48960 Madag-fm-fin.qxd 5/22/08 8:15 PM Page i Making Work Pay in Madagascar Madag-fm-fin.qxd 5/22/08 8:15 PM Page ii Madag-fm-fin.qxd 5/22/08 8:15 PM Page iii Making Work Pay in Madagascar Employment, Growth, and Poverty Reduction Margo Hoftijzer Pierella Paci Madag-fm-fin.qxd 5/22/08 8:15 PM Page iv © 2008 The International Bank for Reconstruction and Development/ The World Bank 1818 H Street NW Washington DC 20433 Telephone: 202-473-1000 Internet: www.worldbank.org E-mail: feedback@worldbank.org All rights reserved. 1 2 3 4 5 11 10 09 08 This volume is a product of the staff of the International Bank for Reconstruction and Development/The World Bank. The findings, interpretations, and conclusions expressed in this volume do not necessarily reflect the views of the Executive Directors of The World Bank or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work. 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All other queries on rights and licenses, including subsidiary rights, should be addressed to the Office of the Publisher, The World Bank, 1818 H Street NW, Washington, DC 20433, USA; fax: 202-522-2422; e-mail: pubrights@worldbank.org. ISBN-13: 978-0-8213-7530-3 eISBN: 978-0-8213-7531-0 DOI: 10.1596/978-0-8213-7530-3 Library of Congress Cataloging-in-Publication Data Making work pay in Madagascar: employment, growth, and poverty reduction/ by Margo Hoftijzer, Pierella Paci. p. cm. Includes bibliographical references and index. ISBN 978-0-8213-7530-3 -- ISBN 978-0-8213-7531-0 (electronic) 1. Labor market--Madagascar. 2. Wages--Madagascar. 3. Poverty--Madagascar. 4. Labor productivity--Madagascar. I. Hoftijzer, Margo. II. Paci, Pierella, 1957- III. World Bank. HD5841.7.A6M34 2008 331.109691--dc22 2008017700 Cover design: Candace Roberts, Quantum Think, Philadelphia, PA, United States Contents Acronyms and Abbreviations xi Acknowledgments xiii Chapter 1 Introduction and Overview 1 Objectives and Structure of This Report 2 Findings 4 Conclusions and a Proposed Way Forward 10 Chapter 2 Definitions and Data 13 Chapter 3 Country Context 19 Population, Income, and Poverty 19 Macroeconomic Context 22 Labor Market Outcomes 32 v vi Contents Chapter 4 Growth, Employment, and Labor Productivity 43 Comparing the Output and Employment Shares of Sectors and Subsectors 43 Decomposing Per Capita Growth into Labor 49 Productivity, Employment, and Demographic Changes Annex 4A.The Shapley Decomposition 54 Annex 4B.The Sources of Change in Labor 55 Productivity Chapter 5 Relating Aggregate and Sectoral Labor 59 Productivity with Earnings Review of Earnings Data 59 Sector Comparability 65 Chapter 6 Linking Employment and Earnings with Poverty 69 Employment and Earnings Profiles of the 70 Population Sectoral Employment, Earnings, and Poverty 77 The Various Sources of Labor Income and 83 Their Links with Poverty Annex 6A. Employment and Population Tables 92 Annex 6B.The Kakwani, Neri, and Son 94 Decomposition of Household Labor Income Annex 6C. Changes in Labor Income and 96 Sources of Change in Rural and Urban Areas and by Province Chapter 7 Good Jobs, Bad Jobs 103 The Probability of Getting a Good Job 103 The Determinants of Earnings 107 Segmentation in the Wage Labor Market 111 Annex 7A. Determinants of Male and Female, Urban and Rural Employment--Tables 117 Chapter 8 Conclusions and Suggestions for a Way Forward 123 References 131 Index 135 Contents vii Figures 3.1 Changes in Annual GDP Growth, 1960­2006 22 3.2 Sectoral Output Growth, 1997­2006 26 3.3 Subsectoral Output Shares, 2006 27 3.4 Employment Status by Level of Urbanization, 2005 37 4.1 Aggregate Contributions to Changes in GDP Per Capita, 51 1999­2005 5.1 Distributions of Monthly Earnings, 2001 and 2005 61 5.2 Distribution of Monthly Earnings by Sector, 64 2001 and 2005 6C.1 Changes in Weekly Household Per Capita Labor 97 Income in Rural and Urban Areas by Expenditure Quintile, 2001­05 6C.2 Sources of Change In Weekly Household Per Capita 98 Labor Income in Rural and Urban Areas for the Overall Populations, the Poor, and the Nonpoor, 2001­05 6C.3 The Provinces of Madagascar 101 Tables 2.1 Definitions 14 2.2 Population and Dependency Rates in Madagascar, 16 1999­2005 3.1 Population, Income, and Poverty in Madagascar, 21 1960­2005 3.2 Macroeconomic Indicators, 1997­2006 25 3.3 Growth Trends in the Main Subsectors, 1999­2006 27 3.4 Basic Labor Market Indicators for Madagascar, 33 2005, 2001, and 1999 3.5 Hierarchical Decomposition of the Labor Market, 34 2005, 2001, and 1999 3.6 Low Earnings and Poverty, by Employment Category, 39 Region, and Gender, 2005 3.7 Employment Status and Earnings by Education Level, 40 2005 4.1 Sector and Subsector Output and Employment Shares, 45 2005, 2001, and 1999 4.2 Sectoral Shares of Output and Employment,All Workers 47 and the Poorest Quintile, 2005, 2001, and 1999 viii Contents 4.3 Sectoral Contributions to Changes in GDP Per Capita, 52 1999­2005 4B.1 The Contribution of Changes in Capital-Labor Ratio 56 and TFP to Changes in Output per Worker, 1999­2005 4B.2 Assessment of the Robustness of Approximations to 57 Capital