Operationalizing Pro-Poor Growth

The Case of El Salvador

Prepared by

José Silvério Marques

For the

World Bank

(September 2004)

Table of Contents Executive Summary ......................................................................................................... vi Introduction....................................................................................................................... 1 I. Historical Context and Growth-Poverty Trends ........................................................ 4

1. Historical context .................................................................................................... 4 2. Growth, Poverty, and Inequity in the 1990s ........................................................... 8

Growth trends...................................................................................................... 8 Poverty trends ................................................................................................... 10 Income distribution trends ................................................................................ 13 Asset distribution .............................................................................................. 14

II. Growth, Distribution of Income, and Poverty ........................................................ 16

1. Sources and Determinants of Growth ................................................................... 16 Changing production and employment patterns ............................................... 16 Productivity trends, 1960-2000......................................................................... 18 Growth determinants......................................................................................... 19

2. Correlates of Poverty ............................................................................................ 21 3. Distribution and Poverty Impact of Growth ......................................................... 23

Interaction of growth, distribution and poverty ................................................ 23 Poverty elasticities ............................................................................................ 23 Pro-Poor Growth Estimates .............................................................................. 24

III. Factors Affecting the Participation of Poor People in Growth ............................ 29

1. Macroeconomic instability.................................................................................... 29 Did Macro Instability Increase?........................................................................ 30 Why did instability not affect growth and poverty outcomes in the early 1990s? ............................................................................................................... 32 Why do people continue to feel insecure? ........................................................ 33

2. Pro-Poor Public Spending..................................................................................... 34 Forging national priorities................................................................................. 35 The peace dividend ........................................................................................... 36 Social Spending ................................................................................................ 37 Improved Social Conditions ............................................................................. 38 Spending Incidence........................................................................................... 39

3. Rural Development ............................................................................................... 40 Agricultural performance.................................................................................. 40 Agrarian Reform ............................................................................................... 43 Growth in rural household income ................................................................... 44 Key drivers of household income growth......................................................... 46

4. Remittances........................................................................................................... 53 5. Labor Market ........................................................................................................ 55 6. Gender................................................................................................................... 59

Women in politics ............................................................................................. 59

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Girls’ education................................................................................................. 59 Women in the labor market............................................................................... 60

IV. Trade-Offs Between Growth and Pro-Poor Growth............................................. 62

1. Macro Stabilization Policies ................................................................................. 62 2. Trade liberalization ............................................................................................... 65

V. Conclusions and Recommendations For Policy Making........................................ 69

Restore rapid growth......................................................................................... 69 Maintain a flexible labor market....................................................................... 70 Strengthen the social protection system............................................................ 71 Act on the drivers of poor households’ income growth................................... 72 Tailor policy interventions to the poorest households ...................................... 72

Pro-poor investment agenda and financing …………………………………72 References ........................................................................................................................ 76 Annexes ............................................................................................................................ 79

Annex 1................................................................................................................. …80 Annex 2..................................................................................................................... 81

Boxes Box 1.1 The Cost of the Armed Conflict ................................................................ 8 Box 1.2 Official Poverty Lines.............................................................................. 11 Box 3.2 Pro-Poor Policies in a Post-Conflict Setting............................................ 34 Box 3.1 Panel Data, Quintiles, and Changes in Income........................................ 45 Tables Table 1.1 El Salvador’s GDP and Sector Growth, 1960s – 1990s ............................ 4 Table 1.2 Selected Education and Health Indicators, 1989/90 .................................. 6 Table 1.3 Population in Poverty, 1976-1988 ............................................................. 7 Table 1.4 GDP Growth, 1990 – 2002 ........................................................................ 9 Table 1.5 Principal National Account Aggregates, 1990-2001 ................................. 9 Table 1.6 Headcount Poverty, 1991- 2002 .............................................................. 10 Table 1.7 Reduction in Poverty (Adjusted Income), 1991- 2000............................ 11 Table 1.8 Changes in Extreme Poverty Lines and Consumer Price Index (CPI) .... 11 Table 1.9 Robustness of Poverty Incidence in El Salvador to Changes in Prices and Poverty Lines, 1991-2002................................................................. 12 Table 1.10 Intensity and Severity of Poverty (Adjusted Income), 1991-2002 .......... 13 Table 1.11 Distribution of (Unadjusted) Income, 1991, 1996, 2002......................... 14 Table 1.12 World Bank’s Estimate of the Gini Coefficient 1991- 2002 ................... 14 Table 1.13 Distribution of land Among Land Owners, 1989 .................................... 15 Table 2.1 Index of Sectoral Production, 2002 ......................................................... 17 Table 2.2 Foreign Exchange Earning ...................................................................... 17 Table 2.3 Sources of Growth, 1960-2000................................................................ 18

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Table 2.4 Estimates of Total Factor Productivity Growth , 1960-2000 .................. 19 Table 2.5 Regression Coefficients ........................................................................... 20 Table 2.6 Explained Changes in Growth Rates During the 1990s .......................... 21 Table 2.7 El Salvador- Determinants of Potential Growth, 2000-10....................... 22 Table 2.8 Age Profile of Poverty, 2002 ................................................................... 23

Table 2.9 El Salvador’s Growth and Inequality Elasticities of Poverty, 1991-2002……………………………………………………………….. 24

Table 2.10El Salvador’s Growth Elasticities of Poverty, 1991-2000 …………….. 24 Table 2.11 Ravallion and Chen Pro-Poor Growth Rate, Total Poverty, 1991- 2002 ....................................................................... 28 Table 3.1 Macroeconomic Volatility, 1960s-90s..................................................... 30 Table 3.2 Volatility of Private Consumption, 19980-99 ......................................... 31 Table 3.3 Volatility of Growth in Real Wages, Employment and Underemployment 1990-2000 ................................................................. 32 Table 3.4 Agriculture and Manufacturing Value Added and Employment Growth,

1992-2001.................................................................................................. 34 Table 3.5 Central Government Expenditures By Management Area, 1996-2003... 37 Table 3.6 Investment in Basic Infrastructure, 1990, 1998-2002 ............................. 38 Table 3.7 Selected Education and Health Indicators 2000 ...................................... 39 Table 3.8 Real Prices of Selected Agricultural Products, 1990-95 ......................... 42 Table 3.9 Changes in Rural Household Income, 1995-2001................................... 44 Table 3.10 Changing Composition of Rural Household Income, 1995-2001 ........... 45 Table 3.11 Factors that Affect the Level and the Changes in Rural Household Income, 1995/2001................................................................ 47 Table 3.12 Decomposition of Changes in Rural Household Income, by Quintile, 1995/2001 ............................................................................ 49 Table 3.13 Changes in Occupation and Labor Income, 1995 and 2001 ................... 50 Table 3.14 Distribution of Annual Hours Worked, 1995 and 2001 ......................... 51 Table 3.15 Distribution of Land by Quintile of Income, 1995, 2001 ...................... 52 Table 3.16 Land Tenure in 1995 and 2001 .............................................................. 53 Table 3.17 Monthly Average Remittances Received by Household, by Poverty Level, 1998 and 2002 ........................................................... 54 Table 3.18 Impact of Remittances on Poverty, 2002................................................. 55 Table 3.19 Rural and Urban Unemployment by Poverty Level, 2002 ...................... 56 Table 3.20 Participation Rates, 1991-2002................................................................ 56 Table 3.21 Workers Occupied, by Occupational Category, 1992, 2002 ................... 57 Table 3.22 Indicators of Gender, 1991, 1996, 2002 .................................................. 58 Table 3.23 Ratio of Average Women’s Salaries to Men’s, by Years of Schooling and Region................................................................................................. 60 Table 4.1 Growth Elasticities of Real Wages, Employment and Underemployment, 1991- 2002 ............................................................................................... 63 Table 4.2 Value Added and Employment in Traded and Non-traded Sectors, 1992-2001 ................................................................................................ 67 Table 4.3 Trade Penetration Elasticities of Real Wages, 1991-2002 ...................... 67 Table 4.4 Average Monthly Wage, by Years of Schooling, 1991, 1996, 2002....... 68

Table 5.1 Estimated Costs of a Pro-Poor Growth Investment Package .................. 74

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Table 5.2 Concentration Indices .............................................................................. 75 Figures Figure 1.1 El Salvador: Savings and Investment, 1960-90........................................ 6 Figure 1.2 Average Years of Schooling for 15 Year Olds, 1995, 2002................... 15 Figure 2.1 Sectoral Contribution to GDP, 1960-90 .................................................. 17 Figure 2.2 Growth Incidence Curves, Total Poverty, National, 1991-1995, .......... 25 1995-2001 Figure 2.3 Growth Incidence Curves, Total Poverty, Urban, 1991-1995, 1995-2001............................................................................................... 25 Figure 2.4 Growth Incidence Curves, Total Poverty, Rural, 1991-1995, 1995-2001............................................................................................... 26 Figure 2.5 Growth Incidence Curves, Total Poverty, National, 1991-2000 ............ 26 Figure 2.6 Growth Incidence Curves, Total Poverty, Urban, 1991-2000................ 27 Figure 2.7 Growth Incidence Curves, Total Poverty, Rural, 1991-2000................. 27 Figure 3.1 Inflation and Unemployment Rates, 1990-2003 .................................... 31 Figure 3.2 Military Spending as % of Total Central Government Spending and Number of Military Personnel ........................................................ 36 Figure 3.3 Average Years of Schooling for Children Aged 15 ............................... 38 Figure 3.4 Infant Mortality Rate Per Socio-Economic Group................................. 39 Figure 3.5 Ratio of the 5th Richest to the 1st Poorest Quintiles Household Percentage Access to Services................................................................ 40 Figure 3.6 Real Effective Exchange Rate and Remittances .................................... 41 Figure 3.7 Per capita Agriculture Production Index, 1991-2003............................. 42 Figure 3.8 Short and Long Term Unemployment, 1990-2002 ................................ 58 Figure 3.9 Unemployment Duration, 1990-2002..................................................... 58 Figure 4.1 Inflation Rate and the Nominal Exchange Rate ..................................... 62

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Acknowledgements This paper has been prepared by José Silvério Marques with the assistance of Orlando Martinez (consultant), Reyes Aterido (World Bank), and Margarita Sanfeliu (FUSADES). Louise Cord, Helena Ribe, Andy Mason, Omar Arias, Humberto Lopez, Ignacio Fiesta, Derek Byerlee and Malcom Ehrenpreis (World Bank) provided very useful comments and suggestions during two workshops held in March and June 2004 in Washington D.C. Written comments were received from the World Bank and DFID. Additional comments were received during a workshop in Frankfurt in July 2004 with the participation of representatives of all sponsoring institutions: Agence Francaise de Developpment, DFID, GTZ, KFW, and the World Bank. Any errors are my own.

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Executive Summary This study illustrates the case of a country which after experiencing a major conflict during the 1980s, achieved substantial poverty reduction led essentially by growth as the country recovered and began to put some of the institutions, markets and policies in place for a broad based growth pattern within a newly established democratic framework. The study explores the factors that affected the ability of the poor to participate in economic growth and to benefit from it, and seeks to identify the policies that will make growth in El Salvador more pro-poor. The pro-poor growth definition used in this study is an absolute definition that requires only that the incidence of poverty declines with growth. After analyzing the historical context and trends in poverty and inequality, the key questions addressed are: What explains El Salvador’s relatively low growth during the last four decades and its continuing high levels of poverty? How have public policies and other development such as remittances affected the participation of poor people in growth? Were there trade-offs between pro-growth and pro-poor policies in the stabilization and structural adjustment policies pursued during the 1990s? What are the poverty correlates and the key drivers of income growth in the poorest households? And, what lessons can be learned from this experience to make growth more pro-poor in the future? The study has four main messages. First, growth and pro-poor spending brought about by the stabilization and structural reforms initiated in the early 1990s, the peace agreements, and the political consensus on the priority of social policies, together with increased remittances, helped to reduce poverty by one-third in the 1990s, not a small feat. Second, given El Salvador’s continuing high levels of poverty and inequality and the recent deceleration in growth and in poverty reduction, further progress in reducing poverty will require restoring rapid growth. Analysis of the determinants of growth indicates that this in turn will require investment in education and infrastructure. At the same time, there is a need to keep the economy open and the labor market flexible while strengthening the country’s social protection system to minimize the impact of negative shocks on poor households. Third, to make growth more pro-poor in El Salvador, there is a need to act on the drivers of income growth of the poor. Analysis of poverty correlates and rural household income growth indicates that these drivers are education and basic infrastructure. Investment in these areas improves the access of the poor to assets and services that enable them to take advantage of economic opportunities such as accessing non-agricultural employment and microenterprises. And fourth, the design of pro-poor growth policy interventions for the poorest households requires a detailed examination of the specific drivers of their income growth, because analysis of income growth by quintile indicates that the poorest households may face constraints not easily identifiable through examination of

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standard household survey data, and that this poorest group may differ substantially from other households. Historical Context and Growth-Poverty Trends Poverty has remained high in El Salvador through episodes of very rapid growth and also of sharp decline in output during the last four decades. In the 1960s the country embarked on an import-substitution effort which initially led to rapid growth; as the easier opportunities for import substitution were exhausted, growth began to decelerate in the 1970s; in the 1980s, El Salvador experienced a destructive internal conflict which caused a large drop in GDP; in the 1990s, it initiated a structural adjustment reform that was initially accompanied by rapid GDP growth, but growth has slowed again since the mid-90s. In general, growth has been low over the last 40 years, averaging 3 percent. Owing to the conflict, today’s real per capita income is similar to that of 30 years ago, and poverty affects about 40 percent of the population. Some policies of the late 1970s and early 1980s were costly for the country. They included the nationalization of the banking system, the institution of price controls, the maintenance of a dual exchange rate regime that penalized traditional exports, and the creation of state export marketing boards for coffee and sugar. In the early 1980s, an agrarian reform confiscated all properties over 245 ha, and transferred this land to landless farmers and new agricultural cooperatives. The agrarian reform was poorly implemented and did not yield the results anticipated, in part because of the intensification of the conflict and the drop in agricultural prices. The conflict cost the lives of over 70,000 Salvadorans and left most of the country’s infrastructure destroyed. Rural areas were those most affected by the destruction and thousands of Salvadorans emigrated, mostly to the US. Over one million Salvadorans live in the US alone. By the end of the 1980s, El Salvador was experiencing an economic and social crisis. Per capita income was only 70 percent of its 1978, pre-conflict, level. In urban areas, unemployment affected 23 percent of those in the bottom income quintile. The country’s economic infrastructure was shattered and social indicators lagged. The government that took office in 1989 implemented a series of reforms designed to stabilize the economy and revive growth. Price controls were dismantled, the exchange rate was left to float, the marketing boards were abolished, the banks were re-privatized, and trade was liberalized. Gradually investment responded, particularly after the signing of the Peace Agreements in 1992, with GDP increasing at an annual average rate of 6 percent during the 1990-1995 period. The deterioration in the terms-of-trade, owing in part to a large drop in coffee prices, and the two earthquakes in 2001 contributed to slowing GDP growth to an average of 2.8 percent per year during the 1996-2002 period. During this period, a number of so-called second generation reforms were implemented, among them the privatization of telecommunications and electricity distribution, together with reforms of the pension system and the judiciary. In 2001, the government decided to dollarize the economy.

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Headcount poverty declined rapidly during the 1990s. The intensity and severity of poverty also declined during the decade. According to official estimates, poverty affected 66 percent of the population in 1991; 33 percent of the population was in extreme poverty and another 33 percent was in moderate poverty. Poverty was higher in rural areas (71 percent) than in urban areas (60 percent). By 1995, total poverty had declined to 54 percent or by 12 percentage points, with the absolute decline being higher in urban areas than in rural areas (14 vs. 7 percentage points). During the 1995-2002 period, poverty was further reduced to 43 percent or by 11 percentage points, with the absolute decline again favoring urban areas (12 vs. 8 percentage points). During the 1991-2002 period, total poverty declined by 23 percentage points, 26 points in urban areas and 15 percentage points in rural areas. World Bank staff have made some adjustment to the official income estimates and confirmed the decline in poverty during the period. Poverty declined by a similar percentage during the first and second part of the 1990s (22 percent and 20 percent), though GDP growth was twice as fast during the former period compared to the latter. While the decline in urban poverty was lower in the second part of the 1990s (26 percent versus 34 percent), the decline in rural poverty was twice as large during the second part of the 1990s compared to the early 1990s (15 percent versus 7 percent). The implied growth elasticities of poverty are discussed below. The distribution of income and assets remains highly unequal. Income distribution deteriorated slightly during the 1990s. Official estimates show that the distribution of income improved somewhat during the first part of the 1990s but then deteriorated during the 1995-2002 period. World Bank estimates also show a slight deterioration in income distribution during the 1990s. Despite the agrarian reform, land assets continue to be concentrated and the poorest children still have two years less of schooling than the richest children. Growth, Distribution of Income, and Poverty El Salvador’s productive structure has changed significantly since the 1960s. Traditional agricultural exports (coffee, cotton, sugar) saw their contribution to the country’s value added decline. In contrast, services and industry rose in importance. The change in the composition of output was not accompanied by an increase in the overall productivity of the economy. Indeed, over the last forty years, growth in El Salvador has been a result mostly of factor accumulation rather than productivity gains. Without considering human capital adjustments, total factor productivity’s annual contribution to growth during the last four decades was -0.3 percent. On the other hand, annual average contributions of capital and labor to growth were 1.5 percent and 1.8 percent, respectively, for an average growth rate of 3 percent during 1960-2000. These results are influenced by the destruction of capital and heavy emigration during the years of the internal conflict. Adjusting for human capital does not significantly change the results. Analysis of the determinants of growth indicates that the structural reforms of the 1990s may have increased the country’s growth potential. The analysis indicates that if El Salvador is to increase its long term growth potential, the country must improve its

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infrastructure, invest in education, and continue to open its economy. While analysis of the correlates of poverty supports the conventional findings related to geographic location, employment and household characteristics, there is evidence that unobserved differences among and between households play an important role in the determinants of poverty (and inequality) in El Salvador. The unobserved (unmeasured) heterogeneity of households and their member in determining per capita income related, for example, to education may include labor market connections, family human capital, school quality, and/or work ethic. The elasticities of poverty to growth and inequality for El Salvador are relatively high compared to their theoretical value, which underscores the importance of growth and improved income distribution for poverty reduction. The implied growth elasticities of poverty were much higher (in absolute terms) during the second part of the 1990s than during the first part. However, this large increase results to a large extent from the fact that the official poverty lines lagged the increase in the consumer price index. Taking this lag into consideration, the elasticity is reduced to values similar to those in the earlier period. Nonetheless, growth was more regionally balanced during 1995/2000 than during 1991-1995. The growth incidence curves and the pro-poor growth rates indicate that these contrasting outcomes are related to changes in rural household incomes. Incomes of the poorest rural households dropped in the first part of the 1990s but recovered in the second part. . Factors Affecting the Participation of Poor People in Growth Some of the key factors that influence pro-poor growth are: macro instability, public spending policies, rural development, remittances, labor market conditions, and gender policies. Macro instability leads to insecurity which, like inequality, impairs growth and poverty reduction, as it deters investment. Insecurity about future employment and income also directly and adversely affects welfare because most households and workers care not only about the level of their standard of living, but also about its security. El Salvador was subject to intense volatility in consumption, wages and employment during the first part of the 1990s, as a consequence of the adjustment process. While the volatility of macro variables declined in the second part of the 1990s, there continued to be a sense of insecurity among the population, a phenomenon which may have reflected political factors as well as the impact of a more open economy. Public spending in the social sectors increased significantly during the 1990s, in part owing to the peace dividend. In 1996, social expenditures represented 31 percent of the budget; by 2003 they represented 46 percent. These expenditures have mainly benefited the lower income groups, a fact which is reflected in improved social indicators and access to basic services by the poor. Nevertheless, El Salvador still spends too little on education compared to the average for Latin America and the Caribbean. While the annual average growth rate of agriculture declined from 2.3 percent during 1990-95 to 0.6 percent during 1996-2001, the per capita income of the poorest rural households increased in the second part of the 1990s at rates over 5 percent. Using panel

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data from FUSADES for the 1995-2001 period, we explore the factors that impacted on income growth among the poorest rural households. The analysis indicates that the key drivers are access to non-agricultural employment and the possibility of establishing micro-enterprises, both of which require improved human capital and access to basic infrastructure. Remittances are an important source of income growth for better-off households. The poorest households are not likely to receive remittances or credit. To invest in human capital or establish micro enterprises, the poorest households may be forced to sell the few assets (cattle) that they possess. Better-off households may use remittances for such purposes. Also, while the poor families sought other sources of income outside agriculture, they intensify their work in agriculture. Many poor families rented land (in) to cultivate it. An increasing dynamic land market appears to have facilitated the resignation of resources that led to the increase in incomes. To the extent that better-off families receive higher remittances than poorer families, remittances contribute to inequality; on the other hand, remittances are an important factor in poverty reduction. With respect to the labor market, we find that women’s participation has increased, particularly in rural areas, most likely owing to the agricultural crisis and the need to find non-agricultural jobs such as in maquila, and that there has not been a major change in the composition of employment categories, though the relative importance of unremunerated family members has tended to decline while that of wage earners has tended to increase. This is consistent with an increase in the formalization of labor relations. On the other hand, the labor market appears to be quite flexible, with most unemployment being of short duration. Finally, gender discrimination in the education sector appears to be on the decline, but it remains a serious problem in the labor market, particularly for women with more education.

Trade-Offs Between Growth and Pro-Poor Growth While growth contributed substantially to poverty reduction during the 1990s, inequality appears to have increased slightly during the same period. Some of the policies that promoted growth may have also increased inequality. Since increased inequality works against poverty reduction, there may exist some trade-off between pro-growth policies and pro-poor policies. We explore how macro stabilization and trade liberalization policies impacted poverty and inequality. While stabilization policies are necessary for growth, they have different effects on wages and unemployment and therefore on poverty and inequality. Wage flexibility may help spread the cost of the adjustment, while unemployment has a more unequal effect. In the case of El Salvador, we find that adjustment has been mostly through changes in wages rather than through unemployment. In the future with the dollarization of the economy, the brunt of future adjustments may fall on employment. Thus, it will make labor market regulations that detract from wage flexibility more costly. As for trade liberalization, though it is expected to promote growth over the longer term, it may have adverse short term effects on income distribution and therefore on poverty

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reduction. In the case of El Salvador, there is no evidence of such a negative short tem impact.

Conclusions and Recommendations For Policy Making After experiencing a major conflict during the 1980s, El Salvador achieved substantial poverty reduction during the 1990s. The preceding analysis indicates that the following elements have contributed to this outcome: Successful implementation of the Peace Accords, the focus of the National

Reconstruction Program on the poor and demobilized, and the gradual building up of democratic institutions all created a favorable environment for growth and social investment;

Successful stabilization and structural adjustment reforms led to high growth rates, particularly in the first part of the 1990s, and a drop in inflation; rapid growth in the early 1990s helped to raise many of the poor closer to the poverty line, as evidenced by a decline in the poverty gap; subsequent growth, though less rapid, helped to push many of them above the poverty line;

A flexible labor market helped reduce the impact of adjustment on unemployment, which usually bears most heavily on poor households; adjustment via wages helped distribute the cost of adjustment among different income groups;

Higher public social/basic infrastructure expenditures were made possible by growth (creating higher fiscal revenues) and by a new political consensus on the importance of education and other social investment;

Gender discrimination fell; Rural incomes rose because of non-agricultural job opportunities (micro

enterprises, maquila, services), supported by infrastructure investment and rising human capital formation, which in turn made it possible for the rural poor to take advantage of opportunities that presented themselves; and

Higher remittances made it possible to finance human capital formation and physical investment in micro enterprises.

These findings suggest the following are the key areas for making growth in El Salvador more pro-poor in the future. Restore rapid growth. Forty-three percent of El Salvador’s population still lives in poverty. Growth has slowed in recent years and this has in turn slowed the rate of poverty reduction. Therefore, the first order of business should be to restore rapid growth. Analysis of the determinants of growth indicates that this requires investment in education and infrastructure, while keeping the economy open. The need to continue to invest in education and basic infrastructure is now an accepted priority in El Salvador, though the political consensus required to mobilize the resources to finance these needs may be lacking. Keeping the economy open is a more

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controversial proposition, however. To minimize the possible negative effects of trade liberalization or trade agreements in countries with very unequal distribution of income such as El Salvador, the World Bank recommends, among other things: i) Improve the distribution of assets, because assets improve households’ and individuals’ capacity to take advantage of new opportunities; and ii) strengthen social safety nets, because the process of adjustment can lead to temporary or permanent income losses. Both income and asset distribution are still highly concentrated in El Salvador. Improving the distribution of assets could be thought to require a new round of land redistribution. But the World Bank’s Rural Development Study published in 1998 indicates that considering the “scarcity of farm land in El Salvador … these estimates underline the unreasonableness of relying primarily on land redistribution to alleviate poverty among the rural poor and the importance of non-land factors”. Thus it appears that the appropriate route to better asset and income distribution is investment in human capital and basic infrastructure, which makes it possible for the poor to take advantage of economic opportunities and move out of poverty. For those who face temporary income losses or are not able to help themselves, there is a need to strengthen the social protection system as discussed below. Maintain a flexible labor market. With the dollarization of the economy it is expected that, in the future, the labor market will absorb the brunt of any required adjustment. Since inflation is now very low, any required adjustment in the future should mostly impact employment and unemployment, because nominal wages are usually inflexible downwards. To facilitate adjustment, labor market flexibility should be maintained. Wage flexibility may help spread the cost of adjustment, while unemployment has a more unequal effect. Labor market flexibility means giving employers and employees alike “efficient mechanisms with which to adapt to the business cycle… It is important to ensure that the mechanisms used to set minimum wages are based not only on purchasing power, but also on considerations related to productivity and employment.” Strengthen the social protection system. The existence of an adequate social protection system (insurance and social assistance programs) will facilitate the adjustment process. Since in the future, most of the consequences adjustment may fall on employment, the question arises as to whether current unemployment policies are appropriate. Severance payments should be evaluated to see whether they are better than alternative mechanisms, such as individual savings accounts. These are a “funded” version of the severance pay program. Social assistance programs should be strengthened, particularly those directed at the most vulnerable groups such as poor children, the disabled and the elderly, while at the some time building institutional capacity and cost effective delivery models. Act on the drivers of poor household income growth. To make growth more pro-poor in El Salvador, there is a need to act on the factors that drive income growth among the poor. Analysis of poverty correlates and of rural household income growth indicates that these are education and basic infrastructure. Investment in these areas improves the access of the poor to assets and services that enable them to take advantage of economic opportunities such as accessing non-agricultural employment and micro-enterprises.

