Self-employed
Entrepreneurs Large firms Public sector professionals Small firms Unskilled
4.1
4.1
9.4
3.1
4.0
4.1
4.4
8.8
2.9
3.9
4.7
5.5
10.4
3.2
4.2
9.2
4.3
5.0
9.8
3.0
3.7
8.9
4.2
5.1
10.1
3.0
3.6
8.0
4.1
5.2
10.3
3.0
3.5
8.8
4.4
5.5
12.6
2.8
3.5
8.5
4.2
5.2
11.6
2.6
3.2
7.7
4.1
5.2
10.1
2.6
3.3
7.2
4.2
5.2
8.5
2.6
3.3
7.9
4.2
5.1
8.7
2.7
3.1
5.9
3.0
3.8
6.1
1.8
2.3
6.8
3.4
4.2
6.4
2.0
2.5
5.5
3.7
4.2
6.0
2.0
2.5
5.1
3.6
4.1
6.0
2.0
2.6
6.3
3.5
4.4
6.0
2.1
2.5
9.5
3.7
4.9
6.3
2.1
2.6
6.8
3.0
3.1
5.9
1.9
2.2
5.9
3.0
3.1
6.1
1.8
2.2
6.3
3.3
3.9
6.2
2.0
2.4
6.8
3.4
4.0
6.4
2.0
2.5
5.4
3.7
4.1
5.9
2.0
2.5
5.1
3.6
4.0
6.0
2.0
2.5
6.3
3.4
4.2
6.0
2.1
2.5
9.4
3.7
4.8
6.3
2.1
2.6
Source: Own calculations based on microdata from the EPH.
Note: in 1992 to 1994 public sector wages refer only to the GBA.
43
Table 7.5 Hours of work By type of work Argentina, 1992-2005
Type of work
EntrepreneursWage earnersSelf-employedZero-income
EPH-15 cities
1992
43.6
44.2
45.2
1993
44.5
44.3
41.8
1994
43.9
43.8
38.1
1995
56.0
43.0
42.9
40.9
1996
56.4
43.1
43.7
36.4
1997
57.6
43.5
42.6
39.1
1998
58.4
43.6
42.6
39.5
EPH - 28 cities
1998
57.8
43.5
43.5
40.4
1999
56.0
43.3
42.6
42.1
2000
57.2
42.9
41.4
40.3
2001
56.1
41.8
39.7
41.2
2003
54.8
41.4
38.2
38.5
EPH-C
2003-II
51.6
41.1
38.7
31.8
2004-I
53.0
42.1
38.9
30.1
2004-II
54.3
41.3
39.7
32.5
2005-I
53.2
42.2
39.9
35.5
2005-II
52.6
41.8
41.1
31.4
with PJH
2002
54.2
39.0
39.3
36.3
2003
54.8
39.3
38.4
38.7
2003-II *
51.9
38.8
39.0
31.9
2003-II
51.7
39.0
39.0
32.0
2004-I
53.1
40.2
39.1
29.9
2004-II
54.5
39.8
39.9
32.5
2005-I
53.2
40.7
40.0
36.4
2005-II
52.7
40.7
41.0
31.6
Formal workers
Informal workers
Salaried workers Self-employed Salaried Self-employed
Entrepreneurs Large firms Public sector professionals Small firms Unskilled Zero-income
45.6
40.5
39.2
42.5
44.8
45.2
46.1
41.6
42.8
42.5
44.4
41.8
46.0
40.3
41.7
41.1
44.1
38.1
56.0
46.6
38.7
40.9
39.7
43.2
40.9
56.4
46.8
39.6
43.3
40.0
43.7
36.4
57.6
47.1
39.1
41.5
40.5
42.8
39.1
58.4
47.3
39.4
41.4
40.6
42.8
39.5
57.8
47.4
39.1
42.0
40.6
43.8
40.4
56.0
47.2
38.2
42.3
41.1
42.6
42.1
57.2
47.0
38.1
41.5
40.6
41.4
40.3
56.1
46.3
37.4
40.5
38.9
39.6
41.2
54.8
46.1
36.7
39.2
38.7
38.0
38.5
51.6
45.8
37.6
37.8
37.5
38.8
31.8
53.0
46.2
39.1
39.2
38.5
38.9
30.1
54.3
45.9
38.9
39.4
36.9
39.8
32.5
53.2
46.6
38.7
41.0
38.3
39.7
35.5
52.6
45.9
39.4
39.7
37.3
41.3
31.4
54.2
44.9
32.6
40.0
38.1
39.2
36.3
54.8
45.4
32.1
39.3
38.5
38.2
38.7
51.9
44.8
32.1
37.5
37.2
39.2
31.9
51.7
44.8
32.3
37.8
37.4
39.2
32.0
53.1
45.2
33.7
39.6
38.3
39.1
29.9
54.5
45.1
34.4
39.4
36.9
40.0
32.5
53.2
45.9
34.5
41.0
38.1
39.8
36.4
52.7
45.3
36.3
39.6
37.0
41.3
31.6
Source: Own calculations based on microdata from the EPH.
Note: in 1992 to 1994 public sector hours of work refer only to the GBA.
44
Table 7.6 Labor income By type of work Argentina, 1992-2005
Formal workers
Informal workers
Type of work
Salaried workers Self-employed Salaried Self-employed
EntrepreneursWage earnersSelf-employed Entrepreneurs Large firms Public sector professionals Small firms Unskilled
EPH-15 cities
1992
662.5
751.8
774.3
775.3
1427.9
534.7
682.7
1993
701.1
699.8
788.7
834.9
1437.6
486.8
617.1
1994
701.4
735.6
798.8
837.3
1500.0
480.0
631.6
1995
1865.5
659.2
645.8
1865.5
754.7
753.9
1415.7
420.2
533.5
1996
1848.0
647.8
631.8
1848.0
750.5
776.4
1374.9
412.5
510.6
1997
1699.7
646.1
618.4
1699.7
727.4
806.2
1478.5
397.3
483.6
1998
1937.7
673.9
653.8
1937.7
775.6
828.2
1683.4
404.8
489.8
EPH - 28 cities
1998
1833.4
638.1
610.3
1833.4
739.5
781.0
1578.9
383.4
468.6
1999
1619.6
625.2
550.6
1619.6
729.0
769.4
1472.3
373.2
429.3
2000
1458.3
624.5
530.4
1458.3
733.9
770.0
1319.1
378.7
425.2
2001
1453.7
611.5
478.5
1453.7
736.0
736.2
1257.9
358.7
378.3
2003
1131.7
436.6
346.3
1131.7
521.9
540.8
842.6
246.9
265.5
EPH-C
2004-II
1004.3
498.0
413.8
1004.3
605.3
633.4
870.0
272.6
330.5
2005-I
1231.5
517.9
414.9
1231.5
625.2
653.3
901.7
295.3
320.1
2005-II
1507.6
548.0
435.2
1507.6
628.8
749.6
878.8
291.6
351.0
with PJH
2002
1218.3
404.9
328.4
1218.3
519.2
416.4
833.4
247.9
255.0
2003
1129.9
431.9
342.6
1129.9
520.2
528.1
833.5
244.8
263.6
EPH-C
2004-II
998.4
484.5
404.3
998.4
599.4
587.2
862.5
268.6
323.4
2005-I
1229.9
504.8
407.9
1229.9
618.0
608.9
901.0
291.6
315.2
2005-II
1501.1
536.0
424.9
1501.1
622.5
710.8
872.7
287.6
343.2
Source: Own calculations based on microdata from the EPH.
Note: in 1992 to 1994 public sector earnings refer only to the GBA.
45
Table 7.7 Hourly wages By sector Argentina, 1992-2005
Primary
Industry
Industry
Utilities &
activities low tech high tech Construction Commerce transportation
EPH-15 cities
1992
4.3
3.1
3.9
3.1
3.6
4.1
1993
7.5
3.1
4.1
3.6
3.3
4.0
1994
4.8
3.3
4.4
3.4
3.5
4.4
1995
6.6
3.2
4.9
3.3
3.3
4.2
1996
4.0
3.2
4.4
3.1
3.2
3.9
1997
4.0
3.0
4.3
3.3
3.0
4.2
1998
3.5
3.1
4.6
3.1
3.1
4.1
EPH - 28 cities
1998
3.8
3.0
4.4
2.9
3.0
3.9
1999
3.6
2.9
4.2
3.0
2.9
3.5
2000
3.5
2.9
4.4
3.0
2.9
3.5
2001
5.1
3.0
4.2
3.2
2.8
3.8
2003
2.8
2.5
3.0
2.0
2.0
2.5
EPH-C
2003-II
5.3
2.1
3.4
2.4
2.1
3.1
2004-I
3.9
2.2
3.7
2.4
2.2
3.6
2004-II
4.3
2.4
4.2
2.4
2.2
3.1
2005-I
4.3
2.6
3.9
2.4
2.4
3.0
2005-II
2.5
3.8
2.4
2.5
3.2
with PJH
2002
2.6
2.6
2.8
2.2
1.9
2.8
2003
2.6
2.4
3.0
2.0
1.9
2.5
EPH-C
2003-II *
4.6
2.0
3.3
2.3
2.1
2.9
2003-II
5.1
2.1
3.3
2.3
2.1
3.0
2004-I
3.9
2.2
3.7
2.4
2.2
3.5
2004-II
4.2
2.4
4.2
2.3
2.2
3.1
2005-I
4.1
2.5
3.9
2.3
2.4
2.9
2005-II
2.5
3.8
2.4
2.5
3.1
Source: Own calculations based on microdata from the EPH.
Skilled services 6.4 6.3 7.0 6.9 6.3 6.3 7.1 6.8 5.8 6.1 6.3 4.3 4.5 4.5 4.9 4.8 5.7 4.7 4.3 4.3 4.4 4.5 4.9 4.8 5.7
public education & administration Health
4.3
4.5
4.4
4.6
5.3
5.6
5.1
5.1
5.3
5.3
5.1
5.1
5.7
5.5
5.4
5.2
5.3
5.4
5.4
5.2
5.1
5.1
3.8
3.8
4.4
4.4
4.4
4.4
4.2
4.2
4.3
4.3
4.9
4.6
2.9
3.5
3.2
3.4
4.0
4.2
4.2
4.3
4.2
4.4
4.1
4.1
4.2
4.2
4.8
4.6
Domestic servants 3.2 3.3 3.6 3.5 3.5 3.6 3.3 3.0 3.0 2.9 2.9 2.1 2.1 2.1 2.2 2.1 2.1 2.1 2.1 2.1 2.1 2.1 2.2 2.1 2.1
46
Table 7.8 Hours of work By sector Argentina, 1992-2005
Primary
Industry
Industry
Utilities &
activities
EPH-15 cities
1992
57.1
1993
49.8
1994
52.6
1995
53.3
1996
50.5
1997
53.2
1998
52.7
EPH - 28 cities
1998
52.9
1999
49.3
2000
55.2
2001
50.5
2003
49.1
EPH-C
2003-II
47.4
2004-I
48.4
2004-II
50.4
2005-I
49.7
2005-II
45.3
with PJH
2002
47.2
2003
43.8
2003-II *
38.7
2003-II
38.6
2004-I
42.6
2004-II
42.0
2005-I
43.5
2005-II
41.7
low tech 46.6 48.3 48.0 47.9 45.5 48.5 48.3 48.0 47.0 47.9 47.2 43.3 42.7 44.3 42.0 43.9 44.2 46.2 42.6 41.9 42.1 42.7 40.8 42.8 43.3
high tech Construction Commerce transportation
47.4
46.6
48.9
50.5
47.1
44.4
49.7
54.8
46.0
45.3
49.4
52.4
43.7
42.3
49.7
53.2
45.3
42.7
49.9
54.3
47.0
41.2
50.9
52.8
46.5
42.9
49.2
55.1
46.6
43.4
49.7
54.8
45.0
43.9
49.5
56.3
44.3
41.8
47.8
53.4
43.2
38.7
47.1
53.2
45.0
38.5
46.6
52.5
43.1
38.6
45.8
50.4
44.9
39.1
46.3
50.9
44.6
39.8
46.6
50.0
45.2
41.4
46.6
51.2
45.0
41.4
46.3
51.8
43.3
38.1
46.4
49.7
44.7
37.9
46.3
52.6
42.8
38.3
45.5
50.5
42.9
38.4
45.7
50.4
44.7
39.2
46.2
50.9
44.5
39.8
46.6
50.1
45.1
41.2
46.4
51.2
45.0
41.0
46.2
51.8
Source: Own calculations based on microdata from the EPH.
Skilled services 42.4 42.4 44.0 44.1 43.9 44.7 45.5 45.5 44.7 45.2 44.7 42.8 42.9 41.5 41.7 42.2 42.6 42.9 42.7 42.8 42.8 41.4 41.6 42.1 42.5
Public Education & administration Health
42.3
38.6
43.9
37.1
42.9
36.8
42.7
36.7
42.0
36.5
41.1
36.8
42.5
37.3
42.1
37.0
41.3
36.0
41.4
36.5
40.8
35.5
40.2
34.8
42.0
33.9
43.0
35.3
43.3
35.7
42.9
35.6
43.7
36.2
33.8
33.1
35.1
32.5
36.5
31.0
36.8
31.2
37.7
32.8
39.5
33.3
39.7
33.4
41.6
34.3
Domestic servants 31.3 31.7 27.9 27.1 26.5 26.5 26.2 26.9 28.1 28.7 26.7 25.7 28.5 28.6 25.7 29.1 26.6 25.5 26.0 28.0 28.4 28.4 26.0 29.1 26.7
Table 7.9 Labor income By sector Argentina, 1992-2005
Primary
Industry
Industry
Utilities &
activities low tech high tech Construction Commerce transportation
EPH-15 cities
1992 1993
929.5 884.6
562.7 586.7
705.4 772.8
578.2 609.3
639.9 614.5
802.3 863.5
1994
1006.1
573.3
750.0
559.8
622.6
805.7
1995
1226.1
583.3
815.9
506.0
586.0
793.7
1996
770.7
559.1
769.1
478.9
588.6
765.4
1997
833.8
558.2
759.0
473.4
581.2
759.0
1998
649.8
EPH - 28 cities
572.4
820.4
476.3
556.4
772.8
1998
656.8
556.9
786.9
450.2
537.9
744.4
1999
677.4
519.8
719.5
477.7
525.9
698.7
2000
715.7
524.8
744.2
430.7
489.6
663.3
2001 2003
937.6 622.6
514.1 403.0
690.0 524.5
386.7 264.9
463.8 324.8
661.1 475.5
EPH-C
2004-II
856.3
411.6
668.1
344.9
395.1
568.8
2005-I
848.4
460.3
682.8
364.6
425.2
571.4
2005-II with PJH
448.4
674.3
368.6
445.3
615.3
2002
527.1
433.2
484.8
245.1
312.7
465.1
2003
620.1
395.5
520.7
263.2
321.7
473.2
EPH-C
2004-II
804.8
402.6
665.2
338.6
388.5
562.8
2005-I 2005-II
755.3 1662.2
447.2 441.0
678.2 670.1
358.9 362.4
420.8 438.1
565.3 611.1
Source: Own calculations based on microdata from the EPH.
Skilled
Public Education &
services administration Health
1013.8 1019.8 1130.6 1177.9 1063.7 1083.2 1238.1
796.9 831.3 848.6 860.3 859.9 832.2 953.5
690.6 723.7 730.8 669.4 690.4 686.7 727.8
1191.4 1036.0 1045.3 1046.0 697.6
881.7 850.7 848.0 804.7 612.4
693.6 674.7 680.1 663.9 463.8
734.7 778.9 852.0
711.5 718.5 839.6
568.1 575.0 626.6
770.6 696.3
404.6 598.5
423.7 459.6
730.7 774.6 848.1
667.2 681.5 802.0
544.0 553.6 606.3
Domestic servants 366.8 366.7 333.6 284.4 272.7 267.1 260.2 243.8 249.8 250.8 231.6 153.3 175.8 188.4 180.9 151.3 152.1 174.0 186.6 180.2
47
Table 7.10 Distribution of labor income Shares Argentina, 1992-2005
Salaried Self- employed Entrepreneurs
workers
Total
EPH-15 cities
1992
73.9
26.1
100.0
1993
73.7
26.3
100.0
1994
74.0
26.0
100.0
1995
68.1
21.1
10.8
100.0
1996
69.2
20.9
9.9
100.0
1997
70.0
20.3
9.7
100.0
1998
69.8
19.8
10.3
100.0
EPH - 28 cities
1998
71.9
18.8
9.3
100.0
1999
72.5
19.1
8.4
100.0
2000
72.6
18.8
8.6
100.0
2001
72.6
18.3
9.2
100.0
2002
72.6
18.3
9.1
100.0
2003
70.7
20.6
8.7
100.0
EPH-C
2004-II 72.8
18.0
9.2
100.0
2005-I 73.2
17.3
9.5
100.0
Source: Own calculations based on microdata from the EPH.
48
Table 7.11 Distribution of wages (primary activity) Gini coefficient Argentina, 1992-2005
Male workers aged 25-55
All
Education
All
Low
Mid
High
EPH-15 cities
1992
0.400
0.412
0.319
0.362
0.419
1993
0.390
0.397
0.305
0.354
0.402
1994
0.392
0.397
0.307
0.345
0.396
1995
0.410
0.420
0.311
0.371
0.417
1996
0.415
0.417
0.321
0.358
0.412
1997
0.413
0.404
0.326
0.331
0.397
1998
0.434
0.438
0.330
0.371
0.418
EPH - 28 cities
1998
0.435
0.436
0.330
0.371
0.419
1999
0.424
0.417
0.335
0.352
0.401
2000
0.433
0.437
0.352
0.377
0.403
2001
0.445
0.444
0.368
0.373
0.427
2003
0.440
0.461
0.335
0.391
0.458
EPH-C
2003-II
0.453
0.439
0.349
0.385
0.428
2004-I
0.455
0.440
0.366
0.381
0.435
2004-II
0.442
0.408
0.338
0.364
0.386
2005-I
0.429
0.412
0.336
0.355
0.403
2005-II
0.458
0.413
0.342
0.346
0.393
with PJH
2002
0.453
0.465
0.364
0.398
0.441
2003
0.439
0.460
0.326
0.391
0.458
EPH-C
2003-II * 0.454
0.438
0.349
0.391
0.425
2003-II
0.458
0.443
0.349
0.390
0.430
2004-I
0.460
0.442
0.367
0.382
0.434
2004-II
0.443
0.411
0.343
0.364
0.386
2005-I
0.431
0.416
0.343
0.357
0.406
2005-II
0.459
0.415
0.345
0.348
0.395
Source: Own calculations based on microdata from the EPH.