Stock and 5.1 Monthly Labor Earnings by Sector, 2005 and 2001 60 5.2 Employment Distribution in the Secondary Sector, 65 2005 and 2001 5.3 Median Earnings, by Sector and Employment Status, 67 2005 and 2001 6.1 Employment Status of the Working-Age Population by 71 Poverty Level 6.2 Employment Status of the Working-Age Employed 72 Population by Poverty Level 6.3 Median Monthly Earnings by Employment Status, 74 by Quintile and Poverty Level, 2005 and 2001 6.4 Structure of Household Income by Quintile and 75 Poverty Level, 2005 and 2001 6.5 Impact of Nonlabor Transfers on Poverty, 2005 and 2001 77 6.6 Structure of Household Income by Sector (Quintile, 78 and Poverty Level), 2005 and 2001 6.7 Decomposition of Changes in Poverty into Intrasectoral 81 and Intersectoral Effects, 2001­05 6.8 Household Labor Income Profile, 2005 and 2001 85 6.9 Simulated Changes in Poverty Due to Changes in 87 Household Labor Income Profile (reference year 2001) 6A.1 Employment Status of the Working-Age Population 92 by Quintile and Poverty Level, 2005, 2001, and 1999 6A.2 Employment Status of the Working-Age Employed 93 Population by Quintile and Poverty Level, 2005, 2001, and 1999 6C.1 Weekly Household Per Capita Labor Income in Urban 97 and Rural Areas by Expenditure Quintile, 2005, 2001, and Percentage Difference (MGA) 6C.2 Changes in Household Per Capita Weekly Labor Income 100 by Province, 2001­05 6C.3 Sources of Changes in Weekly Household Per Capita 100 Labor Income by Province, 2001­05 Contents ix 7.1 Determinants of Daily Earnings, 2005 and 2001 109 7.2 Determinants of Daily Wage Earnings by Gender, 2005 111 7.3. Testing for Segmentation--Determinants of Daily 114 Wage Earnings, 2005 7A.1 Determinants of Male Rural Employment, 2005 118 7A.2 Determinants of Female Rural Employment, 2005 119 7A.3 Determinants of Male Urban Employment, 2005 120 7A.4 Determinants of Female Urban Employment, 2005 121 Boxes 3.1 Export Processing Zones:Are Drivers of Growth at Risk? 29 3.2 A Lagged Link between Growth of Rice Prices and Production 31 3.3 Integrated Growth Poles: New Motors of Growth? 32 6.1 A Closer Look at Child Labor 88 Madag-fm-fin.qxd 5/22/08 8:15 PM Page x Madag-fm-fin.qxd 5/22/08 8:15 PM Page xi Acronyms and Abbreviations AGOA African Growth and Opportunity Act CAS Country Assistance Strategy CPI Consumer Price Index EBA Everything But Arms Initiative of the European Union EPM Enquête Périodique auprès des Ménages (Household Survey) EPZ Export Processing Zone FAO Food and Agriculture Organization FDI Foreign Direct Investment GDP Gross Domestic Product HHS Household Survey ILO International Labor Organization IMF International Monetary Fund INSTAT Institut National de la Statistique (Malagasy National Statistics Bureau) MAP Madagascar Action Plan MGA Malagasy Ariary MoF Malagasy Ministry of Finance xi Madag-fm-fin.qxd 5/22/08 8:15 PM Page xii xii Acronyms and Abbreviations NPL National Poverty Line PRSC Poverty Reduction Support Credit TFP Total Factor Productivity UNDP United Nations Development Programme WDI World Development Indicators MFA Multi-Fiber Arrangements Madag-fm-fin.qxd 5/22/08 8:15 PM Page xiii Acknowledgments This report is the product of a joint venture between the Poverty, Reduction and Aid Effectiveness group (PRMPR) of the World Bank and the World Bank country office in Madagascar (AFTH3). It is part of PRMPR's multi-year program which studies the role of employment in Madagascar in order to encourage inclusive growth and create a broader work program in the labor market initiated by the country office. The authors are very grateful for the input and comments of David Stifel, Faly Hery Rakotomanana, and Elena Celada, particularly because certain sections of the report draw heavily on their work,"Assessing Labor Market Conditions in Madagascar, 2001­2005," which is forthcoming as an Africa Region Working Paper. The report is also based on an earlier draft paper by Hoftijzer and Stifel, which was prepared for the World Bank's Economists' Forum in April 2007.The authors also expresses their gratitude for the valuable comments and contributions of Stefano Palernostro and Laza Razafiarison in Antananarivo, and of the Jobs and Migration Team at the World Bank in Washington, D.C. Finally, the quality of this report has greatly benefited from the com- ments and suggestions of Jeff Dayton-Johnson (Principal Economist, xiii Madag-fm-fin.qxd 5/22/08 8:15 PM Page xiv xiv Acknowledgments Organisation for Economic Cooperation, France), Ravi Kanbur (T. H. Lee Professor of World Affairs, Professor of Economics at Cornell University, United States), Germano Mwabu (Professor and Chair, Economics Department, University of Nairobi, Kenya), Jonathan Temple (Professor of Economics, University of Bristol, United Kingdom), and Jeemol Unni (Professor at the Gujarat Institute of Development Research, National Commission for Enterprises in the Unorganized Sector, India). The input of these distinguished reviewers will also be gratefully drawn upon in the context of PRMPR's future work on employment and inclusive