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Tailor policy interventions to the poorest households. Finally, an important implication of the findings of this study is that the poorest households may face specific constraints that require detailed analysis and tailor-made policy interventions. Arias’s analysis of poverty correlates for different income quintiles (Section II) concludes that household variables that are common in survey data may not fully capture variations in socio-economic performance and in the likely impact of public interventions. Our own analysis in Section III of the drivers of income growth finds that the poorest households have income drivers that differ from other households. Thus, the design of pro-poor growth policy interventions for the poorest households requires a detailed analysis of the specific drivers of their income growth. Pro-poor investment agenda and financing. What specifically should be done? What should comprise a pro-poor growth investment program? The recent Public Expenditure Review (PER) and Poverty Assessment (PA) prepared by the World Bank indicate that in education the government should focus on measures to increase the coverage in the 3rd cycle of basic education and in secondary education, particularly for the lowest income students. At the same time, there is a need to ensure that all people have access to quality healthcare. On basic infrastructure, access to potable water and suitable sanitation facilities in rural areas is increasingly recognized as an important health input as is improved access to all weather roads in rural areas translates into reduces isolation, lowers the costs for goods and services (including accessing education and health facilities), and increases access to markets, enabling people to better take advantage of emerging economic opportunities. On social protection, it will be important to establish an institutional mechanism for coordinating programs that would support greater emphasis on high priority, high return areas (e.g., early childhood interventions), and identification and scaling up of cost-effective models. How much would cost such pro-poor growth package? The PER and PA estimate that the cost would be equivalent to between 3.2 percent and 3.6 percent of GDP. This cost will be distributed gradually over several years. How could this investment package be finance? Within the current fiscal envelop, there is very little margin to increase social spending. In 2003, the fiscal deficit was about 3 percent of GDP and the public debt, 41 percent of GDP. El Salvador has a low tax burden (12.1 percent of GDP in 2003) with most government tax revenues originating from the VAT or value added tax (53 percent of total tax revenues), income taxes (29 percent), import duties (10 percent); and excise taxes on alcoholic beverages, tobacco products, and fuels (4 percent). Any decision to increase taxes to finance a pro-poor growth package will need to take into account its incidence on the poor. A recent study on tax incidence in El Salvador found that in general the system is regressive. The only two taxes that are not regressive are the personal income tax and the gasoline tax. The most regressive taxes are the VAT on domestic goods and the tax on cigarettes. To raise the revenues required to finance the pro-poor growth package the authorities should give priority to reduce tax illusion and evasion, particularly in the income tax. Other additional tax revenue measures that the authorities may consider should ensure a net positive resource transfer to the poor.

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Introduction

Poverty has remained high in El Salvador as the country has gone through episodes of both very rapid growth and sharp declines in output during the last four decades. In the 1960s the country embarked on an import-substitution effort which initially led to rapid growth; as the easier opportunities for import substitution were exhausted, growth began to decelerate in the 1970s; in the 1980s, El Salvador experienced a destructive internal conflict which caused a large drop in GDP; in the 1990s, it initiated a structural adjustment reform that was initially accompanied by rapid GDP growth, but growth has slowed since the mid-1990s. In general, over the last 40 years, growth has been low, averaging 3 percent. Owing in part to the conflict, today’s real per capita income is similar to that of 30 years ago; and poverty, though it was reduced by one-third during the 1990s, still affects about 40 percent of the population. This case study on El Salvador’s growth and poverty reduction experience is part of a broader effort by the World Bank and sponsoring organizations (Agence Francaise de Developpment, Department for International Development, GTZ, KFW) to better understand the factors that contribute to making growth pro-poor. The Operationalizing Pro-poor Growth work program aims to (a) propose a common methodology for defining and measuring pro-poor growth; (b) develop an analytical approach for designing pro-poor growth strategies; (c) provide operational guidance on key macro, sectoral and thematic policies and how they relate to poverty and growth; and (d) investigate how country context and initial conditions might affect the selection and prioritization of such policies. This study contributes to the work program by exploring the factors that affect the ability of the poor to participate in economic growth and to benefit from it, and seeks to identify the policies that will make growth in El Salvador more pro-poor. The pro-poor growth definition used in this study is an absolute definition of pro-poor growth that requires only that the incidence of poverty declines with growth. This study illustrates the case of a country which after experiencing a major conflict during the 1980s, achieved substantial poverty reduction led by growth as the country recovered and began to put some of the institutions, markets and policies in place for a broad based growth pattern within a newly established democratic framework. The key questions addressed in this study are:

1. What explains El Salvador’s relatively low growth during the last four decades and continuing high levels of poverty? What are the key determinants of growth?

2. After the destructive conflict of the 1980s, poverty declined substantially. What

explains the large reduction in poverty during the 1990s? What are the key poverty correlates? How have policies related to macroeconomic development, public expenditure, rural development, remittances, the labor market, and gender

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affected the participation of poor people in growth? Were there trade-offs between pro- growth and pro-poor policies in the stabilization and structural adjustment policies pursued?

3. Despite the deceleration of GDP and agricultural growth, the pace of reduction of

poverty in rural areas during the second part of the 1990s was twice as fast as during the first. What were the key drivers of change in rural household incomes and poverty reduction during the second part of the 1990s? And, what lessons can be learned from this experience to make growth more pro-poor in the future?

To help answer these questions, the study builds on and complements several strands of ongoing work at the World Bank and in-country, including the 2003 Country Economic Memorandum, the Poverty Assessment, the Public Expenditure Review and a recently concluded assessment of progress towards the Millennium Development Goals. The empirical analysis is based largely on three different sets of data. One is the national accounts estimates prepared by the Central Reserve Bank of El Salvador. We use these data to analyze the sources of growth, including factor and sector decomposition to help answer the first question. We find that the lack of productivity growth has been a major cause of low growth, which, together with high levels of inequality have contributed to high poverty rates. The second set of data consists of the household surveys conducted every year by the Directorate General of Statistics, DIGESTYC. Although there are some problems of comparability between surveys, their methodologies are relatively homogeneous for the 1991-2002 period. These surveys have national coverage and the estimates are statistically representative for rural and urban areas. The surveys report household income rather than expenditures. DIGESTYC computes poverty estimates based on national poverty lines for the extremely and the moderately poor, as well as for urban and rural areas. These data will help answer the second set of questions as we explore to what extent social expenditures (education, health and basic infrastructure) have been pro-poor (incidence analysis) and discuss the pro-poor growth estimates. We find that growth has helped reduce poverty substantially in the 1990s, though income inequality has increased slightly, and that public expenditures have benefited the lowest income groups relatively more than others. The household survey data also help establish the importance of remittances for poverty reduction (although they appear to increase inequality), and provide insights into the flexibility of El Salvador’s labor market. The household survey earnings data further help us to examine whether trade-offs between growth and pro-poor growth were involved in the adjustment and trade liberalization policies pursued during the 1990s. We conclude that such trade-offs were not present in the case of El Salvador. The third set of information consists of panel data collected by FUSADES1 every other year since 1995, which will help answer the third set of questions. These data are nationally representative of rural areas and provide a good basis for evaluating the 1 FUSADES is a private sector foundation created in the mid-1980s which, among other things, conducts economic and social research in its Department of Social and Economic Studies.

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determinants of rural household income growth, as well as the key transmission mechanisms between growth and poverty. We find that the key drivers of income growth among the poorest rural households are related to access to non-agricultural employment and the possibility of establishing micro-enterprises, both of which require improvements in human capital and basic infrastructure. The study has four main messages. First, growth and pro-poor spending brought about by the stabilization and structural reforms initiated in the early 1990s, the peace agreements, and the political consensus on the priority of social policies, together with increased remittances, helped to reduce poverty by one-third in the 1990s, not a small feat. Second, given El Salvador’s continuing high levels of poverty and inequality and the recent deceleration in growth and poverty reduction, further progress in reducing poverty will require restoring rapid growth. Analysis of the determinants of growth indicates that this in turn will require investment in education and infrastructure. At the same time, there is a need to keep the economy open and the labor market flexible while strengthening the country’s social protection system to minimize the impact of negative shocks on poor households. Third, to make growth more pro-poor in El Salvador, there is a need to act on the drivers of income growth of the poor. Analysis of poverty correlates and rural household income growth indicate that these drivers are education and basic infrastructure. Investment in these areas improves the access of the poor to assets and services that enable them to take advantage of economic opportunities such as accessing non-agricultural employment and microenterprises. And fourth, the design of pro-poor growth policy interventions for the poorest households requires a detailed examination of the specific drivers of their income growth, because analysis of income growth by quintile indicates that the poorest households may face constraints not easily identifiable through examination of standard household survey data and that this poorest group may differ substantially from other households. The paper is organized as follows: Section I provides the historical context and reviews growth, poverty and income distribution trends. Section II analyzes the sources and the determinants of growth, the poverty correlates, and the interaction between growth, poverty, and income distribution. Section III identifies factors that affect the participation of the poor in growth, including those related to macroeconomic instability, public spending, rural development, remittances, the labor market, and gender policies. Section IV discusses possible trade- offs between growth and pro-poor growth in stabilization and trade liberalization policies. Finally, Section V presents the conclusions and recommendations. The paper includes two annexes that provide empirical estimates.

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I. Historical Context and Growth-Poverty Trends This Section sets the stage for what follows. It gives an overview of the historical context and reviews trends in growth, poverty and income distribution. The discussion on poverty and income distribution is mostly restricted to the 1990s because of data limitations. After pursuing an import substitution strategy in the 1960s and 1970s, and experiencing strong government intervention in the economy and a very destructive conflict in the 1980s, El Salvador was in crisis at the beginning of the 1990s: poverty was widespread, social conditions lagged, the country’s economic infrastructure was shattered, and economic growth was minimal. The 1990s saw a major turnaround. Poverty declined by one-third during the decade owing to the resumption of growth brought about by the stabilization and structural reforms initiated in early 1989 and the peace agreements of 1992, together with increased remittances from Salvadorans living abroad. But despite progress in poverty reduction, evidence is presented at the end of the Section that indicates that assets and incomes continued to be highly concentrated in El Salvador. 1. Historical context El Salvador is a small country with a population of 6.5 million. The population is young with 55 percent of Salvadorans aged under 24 years. Fifty-nine percent of the population lives in urban areas and 41 percent in rural areas. Per capita gross national income in 2002 was US$ 2,080. The country is prone to natural disasters – earthquakes, floods, and droughts – which form an ongoing threat to the country’s population, particularly to those living in high-risk zones. In the last two decades, three major earthquakes hit the country; one in 1986 that mostly impacted the capital city of San Salvador; and two in early 2001 that devastated a large part of the national territory, with 1,160 dead and 8,000 injured. Hurricane Mitch brought floods and destruction in 1998 to the eastern part of the country. The atmospheric phenomenon El Niño impacts the country periodically. Recurrent natural disasters give the population a sense of insecurity that negatively affects welfare (this issue is discussed further in Section III). Table 1.1 El Salvador’s GDP and Sector Growth, 1960s-1990s (simple average of annual rates)

1960s 1970s 1980s 1990s GDP 6.0 3.9 -1.9 4.9 Agriculture 3.8 3.8 -2.5 2.2 Manufacturing 8.7 3.1 -3.3 5.3 Services 5.6 4.1 -1.2 5.6 Memo: LAC’s GDP 2.3 3.5 -0.1 1.6 Source: World Bank’s World Development Indicators (WDI) In the last four decades El Salvador has undergone two major development experiments separated by a major internal conflict. In the 1960s and 1970s, it pursued an import substitution strategy. El Salvador was one of the founders of the Central American

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Common Market (CACM) established in 1960. Under high tariff protection, the CACM facilitated rapid industrialization, which was financed by agriculture surpluses mainly generated by coffee, the country’s principal export crop. GDP grew at 6 percent annually during the 1960s, led by manufacturing, which grew at 8.7 percent per year. GDP growth during this period was more than double the average for Latin America and the Caribbean (LAC) (Table 1.1). In the 1970s there were signs that import substitution opportunities were quickly becoming exhausted, with average annual manufacturing and GDP growth declining to 3.1 percent and 3.9 percent, respectively. In the 1980s, the Central America region, including El Salvador, became engulfed in a conflict which was part of the broader Cold War confrontation. The conflict and a series of misguided policies contributed to a decline in GDP averaging 1.9 percent per year during the 1980s, including large drops in agricultural and manufacturing output. In 1989 a new government took office. It embarked on a major stabilization and structural adjustment program and initiated negotiations with the armed opposition (FMLN), reaching a Peace Accord in early 1992. As the country began building its democratic institutions, the governments pursued an export led growth strategy. GDP grew at an annual rate of 4.9 percent led by manufacturing and services; agricultural growth lagged, increasing at an annual average rate of only 2.2 percent. The stabilization and trade liberalization efforts may have involved trade-offs between pro-growth and pro-poor polices, as discussed in Section IV. The conflict of the 1980s cost the lives of over 70,000 Salvadorans and destroyed the country’s infrastructure. Rural areas were those most affected by the destruction. Thousands of Salvadorans emigrated, mainly to the US. According to the Directorate General of Statistics (DIGESTYC), annual net migration increased from 17,600 during 1970-75, to 32,200 during 1975-1980, to 69,000 during 1980-95 and numbered 43,800 in 1985-1990. In the first part of the 1990s, annual net migration is estimated at 11,400; in the second part, at 7,600. Over one million Salvadorans live in the US alone. Some policies of the late 1970s and early 1980s were costly for the country. They included the nationalization of the banking system, the institution of price controls, the maintenance of a dual exchange rate regime that penalized traditional exports, and the creation of state export marketing boards for coffee and sugar. In the early 1980s, an agrarian reform confiscated all properties over 245 ha, and transferred this land to landless farmers and new agricultural cooperatives. The agrarian reform did not yield the anticipated results, in part because of the intensification of the conflict and the drop in agricultural prices (this issue is discussed further in Section III).2

2 World Bank (1994) “El Salvador: The Challenge of Poverty Alleviation”, Report No. 12315-ES, Annex A, page 4.

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Figure 1.1. El Salvador: Savings and Investment, 1960-90

0.05.0

10.015.020.025.0

1960s 1970s 1980s 1990s

% o

f GD

P

Gross capital formation Gross domestic savings Gross national savings

Source: World Bank (WDI) Investment and domestic savings dropped sharply during the 1980s. Gross capital formation (GCF) had increased from 14.4 percent of GDP in the 1960s to 19.3 percent of GDP in the 1970s, but then fell to 12.9 percent of GDP in the1980s (Figure 1.1). Gross domestic savings (GDS) followed a similar pattern: they increased from 11.9 percent of GDP in the 1960s to 16.6 percent of GDP in the 1970s and then dropped to 6.9 percent of GDP in the 1980s. In LAC as a whole, average GCF and GDS remained above 20 percent of GDP during the 1960-1980 period. By the end of the 1980s, El Salvador was in economic and social crisis. Per capita income was only 70 percent of its 1978, pre-conflict, level. In urban areas, unemployment affected 9 percent of the labor force (23 percent of those in the bottom quintile) and underemployment affected 50 percent (72 percent of those in the bottom quintile). In rural areas, the employment situation must have been even worse.3 The country’s economic infrastructure was shattered. Table 1.2 Selected Education and Health Indicators, 1989/90 (Percentages) Adult

Illiteracy Rate

Gross Primary

Enrollment

Gross Secondary Enrollment

Gross Post-

Secondary Enrollment

Life Expectancy

at birth (years)

Infant Mortality

Rate (per 1000

live births)

Under 5 Mortality

Rate (per 1000

live births) El Salvador 27.5 81 26 16 65.6 45.6 54 LAC 15.2 106 49 17 67.9 41.3 49.4 Source: Marques, José Silvério and Iann Bannon (2003) “Central America: Education Reform in a Post-Conflict Setting, Opportunities and Challeges”, World Bank, Conflict Prevention and Reconstruction Unit, Working Paper 4, Tables 3 and 4 and World Bank (WDI).

3 Data for rural areas in 1985 show unemployment at 16 percent (35 percent of those in the 1st quintile) and underemployment at 54 percent (59 percent of those in the 1st quintile). World Bank (1994) “El Salvador: The Challenge of Poverty Alleviation”, Report No. 12315-ES, Annex A, page 3, table A-2.

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Social indicators lagged. Table 1.2 shows basic education and health indicators for El Salvador and LAC at the end of the 1980s. As the data show, literacy and education coverage and efficiency indicators were much lower in El Salvador than in the region as a whole. While primary gross enrollments in LAC were over 100 percent, in El Salvador 20 percent of primary school age children did not attend school. Enrollments were much lower in rural areas particularly in the areas were the conflict was fierce.4 El Salvador’s health indicators also lagged behind those for LAC. Life expectancy at birth was 2 years lower than in LAC and infant mortality was higher. Poverty must have increased significantly during the 1980s, but poverty data for the late 1970s and 1980s need to be analyzed with extreme care. The surveys conducted during this period were based on the 1970 population census. The military conflict caused major dislocations of the population and made it difficult to carry out the surveys. There were several other methodological shortcomings, which undermined the reliability of the estimates, particularly for comparisons of changes over time.5 Table 1.3 shows poverty estimates by the now extinct Ministry of Planning for the late 1970s and the 1980s. They indicate that in 1985 poverty affected 63 percent of the rural population; in 1988 it affected 61 percent of the urban population. The data show a decline in urban poverty between 1976 and 1985 but a large increase between 1985 and 1988. These surveys do not appear to be comparable. On one hand, it does not seem reasonable that urban poverty would increase by 14 percentage points in just three years (even considering the 1986 earthquake); on the other hand, it is hard to believe that urban poverty declined between 1976 and 1985, given the large drop in GDP and the destruction caused by the conflict. Box 1.1 presents an estimate of the cost of the armed conflict. Table 1.3 Population in Poverty, 1976-1988 (Percentage)

Urban Poverty Rural Poverty Year Total Moderate Extreme Total Moderate Extreme

1976 50 30 20 N/a N/a N/a 1985 47 21 26 63 30 32 1988 61 31 30 N/a N/a N/a Total Poverty: sum of moderate and extreme Extreme poverty: income level insufficient to purchase one basic food basket Moderate poverty: income level insufficient to purchase two basic food baskets Source: World Bank (1994) “El Salvador: The Challenge of Poverty Alleviation”, Report No. 12315-ES, Annex A, page 3, table A-1, based on Ministry of Planning estimates.

4 See Marques, José Silvério and Ian Bannon (2003) 5 According to the World Bank (1994), Annex A, page 2., these shortcomings were: (a) under-reporting of income, which is a major limitation for constructing poverty lines on the basis of income data alone. Most surveys did not gather data on remittances from abroad, which in El Salvador increased from 1.5 percent of GDP in 1980-82 to 4.1 percent of GDP in 1985-89; (b) the sampling frame of these surveys suffered from several changes according to the location of the military conflict over the years; (c) the composition of the basic food basket (BFB) was not properly updated; (d) seasonal effects were not adequately considered since surveys were conducted during different periods of the year; and (e) the survey instrument was changed several times, raising concerns about whether survey questions actually measure the same variables.

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Box 1.1 The Cost of the Armed Conflict Before the eruption of the internal conflict in 1979/80, El Salvador’s per capita GDP was 20 percent higher than the median Latin America income; in the 1990s it was 20 percent lower. In the intervening period, El Salvador suffered a dramatic fall in income; indeed during the first years of the conflict (1979/1982) real per capita GDP fell by about 40 percent (World Bank, 2002a). Lopez (2001, 2003a) has estimated the cost of the conflict in terms of GDP loss and its impact on poverty. In order to estimate the magnitude of the impact, Lopez uses a procedure that permits the decomposition of GDP growth into a “regular” component that captures the dynamics of growth in the absence of extreme shocks and simulates “normal” growth, and a second component that captures aberrant behaviors caused by extraordinary events such as the armed conflict. According to his estimates, if the conflict had not taken place, El Salvador’s per capita GDP would be at least 75 percent higher than its observed 2000 value. Similarly, poverty, as measured by the headcount poverty index, would be about 15 percentage points lower (gross elasticity of poverty to growth of -1.3), or close to that in Costa Rica, a country that did not experience conflict and has made significant progress in improving social conditions. The armed conflict also had a significant negative impact on social outcomes, especially health and education. Using Wodon et al (2001)6 estimates of cross country elasticities for a set of social indicators to growth, Lopez (2003a) calculates how the drop in GDP per capita impacted social indicators. Child malnutrition could have been halved to about 6 percent in the absence of the armed conflict (elasticity of about –1.0), and infant mortality could be about one-fourth less than in 2000 (elasticity of -0.36). In education, there would have been gains in reducing illiteracy, and in primary, secondary (both net) and tertiary (gross) enrollment ratios, with the gains in secondary and tertiary being higher. The elasticities to growth are -0.1 for adult illiteracy, 0.04 for primary enrollment, 0.25 for secondary enrollment and 0.63 for tertiary enrollment. 2. Growth, Poverty, and Inequity in the 1990s Growth trends The government of President Alfredo Cristiani that took office in 1989 implemented a series of reforms designed to stabilize the economy and revive growth. Price controls were dismantled, the exchange rate was left to float, the marketing boards were abolished, the banks were re-privatized, and trade was liberalized.7 Gradually investment responded, particularly after the signing of the Peace Agreements in 1992 (Figure 1.1). Following years of repressed demand during the conflict, domestic demand boomed fueled by rapid credit growth and the large public (and private) expenditures associated 6 Wodon, Q., R. Castro-Fernandez, G. Lopez-Acevedo, C. Siaens, C. Sobrado, and J. P. Tre (2001) “Poverty in Latin America: Trends (1986-98) and Determinants”, LCR, World Bank. 7 FUSADES (Salvadoran Foundation for Economic and Social Development) issued a set of proposals toward the end of the 1980s to tackle the country's severe economic and social problems. After extensive public discussions these proposals became a plank in the electoral platform of presidential candidate Alfredo Cristiani. Following his 1989 election Cristiani invited the FUSADES team to join his administration and implement the program they had developed. See Liévano de Marques (1995), Mirna. “El Salvador- Un País en Transición”, ESEN.

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with the National Reconstruction Program,8 which together with improved terms-of-trade and the recovery of regional demand, helped accelerate growth, with GDP increasing at an annual average rate of 6 percent during the 1990-1995 period, surpassing LAC (Table 1.4). Table 1.4. GDP Growth, 1990-02 (Simple arithmetic average of annual rates)

1990-95 1996-02 El Salvador 6.0 2.8 LAC 3.8 2.0 Source: World Bank’s WDI During the second part of the 1990s, growth decelerated. To curtail the rapid expansion of credit and domestic demand, the Central Bank tightened monetary policy in the mid-1990s, leading to high real interest rates (8 percent in 1997 and 16 percent in 2000).9 The deterioration in the terms-of-trade, owing in part to a large drop in coffee prices and the two earthquakes in 2001 further contributed to a slowing down of GDP growth to an average of 2.8 percent per year during the 1996-2002 period, only slightly above the estimated population growth rate of 2.1 percent (Table 1.4). Table 1.5 Principal National Account Aggregates, 1990-2001

Annual Average Growth Rates (%) 1990-95 1996-2001

Private Consumption 8.3 2.5 Public Consumption -3.7 2.2 Investment 11.4 1.4 Exports of GNFS 13.7 13.8 Imports of GNGS 20.3 7.3 Memorandum: Terms of Trade 7.2 -3.7 a/ Credit Private Sector 13.4 7.6 a/ 1996/00 Source: World Bank (WDI) During the second part of the 1990s, a number of so-called second-generation reforms were implemented, among them the privatization of telecommunications and electricity distribution, and reforms of the pension and judiciary systems. In 2001, the government decided to dollarize the economy. Interest rates have since dropped to near US levels but GDP growth has failed to recover, in part owing to the US and regional economic slowdown. The first ARENA government that took power in 1989 did not face significant opposition to the reform program. The situation inherited from the previous Christian Democratic administration of President Duarte was very difficult. Despite efforts at reform and at 8 The international community in a series of Consultative Group Meetings supported the National Reconstruction Program; the pledges (and disbursements) have totaled approximately US$ 1 billion. 9 Arrears to the commercial banks increased from US$ 120 million in 1995 to over US$ 400 million in 1999. See Campos (2000), Roberto Rivera, “La Economia Salvadoreña al final del Siglo: Desafios para el Futuro”, FLACSO.

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reaching a peace agreement during the second part of the 1980s, the Christian Democrats had failed to end the war or improve the economy. Allegations of corruption and difficult relations with the private sector facilitated the ARENA victory in the 1988 legislative elections. By 1989, ARENA had attracted the support of business and other groups, and Alfredo Cristiani won the presidency by an important margin (55 percent), with a strong mandate to improve economic and social conditions, and to put an end to the conflict. He succeeded on both counts and ARENA would govern for three consecutive periods, with the electorate recently giving it a fourth presidential period, with the election of Antonio Saca as President of the Republic for the 2004-2009 period. Nonetheless, with increasing participation of the FMLN in the National Assembly after the Peace Accords of 1992, the ARENA administrations have faced growing opposition to its policies and demands for greater accountability. Poverty trends Poverty declined rapidly during the 1990s. According to official estimates by the Directorate General of Statistics, DIGESTYC, poverty measured by monetary incomes affected 66 percent of the population in 1991; 33 percent of the population was in extreme poverty and another 33 percent in moderate poverty (Table 1.6). Poverty was higher in rural areas (71 percent) than in urban areas (60 percent). By 1995, total poverty had declined to 54 percent, or by 12 percentage points, with the absolute decline being higher in urban areas than in rural areas (14 vs. 7 percentage points). During the 1995-2002 period, poverty was further reduced to 43 percent, or by 11 percentage points, with the absolute decline again favoring urban areas (12 vs. 8 percentage points). During the entire period, total poverty declined by 23 percentage points, comprising reductions of 26 points in urban areas and 15 percentage points in rural areas. Table 1.6 Headcount Poverty, 1991-2002 (Percentage) 1991 1995 2000 2002

Nation Urban Rural Nation Urban Rural Nation Urban Rural Nation Urban Rural

Total- Official 66 60 71 54 46 64 45 34 59 43 34 56 Total-World Bank 64 59 70 50 39 65 40 29 55 37 29 50 Moderate- Official 33 32 34 32 31 35 25 23 28 24 22 27 Moderate- World Bank

33 34 33 30 27 34 24 20 29 22 19 25

Extreme- Official 33 28 37 22 15 30 19 11 31 19 12 29 Extreme- World Bank

31 25 37 21 12 31 16 8 27 15 9 25

Source: DICESTYC’s Household Surveys for Official estimates and Arias (2004) “Poverty In El Salvador During the 1990s: Evolution and Characteristics”, World Bank, processed, for World Bank estimates. The World Bank’s poverty estimates also show a significant decline in poverty during the 1990s. Due to some shortcoming in DIGESTYC’s income estimates, World Bank staff have re-estimated the income series and re-calculated the poverty rates using the official

10

poverty lines.10 Box 1.2 discusses the official poverty lines and some of their drawbacks. The poverty estimates using adjusted income are shown in Table 1.6. As it can be seen, the World Bank’s estimates for 1991-2002 show a broadly similar trend in the decline in poverty, although the Bank shows a larger decline in poverty than DIGESTYC (27 percentage points versus 23 percentage points). Poverty fell by a similar percentage during the first and second part of the 1990s (22 percent and 20 percent) though GDP growth was twice as fast during the former period compared to the latter (Table 1.7). While the decline in urban poverty was lower in the second part of the 1990s (26 percent versus 34 percent), the decline in rural poverty was twice as large during the second part of the 1990s compared to the early 1990s (15 percent versus 7 percent). The implied growth elasticities of poverty are discussed in Section II and the drivers of rural income growth in Section III. Table 1.7 Reduction in Poverty (Adjusted Income), 1991-2000 (Percentage) 1991/1995 1995/2000 National Urban Rural National Urban Rural Total -22 -34 -7 -20 -26 -15 Moderate -9 -21 3 -20 -26 -15 Extreme -32 -52 -16 -24 -33 -13 Source: Table 1.6 Box 1.2 Official Poverty Lines The official poverty lines are based on the minimum caloric intake requirements estimated by an expenditure survey in 1991. The extreme poverty lines correspond to the monthly cost of basic food baskets that provide a minimum caloric requirement (about 2,200 Kcal/day) for a family of four members. The general poverty lines are obtained by multiplying the extreme lines by a factor of two to allow for non-food expenditures. The official poverty lines use different consumption baskets for urban and rural areas

10 The two major problems and other components of n(e.g., purchase items that aor utilities); and (ii) in the e

(CPI

R

F Source: Arias (2004) based in DIGESTYC data

Table 1.8 Changes in Extreme Poverty Lines and Consumer Price Index

)

1991 1995 2000 2002 % Change 1995/2000

Poverty lines (individual)

Urban 1.00 1.22 1.34 1.36 9.8 ural 1.00 1.30 1.50 1.45 15.4

CPI General 1.00 1.30 1.57 1.66 20.8

ood 1.00 1.37 1.65 1.72 20.4 Non-food 1.00 1.25 1.53 1.62 22.4

to achieve the same minimum caloric requirement. The use of different

with DIGESTYC’s income estimates are: (i) they do not include in-kind income on-monetary income that are particularly important for agricultural households re home-produced or that are received as a donation for education, health, food arly years they did not include the implicit rental value for home-owners.