49
Table 7.12 Correlations hours of work-hourly wages Argentina, 1992-2005
All workers Urban
salaried
workers
EPH-15 cities
1992
-0.18
-0.17
1993
-0.22
-0.17
1994
-0.23
-0.20
1995
-0.18
-0.18
1996
-0.19
-0.17
1997
-0.21
-0.20
1998
-0.16
-0.17
EPH - 28 cities
1998
-0.16
-0.17
1999
-0.19
-0.18
2000
-0.19
-0.17
2001
-0.18
-0.18
2003
-0.16
-0.17
EPH-C
2004-II
-0.10
-0.08
2005-I
-0.18
-0.17
2005-II
-0.07
-0.10
with PJH
2002
-0.13
-0.10
2003
-0.16
-0.16
EPH-C
2004-II
-0.09
-0.08
2005-I
-0.17
-0.16
2005-II
-0.07
-0.09
Source: Own calculations based on microdata from the EPH.
50
Table 7.13 Ratio of hourly wages by educational group Prime-age males Argentina, 1992-2005
High/Medium High/Low Medium/Low
EPH-15 cities
1992
1.86
2.61
1.41
1993
1.85
2.57
1.39
1994
1.84
2.64
1.43
1995
1.99
2.81
1.41
1996
1.93
2.60
1.34
1997
1.95
2.64
1.35
1998
2.09
3.04
1.45
EPH - 28 cities
1998
2.05
2.97
1.45
1999
1.95
2.70
1.38
2000
1.97
2.84
1.44
2001
2.03
2.77
1.37
2003
2.11
3.02
1.43
EPH-C
2003-II
1.93
2.60
1.35
2004-I
1.88
2.49
1.33
2004-II
1.77
2.46
1.39
2005-I
1.87
2.46
1.32
2005-II
1.92
2.56
1.33
with PJH
2002
2.09
3.02
1.44
2003
2.14
3.09
1.45
2003-II *
1.93
2.61
1.35
2003-II
1.97
2.69
1.36
2004-I
1.91
2.55
1.34
2004-II
1.78
2.52
1.41
2005-I
1.89
2.49
1.32
2005-II
1.93
2.57
1.33
Source: Own calculations based on microdata from the EPH.
51
Table 7.14 Mincer equation Estimated coefficients of educational dummies Argentina, 1992-2005
All workers Men
Women
Primary Secondary College
Primary Secondary College
Primary
EPH-15 cities
1992
0.287
0.451
0.557
-0.095 0.462
0.454
0.153
1993
0.090
0.453
0.606
0.005
0.322
0.496
0.113
1994 1995
0.178 0.140
0.429 0.510
0.716 0.707
-0.013 -0.027
0.431 0.277
0.404 0.291
0.187 0.183
1996
0.159
0.475
0.716
0.024
0.292
0.605
0.167
1997
0.173
0.428
0.624
0.072
0.322
0.452
0.178
1998
0.253
0.476
0.779
0.011
0.350
0.208
0.162
EPH - 28 cities
1998
0.227
0.457
0.757
0.042
0.450
0.523
0.161
1999
0.175
0.381
0.753
0.032
0.340
0.341
0.183
2000
0.158
0.487
0.726
-0.023 0.304
0.331
0.118
2001
0.232
0.414
0.785
0.160
0.320
0.198
0.252
2003
0.256
0.414
0.784
0.103
0.399
0.624
0.250
EPH-C 2003-II
0.104
0.408
0.680
0.159
0.420
0.682
0.094
2004-I
0.174
0.394
0.678
0.125
0.297
0.374
0.194
2004-II
0.183
0.390
0.548
0.133
0.408
0.588
0.280
2005-I
0.115
0.358
0.669
0.159
0.247
0.393
0.140
2005-II
0.129
0.390
0.653
0.126
0.377
0.667
0.106
with PJH
2002
0.306
0.472
0.825
0.041
0.432
0.300
0.211
2003
0.240
0.409
0.804
0.083
0.344
0.676
0.233
EPH-C
2003-II * 0.126
0.402
0.688
0.176
0.312
0.377
0.101
2003-II 2004-I
0.084 0.173
0.350 0.399
0.693 0.677
0.132 0.076
0.325 0.251
0.647 0.367
0.100 0.195
2004-II
0.156
0.382
0.545
0.140
0.395
0.573
0.268
2005-I
0.098
0.366
0.677
0.100
0.232
0.389
0.134
2005-II
0.181
0.392
0.650
0.058
0.300
0.492
0.119
Source: Own calculations based on microdata from the EPH.
Urban salaried workers
Men
Women
Secondary College
Primary Secondary
0.432 0.419 0.362 0.403 0.336 0.439 0.456
0.560 0.657 0.687 0.664 0.732 0.598 0.679
0.007 -0.025 0.003 0.020 0.073 -0.053 0.000
0.383 0.387 0.416 0.364 0.232 0.363 0.469
0.442 0.326 0.421 0.364 0.344
0.668 0.670 0.697 0.675 0.723
0.025 -0.057 -0.070 0.109 -0.182
0.457 0.403 0.387 0.393 0.380
0.403 0.318 0.328 0.321 0.371
0.601 0.671 0.588 0.612 0.626
0.196 0.166 0.157 0.204 0.012
0.303 0.269 0.314 0.302 0.336
0.344 0.346
0.753 0.737
0.029 -0.136
0.384 0.349
0.383 0.392 0.322 0.333 0.327 0.375
0.610 0.616 0.673 0.595 0.619 0.623
0.194 0.196 0.172 0.143 0.159 0.006
0.322 0.322 0.270 0.316 0.322 0.338
College 0.226 0.351 0.403 0.480 0.503 0.430 0.450 0.467 0.512 0.577 0.566 0.552 0.631 0.607 0.580 0.559 0.603 0.602 0.585 0.628 0.632 0.610 0.582 0.558 0.609
52
Table 7.15 Mincer equation Dispersion in unobservables and gender wage gap Argentina, 1992-2005
Dispersion in unobservables
Gender wage gap
All workers
Urban salaried
Urban salaried
Men Women Men Women
workers
EPH-15 cities
1992
0.640 0.655 0.528 0.503
0.866
1993
0.605 0.617 0.538 0.509
0.875
1994
0.616 0.613 0.533 0.517
0.904
1995
0.631 0.817 0.540 0.517
0.922
1996
0.626 0.640 0.550 0.520
0.914
1997
0.632 0.644 0.561 0.536
0.898
1998
0.619 0.833 0.560 0.543
0.857
EPH - 28 cities
1998
0.617 0.649 0.563 0.542
0.854
1999
0.612 0.778 0.569 0.545
0.889
2000
0.640 0.825 0.567 0.558
0.869
2001
0.665 0.900 0.596 0.570
0.887
2003
0.666 0.637 0.585 0.536
0.890
EPH-C
2003-II 0.680 0.757 0.617 0.587
0.934
2004-I 0.680 0.942 0.599 0.577
0.890
2004-II 0.695 0.679 0.584 0.562
0.904
2005-I 0.638 0.890 0.586 0.581
0.889
2005-II 0.629 0.688 0.570 0.574
0.882
with PJH
2002
0.695 0.878 0.574 0.530
0.868
2003
0.661 0.633 0.582 0.522
0.892
EPH-C
2003-II * 0.683 0.885 0.617 0.591
0.932
2003-II 0.720 0.769 0.617 0.591
0.929
2004-I
0.689
0.958
0.602
0.577
0.895
2004-II 0.712 0.689 0.586 0.566
0.900
2005-I
0.643
0.889
0.590
0.585
0.887
2005-II 0.637 0.842 0.572 0.580
0.877
Source: Own calculations based on microdata from the EPH.
53
Table 7.16 Share of adults in the labor force Argentina, 1992-2005
Adults (25-64)
Age
Gender
Education
Total (15-24) (25-64) (65 +) Female Male
Low Medium High
15 main cities
1992 0.560 0.492 0.685 0.113
0.483 0.914 0.608 0.708 0.841
1993 0.568 0.494 0.698 0.116
0.507 0.912 0.618 0.714 0.854
1994 0.565 0.504 0.698 0.095
0.508 0.910 0.618 0.714 0.856
1995 0.573 0.502 0.712 0.095
0.533 0.913 0.643 0.730 0.856
1996 0.575 0.506 0.714 0.103
0.530 0.916 0.638 0.729 0.843
1997 0.582 0.494 0.726 0.119
0.550 0.919 0.649 0.739 0.861
1998 0.584 0.463 0.735 0.137
0.563 0.929 0.659 0.742 0.873
28 main cities
1998 0.571 0.450 0.722 0.129
0.545 0.923 0.652 0.729 0.854
1999 0.577 0.444 0.730 0.138
0.563 0.919 0.648 0.745 0.858
2000 0.581 0.439 0.736 0.138
0.571 0.920 0.663 0.752 0.842
2001 0.569 0.417 0.733 0.119
0.565 0.921 0.660 0.739 0.852
2003 0.564 0.401 0.735 0.134
0.575 0.908 0.641 0.750 0.846
EPH-C
2003-II 0.599 0.466 0.767 0.149
0.620 0.927 0.685 0.773 0.853
2004-I 0.602 0.469 0.765 0.161
0.619 0.926
0.690 0.768 0.849
2004-II 0.605 0.462 0.771 0.159
0.625 0.929 0.698 0.782 0.862
2005-I 0.599 0.448 0.764 0.168
0.613 0.927
0.690 0.773 0.855
2005-II 0.608 0.450 0.772 0.176
0.629 0.926 0.702 0.769 0.870
with PJH
2002 0.580 0.414 0.749 0.125
0.600 0.915 0.684 0.752 0.849
2003 0.577 0.414 0.744 0.135
0.596 0.909 0.662 0.760 0.844
2003-II 0.614 0.482 0.776 0.152
0.641 0.929 0.708 0.781 0.855
2004-I 0.616 0.479 0.774 0.164
0.641 0.927
0.714 0.776 0.849
2004-II 0.618 0.471 0.780 0.161
0.646 0.930 0.720 0.789 0.862
2005-I 0.610 0.456 0.769 0.172
0.629 0.928
0.707 0.776 0.856
2005-II 0.617 0.457 0.776 0.179
0.643 0.926 0.713 0.774 0.870
Source: Own calculations based on microdata from the EPH.
54
Table 7.17 Share of adults employed Argentina, 1992-2005
Adults (25-64)
Age
Gender
Education
Total (15-24) (25-64) (65 +) Female Male
Low Medium High
15 main cities
1992 0.522 0.424 0.651 0.107
0.459 0.869
0.572 0.672 0.816
1993 0.516 0.397 0.651 0.113
0.466 0.861
0.567 0.669 0.816
1994 0.495 0.389 0.632 0.083
0.451 0.835
0.550 0.640 0.812
1995 0.476 0.353 0.618 0.080
0.451 0.806
0.538 0.632 0.796
1996 0.473 0.341 0.617 0.090
0.446 0.804
0.529 0.632 0.769
1997 0.500 0.368 0.645 0.106
0.476 0.830
0.563 0.651 0.800
1998 0.509 0.354 0.660 0.122
0.496 0.846
0.570 0.670 0.823
28 main cities
1998 0.500 0.344 0.652 0.115
0.484 0.842
0.568 0.662 0.807
1999 0.497 0.332 0.649 0.123
0.493 0.825
0.561 0.662 0.791
2000 0.495 0.318 0.649 0.120
0.498 0.819
0.564 0.662 0.784
2001 0.464 0.283 0.621 0.103
0.485 0.774
0.532 0.622 0.777
2003 0.470 0.259 0.643 0.121
0.508 0.789
0.543 0.645 0.780
EPH-C
2003-II 0.501 0.307 0.674 0.128
0.530 0.831
0.576 0.678 0.783
2004-I 0.509 0.317 0.679 0.141
0.534 0.837
0.598 0.674 0.778
2004-II 0.525 0.330 0.696 0.144
0.549 0.853
0.615 0.700 0.807
2005-I 0.521 0.321 0.690 0.150
0.540 0.853
0.612 0.694 0.796
2005-II 0.542 0.339 0.711 0.161
0.566 0.868
0.633 0.702 0.827
with PJH
2002 0.476 0.275 0.641 0.108
0.516 0.780
0.565 0.641 0.762
2003 0.487 0.275 0.655 0.121
0.533 0.793
0.572 0.658 0.779
2003-II 0.519 0.327 0.688 0.131
0.558 0.836
0.608 0.690 0.785
2004-I 0.526 0.331 0.691 0.145
0.561 0.840
0.630 0.685 0.778
2004-II 0.540 0.341 0.707 0.147
0.575 0.857
0.643 0.709 0.808
2005-I 0.533 0.331 0.698 0.154
0.559 0.855
0.635 0.699 0.796
2005-II 0.552 0.346 0.716 0.164
0.581 0.868
0.647 0.708 0.827
Source: Own calculations based on microdata from the EPH.
55
Table 7.18 Unemployment rates Argentina, 1992-2005
Age
Gender
Total (15-24) (25-64) (65 +) Female Male
15 main cities
1992 0.068 0.137 0.050 0.056
0.051 0.049
1993 0.092 0.197 0.066 0.029
0.081 0.057
1994 0.123 0.227 0.094 0.130
0.112 0.083
1995 0.169 0.297 0.132 0.159
0.154 0.118
1996 0.177 0.326 0.137 0.122
0.158 0.123
1997 0.141 0.256 0.112 0.113
0.134 0.097
1998 0.128 0.236 0.101 0.108
0.119 0.089
28 main cities
1998 0.125 0.235 0.097 0.108
0.111 0.087
1999 0.139 0.253 0.111 0.107
0.124 0.102
2000 0.148 0.275 0.117 0.131
0.129 0.110
2001 0.184 0.322 0.153 0.138
0.141 0.160
2003 0.166 0.355 0.125 0.101
0.116 0.131
EPH-C
2003-II 0.164 0.342 0.121 0.137
0.144 0.104
2004-I 0.154 0.324 0.113 0.121
0.136 0.096
2004-II 0.133 0.285 0.098 0.092
0.121 0.081
2005-I 0.131 0.283 0.096 0.107
0.118 0.080
2005-II 0.109 0.248 0.079 0.084
0.100 0.063
with PJH
2002 0.179 0.335 0.145 0.136
0.140 0.148
2003 0.157 0.335 0.119 0.100
0.106 0.128
2003-II 0.154 0.322 0.114 0.138
0.130 0.101
2004-I 0.146 0.309 0.107 0.118
0.124 0.094
2004-II 0.126 0.276 0.093 0.090
0.111 0.079
2005-I 0.125 0.274 0.092 0.104
0.110 0.078
2005-II 0.106 0.242 0.077 0.084
0.096 0.062
Source: Own calculations based on microdata from the EPH.
Adults (25-64) Education Low Medium High
0.058 0.082 0.110 0.163 0.171 0.132 0.135
0.051 0.062 0.104 0.133 0.133 0.118 0.097
0.030 0.045 0.052 0.070 0.089 0.071 0.057
0.129 0.134 0.149 0.194 0.152
0.092 0.112 0.120 0.159 0.140
0.055 0.078 0.069 0.088 0.078
0.159 0.133 0.119 0.113 0.098
0.124 0.122 0.106 0.103 0.087
0.082 0.083 0.063 0.070 0.049
0.174 0.136 0.141 0.118 0.107 0.103 0.093
0.148 0.134 0.116 0.117 0.102 0.100 0.085
0.102 0.078 0.082 0.084 0.063 0.070 0.049
56
Table 7.19 Duration of unemployment Argentina, 1992-2005
Adults (25-64)
Age Total (15-24) (25-64) (65 +)
Gender
Female
Male
Education
Low
Medium
15 main cities
1992
3.8
4.0
3.7
4.3
4.2
3.3
3.1
4.0
1993
5.1
5.2
5.1
3.4
5.4
4.9
4.3
5.9
1994
5.4
5.2
5.5
8.8
6.6
4.5
4.9
5.8
1995
6.8
6.3
7.0 11.6
8.2
5.9
1996
8.1
7.9
8.1 13.2
10.6
6.1
6.1
7.4
7.2
8.3
1997
6.6
6.6
6.3 13.0
7.8
5.0
5.3
7.1
1998
6.2
5.7
6.7
4.0
9.0
4.6
5.9
6.8
28 main cities
1998
6.1
5.5
6.5
4.7
8.7
4.7
1999
6.4
5.9
6.6
8.7
8.6
4.9
5.8
6.6
5.5
6.5
2000
6.6
6.3
6.8
6.0
8.9
5.1
6.1
7.1
2001
6.8
7.0
6.7
9.5
8.3
5.7
6.1
6.8
2003
8.5
7.6
9.0 11.1
12.0
7.2
8.0
9.3
EPH-C
2003-II 11.7 10.6 12.5 13.2
13.9
11.0
12.3
11.9
2004-I 10.6 9.2 11.6 11.1
13.3
9.7
10.5
11.8
2004-II 10.0 9.2 10.6 9.8
11.9
9.3
9.6
11.1
2005-I
9.8
8.4 10.7 11.4
12.0
9.3
9.5
11.7
2005-II 10.2 8.8 11.0 13.5
12.6
9.1
10.5
10.9
with PJH
2002
8.9
8.7
8.8 18.4
11.1
7.1
7.2
9.4
2003
8.5
7.6
8.9 11.1
11.8
7.2
7.9
9.3
2003-II 11.7 10.6 12.4 13.0
13.8
11.0
12.4
11.8
2004-I 10.7 9.2 11.6 11.1
13.4
9.8
10.7
11.9
2004-II 10.0 9.2 10.6 9.8
12.0
9.2
2005-I
9.8
8.4 10.7 11.4
12.0
9.3
2005-II 10.2 8.8 11.0 13.7
12.6
9.1
9.6
11.2
9.5
11.8
10.6
10.9
Source: Own calculations based on microdata from the EPH.
High 4.8 5.8 6.5 10.0 10.3 7.5 9.0 8.4 9.3 8.5 8.4 10.2 13.6 12.7 11.6 10.8 12.2 11.1 10.2 13.5 12.7 11.7 10.7 12.2
Table 7.20 Labor force, employment rate and unemployment rate Argentina, 2003-2005 Encuesta Permanente de Hogares Continua
Alternative 1
Alternative 2
Labor force Employment Unemployment
Labor force Employment
I-2003
45.6
36.3
20.4
44.2
33.5
II-2003
45.6
37.4
17.8
44.4
35.1
III-2003
45.7
38.2
16.3
44.7
35.9
IV-2003
45.7
39.1
14.5
44.6
36.7
I-2004
45.4
38.9
14.4
44.3
36.6
II-2004
46.2
39.4
14.8
45.3
37.4
III-2004
46.2
40.1
13.2
45.1
38.0
IV-2004
45.9
40.4
12.1
45.0
38.5
I-2005
45.2
39.4
13.0
44.4
37.7
II-2005
45.6
40.1
12.1
44.7
38.4
III-2005
46.2
41.1
11.1
45.3
39.7
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.