growth. Madag-txt-fin.qxd 5/22/08 8:17 PM Page 1 C H A P T E R 1 Introduction and Overview There is little doubt that economic growth contributes significantly to poverty reduction;1 however, countries clearly differ in the degree to which income growth translates into reduced levels of poverty. Although cross-country estimates suggest that differences in the responsiveness of poverty to income growth account for a small fraction of overall differ- ences in poverty changes across countries, from the point of view of an individual country these differences may have significant implications for poverty reduction, especially in the short term.2 Labor markets--in particular, employment and earnings--are believed to play an important role in the way that growth translates into poverty reduction. After all, the poor derive most of their income from labor. However, there is still insufficient understanding of the concrete linkages among growth, employment and earnings, and poverty reduction, and consequently of the relevant policy measures that would improve the effectiveness of labor markets as a mechanism for translating growth into increased income-generation opportunities for the poor. The ease with which the poor may take up the opportunities afforded by growth may depend crucially on the structure of employment, the 1 Madag-txt-fin.qxd 5/22/08 8:17 PM Page 2 2 Making Work Pay in Madagascar: Employment, Growth, and Poverty Reduction returns to labor and their distribution, and the existence of imperfections and frictions in the labor market. For example, flexible labor markets and low barriers to mobility might make it easier for the poor to benefit from growth by allowing them to move more easily to growing sectors of high labor productivity and high earnings, particularly when growth is accom- panied by an increased demand for unskilled labor, or when the poor are able to acquire the skills that are required by the growing sectors. On the other hand, labor market imperfections or rigidities may pre- vent the poor from benefiting optimally from economic growth. The concern that this has been the case in some developing countries is reflected in the emphasis placed on "jobless growth" as an explanatory factor for the disappointing levels of poverty reduction in countries that had experienced relatively high levels of growth. Though this has led to debates on how to foster employment-intensive growth, it has also led to the recognition that poverty reduction through the labor market may be dependent more on the increased labor productivity and earnings capacity of the poor than on increased employment opportunities (ILO 2003). Another debated question that is largely unresolved has been whether policy interventions should focus on increasing labor productivity and earnings for the work that is currently being carried out by the poor (e.g., in agriculture) or whether interventions should concentrate on increas- ing employment opportunities in the higher labor productivity sectors where few poor are active, so that more of the poor can move there (Fields 2006). Objectives and Structure ofThis Report This report is aimed at providing a better understanding of how the labor market functions as a transmission mechanism between growth and poverty reduction, by studying the links in the case of Madagascar. In this way, the study intends to contribute to the discussion concerning the identification of policy priorities and interventions in Madagascar that will increase the reduction of poverty at given growth levels. The report is also part of a broader research framework comprising several country studies and a cross-country analysis aimed at generating more insights into the linkages among growth, labor, and poverty reduction in low- income countries. Madag-txt-fin.qxd 5/22/08 8:17 PM Page 3 Introduction and Overview 3 This report draws mainly on data from household surveys conducted in 1999, 2001, and 2005, and on national accounts data. Because the Malagasy population experienced the consequences of an economic crisis during part of the period under observation, the study was able to explore the growth and poverty-reduction linkages in a period during which out- put per capita actually decreased (2001­05) and, as far as the data per- mitted, to compare observed developments in a period in which per capi- ta growth was positive (1999­2001).3 The report is structured into eight chapters, beginning with this intro- duction. Chapter 2 describes the data and the main definitions used in this report. Chapter 3 provides the socioeconomic context of the study, with a particular emphasis on growth, poverty, and labor market charac- teristics. For the last item, this chapter uses an alternative set of labor indi- cators developed to capture labor market conditions in low- and middle- income countries--where low labor productivity and subsistence employment prevail--in a better way than they are captured by standard labor indicators (Stifel, Rakotomanana, and Celada 2007). The report then focuses on the linkages among