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food baskets for urban and rural areas is harder to justify on welfare grounds. The value of the urban basket is about 60 percent higher (down from 70 percent in 1991), essentially reflecting higher quality food components (for instance French bread versus more ‘tortillas”). This may lead to understatement of both the gap between urban and rural poverty and the actual national poverty level. The cost of these bundles of food is updated with new prices every year. The “real” value of the consumption baskets used to define poverty lines have declined in recent years when deflated by the Consumer Price Index (CPI). While the extreme urban and rural poverty lines increased by 36 and 45 percent during 1991-2002, the food and non-food components of the CPI rose by 72 and 62 percent, respectively (Table 1.8). It should be noted that during the first part of the 1990s, the rural poverty line changed in line with the CPI (30 percent) while the urban poverty line declined when deflated by the CPI. During 1995-2000, while the general CPI changed by 21 percent, the urban and rural poverty lines changed by only 10 percent and 15 percent, respectively. Arias (2004) makes several simulations of poverty estimates using alternative poverty lines (Table 1.9). First, he indexes the urban and rural poverty lines to the change in the CPI during the 1991-2002 period and considered three scenarios: i) a full adjustment by the change in the general CPI (assuming that the poor faced the full increase in the cost of living); ii) adjusting the extreme poverty lines by the change in the food component of the CPI (which makes the extreme poor face only the general increase in food prices) and the portion corresponding to non-food items by changes in the non-food CPI (which imputes the increase in the cost of non-food items only to the moderate/relative poor); and iii) adjusting only the non-food component of the poverty line to the change in the non-food CPI. Second, he takes the urban poverty line to be applicable to both urban and rural areas. This assumes that the consumption basket is needed in both areas to achieve a similar level of welfare (although failing to reflect regional price variation). The estimates of both the level, the magnitude of changes and the geographic distribution of poverty are clearly sensitive to the existing official poverty lines. However, the significant declining trend in poverty continues to hold throughout the simulations.

Ext. Mod. Total Ext. Mod. Total Ext. Mod. Total Ext. Mod. Total Ext. Mod. Total Ext. Mod. Total

National 31.2 33.2 64.4 20.5 30.2 50.6 15.6 26.2 41.9 15.9 23.7 39.6 15.7 23.2 38.9 15.4 21.8 37.2Urban 25.1 33.6 58.7 11.6 27.3 38.9 9.0 23.3 32.3 8.1 20.3 28.5 9.1 20.3 29.3 9.1 19.4 28.5Rural 36.7 32.8 69.5 31.2 33.6 64.8 24.8 30.3 55.2 26.8 28.5 55.3 25.1 27.4 52.4 24.5 25.3 49.8

Full CPI Adjustment 1 31.2 33.2 64.4 21.3 30.8 52.1 17.3 28.0 45.2 17.9 26.8 44.7 19.1 26.0 45.2 18.9 25.6 44.5Adj. CPI food and non-food 2 31.2 33.2 64.4 23.1 29.5 52.5 19.1 26.7 45.8 19.3 25.8 45.1 20.1 25.4 45.5 19.8 25.0 44.8Adj. CPI non-food 3 31.2 33.2 64.4 20.5 30.1 50.6 15.6 26.7 42.3 15.9 25.5 41.4 15.7 25.6 41.2 15.4 25.0 40.4

National 43.6 28.8 72.4 30.5 28.9 59.5 24.0 27.0 51.0 22.6 24.9 47.5 22.1 25.1 47.1 21.8 24.2 46.0Urban 25.1 33.6 58.7 11.6 27.3 38.9 9.0 23.3 32.3 8.1 20.3 28.5 9.1 20.3 29.3 9.1 19.4 28.5Rural 60.5 24.5 85.0 53.6 30.8 84.4 44.9 32.2 77.1 42.9 31.4 74.3 40.6 32.0 72.5 40.0 31.2 71.1

* Includes non-monetary income estimations.

Table 1.9 Robustness of Poverty Incidence in El Salvador to Changes in Prices and Poverty Lines, 1991-2002(% individuals)

Using Urban Poverty Line

1 1991 P.L. x CPI. 2 E.P.L x CPI food + E.P.L. x CPI non-food. 3 E.P.L. + E.P.L. x CPI non-food, where: P.L.=Poverty Line, E.P.L.=Extreme Poverty Line.

2000 2001 2002

CPI Adjustments

1991* 1995 1999

WB PA (official poverty lines)

Source: Arias (2004)

Comparing to the World Bank’s income adjusted poverty estimates, poverty incidence in 2002 is estimated to be 3 to 8 percentage points higher if poverty consumption baskets are adjusted to reflect (in different degrees) the actual change in general prices. Extreme poverty becomes 3 to 5

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points higher. Total poverty is estimated to decline by 20 to 24 points and extreme poverty by 11 to 16 points even if the cost of living for the poor is fully adjusted to national trends. The decline in national poverty is almost the same when we use the single urban poverty lines although it naturally becomes much slower in rural areas. About 46 percent of the Salvadorians would be considered poor and 22 percent extremely poor (71 and 40 percent of the rural population, respectively) if we apply the same (urban) poverty yardstick nationally. Arias concludes that the simulation results, albeit imperfect, leave us more confident of the robustness of the estimated significant poverty decline in El Salvador in the last decade. Table 1.10 Intensity and Severity of Poverty (Adjusted Income), 1991-2002 (percentage) 1991 1995 2000 2002 Intensity National 29.8 22.5 17.0 16.5 Urban 25.7 14.6 10.2 11.1 Rural 33.7 32 26.6 24.2 Severity National 17.7 13.6 10.0 10.0 Urban 14.7 7.7 5.3 6.2 Rural 20.5 20.6 16.6 15.4 Source: Arias (2004) “Poverty in El Salvador during the 1990s: Evolution and Characteristics”, processed, World Bank The intensity and severity of poverty also declined during the 1990s. The intensity of poverty or the poverty gap measures the average distance of the poor from the poverty line. The severity of poverty is the square of the intensity and therefore gives more weight to the poor who are further below the poverty line. The estimates of the intensity and severity of poverty are shown in Table 1.10, using income adjusted poverty estimates. It can be seen that both variables declined substantially for urban areas in 1991-95; for rural areas they remained at the same level. During 1995-2000, both the intensity and severity measures declined in urban and rural areas. Income distribution trends The distribution of income has deteriorated slightly during the 1990s. Official estimates show that the distribution of income per quintile improved somewhat during the first part of the 1990s but then deteriorated, with a net negative result for the full period (Table 1.11). Similarly, estimates of the Gini coefficients (last column of Table 1.9) indicate that the income distribution improved (Gini dropped) during 1991-96 but then worsened (Gini increased) during 1996-2002, with a slightly negative result for the full period. Substantial inequality in income distribution in El Salvador deters growth and poverty reduction, as discussed in Section II.

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Table 1.11 Distribution of (Unadjusted) Income, 1991, 1996, 2002 (Percentage)

Source: DIGESTYC

Poorest 1 quintile

2 quintile 3 quintile 4 quintile Richest 5 quintile

Gini

1991 3.1 7.8 12.7 20.5 56.0 0.527 1996 3.3 7.9 13.1 21.1 54.7 0.517 2002 2.8 7.5 12.8 21.2 55.7 0.534 Change 1991/96 0.2 0.1 0.4 0.6 -1.3 -0.01 Change 1996/02 -0.5 -0.4 -0.3 0.1 1 0.017 Change 1991/02 -0.3 -0.3 0.1 0.7 -0.3 0.007

World Bank estimates confirm these trends. Table 1.12 shows Bank estimates of the Gini for total, monetary and labor income. For monetary and labor income, the trend is similar to the previous estimates, with a improvement in income distribution during the early 1990s and deterioration after the mid-1990s. However, the Gini for total income, which includes a non-monetary income adjustment, indicates a deterioration in the distribution of income between 1991-95 and 1995-2000, even though the Gini for urban and rural incomes improved during the first period and deteriorated in the second period. According to Arias (2004), this reflects a greater urban-rural disparity in the first part of the 1990s, as will be discussed in Section II when the evolution of the entire income distribution is examined (growth incidence curves). Table 1.12 World Bank’s Estimate of the Gini Coeficient, 1991-2002 (Percentage)

Year 1991 1995 2000 2002 Total income 0.509 0.518 0.523 0.519 Urban 0.474 0.463 0.473 0.473 Rural 0.476 0.444 0.449 0.464 Monetary income 0.503 0.493 0.510 0.520 Labor income 0.536 0.509 0.524 0.530 a/ includes non monetary income adjustments Source: Arias (2004) “Poverty in El Salvador during the 1990s: Evolution and Characteristics”, processed, World Bank Asset distribution The distribution of human capital and physical assets may be approximated by years of schooling and land ownership. Although education enrollments and the number of years of schooling of the poorest quintiles rose faster than for the richest quintiles during the 1990s, albeit from a lower base, the poorest children still have an average of about two years’ less schooling than rich children (Figure 1.2). This clearly puts poor children at a disadvantage, as educational attainment is closely related to household income.

14

Figure 1.2 Average Years of Schooling for 15 Year Olds, 1995, 2002

4

5

6

7

8

1 2 3 4 5Income Quintiles

Year

s of

Sch

oolin

g

1995 2002

Source: DIGETYC’s Household Surveys Land is scarce in El Salvador. Despite a comprehensive agrarian reform implemented in the early 1980s in which 295,000 ha were distributed to cooperatives and small farmers (84,000 beneficiaries), by the late 1980s land remained concentrated as shown in Table 1.13; about 70 percent of all landowners had 11 percent of the land, with their average plot size being less than one hectare.11 The distribution of an additional 78,000 ha under the Peace Accords of 1992 (30,000 beneficiaries) did not substantially change the situation. Table 1.13 Distribution of land Among Land Owners, 1989 a/

0-2 ha 2-5 ha 5-20 ha 20-50 ha 50 ha + Total % of landowners 69.9 14.2 11.3 3.2 1.3 100.0 % of land area 10.7 10.8 24.5 22.5 31.6 100.0 Memo: No. landowners 196181 39978 31822 9072 3786 280839 Area 131291 132461 300187 275671 387547 1227157 a/ excludes cooperatives, land renters, sharecroppers. Source: El Salvador Rural Development Study, World Bank (1998), Table C-2 In sum, despite an impressive decline in poverty during the 1990s owing to the acceleration of growth, poverty in El Salvador still affects over 40 percent of the population and the distribution of income and assets remains highly unequal. With the recent slowdown in growth and poverty reduction, there is a need to identify the key factors that may help to put El Salvador on higher growth and poverty reduction paths. This is the focus of the discussion in the following Sections.

11 The agrarian reform is further discussed in Section III.

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II. Growth, Distribution of Income, and Poverty

This Section analyzes the impact of growth and the distribution of income on poverty. It is divided into two parts. The first part analyzes changes in the pattern of production and employment, and trends in productivity. It investigates the reasons for El Salvador’s historically low growth record and concludes that stagnation in factor productivity has undermined growth – a problem that, in turn, partially explains continuing high poverty levels. The analysis of the determinants of growth shows that the structural reforms of the 1990s not only contributed to growth and poverty reduction but may also have increased the country’s growth potential. It indicates that to further increase its long-term growth potential, El Salvador must invest in education, improve its infrastructure, and maintain an open economy. The second part discusses poverty correlates and growth elasticities of poverty. While the analysis supports the conventional findings with respect to correlates of poverty, there is evidence that unobserved differences among and within households play an important role in the determinants of poverty (and inequality) in El Salvador. The growth and inequality elasticities of poverty are relatively high, underscoring the importance of growth and improved income distribution for poverty reduction. Growth incidence curves and pro-poor growth rates presented at the end of the Section indicate that growth was more regionally balanced during 1995-2000 than during 1991-1995, though during the latter period growth was faster and the absolute reduction in poverty was larger. The growth incidence curves and the pro-poor growth rates indicate that these contrasting outcomes are related to changes in rural household incomes. 1. Sources and Determinants of Growth Changing production and employment patterns El Salvador’s productive structure has changed significantly since the 1960s. Traditional agricultural exports (coffee, cotton, sugar) saw their contribution to the country’s value added decline. In contrast, services and industry rose in importance. Figure 2.1 shows that agriculture’s contribution to GDP declined from 42 percent to 14 percent in the 1990s; while services’ contribution increased from 36 percent to 58 percent and industry’s contribution from 23 percent to 28 percent. Within services, the fastest growing subsector was financial services, which since 1978 nearly tripled in real terms; the commerce, restaurants and hotels subsector increased by 36 percent, well above the growth in the manufacturing and construction subsectors, which increased by 16 percent and 14 percent, respectively (Table 2.1). This transition from an agriculture-based to an urban, industrial and service-based society is a trend common to many Latin American countries.12 These changes demand new labor force skills, with education becoming the

12 See de Ferranti, David, Guillermo Perry, Francisco Ferreira, and Michael Walton (2003). “Inequality in America Latin and the Caribbean: Breaking with History?”, Advance Conference Edition. World Bank, Latin America and Caribbean Studies, , Chapter 9 pages 304

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most important economic assets for the majority of the population as underscored by the analysis in this and the following Sections.

Figure 2.1 Sectoral Contribution to GDP, 1960s-90s

0

20

40

60

% o

f GD

P

Agriculture 42 39 28 14

Industry 23 25 24 28

Services 36 37 49 58

60s 70s 80s 90s

Source: World Bank (WDI) The changing composition of output is also reflected in foreign exchange earnings. Agricultural exports represented 80 percent of total foreign exchange earnings in 1978; by 2002 they accounted for only 6 percent (Table 2.2). Remittances from Salvadorans living in the US now represent two-thirds of total foreign exchange earnings; export processing zones or maquila and non-traditional exports represent 16 percent and 12 percent, respectively. The increase in maquila export earnings (net) during the period more than compensated for the loss in earnings from traditional agro-exports. How the changing patterns of production and employment affected poverty, particularly in rural areas, is explored in Section III.

Table 2.1 Index of Sectoral Production, 2002 (1978= 100) 2002 GDP 133 Agriculture 92 Manufacturing 116 Construction 114 Financial 275 Commerce, Restaurants, hotels 136 Source: Central Bank

Table 2.2 Foreign Exchange Earnings a/ US$ million % 1978 2002 1978 2002 Agro-exports b/ 514 161 80 6 Non-traditional exports 54 335 8 12 Maquila, net 21 475 3 16 Total Exports 589 971 92 33 Remittances 51 1935 8 67 Total Exports plus Remittances 640 2906 100 100 a/ Excludes exports to Central America b/ Coffee, sugar, cotton and shrimp. Source: PNUD (2003), “El Salvador: Human Development Report”. table 6.1 based on Central Bank data

17

Productivity trends, 1960-2000 The change in the composition of output was not accompanied by an increase in the overall productivity of the economy. Indeed, over the last forty years, growth in El Salvador has been mostly a result of factor accumulation rather than productivity gains. Following the well-known Solow growth accounting procedure, Table 2.3 shows recent FUSADES estimates of total factor productivity (TFP). It can be observed that without considering human capital adjustments, the average annual contribution of TFP to growth during the last four decades was -0.3 percent. On the other hand, annual average capital and labor contributions to growth were 1.5 percent and 1.8 percent, respectively, for an average growth rate of 3 percent during 1960-2000. These results are influenced by the destruction of capital and large emigration during the years of the internal conflict, with labor and capital declining during the early 1980s. It should be noted that the periods with higher growth rates (the first half of the 1960s and the first half of the 1990s) are also those in which the contribution of TFP to growth were the highest. Table 2.3 Sources of Growth, 1960-2000 Average Annual Growth Rate Contribution to GDP growth GDP Capital Labor TFP Capital Labor 1960-64 6.3 3.3 3 3.2 1.3 1.8 1965-69 4.8 4 3.5 1.1 1.6 2.1 1970-74 4.5 4.8 4.8 -0.3 1.9 2.9 1975-79 3.1 7.2 4.2 -2.3 2.9 2.5 1980-84 -5.4 -0.9 -2.1 -3.9 -0.3 -1.2 1985-89 1.2 2.5 4.7 -2.6 1 2.8 1990-95 5.8 4.2 2.7 2.5 1.7 1.6 1996-00 2.9 4.3 3.3 -0.8 1.7 2 1960-69 5.6 3.7 3.2 2.2 1.5 1.9 1970-79 3.8 6 4.5 -1.3 2.4 2.7 1980-89 -2.1 0.8 1.3 -3.2 0.3 0.8 1990-00 4.5 4.3 2.9 1 1.7 1.8 1960-00 3 3.7 3 -0.3 1.5 1.8 Source: FUSADES (2003) “Informe de Desarrollo Economico y Social: Competitividad para el Desarrollo”, Table 2, page 9. Adjusting for human capital does not change the results significantly. Table 2.4 shows FUSADES estimates of TFP growth including an adjustment for human capital using the rate of secondary education gross enrollment as a proxy. Comparison of the first two rows in the table shows no significant changes in TFP growth. Other recent estimates confirm the weak contribution of TFP to growth. Table 2.4 presents estimates from Loayza, Fajnzylber, and Calderon (2002). Those are generally lower than FUSADES’ estimates.13 These authors use educational attainments of different groups weighted by the social returns to schooling when adjusting for human

13 Loayza, N., Fajnzylber, P., Calderon, C. (2002) “Economic Growth in Latin America and Caribbean: Stylized Facts, Explanations, and Forecasts”, World Bank

18

capital. The effect of the adjustment is to lower the contribution of TFP to growth since education attained increased in all periods.14 According to the authors, El Salvador did poorly in terms of TFP growth during the 1960s, 1970s, and 1980s when compared with the median for Latin America, but the reverse appears to be true in the 1990s (see Memoranda items in Table 2.4). Table 2.4 Estimates of Total Factor Productivity Growth Rates, 1960-2000

1960s 1970s 1980s 1990s 1960-00 1990-95 1996-00FUSADES Traditional Solow 2.2 -1.3 -3.2 1 -0.3 2.5 -0.8 FUSADES incl. human capital 1/

1.7 -1.2 -3.1 0.9 -0.4 2.2 -0.7

Loayza Traditional Solow 0.8 -2.2 -2.2 0.9 Loayza incl. human capital 2/ 0.0 -2.6 -3.0 0.25 Memoranda: Median for LAC Loayza Traditional Solow 1.72 1.03 -1.43 0.41 Loayza incl. human capital 2/ 0.41 0.03 -2.15 0.06 1/ human capital adjustment proxied by the rate of secondary gross enrollment. 2/ human capital adjustment based on educational attainment of the population weighted by social returns to schooling. Source: FUSADES (2003) Table 2, page 9. Loayza, N., Fajnzylber, P., Calderon, C. (2002) Table A.9 Indeed, FUSADES estimates TFP growth (including human capital adjustment) of 0.9 percent per year during the 1990s. Since the contribution of TFP to growth was positive during the 1990s, one could argue that rapid GDP growth during the period was not exclusively a result of cyclical factors but could also be attributed to the improved policy framework that was put in place, which augmented the overall productivity of the economy. If this conclusion is correct, then it gives an indication of whether the reforms implemented were on the right track and whether or not a correction in the course of action taken is needed. Building on Loayza, Fajnzylber, and Calderon (2002), Lopez (2003b) has recently analyzed this issue. We review his findings in the context of the discussion of growth determinants that follows. Growth determinants Loayza, Fajnzylber, and Calderon (2002) have recently estimated the determinants of growth for a large sample of countries, including El Salvador. They fitted an econometric model for per capita GDP growth on a set of variables that proxy structural policies and institutional conditions, stabilization, and external conditions. Specifically, the variables related to structural policies and institutional conditions are education, financial depth, government burden, public services and infrastructure, governance, and international trade openness. As for stabilization policies, the variables selected are related to fiscal,

14 Loayza, Fajnzylber, and Calderon (2002) also present a third estimate that includes (in addition to human capital) an adjustment for input use (capital adjusted by the rate of labor employment and labor adjustment by hours worked). With this adjustment, TFP growth is negative in the 1990s (-0.45) but the authors warn that “efforts to adjust for the true rate of utilization of labor and particular capital stocks, using rates of unemployment and hours worked, are still imperfect, so that we are overstating the actual usage of these factors of production during recessions, and correspondingly understating the contribution of TFP growth”. Ibid, page 15.

19

monetary and financial policies that contribute to a stable macroeconomic environment. External conditions are also incorporated in the model to take into account the possibility of the transmission of cycles across countries, via international trade, external financing flows, and investors’ perceptions of economic conditions. The authors also include as explanatory variables the initial level of real GDP per capita and the output gap, to take other factors into account.15 Finally, to mitigate business cycles events, the regression is performed on five (78 countries and 350 observations) and 10 year averages (65 countries and 175 observations) using the Generalized Methods of Moments estimator (GMM). The estimation results are shown in Table 2.5. Table 2. 5 Regression Coefficients GDP per capita growth rates 10-year 5-year

Initial GDP per capita -0.033 -0.018 Initial output gap -0.167 -0.237 Education 0.006 0.017 Financial depth 0.006 0.006 Trade openness 0.025 0.01 Government burden -0.017 -0.015 Public Infrastructure 0.024 0.007 Governance -0.006 -0.001 Price Stability -0.021 -0.005 Cyclical Volatility -0.508 -0.277 External imbalances -0.001 -0.006 Banking crisis -0.006 -0.029 External conditions 0 0.072 Source: Loayza, Fajnzylber, and Calderon (2002), Table II.3 Using the above regression coefficients and the actual values of the explanatory variables for El Salvador, Loayza, Fajnzylber, and Calderon (2002) estimate the contribution of each variable to projected changes in El Salvador’s growth rates and compare them with the actual values. For the 1990s, Table 2.6 indicates that the drop in the GDP growth rate in the second part of the 1990s does not seem to be explained by structural or stabilization policies but by other (i.e., cyclical) factors. According to the authors, structural reforms and stabilization polices added 1.5 and 0.3 percentage points a year to El Salvador’s long term growth rate during the 1990s. From these results, Lopez (2003b) concludes that long-run growth may have actually increased over the 1990s and the reform program may have been much more successful than suggested by a decline in growth rates of 3 percentage points (from the first to the second half of the 1990s). 16

15 The initial level of real GDP per capita is related to the so-called transational and convergence and the output gap to the so-called cyclical reversion. See Loayza, Norman, Pablo Fajnzylber, and Cesar Calderon (2002) 16 Lopez (203b) page 25

20

Loayza, Fajnzylber, and Calderon (2002) have also estimated the potential contribution of key variables to additional growth in the country using the regression coefficients shown in Table 2.5 and considering one scenario that assumes that the explanatory variables continue on their recent trend and another, more optimistic scenario that assumes that the explanatory variables move to the top 25 percent of LAC countries and the world. As can be seen from Table 2.7, per capita growth could reach 3.9 percent or 5.2 percent during the 2000-10 period under the “present trend” and the “optimistic” scenarios, respectively. Under the first scenario, infrastructure would make the principal contribution to growth by adding 0.83 percentage points a year to the rate of per capita growth followed by trade openness with 0.58 percentage points; under the optimistic scenario, education would make the principal contribution with 1.22 percentage points followed by infrastructure with 0.66 percentage points. These estimates indicate that if El Salvador is to increase its long term growth prospects, it must further improve its infrastructure, invest in education, and continue to open its economy to trade. Table 2.6 Explained Changes in Growth Rates During the 1990s (Percentage)

Actual Change Projected Change Structural Reforms Stabilization Policies Other a/ 1991/95 vs. 1986/90

3.26 1.14 1.34 0.27 -0.46 1996/99 vs. 1991/95

-2.8 -0.1 1.48 0.25 -1.73 a/ Refers to external conditions, transitional convergence and cyclical reversion Source: Loayza, Fajnzylber, and Calderon (2002), Table II.5 Table 2.7 El Salvador-Determinants of Potential Growth, 2000-10

Scenarios/ Variables

Present Trend Optimistic

Additional percentage points of annual growth 2/

Structural 1.98 2.16 Education 0.37 1.22 Financial depth 0.39 0.02 Trade openness 0.59 0.26 Government burden -0.20 0.00 Infrastructure 0.83 0.66 Other 1/ -0.78 0.40 Projected additional growth 1.20 2.56 Growth per capita 90/99 2.67 2.67 Potential growth per capita 2000-10 3.87 5.23 1/ Stabilization policies, external conditions, transitional convergence and cyclical recovery 2/ Relative to 1990-99 Source: Loayza, Fajnzylber, and Calderon (2002), Tables III.2 and III.3 2. Correlates of Poverty Poverty continues to be predominantly a rural problem. Forty-one percent of the population lives in rural areas, but 56 percent of the rural population is in poverty and 27 percent is in extreme poverty. Poverty also disproportionately affects the young. Table 2.8 shows the age profile of poverty. As can be observed, those younger than 18 years

21

old represent 41 percent of the population but 52 percent of them are in poverty and 24 percent are in extreme poverty. A similar situation applies to the elderly: they represent 10 percent of the population, but 38 percent are in poverty. This situation results from the weakness of the social safety net, including the limited coverage of the pension system.17 Table 2.8 Age profile of poverty, 2002

Age Group % Total Population

% in Poverty % in Extreme Poverty

0-17 40.8 51.6 24.4 18-59 49.1 36.8 15.3 60+ 10.1 38.2 16.8 Source: UNDP (2003) El Salvador-Human Development Report, Table 2.19, page 59 based on household survey. Poverty correlates, or the variables that are associated with the probability of being poor, can be estimated using regression analysis. In these regressions, several explanatory variables such as household characteristics, employment and geographic location have been found to be significant. Recent World Bank estimates for El Salvador yielded the following results:18

• Labor income is the main component in per capita income, representing about 85 percent.

• Sector of employment is an important determinant of income. Working in industry or services rather than in the agricultural sector increases per capita income, in both urban and rural areas. Having a household head employed in the mining, manufacturing, electricity, construction, commerce, transport, or services sectors increases per capita income by up to 70 percent as compared to working in agriculture.

• Not surprisingly, unemployment is a key determinant of poverty. Having a household head or spouse who is seriously underemployed (i.e., working less than 20 hours per week) reduces expected per capita income by 10 to 40 percent. Milder underemployment (i.e. working between 20 and 39 hours a week) reduces per capita income by 5 to 20 percent.

• Geography is also a determinant of labor income. Differences in labor force participation and wages between departments are due more to differences in departmental characteristics than to differences in the characteristics of the households living in the various departments.19

• Educational attainment is strongly related to headcount poverty: a household with a head who has attended university has an expected level of per capita income 70-75 percent higher than an otherwise similar household whose head has no education at all. The marginal returns to education appear to increase with education levels: in urban areas an increase from six to seven years of schooling generates an increase in

17 See Marques, José Silvério (2002) “El Salvador: Social Safety Net Assessment, World Bank, Report No. 24190, and World Bank (2002b) “El Salvador: Policy Notes on Elements to Strenghten the Social Safety Net”, Report No, 24191-ES, Volume II. 18 World Bank (2002a) 19 Administratively, El Salvador is divided in 14 departments.

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labor income of 6.5 percent, as compared to a 13.1 percent increase from 15 to 16 years of schooling.