Source: INDEC, boletines de prensa.
Unemployment 24.3 21.0 19.6 17.7 17.4 17.4 15.7 14.5 14.9 13.9 12.5
57
Table 7.21 Age, gender and educational structure of employment Argentina, 1992-2005
Gender
Age
Female Male
(0-14) (15-24) (25-40) (41-64)
15 main cities
1992
37.2
62.8
0.5
19.1
39.0
38.6
1993
37.6
62.4
0.5
18.3
38.4
39.8
1994
37.2
62.8
0.3
18.8
39.7
38.7
1995
38.1
61.9
0.3
18.3
40.9
38.2
1996
38.0
62.0
0.6
20.4
37.4
38.9
1997
38.3
61.7
0.2
17.8
39.3
39.6
1998
39.5
60.5
0.2
17.4
39.3
39.7
28 main cities
1998
39.0
61.0
0.3
17.3
39.8
39.4
1999
40.0
60.0
0.4
19.6
36.8
39.8
2000
40.2
59.8
0.3
19.0
37.7
39.8
2001
40.9
59.1
0.2
15.5
39.8
41.5
2003
40.8
59.2
0.1
14.0
40.3
41.9
EPH-C
2003-II
40.5
59.5
0.6
17.8
35.9
41.8
2004-I
40.4
59.6
0.7
18.1
36.5
40.6
2004-II
40.1
59.9
0.6
18.0
36.8
40.6
2005-I
40.0
60.0
0.6
17.8
36.5
40.8
2005-II
40.7
59.3
0.6
17.6
37.5
40.0
with PJH
2002
42.2
57.8
0.3
17.4
37.9
41.3
2003
42.9
57.1
0.1
14.1
41.0
41.3
2003-II
42.5
57.5
0.5
18.0
36.8
41.0
2004-I
42.6
57.4
0.6
18.0
37.3
40.2
2004-II
42.1
57.9
0.6
17.8
37.5
40.3
2005-I
41.8
58.2
0.5
17.7
37.1
40.6
2005-II
42.3
57.7
0.6
17.3
38.0
39.9
Source: Own calculations based on microdata from the EPH.
(65 +) 2.8 3.0 2.4 2.4 2.7 3.1 3.3 3.2 3.3 3.2 3.0 3.7 3.9 4.1 4.0 4.2 4.3 3.1 3.5 3.7 3.9 3.8 4.1 4.2
Education Low Medium
39.8
38.8
38.0
39.1
37.7
39.2
40.9
37.7
36.4
38.9
37.7
37.7
36.3
38.1
37.4
37.7
35.8
38.5
35.6
38.2
34.8
37.5
30.6
39.0
28.6
39.2
29.4
38.7
31.8
40.3
31.6
40.0
31.4
39.2
34.2
38.0
32.7
38.8
30.7
39.1
31.6
38.5
34.0
39.8
33.9
39.3
33.1
38.9
High 21.4 22.9 23.2 21.5 24.7 24.6 25.5 24.9 25.7 26.2 27.8 30.4 32.2 32.0 27.9 28.4 29.4 27.8 28.4 30.2 29.9 26.2 26.8 28.0
58
Table 7.22 Regional structure of employment Argentina, 1992-2005
GBA Pampeana Cuyo
NOA Patagonia
15 main cities
1992
74.5
15.7
2.6
4.6
2.7
1993
75.6
15.0
2.6
4.2
2.6
1994
74.2
15.7
2.8
4.5
2.8
1995
73.4
15.4
2.9
5.1
3.2
1996
73.3
15.3
3.0
4.9
3.5
1997
73.1
15.6
2.9
5.1
3.3
1998
73.5
15.3
2.9
5.0
3.3
28 main cities
1998
57.2
21.8
6.1
8.0
2.6
1999
57.0
21.7
6.2
8.2
2.6
2000
55.9
22.6
6.2
8.3
2.7
2001
55.1
22.6
6.3
8.6
2.9
2003
55.7
22.6
6.3
8.2
3.0
EPH-C
2003-II
56.2
22.5
6.3
8.6
2.4
2004-I
56.5
22.3
6.4
8.3
2.5
2004-II
56.6
22.3
6.1
8.5
2.5
2005-I
55.8
22.7
6.3
8.6
2.5
2005-II
56.5
22.4
6.0
8.5
2.5
with PJH
2002
55.4
22.6
6.3
8.4
2.8
2003
55.2
22.6
6.1
8.6
2.9
2003-II
55.7
22.3
6.3
9.0
2.4
2004-I
55.9
22.1
6.4
8.7
2.5
2004-II
56.2
22.1
6.1
8.8
2.5
2005-I
55.5
22.5
6.3
8.9
2.5
2005-II
56.0
22.4
6.1
8.8
2.4
Source: Own calculations based on microdata from the EPH.
NEA 4.3 4.3 4.3 4.5 4.2 4.1 4.1 4.0 4.2 4.1 4.6 4.6 4.4 4.4 4.3 4.4 4.3
59
Table 7.23
Structure of employment
By type of work
Argentina, 1992-2005
Labor relationship
Entrepreneurs Wage earners Self-employed Zero income
(i)
(ii)
(iii)
(iv)
GBA
1992
5.2
70.0
23.6
1.2
1993
5.5
68.7
24.6
1.3
1994
4.6
70.1
23.9
1.5
15 main cities
1995
4.9
71.0
22.7
1.4
1996
4.5
72.2
21.7
1.6
1997
4.8
72.7
21.1
1.4
1998
4.7
73.4
20.7
1.2
28 main cities
1998
4.6
72.5
21.6
1.3
1999
4.5
72.5
21.6
1.4
2000
4.6
72.1
22.1
1.2
2001
4.4
71.3
23.4
0.9
2003
4.2
69.9
24.7
1.2
EPH-C
2003-II
4.1
71.9
22.2
1.8
2004-I
4.1
73.0
21.2
1.7
2004-II
4.4
72.9
21.3
1.4
2005-I
4.0
73.7
21.2
1.1
2005-II
4.3
73.9
20.5
1.3
with PJH
2002
4.0
72.0
23.0
1.0
2003
3.8
71.8
23.2
1.1
2003-II
3.8
73.7
20.9
1.7
2004-I
3.8
74.5
20.2
1.6
2004-II
4.1
74.1
20.4
1.4
2005-I
3.8
74.7
20.4
1.1
2005-II
4.1
74.5
20.2
1.2
Type of firm Large (v)
Small (vi)
Public (vii)
36.1
48.3
15.6
35.1
49.4
15.6
36.7
47.7
15.6
37.0
47.5
15.5
35.1
48.7
16.3
34.7
49.0
16.3
33.2
49.8
16.9
31.8
50.5
17.6
30.9
51.4
17.7
31.2
52.3
16.5
33.3
51.3
15.4
33.1
51.1
15.8
34.0
50.5
15.5
35.5
48.9
15.6
28.8
48.8
22.4
29.3
48.5
22.2
29.8
49.1
21.1
31.9
48.8
19.3
31.8
49.0
19.2
33.0
48.7
18.4
34.5
47.9
17.6
Labor category
Entrepreneurs
(i)
15 main cities
1995
5.2
1996
4.6
1997
4.9
1998
4.8
28 main cities
1998
4.7
1999
4.6
2000
4.8
2001
4.5
2003
4.3
EPH-C
2003-II
4.3
2004-I
4.3
2004-II
4.6
2005-I
4.2
2005-II
4.5
with PJH
2002
4.1
2003
4.0
2003-II
4.0
2004-I
4.0
2004-II
4.3
2005-I
4.0
2005-II
4.3
Salaried workers Self-employed SalariedSelf-employeWorkers with
Large firms Public sector professionals Small firms Unskilled zero income
(ii)
(iii)
(iv)
(v)
(vi)
(vii)
34.1
15.3
33.4
15.2
35.0
15.3
35.4
15.2
3.3
20.3
20.5
1.5
3.2
22.6
19.2
1.7
3.0
21.8
18.5
1.4
3.0
22.2
18.1
1.3
33.5
16.0
33.2
16.1
31.6
16.5
30.5
17.3
29.8
17.2
3.0
22.3
19.1
1.4
2.9
22.4
19.3
1.4
2.9
22.9
20.1
1.2
3.1
22.6
21.0
1.0
3.8
22.0
21.6
1.3
29.5
16.5
31.7
15.4
31.5
15.7
32.4
15.4
33.8
15.5
3.3
24.5
20.0
1.9
3.3
24.6
19.0
1.7
3.5
24.2
18.9
1.5
3.8
24.4
18.5
1.2
3.6
23.3
18.0
1.3
27.5
22.2
28.3
21.7
28.2
21.0
30.4
19.3
30.3
19.2
31.4
18.3
32.9
17.6
3.3
21.7
20.3
1.1
3.6
21.0
20.3
1.2
3.1
23.1
18.9
1.7
3.1
23.6
18.1
1.6
3.3
23.3
18.1
1.4
3.6
23.7
17.9
1.1
3.4
22.8
17.7
1.3
Source: Own calculations based on microdata from the EPH.
60
Table 7.24 Structure of employment Share of informal workers (productive definition) Argentina, 1992-2005
Adults (25-64) Youths (15-24)
Age
Gender
Education
Gender
Total (15-24) (25-64) (65 +) Female Male
Low Medium High
Female Male
15 main cities
1995 0.422 0.496 0.397 0.634 0.440 0.370
0.546 0.410 0.123
0.506 0.489
1996 0.435 0.517 0.408 0.654 0.440 0.388
0.584 0.422 0.139
0.518 0.516
1997 0.417 0.482 0.395 0.596 0.423 0.377
0.567 0.401 0.139
0.482 0.482
1998 0.416 0.481 0.393 0.586 0.424 0.371
0.567 0.409 0.134
0.491 0.475
28 main cities
1998 0.428 0.504 0.402 0.613 0.432 0.383
0.571 0.420 0.141
0.523 0.492
1999 0.432 0.510 0.405 0.643 0.438 0.383
0.587 0.424 0.143
0.499 0.518
2000 0.442 0.515 0.419 0.650 0.439 0.405
0.592 0.450 0.154
0.512 0.517
2001 0.446 0.542 0.420 0.626 0.431 0.413
0.606 0.449 0.162
0.563 0.528
2003 0.448 0.581 0.419 0.580 0.408 0.427
0.634 0.467 0.155
0.572 0.587
EPH-C
2003-II 0.464 0.578 0.433 0.614 0.455 0.418
0.646 0.507 0.170
0.588 0.572
2004-I 0.453 0.582 0.416 0.645 0.440 0.400
0.615 0.482 0.168
0.616 0.561
2004-II 0.445 0.547 0.414 0.647 0.438 0.397
0.616 0.463 0.147
0.608 0.510
2005-I 0.441 0.520 0.415 0.635 0.450 0.390
0.616 0.462 0.155
0.556 0.499
2005-II 0.426 0.513 0.396 0.625 0.415 0.383
0.603 0.447 0.143
0.553 0.490
with PJH
2002 0.430 0.553 0.400 0.632 0.370 0.422
0.563 0.438 0.159
0.506 0.588
2003 0.425 0.536 0.399 0.579 0.375 0.417
0.564 0.444 0.156
0.498 0.568
2003-II 0.437 0.532 0.409 0.598 0.413 0.407
0.570 0.477 0.171
0.511 0.547
2004-I 0.433 0.548 0.398 0.635 0.403 0.395
0.549 0.461 0.168
0.547 0.549
2004-II 0.429 0.524 0.399 0.638 0.406 0.394
0.559 0.448 0.147
0.562 0.499
2005-I 0.427 0.499 0.403 0.623 0.422 0.388
0.563 0.451 0.155
0.511 0.492
2005-II 0.418 0.500 0.391 0.617 0.400 0.384
0.572 0.437 0.144
0.526 0.483
Source: Own calculations based on microdata from the EPH.
Norte: Informal=salaried workers in small firms, non-professional self-employed and zero-income workers
61
Table 7.25 Structure of employment Share of informal workers (social-protection definition) Argentina, 1992-2005
Age
Gender
Total (15-24) (25-64) (65 +)
Female Male
15 main cities 1992
0.312 0.507 0.241 0.548
0.290 0.210
1993
0.319 0.487 0.259 0.554
0.320 0.219
1994
0.291 0.475 0.231 0.404
0.282 0.198
1995
0.331 0.532 0.269 0.517
0.325 0.230
1996 1997
0.351 0.362
0.530 0.537
0.295 0.307
0.624 0.570
0.343 0.356
0.264 0.275
1998
0.371 0.562 0.311 0.582
0.353 0.281
28 main cities
1998
0.379 0.590 0.315 0.573
0.359 0.284
1999
0.383 0.583 0.325 0.565
0.367 0.294
2000 2001
0.385 0.387
0.598 0.604
0.331 0.333
0.446 0.490
0.383 0.375
0.292 0.301
2003
0.388 0.656 0.330 0.531
0.340 0.323
EPH-C
2003-II
0.437 0.708 0.374 0.621
0.420 0.338
2004-I
0.433 0.676 0.371 0.594
0.408 0.343
2004-II 2005-I
0.435 0.430
0.690 0.657
0.374 0.376
0.592 0.535
0.408 0.429
0.348 0.336
2005-II
0.423 0.642 0.368 0.588
0.402 0.341
with PJH
2002
0.441 0.692 0.387 0.498
0.430 0.349
2003
0.449 0.691 0.398 0.538
0.436 0.363
2003-II 2004-I
0.494 0.486
0.738 0.702
0.438 0.433
0.644 0.613
0.506 0.497
0.379 0.378
2004-II
0.483 0.708 0.431 0.612
0.493 0.377
2005-I
0.474 0.676 0.427 0.560
0.502 0.364
2005-II
0.458 0.658 0.410 0.603
0.469 0.359
Source: Own calculations based on microdata from the EPH.
Norte: Informal= Absence of right to have a pension when retired.
Adults (25-64) Education Low Medium High
0.347 0.361 0.339 0.372 0.440 0.432 0.465
0.211 0.234 0.209 0.237 0.253 0.293 0.269
0.111 0.141 0.098 0.141 0.154 0.155 0.168
0.469 0.474 0.478 0.516 0.503
0.272 0.300 0.318 0.306 0.332
0.169 0.179 0.176 0.174 0.184
0.566 0.540 0.571 0.551 0.561
0.390 0.388 0.353 0.359 0.360
0.205 0.212 0.203 0.221 0.196
0.585 0.598 0.647 0.626 0.645 0.625 0.616
0.383 0.393 0.453 0.444 0.403 0.396 0.398
0.178 0.197 0.222 0.223 0.212 0.230 0.203
Youths (15-24) Gender Female Male
0.504 0.454 0.436 0.531 0.546 0.519 0.553
0.509 0.508 0.499 0.533 0.519 0.549 0.568
0.590 0.585 0.581 0.638 0.656
0.591 0.582 0.609 0.580 0.656
0.703 0.698 0.712 0.659 0.643
0.711 0.663 0.676 0.655 0.642
0.700 0.713 0.751 0.741 0.742 0.695 0.670
0.685 0.672 0.728 0.674 0.687 0.663 0.650
62
Table 7.26 Structure of employment By sector Argentina, 1992-2005
Primary
Industry
Industry
Utilities &
activities low tech high tech Construction Commerce transportation
15 main cities
1992
0.9
8.7
12.3
6.4
25.0
7.5
1993
0.8
8.1
11.7
7.1
25.2
7.8
1994
0.8
6.7
12.1
7.5
23.5
8.9
1995
0.9
7.1
11.1
7.0
22.5
9.0
1996
0.7
6.9
10.3
7.3
23.0
9.2
1997
0.8
6.5
10.6
7.5
21.8
9.0
1998
0.7
5.6
10.4
8.1
22.2
8.5
28 main cities
1998
1.0
5.8
9.6
8.5
23.3
8.2
1999
1.0
5.6
9.0
8.4
22.8
9.0
2000
0.8
5.8
8.2
7.9
24.0
8.7
2001
1.1
5.4
8.3
7.2
24.0
8.5
2003
1.3
6.3
7.2
6.7
23.2
8.2
EPH-C
2003-II
1.4
7.9
6.6
7.3
24.8
7.7
2004-I
1.5
7.8
6.9
7.9
25.0
7.4
2004-II
1.3
7.9
7.1
8.0
25.4
7.8
2005-I
1.1
7.7
7.3
8.1
24.0
7.6
2005-II
1.3
7.6
6.9
8.7
24.3
7.6
with PJH
2002
1.4
5.5
7.3
6.7
21.8
7.6
2003
1.4
6.1
6.7
6.5
22.0
7.6
2003-II
1.7
7.7
6.1
7.1
23.3
7.1
2004-I
1.7
7.8
6.4
7.6
23.7
6.9
2004-II
1.5
7.9
6.6
7.8
24.2
7.4
2005-I
1.4
7.7
6.9
8.0
23.1
7.2
2005-II
1.4
7.6
6.6
8.6
23.6
7.3
Source: Own calculations based on microdata from the EPH.