output, employment, and labor pro- ductivity (chapter 4); between labor productivity and earnings (chapter 5); and between employment and earnings, and poverty (chapter 6). Specifically, chapter 4 uses macroeconomic data to compare sectoral shares in output and employment, as well as their changes over time. It also explores the extent to which per capita output growth is associated with changes in employment and changes in labor productivity. Chapter 4 takes a look at the linkages between macro- and microeconomic data by reviewing the ways in which changes in aggregate and sectoral labor productivity translate into individual earnings as gathered from the household surveys. Chapter 5 also reviews the relationships between pro- ductivity and earnings by looking at the linkages between changes in aggregate and sectoral labor productivity data (macro) and changes in individual earnings as gathered from the household surveys (micro). Chapter 6 examines the origins and determining factors of household earnings and employment and assesses their impact on poverty and poverty reduction. Chapter 7 analyzes the individual and household char- acteristics that are associated with having either "good" jobs or "bad" jobs and reviews the question of whether there may be barriers preventing the movement of workers from bad to good labor market segments. Finally, chapter 8 describes the main conclusions of this report and provides sug- gestions for future work based on these conclusions. Madag-txt-fin.qxd 5/22/08 8:17 PM Page 4 4 Making Work Pay in Madagascar: Employment, Growth, and Poverty Reduction Findings The report arrives at the findings discussed below. The structure of the labor market in Madagascar is typical of low-income countries Madagascar's labor market characteristics are typical for a low-income country: labor force participation and employment rates are high, formal- ity and wage employment rates are low, a large share of the population is active in agriculture, and there is a relatively high incidence of child labor. In addition, both the overall population and the labor force are growing at a rapid rate, increasing the need for a steady pace of job creation mere- ly to maintain the current level of the employment rate. A large share (88 percent) of the adult population is employed, but for many people, employment does not provide a way out of poverty; almost two-thirds of the working adults are "working poor" living in poor house- holds. By far the lowest returns to labor occur in agriculture. Median monthly earnings in the primary sector are only about 40 percent of those in the secondary and tertiary sectors (not controlling for worker charac- teristics). This difference in earnings corresponds to the relatively low labor productivity in the primary sector, which, when defined as average output per worker, is less than 15 percent of labor productivity in the sec- ondary or tertiary sectors. Jobs that are better paid tend to be nonagricul- tural, wage, and urban, and in the formal sector. The structure of the labor market differs markedly between rural and urban areas. In rural areas--where 80 percent of the workers live--almost 90 percent of employment is in agriculture. The most common organiza- tional unit of labor is the household enterprise (86 percent of rural work- ers). Even in secondary urban centers (comprising 12 percent of workers), three-quarters of employment is still in the form of family labor. In large urban centers (which have 8 percent of workers) on the other hand, two- thirds of employment consists of wage jobs, but agriculture is still impor- tant in urban areas, providing 47 percent of employment. Services account for a similarly large share, while industry accounts for only 8 percent. Returns to labor also vary by job location. Monthly median earnings are lowest in rural areas and highest in large urban centers.This difference also prevails between jobs in agriculture and wage and nonwage nonagri- cultural jobs. For example, the median nonagricultural wage worker in large urban centers earns 12 percent more than the median worker with Madag-txt-fin.qxd 5/22/08 8:17 PM Page 5 Introduction and Overview 5 the same type of job in a secondary city and earns 29 percent more than the median nonagricultural wage worker in a rural area. Employment and earnings patterns in the period under observation were much affected by a short but severe crisis, and by increases in the price and production of rice The period under observation in this report (1999­2005) is characterized by two developments.The first of these was a short but severe crisis start- ing at the end of 2001, and the subsequent economic rebound. Second, there was an increase in world rice prices that--in combination with a sharp depreciation of the local currency aided by public investments in rural areas--increased agricultural output and revenues. Prior to the crisis, Madagascar had experienced relatively high growth rates, averaging 4.6 percent per year between 1997 and 2001. In 2002, however, GDP fell by almost 13 