Notwithstanding the above, Arias (2004) has run quintile regressions on the 1991 and 2002 household survey data. On the basis of his findings, he cautions against deriving policy implication for the poorest households based on traditional poverty correlates because these may not tell the full story. The poorest households may face specific constraints or opportunities that may need to be taken into consideration. He concludes that:

…while supporting conventional findings on the correlates of poverty, we find evidence that unobserved household heterogeneity, by affecting the income returns to some assets and job characteristics, plays an important role in the determinants of poverty (and inequality) in El Salvador (...) These results are consistent with a flurry of recent empirical studies that question the common presumption that the individual and household variables that are common in survey data can fully capture the variations in socio-economic performance and in the likely impact of public interventions.

According to Arias the unobserved (unmeasured) differences among households and their members in determining per capita income related, for example, to education may include labor market connections, family human capital, school quality, and/or work ethics. It is interesting to note that the income penalty associated with households headed by females in 1991 had, according to Arias’ estimates, disappeared by 2002. An exception was that of households in the upper quintiles, who experienced a smaller but still significant income penalty (6-9 percent), compared to similar households headed by males. 3. Distribution and Poverty Impact of Growth Interaction of growth, distribution and poverty A large body of evidence supports the proposition that growth leads to poverty reduction; however, the impact of growth on income distribution is uncertain – that is, growth may or may not lead to a different distribution of income. Also, it is now well established that the impact of growth on poverty depends on what happens to the distribution of income. Greater inequality generally means a diminished capacity to reduce poverty, for any given level of growth.20 Moreover, high inequality may also reduce the rate of growth because it may lead to political instability, violence or underinvestment in human capital. Poverty elasticities In El Salvador, there was a strong correlation between growth and poverty as well as between inequality and poverty in the 1990s. Lopez (2003c) has recently estimated the growth (of per capita GDP) and inequality (Gini) elasticities of poverty using national accounts and household survey income-adjusted data (Table 2.9). The growth and 20 See Klasen (2001) for an illustrative example.

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inequality elasticities of extreme poverty estimated for El Salvador are larger than the theoretical levels estimated by Lopez for countries at similar levels of development and similar distribution of income. The real decline in the official poverty line (i.e., when deflated by the consumer price index or the GDP deflator) may explain in part the relatively high growth elasticity of poverty (see below). Table 2.9 El Salvador’s Growth and Inequality Elasticities of Poverty, 1991-2002 Extreme Poverty Total Poverty Average Growth of per capita GDP -1.5 -1.1 -1.3 Inequality (Gini) 2.8 1.1 1.9 Source: Lopez (2003c) “El Salvador: Towards the MDGs”. Processed. World Bank. Table 2.10 shows the growth (of GDP per capita) elasticities of (total) poverty using the official poverty line for 1991-95 and 1995-2000. The elasticity is much higher in the second period (in absolute terms) than in the first part of the 1990s, as growth decelerated but poverty declined by about the same rate. However, this large increase in implied elasticity results to a large extent from the fact that the official poverty lines lagged the increase in the consumer price index. While during 1995-2000, the CPI increased by 21 percent, the urban poverty line increased by only 9.8 percent and the rural poverty line by 15.4 percent. Lopez indicates that taking this lag into consideration, the elasticity is reduced to values similar to those in the earlier period. Table 2.10 El Salvador’s Growth Elasticities of Poverty, 1991-2000 1991-1995 1995-2000 1991-2000 With official poverty lines -1.1 -4.8 -1.5 Memorandum: % change in poverty -22 -20 -38 % change in per capita income 20 4.5 25 % change in official poverty line- urban 22 9.8 34 % change in official poverty line- rural 30 15.4 50 % change in consumer price index 30 21 57 Source: Box 1.2 (Table 1.8) and own estimates Pro-Poor Growth Estimates Growth incidence curves have been estimated for three periods: 1991-1995, 1995-2000, and 1991-2000 (Figures 2.2-2.7). Focusing on the incidence curve for the 1991-1995 period at the national level, it can be observed in Figure 2.2 that the growth of household per capita income of the lowest percentiles was not only below the mean but was also negative; in contrast, during the 1995-2000 period, the growth in income per capita of the lowest percentiles was positive and above the mean. According to Arias (2004) the deterioration in income growth among households in the lower percentiles is less evident for monetary income (compared to total adjusted income), which may imply that the fall in income for the lowest percentiles could reflect imperfect adjustments to the 1991 survey of non-monetary incomes of very poor households. Nonetheless, some decline in overall income of the households in the lowest percentiles may have taken place during the first part of the 1990s. The growth incidence curves for urban and rural areas

24

(Figures 2.3 and 2.4), indicate that the decline in the rates of growth of per capita incomes in 1991-95 took place in the rural areas.

Figure 2.2 Growth incidence curve for El Salvador, National, 1991-95, 1995-00

-15

-10

-5

0

5

10

0 10 20 30 40 50 60 70 80 90

Household Percentile

Annu

al g

row

th in

inco

me

per c

apita

Growth rate 1991-1995

Growth rate 1995-2000

Mean 1991-1995

Mean 1995-2000

PR 1

991

PR

19

95

PR 2

000

ann

Source: Arias (2004) Figure 2.3 Growth incidence curve for El Salvador (Urban), 1991-1995, 1995-2000

-4.0

-2.0

0.0

2.0

4.0

6.0

8.0

10.0

12.0

0 10 20 30 40 50 60 70 80 90

Percentile of household per capita income

Annual growth in income per capita

Growth rate 1991-1995 Growth rate 1995-2000

Mean 1991-1995

Mean 1995-2000

PR 1991 PR 1995PR 2000

Source: Arias (2004)

25

Figure 2.4 Growth incidence curve for El Salvador (Rural), 1991-1995, 1995-2000

-20.0

-15.0

-10.0

-5.0

0.0

5.0

10.0

0 10 20 30 40 50 60 70 80 90

Percentile of the household income per capita

Ann

ual g

row

th in

inco

me

per c

apita

Grow th rate 1991-1995

Grow th rate 1995-2000

Mean 1991-1995

Mean 1995-2000

PR

1991PR

1995PR

2000

Source: Arias (2004)

Figure 2.5 Growth incidence curve for El Salvador (National) , 1991-2000

-2.0

-1.0

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

0 10 20 30 40 50 60 70 80 90

Annual growth in per capita income

Median Mean

Poverty rate 1991

Percentile of household per capita income

Extreme poverty rate 1991

Source: Arias (2004)

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Figure 2.6 Growth incidence curve for El Salvador (Urban),

1991-2000

0.0

1.0

2.0

3.0

4.0

5.0

6.0

0 10 20 30 40 50 60 70 80 90

Annual growth in income per capita

Median Mean

Poverty rate 1991

Percentile of household per capita income

Extreme poverty rate 1991

Source: Arias (2004)

Figure 2.7 Growth incidence curve for El Salvador (Rural), 1991-2000

-5.0

-4.0

-3.0

-2.0

-1.0

0.0

1.0

2.0

3.0

4.0

0 10 20 30 40 50 60 70 80 90

Annual growth in income per capita

Median

Mean Poverty rate 1991

Percentile of household per capita income

Extreme poverty rate 1991

Source: Arias (2004)

27

The pro-poor growth rates for El Salvador have been estimated for 1991-2000 using adjusted household income data. These estimates are based on the Ravallion and Chen (2003) definition of pro-poor growth, which is derived by calculating the mean of the growth rates of all percentiles up to the headcount ratio. Annex 1 presents more detailed estimates for extreme poverty and the bottom quintile, and by urban and rural areas. Table 2.11 Ravallion and Chen Pro-Poor Growth Rate, Total Poverty, 1991-2002 1991/1995 1995/2000 1991/2000 Pro-Poor Growth Rate a/ 4.74 3.77 4.14 Urban 8.42 2.3 5.05 Rural -0.73 3.15 1.33 a/ Derived from the mean of growth rates at all percentiles up to the headcount ratio. Source: Annex 1 It can be observed in Table 2.11 that the Ravallion and Chen pro-poor growth rates at the national level are all positive which implies that growth was pro-poor during all periods. The rate of pro-poor growth was greater during the first part of the 1990s than during the second part. On the other hand, growth was not pro-poor during the first part of the 1990s in rural areas: in fact the rate was negative. The estimates confirm the urban bias of growth in the early 1990s. For 1995/2000, rural incomes recovered, and the rate of pro-poor growth was greater in rural than in urban areas. In sum, growth was more regionally balanced during 1995/2000 than during 1991-1995, though during the latter period growth was faster and the absolute reduction in poverty was larger. The growth incidence curves and the pro-poor growth rates indicate that these contrasting outcomes are related to changes in rural household incomes. In Section III we explore the factors responsible for this result.

28

III. Factors Affecting the Participation of Poor People in Growth This Section focuses on the factors that influence pro-poor growth. It covers six areas of interest: macro instability, public spending policies, rural development, remittances, labor market, and gender. Whenever relevant, the political economy aspects at play are also discussed. To anticipate the results, with respect to macro volatility we find that El Salvador experienced intense volatility in consumption, wages and employment during the first part of the 1990s, as a consequence of the adjustment process. While the volatility of macro variables declined in the second part of the 1990s, there continued to be a sense of insecurity among the population, which may have reflected political factors as well as the impact of a more open economy. We find that public expenditures increased significantly in the social sectors, a trend that was made possible in part by the peace dividend. These social expenditures mostly benefited lower income groups. As for rural development, the drop in rural incomes for the poorest households in the first part of the 1990s is attributed to the poor performance of agriculture, mainly reflecting the decline in real producer prices induced by an appreciation of the real exchange rate, which in turn was caused in part by the increase in remittances. Agriculture has not recovered since. During the second part of the 1990s, we find that the drivers of income growth in the poorest rural households were access to non-agricultural employment and the possibility of establishing micro-enterprises, both of which required improved human capital and access to basic infrastructure. Better-off families receive higher remittances than poorer ones and to this extent remittances contribute to inequality; on the other hand, remittances are an important factor though not the determinant factor in poverty reduction. With respect to the labor market, we find that women’s participation has increased particularly rapidly in rural areas and that there has not been a major change in the composition of employment categories, though the relative importance of non-remunerated family members has tended to decline while that of wage earners has tended to increase, which is consistent with greater formalization of labor relations; on the other hand, the labor market appears quite flexible, with most unemployment being of short duration. Finally, gender discrimination in the education sector appears to be on the decline, but it remains a serious problem in the labor market, particularly for women with more education. 1. Macroeconomic instability Insecurity, like inequality, impairs growth and poverty reduction, as it deters investment. Insecurity about future employment and income also directly and adversely impacts welfare “because most households and workers care not only about the level of their standard of living, but also about their security”.21 The poor are more vulnerable to macro instability than the rich because they have less means to compensate for losses in income and often are unable to contract insurance or apply other forms of self-protection. 21 World Bank (2000), “Securing our Future in a Global Economy”, page 5.

29

Therefore, high rates of growth may be associated with lower rates of pro-poor growth, if growth is very volatile. The population of El Salvador faces risks caused by: (i) adverse natural phenomena; (ii) idiosyncratic shocks (see below); and (iii) macro instability. In the last twenty years, the country has experienced three major earthquakes and has periodically suffered the impact of hurricanes and droughts. Recurrent natural disasters give the population a sense of insecurity. Idiosyncratic shocks are those that affect families or individuals. These shocks may be associated with the loss of income (or job), disease, family disintegration, violence, and isolation. Macro instability refers to the unpredictable movement in key economic variables such as prices, income, employment, and wages that lead households and workers to feel insecure. In this sub-section we focus on macro instability and explore how it affected El Salvador during the 1990s. We investigate whether or not instability has increased during the 1990s and then discuss the possible implications of the findings for pro-poor growth. Did Macro Instability Increase? During the 1980s, the volatility of El Salvador’s output growth rate (measured by the standard deviation of GDP growth rates) was greater than the median for Latin America (LAC), in part owing to the conflict. In turn, LAC’s output instability was much higher than that of industrial countries. Both LAC’s and El Salvador’s GDP volatility declined substantially during the 1990s (Table 3.1), with El Salvador’s GDP volatility now being much lower than LAC’s. The volatility of private consumption, which is a more accurate measure of the change in the standard of living of the population, was much higher than the volatility of GDP in El Salvador during the 1990s. The higher volatility of private consumption mirrors the volatility of real wages. Table 3.1 Macroeconomic Volatility, 1960s-90s (Standard Deviation of growth rates, in percent) ` 1960s 1970s 1980s 1990s Gross Domestic Product El Salvador 2.8 3.1 5.7 1.9 Latin America (median) 2.7 3.5 4.8 3.0 Private Consumption El Salvador 4.2 6.3 6.0 6.9 Latin America (median) 4.2 5.2 6.1 4.9 Real Wages El Salvador N/a 7.1 14.1 13.0 Latin America (median) N/a 6.9 14.1 6.6 Terms of Trade El Salvador 1.7 9.5 3.2 1.5 Latin America (median ) a/ 1.6 4.1 2.6 2.3 a/ standard deviation for variations in the prices of exports and imports multiplied by their weight in GDP. Source: de Ferranti et al (2000) “Securing our Future in a Global Economy””, World Bank, Tables 2.2 , 2.3 and 4.1.

30

Exploring in more detail the events of the 1990s, Table 3.2 shows that the higher volatility of private consumption in the 1990s resulted from changes during the first part to the 1990s; the volatility of this variable declined substantially in the second part of the decade. Table 3.2 Volatility of Private Consumption, 1980-99 (Standard deviation of growth rates, in percent)

1980-89 1990-1999 1990-94 1995-99 5.8 7.5 9.3 3.1

Source: Author’s estimates on the basis of WDI data As mentioned in Section II, El Salvador’s productive structure has change significantly during the last four decades. These changes intensified with the structural reforms implemented during the 1990s, while at the same time the country was transitioning from war to peace. The outcome of increased volatility during this period should not come as a surprise Macro disturbances are transmitted to households mainly through two channels: i) inflation and its impact on the purchasing power of the poor; and ii) the labor market and its impact on wages and employment. Inflation was 24 percent in 1990. It was reduced to about 10 percent in 1992, but then increased to almost 20 percent in 1993. After that, it declined rapidly (Figure 3.1) and since dollarization in 2001 it expectations were reducethe dollar in 1993/1994 which was undertaken unemployment was 10 pe10 percent in 1993. It sThus, in the early 1990s,than in the latter part of t

nemployment Rates, 003

1997

1998

1999

2000

2001

2002

2003

e Unemployment

Source: Central Bank and DIGESTYC

Figure 3.1 Inflation and U19990-2

0

5

10

15

20

25

30

1990

1991

1992

1993

1994

1995

1996

Perc

enta

ge

Inflation Rat

has been below 3 percent, similar to US levels. Inflationary d by the exchange rate policy. The rate was de facto pegged to

at 8.75 colones per US$1 until dollarization in January of 2001, at the same rate. On the other hand, the rate of (open) rcent in 1990. It fell somewhat but then increased again to about ubsequently fell, remaining at around 7 percent (Figure 3.1).

both inflation and unemployment were higher and more volatile he 1990s.

31

In fact, Table 3.3 shows that during 1991-95 real wages were much more volatile (13.8) than employment or unemployment. In 1995-2000, the volatility of real wages fell (8.5) as did that of employment and unemployment. During the 1990s, El Salvador’s unemployment volatility was much lower than LAC’s or the developed countries’, while real wage volatility was much higher. Table 3.3 Volatility of Growth in Real wages, Employment and Underemployment, 1990-2000. (Standard deviation of growth rates, in percent)

Employment Unemployment Real wage

El Salvador 1991-1995 4.1 8.3 13.8 1995-2000 2.7 3.2 8.5 1991-2000 3.2 5.8 10.9 Memoranda: 1/ Latin America: 1990-2000 2.1 19.4 6.6 Developed Countries: 1990-2000 1.8 17.0 1.9 Source: For El Salvador: author’s estimates based on household survey data. For Latin America and Developed Countries, IDB (2004) Goods Jobs Wanted, Labor Markets in Latin America, ESPR. Table 4.3, page 121. Why did instability not affect growth and poverty outcomes in the early 1990s? The instability in the first part of the 1990s did not seem to have hurt growth because of initial conditions and other forces at work. Clearly, the instability provoked by economic adjustment cannot be compared to the insecurity experienced during the conflict, with its daily acts of rural and urban guerrilla warfare. Thus, the 1992 peace agreements brought some degree of tranquility and hope to Salvadoran society. The large surge in public and private spending associated with the reconstruction of the economy and the need to satisfy pent-up demand during the years of conflict helped accelerate growth from a depressed base, even though prices and wages were being adjusted. Poverty declined by 22 percent during 1990-95 because of a very rapid decline in urban poverty; a fall of 34 percent compared to 7 percent in rural areas (Table 1.7). In the second part of the 1990s, poverty fell by 20 percent as urban and rural poverty declined by 26 percent and 15 percent, respectively. Why did the decline in rural poverty lag during the first part of the 1990s? First it should be noted that the conflict was much more intense in rural areas, though there was urban warfare and even the offensive in the capital in November 1999. Some of the most isolated rural areas in the country were controlled by the guerrillas, and after the peace agreements in 1992 these areas had to be restored to conditions comparable to those in the rest of the country. Administrative authorities took back their positions; teachers returned to schools; farm owners returned to their properties; populations and demobilized personnel returned to their places of origin; etc. It therefore took more time for rural areas to adjust to the new peace situation. On the other hand, during the first part of the 1990s, as will be discussed later in this Section, agricultural growth lagged, reflecting a decline in agricultural producer prices induced by the appreciation of the real exchange rate. Another factor deterring the

32

recovery of agriculture was the level of insecurity felt in rural areas, as crime increased substantially after the peace accords and the demobilization of ex-combatants. It also took longer for government programs to impact rural areas, because of the time needed to organize communities, identify their needs, and bring new investment and programs to them. Therefore, the impact on rural poverty took more time to be felt. Why do people continue to feel insecure? Notwithstanding the drop in GDP and consumption and wage volatility, along with a significant reduction in inflation and lower unemployment since the mid-1990s, there continues to be a sentiment of insecurity in the country. How can the government claim that the macroeconomic situation is stable and sound, if families do not feel it? Questions such as this have been heard in recent years across the country. Families feel insecure even though the macro aggregates are stable. There are several possible explanations for this, besides natural disasters. On the political front, the polarization between the two main political parties, ARENA and FMLN, and the confrontational political discourse of some political leaders adds to uncertainty over the future as it keeps alive recent conflict. This polarization was evident in the recent (March 21, 2004) presidential election, in which ARENA won a fourth consecutive 5-year presidential mandate with 57.7 percent of the votes; the FMLN obtained 35.6 percent, while the other two groups in contention obtained only 3.9 percent (the PDC/CDU center left coalition) and 2.7 percent (the PCN).22 Voter turnout was at a record high level. On the economic front, the opening up of the economy to international competition and other market oriented reforms may have increased the “sensitivity of goods demand to prices and, as a result, the sensitivity of the demand for labor to wages”, 23 which may explain in part the larger movement in real wages depicted in Table 3.1, a development that fuels insecurity. The sense of insecurity may also result from a changing relationship between the firm and the employee. In El Salvador, some firms, for instance in the maquila sector, establish themselves rapidly in the country – but, equally rapidly, close operations and disappear, leaving many workers without jobs. The decline in agriculture, and particularly the coffee crisis, has also brought major dislocation to the rural economy and insecurity for rural households. Although the export base has become more diversified and the country is now less subject to the price volatility of a few commodity exports (Table 3.1 shows how terms-of-trade volatility has fallen in recent years), the decline in traditional exports has affected thousands of campesino families that depend on these crops. For instance, in the early 1980s coffee employed about 200,000 workers a year; during the 2000/2001 season it employed 120,000; and during the 2003/2004 season only 50,000.24 Table 3.4 shows that employment in agriculture during the

22 Note that the PDC/CDU and PCN should disappear as political parties because they obtained less than 6 percent and 3 percent of the votes, respectively. 23 de Ferranti et al (2000), page 21. 24 Refers to annual equivalent of 250 days of work. Salvadoran Coffee Council’s web page. (www.consejocafe.org.sv).

33

1992/2001 period dropped at an annual rate of 1.6 per year while it increased by 3.8 percent in manufacturing. Table 3.4 Agriculture and Manufacturing Value Added and Employment Growth, 1992-2001 (Simple average of annual rates) Agriculture ManufacturingValue Added 1.1 5.1 Employment -1.6 3.8 Source: FUSADES (2003) “Informe de Desarrollo Economico y Social: Competititvidad para el Desarrollo”, based on Central Bank and household surveys data In sum, the higher levels of volatility of consumption, wages and employment during the first part of the 1990s were a consequence in part of the adjustment process, which gradually corrected major macro disequilibria and led to very low rates of inflation and relatively low (open) unemployment. While the volatility of macro variables declined in the second part of the 1990s, there continued to be a sense of insecurity among the population which may have resulted from political factors as well as the impact of a more open economy. Insecurity detracts from efforts to make growth more pro-poor. 2. Pro-Poor Public Spending The level of government spending (for instance, in relation to GDP) is not a determinant of the rate of pro-poor growth. Clearly, a country may spend a lot on the military and very little on social programs; therefore its public spending contributes little to poverty reduction. Thus, more important then the level of spending is the composition of expenditures, the efficiency of social spending, and who are the ultimate beneficiaries of public programs. In this sub-section, we explore how the composition of public spending changed in the post-conflict period, whether there was a peace dividend, and who benefited from government spending. Box 3.1 highlights some factors that may contribute to make growth pro-poor in post-conflict situations. Box 3.1 Pro-Poor Policies in a Post Conflict Setting After the signing of the Peace Agreements in 1992, El Salvador had a renewed sense of hope. Peace brings not just a cessation of hostilities, but also the opportunity for transformation, especially in terms of addressing the root causes of the conflict and building a better future for the next generations. The immediate post-conflict period offers a window of opportunity, often brief however, to undertake these transformations by adopting bold and longer-term reform processes.25 Redressing sharp inequalities and/or meeting the legitimate demands of the poor, whose unmet demands may have contributed to the conflict in the first place, is often at the center of the post-conflict reconciliation and reconstruction agenda. Therefore, pro-poor policies should be an inherent characteristic of reconstruction and reconciliation policies. Reconstruction programs usually involve measures to bring some kind of stability to the economy as a precondition for economic recovery, together with structural economic reforms to accelerate

25 Marques (2003)

34

growth, the reconstruction of physical infrastructure, and the rebuilding of human, social and cultural capital. Other components that are also usually part of these programs are the reinsertion of ex-combatants into civilian life, and demining.26 In each of these areas there are opportunities to ensure that policies are pro-poor. For instance, in the case of El Salvador, price liberalization was accompanied by subsidies on electricity, water and transport to reduce the negative impact on the poorest households; direct subsidies were also granted for low income housing; the reconstruction of infrastructure favored labor intensive methods as far as possible; the Social Investment Fund awarded thousands of contracts to small companies to respond to community needs and help rebuild or establish basic community services; education reform gave priority to the previously underserved poorest areas and many ex-combatants received scholarships to further their education; and land was distributed to the demobilized. Forging national priorities The government of President Cristiani that took office in mid-1989 (June 1989-May 1994), in addition to reaching a peace settlement with the guerrillas, had as its key priority improving social conditions and reducing poverty. The government economic and social program emphasized public spending on education and health. Soon after taking office, the government requested World Bank support for the social sectors. The first two World Bank operations approved in 1991 were the Social Sector Rehabilitation project and the First Structural Adjustment loan.27 The first of these operations focused on education and health. The administrations of Presidents Calderon (June 1994-May 1999) and Flores (June 1999—May 2003) that followed continued to make the social sectors a priority and over time the World Bank approved three other education projects and recently a new health project. The government of President Cristiani, recognizing the need to improve the country’s safety net, particularly in order to compensate those most affected by stabilization and structural adjustment programs, implemented several compensatory measures including direct subsidies for transport, housing, and basic services, and created a Social Investment Fund (FIS). The FIS was established in 1990 with the mandate of financing basic infrastructure sought by poor communities. In 1997, the mandate of the Social Investment Fund was broadened to include local development, its name was changed to the Social Investment Fund for Local Development (FISDL), and it became a permanent institution. With the Peace Agreement in 1992, El Salvador’s political landscape changed dramatically. In 1994, the ex-guerrilla movement FMLN, now a political party, participated for the first time in elections for the National Assembly and gained a significant presence.28 The government was then required to forge a broader consensus 26 See “El Salvador- National Reconstruction Program”, Report to the Consultative Group Meeting, Ministry of Planning, Government of El Salvador, March 1992; and World Bank’s Experience with Post-Conflict Reconstruction, OED, 1998 27 These two projects were approved in early1991. The World Bank had a long hiatus in its lending to El Salvador because of the war and concerns over creditworthiness. Between 1979 and 1991, the only project approved was an emergency earthquake reconstruction operation in 1987. 28 In the first election in which the FMLN participated (1994), the party obtained 21 seats in the National Assembly out of a total of 84 seats, and 14 municipalities out of a total of 262. Since 2003, the FMLN has

35

on public expenditure priorities. Education emerged as the sector where there was the strongest consensus on its importance for the country’s development prospects and poverty reduction. In health, there has been some consensus on the need to improve primary healthcare, but less agreement on how to improve service delivery and the efficiency and equity of resource use through new financing and management modalities. Similarly, while there has been a general consensus on the need to decentralize service delivery and strengthen local governments, the specific division of responsibilities between the Central Government and the Municipalities, together with financing arrangements and accountability mechanisms, are some of the issues that have proven more controversial. The peace dividend The reduction in military expenditures brought about by the Peace Agreement made it possible to reorient public expenditures towards the social sectors. During the 1990s, military spending dropped from nearly 25 percent of total government spending to about 10 percent, and the number of military personnel from 60,000 to 15,000 (Figure 3.2). According to the World Bank (WDI) in 1989, military spending in El Salvador was equivalent to 4for LAC was 1.5 percent declined to 0.9 percent of Gthis reduction in perspectiveequivalent to 3.2 percent of G It should be noted, howeverfinanced from external sourcthe treasury. New institutiCivilian Police, etc.) and tincluding provision of pensio

had the majority in the National A80 municipalities, including 6 of th

Figure 3.2 Military Spending as % of Total Central Government Spending and Number of Military Personnel

0

5

10

15

20

25

1991 1992 1993 1994 1995 1996 1997 1998 1999

Porc

enta

je

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

Mili

tary

Per

sonn

el

Military Spending Military Effectives

Source: World Bank’s WDI

.6 percent of gross domestic income (GDI) while the average of GDI. By 1999, military spending in El Salvador had DI while for LAC it remained at the previous level. To put , in 2003 public expenditure on education in El Salvador was

DP.

, that a large share of military expenditures in the 1980s was es and that the peace agreements also made new claims on

ons had to be created (Human Rights, Judiciary, National he National Reconstruction Program had to be financed, ns to wounded veterans and surviving family members.

36

ssembly, with 31 seats against the 27 held by ARENA, and the control of e 14 municipalities that head departments.