Skilled services 7.9 7.8 8.7 9.9 10.0 10.2 10.5 9.5 9.7 9.6 9.0 10.2 9.8 9.8 9.3 10.5 9.9 9.1 9.4 9.0 9.1 8.7 9.9 9.4
Public Education & administration Health
6.2
17.6
6.4
17.1
6.7
17.2
7.1
17.8
7.6
17.3
7.1
18.5
7.2
19.2
7.7
18.9
7.6
19.1
7.8
19.3
8.4
20.2
7.5
22.2
7.6
18.9
7.3
18.7
7.2
18.4
7.1
18.9
7.2
18.9
10.3
23.3
9.1
24.1
8.9
21.3
8.7
20.5
8.1
20.2
7.7
20.4
7.6
20.3
Domestic servants 7.6 8.0 7.8 7.8 7.5 8.0 7.6 7.6 7.7 7.9 7.9 7.3 8.0 7.7 7.7 7.8 7.6 6.9 7.0 7.6 7.5 7.5 7.7 7.7
Table 7.27 Structure of employment By sector (CIIU -1 digit) Argentina, 1992-2005
Agro
15 main cities
1992
0.9
1993
0.8
1994
0.8
1995
0.9
1996
0.7
1997
0.8
1998
0.7
28 main cities
1998
1.0
1999
1.0
2000
0.8
2001
1.1
2003
1.3
EPH-C
2003-II
0.9
2004-I
1.0
2004-II
0.8
2005-I
0.7
2005-II
0.9
with PJH
2002
1.4
2003
1.5
2003-II
1.3
2004-I
1.2
2004-II
1.0
2005-I
1.0
2005-II
1.0
Restauranansportation Fishing MiningManufacturing Utilities Construction Commerce & hotels& commu Finance
21.4
0.9
6.5
20.1
0.6
7.2
19.0
0.8
7.6
18.5
0.7
7.1
17.5
0.9
7.4
17.4
0.7
7.7
16.3
0.6
8.2
22.5
2.9 6.7
2.4
22.6
3.0 7.3
2.2
20.7
3.1 8.3
2.7
19.5
3.3 8.3
2.8
20.4
3.0 8.5
2.6
19.4
2.8 8.4
2.9
19.7
2.9 8.0
2.8
15.6
0.6
8.7
14.9
0.6
8.5
14.3
0.6
8.0
14.1
0.6
7.3
13.7
0.6
6.8
20.8
2.9 7.7
2.5
20.2
3.0 8.6
2.4
21.1
3.4 8.3
2.5
21.1
3.3 8.1
2.5
20.9
2.8 7.8
2.4
0.1
0.3
14.5
0.6
7.3
0.1
0.4
14.7
0.5
7.9
0.1
0.4
15.0
0.5
8.0
0.1
0.3
14.9
0.5
8.1
0.1
0.3
14.5
0.5
8.7
21.9
2.9 7.1
1.8
21.4
3.6 6.9
1.8
21.9
3.5 7.3
1.6
20.4
3.5 7.1
1.8
20.9
3.4 7.1
1.8
13.1
0.5
6.8
13.1
0.5
6.6
0.1
0.3
13.8
0.5
7.1
0.1
0.4
14.3
0.5
7.6
0.1
0.4
14.5
0.5
7.8
0.1
0.3
14.6
0.5
8.0
0.1
0.3
14.2
0.5
8.6
19.3
2.9 7.3
2.3
19.8
2.7 7.2
2.2
20.6
2.8 6.6
1.7
20.3
3.4 6.4
1.7
20.8
3.4 6.9
1.5
19.6
3.5 6.7
1.7
20.3
3.3 6.8
1.7
Source: Own calculations based on microdata from the EPH.
Business Public
Healt & Other Domestic Foreign
services administrationTeachingcial servic services servants rganization
5.7
6.3
6.3
5.6
4.3
7.7
5.8
6.5
7.1
5.3
3.4
8.1
6.2
6.7
7.1
5.5
3.7
7.9
7.3
7.2
7.0
5.6
4.0
7.9
7.5
7.8
6.2
6.1
3.8
7.7
7.5
7.2
6.7
6.8
3.6
8.2
7.9
7.4
7.5
6.2
4.1
7.7
7.2
7.8
7.6
6.1
3.8
7.7
7.5
7.8
7.7
5.8
4.1
7.9
7.4
8.0
7.7
5.8
4.3
8.0
6.7
8.6
8.5
5.6
4.5
8.1
8.0
7.6
9.6
6.1
4.9
7.5
8.0
7.5
8.2
5.5
5.3
8.0
0.1
8.0
7.2
7.6
5.2
5.8
7.7
0.0
7.7
7.2
7.4
5.6
5.4
7.7
0.0
8.7
7.1
7.4
5.6
5.9
7.8
0.0
8.1
7.2
7.7
5.5
5.7
7.6
0.0
6.9
10.5
9.5
6.6
5.9
7.0
7.4
9.2
9.8
6.9
5.9
7.1
7.4
8.9
8.4
7.3
5.6
7.6
0.0
7.4
8.7
7.7
6.7
6.1
7.5
0.0
7.2
8.1
7.6
7.0
5.7
7.5
0.0
8.2
7.7
7.4
6.7
6.4
7.7
0.0
7.7
7.5
7.7
6.5
6.0
7.7
0.0
63
Table 7.28 Child labor By equivalized household income quintiles Argentina, 1992-2005
Gender
Equivalized income quintile
Total
Female Male
1
2
3
4
15 main cities
1992
0.021
0.012
0.030
0.016
0.015
0.032
0.022
1993 1994
0.020 0.013
0.010 0.005
0.029 0.022
0.019 0.015
0.019 0.013
0.041 0.017
0.005 0.010
1995
0.010
0.007
0.013
0.011
0.004
0.007
0.021
1996
0.012
0.009
0.015
0.010
0.016
0.011
0.020
1997
0.009
0.004
0.014
0.010
0.014
0.002
0.003
1998
0.010
0.007
0.012
0.020
0.011
0.009
0.001
28 main cities
1998
0.011
0.008
0.014
0.020
0.011
0.011
0.007
1999
0.008
0.003
0.012
0.014
0.004
0.007
0.004
2000
0.005
0.003
0.007
0.008
0.006
0.001
0.000
2001
0.007
0.003
0.010
0.009
0.009
0.005
0.001
2003
0.003
0.001
0.005
0.004
0.002
0.002
0.000
EPH-C 2003-II
0.016
0.012
0.019
0.024
0.011
0.023
0.013
2004-I
0.020
0.012
0.028
0.030
0.022
0.021
0.013
2004-II 0.015
0.012
0.018
0.026
0.012
0.008
0.007
2005-I
0.015
0.012
0.018
0.022
0.007
0.016
0.002
2005-II 0.019
0.012
0.025
0.033
0.013
0.004
0.008
with PJH
2002
0.007
0.004
0.009
0.009
0.008
0.002
0.001
2003
0.003
0.001
0.005
0.004
0.002
0.002
0.000
2003-II
0.016
0.012
0.019
0.024
0.011
0.023
0.014
2004-I
0.020
0.012
0.028
0.030
0.022
0.021
0.013
2004-II 2005-I
0.015 0.015
0.012 0.012
0.019 0.018
0.026 0.022
0.012 0.007
0.008 0.016
0.007 0.002
2005-II 0.019
0.012
0.025
0.033
0.013
0.004
0.008
Source: Own calculations based on microdata from the EPH.
5 0.002 0.007 0.007 0.010 0.000 0.000 0.000 0.000 0.006 0.001 0.000 0.000 0.001 0.003 0.001 0.002 0.012 0.004 0.000 0.001 0.003 0.001 0.002 0.012
Table 7.29 Right to receive social security (pensions) By gender and education Argentina, 1992-2005
Age
Gender
Total
(15-24)
(25-64)
(65 +)
Female
15 main cities
1992
0.692
0.493
0.759
0.452
0.710
1993
0.685
0.513
0.741
0.446
0.680
1994
0.711
0.525
0.769
0.596
0.718
1995
0.671
0.468
0.731
0.483
0.675
1996
0.650
0.470
0.705
0.376
0.657
1997
0.639
0.463
0.693
0.430
0.644
1998
0.630
0.438
0.689
0.418
0.647
28 main cities
1998
0.622
0.410
0.685
0.427
0.641
1999
0.618
0.417
0.675
0.435
0.633
2000
0.616
0.402
0.669
0.554
0.617
2001
0.614
0.396
0.667
0.510
0.625
2003
0.612
0.344
0.670
0.469
0.660
EPH-C
2003-II
0.564
0.292
0.626
0.379
0.580
2004-I
0.569
0.324
0.629
0.406
0.592
2004-II
0.566
0.310
0.626
0.408
0.592
2005-I
0.571
0.343
0.624
0.465
0.571
2005-II
0.579
0.358
0.632
0.412
0.598
with PJH
2002
0.560
0.308
0.613
0.502
0.570
2003
0.552
0.309
0.602
0.462
0.564
2003-II
0.507
0.262
0.562
0.356
0.494
2004-I
0.516
0.298
0.567
0.387
0.503
2004-II
0.518
0.292
0.569
0.388
0.507
2005-I
0.527
0.324
0.573
0.440
0.498
2005-II
0.543
0.342
0.590
0.397
0.531
Source: Own calculations based on microdata from the EPH.
Male 0.790 0.781 0.802 0.770 0.736 0.725 0.719 0.716 0.706 0.708 0.699 0.677 0.662 0.657 0.652 0.664 0.659 0.651 0.637 0.621 0.622 0.623 0.636 0.641
Adults (25-64)
Education
Low
Medium
0.653 0.639 0.661 0.628 0.560 0.568 0.535
0.789 0.766 0.791 0.763 0.747 0.707 0.731
0.531 0.526 0.522 0.484 0.497
0.728 0.700 0.682 0.694 0.668
0.434 0.460 0.429 0.449 0.439
0.610 0.612 0.647 0.641 0.640
0.415 0.402 0.353 0.374 0.355 0.375 0.384
0.617 0.607 0.547 0.556 0.597 0.604 0.602
High 0.889 0.859 0.902 0.859 0.846 0.845 0.832 0.831 0.821 0.824 0.826 0.816 0.795 0.788 0.797 0.779 0.804 0.822 0.803 0.778 0.777 0.788 0.770 0.797
64
Table 7.30 Access to labor health insurance By gender and education Argentina, 1992-2004
Age
Gender
15 main cities
Total
(15-24)
(25-64)
(65 +)
Female
1992
0.664
0.478
0.726
0.448
0.670
1993
0.653
0.474
0.712
0.397
0.648
1994
0.689
0.510
0.746
0.531
0.695
1995 1996
0.655 0.620
0.452 0.450
0.714 0.671
0.482 0.375
0.657 0.619
1997
0.623
0.453
0.675
0.417
0.620
1998
0.620
0.434
0.678
0.405
0.636
28 main cities
1998 1999
0.610 0.606
0.404 0.405
0.672 0.663
0.412 0.421
0.629 0.617
2000
0.585
0.384
0.635
0.510
0.583
2001
0.584
0.380
0.633
0.472
0.596
2003
0.605
0.331
0.664
0.465
0.656
EPH-C
2003-II 2004-I
0.560 0.568
0.291 0.321
0.620 0.626
0.406 0.426
0.578 0.592
2004-II
0.569
0.319
0.629
0.419
0.593
2005-I
0.573
0.345
0.627
0.461
0.574
2005-II
0.579
0.358
0.632
0.419
0.601
with PJH 2002
0.549
0.309
0.600
0.492
0.558
2003
0.546
0.297
0.598
0.458
0.561
2003-II
0.505
0.267
0.558
0.384
0.493
2004-I
0.516
0.300
0.566
0.408
0.504
2004-II 2005-I
0.522 0.532
0.302 0.328
0.572 0.577
0.401 0.440
0.508 0.502
2005-II
0.545
0.345
0.592
0.407
0.536
Source: Own calculations based on microdata from the EPH.
Male 0.761 0.753 0.779 0.754 0.705 0.711 0.706 0.702 0.696 0.674 0.663 0.670 0.653 0.652 0.656 0.667 0.657 0.637 0.631 0.614 0.619 0.627 0.641 0.640
Adults (25-64)
Education
Low
Medium
0.616 0.599 0.634 0.606 0.537 0.550 0.522
0.764 0.745 0.768 0.746 0.714 0.696 0.719
0.517 0.515 0.492 0.457 0.491
0.715 0.690 0.652 0.661 0.661
0.424 0.446 0.427 0.444 0.437
0.603 0.611 0.647 0.640 0.640
0.410 0.397 0.348 0.365 0.355 0.374 0.385
0.602 0.601 0.543 0.556 0.598 0.604 0.603
High 0.852 0.833 0.886 0.852 0.797 0.818 0.822 0.819 0.805 0.780 0.787 0.812 0.792 0.794 0.804 0.793 0.807 0.803 0.799 0.776 0.783 0.795 0.785 0.800
65
Table 7.31 Labor benefits Argentina, 1992-2005
Permanent job 13th month Holidays
Employment pro
15 main cities
1992
0.709
0.709
1993
0.706
0.703
1994
0.725
0.724
1995
0.694
0.690
1996
0.827
0.661
0.658
1997
0.827
0.634
0.630
1998
0.840
0.633
0.632
28 main cities
1998
0.834
0.628
0.626
1999
0.853
0.625
0.623
2000
0.849
0.624
0.625
2001
0.838
0.622
0.621
2003
0.844
0.618
0.620
EPH-C
2003-II
0.837
0.592
0.592
2004-I
0.846
0.599
0.602
2004-II
0.835
0.589
0.588
2005-I
0.841
0.596
0.601
2005-II
0.848
0.600
0.599
with PJH
2002
0.791
0.563
0.563
2003
0.798
0.557
0.558
2003-II
0.745
0.532
0.533
0.068
2004-I
0.763
0.544
0.548
0.061
2004-II
0.760
0.540
0.541
0.055
2005-I
0.776
0.554
0.560
0.047
2005-II
0.797
0.564
0.566
0.037
Source: Own calculations based on microdata from the EPH.
66
Table 8.1 Educational structure Adults 25-65 Argentina, 1992-2005
Low
EPH - 15 cities (1)
1992
47.7
1993
45.4
1994
45.6
EPH- 15 cities (2)
1995
47.5
1996
43.4
1997
43.7
1998
42.9
EPH - 28 cities
1998
43.4
1999
41.9
2000
41.9
2001
41.1
2002
39.5
2003
38.4
EPH-C
2004-II
38.3
2005-I
38.4
2005-II
37.2
All Medium 34.5 35.7 35.6 34.5 35.8 35.4 35.8 35.5 35.9 35.4 35.7 36.4 37.0 36.6 36.1 36.4
High 17.8 18.8 18.8 18.0 20.8 20.9 21.3 21.1 22.2 22.7 23.2 24.2 24.7 25.1 25.5 26.4
Males
Low
Medium
High
46.1
35.5
18.5
44.8
35.7
19.5
45.0
36.2
18.8
47.2
34.7
18.2
42.6
36.6
20.7
43.6
36.2
20.2
42.6
37.2
20.3
43.4
36.5
20.1
42.0
37.4
20.6
41.9
36.7
21.4
41.4
37.0
21.6
40.3
37.1
22.7
39.1
37.6
23.3
38.8
38.2
23.0
38.9
37.2
23.9
38.6
37.0
24.4
Females
Low
Medium
49.2
33.6
46.0
35.8
46.2
35.1
47.8
34.3
44.1
35.1
43.9
34.7
43.2
34.6
43.4
34.7
41.9
34.5
41.9
34.3
40.8
34.5
38.7
35.7
37.7
36.4
37.9
35.2
37.9
35.2
36.1
35.9
High 17.2 18.2 18.7 17.9 20.8 21.5 22.2 21.9 23.6 23.9 24.7 25.5 25.9 26.9 27.0 28.0
Source: Own calculations based on microdata from the EPH.
Working males
Low
Medium
High
44.7
36.7
18.6
44.3
36.9
18.8
44.5
37.0
18.5
45.9
35.5
18.6
40.8
37.5
21.6
41.9
37.7
20.4
41.5
38.0
20.6
42.4
37.4
20.3
41.1
38.4
20.5
40.6
38.1
21.4
39.9
38.4
21.7
40.0
37.4
22.6
37.7
38.8
23.4
37.4
38.9
23.8
37.3
37.9
24.8
37.2
37.5
25.3
Working females
Low
Medium
High
38.7
33.2
28.2
35.0
36.6
28.4
36.2
33.8
30.1
37.6
34.5
27.8
34.2
34.3
31.6
34.4
32.7
32.9
33.6
33.1
33.3
33.8
33.2
33.0
31.7
34.0
34.4
32.2
33.3
34.5
31.5
32.8
35.8
31.8
34.3
33.9
26.4
35.5
38.2
30.9
33.9
35.2
31.1
33.8
35.2
28.7
33.9
37.5
Table 8.2 Years of education By age and gender Argentina, 1992-2005
(25-65)
(10-20)
Female Male All Female Male All
EPH - 15 cities (1)
1992
9.3 9.5 9.4
7.8 7.6 7.7
1993
9.4 9.6 9.5
7.9 7.7 7.8
1994
9.5 9.6 9.5
7.8 7.7 7.8
EPH- 15 cities (2)
1995
9.6 9.6 9.6
7.8 7.5 7.7
1996
9.6 9.7 9.7
7.6 7.3 7.5
1997
9.9 9.9 9.9
8.1 7.8 7.9
1998
9.9 9.9 9.9
8.2 7.8 8.0
EPH - 28 cities
1998
9.9 9.9 9.9
8.1 7.7 7.9
1999 10.1 10.0 10.0 8.2 7.8 8.0
2000 10.1 10.0 10.1 8.2 7.8 8.0
2001 10.2 10.1 10.2 8.3 7.8 8.0
2002 10.4 10.1 10.2 8.3 7.8 8.0
2003 10.4 10.2 10.3 8.6 8.2 8.4
EPH-C
2003 10.8 10.4 10.6 8.1 7.9 8.0
2004-I 10.7 10.4 10.6 8.3 8.0 8.2
2004-II 10.5 10.3 10.5 8.2 7.7 7.9
2005-I 10.6 10.4 10.5 8.4 7.9 8.2
2005-II 10.7 10.4 10.6 8.1 7.6 7.9
(21-30) Female Male All
(31-40) Female Male All
10.9 10.8 10.8 11.0 10.9 11.0 11.2 10.9 11.0
10.1 10.0 10.0 10.1 10.0 10.1 10.2 10.0 10.1
11.1 10.6 10.9 11.2 10.6 10.9 11.2 10.7 11.0 11.4 10.8 11.1
10.4 10.2 10.3 10.4 10.1 10.3 10.6 10.4 10.5 10.6 10.3 10.5
11.4 10.7 11.1 11.6 10.8 11.2 11.5 10.7 11.1 11.6 11.0 11.3 11.7 11.1 11.4 11.9 11.2 11.6
10.5 10.3 10.4 10.7 10.5 10.6 10.9 10.6 10.7 10.9 10.5 10.7 11.1 10.6 10.8 11.1 10.7 10.9
11.9 11.1 11.6 11.9 11.3 11.6 11.8 11.2 11.5 11.7 11.2 11.5 11.9 11.2 11.6
11.5 11.0 11.3 11.3 10.8 11.1 11.2 10.8 11.0 11.2 11.0 11.1 11.4 10.9 11.2
Source: Own calculations based on microdata from the EPH.