percent. As the crisis--which was of a political nature--was largely urban, it particularly affected the secondary and tertiary sectors.After the crisis ended, these sectors rebounded quick- ly, and by 2004 GDP had returned to its precrisis level. Nevertheless, as a result of the population growth that had occurred in the interim, out- put per capita levels took longer to recover. By 2005, GDP per capita was still 5 percent lower than in 2001. Even though output levels were quickly restored, the crisis had signif- icant effects on the employment and earnings structure of the Malagasy population. These consequences of the crisis, which were still clearly vis- ible in 2005, are summarized here. The most striking effect of the crisis was the massive inflow of labor into agriculture. Between 2001 and 2005 the share of agricultural work- ers increased by 8 percentage points, to 77.7 percent, increasing the num- ber of primary sector workers by almost one-third. In the same period the number of secondary sector workers fell by more than half, while the number of workers in the tertiary sector increased by 7 percent, which was half the rate of growth of the working-age population in that period. The rise in the employment share of agriculture can thus be attributed to a combination of the secondary sector's shedding workers and the terti- ary sector's generating an insufficient number of jobs to absorb enough labor entrants to maintain its share in employment. The crisis also caused significant changes in labor productivity (defined as average output per worker), which are closely associated Madag-txt-fin.qxd 5/22/08 8:17 PM Page 6 6 Making Work Pay in Madagascar: Employment, Growth, and Poverty Reduction with the observed fall in GDP per capita. A growth decomposition sug- gests that, if the impact of the decline in labor productivity had not been offset by the effects of the rise in employment and the fall in the dependency rate, GDP per capita would have fallen by 13 percent between 1999 and 2005 instead of by the 3.6 percent that was actually observed. A similar decomposition by sector reveals that the primary sector in particular suffered from a fall in labor productivity. Given the large influx of labor into agriculture, this outcome is not surprising. The secondary sector, on the other hand, experienced a substantial increase in labor productivity, as the sector saw a large share of workers depart while output levels did not change significantly. Neither sector seems to have contributed positively to changes in GDP per capita over the peri- od 2001­05. In the primary sector the positive contributions of increased employment were more than offset by the fall in labor produc- tivity, while in the secondary sector the opposite occurred, with the pos- itive effects of higher labor productivity not matching the negative con- tribution of the fall in employment. In the tertiary sector neither changes in labor productivity nor changes in employment could be associated with growth in GDP per capita. A comparison of labor productivity data with information on microlevel earnings allows some cautious interpretations of how macrodevelopments affected individual workers. In the secondary sector, mean monthly earnings fell by 30 percent in the same period that aver- age output per worker more than doubled. A closer look at the changes in the composition of workers in this sector seems to suggest that the less productive workers (self-employed and family enterprise workers), in particular, left the sector between 2001 and 2005, which would provide an explanation (if not the only one) for the substantial increase in labor productivity. The large fall in mean earnings--while median earnings fell only modestly--may possibly be attributed to the highest-paid wage workers experiencing either a fall in their earnings or the loss of their jobs as a consequence of the crisis. Tertiary sector workers seem to have expe- rienced a pull-down effect from the secondary sector, as the relatively high tertiary sector earnings converged with secondary sector levels while output per worker remained largely unchanged. In the primary sector, somewhat surprisingly, the large influx of labor and the subsequent fall in average output per worker coincided with an increase in both mean and median earnings. It is assumed that this diver- gence is largely due to a number of data issues, including the exacerbation Madag-txt-fin.qxd 5/22/08 8:17 PM Page 7 Introduction and Overview 7 of both the increase in output and the fall in labor productivity, which do not allow meaningful conclusions to be drawn from the comparison between the two variables. Among other factors, output data are assumed to have not fully cap- tured the increase in both the quantity and the price of Madagascar's main crop, rice. These increases, however, are likely to have been among the main drivers of the 20 percent increase in average hourly earnings, which were in turn largely responsible for the rise in household per capi- ta labor income of 15 percent between 2001 and 