Social Spending Social expenditures represented 31 percent of the budget in 1996 and increased to 46 percent by 2003 (Table 3.5). Both education and health expenditures increased as shares of the total budget. Transfers registered the largest increase, since they include the resources passed on to the municipalities, which by law correspond to 6 percent of central government revenues (7 percent since 2004), as well as transfers to the public pension system. The large increase in social expenditures in 2001 was associated with earthquake reconstruction, particularly of low-income housing and of damaged schools and health clinics. Table 3.5 Central Government Expenditures, by Management Area, 1996-2003 (% of Total Expenditures)

1996 1997 1998 1999 2000 2001 2002 2003 Administration 13.1 12.5 12.7 13.5 13.1 11.7 9.2 9.4 Justice and Security 15.0 15.0 15.5 15.9 15.0 13.2 13.4 13.5 Social Development a/ 31.2 32.1 33.9 36.3 36.0 41.5 43.5 45.7 Education 14.6 16.4 16.8 17.5 17.3 19.7 18.4 19.5 Health 9.1 8.8 9.2 9.6 9.9 8.8 9.5 9.7 Transfers b/ 0.4 1.3 5.5 5.9 5.7 10.2 14.2 15.2 Economic Development c/ 13.9 14.4 14.9 11.0 13.2 12.3 9.2 10.0 Public Debt 20.1 18.0 15.6 16.1 14.6 13.9 17.9 17.9 General Obligations 6.6 8.0 6.5 6.8 8.0 5.6 6.7 3.6 Public Enterprises - - 1.0 0.3 0.2 1.9 - - Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 Memorandum: Amount of Social Expenditures (en US$ million)

492 531 649 684 746 992 1,126 1,137

Growth of Social Expenditures N/a 7.9 22.3 5.3 9.1 33.1 13.5 1.0 Note: 1996-2001, executed budgets; 2002-2003 approved budgets. a/ Includes small scale basic infrastructure such as water supply, rural roads and rural electricity financed by the Social Investment Fund for Local Development (FISDL), and the municipalities. b/ Includes transfers to the municipalities, war veterans, FISDL, and to the pension system. c/ Includes major public infrastructure investment such as ports, airports, main roads, and irrigation schemes. Source: Ministry of Finance Investment in basic infrastructure has also increased substantially in recent years. Table 3.6 shows the investment financed by public institutions such as ANDA (the Water and Sanitation Authority), the FISDL, the Ministry of Health, and the Ministry of Public Works. The average level of investment in water over the last five years has been much higher than in 1990. Note also the large increase in the financing of rural roads, which is part of the Sustainable Rural Roads program supported by the IDB. Notwithstanding these increases, according to the World Bank (WDI), the public sector in El Salvador spends about 3.2 percent of GDP on education and 3.8 percent of GDP on health while the average for Latin America is 4.4 percent of GDP and 3.3 percent of GDP, respectively. On the other hand, it is important to note that social public spending in El Salvador is generally efficient and well targeted on the poor, as decision on the use of funds are increasingly being made at the local level. For instance, in the case of

37

education, an increasing share of expenditures are undertaken by schools directly with funds transferred by the Ministry of Education; the Ministry of Health has decentralized the financing of primary healthcare to local health systems or SIBASI; and the FISDL transfers funds to municipalities and communities to help finance basic infrastructure.29 Table 3.6 Investment in Basic Infrastructure, 1990, 1998-2002 (US$ millions) Sector 1990 1998 1999 2000 2001 2002 Average

1998-2002 Water 17.7 a/ 29.3 44.4 38.5 41.1 14.0 33.5 ANDA 17.7 a/ 28.0 40.9 35.2 37.8 11.0 30.6 FISDL 1.3 3.5 3.4 3.3 2.9 2.9 Sanitation 8.9 8.5 9.3 10.5 12.1 9.9 ANDA 1.9 2.1 0.8 1.2 2.6 1.7 FISDL 0.8 0.9 1.4 1.3 1.2 1.1 Ministry of Health 6.2 5.6 7.1 7.9 8.3 7.0 Rural Roads 0.0 3.8 15.8 33.0 87.6 35.1 FISDL 0.0 3.8 15.8 15.4 17.2 13.1 Ministry of Public Works - - - 17.6 70.4 22.0 a/ Includes water and sanitation Source: Ministry of Finance, ANDA, FISDL and FINET Improved Social Conditions Social conditions improved substantially during the 1990s. Between 1990/91 and 2000, illiteracy declined significantly and education enrollment at the pre-primary, primary and secondary levels increased noticeably (Table 3.7). With respect to health, life expectancy rose by 4.5 years while infant mortality declined sharply. These gains were greater than those achieved in LAC as a whole. Access to basic services also improved: access to water reached 76 percent of households in 2002 comparereached 93 percent of househreaches 88 percent of househo

29 See Marques (2002,2003)

Figure 3.3 Average Years of Schooling for Children Aged 15 Years

4

5

6

7

8

1 2 3 4 5

Income Quintiles

Year

s of

sch

oolin

g

1995 2002

Source: Household Surveys, DIGESTYC

d to 55 percent in 1991; access to sanitary installations olds in 2002 up from 78 percent in 1991; electricity now lds, compared to 70 percent at the beginning of the period.

38

Table 3.7 Selected Education and Health Indicators 2000 Illiteracy

a/ Gross

Enrollment Pre-

Primary (%)

Gross Enrollment

Primary (%)

Gross EnrollmentSecondary

(%)

Life Expectancy

at birth (years)

Infant Mortality

Rate (per 1000 live

births)

Under 5 Mortality Rate (per 1000 live

births)

2000 El Salvador 20 44 109 54 70 29 35 LAC 11 58 130 86 70 29 37 Change Since 1990/91 El Salvador -8 24 28 29 4.5 -16 -19 LAC -4 11 24 37 2.5 -12 -13 a/ population 15 years and older. Source: DIGESTYC and World Bank’s WDI Spending Incidence Access to services has improved significantly for the poor. Figures 3.3-3.5 show that the gap in access to services between the rich and the poor has closed during the 1990s. For example, the average number of years of schooling increased faster for those in the lowest three quintiles; infant mortality dropped more for the lower and middle income groups. Meanwhile, the ratios between the percentage of the richest households to the percentage of the poorest households with access to water, sanitary services and electricity declined during the 1990s, as the service gap was gradually closed.

Source: FESAL, Asociación Demografica Salvadorena

Figure 3.4 Infant Mortality Rate Per Socio-Economic Group

0

10

20

30

40

50

60

Low Midlle High

Per 1

,000

bor

n al

ive

FESAL 1993 FESAL 2002

In sum, during the 1990s public expenditures increasingly favored the social sectors, a trend that in part was made possible by the peace dividend. These expenditures have mostly benefited the lower income groups as reflected in improved social indicators and access to basic services among the poor. Nevertheless, El Salvador still spends too little on education compared to the average for LAC and many poor families still do not have regular access to quality health services.

39

3. Rural Development Poverty continues to be predominantly a rural problem in El Salvador. While about 40 percent of the population lives in rural areas, 56 percent of the rural population lives in poverty and 27 percent lives in extreme poverty. A large share of the rural population depends on agriculture and related activities for its livelihood (in 2002 44 percent of the employed rural labor force worked in agriculture). People working in agriculture are more likely to be poor than people working in other sectors. Indethat compared to working inmining, manufacturing, electriincreases per capita income agricultural policy frameworkduring the second part of the d Agricultural performance Agriculture’s contribution to Gcreation and export earnings. 1.4 percent during 1990-20012.1 percent; agriculture’s sharpercent in the early 2000s; agyear during the 1990s and itpercent in 1992 to less than 25to merchandise exports fell froearly 2000s.30 Within agricultperformed well during the 19nearly disappeared.31

30 World Bank’s WDI. 31 There is an ongoing attempt to revgarments to the US under the free tra

ed, the poverty correlates discussed in Section II indicated agriculture, having a household head employed in the city, construction, commerce, transport, or service sectors

by up to 70 percent. In this sub-section we review the of the 1990s and probe the drivers of rural income growth ecade.

DP has been declining, along with its contribution to job The annual growth of agricultural value added averaged , below the population growth rate, which is estimated at e of total GDP declined from 28 percent in the 1980s to 9 ricultural employment dropped at a rate of 1.6 percent a

s contribution to total employment was reduced from 36 percent in the early 2000s; and agriculture’s contribution m nearly 60 percent in the early 1990s to 35 percent in the ure some subsectors such as basic grains, sugar and poultry 90s, while coffee declined sharply and cotton production

ive cotton production, given the increased potential for exporting de agreement.

Figure 3.5 Ratio of the 5th Richest to the 1st Poorest Quintiles Household Percentage Access

to Services

0.0

0.5

1.0

1.5

2.0

2.5

Access to Water Access toElectricity

Access toSanitary Services

Rat

io

1991 1998 2002

40

Most of El Salvador’s agricultural land is devoted to basic grains (70 percent) with two-thirds of producers cultivating very small plots of less than 2 hectares (ha).32 For many crops, and especially for staples such as maize and beans, most of the increase in production has come from an increase in area under cultivation on hillsides, which is often quite fragile. Only about one-half of maize production is marketed, which illustrates the continuing high levels of self-consumption.33 Several factors explain the poor performance of the agricultural sector. One has been the lack of clarity over the country’s agricultural sector policy. In the early 1990s, the Cristiani Administration focused on eliminating most of the distortions that affected the sector (such as price controls, guaranteed prices, and marketing boards), establishing a market based exchange rate and liberalizing trade.34 During the Calderon Administration, some top officials considered that the sector was destined for steady decline and it was not therefore among the administration’s priorities. During the Flores Administration, there was an effort to modernize the sector through better information systems, improved research and technical assistance to producers, improved production chains and marketing arrangements (including the establishment of cooperative agreements between producers and large consumers), and strengthened property rights through land registry.35

Figure 3.6 Real Effective Exchange Rate and Remittances

100

110

120

130

140

150

160

170

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

REE

R 1

990=

100

0

500

1000

1500

2000

2500

US$

mill

ions

REER Remittances

Source: Central Bank and IMF.

32 It should be noted that the last agricultural census in El Salvador dates from 1971. 33 World Bank (1998) 34 During the first part of the 1980s, coffee exports were liquidated at the then “official exchange rate” of 2.50 colones per dollars while all other exports were liquidated at the market rate of 5 colones. Coffee producers had to pay for imported inputs at the market rate. This discrimination against coffee, plus the destruction brought about by the conflict, caused a high level of debt for coffee producers, a situation that still affects the sector. 35 These efforts were supported by two World Bank operations: the Agriculture Sector Reform and Investment Project approved in 1993, which has already terminated; and the Land Administration Project approved in 1996, which is currently under execution.

41

The dismantling of barriers and the agricultural pricing reforms of the early 1990s took place in an environment in which real agricultural prices were declining. This lowered the profitability of agriculture and therefore investment in the sector. As already mentioned, a sense of insecurity in rural areas continued after the signing of the peace accords, further deterring investment in agriculture. The sector was already under financial strain owing to the years of conflict and the appreciation of the real exchange rate, which continued during the 1990s. Indeed, between 1990 and 1995, the exchange rate appreciated (in real effective terms) by 30 percent, owing in part to the influx of remittances as shown in Figure 3.6. Specifically, real prices of most agricultural tradables experienced declines on the order of 20-34 percent between 1990-1995 (Table 3.8), with the most important declines occurring during 1993-95. The World Bank’s Rural Development Study (1998) concludes that the most important factor explaining the decline in domestic producer prices was real exchange rate appreciation. Marketing costs, including the financial component of storage operations, also appear to have influenced the drop in farm prices compared to what they might have been under a competitive marketing structure. According to the study, the negative impact of marketing costs can be attributed to a government failure to encourage greater competition in the marketing chain, which would facilitate the development of competitive storage services, financing, and processing from the farmgate to the consumer (or the export point). Table 3.8: Real Prices of Selected Agricultural Products, 1990-95 (1990=100)

Maize Rice Milk Cattle 1990 100 100 100 100 1995 66 80 67 67 Source: “El Salvador Rural Development Study”, World Bank, 1998

Figure 3.7 Per Capita Agriculture Production Index, 1991-2003

707580859095

100105110115120

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

Inde

x 19

89-9

1=10

0

Source: FAO Agricultural production has not recovered, owing in part to adverse natural conditions (El Niño in 1997; the hurricane Mitch in 1998; the earthquakes in 2001) and the coffee crisis since 1997. FAO’s agricultural production index (Figure 3.7) indicates that per capita

42

production has declined by 8 percent between 1990 and 1995 and by another 9 percent between 1995 and 2003. Agrarian Reform The agrarian reform implemented in the early 1980s redistributed approximately 300,000 ha of land: 215,000 ha that were expropriated from farms larger than 245 ha for 37,000 beneficiaries organized into 346 cooperatives and 80,000 ha for 47,000 beneficiaries who worked the land.36 This expropriated land included about 20 percent of the country’s land with the greatest agricultural potential. The Peace Accords of 1992 further distributed land to ex-combatants: about 30,000 beneficiaries received 78,000 ha. According to a World Bank study a significant proportion of land affected by the agrarian reform and Peace Accords was not under cultivation; some of the cooperatives were converted into individual tenancy and others operated under mixed individual and cooperative tenancy. In the crop year 1993/94 about 6% (or 17,700 ha) of land distributed under the agrarian reform was not under cultivation. Of the land transferred to ex-combatants, 28% (or 5,200 ha) was not under cultivation. According to the study a number of factors were responsible for this situation, including lack of credit, decreasing competitiveness of the sector, and natural factors (e.g., adverse weather).37 In 1996, the National Assembly approved legislation which permitted the free sale of land and allowed producers to use the proceeds to reduce arrears to the financial system or state agencies.38 The agrarian reform corrected the sharpest inequality in the distribution of assets in the country but has also led, according to the World Bank, “to legal insecurity of land holdings, low investment levels, the emergence of arrears and arrears clearance initiatives, and low managerial levels in the exploitation of land transferred to cooperatives.”39 In addition, the sector has faced other problems such as uncompetitive marketing structures for some agricultural products and purchased inputs; poor agricultural technology generation and transfer systems; weak rural financial system; and low investment in rural roads and other infrastructure. El Salvador’s land reform experience is not unique in Latin America. de Ferranti et al (2004) put the experience of these countries in the following terms: 40

They did not usher in the hoped-for transformation of social and economic inequalities in Latin America. Nor, for the most part, did they generate the kind of vibrant smallholder sectors that were so important in East Asia’s dynamic development path. However, the reason was not because land reform was in principle ill conceived. Rather, major land

36 The second phase of the agrarian reform, which was meant to expropriate all land in farms of over 100 ha, was canceled by the 1983 Constitution which set 245 ha as a ceiling. 37 World Bank (1998), page 6. 38 Law of Special Regimes for Land Held by Associations of Cooperative Communities and Producer Communities (Legislative Decree 719, May 1996). 39 World Bank (1998), page 6. 40 de Ferranti et al (2003), Inequality in Latin America and the Caribbean: Breaking with History?, World Bank Latin American and Caribbean Studies, Advance Conference Edition, Chapter 8 pages 331.

43

reform efforts were often poorly designed—notably where ill-fated production cooperatives were emphasized—and more fundamentally were incomplete.

Growth in rural household income

While the annual average growth rate of agriculture fell from 2.3 percent during 1990-95 to 0.6 percent during 1996-2001, the per capita income of the poorest rural household increased in the second part of the 1990s at rates of over 5 percent. Section II offered evidence (Figure 2.4, growth incidence curve) that after declining during the first part of the 1990s, real per capita incomes of the poorest rural household grew at over 7 percent a years during the second part of the 1990s. With the help of panel data from FUSADES, this sub-section probes the key drivers of this increase in household incomes.41 Table 3.9 Changes in Rural Household Incomes, 1995-2001

Percentiles Average Household income (1995 colones) Change (%) 1995 1997 1999 2001 95/97 97/99 99/01 95/01

10 3590 2765 4421 7603 -23 60 72 212 25 7250 6360 9856 13073 -12 55 33 180 50 13860 12234 19780 22953 -12 62 16 166 75 23438 23417 38105 39690 0 63 4 169 90 34550 41566 57621 61526 20 39 7 178

Average 18063 18207 28513 32187 1 57 13 178 Note: includes only 451 households of the panel Source: Sanfeliu, Margarita B. and Mauricio Shi (2003) “ Dinámica del Ingreso Rural en El Salvador”.Processed, FUSADES. During the 1995/01 period, the average real income of the 451 rural households in the FUSADES sample increased by 178 percent, or by about 10 percent a year (Table 3.9) (see Box 3.2). The rate of increase in average real incomes of the poorest 10 percent of households was greater than the average, which is consistent with our findings in the previous section. In a short period of six years, there has been a drastic change in the sources of income for rural households. While in 1995 almost one-half of their income originated from agricultural activities, by 2001 only one-quarter came from this source (Table 3.10). In contrast, the share of non-agricultural income rose from 47 percent to 55 percent and remittances and other help grew from 8 percent to 16 percent. 41 In 1995 with the support from the World Bank and Ohio State University, FUSADES carried out a rural household survey to study the determinants of rural poverty. Every two years FUSADES has visited the same families with the objective of evaluating their income dynamics. This subsection is based on the resulting panel data available for 451 families for the years 1995, 1997, 1999 and 2001. It draws on the recent FUSADES study prepared for the World Bank. Sanfeliu, Margarita B. and Mauricio Shi (2003) “Dinámica del Ingreso Rural en El Salvador”,.processed, FUSADES, as well as on new estimates prepared for this country study.

44

Table 3.10 Changing Composition of Rural Household Income, 1995-2001

Average Income (1995 colones)

% of total income

1995 2001 1995 2001 Agriculture 7953 8518 44 26 Own production 3113 5460 17 17 Salaries 4253 2979 24 9 Other 587 80 3 Non Agriculture 8423 17744 47 55 Enterprise activities

736 6594 4 21

Salaries 7391 10375 41 32 Other 295 776 2 2 Remittances and other help

1491 3844 8 16

Remittances from abroad

1125 4253 6 13

Subsidies 194 704 1 2 Source: Sanfeliu, Margarita B. and Mauricio Shi (2003) “ Dinámica del Ingreso Rural en El Salvador”. Processed, FUSADES. Box 3.2 Panel Data, Quintiles and Changes in Income According to FUSADES, there are three major reasons why changes in household incomes appear relatively large in the panel data. One is that the same families are tracked down in the surveys and, therefore, as families mature their income is expected to increase; that is, the sample excludes new, younger families that may start with lower incomes. At a certain point, however, household income will begin to decline, as children leave the household and adults reach retirement age. Secondly, the definition of income in the survey and its modeling allows for negative income values when the household makes losses during a particular year; if the household then recovers, this could lead to large increase in income in the following years. Finally, the manner in which quintiles or percentiles are defined influence the estimates of income growth. There are three principal ways of defining the quintiles or percentiles: income of the household in the base year; income of the household in the current year; or average income of the household over a period. In Table 3.9, the percentiles are based on income in the current year. In Table 3.12, the quintiles are based on the average income of the household during 1995-2001. According to FUSADES, only 15 percent of households in the panel remained in the same income deciles in the 1997 survey when compared to their position in the 1995 survey; 14 percent of households in the 1999 survey, compared to the 1997 survey; and 21 percent of households in the 2001 survey compared to the 1999 survey. About 40 percent of households changed their relative income position by more than two deciles between the 1995 and the 1997 surveys; 33 percent of households between the 1997 and

45

the 1999 surveys; and 29 percent of households between the 1999 and the 2001 surveys. There were households that moved from the bottom to the top deciles and vice versa. Again, this may be related, for example, to an extremely bad agricultural year followed by some recovery; or even to the emigration of a household member during a given year, leading to a large drop in income until remittances start. The implication is that incomes may vary substantially depending on the definition adopted for the quintiles. Key drivers of household income growth What were the key drivers of these changes? Table 3.11 shows the key factors that influenced the changes in household income. These factors are grouped by those pertaining to household characteristics; human capital; physical assets; and access to markets for goods and services, to labor markets, to credit, to remittances, and to basic infrastructure.42 The principal factors that impact income growth of the “average household” are: Household characteristics. The number of dependents is negatively related to the

level and the change in per capita household income. Human capital. The average schooling of the members of the household who

work is positively associated with the level and the change in household income. The link between more schooling, better jobs, and better pay is well established.

Physical assets. The level of assets owned by households is positively associated with the level and changes in household income. The area planted is also positively associated with the level of household income.

Access to goods and services markets. The production of basic grains is negatively associated with the level of and changes in household income. Families that plant basic grains are more likely to have lower levels of income or negative changes in incomes. In contrast, the production of nontraditional products is positively associated with the level of income. Families with micro-enterprises are also more likely to have higher levels of income and positive changes in income.

Labor market participation. The number of sources of a household’s nonagricultural income is positively associated with the level of and the change in household income; the number of sources of income from agriculture is not significant.

Access to credit. This variable is not a significant determinant of the level or changes in income, possibly because very few families in rural areas still have access to credit.

Remittances. Transfers from family members abroad are positively associated with higher levels of and changes in household incomes.

Access to infrastructure. The distance to a bus stop (a good proxy for access to markets and services) is negatively associated with the level of household income, that is, the further away the household is from a bus stop, the lower the household’s level of income.

42 Note that Sanfeliu (2003) uses a different grouping.

46

Table 3.11 Factors that Affect the Level of and the Change in Rural Household Incomes, 1995/2001

Factors that explain the level of income of

households 2001 a/

Factors that explain the change in the

income of households 1995/01 a/

Strategies that Impacted the change in income of

households 1995/01 a/ Income in base year (-) ** Household Characteristics No. of children aged under 10 years/household members (-) **

No. of children aged under 10 years/household members (-) **

Human Capital Years of schooling 15 to 65 years (+) ** Years of schooling 15 to 65 years

(+) **

Physical Assets Planted Area (+)**

Household asset index (+) ** b/ Household asset index (+) ** Access to Goods and Services markets Plants basic grains (- ) ** Plants basic grains (-) *

Income in base year (-) **

Produces nontraditional products (+) ** Produces nontraditional prod. (+) *

Has micro-enterprise n/s Has micro-enterprise (+) * Labor Market Participation No. of agricultural sources of income n/s No. of non agricultural sources of income (+) **

No. of non agricultural sources of income(+) **

Access to Credit Received credit n/s Remittances Receive remittances (+) ** Receive remittances (+) ** Receive remittances (+) ** Access to Infrastructure Distance to bus stop (-) ** Regional Variables N/s-not significant; * found significant at 10%; ** found significant at 5%. a/ The estimates are based on OLS. For the factors that affected the level of household income, the dependent variable is the log of each year’s household income and the independent variables take the value in the year under analysis; for the factors that affect the change in household incomes, the dependent variable is the log of the relative change in household income between the years being considered and the independent variables take their base year values; for the strategies that affect change in household income , the dependent variable is the log of the relative change in household income and the independent variable is the change in value of these variables. b/ An index built on the basis of a list of goods owned by the household, such as vehicles, machinery, appliances, etc. Source: Adapted from Sanfeliu, Margarita B. and Mauricio Shi (2003) “ Dinámica del Ingreso Rural en El Salvador”. Processed, FUSADES. The last column of Table 3.11 presents strategies that have contributed to household income change. Three strategies are found to be significant for increasing the likelihood of additional income: households obtain jobs outside agriculture, engage in the production of nontraditional products, or have some household members migrate in order to begin receiving remittances.

47

To further extend the analysis, we may inquire, first, what is the relative importance of the different factors in explaining rural household income growth? And, second, what is the relevance of these findings for the poorest households? To address these questions we draw on an analytical framework used recently by Cord, et al. (2003). This framework seeks to explain the changes in household incomes by analyzing the relationship between household characteristics and per capita income growth using an econometric model applied to panel data. Annex 2 describes the framework and presents the detailed results using FUSADES’ panel data. The summary results of the Fixed Effects (FE) estimates that rely on within-household variations of the data and the Random Effects (RE) estimates that rely on across-household variability are presented in Table 3.12 The factors considered as potentially explaining the change in income are the same as those considered previously. Analysis of Table 3.12 indicates the following:

• For the average household (global), the factor that impacts on income growth the most is access to remittances, followed by has a micro-enterprise. These two factors may be related, since remittances may help finance the establishment of micro-enterprises. Ownership of physical assets (bundle of assets) comes as a distant third factor in importance: land is not apparently an important factor. The fourth factor in importance is access to non-agricultural labor markets (has non-agricultural salaried members). Access to infrastructure, particularly roads (RE), also contributes to income growth.

• For the poorest quintile (Q1), the relative importance of the above factors is significantly different. Remittances are not an important determinant of income growth because the majority of these families do not receive them (see Table 3.17 and discussion on remittances below). The principal factor in determining income growth is now access to non-agricultural employment followed by “has a micro-enterprise” (RE); ownership of physical capital (bundle of assets) comes in third place (RE). The selling of cattle, possibly to finance the establishment of micro-enterprises or invest in human capital, comes fourth in importance (RE). This latter factor was not significant for the average household. Access to potable water appears as an important factor in the FE estimates.

Some factors do not appear as important in the decomposition of income growth, perhaps because of the short time involved (6 years), but they are closely associated with the factors identified and may even be considered required preinvestment factors. For instance, some level of education and skill is required to build and run a micro-enterprise or being able to obtain a non-agricultural job; without access to basic infrastructure it is not possible to run most micro-enterprises. The importance of education and infrastructure in helping to determine the level and change in household income was already established (Table 3.11). Also, the World Bank’s Rural Development Study found that returns to education significantly increase wage income, particularly in off-farm non-agricultural activities.43 43 World Bank (1998a), page xv.

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Table 3.12 Decomposition of Change in Rural Household Income, by Quintile, 1995/2001 a/ Fixed Effect Random Effects

Global Q 1 Global Q 1 Q 2 Q 3 Q 4 Q 5 Real Per Capita Annual Income (change1995/2001=100) 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0%

Household Characteristics 23.1% 41.2% 22.4% -4.2% -47.6% 36.2% 0.0% 21.4%

Household size (log) 41.2% 11.5% 2.6%

Number of persons over 15 years (log) -15.7% 3.6%

Average age of members (log)

Economic dependence (log) 23.1% 22.4% 36.2% 21.4%

Woman head of HH (dummy) -54.1%

Human Capital 4.3% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Years of schooling 15 to 65 years (log) 4.3% %

Physical Capital 26.9% 0.0% 29.5% 35.9% 49.0% 0.0% -94.4% -0.9%

Total land area available (log) -48.2%

Index of assets (log) b/ 21.7% 26.9% 35.9% 13.6%

Owns cattle (dummy) 5.2% 2.6% 29.1% -30.4%

Owns poultry (dummy) 19.7% -15.8% -14.5%

Access to Goods and Services markets 55.6% 68.7% 62.7% 59.6% -34.4% 0.0% 158.6% 35.2%

Cultivates basic grains (dummy) -8.0% -9.5% -27.5% -2.8%

Cultivates non tradit. products (dummy)

Has micro-enterprise (dummy) 63.6% 72.1% 53.4% 158.6% 38.0%

Sold animals or animal product (dummy) 68.7% 33.7% -34.4%

Sold agricultural products (dummy)

Labor market participation 17.9% 69.2% 16.5% 55.0% 28.8% 19.2% 5.2% 0.0%

Has agricultural salaried members (dum.)

Has non agricultural salaried members (dum.) 17.9% 69.2% 16.5% 55.0% 28.8% 19.2% 5.2%

Access to credit 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Received credit (log)

Access to Remittances 86.5% 0.0% 72.3% 0.0% 77.4% 152.3% 119.7% 27.8%

Received remittances (dummy) 86.5% 72.3% 77.4% 152.3% 119.7% 27.8%

Infrastructure 0.0% 36.8% -11.3% 0.0% 19.0% -13.6% 0.0% -7.4%

Has electricity (dummy) -14.6% -13.6% -7.4%

Has potable water (dummy) 36.8%

Distance to paved road (log) 3.3% 19.0%

Distance to bus stop (log)

Residual -114.2% -115.7% -92.0% -46.3% 7.5% -94.3% -89.2% 23.9% a/ Difference between the log of mean of each variable between 1995 and 2001, multiplied by the correspondent regression coefficient that is significant at 90%. The difference in real per capita income has been made equal to 100%. Quintiles are determined by average household income during the period. b/ An index built on the basis of a list of goods owned by the household such as vehicles, machinery, electro domestics, etc. Source: Annex 3.