(41-50)
(51-60)
Female Male All Female Male All
9.2 9.3 9.2 9.4 9.4 9.4 9.4 9.6 9.5
8.0 8.8 8.4 8.2 8.7 8.5 8.2 8.9 8.6
9.3 9.3 9.3 9.5 9.5 9.5 9.8 9.6 9.7 9.9 9.9 9.9
8.4 8.8 8.6 8.4 8.7 8.6 8.6 8.9 8.7 8.7 9.1 8.9
9.8 9.8 9.8 10.1 10.0 10.1 10.1 9.8 10.0 10.2 9.8 10.0 10.4 9.9 10.2 10.4 10.2 10.3
8.6 9.0 8.8 8.7 9.0 8.8 8.6 9.3 8.9 9.0 9.4 9.2 9.0 9.3 9.2 9.1 9.3 9.2
10.6 10.1 10.4 10.7 10.2 10.5 10.4 10.1 10.2 10.4 10.1 10.3 10.8 10.5 10.6
9.6 9.6 9.6 9.6 9.6 9.6 9.4 9.5 9.5 9.7 9.7 9.7 9.6 9.5 9.5
(61+) Female Male All 6.7 7.6 7.1 7.0 7.8 7.3 6.9 7.9 7.3 6.8 8.0 7.3 6.9 7.9 7.3 7.1 8.0 7.5 7.0 8.0 7.4 6.9 7.9 7.3 7.0 7.9 7.3 7.1 8.0 7.5 7.2 8.1 7.5 7.4 8.3 7.7 7.3 8.3 7.7 7.7 8.5 8.1 7.7 8.4 8.0 7.6 8.5 8.0 7.6 8.5 8.0 7.6 8.5 8.0
67
Table 8.3 Years of education By household equivalized income quintiles Adults 25-65 Argentina, 1992-2005
1
2
3
4
5
Mean
EPH - 15 cities (1)
1992
7.2
7.8
8.5
9.7
12.2
9.4
1993
7.0
8.0
8.5
9.6
12.1
9.3
1994
7.1
8.0
8.4
9.6
12.3
9.3
EPH- 15 cities (2)
1995
7.2
7.9
8.8
9.6
12.5
9.5
1996
7.0
7.9
8.8
9.8
12.7
9.6
1997
7.2
8.0
8.9
10.2 12.8
9.8
1998
7.0
8.1
8.8
10.2 13.1
9.8
EPH - 28 cities
1998
7.0
8.1
8.9
10.1 13.0
9.8
1999
7.2
8.3
9.1
10.3 13.0
9.9
2000
7.2
8.2
9.2
10.3 13.2 10.0
2001
7.2
8.2
9.2
10.5 13.2 10.0
2002
7.4
8.3
9.3
10.4 13.4 10.1
2003
7.4
8.4
9.4
10.7 13.3 10.2
EPH-C
2003 *
7.8
8.7
9.5
10.8 13.4 10.3
2003
7.9
8.7
9.5
10.9 13.5 10.4
2004-I
7.8
8.7
9.8
11.0 13.5 10.5
2004-II
7.8
8.7
9.6
10.9 13.4 10.4
2005-I
7.9
8.6
9.7
11.0 13.3 10.4
2005-II
7.7
8.7
9.6
11.2 13.6 10.5
Source: Own calculations based on microdata from the EPH.
68
Table 8.4 Years of education By age and income Argentina, 1992-2005
(10-20) 1234 EPH - 15 cities (1) 1992 6.8 7.4 7.6 8.2 1993 6.8 7.4 7.8 8.2 1994 6.8 7.4 7.7 8.1 EPH- 15 cities (2) 1995 6.7 7.2 7.9 8.1 1996 6.3 7.0 7.6 8.2 1997 6.9 7.4 8.0 8.6 1998 6.8 7.6 8.0 8.7 EPH - 28 cities 1998 6.8 7.5 8.1 8.6 1999 7.0 7.6 8.1 8.6 2000 6.9 7.6 8.2 8.8 2001 6.9 7.7 8.1 8.6 2002 7.1 7.6 8.0 8.7 2003 7.5 7.9 8.6 9.0 EPH-C 2003 * 7.2 7.6 8.0 8.4 2003 7.3 7.6 8.0 8.5 2004-I 7.4 7.9 8.5 8.8 2004-II 7.1 7.7 8.2 8.5 2005-I 7.3 8.0 8.4 8.9 2005-II 6.9 7.7 8.2 8.8
5 Mean 8.5 7.6 8.6 7.7 8.7 7.6 8.7 7.6 8.5 7.4 9.0 7.9 9.0 7.8 9.0 7.8 9.1 7.9 9.0 7.9 9.2 7.9 9.1 7.9 9.2 8.3 8.8 7.8 8.8 7.9 8.9 8.1 9.1 7.9 9.3 8.2 8.9 7.9
(21-30) 1 2 3 4 5 Mean 8.3 9.2 10.4 11.2 13.3 10.8 8.2 9.6 10.1 11.5 13.0 10.8 8.4 9.4 10.3 11.1 13.1 10.8 8.5 9.0 10.2 11.3 13.1 10.8 8.1 9.3 10.4 11.4 13.3 10.8 8.6 9.4 10.3 11.5 13.5 10.9 8.3 9.4 10.2 11.9 13.8 11.0 8.3 9.4 10.4 11.7 13.7 10.9 8.6 9.5 10.8 11.8 13.5 11.0 8.5 9.7 10.6 11.7 13.7 11.1 8.6 9.7 10.6 12.0 13.7 11.1 8.8 9.9 10.9 11.9 13.9 11.2 9.1 10.1 11.2 12.1 14.0 11.4 9.2 9.9 11.0 12.0 13.8 11.3 9.2 10.0 11.1 12.1 13.9 11.4 9.3 10.3 11.3 12.4 13.8 11.6 9.1 10.1 11.2 12.3 13.7 11.4 9.1 10.1 11.2 12.2 14.0 11.4 9.2 9.9 11.2 12.4 14.0 11.5
(31-40) 1 2 3 4 5 Mean 7.8 8.4 9.4 10.6 13.1 10.1 7.2 8.7 9.2 10.7 13.0 9.9 7.3 8.5 9.2 10.6 13.4 10.0 7.6 8.4 9.6 10.7 13.7 10.2 7.4 8.4 9.5 11.0 13.4 10.2 7.8 8.7 9.7 11.3 13.9 10.4 7.5 8.7 9.6 10.9 14.4 10.4 7.4 8.6 9.7 10.9 14.1 10.4 7.6 8.8 9.6 11.5 13.8 10.5 7.5 8.8 10.0 11.2 14.2 10.6 7.7 8.7 10.1 11.3 14.2 10.6 7.8 8.8 10.1 11.3 14.4 10.7 7.8 9.2 9.8 11.8 14.1 10.8 8.4 9.1 10.3 11.9 14.4 11.0 8.5 9.2 10.4 12.0 14.5 11.2 8.1 9.2 10.6 12.1 14.3 11.1 8.2 9.3 10.3 11.9 14.2 11.0 8.3 9.1 10.5 11.8 14.2 11.0 8.0 9.4 10.4 12.0 14.3 11.1
(41-50) 1 2 3 4 5 Mean EPH - 15 cities (1) 1992 7.1 7.6 8.0 9.5 12.0 9.1 1993 7.0 7.6 8.2 9.2 12.1 9.2 1994 6.9 7.7 8.3 9.4 12.2 9.2 EPH- 15 cities (2) 1995 7.0 7.7 8.8 9.4 12.5 9.3 1996 7.1 7.5 8.5 9.4 13.1 9.4 1997 6.9 7.6 8.7 10.1 12.8 9.6 1998 6.9 8.1 8.6 10.0 13.1 9.7 EPH - 28 cities 1998 6.9 8.0 8.6 10.0 13.0 9.6 1999 7.2 8.2 9.0 10.1 13.1 9.9 2000 7.1 7.9 9.1 10.3 13.4 9.9 2001 7.0 8.0 8.9 10.5 13.3 9.8 2002 7.1 8.0 9.4 10.3 13.8 10.0 2003 7.1 8.0 9.5 10.5 13.7 10.1 EPH-C 2003 * 7.4 8.5 8.9 10.8 13.5 9.9 2003 7.5 8.6 9.0 10.9 13.6 10.1 2004-I 7.7 8.7 9.6 10.7 13.6 10.4 2004-II 7.6 8.7 9.4 10.6 13.4 10.2 2005-I 7.7 8.7 9.3 10.9 13.3 10.2 2005-II 7.8 8.8 9.7 11.3 13.6 10.5
(51-60) 1 2 3 4 5 Mean 6.3 6.7 7.5 8.2 11.0 8.3 6.4 6.7 7.3 8.4 10.9 8.4 6.7 6.7 6.9 8.2 10.9 8.3 6.3 6.9 7.3 8.2 11.1 8.4 6.1 6.8 7.4 8.4 11.5 8.4 5.8 6.9 7.4 8.8 11.6 8.6 6.0 6.8 7.5 8.6 11.8 8.7 5.9 6.8 7.4 8.6 11.7 8.7 6.2 7.0 7.5 8.6 11.6 8.6 6.2 7.0 7.4 8.8 11.9 8.8 6.2 6.9 7.9 9.0 12.1 9.0 6.4 7.2 7.5 9.1 12.1 9.0 6.0 6.6 7.9 9.3 12.2 9.1 6.7 7.4 8.0 9.3 12.5 9.2 6.8 7.5 8.1 9.4 12.6 9.4 7.0 7.4 8.4 9.6 12.5 9.4 6.5 7.2 8.1 9.6 12.5 9.3 6.9 7.4 8.6 9.8 12.5 9.6 6.4 7.5 8.0 9.7 12.6 9.4
(61+) 1 2 3 4 5 Mean 5.9 6.1 6.6 7.7 9.5 7.1 5.8 6.2 6.8 7.4 9.9 7.2 5.6 6.4 6.8 7.5 9.6 7.2 5.5 6.0 6.6 7.2 9.7 7.1 5.4 6.2 6.5 7.1 10.0 7.3 5.6 6.0 6.4 7.5 10.2 7.3 5.5 5.8 6.3 7.2 10.6 7.3 5.3 5.7 6.2 7.1 10.3 7.2 5.0 6.0 6.3 7.4 9.8 7.2 5.0 6.0 6.4 7.4 9.9 7.3 4.9 5.8 6.4 7.3 10.3 7.4 4.8 5.9 6.4 7.6 10.0 7.5 5.2 6.1 6.3 7.7 10.2 7.6 6.0 6.3 6.5 7.7 10.3 7.7 6.2 6.4 6.6 7.8 10.4 7.9 5.8 6.0 7.0 7.9 10.8 7.9 5.2 6.0 7.0 8.0 10.6 7.9 5.6 5.9 6.8 8.0 10.7 7.9 5.6 6.2 6.9 8.2 11.0 7.9
Source: Own calculations based on microdata from the EPH.
69
Table 8.5 Gini coefficient Years of education By age Argentina, 1992-2003
(25-65) (10-20) (21-30) (31-40) (41-50) (51-60)
EPH - 15 cities (1)
1992
0.237 0.214 0.195 0.216 0.242 0.250
1993
0.237 0.212 0.190 0.217 0.243 0.254
1994
0.233 0.213 0.185 0.212 0.236 0.254
EPH- 15 cities (2)
1995
0.235 0.209 0.181 0.214 0.234 0.264
1996
0.236 0.243 0.180 0.212 0.241 0.263
1997
0.234 0.215 0.182 0.209 0.236 0.266
1998
0.231 0.216 0.177 0.208 0.233 0.259
EPH - 28 cities
1998
0.233 0.219 0.180 0.211 0.237 0.262
1999
0.229 0.219 0.175 0.207 0.230 0.261
2000
0.229 0.218 0.177 0.207 0.233 0.263
2001
0.225 0.220 0.172 0.205 0.228 0.255
2002
0.225 0.223 0.169 0.200 0.228 0.261
2003
0.222 0.209 0.162 0.197 0.226 0.258
EPH-C
2003
0.224 0.219 0.168 0.198 0.235 0.256
2004-I 0.224 0.213 0.166 0.205 0.228 0.256
2004-II 0.220 0.215 0.166 0.197 0.222 0.260
2005-I 0.219 0.206 0.169 0.195 0.220 0.251
2005-II 0.218 0.214 0.165 0.197 0.216 0.253
Source: Own calculations based on microdata from the EPH.
(61+) 0.280 0.276 0.276 0.292 0.286 0.290 0.297 0.300 0.292 0.294 0.290 0.281 0.283 0.286 0.284 0.287 0.296 0.288
Table 8.6 Literacy By age and gender Adults aged 25 to 65 Argentina, 1992-2005
(15-24)
(25-65)
Female Male
Mean
Female Male Mean
EPH-15 cities
1992
0.99
0.99
0.99
0.98
0.99
0.99
1993
0.99
0.99
0.99
0.98
0.99
0.99
1994
1.00
0.99
0.99
0.99
0.99
0.99
1995
1.00
1.00
1.00
0.99
0.99
0.99
1996
1.00
0.99
1.00
0.99
0.99
0.99
1997
1.00
0.99
0.99
0.99
0.99
0.99
1998
0.99
0.99
0.99
0.99
0.99
0.99
EPH - 28 cities
1998
0.99
0.99
0.99
0.98
0.99
0.98
1999
0.99
0.99
0.99
0.98
0.99
0.99
2000
0.99
0.99
0.99
0.99
0.99
0.99
2001
0.99
0.99
0.99
0.99
0.99
0.99
2002
1.00
0.99
0.99
0.99
0.98
0.99
2003
1.00
0.99
0.99
0.99
0.98
0.99
EPH-C
2004-II
0.99
0.99
0.99
0.99
0.99
0.99
2005-I
1.00
0.99
0.99
0.99
0.99
0.99
2005-II
1.00
0.99
0.99
0.99
0.99
0.99
Source: Own calculations based on microdata from the EPH.
(65 +) Female Male
0.94
0.97
0.95
0.97
0.96
0.97
0.96
0.98
0.95
0.98
0.97
0.98
0.97
0.97
0.96
0.97
0.96
0.97
0.96
0.97
0.96
0.97
0.96
0.98
0.97
0.97
0.96
0.98
0.96
0.98
0.96
0.98
Mean 0.95 0.96 0.96 0.97 0.96 0.97 0.97 0.97 0.97 0.96 0.96 0.97 0.97 0.97 0.97 0.97
70
Table 8.7 Literacy By household equivalized income quintiles Argentina, 1992-2005
Age 15 to 24
Age 25 to 65
1
2
3
4
5
Mean
1
2
3
4
5
Mean
EPH-15 cities
1992
0.98
0.98
0.99
0.99
1.00
0.99
0.96
0.97
0.98
0.99
1.00
0.98
1993
0.99
0.99
0.99
1.00
1.00
0.99
0.96
0.98
0.99
0.99
1.00
0.99
1994
0.99
0.99
0.99
1.00
1.00
0.99
0.97
0.98
0.99
0.99
1.00
0.99
1995
0.98
0.99
1.00
1.00
1.00
0.99
0.97
0.99
0.99
0.99
1.00
0.99
1996
0.99
0.99
1.00
1.00
1.00
1.00
1997
0.98
0.99
1.00
1.00
1.00
0.99
0.96
0.98
0.99
0.99
1.00
0.99
0.96
0.98
0.99
0.99
1.00
0.99
1998
0.98
0.99
0.99
1.00
1.00
0.99
0.95
0.98
0.99
0.99
1.00
0.99
EPH - 28 cities
1998
0.98
0.99
0.99
1.00
1.00
0.99
0.95
0.98
0.99
0.99
1.00
0.98
1999
0.98
0.99
0.99
0.99
1.00
0.99
2000
0.98
0.99
1.00
1.00
1.00
0.99
0.97
0.98
0.99
0.99
1.00
0.99
0.96
0.98
0.99
0.99
1.00
0.99
2001
0.99
0.99
0.99
1.00
1.00
0.99
0.96
0.98
0.99
1.00
1.00
0.99
2002
0.99
0.99
1.00
1.00
1.00
0.99
0.96
0.98
0.98
0.99
1.00
0.99
2003
0.99
0.99
1.00
1.00
1.00
0.99
0.96
0.98
0.98
0.99
1.00
0.98
EPH-C
2004-II
0.99
0.99
0.99
1.00
0.99
0.99
0.97
0.98
0.99
1.00
1.00
0.99
2005-I
0.99
1.00
0.99
1.00
1.00
0.99
2005-II
0.99
0.99
0.99
1.00
1.00
0.99
0.97
0.98
0.99
0.99
1.00
0.99
0.96
0.98
0.99
1.00
1.00
0.99
Source: Own calculations based on microdata from the EPH.
Table 8.8 Enrollment rates By age and gender Argentina, 1992-2005
3 to 5 years-old
6 to 12 years-old
13 to 17 years-old
Female Male Mean Female Male Mean Female Male Mean
EPH-15 cities
1992 0.35 0.34 0.34
0.98 0.98 0.98
0.83 0.74 0.78
1993 0.32 0.36 0.34
0.98 0.99 0.98
0.81 0.76 0.78
1994 0.30 0.30 0.30
0.98 0.98 0.98
0.83 0.77 0.80
1995 0.27 0.32 0.29
0.99 0.99 0.99
0.81 0.77 0.79
1996 0.33 0.34 0.34
0.99 0.98 0.99
0.81 0.78 0.79
1997 0.35 0.34 0.34
0.99 0.99 0.99
0.85 0.82 0.83
1998 0.44 0.40 0.42
0.99 0.99 0.99
0.89 0.85 0.87
EPH - 28 cities
1998 0.38 0.36 0.37
0.99 0.99 0.99
0.88 0.84 0.86
1999 0.41 0.41 0.41
0.99 0.99 0.99
0.90 0.86 0.88
2000 0.43 0.43 0.43
0.99 0.99 0.99
0.91 0.90 0.90
2001 0.41 0.38 0.40
0.99 0.98 0.99
0.93 0.90 0.91
2002 0.43 0.40 0.42
0.99 0.99 0.99
0.93 0.90 0.91
2003 0.50 0.51 0.51
1.00 1.00 1.00
0.94 0.91 0.93
EPH-C
2003-II 0.55 0.55 0.55
0.99 0.99 0.99
0.91 0.88 0.89
2004-I 0.63 0.62 0.63
0.99 0.99 0.99
0.92 0.90 0.91
2004-II 0.58 0.58 0.58
0.99 0.99 0.99
0.89 0.89 0.89
2005-I 0.64 0.64 0.64
0.99 0.99 0.99
0.92 0.90 0.91
2005-II 0.61 0.58 0.60
0.99 0.99 0.99
0.91 0.91 0.91
Source: Own calculations based on microdata from the EPH.