2005. A reduction in both the incidence and the depth of poverty, as the average GDP per capita fell, can be explained by a reduction in inequality The changes described above have had numerous implications for earn- ings inequality and the incidence and depth of poverty. As the 1999 household survey does not allow for the construction of comparable earn- ings data, the earnings-related observations in this report are limited to 2001 and 2005. Between 2001 and 2005, earnings increased in the lower and middle parts of the earnings distribution (largely primary sector workers) and fell in the upper end of the distribution (the highest-paid secondary and tertiary workers).As a result, earnings inequality fell in this period. Another result of the convergence of sector earnings in combination with the influx of labor into agriculture was that the primary sector became a more important source of income for the better-off households. For example, in 2005 the richest quintile of households derived almost half of its income from agriculture, compared with 22 percent in 2001. For the poorest households, the tertiary sector became a more important source of income (12.6 percent in 2005 compared with 5.4 percent in 2001), largely at the expense of primary sector income (81.0 percent in 2005 compared with 87.2 percent in 2001). In the absence of panel data, it is unclear whether these changes reflect a move of the poorest house- holds from agriculture to services or whether the poorest quintile com- prised a different set of households in 2005 and in 2001. Between 2001 and 2005, the rural poverty rate fell by 3.8 percentage points (to 73.5 percent), while poverty in urban areas was almost 8 per- centage points higher in 2005 than before the crisis in 2001 (52.0 percent compared with 44.2 percent). These changes may have been caused by a number of rural households moving to higher quintiles while urban Madag-txt-fin.qxd 5/22/08 8:17 PM Page 8 8 Making Work Pay in Madagascar: Employment, Growth, and Poverty Reduction households were reranked to lower positions in the distribution. In that case, the increased importance of the tertiary sector as a source of income for the poorer households would be at least partly explained by the increase in urban households among the poor, rather than by the moving of poor households into tertiary sector activities. Similarly, the increased importance of the primary sector as an income source for the better-off households would be attributed to the increased number of rural house- holds among the better-off. Another possible explanation for the changes in urban and rural poverty rates could be that a net migration of poorer households from rural to urban areas occurred, which would imply that households did not move up or down to other quintiles but merely changed from being rural poor to being urban poor.Again, in the absence of panel data, neither hypothesis concerning the causes of the changes in rural and urban poverty rates can be tested. Nationwide, the headcount poverty rate was 1 percentage point lower in 2005 than in 2001. An analysis of the sources of household income-- hourly earnings, hours worked, household participation, and (inverse) unemployment rates--and of their changes over time suggests that this reduction in poverty was achieved by an increase in the share of working adults in the relatively better-off households. Because the national pover- ty line puts the poverty rate in Madagascar at 68.7 percent (2005), changes in the poverty rate largely reflect changes in the conditions of those households, which are placed around the 70th percentile of the expenditure distribution. Compared with 2001, members of these house- holds received lower average hourly earnings, worked fewer hours, and were more likely to be unemployed in 2005. These developments, which all had a negative impact on household labor income, can be explained by the loss of secondary and tertiary sector employment, which tends to be higher paid than in the primary sector, and by the move of labor into agri- culture that occurred as a result of the crisis. Presumably, households increased their labor force participation to cope with the adverse effects of the crisis, and a share of the households managed to escape poverty through this strategy. One of the implications of having a large share of the population liv- ing well below the poverty line is that modest improvements in the well- being of the poorest may not affect the incidence of poverty but rather may affect its depth. Between 2001 and 2005, the depth of poverty fell by more than 8 percentage points, from 34.9 percent to 26.8 percent. Madag-txt-fin.qxd 5/22/08 8:17 PM Page 9 Introduction and Overview 9 This improvement can be attributed solely to an increase in hourly earn- ings, which rose by 42 percent for the poor. Changes in all other sources of household income negatively affected the poorer households' income in this period: both the household participation rate and the number of hours worked per employed adult fell, and household