49

The results for the household characteristics are not easy to interpret: for the poorest quintile, while household size has a positive sign, the number of family members aged over 15 years (RE) has a negative sign. Contrary to what one would expect, for the average household higher economic dependence is positively related with income growth. On the other hand, in both cases the cultivation of basic grains, which is associated with subsistence agriculture, appears negatively related to income growth, as expected. Land does not show up as an important factor in income growth. Between 1995 and 2001 the occupation of the families changed considerably, which have contributed to the change in income. While most families continue to work the land and agricultural activities appear to have intensified particularly for those with lower income, a large number of families also establish their own enterprises and sought salaried jobs in non agriculture activities. Table 3.13 indicate that for the bottom quintile, 7 percent and 22 percent of families had micro enterprises and salaried jobs in no agriculture activities, respectively, in 1995; by 2001, the corresponding percentages were 12 and 40. For the top quintile, more than half of the families had micro-enterprise in 2001 compared to 21 percent in 1995. Labor income from these new sources increased accordingly. Table 3.13 Changes in Occupation and Labor Income, 1995 and 2001 Quintile Agriculture

or husbandry In their land

Own Enterpr

ises

Salaried Agriculture

Salaried No Agriculture

Agriculture or husbandry In their land

Own Enterpris

es

Salaried Agriculture

Salaried No Agriculture

1995 2001 Occupation (% of familias)

1 78.9% 6.7% 64.4% 22.2% 90.0% 12.2% 72.2% 40.0% 2 76.9% 9.9% 69.2% 39.6% 82.2% 35.6% 44.4% 54.4% 3 72.5% 12.1% 57.1% 47.3% 80.2% 26.4% 49.5% 56.0% 4 68.9% 5.6% 40.0% 60.0% 66.7% 38.9% 34.4% 63.3% 5 64.0% 21.3% 34.8% 59.6% 76.4% 51.7% 14.6% 59.6%

Global 72.3% 11.1% 53.2% 45.7% 78.9% 32.8% 43.0% 54.5% Labor Income (% of the labor income of the families)

1 20.5% 1.8% 48.4% 29.3% 23.6% 9.0% 34.3% 33.0% 2 21.5% 3.6% 41.0% 33.9% 18.4% 20.3% 17.2% 44.2% 3 18.4% 2.8% 38.1% 40.7% 14.0% 14.4% 19.2% 52.4% 4 15.7% 0.6% 19.1% 64.6% 10.4% 23.2% 11.7% 54.6% 5 20.3% 9.3% 14.7% 55.7% 30.2% 37.5% 1.6% 30.7%

Global 19.2% 4.5% 27.8% 48.5% 21.6% 26.0% 11.6% 40.9%

Note: categories are not exclusive Source: Special tabulation from the FUSADES’s Basis data base. Which non-agricultural activities were the sources of income growth for the rural population? Sanfeliu (2003) indicates that between 1995 and 2001, total labor income from micro-enterprises increased eightfold while that from non-agricultural wage activities increased by 40 percent. Within the latter, total wage income from maquila grew during the same period by 383 percent; from fisheries, 99 percent; from services, 75 percent; and from commerce and industry, between 20 and 30 percent. Income from construction declined by 7 percent. In 2001, 55 percent of rural incomes originated from non-agricultural activities, 26 percent from agricultural activities and 19 percent from remittances and other transfers. Micro-enterprise income contributed 20 percent of total

50

income and non-agricultural labor income, 35 percent. Services, construction and maquila contributed 19 percent, 4 percent and 3 percent, respectively, to total rural income. Data on the distribution of annual hours worked per activity (Table 3.14) indicate that in 1995 families in the bottom quintile dedicated 48 percent of their working hours to work their own land while the top quintile dedicated 34 percent; in 2001 the corresponding percentage were 51 percent and 25 percent. The families in the bottom quintiles also increase the amount of hours dedicated to salaried no agricultural activities from 17 percent to 22 percent of total hours worked, while reducing the hours in salaried agricultural work. On the other hand, the families in the top quintile increased significantly the hours dedicated to micro-enterprise activities, from 12 percent to 30 percent of all hours worked. Table 3.14 Distribution of Annual Hours Worked, 1995 and 2001 Quintile Agriculture or husbandry in

their land Own

Enterprises Salaried

Agriculture Salaried No Agriculture

Total Annual Working Hours

1995 1 48.1% 2.3% 33.0% 16.7% 299,958 2 37.8% 3.9% 35.3% 23.0% 376,929

3 31.2% 3.6% 37.1% 28.1% 468,085

4 30.5% 4.0% 22.4% 43.1% 339,629

5 33.9% 12.0% 15.3% 38.9% 439,569

Global 35.6% 5.4% 28.5% 30.4% 1,924,169

2001 1 51.3% 4.7% 21.9% 22.1% 420,490

2 33.5% 16.2% 18.4% 31.9% 408,492

3 27.1% 12.6% 20.3% 40.1% 476,618

4 23.8% 24.9% 12.5% 38.8% 430,736

5 24.8% 39.2% 2.8% 33.2% 515,224 Global 31.6% 20.2% 14.8% 33.4% 2,251,560

Source: Special Tabulation from the FUSADES’s Basis data base. Have changes in land tenure influenced the change in income? FUSADES’ rural household surveys data on land tenure for 1995 and 1997 are not strictly comparable with the surveys conducted in 1999 and 2001 because two different questionnaires were used, with much more detailed questions being asked in the more recent surveys. Nonetheless, Table 3.15 on the distribution of land by quintile of income gives some interesting insights. Of the rural households in FUSADES sample in 1995, about two thirds had land. Eighty two percent of families that owned land controlled 23 percent of the land area, while one percent of the families in the sample that owned land controlled 23 percent of the land area. The bottom quintile’s average plot size was only 0.9 ha; for the top quintile it was 2 ha; the overall average plot was only 1.6 ha. Comparing 1995 and 2001, there was a small increase in the size of the average plot to 1.86 ha and an apparent reduction in land concentration at the top (1.3 percent of the families now controlled 15.9 percent of the land area). These data points to the fact that land remains highly

51

concentrated in rural El Salvador (10 percent of the families controlled 60 percent of the land area) as well as to the small, economic unviable dimension of most farms. Table 3.15 Distribution of Land by Quintile of Income, 1995, 2001

Of which Quintile

Families that Own Land <2ha <5ha <20ha <50ha

Average Area (ha)

1995 1 57.8% 86.5% 13.5% 0.0% 0.0% 0.91 2 64.8% 86.4% 10.2% 3.4% 0.0% 0.92 3 60.4% 81.8% 10.9% 5.5% 1.8% 2.13 4 63.3% 86.0% 7.0% 5.3% 1.8% 1.90 5 74.2% 71.2% 19.7% 7.6% 1.5% 2.05 Global (famlies) 64.1% 82.0% 12.5% 4.5% 1.0% 1.60 No. Family owners 289 237 36 13 3 Total Area 462.30 108.18 117.22 128.58 108.31 % area 100.0% 23.4% 25.4% 27.8% 23.4% Average Area 1.60 0.46 3.26 9.89 36.10

2001 1 60.0% 83.3% 16.7% 0.0% 0.0% 0.97 2 65.9% 83.3% 6.7% 10.0% 0.0% 1.85 3 70.3% 79.7% 14.1% 4.7% 1.6% 1.55 4 74.4% 86.6% 6.0% 6.0% 1.5% 1.45 5 83.1% 60.8% 14.9% 21.6% 2.7% 3.14 Global (familias) 70.7% 78.1% 11.6% 9.1% 1.3% 1.86 No. Family owners 319 249 37 29 4 Total Area 592.35 117.36 108.88 271.77 94.34 % area 100.0% 19.8% 18.4% 45.9% 15.9% Average Area 1.86 0.47 2.94 9.37 23.58

Source: Special tabulation from the FUSADES’s Basis data base. In 1995, 94 percent of the families in the top quintile owned land compared to 75 percent of the families in the bottom quintile (Table 3.16). On the other hand, about one-third of the families in the bottom quintile rented land (in) compared to 10 percent of the families in the top quintile. The percentage of families in the bottom quintile that owned land declined between 1995 and 2001, from 75 percent to 69 percent while the share of those that rented land (in) doubled from 32 percent to 63 percent. Indeed, between 1995 and 2001, the share of families that rented land (in) reportedly increased sharply for all quintiles and the average land rental price paid by the families in the sample increased from 408 colones per manzana in 1995 to 563 colones per manzana in 2001, or by 12.5 percent in real terms. In sum, analysis of the factors that impact the income growth for poorest rural households indicates that the drivers are access to non-agricultural employment and the possibility of establishing micro-enterprises, both of which require improved human capital and access to basic infrastructure. Remittances are an important source of income growth for better-off households followed by the possibility of establishing micro-enterprises. Also, while the poor families sought other sources of income outside agriculture, they intensify their work in agriculture. Some poor families sold their land; many others rented land (in) to cultivate it. An increasing dynamic land market appears to have facilitated the resignation of resources that led to the increase in incomes.

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Table 3.16 Land Tenure in 1995 and 2001 Quintile a/ Families Average Own Land Rent Land Out Rent Land In Cultivate the land

w/ Land Area (ha)

Families b/ Average Area

Families b/ Average Area

Families b/ Average Area

Families b/ Average Area

1995

1 76.7% 0.94 75.4% 0.91 4.3% 0.40 31.9% 0.82 82.6% 0.83 2 75.8% 1.01 85.5% 0.92 0.0% - 30.4% 0.74 71.0% 0.97

3 68.1% 2.06 88.7% 2.13 9.7% 2.85 25.8% 0.67 75.8% 1.18

4 73.3% 1.87 86.4% 1.90 1.5% 0.35 24.2% 0.94 65.2% 0.87

5 78.7% 2.18 94.3% 2.05 7.1% 2.24 10.0% 2.50 67.1% 1.36

Global 74.5% 1.60 86.0% 1.60 4.5% 1.99 24.4% 0.94 72.3% 1.03

2001

1 86.7% 1.13 69.2% 0.97 2.6% 0.87 62.8% 0.73 93.6% 0.73

2 85.7% 1.84 76.9% 1.85 5.1% 3.49 47.4% 0.88 75.6% 0.85

3 83.5% 1.57 84.2% 1.55 10.5% 2.89 42.1% 0.63 76.3% 0.90

4 81.1% 1.71 91.8% 1.45 4.1% 4.66 38.4% 0.97 63.0% 0.97 5 88.8% 3.41 93.7% 3.14 15.2% 5.44 27.8% 1.68 67.1% 1.73

Global 85.1% 1.94 83.1% 1.86 7.6% 4.07 43.8% 0.91 75.3% 1.01

Note: Categories are not exclusive. a/ Based on average incomes in the 1995-2001 period b/ Percentage of familie that have land Source: Special Tabulation from the FUSADES’s Basis data base. An important implication of the analysis is that the poorest households may face specific constraints that require detailed analysis and tailor-made policy interventions. The poorest households are not likely to receive remittances or credit and may be required to use their existing assets such as land and labor in a more intensive manner or sell the few assets that they possess to invest in human capital or in micro-enterprises. Better-off households may use remittances for such purposes. 4. Remittances Remittances from Salvadorans who emigrated to the US have increased rapidly during the 1990s and have become an important factor in household income growth. Overall remittances increased from less than US$ 400 million in 1990 to over US$ 2 billion in 2002. At the macroeconomic level, this influx of capital has helped to close the savings gap; at the micro level it has increased family income. As already mentioned, remittances have also contributed to the appreciation of the real exchange rate and, to that extent, have had an adverse impact on exports and employment creation. Table 3.17 shows the average monthly remittance received by households classified by level of poverty in 1998 and 2002. Between 1998 and 2002, the monthly average remittance increased by 40 percent for urban households and 65 percent for rural households. For rural families in extreme poverty, they increased by 7 percent compared to 66 percent for urban families in extreme poverty. In 2002 at the national level, households in extreme poverty received only 35 percent of the average monthly remittance received by all families in the country; in contrast, the better-off households received 116 percent of the average remittance. Thus, the level of remittances received

53

and the poverty status of the family are closely related, with non-poor households receiving on average an amount of remittances twice as high as poor households; families in urban areas receive on average higher remittances than those in rural areas. Thus, to this extent, remittances contribute to inequity. Table 3.17 Monthly Average Remittances Received by Household, By Poverty Level, 1998 and 2002 (colones and %)

1998 2002 2002/1998 Change %

2002 Total Country = 100

Total Country 910 1318.31 45 100 Poverty 532.81 747.85 40 57 Extreme Poverty 342.88 459.22 34 35 Relative Poverty 624 877.16 41 67 Non-Poor 1148 1531 33 116 Urban 999.62 1396.8 40 106 Poverty 549.61 798.76 45 61 Extreme Poverty 322.5 533.75 66 40 Relative Poverty 634.45 905.21 43 69 Non-Poor 1205 1588 32 120 Rural 726.31 1196.39 65 91 Poverty 515.75 687.29 33 52 Extreme Poverty 357.8 383.73 7 29 Relative Poverty 611.58 841.27 38 64 Non-Poor 1017 1432 41 109 Source: DIGESTYC and Household Surveys To what extent do remittances contribute to poverty outcomes? Table 3.18 shows the proportion of families that receive remittances and the ratio of the average monthly remittance received by households to the poverty line, by poverty level. In 2002, a little over 20 percent of urban and rural household received remittances. The average amount of monthly remittances received by households in extreme poverty represented a little less than one-half of the poverty line. If these families did not receive remittances, the intensity of poverty would be much greater. For families in relative poverty in rural areas, remittances received exceeded the poverty line, which implies that without remittances many families would be in extreme poverty. If one assumes that incomes and remittances are equally distributed among these families, as a first approximation, if these families had not received remittances, extreme poverty in rural areas would have increased in 2002 from 9.3 percent to 14.2 percent.44 For the non-poor, the amount of remittances received substantially exceeded the established poverty lines. Therefore, remittances contribute significantly to poverty reduction. To assess the order of magnitude of the impact of remittances on poverty reduction, the recently completed World Bank Poverty Assessment presents estimates of poverty rates with and without remittances. The results indicate that in the absence of remittances, the decline in total poverty between 1991 and 2002 would have been 23.9 percentage points (from 66.8 to 42.9 percent) rather than 27.2 percentage points (from 64.4 to 37.2 percent),

44 (24*20.4/100= 4.9%).

54

a difference of 3.4 percentage points. The decline in extreme poverty from 1991-2002 would have been 13.2 percentage points (33.7 to 20.5 percent) as opposed to 15.8 percentage points (31.2 to 15.4 percent), a difference of 2.6 percentage points. Table 3.18 Impact of Remittances on Poverty, 2002

%

of Households % of Household that Receive Remittances

Monthly Remittances as % of Poverty Line

Urban 21.5 Extreme Poverty 10 14.5 48.0 Relative Poverty 19 19.4 81.4 Non-Poor 70 23.0 142.9 Rural 23.4 Extreme Poverty 25 10.0 47.0 Relative Poverty 24 20.4 103.1 Non-Poor 51 31.5 175.5 Memorandum: Urban poverty line a/ 1111.6 Rural poverty line a/ 815.9 a/ colones per month Source: DIGESTYC According to the Poverty Assessment, the interpretation of these numbers should be done with caution. On one hand, in the absence of out-migration and remittances, families probably would have increased their labor supply in the local market and, thus, their earnings. This would mean that the incomes of those receiving remittances would be higher than suggested by a calculation without remittances. This would mean correspondingly lower poverty rates. On the other hand, the existence of additional income via remittances has likely led to domestic demand for goods and services that would not have existed in the absence of those remittances. This increased demand for goods and services resulted in higher incomes (and perhaps less poverty) for some number of families that did not receive remittances. Just how those two (opposing) impacts net out is difficult to assess with certainty. Nonetheless, these simulations suggest that while remittances have made an important contribution to poverty reduction in recent years, they have not played the dominant role in recent achievements. 5. Labor Market The labor market in El Salvador is characterized by relatively low rates of unemployment and high rates of underemployment. According to DIGESTYC, the rate of unemployment at the national level was 8.7 percent in 1991 and declined to 6.3 percent in 2002. The unemployment rates for rural and urban areas in 2002 were quite similar: 6.3 and 6.2 percent, respectively. The unemployment rate is higher for the poor than for the non-poor in both rural and urban areas (Table 3.19). Table 3.19 Rural and Urban Unemployment, by Poverty Level, 2002

55

(Percentage of total population 10 years and older) Urban Underemployment Poverty Level Rural

Unemployment Unemployment Rate a/

Urban

Total Visible Invisible

Total 7.4 6.2 29.7 4.1 25.6 Poverty 10.1 11.3 48.0 7.0 41.0 Extreme Poverty 11.0 16.2 54.8 7.5 47.3 Relative Poverty 9.2 8.9 44.7 6.7 38.0 Non-Poor 4.4 4.3 22.8 3.0 19.8 a/ Data for 1999 Source: DIGESTYC and Marques, José Silvério (2002) “El Salvador Social Safety Net Assessment”, World Bank, Report No. 24190-ES, based on the EHPM, 1999. Although the unemployment rate is relatively low, almost one-third of the urban labor force was underemployed in 2002: visible urban underemployment (those who worked involuntarily less the 40 hours weekly) was 4 percent, while invisible underemployment (those who worked more than 40 hours weekly but received less than the established minimum wage) was 26 percent (Table 3.19).45 Forty percent of the poor who are occupied (almost one-half for the extreme poor) work more than 40 hours weekly but receive less than the minimum wage. So, for the employed poor, the main problem is not the number of hours worked but the level of income received for workweeks of more than 40 hours.46 In this context, it should be noted, as will further discussed in the next Section, that when firms need to adjust their employment levels the first employees to be laid off are usually the unskilled, lowest paid workers who are easier to replace in labor abundant countries. Firms may also adjust by employing people on a less than full time basis or by paying less than the officially established wage. Table 3.20 Participation Rates, 1991-2002

National

Urban

Rural Total Men Women Total Men Women Total Men Women 1991 61.9% 84.9% 42.5% 65.8% 80.9% 53.8% 57.7% 88.8% 29.6% 1995 62.9% 84.5% 44.5% 65.4% 80.8% 53.0% 59.2% 89.4% 31.7% 2000 63.3% 81.8% 47.4% 65.7% 78.7% 55.0% 59.3% 86.3% 34.3% 2002 62.2% 80.0% 47.2% 64.5% 77.0% 54.5% 58.4% 84.8% 34.6% Change 1995-91 1.0% -0.4% 2.0% -0.3% -0.1% -0.8% 1.5% 0.6% 2.0% Change 2000-95 0.4% -2.7% 2.9% 0.3% -2.1% 2.1% 0.1% -3.0% 2.6% Change 2002-91 0.3% -4.8% 4.7% -1.2% -4.0% 0.7% 0.7% -4.0% 5.0% Source: DIGESTYC Female participation rates in the labor market have increased, particularly in rural areas (Table 3.20). The crisis in agriculture, which made many women seek jobs in non-agricultural activities such as maquila, may be one of the reasons underlying the increase

45 Note that underemployment is not estimated for rural areas given the prevalence of seasonal work and the difficulties of defining the concept. 46 See Marques, José Silvério (2002) “El Salvador Social Safety Net Assessment”, World Bank, Report No. 24190-ES

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in female participation rates from 30 percent in 1991 to 35 percent in 2002. In contrast, male participation rates have declined in both urban and rural areas. One possible explanation for this decline is increased student retention in schools (lower drop-out rates). Overall, labor market participation increased marginally over the 1991-2002 period. According to DIGESTYC data, El Salvador’s occupational structure has not changed significantly over the last several years. The share of employers has declined slightly, from 7 percent in 1992 to 5 percent in 2002, while the share of wage earners has increased (Table 3.21). The decline in the proportion of non-remunerated family members and the increase in that of wage earners indicate an increase in the formalization of the labor relations. As expected, rural areas have relatively more self-employed workers, non-remunerated family members, and temporary wage earners than urban areas; on the other hand, there are relatively fewer permanent wage earners in rural areas than in urban areas. Table 3.21 Workers Occupied, by Occupational Category, 1992, 2002 (Percentage of the total occupied)

2002 Occupation 1992 National Urban Rural

Employer 7 5 5 4 Self employed 30 30 28 34 Non remunerated family member 11 9 6 15 Permanent wage earners 33 36 45 20 Temporary wage earners 14 16 13 21 Trainee 1 0 0 0 Domestic service 4 4 3 5 Source: DIGESTYC The relative stability in the shares of different occupational categories does not imply that the labor market is either static or inflexible, however. Many individuals may be changing occupational categories and these movements may cancel out. An approximation for the flexibility of the labor market is given by the duration of unemployment. The quicker the labor market is able to match labor market demand and supply, the shorter the duration of unemployment, and the more flexible the labor market. The relative importance of short term and long-term unemployment in El Salvador, LAC, the US and Europe is shown in Figure 3.8. Europe has a very rigid labor market: 42 percent of unemployment is long term (over 12 months), while only 11 percent of the unemployed find a new job within the first month of unemployment. In contrast, the US has a very flexible labor market with 7 percent long-term unemployment and 39 percent short-term unemployment. 47

47 IDB (2004), Table 1.2.

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Figure 3.8 Short and Long Term Unemployment, 1990-2002

0

10

20

30

40

50

Short Term (< 1 month) Long Term (> 12 months)

% o

f Une

mpl

oyed

El Salvador LAC US Europe

Source: Author’s estimates based on household surveys for El Salvador; and IDB (2004) Table 1.2 for other. Unemployment duration in El Salvador is close to that in the US and is about the average for LAC. Its long-term unemployment is 10 percent and its short-term unemployment 36 percent; the respective averages for LAC are 11 and 36 percent. Estimates of the average period of unemployment in El Salvador (Figure 3.9) indicate that it has tended to decline in the last several years, with unemployment duration averaging less than 2 months in recent years.

Figure 3.9 Unemployment Duration, 1991-2002

0

1

2

3

4

5

6

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

Mon

ths

with

out a

Job

Note: Data exclude those who are without a job because they are waiting to enter their first job or because they have given up looking for a job. The mid-point of the range of unemployment periods was assumed; for periods over one year, a 12-month unemployment period was considered. To arrive at average unemployment duration, these time frames were weighted by the number of workers declared to be unemployed. Source: Author’s estimates on the basis of household surveys.

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6. Gender There is evidence at the international level that gender inequality impedes growth and poverty reduction. Women play key roles in promoting productivity growth at both the household and national levels. In El Salvador, two factors have significantly influenced the role of women in society over the last several years: the war and emigration. The war gave many women the opportunity to take up jobs that otherwise would have been held by male combatants. The war also increased the number of female heads of households. Emigration further contributed to the increased number of female-headed households as most of the first migrants were young husbands or male companions. In this sub-section we briefly analyze the current situation of women in politics, education and the labor force. Women in politics The increased participation of women in the economy has been to some extent paralleled by their increased participation in politics. For instance, the number of congresswomen increased from 7 in 1990 to 9 in 2002 but their share was reduced from 12 percent to 11 percent due to the increase in the total number of members of the National Assembly in 1992 from 60 to 84. By comparison, in 2000 there had been 14 congresswomen or 17 percent of the total. Women have also occupied key government posts during the last three administrations, including the Ministry of Planning (now extinct) in the early 1990s, which led the stabilization, structural reform and national reconstruction efforts, and the Ministry of Education during three administrations, which led the successful education reform. A number of women are also mayors or municipal council members. For the first time, the recent elections chose a woman as Vice President of the Republic. Girls’ education The 2001 household survey indicated that gender inequity had been reduced significantly. Table 3.22 shows that the ratio of girls to boys in primary and secondary schooling is now quite similar as are the levels of literacy of young adults. The household survey indicates that in 2001, for the 6-14 age cohort, 31 percent of girls had no schooling, compared to 32 percent of boys; meanwhile, the same proportion of men and women (21.4 percent) had completed primary school. Table 3.22 Indicators of Gender, 1991, 1996, 2002 1991 1996 2002 National Urban Rural National Urban Rural National Urban Rural Ratio of girls to boys enrolled in primary and secondary school

93 84 97 96 95 97 97 99 93

Ratio of literacy rate of young women to young men (15-24 years)

99 99 99 100 99 102 100 100 101

Source: DIGESTYC’s Household Survey

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The government, through the Ministry of Education, has been very proactive in promoting the enrollment and retention of girls in schools. Two programs, Initial Education within the Framework of the Schools for Fathers and Mothers, supported by the World Bank, and the Education for Life program, executed with the support of the Secretary of the Family, are the leading initiatives in this area. Although we are not aware of any evaluation of these programs, they can be expected to have made some contribution to increasing girls’ participation in education. A recent review of gender issues in El Salvador by UNDP recognizes the progress made and stresses the need to “eliminate the barriers to the exercise of citizenship by women, improve the institutionalization of gender equity , promote policies with a gender focus, and involve men more actively.”48 Women in the labor market In the labor market, however, major differences remain between the pay levels of women and men. The International Labor Office’s data for manufacturing wages indicate that during 1990-95 women earned an hourly average of 7.5 percent less than men; this difference had increased to 23 percent during 1998-2000. Household survey data show that at the end of the 1990s, women’s wages were on average between 13 percent and 27 percent lower than men’s, depending on the number of years of schooling (Table 3.23). This differential increases with the number of years of schooling. In urban areas, the differential is smaller for those with 10-12 years of schooling but increases for those with more than 13 years of schooling. In rural areas, the differential is smaller for women without formal education or for those with more than 13 years of schooling. Note that this is consistent with the findings of the poverty correlates discussed in Section II, which indicated that the income penalty associated with households headed by females had disappeared, with the exception of households in the upper quintiles who presumably are also those with more years of education. Table 3.23 Ratio of Average Women’s Salaries to Men’s, by Years of Schooling and Region (Men Wages =100) Years of Schooling (Completed) 0 1-3 4-6 7-9 10-12 13+ Total Women/Men 87 77 74 76 80 73 Urban Women/Men 77 72 70 73 80 73 Rural Women/Men 87 76 76 78 70 86 Source: Marques, José Silvério (2002) “El Salvador Social Safety Net Assessment”, World Bank, Report No. 24190-ES, Table 3.21, on the basis of 1999 Household Survey data. The type of jobs taken by the female labor force influences these wage differentials. Household survey data show high female participation in manufacturing industry including maquila, commerce, hotels and restaurants, and domestic services. About two-

48 See UNDP (2004), Gender Equity in El Salvador, April 2004

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thirds of employed women work in these sectors. The average pay of manufacturing jobs or domestic services is below the average wage, but slightly above the average in the case of “commerce, hotels and restaurants”. However, the average wage of women in this latter sector is well below the average for men. This evidence is consistent with Arias’ (2004) findings on the determinants of poverty. According to his estimates, female-headed households faced an average per capita income penalty of about 13 percent in the early 1990s, which had fallen by the beginning of the 2000s. However, female-headed households in the upper quintiles of the distribution, presumably with a greater number of years of schooling, continued to face per capita incomes 6-9 percent lower than their male-headed household counterparts. In sum, gender discrimination appears to be on the decline in the education sector but still remains a serious problem in the labor market, particularly for women with higher education.