18 to 23 years old Female Male Mean 0.45 0.38 0.41 0.45 0.39 0.42 0.46 0.37 0.42 0.47 0.38 0.43 0.47 0.38 0.42 0.47 0.41 0.44 0.49 0.43 0.46 0.49 0.42 0.45 0.53 0.44 0.49 0.53 0.45 0.49 0.53 0.46 0.49 0.52 0.50 0.51 0.53 0.49 0.51 0.49 0.44 0.47 0.52 0.46 0.49 0.51 0.42 0.47 0.52 0.46 0.49 0.51 0.43 0.47
71
Table 8.9 Enrollment rates By household equivalized income quintiles Argentina, 1992-2005
3 to 5 years-old
6 to 12 years-old
1 2 3 4 5 Mean 1 2 3 4 5 Mean 1 EPH-15 cities
1992 0.22 0.33 0.30 0.43 0.51 0.34 0.97 0.98 0.98 0.99 0.99 0.98 0.72
1993 0.26 0.33 0.29 0.41 0.47 0.33 0.97 0.99 0.98 0.99 1.00 0.98 0.72
1994 0.21 0.30 0.29 0.39 0.33 0.29 0.97 0.98 0.98 1.00 1.00 0.98 0.71
1995 0.20 0.26 0.35 0.38 0.34 0.29 0.98 0.98 1.00 0.99 1.00 0.99 0.67
1996 0.23 0.28 0.40 0.41 0.45 0.33 0.98 0.98 0.99 1.00 1.00 0.99 0.65 1997 0.28 0.33 0.37 0.39 0.41 0.34 0.98 0.99 0.99 0.99 1.00 0.99 0.73
1998 0.32 0.37 0.46 0.58 0.60 0.42 0.98 0.99 1.00 1.00 1.00 0.99 0.78
EPH - 28 cities
1998 0.28 0.33 0.41 0.48 0.53 0.37 0.98 0.99 1.00 1.00 1.00 0.99 0.77
1999 0.31 0.36 0.43 0.48 0.59 0.40 0.99 1.00 0.99 0.99 1.00 0.99 0.82 2000 0.33 0.39 0.45 0.55 0.62 0.44 0.98 1.00 1.00 1.00 1.00 0.99 0.84 2001 0.31 0.36 0.40 0.48 0.52 0.39 0.97 0.98 1.00 1.00 1.00 0.99 0.86
2002 0.30 0.37 0.40 0.55 0.60 0.41 0.99 0.99 1.00 0.99 1.00 0.99 0.85
2003 0.43 0.47 0.53 0.60 0.63 0.50 0.99 1.00 0.99 1.00 1.00 1.00 0.87
EPH-C
2003-II * 0.46 0.46 0.57 0.62 0.77 0.54 0.98 0.99 0.99 0.99 0.99 0.99 0.82 2003-II 0.46 0.47 0.57 0.63 0.78 0.55 0.98 0.99 0.99 0.99 0.99 0.99 0.83
2004-I 0.54 0.63 0.64 0.72 0.71 0.63 0.99 0.99 0.99 1.00 1.00 0.99 0.86
2004-II 0.48 0.55 0.59 0.67 0.75 0.58 0.98 0.99 0.99 0.99 1.00 0.99 0.82
2005-I 0.51 0.61 0.70 0.71 0.78 0.64 0.98 0.99 0.99 0.99 0.99 0.99 0.86
2005-II 0.48 0.53 0.63 0.69 0.81 0.60 0.99 0.98 0.99 1.00 0.99 0.99 0.85
Source: Own calculations based on microdata from the EPH.
13 to 17 years-old 2 3 4 5 Mean 0.76 0.75 0.82 0.94 0.79 0.78 0.76 0.80 0.90 0.78 0.77 0.79 0.85 0.93 0.79 0.75 0.84 0.86 0.95 0.79 0.76 0.84 0.85 0.98 0.79 0.82 0.82 0.90 0.96 0.83 0.84 0.89 0.94 0.98 0.87 0.82 0.88 0.93 0.98 0.86 0.86 0.89 0.92 0.98 0.88 0.87 0.94 0.96 0.98 0.90 0.89 0.94 0.96 0.99 0.91 0.88 0.96 0.97 0.99 0.91 0.91 0.94 0.98 0.99 0.92 0.89 0.89 0.93 0.98 0.89 0.89 0.90 0.93 0.97 0.89 0.89 0.93 0.98 0.99 0.92 0.89 0.93 0.94 0.98 0.90 0.89 0.93 0.96 0.99 0.91 0.91 0.93 0.95 0.98 0.91
18 to 23 years old 1 2 3 4 5 Mean 0.34 0.34 0.32 0.43 0.55 0.40 0.30 0.35 0.36 0.44 0.53 0.41 0.27 0.35 0.38 0.36 0.58 0.39 0.29 0.28 0.37 0.44 0.68 0.42 0.23 0.29 0.38 0.47 0.65 0.41 0.25 0.29 0.42 0.45 0.69 0.43 0.24 0.34 0.39 0.49 0.69 0.43 0.26 0.33 0.41 0.49 0.67 0.43 0.33 0.35 0.46 0.53 0.67 0.47 0.31 0.37 0.47 0.53 0.74 0.48 0.32 0.37 0.45 0.54 0.72 0.48 0.31 0.38 0.45 0.57 0.79 0.49 0.33 0.41 0.45 0.56 0.75 0.49 0.30 0.35 0.45 0.53 0.67 0.45 0.30 0.36 0.46 0.54 0.68 0.46 0.32 0.37 0.44 0.60 0.73 0.48 0.26 0.38 0.45 0.54 0.71 0.46 0.31 0.41 0.46 0.54 0.75 0.48 0.27 0.34 0.43 0.56 0.71 0.46
Table 8.10 Educational mobility By age group Argentina, 1992-2005 13-19 20-25 EPH-15 cities 1992 0.89 0.81 1993 0.88 0.81 1994 0.88 0.80 1995 0.87 0.79 1996 0.89 0.80 1997 0.87 0.80 1998 0.87 0.78 EPH - 28 cities 1998 0.86 0.77 1999 0.87 0.78 2000 0.87 0.77 2001 0.87 0.77 2002 0.89 0.78 2003 0.89 0.80 EPH-C 2004-II 0.87 0.78 2005-I 0.86 0.76 2005-II 0.86 0.77 Source: Own calculations based on microdata from the EPH.
72
Table 9.1 Housing By household equivalized income quintiles
1
EPH-15 cities
1992
0.62
1993
0.63
1994
0.61
1995
0.62
1996
0.61
1997
0.58
1998
0.58
EPH - 28 cities
1998
0.56
1999
0.58
2000
0.57
2001
0.57
2002
0.56
2003
0.57
EPH-C
2003 *
0.54
2003
0.54
2004-I
0.56
2004-II 0.57
2005-I
0.54
2005-II 0.53
Share of housing owners
2
3
4
5
Mean
0.72 0.72 0.76 0.74 0.72 0.74 0.74 0.73 0.76 0.72 0.71 0.71 0.73 0.77 0.71 0.68 0.72 0.73 0.77 0.71 0.70 0.69 0.72 0.76 0.71 0.65 0.72 0.72 0.77 0.70 0.66 0.72 0.72 0.76 0.70
0.65 0.71 0.72 0.76 0.69 0.66 0.70 0.72 0.79 0.70 0.67 0.70 0.72 0.77 0.70 0.68 0.70 0.74 0.77 0.71 0.67 0.71 0.73 0.78 0.71 0.67 0.69 0.71 0.77 0.70
0.63 0.67 0.70 0.75 0.67 0.62 0.67 0.69 0.75 0.67 0.62 0.68 0.70 0.75 0.68 0.63 0.68 0.71 0.74 0.68 0.64 0.67 0.70 0.73 0.67 0.63 0.68 0.70 0.71 0.66
Number of rooms
1
2
3
4
5 Mean
2.6 2.6 2.8 2.9 3.3 2.9 2.6 2.7 2.9 3.0 3.3 2.9 2.6 2.7 2.8 3.0 3.2 2.9 2.6 2.7 2.9 3.0 3.3 2.9 2.6 2.7 2.8 3.0 3.3 2.9 2.5 2.7 2.9 3.0 3.4 2.9 2.5 2.7 2.8 3.0 3.4 2.9
2.4 2.7 2.8 3.0 3.4 2.9 2.4 2.7 2.7 2.9 3.3 2.9 2.5 2.7 2.8 2.9 3.3 2.9 2.4 2.7 2.7 2.9 3.3 2.9 2.4 2.6 2.8 2.9 3.3 2.8 2.4 2.6 2.7 2.9 3.3 2.8
2.5 2.7 2.8 2.9 3.3 2.9 2.5 2.7 2.8 2.9 3.3 2.9 2.5 2.7 2.9 3.0 3.3 2.9 2.5 2.8 2.9 3.0 3.3 3.0 2.5 2.8 2.9 3.1 3.3 3.0 2.5 2.7 2.9 3.1 3.3 3.0
Persons per room
1
2
3
4
5 Mean
2.0 1.4 1.4 1.2 1.0 1.4 2.0 1.5 1.4 1.2 0.9 1.3 2.0 1.5 1.3 1.2 0.9 1.3 2.1 1.6 1.3 1.2 0.9 1.3 2.0 1.6 1.4 1.1 0.9 1.3 2.1 1.6 1.3 1.1 0.8 1.3 2.2 1.6 1.4 1.1 0.8 1.3
2.3 1.7 1.4 1.1 0.9 1.4 2.2 1.7 1.3 1.1 0.9 1.4 2.2 1.7 1.4 1.1 0.9 1.4 2.3 1.8 1.4 1.1 0.9 1.4 2.4 1.8 1.4 1.2 0.8 1.4 2.3 1.8 1.4 1.1 0.8 1.4
2.1 1.8 1.4 1.1 0.8 1.4 2.1 1.8 1.4 1.1 0.8 1.3 2.2 1.8 1.3 1.1 0.8 1.4 2.2 1.7 1.3 1.1 0.8 1.3 2.2 1.7 1.3 1.1 0.8 1.3 2.1 1.7 1.3 1.1 0.8 1.3
Share of "poor" dwellings
Share of dwellings of low-quality materials
1
2
3
4
5 Mean
1
2
3
4
5 Mean
EPH-15 cities
1992
0.06 0.04 0.03 0.03 0.01 0.03
0.04 0.03 0.02 0.01 0.00 0.02
1993 1994
0.07 0.05 0.03 0.03 0.01 0.04 0.09 0.04 0.05 0.02 0.01 0.04
0.05 0.03 0.02 0.01 0.00 0.02 0.04 0.03 0.02 0.01 0.00 0.02
1995
0.08 0.06 0.02 0.03 0.01 0.04
0.04 0.03 0.02 0.01 0.01 0.02
1996
0.08 0.04 0.04 0.03 0.01 0.03
0.04 0.02 0.01 0.01 0.00 0.01
1997
0.07 0.04 0.03 0.01 0.01 0.03
0.04 0.03 0.01 0.01 0.00 0.02
1998
0.04 0.03 0.01 0.01 0.00 0.02
0.03 0.02 0.01 0.01 0.00 0.01
EPH - 28 cities
1998
0.07 0.03 0.02 0.01 0.00 0.02
0.04 0.02 0.02 0.01 0.00 0.02
1999
0.07 0.04 0.03 0.02 0.01 0.03
0.04 0.02 0.02 0.01 0.00 0.02
2000
0.08 0.04 0.03 0.02 0.00 0.03
0.04 0.02 0.01 0.01 0.00 0.01
2001
0.08 0.05 0.03 0.02 0.00 0.03
0.04 0.03 0.02 0.01 0.00 0.02
2002 2003
0.06 0.04 0.02 0.02 0.01 0.02 0.04 0.04 0.03 0.01 0.01 0.02
0.04 0.02 0.01 0.01 0.00 0.02 0.03 0.03 0.01 0.01 0.00 0.01
Source: Own calculations based on microdata from the EPH.
73
Table 9.2 Housing By age
Share of housing owners
[15,24] [25,40] [41,64] [65+) Mean
EPH-15 cities
1992
0.34 0.58 0.80 0.85 0.73
1993
0.31 0.57 0.81 0.86 0.74
1994
0.30 0.54 0.80 0.85 0.72
1995
0.29 0.54 0.79 0.85 0.72
1996
0.26 0.55 0.79 0.86 0.72
1997
0.27 0.53 0.78 0.84 0.70
1998
0.27 0.52 0.78 0.85 0.71
EPH - 28 cities
1998
0.25 0.52 0.78 0.84 0.70
1999
0.30 0.56 0.79 0.85 0.71
2000
0.25 0.57 0.78 0.84 0.71
2001
0.25 0.55 0.78 0.86 0.71
2002
0.31 0.57 0.79 0.85 0.72
2003
0.25 0.53 0.77 0.86 0.70
EPH-C
2003
0.25 0.52 0.76 0.85 0.69
2004-I
0.24 0.49 0.75 0.85 0.68
2004-II 0.24 0.50 0.75 0.84 0.68
2005-I
0.25 0.48 0.74 0.84 0.67
2005-II 0.24 0.47 0.73 0.84 0.66
Number of rooms [15,24] [25,40] [41,64] [65+) Mean 2.0 2.7 3.1 2.9 2.9 2.1 2.8 3.2 3.0 3.0 2.1 2.7 3.2 2.9 2.9 2.1 2.7 3.2 2.9 2.9 2.1 2.7 3.2 3.0 3.0 2.1 2.6 3.2 3.0 3.0 2.1 2.6 3.2 3.0 2.9 2.1 2.6 3.2 3.0 2.9 2.2 2.6 3.2 2.9 2.9 2.0 2.6 3.2 2.9 2.9 2.0 2.6 3.1 3.0 2.9 2.1 2.5 3.1 3.0 2.9 2.0 2.5 3.1 3.0 2.9 2.0 2.6 3.2 3.0 3.0 2.2 2.6 3.2 3.0 3.0 2.1 2.6 3.3 3.1 3.0 2.1 2.6 3.3 3.1 3.0 2.1 2.6 3.3 3.1 3.0
Persons per room [15,24] [25,40] [41,64] [65+) Mean 1.7 1.8 1.4 0.8 1.4 1.5 1.7 1.3 0.8 1.3 1.5 1.8 1.3 0.8 1.3 1.5 1.7 1.3 0.8 1.3 1.4 1.7 1.3 0.8 1.3 1.5 1.7 1.3 0.8 1.3 1.3 1.8 1.3 0.8 1.3 1.4 1.8 1.3 0.8 1.4 1.5 1.7 1.4 0.8 1.4 1.5 1.7 1.4 0.8 1.4 1.5 1.8 1.4 0.8 1.4 1.5 1.8 1.4 0.8 1.4 1.4 1.8 1.4 0.8 1.4 1.7 1.8 1.3 0.8 1.3 1.6 1.8 1.3 0.8 1.3 1.6 1.7 1.3 0.8 1.3 1.7 1.7 1.3 0.8 1.3 1.6 1.7 1.3 0.8 1.3
Share of "poor" dwellings
Share of dwellings of low-quality materials
[15,24] [25,40] [41,64] [65+) Mean [15,24] [25,40] [41,64] [65+) Mean
EPH-15 cities
1992
0.07 0.04 0.03 0.02 0.03
0.02 0.02 0.02 0.01 0.02
1993
0.08 0.05 0.03 0.01 0.03
0.03 0.02 0.02 0.01 0.02
1994
0.09 0.06 0.03 0.01 0.04
0.02 0.02 0.02 0.01 0.02
1995
0.09 0.06 0.03 0.01 0.03
0.04 0.02 0.02 0.01 0.02
1996
0.09 0.06 0.02 0.01 0.03
0.03 0.02 0.01 0.01 0.01
1997
0.08 0.05 0.02 0.01 0.03
0.02 0.02 0.01 0.01 0.02
1998
0.05 0.03 0.01 0.01 0.02
0.01 0.02 0.01 0.01 0.01
EPH - 28 cities
1998
0.06 0.04 0.02 0.01 0.02
0.02 0.02 0.01 0.02 0.02
1999
0.06 0.04 0.02 0.01 0.03
0.02 0.02 0.01 0.02 0.02
2000
0.08 0.04 0.02 0.01 0.03
0.02 0.01 0.01 0.01 0.01
2001
0.09 0.05 0.02 0.01 0.03
0.02 0.02 0.01 0.01 0.02
2002
0.06 0.04 0.01 0.01 0.02
0.02 0.02 0.01 0.01 0.01
2003
0.10 0.03 0.01 0.00 0.02
0.02 0.02 0.01 0.01 0.01
Source: Own calculations based on microdata from the EPH.
74
Table 9.3 Housing By education of the household head
Share of housing owners
Low Medium High Mean
EPH-15 cities
1992
0.73 0.71 0.76 0.73
1993
0.73 0.74 0.74 0.74
1994
0.72 0.73 0.73 0.72
1995
0.72 0.70 0.73 0.72
1996
0.74 0.69 0.70 0.72
1997
0.71 0.68 0.72 0.70
1998
0.72 0.69 0.73 0.71
EPH - 28 cities
1998
0.71 0.68 0.71 0.70
1999
0.72 0.70 0.72 0.71
2000
0.72 0.70 0.70 0.71
2001
0.73 0.69 0.71 0.71
2002
0.74 0.71 0.69 0.72
2003
0.73 0.69 0.67 0.70
EPH-C
2003
0.72 0.66 0.69 0.69
2004-I
0.70 0.64 0.67 0.68
2004-II 0.70 0.64 0.69 0.68
2005-I
0.69 0.62 0.69 0.67
2005-II 0.68 0.62 0.67 0.66
Number of rooms Low Medium High Mean 2.7 3.0 3.6 2.9 2.7 3.1 3.5 3.0 2.7 3.1 3.4 2.9 2.7 3.0 3.5 2.9 2.8 3.0 3.4 3.0 2.7 3.1 3.4 3.0 2.7 3.0 3.5 2.9 2.7 3.0 3.5 2.9 2.7 3.0 3.4 2.9 2.7 3.0 3.4 2.9 2.7 3.0 3.4 2.9 2.7 2.9 3.4 2.9 2.7 2.9 3.4 2.9 2.7 3.0 3.4 3.0 2.7 3.0 3.4 3.0 2.7 3.0 3.4 3.0 2.8 3.0 3.5 3.0 2.7 3.0 3.4 3.0
Persons per room Low Medium High Mean 1.5 1.3 1.0 1.4 1.5 1.3 1.0 1.3 1.5 1.2 0.9 1.3 1.5 1.2 1.0 1.3 1.5 1.3 0.9 1.3 1.5 1.2 0.9 1.3 1.5 1.3 0.9 1.3 1.6 1.3 0.9 1.4 1.5 1.3 1.0 1.4 1.6 1.3 1.0 1.4 1.6 1.3 1.0 1.4 1.6 1.4 1.0 1.4 1.6 1.4 1.0 1.4 1.5 1.3 0.9 1.3 1.5 1.4 1.0 1.3 1.5 1.3 0.9 1.3 1.5 1.3 0.9 1.3 1.5 1.3 0.9 1.3
Share of "poor" dwellings
Share of dwellings of low-quality materials
Low Medium High Mean
Low Medium High Mean
EPH-15 cities
1992 1993
0.04 0.02 0.01 0.03 0.05 0.02 0.01 0.03
0.03 0.01 0.00 0.02 0.03 0.01 0.00 0.02
1994
0.05 0.02 0.01 0.04
0.03 0.01 0.00 0.02
1995
0.05 0.02 0.01 0.03
0.03 0.01 0.00 0.02
1996
0.05 0.02 0.00 0.03
0.02 0.01 0.00 0.01
1997 1998
0.04 0.02 0.01 0.03 0.03 0.01 0.00 0.02
0.02 0.01 0.00 0.02 0.02 0.01 0.00 0.01
EPH - 28 cities
1998
0.03 0.01 0.00 0.02
0.02 0.01 0.00 0.02
1999
0.04 0.02 0.01 0.03
0.03 0.01 0.00 0.02
2000 2001
0.04 0.02 0.01 0.03 0.04 0.02 0.01 0.03
0.02 0.01 0.00 0.01 0.02 0.01 0.00 0.02
2002
0.03 0.02 0.01 0.02
0.02 0.01 0.00 0.01
2003
0.03 0.02 0.01 0.02
0.02 0.01 0.00 0.01
Source: Own calculations based on microdata from the EPH.