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IV. Trade-Offs Between Growth and Pro-Poor Growth The previous Sections have shown that while growth helped to reduce poverty substantially during the 1990s, inequality appears to have increased slightly over the same period. Some of the policies that led to growth may have also increased inequality. Since increased inequality works against poverty reduction, there may exist some trade-offs between pro-growth policies and pro-poor policies. In this section we explore how macro stabilization and trade liberalization policies have impacted poverty and inequality. While stabilization policies are necessary for growth, they impact wages and unemployment differently and therefore also affect poverty and inequality in a different way. Wage flexibility may help spread the cost of the adjustment, while unemployment has a more unequal effect. In the case of El Salvador, adjustment has occurred mostly through changes in wages rather than through unemployment. With the dollarization of the economy, the brunt of future adjustments may fall on employment. Thus, labor market regulations that detract from wage flexibility will become more costly. Meanwhile, trade liberalization is expected to promote growth over the longer term, but may in the short term adversely impact income distribution and therefore poverty reduction. In the case of El Salvador, there is no evidence of such a negative short term impact. 1. Macro Stabilization Policies Fiscal, monetary and exchange rate policies that seek to reduce high rates of inflation and unsustainable external indebtedness are necessary for growth in the short and long run. Investors avoid countries that face major macro disequilibria or recurrent macro crises. Macroeconomic stability also benefits the poor. Lopez (2004) reports several studies that corroborate this assertion. For instance one study, using panel data for 38 countries, finds that high inflation tends to worsen inequality and increase poverty; other authors find that high inflation and macroeconomic instability are negatively associated with the incomes of the poor.49 Lopez indicates that inflation is a “penalty for the poor and countries with lower inflation have a tendency to be more equal. Given that low inflation is also positively

62

49 See Lopez, Humberto (2004) “Pro growth, pro poor: Is there a trade off?”. Processed, World Bank, page 12

Figure 4.1 Inflation Rate and the Nominal Exchange Rate

88.28.48.68.8

99.29.49.69.810

1990

1992

1994

1996

1998

2000

2002

Perc

enta

ge

0

5

10

15

20

25

30

Exchange Rate (left) Inflation Rate

Source: Central Bank

associated with faster growth, polices aimed at reducing inflation would also belong to the win-win category.”50 As discussed in Section I, the high inflation experienced by El Salvador in the early 1990s originated from expansionary fiscal and monetary policies during the 1980s and this was corrected by gradually reducing the fiscal deficit, tightening monetary policy, and anchoring inflationary expectations to a pegged exchange rate since 1993/94, and dollarizing the economy in 2001. Inflation declined from over 25 percent in the early 1990s to less than 5 percent in the latter 1990s (Figure 4.1). The labor market is an important vehicle through which adjustment to a different level of inflation takes place, and it impacts household income and therefore poverty and income distribution. Within the labor market, there is a trade-off between adjustment of real wages and adjustment of employment or unemployment. In general, the more elastic the supply of labor, the more adjustment takes place through changes in employment compared to changes in wages. In contrast, the more inelastic the supply of labor, the more adjustment takes place through changes in wages. These adjustments will not create problems if everybody who wishes to be employed at the going wage rate is indeed employed. However, if wages cannot adjust downwards to their equilibrium level because of minimum wage regulations or other factors, then people who would be prepared to work at lower wages are left without a job, and unemployment ensues. Usually those who are first let go when firms downsize are lower skilled and lower paid employees who are easier to replace in labor abundant countries. In contrast, those who keep their jobs receive a salary above market clearing levels. Thus, wage flexibility helps to spread the cost of adjustment among workers and particularly benefits the less skilled, lower wage workers. Table 4.1 Growth Elasticities of Real Wages, Employment and Underemployment, 1991-2002 Employment Unemployment Real Wage El Salvador: 1991-2002 0.78 -0.05 0.7 LAC: 1990-2000 0.24 -0.028 1 Developed Countries: 1990-2000 0.5 -0.6 0.3 Source: El Salvador: own estimates based on household survey. Latin America and Developed Countries, IDB (2004) “Goods Jobs Wanted, Labor Markets in Latin America”, page 122. The IDB reports that in developed countries, a 1 percent drop in GDP would lead to a 0.6 percentage point increase in unemployment, a 0.5 percentage point decrease in employment, and a 0.3 percentage point decrease in wages (Table 4.1). In Latin America, growth elasticities of employment (0.24 percentage points) and unemployment (-0.028 percentage points) are smaller (in absolute value) but the growth elasticitity of real wage is larger (1 percentage point). In El Salvador a 1 percent drop in GDP would lead to a 0.8 percentage point decrease in employment, a 0.05 percentage point increase in unemployment and a 0.7 percentage point decline in the real wage. LAC and El Salvador adjust more via changes in real wages than in unemployment. 50 Lopez (2004)

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This situation may be changing, however. In Latin America during the 1990s, the reduction in inflation that resulted from stabilization policies impacted the labor market. During the period of high inflation, adjustment took place through change in real wages. With the reduction in inflation, this adjustment mechanism has become inoperative. The stabilization and the disinflation process may have affected how wages, employment, and unemployment respond to output shocks. In countries that went through a disinflation process, growth elasticitiy of employment appears to have increased and growth elasticity of wage to have decreased. Argentina is an interesting example because, like El Salvador, it adopted a pegged exchange rate regime in the early 1990s (including a hard peg or currency board since 1991 until the default in December of 2001 when the currency board was scrapped); its inflation rate declined from almost 200 percent in late 1989 to lower single digits between 1991 and 2001. According to the IDB, Argentina’s growth elasticity of wage in the 1980s was about 10 times higher than in the 1990s, and growth elasticity of employment in the 1990s was twice as large as in the 1980s. Thus, the reduction in inflation may have reduced wage flexibility and increased employment volatility. Indeed, the Tequila crisis that began in Mexico in 1993/94 and spread throughout Latin America affected Mexican workers mostly through lower wages but impacted Argentinean workers mostly through higher unemployment. The impact on poverty was higher in Argentina than in Mexico since unemployment affected mostly those with lower skills and lower incomes. According to the IDB, after the Tequila crisis, poverty increased by 20 percent in Mexico (7 percentage points) and by more than 50 percent in Argentina (9 percentage points), notwithstanding the fact that the drop in total output was much larger in Mexico. In the case of El Salvador, dollarization has brought several advantages, although its proponents underplayed the possible costs. Among the advantages of dollarization is that it removes exchange rate risk and therefore make it possible to reduce interest rates. On the other hand, in addition to exchange rate policy, dollarization takes away from the authorities the option of conducting monetary policy. For the proponents of dollarization this is a plus, as they point to the irresponsible use of these instruments by some governments in the past; for others, however, this could make future adjustments more painful, since the government is left with only fiscal policy, which is usually quite inflexible in the short term. Thus, the brunt of future adjustments may fall on the labor market and, in particular, on employment.51 In sum, while stabilization policies are necessary for growth, the adjustment process may impact more or less strongly on wages and unemployment, and therefore have a differentiated impact on poverty and inequality. Wage adjustments may help spread the cost of the crisis, while unemployment may have a more unequal effect. The dollarization of the economy in El Salvador implies that the brunt of future adjustments may fall on the labor market and in particularly on employment. Thus, it makes labor market regulations that detract from wage flexibility more costly.

51 One should not rule out the possibility that nominal dollar wages could decline in the future.

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2. Trade liberalization El Salvador went through a major trade liberalization reform in the early 1990s. In June 1989, there were 25 tariff rates ranging from 0-290 percent. By April 1990, the number of rates had been reduced to 9 and the range to 1 to 50 percent; by December 1994 there were 3 tariff rates of 5, 10 and 20 percent. Most non-tariff barriers were eliminated when El Salvador became a member of the WTO in 1994. In 1995, the government announced that it would reduce tariffs to a 6 to 1 percent range. Given pressure from various interest groups, the plan was scrapped. Since then, there has been some backtracking as tariffs have been increased to favor particular groups. Also, there continues to be a lack of competition in the importation of some key agricultural inputs. Nevertheless, the great majority of tariff positions today are between 0-15 percent: final consumption goods have a tariff of 15 percent; intermediate goods 10 percent if produced in Central America and 5 percent if not produced in Central America; petroleum 1 percent; and raw material and capital goods 0 percent. Some goods with tariffs exceeding 15 percent, include cheese, sugar, and ethylic alcohol. These goods have the maximum 40 percent tariff permitted by the WTO. Processed rice has a tariff of 35 percent. Most cross-country and individual country studies have concluded that there is a positive relationship between economic growth and trade openness. Specific studies for Latin America have arrived at the same conclusion.52 Conversely, no study has suggested that opening up an economy to trade has adverse effects on growth. The relationship between trade openness and inequity is less clearly established, however, particularly in the short run. According to classic Ricardian comparative advantage theory, trade liberalization should stimulate demand for the factor of production that is abundant in the country, increasing the remuneration of this factor. If labor is the abundant factor and there is plenty of unskilled labor, then the price of labor should increase and the wage differential between skilled and non-skilled workers should decline. Therefore, the theory would predict that trade liberalization in a labor abundant country should reduce inequality. However, in the real world, initial conditions and the path of adjustment may yield a somewhat different outcome. For instance, if the pre-liberalization regime was characterized by protection for unskilled labor, trade liberalization may have a negative impact on poverty levels, at least in the short run. Also, the rapid elimination of protection provided by import tariffs and quotas may lead to the destruction of jobs in protected industries at a much faster pace than the economy is able to create new jobs, thus increasing unemployment. The policy implication is not to avoid trade liberalization, but to effectively address any potential short-term costs. As Lopez (2004) indicates, the empirical evidence on the impact of trade liberalization on inequality is divided: some authors find that trade openness positively affects income distribution while other authors find the opposite result. Lopez’ own findings are that more trade openness is more conducive to inequality.

52 See IDB (2004), page 143.

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The negative impact of trade liberalization on income distribution could come through an increase in unemployment, a decline in wages, or a substitution of “good” jobs for low-paying, low-quality jobs. After a careful review of the empirical evidence, a recent IDB study concluded that for Latin America:

• Trade liberalization has not had much effect on employment allocation and on unemployment. The theory of comparative advantage predicted that resources would move massively toward activities that were potentially more efficient and more intensive in the use of the most abundant resources, but did not happen. The fact that little employment was reallocated between sectors does not mean that companies or workers did not suffer traumas due to liberalization or that the labor market lacked the vitality to respond to such an important policy change. On the contrary, the continual gross creation and destruction of jobs in each company and the appearance and disappearance of companies in each industry were mechanisms that helped adjust production methods and organization, change the composition of production, and reorient production toward particular markets in response to liberalization.

• Trade liberalization seems to have lowered real wages but it does not seem to explain a widening wage gap between skilled and unskilled workers. Again this is not what neoclassical analysis would predict. Liberalization should lead to more productive use of all resources, including labor, elimination of rents that favor capitalists, and an increase in wages. What seems to have happened (although there is no direct supporting evidence) is that workers were sharing in these rents and were then forced to give them up in order to hold onto their jobs. The fact that in many countries tariffs (and import controls) were higher for those sectors that were more labor intensive (especially in unskilled labor) also helps to explain this paradox. As for the wage gap, this effect may actually be less pronounced than is currently believed, and the explanation may be found more in the area of technology and other little understood mechanisms than in the mechanics of relative prices directly associated with international trade.

• The new jobs generated in the export sector are comparable to alternative jobs in quality and pay, or are even better. Contrary to what is often claimed, the increase in labor-intensive exports such as maquila (even before liberalization) has helped to raise workers’ wages and improve their living conditions.

In the case of El Salvador, there is no evidence of major reallocation of resources on the employment front as a result of trade liberalization. Table 4.2 shows that employment has increased faster in non-traded sectors than in traded sectors, with services providing the bulk of new jobs. Maquila jobs have also increased significantly from 6,500 in 1991 to 50,000 in 1996 and to over 100,000 currently, but this is not the result of trade liberalization since this sector is an outgrowth of the protectionist period. There is no evidence of major new unemployment being created by trade liberalization. In agriculture, some relatively high-cost products continue to be protected. The World Bank’s Rural Development study analyzed the level of protection of the agricultural sector after major trade liberalization had taken place in the early 1990s, and concluded that the “level of protection was not low”. In 1995, effective rates of protection varied from 35 percent for chicken to 15 percent for milk; sugar, rice, and sorghum had rates between these two levels. The only exception to the above statement was white maize, whose effective rate of protection was only 3 percent.

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Table 4.2 Value Added and Employment in Traded and Non-traded Sectors, 1992-2001 (Simple average of annual rates) Tradables Non-Tradables Value Added 3.6 4.1 Employment 0.3 6.0 Source: FUSADES (2003) “Informe de Desarrollo Economico y Social: Competititvidad para el Desarrollo”, based on Central Bank and household surveys data In manufacturing there is some evidence that many import substituting firms begun directly importing the products with which they previously competed, bought production licenses, or sold their operations to competitors, strategies that reduced the need to close down operations and lay off workers. Trade liberalization was accompanied by investment to non-tradable activities and particularly ino services as shown in Table 4.2. On the wage front, IDB estimates for ten countries in Latin America indicate that for tradable sectors (agriculture and industry), the impact of a 1 percent increase in trade penetration (the sum of imports and of exports to GDP) could imply a decline of 0.2 percent in real wages; no significant impact is found for the non-tradable sectors (Table 4.3). In El Salvador, the increase in trade penetration is associated with an increase in real wages, as theory would predict. In fact, a 1 percent increase in trade penetration would increase real wages by about 1 percentage point; the impact is positive for both tradables and nontradables. Table 4.3 Trade Penetration Elasticities of Real Wages, 1991-2002 a/

Effect of 1 percent increase in Trade Penetration

El Salvador, 1991-2002 Trade penetration effect on economy wide wages 0.964 Trade penetration effect on tradable wages 0.445 Trade penetration effect on nontradable wages 0.738 Latin America , 1990-2000 Trade penetration effect on tradable wages -0.204 Trade penetration effect on nontradable wages Not significant a/ the dependent variable is (log) real wages. The independent variable is trade penetration or the sum of imports and exports over GDP. Source: For El Salvador, own estimates based on household survey data. For Latin America and Developed Countries, IDB (2004), page 154 (Table 5.7). According to the IDB (2004), there is a consensus that the impact of trade liberalization on the wage gap between skilled and unskilled workers has been modest and indirect in Latin America, which possibly reflects the influence of technological change. For El Salvador, during 1991/2002 the wage gap between the extremes increased slightly (for those with more than 13 grades of education compared those with no schooling), but for other groups (grades 9-12 and 1-6 compared to no schooling) the gap declined (Table 4.4). As El Salvador prepares to implement major free trade agreements with the US, Canada and eventually the EU, developments whose potential benefits are unlikely to be disputed by the majority of economists, one wonders how the government is going to help

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facilitate the adjustment and/or compensate the losers. The usual problem with reforms is that the winners immediately reap the benefits while the losers have to make do with promises that support will some day be forthcoming. It is not surprising, therefore, that most of the reforms that are approved are those in which the winners are powerful. It is also not surprising that skepticism about reforms has increased. Table 4.4 Average Monthly Wage, by Years of Schooling, 1991, 1996, 2002

1 No

schooling

2 Primary (grades

1-6)

3 Secondary

(grades 9-12)

4 Higher Education

(13 grades and over)

4/1 3/2 3/1 2/1

US$ Ratio 1991 69.31 97.39 143.44 281.42 4.06 1.47 2.07 1.41 1996 85.87 137.13 218.91 392.84 4.57 1.60 2.55 1.60 2002 126.37 170.34 243.78 575.73 4.56 1.43 1.93 1.35 Source: Own estimates based on the household surveys In sum, in the case of El Salvador, there is no evidence that trade liberalization provoked major unemployment, or that it contributed to a decline in real wages; on the contrary, and in contrast with the rest of Latin America, it seems that there was a positive relationship between real wages and trade liberalization, both in the tradable and non-tradable sectors. There is also no clear-cut evidence that trade liberalization contributed to widening the wage differentials between skilled and unskilled workers. Therefore, those who oppose trade liberalization on the grounds of its (short term) costs do not appear to have a case.

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V. Conclusions and Recommendations For Policy Making

After experiencing a major conflict during the 1980s, El Salvador achieved substantial poverty reduction during the 1990s. The preceding analysis indicates that the following elements have contributed to this outcome: The successful implementation of the Peace Accords, the focus of the National

Reconstruction Program on the poor and demobilized, and the gradual building of democratic institutions created a favorable environment for growth and social investment;

Successful stabilization and structural adjustment reforms led to high growth rates, particularly in the first part of the 1990s, and a drop in inflation; the rapid growth in the early 1990s raised many poor people closer to the poverty line, as evidenced by a decline in the poverty gap. The growth that followed, though less rapid, helped to push many of them above the poverty line;

A flexible labor market helped reduce the impact of adjustment on unemployment which usually affects poor households most severely; adjustment via wages helped distribute the cost of adjustment among different income groups;

Higher public social/basic infrastructure expenditures were made possible by growth (and the consequential rise in fiscal revenues) and a new political consensus on the importance of education and other social investment;

Gender discrimination in education fell; Rural incomes increased because of non-agricultural job opportunities (micro

enterprises, maquila, services) supported by infrastructure investment and increased human capital formation, which in turn permitted the rural poor to take advantage of opportunities that presented themselves; and

Higher remittances made it possible to finance human capital formation and physical investment in micro enterprises.

On this basis, the following are the key areas that should be considered to make growth in El Salvador more pro-poor in the future. Restore rapid growth Forty-three percent of El Salvador’s population still lives in poverty. Growth has slowed in recent years and this has reduced the rate of poverty decline. Therefore, the first order of business should be to restore rapid growth. Our analysis of the determinants of growth indicates that this requires investment in education and infrastructure, while keeping the economy open. The need to continue to invest in education and basic infrastructure is now an accepted priority in El Salvador, though the political consensus required to mobilize the resources to finance these needs may be lacking. Keeping the economy open is a more controversial proposition. As discussed in Section IV, trade liberalization is expected to promote growth over the longer term, but in the short term it may adversely effect income distribution and therefore poverty reduction. In the case of El Salvador, there is no clear

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evidence of an adverse short-term impact of trade liberalization during the 1990s. Nevertheless, this does not mean that the future impact will be negligible or non-existent. Indeed, with the new wave of free trade agreements with the US and Canada and possibly a future agreement with the EU, there is a need to ensure that any possible short-term negative impact is minimized. To minimize the possible negative effects of trade liberalization or trade agreements in countries with very unequal distribution of income such as El Salvador, the World Bank in addition to an appropriate sequencing of policies and deepening of markets to provide more equal opportunities for potential market participants, recommends that countries: 53

i) Improve the distribution of assets because assets improve the household’s or individual’s capacity to take advantage of new opportunities;54 and ii) strengthen social safety nets, because the process of adjustment can lead to temporary or permanent income losses.

Income and asset distribution is still highly concentrated in El Salvador and improving the distribution of assets could be thought to require a new round of land redistribution. But the World Bank’s Rural Development Study indicates that considering the “scarcity of farm land in El Salvador … these estimates underline the unreasonableness of relying primarily on land redistribution to alleviate poverty among the rural poor and the importance of non-land factors”.55 Thus it appears that the appropriate route to improve asset and income distribution is investment in human capital and basic infrastructure, which makes it possible for the poor to take advantage of economic opportunities and exit poverty. For those who face temporary income losses or are not able to help themselves, there is a need to strengthen the social protection system as discussed below. Maintain a flexible labor market With the dollarization of the economy it is expected that in the future the labor market (wages, employment and unemployment) will absorb the brunt of any required adjustment, as the exchange rate and other key prices in the economy are mostly determined outside the country; interest rates are mostly determined in the US and product prices in international markets, given increased capital mobility and trade liberalization. Since inflation is now at very low levels, any required adjustment in the future should mostly impact employment and unemployment, because nominal wages are usually inflexible downwards. To facilitate adjustment, labor market flexibility should be maintained. As already mentioned, wage flexibility may help spread the cost of adjustment, while unemployment has a more unequal effect. Labor market flexibility means giving employers and 53 de Ferranti et al (2003), Chapter 8, page 9 54 The poor usually do not have the instruments to manage risk, and this impedes them from getting involved in more risky but potentially more profitable activities, which in turn hinders them from gradually moving out of poverty. See Holzmann, Robert and Steen Jorgensen (2000) “Social Risk Management: A New ConceptualFframework for Social Protection and beyond,” World Bank. 55 World Bank (1998a) page 43.

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employees alike “efficient mechanisms with which to adapt to the business cycle… It is important to ensure that the mechanisms used to set minimum wages are based not only on purchasing power, but also on considerations related to productivity and employment. If managed effectively, these policies can be consistent with greater support for workers. This can be done through both reduced informality and the spin-off effects of a generally more supportive approach to labor in terms of institutional functioning.”56 Strengthen the social protection system The existence of adequate social protection systems (social insurance and social assistance programs) will facilitate the adjustment process. Since the future consequences of adjustment may fall mainly on employment, this raises the question of whether current unemployment policies are appropriate. El Salvador, like many other Latin American countries, does not have unemployment insurance but instead has a severance payment system under which the labor code mandates employers to pay an end-of-service gratuity to workers they fire without a “justified” cause (that is, for nondisciplinary reasons). Severance pay is a multiple of the worker’s salary; in some countries, it cannot exceed a specified amount or multiple of the worker’s salary, and in others the formula is different when job separation reflects economic factors. Employers are usually not mandated to set aside resources to pay the end-of-service gratuity. In El Salvador, Chapter VII, Article 58 of the Labor Code establishes that employers must give employees fired without a justified cause severance pay equivalent to 30 daily basic salaries for each month of work. Severance payments should be evaluated to see whether they are preferable to alternative mechanisms such as individual savings accounts. These are a “funded” version of the severance pay program. Workers have individual accounts to which some percentage of their salary is transferred on a regular basis. In the event of job separation, whether voluntary or involuntary, workers can draw resources from their accounts. Any resources left in these accounts at retirement can be used toward old-age pensions. Workers can also “borrow” from their accounts under specific circumstances. A program along these lines has existed in Brazil for more than three decades. More recently, Colombia has replaced its severance pay program with fully funded individual accounts of this type. Unlike unemployment insurance and severance pay, this program involves no net transfer of resources to workers who lose their jobs. For informal sector workers, the main support mechanism when they become unemployed is public works. Training and re-training programs to facilitate insertion into the labor market are important for both formal and informal market workers.57 Social assistance programs should be strengthened as argued in the Word Bank’s recent Social Safety Net Assessment and related policy note.58 Specifically, there is a need to strengthen programs for the most vulnerable groups such as poor children, the disabled

56 de Ferranti et al (2003), Chapter 8 pages 14-15 57 de Ferranti et al (2000), page 91. 58 World Bank (2002a) and World Bank (2002c)

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and the elderly, while at the some time building institutional capacity and cost effective delivery models. Act on the drivers of poor households’ income growth To make growth more pro-poor in El Salvador, there is a need to act on the drivers of the income growth of the poor. Analysis of poverty correlates and of rural household income growth indicates that these are education and basic infrastructure. Investment in these areas improves the access of the poor to assets and services that enable them to take advantage of economic opportunities such as accessing non-agricultural employment and micro-enterprises. The analysis of the determinants of growth in Section II indicated that to increase the country’s long term growth potential, El Salvador must improve its infrastructure, invest in education, and continue to open its economy. From the analysis of FUSADES’ rural household panel data in Section III, we have identified non-agricultural employment and the possibility of establishing micro-enterprises, both of which require improved human capital and access to basic infrastructure, as the key drivers of income growth for the poorest households. These micro determinants of household income growth are therefore consistent with the macro findings, both stressing the need to invest in education and infrastructure in order to promote market participation. Tailor policy interventions to the poorest households Finally, an important implication of the analysis is that the poorest households may face specific constraints that require detailed analysis and tailor-made policy interventions. Arias’s analysis of poverty correlates for the different quintiles (Section II) concludes that household variables that are common in survey data may not fully capture variations in socio-economic performance and in the likely impact of public interventions. Our own analysis in Section III of the drivers of income growth finds that the poorest households have income drivers that differ from other households. Thus, the design of pro-poor growth policy interventions for the poorest households requires a detailed analysis of the specific drivers of their income growth. Pro-poor investment agenda and financing

What specifically should be done in education, basic infrastructure, and social protection? What should comprise a pro-poor growth investment program? The recent Public Expenditure Review (PER) and Poverty Assessment (PA) prepared by the World Bank indicate that in education the government should focus on measures to: i) increase the coverage in the 3rd cycle of basic education and in secondary education, particularly for the lowest income students; and ii) improve education quality at all levels of education. At the same time, there is a need to ensure that all people have access to quality healthcare as another key element of building the human capital of all Salvadorans. While there have been important advances in recent years, it is estimated that nearly one-quarter of the population, mostly the poor, still has limited or no access to public health services.

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In this case there is a need to provide access to a minimum package of healthcare/nutrition services to those who still lack regular health services

On basic infrastructure, access to potable water and suitable sanitation facilities in rural areas is increasingly recognized as an important health input, critically affecting the incidence of disease and infant and child mortality. According to the Poverty Assessment, a key policy challenge in the water sector will be to reinitiate and complete water sector reforms and ensure that all Salvadorans have access to safe water and sanitation. Improved access to all weather roads reduces isolation, lowers the costs for goods and services (including accessing education and health facilities), and increases access to markets, enabling people to better take advantage of emerging economic opportunities. Efforts to ensure that the poor benefit from rural road investments should focus on: i) continuing to expand and improve the rural road network; and ii) continuing development of local and municipal capacity to build and maintain rural road infrastructure. On social protection, according to the World Bank’s Social Safety Net Assessment of 2002, the impact of existing programs is modest due to low levels of public spending. Within the existing budget envelop, resources tend to be under-allocated to priority risk groups. To ensure that El Salvador’s safety net contributes appropriately to sustainable poverty reduction, it will be important to: i) develop a coherent, strategic vision for the sector; ii) establish an institutional mechanism for coordinating programs, that would support greater emphasis on high priority, high return areas (e.g., early childhood interventions), and identification and scaling up of cost-effective models; iii) continue recent efforts to strengthen the information base for program targeting and programs’ abilities to monitor and evaluate their impact. How much would cost such pro-poor growth package? The PER and PA estimate that the cost would be equivalent to between 3.2 percent and 3.6 percent of GDP (Table 5.1). This cost will be distributed gradually over several years. How could this pro-poor growth investment package be finance? Within the current fiscal envelop, there is very little margin to increase social spending. In 2003, the fiscal deficit was about 3 percent of GDP and the public debt, 41 percent of GDP. El Salvador has a low tax burden (12.1 percent of GDP in 2003) with most government tax revenues originating from the VAT or value added tax (53 percent of total tax revenues), income taxes (29 percent), import duties (10 percent); and excise taxes on alcoholic beverages, tobacco products, and fuels (4 percent). Import duties have declined in the last several years owing to trade liberalization and are expected to continue to decline; indeed, it is estimated that the recently negotiated trade agreement with the US (CAFTA) will cost the treasury in import duties about 0.5 of GDP when all tariff elimination commitments are phased in over a 10 to 20 year period. The government has announced that it plans to increase the excise taxes on tobacco and alcoholic beverages to finance the health sector. It plans to create a health fund to facilitate the access of the poor to quality health services. On the other hand, the PER reports that a number of technical proposals have been made in recent years to improve collection and eliminate distortions in the personal and corporate income tax regimes.