Table 9.4 Services By household equivalized income quintiles
Water
Hygienic restrooms
1
2
3
4
5 Mean
1
2
3
4
5 Mean
1
EPH-15 cities
1992
0.93 0.97 0.96 0.98 1.00 0.97
0.73 0.84 0.88 0.91 0.98 0.88
1993
0.93 0.97 0.98 0.99 1.00 0.98
0.71 0.84 0.89 0.92 0.97 0.88
1994
0.94 0.97 0.98 0.99 0.99 0.97
0.74 0.83 0.88 0.93 0.98 0.88
1995
0.93 0.96 0.98 0.99 1.00 0.98
0.77 0.86 0.92 0.96 0.99 0.91
1996
0.93 0.96 0.99 0.99 1.00 0.98
0.74 0.87 0.91 0.96 0.99 0.91
1997
0.93 0.98 0.98 0.99 1.00 0.98
0.64 0.82 0.88 0.95 0.98 0.87
1998
0.94 0.98 0.99 1.00 1.00 0.98
0.58 0.79 0.87 0.95 0.99 0.86
0.25
EPH - 28 cities
1998
0.93 0.97 0.99 1.00 1.00 0.98
0.58 0.79 0.87 0.94 0.99 0.86
0.29
1999
0.96 0.98 0.99 0.99 1.00 0.99
0.62 0.79 0.88 0.93 0.98 0.86
0.31
2000
0.94 0.98 0.99 1.00 1.00 0.98
0.61 0.81 0.87 0.95 0.98 0.87
0.31
2001
0.95 0.98 0.99 1.00 1.00 0.99
0.60 0.80 0.87 0.95 0.99 0.87
0.28
2002
0.95 0.97 0.99 0.99 1.00 0.99
0.61 0.75 0.87 0.95 0.99 0.86
0.30
2003
0.93 0.98 0.99 1.00 1.00 0.98
0.60 0.79 0.88 0.96 0.99 0.87
0.31
Source: Own calculations based on microdata from the EPH.
Sewerage
2
3
4
0.40 0.47 0.62 0.42 0.50 0.62 0.42 0.52 0.61 0.44 0.53 0.65 0.40 0.54 0.67 0.42 0.53 0.65 0.44 0.52 0.70
5 Mean 0.83 0.55 0.82 0.56 0.82 0.57 0.82 0.58 0.83 0.58 0.83 0.58 0.84 0.60
Electricity
1
2
3
4
5 Mean
0.99 0.99 1.00 1.00 1.00 1.00 0.99 1.00 1.00 1.00 1.00 1.00 0.99 1.00 1.00 1.00 1.00 1.00 0.98 1.00 1.00 1.00 1.00 1.00 0.98 1.00 1.00 1.00 1.00 1.00 0.98 0.99 1.00 1.00 1.00 1.00 0.98 0.99 1.00 1.00 1.00 1.00 0.98 1.00 1.00 1.00 1.00 1.00 0.98 1.00 1.00 1.00 1.00 0.99
75
Table 10.1 Household size
Equivalized income quintile
1
2
3
4
5
Mean
EPH-15 cities
1992
4.3
3.3
3.6
3.4
2.8
3.4
1993
4.2
3.5
3.6
3.2
2.8
3.4
1994
4.3
3.5
3.4
3.3
2.7
3.4
1995
4.5
3.7
3.5
3.2
2.7
3.4
1996
4.6
3.8
3.4
3.1
2.7
3.4
1997
4.4
3.7
3.4
3.2
2.7
3.4
1998
4.6
3.7
3.4
3.1
2.7
3.4
EPH - 28 cities
1998
4.6
3.9
3.4
3.1
2.7
3.4
1999
4.6
4.0
3.3
3.1
2.7
3.4
2000
4.6
4.0
3.5
3.1
2.7
3.4
2001
4.8
4.1
3.4
3.0
2.6
3.4
2002
4.8
4.1
3.4
3.0
2.6
3.4
2003
4.8
4.2
3.4
3.0
2.6
3.4
EPH-C
2003 *
4.3
4.1
3.4
2.9
2.5
3.3
2003
4.3
4.0
3.3
2.9
2.4
3.2
2004-I
4.5
4.2
3.3
3.0
2.6
3.4
2004-II
4.5
4.0
3.4
3.0
2.5
3.3
2005-I
4.6
4.1
3.3
3.0
2.6
3.4
2005-II
4.5
4.0
3.3
3.0
2.5
3.3
Source: Own calculations based on microdata from the EPH.
Table 10.2 Number of children
Parental income quintile
1
2
3
4
5
Mean
EPH-15 cities
1992
1.8
1.7
1.7
1.4
1.3
1.6
1993
1.7
1.7
1.6
1.4
1.2
1.5
1994
1.6
1.8
1.5
1.3
1.2
1.5
1995
1.7
1.6
1.6
1.3
1.2
1.5
1996
1.7
1.7
1.5
1.2
1.2
1.5
1997
1.7
1.6
1.5
1.2
1.1
1.4
1998
1.8
1.6
1.4
1.3
1.1
1.4
EPH - 28 cities
1998
1.8
1.7
1.5
1.3
1.1
1.5
1999
1.7
1.6
1.6
1.3
1.2
1.5
2000
1.8
1.7
1.4
1.3
1.1
1.5
2001
1.9
1.6
1.5
1.3
1.1
1.5
2002
1.8
1.7
1.5
1.3
1.1
1.4
2003
1.7
1.7
1.5
1.2
1.1
1.4
EPH-C
2003 *
1.8
1.5
1.5
1.3
1.2
1.4
2003
1.7
1.5
1.4
1.3
1.1
1.4
2004-I
1.6
1.5
1.4
1.2
1.1
1.3
2004-II
1.6
1.5
1.3
1.2
1.0
1.3
2005-I
1.6
1.5
1.4
1.1
1.1
1.3
2005-II
1.6
1.5
1.2
1.2
1.1
1.3
Source: Own calculations based on microdata from the EPH.
Education of household head
Low
Medium
High
3.6
3.6
3.2
3.5
3.5
3.2
3.5
3.4
3.0
3.6
3.3
3.1
3.6
3.4
3.0
3.6
3.3
3.0
3.6
3.3
3.0
3.6
3.4
3.0
3.6
3.5
3.0
3.7
3.4
3.0
3.7
3.4
3.0
3.6
3.4
3.0
3.7
3.4
2.9
3.5
3.4
2.9
3.5
3.4
2.9
3.6
3.4
2.9
3.5
3.4
2.9
3.6
3.3
2.9
3.5
3.4
2.9
Mean 3.5 3.5 3.4 3.4 3.4 3.4 3.4 3.4 3.4 3.5 3.5 3.4 3.4 3.4 3.4 3.4 3.3 3.4 3.3
Parental education
Low
Medium
High Mean
1.9
1.5
1.1
1.6
1.7
1.5
1.1
1.5
1.8
1.3
1.1
1.4
1.8
1.3
1.1
1.5
1.9
1.3
1.0
1.4
1.8
1.3
1.0
1.4
1.8
1.4
0.9
1.4
1.9
1.4
1.0
1.5
1.8
1.4
1.0
1.5
1.8
1.4
1.0
1.5
1.9
1.3
1.0
1.5
1.9
1.4
0.9
1.4
1.8
1.4
0.9
1.4
1.8
1.4
0.9
1.4
1.8
1.4
0.9
1.4
1.8
1.3
0.9
1.4
1.7
1.4
0.9
1.3
1.8
1.3
0.9
1.4
1.7
1.4
0.9
1.3
76
Table 10.3 Dependency rates Household size over income earners
Equivalized income quintile
1
2
3
4
Argentina
5
Mean
EPH-15 cities
1992
3.0
1.7
1.7
1.5
1.4
1.7
1993
3.0
1.8
1.7
1.5
1.4
1.7
1994
3.1
1.9
1.7
1.5
1.3
1.7
1995
3.3
2.0
1.7
1.5
1.3
1.7
1996
3.4
2.0
1.7
1.5
1.4
1.7
1997
3.2
1.9
1.7
1.5
1.3
1.7
1998
3.0
2.0
1.7
1.4
1.3
1.7
EPH - 28 cities
1998
3.1
2.1
1.7
1.5
1.3
1.7
1999
3.3
2.2
1.7
1.5
1.3
1.7
2000
3.3
2.2
1.7
1.5
1.3
1.7
2001
3.7
2.3
1.8
1.5
1.3
1.7
2002
3.5
2.3
1.7
1.5
1.3
1.7
2003
3.3
2.2
1.7
1.5
1.3
1.7
EPH-C
2003 *
3.3
2.1
1.7
1.5
1.3
1.7
2003
3.3
2.1
1.7
1.5
1.3
1.7
2004-I
3.1
2.2
1.7
1.5
1.3
1.7
2004-II
3.0
2.0
1.7
1.5
1.3
1.6
2005-I
2.9
2.1
1.6
1.5
1.3
1.6
2005-II
2.7
2.0
1.6
1.4
1.3
1.6
Source: Own calculations based on microdata from the EPH.
Education of household head
Low
Medium
High
Mean
1.8
2.0
1.8
1.9
1.8
1.9
1.7
1.8
1.8
1.9
1.7
1.8
1.8
1.8
1.8
1.8
1.9
1.9
1.7
1.9
1.8
1.8
1.7
1.8
1.8
1.8
1.6
1.8
1.8
1.8
1.6
1.8
1.8
1.9
1.7
1.8
1.9
1.9
1.7
1.8
2.0
1.9
1.7
1.9
1.9
2.0
1.7
1.9
1.9
1.9
1.7
1.9
2.0
2.2
2.0
2.1
2.0
2.2
2.0
2.1
2.0
2.1
1.9
2.0
1.9
2.1
1.8
1.9
1.9
2.0
1.8
1.9
1.8
2.0
1.7
1.8
Table 10.4 Mean age
Equivalized income quintile
1
2
3
4
5
EPH-15 cities
1992
27.4
32.4
31.8
33.4
34.1
1993
27.2
31.8
32.1
34.2
35.2
1994
27.0
31.4
33.6
33.6
35.8
1995
25.7
30.4
33.1
34.3
36.2
1996
25.1
30.9
32.7
35.3
36.3
1997
25.8
30.3
33.6
35.1
37.3
1998
24.6
29.5
33.1
35.5
36.7
EPH - 28 cities
1998
24.2
28.9
32.7
35.4
36.5
1999
24.4
28.1
33.1
35.0
36.3
2000
24.2
28.8
32.1
35.0
36.7
2001
23.7
28.3
32.8
35.5
37.4
2002
22.9
28.1
32.5
35.6
37.7
2003
23.3
28.0
32.7
36.3
38.3
EPH-C
2003 *
25.0
28.5
32.4
35.8
38.6
2003
25.3
28.7
32.7
36.0
38.8
2004-I
25.1
28.9
33.7
35.8
38.0
2004-II
24.8
29.4
33.2
36.3
38.0
2005-I
24.7
28.9
33.8
36.1
38.8
2005-II
24.8
29.6
34.7
36.0
37.4
Source: Own calculations based on microdata from the EPH.
Mean 31.8 32.1 32.3 31.9 32.1 32.4 31.9 31.5 31.4 31.4 31.5 31.3 31.7 31.9 32.3 32.3 32.3 32.5 32.5
77
Table 10.5 Correlation between couples
Years of Hourly
Hours of work
education wages All couples Workers
Argentina
EPH-15 cities
1992
0.651
0.475
0.133
0.190
1993
0.658
0.463
0.112
0.153
1994
0.657
0.482
0.116
0.209
1995
0.665
0.482
0.112
0.193
1996
0.660
0.462
0.122
0.183
1997
0.661
0.435
0.120
0.213
1998
0.665
0.456
0.113
0.176
EPH - 28 cities
1998
0.667
0.473
0.115
0.191
1999
0.662
0.457
0.114
0.201
2000
0.659
0.512
0.139
0.220
2001
0.662
0.418
0.123
0.213
2002
0.660
0.372
0.108
0.190
2003
0.664
0.461
0.127
0.210
EPH-C
2003
0.672
0.371
0.123
0.152
2004-I
0.659
0.461
0.115
0.139
2004-II
0.690
0.447
0.122
0.145
2005-I
0.674
0.412
0.102
0.121
2005-II
0.687
0.198
0.107
0.153
Source: Own calculations based on microdata from the EPH.
78
Table 11.1 Coverage of PJH Share of households with PJH by equivalized income quintiles
Quintiles
1
2
3
4
5
EPH, 2003 0.34
0.25
0.09
0.03
0.01
EPH-C
2003 *
0.32
0.24
0.10
0.05
0.01
2003
0.32
0.23
0.10
0.04
0.01
2004-I
0.32
0.22
0.10
0.03
0.01
2004-II
0.34
0.21
0.08
0.03
0.01
2005-I
0.34
0.19
0.08
0.02
0.01
2005-II
0.30
0.17
0.06
0.03
0.00
Source: Own calculations based on microdata from the EPH.
Mean 0.11 0.12 0.12 0.11 0.11 0.10 0.09
Table 11.2 Coverage of PJH Share of households with PJH by education of household head
Low
Medium
High
Mean
EPH, 2003 0.16
0.09
0.02
0.11
EPH-C
2003 *
0.17
0.09
0.02
0.11
2003
0.17
0.09
0.02
0.11
2004-I
0.16
0.10
0.02
0.11
2004-II
0.16
0.08
0.01
0.10
2005-I
0.15
0.07
0.01
0.10
2005-II
0.14
0.07
0.01
0.09
Source: Own calculations based on microdata from the EPH.
Table 11.3 Coverage of PJH Benefits (in pesos) of PJH by household
1
2
3
4
5
EPH, 2003 35.4
13.2
4.3
1.1
0.5
EPH-C
2004-II
25.9
11.8
3.7
0.9
0.2
2005-I
24.0
9.2
2.8
0.4
0.0
2005-II
22.8
7.2
1.9
1.0
0.0
Source: Own calculations based on microdata from the EPH.
Mea n 8.4 6.9 5.8 5.2
79
Table 11.4 Incidence of PJH Distribution of PJH beneficiaries by equivalized income quintile
Households
1
2
3
4
5
EPH, 2003 42.2
35.1
15.8
5.6
1.2
EPH-C
2003 *
41.3
32.3
16.7
8.5
1.2
2003
41.4
32.0
16.7
8.6
1.3
2004-I
42.8
32.2
17.5
6.3
1.3
2004-II
45.3
31.5
15.0
6.5
1.7
2005-I
47.8
30.3
15.3
5.2
1.5
2005-II
49.2
30.6
13.0
6.2
1.0
Individuals
1
2
3
4
5
EPH, 2003 42.5
35.2
15.9
5.5
1.1
EPH-C
2003 *
40.7
33.5
16.3
8.4
1.2
2003
40.8
33.2
16.2
8.5
1.2
2004-I
42.1
33.3
17.1
6.2
1.2
2004-II
45.2
31.7
14.9
6.6
1.6
2005-I
48.3
30.0
15.1
5.4
1.4
2005-II
49.1
31.3
12.6
6.1
1.0
Source: Own calculations based on microdata from the EPH.
Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0
Table 11.5 Incidence of PJH Distribution of PJH benefits by equivalized income quintile
1
2
3
4
5
EPH, 2003 42.5 35.2 15.9
5.5
1.1
EPH-C
2004-II
47.6 31.7 13.9
5.7
1.1
2005-I
51.0 29.6 14.5
4.0
0.9
2005-II
52.8 30.1 11.4
5.0
0.7
Source: Own calculations based on microdata from the EPH.
Total 100.0 100.0 100.0 100.0
80
Table A.1 Poverty profile Argentina, 2005 Demographic variables
USD 2 Non-poor (i)
Poor (ii)
Official moderate
Non-poor
Poor
(iii)
(iv)
Population share
88.4
11.6
66.2
33.8
Population share by age [0,15] [16,25] [26,40] [41,64] [65+] Age distribution [0,15] [16,25] [26,40] [41,64] [65+] Total
80.0 89.0 90.5 92.4 96.4 24.8 17.5 21.5 24.9 11.4 100.0
20.0 11.0 9.5 7.6 3.6 47.4 16.6 17.1 15.6 3.2 100.0
50.3 64.4 70.4 73.9 84.4 20.8 16.9 22.3 26.6 13.4 100.0
49.7 35.6 29.6 26.1 15.6 40.2 18.3 18.3 18.3 4.8 100.0
Mean age
33.8
22.8
36.0
25.6
Gender
Share males
0.475
Household size and structure
Family size
3.2
0.469 4.8
0.469 2.9
0.484 4.5
Children (<12)
1.2
2.5
1.0
2.1
Dependency rate
0.66
0.28
0.70
0.39
Female-headed hh.
0.31
0.37
0.32
0.30
Source: Own calculations based on microdata from the EPH.