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Some of these include eliminating the accelerated depreciation for investment, eliminating some deductions to personal and corporate income, and taxing interest income. Such changes could yield 0.4 percent of GDP from personal taxes and 0.6 percent in corporate taxes. Table 5.1 : Estimated Costs of a Pro-Poor Growth Investment Package Sector

Objective

Current Public Spending as a Percentage of

GDP a/

Target Public Spending as a Percentage of

GDP b/

Incremental Spending

Percentage of GDP

Education 100 percent completion of basic education (3rd cycle); 70 percent net secondary school enrollments by 2015

3.2%

5.0%

1.8%

Health Provide access to a minimum package of healthcare/nutrition services to those who still lack regular health services

3.8%

4.0-4.1%

0.2-0.3%

Water Ensure access to safe water and sanitation among the poor who currently lack access

0.8% c/

0.9-1.1% d/

0.1-0.3%

Roads Rehabilitating an additional 1,000 kilometers of rural roads over 5 years; annual maintenance of 3,000 kilometers of rural (secondary and tertiary) roads

1.3%

1.4-1.5%

0.1-0.2%

Safety Net Ensure access to basic service by the poorest, most vulnerable Salvadorans

0.5%

1.5%

1%

Total 9.6% 14.8-13.2% 3.2-3.6%

a/ 2000-2003, unless otherwise noted. b/ World Bank staff estimates, except where noted. c/ 1993-2002 average. d/ National Committee of Water and Sanitation Institutions (CONIAPOS) estimate (1994). Source: World Bank’s 2004 Poverty Assessment and Public Expenditure Review. Any decision to increase taxes to finance a pro-poor growth package will need to take into account its incidence on the poor. A recent study on tax incidence in El Salvador by Acevedo and Orellana (2003) found that in general the system is regressive. As Table 5.2 indicates, the concentration of income measured by the Gini is greater after the payment of taxes compared to the situation before taxes (51.68 versus 50.19). The only two taxes that are not regressive are the personal income tax and the gasoline tax. The most regressive taxes are the VAT on domestic goods and the tax on cigarettes. El Salvador’s corporate income tax rate is a flat 25 percent and the marginal personal income tax rate varies from 10 percent to 30 percent. Most poor do not pay income tax because they earn less than the cut off exemption amount of US$ 2514 (the extreme poverty line for the typical urban household was US$ 1524 in 2002) or are in the “informal sector”. The VAT rate is currently 13 percent. Most VAT exemptions have been eliminated including in 2000 those on medicines, basic grains, milk, fruits and vegetables. In 2004, the National Assembly approved a new exemption for agricultural inputs but the government has challenged it in the Supreme Court, which still has to resolve. Micro businesses (assets lower than US$ 2285 or annual sales lower than

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US$5714) are exempt from the VAT. The VAT has relatively high productivity. 59 According to the PER further revenues from this source would likely require higher rates. Each percentage point increase in the VAT rate could yield about 0.3 percent of GDP in revenues. Table 5.2 Concentration Indices

Gini Before Taxes

Gini of Tax Payments

Kakwani Index a/

Gini After Taxes

Income Tax 50.19 59.67 9.48 50.01 Personal 50.19 64.05 14.34 50.04 Corporate 50.19 33.46 -16.73 50.83 VAT 50.19 26.62 -23.57 51.54 Domestic 50.19 19.22 -30.97 51.02 Imports 50.19 33.46 -16.73 50.67 Excise Taxes 50.19 38.61 -11.58 50.25 Alcoholic beverages 50.19 34.76 -15.43 50.21 Non Alcoholic beverages 50.19 30.51 -19.68 50.21 Cigarettes 50.19 19.35 -30.84 50.21 Gasoline 50.19 58.07 7.88 50.18 Import Taxes 50.19 33.46 -16.73 50.35 Total 50.19 33.30 -16.89 51.68 a/ Gini of Tax Payments minus Gini Before Taxes Source: Acevedo, Carlos and Mauricio Orellana “El Salvador- Diagnostico del Sistema Tributario y Recomendaciones de Política para Incrementar la Recaudación”, IDB, Diciembre 2003 To raise the revenues required to finance the pro-poor growth package the authorities should give priority to reduce tax illusion and evasion, particularly in the income tax. Other additional tax revenue measures that the authorities may consider should ensure a net positive resource transfer to the poor.

59 According to the Public Expenditure Review, in 2003 VAT revenue productivity, defined as VAT revenue as a share of domestic consumption divided by the VAT rate was 0.49 in El Salvador, higher than the ratio of 0.46 in Central America and 0.43 in Latin America.

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References

Acevedo, Carlos and Mauricio Orellana “El Salvador- Diagnostico del Sistema Tributario y Recomendaciones de Política para Incrementar la Recaudación”, IDB, Diciembre 2003 Arias, Omar. 2004. “Poverty in El Salvador During the 1990s: Evolution and Characteristics”, processed, World Bank. Calvo, Guillermo, and Frederic S. Mishkin. 2003. “The Mirage of Exchange Rate Regimes for Emerging Market Economies”, Working PaperNo.9808, Cambridge, MA. Campos, Roberto Rivera. 2000. “La Economia Salvadoreña al final del Siglo: Desafios para el Futuro”. FLACSO. Cord, Louise, Ramon Lopez, Monika Huppi, and Oscar Melo. 2003. “Growth and Rural Poverty in the CIS7: Cases Studies of Georgia, the Kyrgyz Republic and Moldova”, processed, World Bank. Datt, Gaurav and Martin Ravallion (1992), “Growth and Redistribution Components of Change in Poverty Measures: A Decomposition with Application to Brazil and India in the 1980s”, Journal of Development Economics, 38, North-Holland. de Ferranti, David, Guillermo Perry, S. Gill, and Luis Serven. 2000. “Securing our Future in a Global Economy”, World Bank de Ferranti, David, Guillermo Perry, Francisco Ferreira, and Michael Walton. 2003. “Inequality in America Latin and the Caribbean: Breaking with History?”, World Bank, Latin America and Caribbean Studies, Advance Conference Edition. Government of El Salvador. 2002. El Salvador - National Reconstruction Program, Report to the Consultative Group Meeting, Ministry of Planning. Government of El Salvador. 2004. “El Salvador - Progress on the Millennium Development Goals, First Country Report”. FUSADES. 2003. “Informe de Desarrollo Economico y Social: Competititvidad para el Desarrollo”. Holzmann, Robert and Steen Jorgensen. 2000. “Social Risk Management: A new conceptual framework for Social Protection and Beyond”, World Bank, February, 2000. Klasen , Stephan . 2001. “In Search of the Holy Grail: How to Achieve Pro- Poor Growth?”, processed, July 2000. IDB. 2004. “Goods Jobs Wanted, Labor Markets in Latin America”. ESPR.

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Liévano de Marques, Mirna. 1995. “El Salvador- Un País en Transición”, ESEN. . Loayza , N., Fajnzylber, P., Calderon, C. 2002. “Economic Growth in Latin America and Caribbean: Stylized Facts, Explanations, and Forecasts,” World Bank, Lopez, Humberto. 2001. “The Cost of Armed Conflict in Central America”, LCR; World Bank; Lopez, Humberto. 2003a. “The Economic and Social Cost of Armed Conflict in El Salvador”, CPRU Dissemination Note Number 8, World Bank, January 2003. Lopez, Humberto. 2003b. “Growth in El Salvador”. Background Paper for the country Economic Memorandum,. World Bank. Lopez, Humberto. 2003c. “El Salvador: Towards the MDGs”, processed, World Bank. Lopez , Humberto. 2004. “ Pro growth, pro poor: Is there a trade off?”. World Bank. Marques, José Silvério (2002) “El Salvador Social Safety Net Assessment”, World Bank, Report No. 24190-ES Marques, José Silvério and Ian Bannon. 2003. “Central America: Education Reform in a Post-Conflict Setting, Opportunities and Challenges”, World Bank, Conflict Prevention and Reconstruction Unit, Working Paper 4, April 2003. Sanfeliu, Margarita B. and Mauricio Shi. 2003. “ Dinámica del Ingreso Rural en El Salvador”, processed, FUSADES. Wodon, Q., R.; Castro- Fernández, G. Lopez: Acevedo, C. Siaens: C. Sobrado, and J. P. Tre, . 2001. “Poverty in Latin America: Trends (1986-98) and Determinants”. LCR, World Bank. World Bank. 1994. “El Salvador: The Challenge of Poverty Alleviation”. Report No. 12315-ES. World Bank. 1998a. “ El Salvador- Rural Development Study”. World Bank 1998b. “World Bank’s Experience with Post-Conflict Reconstruction.” OED. World Bank. 2002a. “El Salvador: Policy Notes on Poverty and Social Spending”, Report No. 24191-ES, Volume I World Bank. 2002b. “El Salvador: Policy Notes on Elements to Strengthen the Social Safety Net”. Report No. 24191-ES, Volume II.

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World Bank. 2002.c “ El Salvador: Social Safety Net Assessment”. Report No. 24190-ES. UNDP. 2003. “El Salvador: Human Development Report”. UNDP. 2004. “Gender Equity in El Salvador”.

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Annexes

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Annex 1

Pro-Poor Growth Rates Estimates Tables A.1.1-A.1.3 present the summary estimates of the Ravallion and Chen (2003) pro-poor growth rates from the adjusted income household survey data.

Table A.1.1 Pro-Poor Growth Estimates, 1991-2000 National Urban Rural Total Poverty Growth in mean income 5.17 5.07 1.44 Growth in median 5.21 4.97 2.26 Mean Percentile Growth Rate 4.64 5.1 1.62 Pro-Poor Growth Rate 4.14 5.05 1.33 Growth up to the percentile:

10 0.87 5.18 -0.74 15 1.76 5.2 -0.5 20 2.37 5.22 -0.37 25 2.82 5.23 -0.3 30 3.16 5.23 -0.11

Source: World Bank staff estimates

Table A.1.2 Pro-Poor Growth Estimates, 1991-1995 National Urban Rural Total Poverty

Growth in mean income 6.62 7.26 -0.59 Growth in median 7.05 8.12 1.54 Mean Percentile Growth Rate 5.65 7.91 -0.18 Pro-Poor Growth Rate 4.74 8.42 -0.73 Growth up to the percentile:

10 -1.71 8.78 -2.66 15 -1.14 9 -1.77 20 0.16 9.05 -1.33 25 1.46 9.05 -1.06 30 2.33 8.97 -0.89

Source: World Bank staff estimates

Table A.1.3 Pro-Poor Growth Estimates, 1995-2000 National Urban Rural Total Poverty Growth in mean income 4.02 3.36 3.1 Growth in median 3.77 2.52 2.84 Mean Percentile Growth Rate 3.94 2.95 3.34 Pro-Poor Growth Rate 3.77 2.3 3.15 Growth up to the percentile:

10 5.79 2.39 6.85 15 4.9 2.26 5.94 20 4.35 2.26 5.09 25 4.08 2.28 4.49 30 3.97 2.33 4.01

Source: World Bank staff estimates

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Annex 2 Estimates of Rural Household Income Growth Drivers

The following tables present the results of estimates of the decomposition of the changes in rural household income growth, per quintile, using FUSADES/BASIS panel data available for 451 families for the years 1995, 1997, 1999 and 2001. It follows the methodology used by Cord, Louise, Ramón López, Monika Huppi and Oscar Melo “Growth and Rural Poverty in the CIS7. Case Studies of Georgia, the Kyrgyz Republic, and Moldova” (2003), and extends their results by applying it to household income quintiles. The decomposition of changes in income involves the following three steps. First, regressions were run to correlate the level of (log) income of each household with household characteristics (logs and dummies) such as the composition of the family and level of education, physical capital endowment, the participation of the household in markets (for goods and services, labor and credit), reception of remittances, and access to infrastructure as well as the effects of external factors such as earthquakes and the coffee crisis. Fixed effects (FE) and random effects (RE) estimators were calculated. RE estimators rely on across-household variability while FE relies on within-household variations in the data. Regressions were run for: i) all households; and ii) each quintile separately, according to mean income obtained during the four years (1995, 1997, 1999 and 2001). The R2 for the RE estimates is greater than for the FE. Second, we estimated the difference between 1995 to 2001 of the log of the mean of household per capita income and the log of each variable used in the regression formula for all households and per quintile. Third, the result obtained in the second step was multiplied by the coefficient obtained in the regression analysis in the first step (using only those variables significant at the 90 percent level), in order to ascertain the contribution of each variable to growth during the period. The contribution of external factors and “unexplained” factors were calculated as residuals. These estimates were done separately using regression coefficients with FE and RE estimators.

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Table A.2.1 Quintiles based on Average Income, Average Value of Variables in 1995

1995 Variables

Global Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5

Per capita annual income (1995 colones) 3,472.22 1,199.15 2,173.12 2,638.12 4,091.21 7,349.25 Household Characteristics Household size (log) 6.04 7.12 6.59 6.13 5.29 5.01 Number of persons over 15 years (log) 3.46 3.27 3.38 3.79 3.24 3.61 Average age of members (log) 26.70 21.50 23.88 26.40 29.21 32.77 Economic dependence (log) 3.05 3.80 3.27 2.78 2.95 2.41 Woman head of HH (dummy) 0.08 0.03 0.10 0.09 0.07 0.10 Human Capital Years of schooling 15 to 65 years (log) 3.77 2.34 2.79 3.84 4.28 5.69 Physical Capital Total land area available (log) 1.21 0.88 0.55 3.15 0.40 1.11 Index of assets (log) 1.87 0.94 1.25 1.50 2.02 3.71 Owns cattle (dummy) 0.18 0.18 0.12 0.18 0.19 0.21 Owns poultry (dummy) 0.42 0.42 0.51 0.46 0.40 0.30 Access to Goods and Services markets Cultivates basic grains (dummy) 0.49 0.66 0.47 0.50 0.41 0.40 Cultivates non traditional products (dummy) 0.10 0.06 0.10 0.12 0.10 0.13 Has micro enterprise (dummy) 0.11 0.07 0.10 0.12 0.06 0.21 Sold animals or animal product (dummy) 0.26 0.30 0.27 0.29 0.21 0.20 Sold agricultural products (dummy) 0.31 0.31 0.33 0.36 0.23 0.31 Labor market participation Has agricultural salaried members (dummy) 0.53 0.64 0.70 0.58 0.40 0.35 Has non agricultural salaried members (dummy) 0.46 0.22 0.39 0.49 0.60 0.60 Access to credit Received credit (log) 0.10 0.06 0.12 0.06 0.12 0.12 Access to Remittances Received remittances (dummy) 0.14 0.06 0.11 0.08 0.17 0.26 Infrastructure Has electricity (dummy) 0.55 0.33 0.43 0.56 0.57 0.84 Has potable water (dummy) 0.39 0.30 0.32 0.38 0.48 0.47 Distance to paved road (log) 5.62 7.21 6.58 4.69 4.16 5.07 Distance to bus stop (log) 2.16 2.66 2.44 1.73 1.37 2.60

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Table A.2.2 Quintiles based on Average Income, Average Value of Variables in 2001 2001

Variables Global Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5

Per capita annual income (1995 colones) 6,182.16 1,882.14 3,292.59 4,724.99 6,485.17 14,651.16 Household Characteristics Household size (log) 6.04 7.69 6.49 5.99 5.37 4.65 Number of persons over 15 years (log) 3.43 3.71 3.49 3.54 3.32 3.08 Average age of members (log) 30.75 23.58 27.74 31.35 32.23 38.89 Economic dependence (log) 2.16 2.49 2.25 2.08 2.17 1.80 Woman head of HH (dummy) 0.17 0.11 0.21 0.23 0.12 0.16 Human Capital Years of schooling 15 to 65 years (log) 4.21 3.14 3.53 4.02 4.67 5.82 Physical Capital Total land area available (log) 2.56 1.63 2.47 1.87 2.21 4.56 Index of assets (log) 3.00 1.76 2.48 2.82 3.08 4.91 Owns cattle (dummy) 0.19 0.26 0.18 0.10 0.16 0.24 Owns poultry (dummy) 0.58 0.68 0.67 0.60 0.47 0.46 Access to Goods and Services markets Cultivates basic grains (dummy) 0.58 0.80 0.62 0.57 0.47 0.44 Cultivates non traditional products (dummy) 0.14 0.08 0.14 0.12 0.10 0.24 Has micro enterprise (dummy) 0.33 0.12 0.36 0.27 0.39 0.52 Sold animals or animal product (dummy) 0.38 0.49 0.42 0.39 0.29 0.33 Sold agricultural products (dummy) 0.42 0.53 0.40 0.43 0.31 0.42 Labor market participation Has agricultural salaried members (dummy) 0.43 0.72 0.44 0.50 0.34 0.15 Has non agricultural salaried members (dummy) 0.55 0.40 0.54 0.57 0.63 0.60 Access to credit Received credit (log) 0.21 0.14 0.19 0.19 0.21 0.30 Access to Remittances Received remittances (dummy) 0.30 0.21 0.22 0.28 0.43 0.36 Infrastructure Has electricity (dummy) 0.75 0.61 0.69 0.70 0.81 0.97 Has potable water (dummy) 0.51 0.37 0.54 0.51 0.51 0.63 Distance to paved road (log) 3.53 4.82 3.88 3.59 2.59 2.80 Distance to bus stop (log) 1.63 2.27 2.11 1.73 1.17 0.87

83

Table A.2.3 Quintiles based on Average Income, Difference in Log of Mean Variables log(mean in 2001) - log(mean in 1995) Global Q 1 Q 2 Q 3 Q 4 Q 5 Per capita annual income (1995 colones) 57.7% 45% 42% 58% 46% 69%Household Characteristics Household size (log) 0% 8% -2% -2% 1% -7% Number of persons over 15 years (log) -1% 13% 3% -7% 2% -16% Average age of members (log) 14% 9% 15% 17% 10% 17% Economic dependence (log) -35% -42% -37% -29% -31% -29% Woman head of HH (dummy) 76% 120% 75% 97% 61% 44%Human Capital Years of schooling 15 to 65 years (log) 11% 29% 24% 5% 9% 2%Physical Capital Total land area available (log) 75% 62% 150% -52% 170% 141% Index of assets (log) 47% 63% 68% 63% 42% 28% Owns cattle (dummy) 6% 36% 37% -58% -19% 10% Owns poultry (dummy) 31% 47% 27% 28% 15% 42%Access to Goods and Services markets Cultivates basic grains (dummy) 17% 20% 29% 13% 13% 8% Cultivates non tradit. products (dummy) 30% 34% 37% 0% 0% 56% Has micro enterprise (dummy) 109% 61% 127% 78% 195% 88% Sold animals or animal product (dummy) 41% 49% 46% 30% 31% 48% Sold agricultural products (dummy) 30% 54% 18% 20% 29% 28%Labor market participation Has agricultural salaried members (dum.) -21% 11% -45% -14% -15% -87% Has non agricultural salaried members (dummy) 18% 59% 34% 15% 5% 0%Access to credit Received credit (log) 77% 96% 44% 122% 55% 90%Access to Remittances Received remittances (dummy) 80% 134% 69% 127% 96% 33%Infrastructure Has electricity (dummy) 32% 61% 46% 23% 36% 14% Has potable water (dummy) 28% 20% 52% 30% 7% 29% Distance to paved road (log) -46% -40% -53% -27% -47% -59% Distance to bus stop (log) -28% -13% -23% -34% -39% -46%

84

Table A.2.4 Quintiles based on Average Income, Coefficient Estimates, Random Effects

Coefficients (Random Effects) Global Q 1 Q 2 Q 3 Q 4 Q 5 R2 0.46 0.48 0.32 0.35 0.31 0.48Household Characteristics Household size (log) -0.52 0.68 -0.74 -0.05 -0.54 -0.24 Number of persons over 15 years (log) 0.04 -0.56 0.48 -0.38 0.40 -0.08 Average age of members (log) 0.04 -0.31 -0.23 0.07 0.37 0.01 Economic dependence (log) -0.37 -0.20 0.12 -0.73 -0.27 -0.51 Woman head of HH (dummy) -0.13 0.26 -0.30 -0.03 -0.24 -0.33Human Capital Years of schooling 15 to 65 years (log) 0.06 0.11 0.02 0.03 0.20 0.03Physical Capital Total land area available (log) 0.04 0.06 0.02 0.00 -0.13 0.00 Index of assets (log) 0.33 0.26 0.11 0.15 0.17 0.33 Owns cattle (dummy) 0.25 0.28 0.32 0.31 0.72 0.30 Owns poultry (dummy) -0.07 -0.10 0.31 -0.05 -0.47 -0.24Access to Goods and Services markets Cultivates basic grains (dummy) -0.32 -0.62 -0.02 -0.20 0.12 -0.24 Cultivates non tradit. products (dummy) 0.10 -0.17 0.06 0.27 0.28 0.00 Has micro enterprise (dummy) 0.38 0.40 0.06 0.20 0.38 0.30 Sold animals or animal product (dummy) -0.03 0.31 -0.31 -0.14 0.26 0.01 Sold agricultural products (dummy) 0.03 0.17 -0.13 0.12 0.29 0.10Labor market participation Has agricultural salaried members (dum.) 0.06 0.16 0.16 0.16 0.07 0.13 Has non agricultural salaried members (dummy) 0.54 0.42 0.36 0.76 0.45 0.31Access to credit Received credit (log) 0.13 0.00 0.22 -0.10 0.21 0.11Access to Remittances Received remittances (dummy) 0.52 -0.15 0.47 0.70 0.58 0.58Infrastructure Has electricity (dummy) -0.26 -0.13 0.03 -0.34 -0.44 -0.38 Has potable water (dummy) -0.04 0.23 -0.01 -0.08 -0.08 0.16 Distance to paved road (log) -0.04 -0.04 -0.15 0.03 0.03 -0.02 Distance to bus stop (log) -0.03 -0.04 0.04 -0.08 0.02 -0.02

85

Table A.2.5 Quintiles based on Average Income, Coefficient Estimates, Fixed Effects

Coefficients (Fixed Effects) Global Q 1 Q 2 Q 3 Q 4 Q 5 R2 0.40 0.11 0.14 0.120.17 0.25Household Characteristics Household size (log) -0.03 2.42 -1.21 0.96 -0.82 0.16 Number of persons over 15 years (log) -0.28 -0.78 0.52 -1.12 0.34 -0.22 Average age of members (log) 0.19 0.74 -0.30 -0.53 -0.19 1.22 Economic dependence (log) -0.39 -0.23 0.08 -0.92 -0.22 -0.13 Woman head of HH (dummy) 0.12 1.10 0.02 0.31 0.66 -0.43Human Capital Years of schooling 15 to 65 years (log) 0.23 0.08 -0.10 0.56 0.25 0.20Physical Capital Total land area available (log) 0.03 0.08 -0.01 -0.01 -0.14 0.03 Index of assets (log) 0.27 -0.25 0.23 0.64 0.42 0.62 Owns cattle (dummy) 0.49 -0.14 0.42 0.95 1.21 -0.16 Owns poultry (dummy) -0.06 -0.16 0.23 0.19 -0.85 -0.03Access to Goods and Services markets Cultivates basic grains (dummy) -0.27 -0.60 0.08 -0.73 0.38 -0.34 Cultivates non tradit. products (dummy) 0.04 -0.53 0.45 0.08 0.72 0.02 Has micro enterprise (dummy) 0.34 0.41 0.06 0.10 0.29 0.97 Sold animals or animal product (dummy) -0.04 0.64 -0.43 -0.63 0.63 -0.06 Sold agricultural products (dummy) 0.15 0.09 -0.41 0.39 0.37 0.30Labor market participation Has agricultural salaried members (dum.) 0.13 0.33 -0.02 -0.11 0.16 0.16

0.58 0.53 0.40 0.86 0.72 0.80Access to credit Received credit (log) -0.03 -0.26 -0.09 -0.05 -0.09 -0.20Access to Remittances Received remittances (dummy) 0.62 -0.61 0.46 1.43 0.41 0.89Infrastructure Has electricity (dummy) -0.06 -0.61 -0.25 0.37 -0.42 -0.30 Has potable water (dummy) 0.03 0.83 -0.19 -0.24 -0.12 0.14 Distance to paved road (log) -0.03 0.05 -0.33 -0.12 -0.08 0.04 Distance to bus stop (log) -0.02 -0.10 -0.03 -0.04 0.07 0.06

Has non agricultural salaried members (dummy)

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Table A. 2.6 Quintiles Based on Average Income, Decomposition of Change in Income, Random Effects, 1995-2001 a/

Random Effect Global Q 1 Q 3 Q 2 Q 4 Q 5 Per capita annual income (1995 colones) 57.7% 45.1% 41.6% 58.3% 46.1% 69.0%Household Characteristics 12.9% -1.9% -19.8% 21.1% 0.0% 14.8% Household size (log) 0.0% 5.2% 1.1% Number of persons over 15 years (log) -7.1% 1.5% Average age of members (log) Economic dependence (log) 12.9% 21.1% 14.8% Woman head of HH (dummy) -22.5% Human Capital 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% Years of schooling 15 to 65 years (log) Physical Capital 17.0% 16.2% 20.4% 0.0% -43.5% -0.6% Total land area available (log) -22.2%

15.5% 16.2% 9.4% Owns cattle (dummy) 1.5% 12.1% -14.0% Owns poultry (dummy) 8.2% -7.3% -10.0%Access to Goods and Services markets 36.2% 26.9% -14.3% 0.0% 73.1% 24.3% Cultivates basic grains (dummy) -5.5% -12.4% -1.9% Cultivates non tradit. products (dummy) Has micro enterprise (dummy) 41.6% 24.1% 73.1% 26.2% Sold animals or animal product (dummy) 15.2% -14.3% Sold agricultural products (dummy) Labor market participation 9.5% 24.8% 12.0% 11.2% 2.4% 0.0% Has agricultural salaried members (dum.) Has non agricultural salaried members (dummy) 9.5% 24.8% 12.0% 11.2% 2.4% 0.0%Access to credit 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% Received credit (log) Access to Remittances 41.7% 0.0% 32.2% 88.8% 55.2% 19.2% Received remittances (dummy) 41.7% 32.2% 88.8% 55.2% 19.2%Infrastructure -6.5% 0.0% 7.9% -7.9% 0.0% -5.1% Has electricity (dummy) -8.4% -7.9% -5.1%

Distance to paved road (log) 1.9% 7.9% Distance to bus stop (log) Residual -53.1% -20.9% 3.1% -55.0% -41.1% 16.5%

Index of assets (log)

Has potable water (dummy)

a/ Difference between the log of mean of each variable multiplied by the correspondent regression coefficient that is significant at 90%.

87

Table A. 2.7 Quintiles Based on Average Income, Decomposition of Change in Income, Fixed Effects, 1995-2001 a/

Fixed Effect Global Q 1 Q 2 Q 3 Q 4 Q 5 Per capita annual income (1995 colones) 57.7% 45.1% 41.6% 58.3% 46.1% 69.0%Household Characteristics 13.3% 18.6% 0.0% 34.1% 0.0% 21.0% Household size (log) 18.6% Number of persons over 15 years (log) 7.4% Average age of members (log) 21.0% Economic dependence (log) 13.3% 26.6% Woman head of HH (dummy) Human Capital 2.5% 0.0% 0.0% 2.5% 0.0% 0.0% Years of schooling 15 to 65 years (log) 2.5% 2.5% Physical Capital 15.5% 0.0% 0.0% -13.8% 0.0% 17.4% Total land area available (log) Index of assets (log) 12.5% 40.6% 17.4% Owns cattle (dummy) 3.0% -54.4% Owns poultry (dummy) Access to Goods and Services markets 32.1% 31.0% -7.5% -27.8% 0.0% 86.2% Cultivates basic grains (dummy) -4.6% -9.2% Cultivates non tradit. products (dummy) Has micro enterprise (dummy) 36.7% 86.2% Sold animals or animal product (dummy) 31.0% -18.6% Sold agricultural products (dummy) -7.5% Labor market participation 10.3% 31.2% 13.5% 12.7% 0.0% 0.0% Has agricultural salaried members (dum.) Has non agricultural salaried members (dummy) 10.3% 31.2% 13.5% 12.7% 0.0%Access to credit 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% Received credit (log) Access to Remittances 49.9% 0.0% 0.0% 181.7% 0.0% 29.5% Received remittances (dummy) 49.9% 181.7% 29.5%Infrastructure 0.0% 16.6% 17.5% 0.0% 0.0% 0.0% Has electricity (dummy)

16.6% Distance to paved road (log) 17.5%

Residual -65.9% -52.2% 18.0% -131.1% 46.1% -85.0%

Has potable water (dummy)

Distance to bus stop (log)

a/ Difference between the log of mean of each variable multiplied by the correspondent regression coefficient that is significant at 90%.

88