Table A.2 Poverty profile Argentina, 2005 Regions
USD 2
Official moderate
Non-poor
Poor
Non-poor
Poor
(i)
(ii)
(iii)
(iv)
Population share
GBA
90.7
9.3
69.1
30.9
Pampeana
88.2
11.8
69.3
30.7
Cuyo
87.3
12.7
63.1
36.9
NOA
81.1
18.9
52.0
48.0
Patagonia
94.9
5.1
78.5
21.5
NEA
77.1
22.9
46.1
53.9
Distribution
GBA
55.1
43.0
56.1
49.1
Pampeana
22.7
23.2
23.8
20.7
Cuyo
6.3
7.0
6.1
6.9
NOA
8.8
15.7
7.6
13.6
Patagonia
2.6
1.1
2.9
1.6
NEA
4.4
10.1
3.5
8.1
Total
100.0
100.0
100.0
100.0
Source: Own calculations based on microdata from the EPH.
81
Table A.3 Poverty profile Housing
2005 Home ownership Number of rooms Persons per room
USD 2 Non-poor (i)
Poor (ii)
0.673
0.506
3.003
2.451
1.210
2.391
Official moderate
Non-poor
Poor
(iii)
(iv)
0.683
0.587
3.071
2.618
1.055
2.060
2003
USD 2 Non-poor (i)
Poor (ii)
Official moderate
Non-poor
Poor
(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: Own calculations based on microdata from the EPH.
82
Table A.4 Poverty profile Argentina, 2005 Education
Years of education Total [10,20] [21,30] [31,40] [41,50] [51,60] [61+] Educational groups Adults Low Medium High Total Male adults Low Medium High Total Female adults Low Medium High Total Household heads Low Medium High Total Literacy rate
USD 2 Non-poor (i)
Poor (ii)
8.5
5.3
8.2
6.6
11.8
9.0
11.5
7.7
10.8
7.4
9.6
6.1
8.0
5.2
35.3 37.1 27.6 100.0 36.8 38.1 25.1 100.0 34.0 36.3 29.7 100.0 42.5 33.6 24.0 100.0 0.99
67.6 27.7 4.7 100.0 68.7 25.7 5.6 100.0 66.8 29.3 4.0 100.0 69.2 26.3 4.5 100.0 0.97
Official moderate
Non-poor
Poor
(iii)
(iv)
9.2
6.0
8.6
7.2
12.4
9.3
12.3
8.4
11.5
8.1
10.2
6.8
8.3
5.6
28.6 38.1 33.3 100.0 29.6 40.0 30.4 100.0 27.7 36.5 35.8 100.0 37.2 34.7 28.2 100.0 0.99
63.0 31.5 5.5 100.0 65.4 29.2 5.4 100.0 60.9 33.5 5.7 100.0 67.4 27.8 4.8 100.0 0.97
School attendance
[3,5]
0.63
0.46
0.68
0.50
[6,12]
0.99
0.98
0.99
0.99
[13,17]
0.92
0.83
0.94
0.87
[18,23]
0.46
0.21
0.52
0.28
Source: Own calculations based on microdata from the EPH.
83
Table A.5 Poverty profile Argentina, 2005 Employment
In the labor force Total [16,24] [25,55] [56+] Men [25,55] Women [25,55] Employed Total [16,24] [25,55] [56+] Men [25,55] Women [25,55] Unemployment rate Total [16,24] [25,55] [56+] Men [25,55] Women [25,55]
USD 2 Non-poor (i)
Poor (ii)
0.563 0.507 0.817 0.350 0.955 0.693
0.456 0.481 0.710 0.419 0.895 0.573
0.512 0.394 0.765 0.328 0.911 0.634
0.328 0.283 0.545 0.275 0.703 0.429
0.090 0.223 0.063 0.064 0.045 0.085
0.280 0.411 0.231 0.344 0.214 0.251
Official moderate
Non-poor
Poor
(iii)
(iv)
0.584 0.511 0.844 0.341 0.961 0.736
0.482 0.491 0.718 0.408 0.921 0.551
0.543 0.417 0.802 0.325 0.926 0.688
0.384 0.319 0.607 0.330 0.811 0.440
0.070 0.184 0.049 0.049 0.036 0.066
0.203 0.350 0.154 0.193 0.120 0.202
Unemployment spell
10.4
9.8
(months)
10.4
10.2
Child labor
0.013
0.038
0.008
0.027
Source: Own calculations based on microdata from the EPH.
Table A.6 Poverty profile Argentina, 2005 Wages, hours and earnings
USD 2
Official moderate
Non-poor
Poor
Non-poor
Poor
(i)
(ii)
(iii)
(iv)
Worked hours
Total
41.9
32.3
42.8
36.7
[16,24]
38.8
33.3
39.5
35.6
[25,55]
43.1
33.1
43.9
38.0
[56+]
39.7
27.4
40.6
32.9
Men [25,55]
48.9
39.5
49.7
44.0
Women [25,55]
35.6
25.2
36.6
28.7
Hourly wages
Total
6.1
2.1
6.7
2.7
[16,24]
3.6
1.5
4.0
2.1
[25,55]
6.3
2.4
7.0
2.9
[56+]
7.3
1.9
8.0
2.8
Men [25,55]
6.1
2.3
6.9
2.9
Women [25,55]
6.6
2.5
7.2
2.8
Earnings
Total
923.7
183.1
1041.5
328.4
[16,24]
517.3
144.5
588.8
248.5
[25,55]
1001.8
203.3
1121.6
365.6
[56+]
949.3
129.6
1063.0
254.0
Men [25,55]
1122.3
244.6
1278.9
429.7
Women [25,55]
835.8
133.9
921.1
240.8
Source: Own calculations based on microdata from the EPH.
84
Table A.7 Poverty profile Argentina, 2005 Employment structure
USD 2
Non-poor
(i)
Labor relationship
Entrepreneur
3.2
Salaried worker
70.1
Self-employed
16.8
Zero income
0.8
Unemployed
9.0
Total
100.0
Labor group
Entrepreneurs
3.7
Salaried-large firms
35.5
Salaried-public sector
18.3
Self-employed professionals
3.3
Salaried-small firms
22.4
Self-employed unskilled
15.9
Zero income
0.9
Total
100.0
Formality (based on labor group)
Formal
60.7
Informal
39.3
Total
100.0
Formality (based on social security rights)
Formal
57.6
Informal
42.4
Total
100.0
Sectors
Primary activities
1.2
Industry-labor intensive
7.8
Industry-capital intensive
7.0
Construction
7.9
Commerce
22.2
Utilities & transportation
7.5
Skilled services
9.5
Public administration
8.1
Education & Health
21.1
Domestic servants
7.7
Total
100.0
Poor (ii) 0.9 47.9 21.0 2.2 28.0 100.0 1.3 16.2 15.9 0.8 33.1 29.5 3.2 100.0 34.2 65.8 100.0 5.9 94.1 100.0 3.1 7.9 3.6 18.7 27.6 4.3 2.4 4.2 15.4 12.9 100.0
Official moderate
Non-poor
Poor
(iii)
(iv)
3.7 72.4 16.2 0.7 7.0 100.0
1.1 57.1 19.9 1.5 20.3 100.0
4.1 38.0 19.4 3.9 19.7 14.1 0.8 100.0
1.5 22.4 14.1 0.6 34.1 25.4 2.0 100.0
65.4 34.6 100.0
38.6 61.4 100.0
64.8 35.2 100.0
19.6 80.4 100.0
1.1 7.2 7.3 5.9 21.7 7.9 10.7 8.8 22.9 6.5 100.0
2.0 9.7 5.1 17.4 25.4 5.4 3.6 4.7 14.1 12.7 100.0
Permanent job
0.82
0.29
0.87
0.51
Right to pensions
0.59
0.06
0.66
0.20
Labor health insurance
0.59
0.06
0.66
0.20
Source: Own calculations based on microdata from the EPH.
85
Table A.8 Poverty profile Argentina, 2005 Poverty-alleviation programs (Programa Jefes)
USD 2 Non-poor (i)
Poor (ii)
Official moderate
Non-poor
Poor
(iii)
(iv)
Households with PJH
0.069
0.337
0.033
0.264
Mean income from PJH
3.2
28.0
1.3
17.0
Distribution
Beneficiaries
33.6
66.4
23.6
76.4
Transfers
33.1
66.9
24.1
75.9
Source: Own calculations based on microdata from the EPH.
Table A.9 Poverty profile Argentina, 2005 Incomes
USD 2 Non-poor (i)
Poor (ii)
Official moderate
Non-poor
Poor
(iii)
(iv)
Household per capita income Household total income
535.2 1702.9
53.9 258.6
664.6 1933.1
117.9 534.7
Gini per capita income
0.461
0.202
0.399
0.260
Individual income
Labor
81.1
62.8
81.3
Non-labor
18.9
37.2
18.7
Total
100.0
100.0
100.0
Source: Own calculations based on microdata from the EPH.
76.0 24.0 100.0
86
Table A.10 Poverty profile Argentina, 2005 Decomposition of household per capita income
A. Household incomes and size
Non-poor (i)
Poor (ii)
Household per capita income
535.2
53.9
Household total income
1702.9
258.6
Household size
3.2
4.8
Individual labor income
923.7
183.1
Number of labor income earners
1.3
0.8
Household non-labor income
465.7
105.6
B. Simulations
$
Poor's per capita income
53.9
Poor's per capita income
with the non-poor's
1. Household size
81.3
2.Individual labor income
182.9
3.Number of labor income earners
73.2
4.Household non-labor income
129.0
5.Household total income
355.1
6.Household total income and size
535.2
Source: Own calculations based on microdata from the EPH.
87
Table A.11 Basic needs indicator (NBI) Argentina, 2001
1980
1991
2001
Changes
Persons
%
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
Pampeana
Buenos Aires (includes GBA)
2,607,922
24.3
2,128,736
17.2
2,161,064
15.8
-7.1
-1.4
-8.5
Cуrdoba
529,753
22.4
413,573
15.1
393,708
13.0
-7.3
-2.1
-9.4
Entre Rнos
292,979
32.6
207,794
20.6
202,578
17.6
-12.0
-3.0
-15.0
La Pampa
44,379
21.9
34,705
13.5
30,587
10.3
-8.4
-3.2
-11.6
Santa Fe
595,239
24.5
489,854
17.6
440,346
14.8
-6.9
-2.8
-9.7
Cuyo Mendoza San Juan San Luis
287,076
24.4
246,789
17.6
241,053
15.4
142,404
30.8
103,865
19.8
107,372
17.4
67,019
31.9
61,057
21.5
57,072
15.6
-6.8 -11.0 -10.4
-2.2 -2.4 -5.9
-9.0 -13.4 -16.3
Patagonia Chubut Neuquйn Rнo Negro Santa Cruz Tierra del Fuego
87,343
34.8
76,608
21.9
62,872
15.5
-12.9
-6.4
-19.3
93,507
40.2
81,391
21.4
79,547
17.0
-18.8
-4.4
-23.2
145,707
38.9
116,323
23.2
97,486
17.9
-15.7
-5.3
-21.0
27,245
26.3
22,860
14.7
19,985
10.4
-11.6
-4.3
-15.9
6,356
27.5
14,862
22.4
14,033
14.1
-5.1
-8.3
-13.4
NOA Catamarca Jujuy La Rioja Salta Santiago del Estero Tucumбn
87,039
42.6
73,944
28.2
71,145
21.5
196,892
48.8
180,025
35.5
175,179
28.8
59,224
36.6
59,311
27.0
58,869
20.4
305,776
46.8
318,532
37.1
338,484
31.6
302,681
51.7
254,830
38.2
250,747
31.3
406,748
42.4
314,828
27.7
318,209
23.9
-14.4 -13.3 -9.6 -9.7 -13.5 -14.7
-6.7 -6.7 -6.6 -5.5 -6.9 -3.8
-21.1 -20.0 -16.2 -15.2 -20.4 -18.5
NEA Corrientes Chaco Formosa Misiones
303,818
46.9
248,144
31.4
264,277
28.5
359,857
52.1
329,139
39.5
323,354
33.0
159,072
54.4
155,072
39.1
162,862
33.6
263,424
45.4
262,812
33.6
260,271
27.1
-15.5 -12.6 -15.3 -11.8
-2.9 -6.5 -5.5 -6.5
-18.4 -19.1 -20.8 -18.3
Source: Census data
88
Figure 3.1 Growth-incidence curves Household per capita income Proportional changes by percentile Argentina, 1992-2005 80
60 2003-2005 40
20 0 0 -20 -40 -60
1992-2005
10
20
30
40
50
60
70
80
90
100
1992-1998
1998-2003
Source: Own calculations based on microdata from the EPH.
Figure 3.2 Growth-incidence curves Household per capita income Proportional changes by percentile Argentina, 2002-2005 50
40
30 20 10 0 0 -10
2003-2004
2004-2005 2002-2003
10
20
30
40
50
60
70
80
90
100
Source: Own calculations based on microdata from the EPH.
89
Figure 4.1 Poverty Argentina, 1992-2005 USD 1 and USD 2 lines USD 1 a day 12 10 8 6 4 2 0
USD 2 a day 30 25 20 15 10 5 0
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
H
PG
FGT(2)
H
PG
FGT(2)
Source: Own calculations based on microdata from the EPH. Note: H=headcount ratio, PG=poverty gap, FGT(2)=Foster, Greer and Thornbecke index with parameter 2.
Figure 4.2 Poverty Argentina, 1992-2005 Official poverty lines Official extreme poverty line 30 25 20 15 10 5 0
Official moderate poverty line 70 60 50 40 30 20 10 0
H
PG
FGT(2)
H
PG
FGT(2)
Source: Own calculations based on microdata from the EPH. Note: H=headcount ratio, PG=poverty gap, FGT(2)=Foster, Greer and Thornbecke index with parameter 2.
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
90
Figure 4.3
Density of the (log) income used to compute poverty with official lines
Non parametric estimation
Argentina
1992
1998
.5
.4
.4
.3
.3
D e n sity
Density
.2
.2
.1
.1
0
0
0
2
4
6
8
10
log equivalized income
0
2
4
6
8
10
log equivalized income
2002
2005
.4
.4
.3
.3
.2
Density
.2
D e n s ity
.1
.1
0
0
0
2
4
6
8
10
log equivalized income
2
4
6
8
10
log equivalized income
Source: Own calculations based on microdata from the EPH. Note: first vertical line corresponds to the official extreme poverty line of each year, second vertical line corresponds to the official moderate poverty line of each year.
Figure 4.4 Poverty headcount ratio Official poverty line Greater Buenos Aires, 1974-2005 60
50
40
30
20
10
0 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05
Source: Own calculations based on the EPH.
91
Figure 4.5 Change in poverty headcount ratio LAC countries Change in poverty (points) 15.0 10.0 5.0 0.0 -5.0 -10.0 -15.0 Change in poverty (%) 300 250 200 150 100 50 0 -50 -100 Source: Gasparini et al. (2005). Figure 4.6 Poverty headcount ratio LAC countries Early 2000s 70 60 50 40 30 20 10 0 Source: Gasparini et al. (2005). 92
Uru
Chi
Cri
Chi, 90-03
Jam, 90-02
Arg
Cri, 92-03
Nic, 93-01
Dom
Bra, 90-03
Els, 91-03
Pan
Bol, 93-02
Chi, 90-03
Bra
Jam, 90-02
Bol, 93-02
Col
Nic, 93-01
Bra, 90-03
Mex
Els, 91-03
Cri, 92-03
Ven
Pan, 95-02
Pan, 95-02
Per
Per, 97-02
Gua
Mex, 92-02
Per, 97-02
Sur
Ecu, 94-98
Mex, 92-02
Hon
Hon, 97-03
Uru, 89-03
Jam
Par, 97-02
Ecu, 94-98
Els
Uru, 89-03
Hon, 97-03
Ecu
Ven, 89-00
DR, 00-04
Par
DR, 00-04
Par, 97-02
Bol
Col, 92-00
Arg, 92-04
Nic
Arg, 92-04
Col, 92-00
Hai
Ven, 89-00
Figure 4.7 Poverty Argentina, 1992-2005 50% median poverty line 30 25 20 15 10 5 0
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
H
PG
FGT(2)
Source: Own calculations based on microdata from the EPH. Note: H=headcount ratio, PG=poverty gap, FGT(2)=Foster, Greer and Thornbecke index with parameter 2.
Figure 4.8 Poverty indicator Endowments Argentina, 1992-2003 0.5 0.45 0.4 0.35 0.3 0.25 0.2 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
93
74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05
Figure 5.1 Gini coefficient Distribution of household per capita income Greater Buenos Aires, 1974-2005 0.55 0.50 0.45 0.40 0.35 0.30 Source: Author's calculations based on the EPH. 94
Figure 5.2 Gini coefficient Distribution of household per capita income Around 1990 and around 2000 Early 1990s 60 55 50 45 40 Early 2000s 60 55 50 45 40 Source: Own estimates from Gasparini (2003). Figure 5.3 Change in the Gini coefficient Between early 1990s and early 2000s Distribution of household per capita income 8 6 4 2 0 -2 -4 Source: Own estimates from Gasparini (2003). 95
Argentina Venezuela Paraguay Peru Uruguay Bolivia Chile El Salvador Ecuador Costa Rica Nicaragua Colombia Panama Jamaica Mexico Brazil Honduras
Uruguay Costa Rica Venezuela Peru Jamaica Argentina El Salvador Mexico Honduras Nicaragua Panama Colombia Bolivia Chile Brazil
Uruguay Venezuela Argentina Costa Rica Peru Jamaica El Salvador Mexico Nicaragua Bolivia Panama Chile Honduras Colombia Brazil
Figure 6.1 Generalized Lorenz curves Distribution of household per capita income 1992 and 2005 400
350
300
250
200 1992 150
100 2005 50
0
0
10
20
30
40
50
60
70
80
90
100
Source: Author's calculations based on the EPH.
96
Figure 6.2 Aggregate welfare, 1992-2005 Inequality from EPH and mean income from national accounts 130 120 110 100 90 80 70 60
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
Per capita income
Sen
Atk(1)
Atk(2)
Source: Own estimates from EPH and National Accounts. Note: Atk(e): CES welfare function with parameter e. Figure 6.3 Aggregate welfare, 1992-2005 Inequality and mean income from EPH 110 100 90 80 70 60 50 40
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
Per capita income
Sen
Atk(1)
Atk(2)
Source: Own estimates from EPH. Note: Atk(e): CES welfare function with parameter e.
97
Figure 7.1 Marginal return to a college education All working males, 1992-2003 0.80
0.75
0.70
0.65
0.60
0.55
0.50 1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
Source: Own estimates from microdata of the EPH.
Figure 7.2 Labor force, employment and unemployment Greater Buenos Aires, 1974-2005
60
50
40
30
20
10
0
unemployment
activity
employment
Source: Own estimates from microdata of the EPH. * estimate for second half of 2005
1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
98

L Gasparini

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