unemployment increased. The increase in hourly earnings can be explained by higher earnings in the primary sector and by increased reliance on the tertiary sector (where earnings are higher than in agriculture) as a source of income. The fall in hours worked and the decline in the number of peo- ple in the poorest households who are working may be related to the massive influx of labor into agriculture, which may have created an over- supply of labor in this sector. Education and gender are important for obtaining good jobs Educational attainment plays an important role in determining the likeli- hood of obtaining a "good" job. A higher level of education is associated with the smaller probability of being employed in agriculture (where earnings tend to be lower), and with the higher probability of obtaining formal (that is, good) employment. The influence of determinants other than education--such as age, migrant status, or household status--on the probability of working in agriculture or in informal or formal employ- ment is generally less substantial and straightforward. Education is also an important determinant of the level of earnings an individual receives in various types of employment (agriculture, wage nonagriculture, nonwage nonagriculture). Returns to education are high- est for wage workers. For example, wage workers with a primary educa- tion earned 23 percent more than wage workers without any education, while the returns to primary schooling were 12 percent for nonwage workers and 8 percent for those in agriculture. Wage workers with an upper secondary education earned 69 percent more than those without schooling, while those with postsecondary schooling earned, on average, 105 percent more. For primary and lower secondary education, returns were greater for nonwage labor than for agricultural workers. When focusing on nonagricultural wage workers, and distinguishing between wage earners in the public, private formal, and private informal sectors, significant differences in returns to education are found. In particular, the returns to secondary education for wage workers in the public sector are approximately 25 percent higher than for wage workers Madag-txt-fin.qxd 5/22/08 8:17 PM Page 10 10 Making Work Pay in Madagascar: Employment, Growth, and Poverty Reduction in the private formal sector. This may be a sign of existing segmentation between these two parts of the labor market. In addition to education, gender is an important determinant of earn- ings. When controlling for education, experience, and other factors that determine employment selection, it was found that in nonagricultural employment (both wage and nonwage), women's earnings were about two-thirds of men's earnings. Unfortunately, the calculation method for the larger part of agricultural earnings did not allow a similar analysis of a possible gender gap in agricultural earnings. In an examination of wage employment, the gender gap in the informal sector is significantly greater than in the private formal sector. Whereas even in the formal sector women on average earned 26 percent less than men with otherwise sim- ilar characteristics, this gender gap was as high as 40 percent in the infor- mal sector. Conclusions and a ProposedWay Forward The findings of this report allow a number of conclusions to be drawn, from which a number of policy directions and suggestions for future actions and analysis can be distilled: · To reduce poverty through employment, policies should focus on cre- ating more relatively high-earning jobs rather than on merely creating more jobs. · There is a large difference in labor productivity and earnings between the primary sector on the one hand, and the secondary and tertiary sec- tors on the other. Therefore, policies intended to alleviate poverty through increasing earnings may be most effective when they focus on employment-intensive growth in services and, in particular, industry, allowing workers to move from agriculture to a more productive sec- tor. However, the agricultural sector is likely to remain the main em- ployer of the poor in the short and medium terms. Therefore, the gov- ernment faces a trade-off in concentrating on moving poor workers to more productive sectors by strengthening labor-intensive output expansion in these sectors, or improving the well-being of the large groups that stay behind in agriculture. Sound cost-benefit analyses of both options could aid the government in determining the most appro- priate policy mix to accommodate both objectives. Madag-txt-fin.qxd 5/22/08 8:17 PM Page 11 Introduction and Overview 11 · Further analysis is needed to better understand the crisis-related exit of mostly less productive workers from the secondary sector and the lack of their return to this sector well after the crisis ended. Better insights into the functioning of the secondary sector labor mechanisms, includ- ing any possible market failures, could be beneficial in the design of policies that support employment-intensive growth in both the sec- ondary and tertiary sectors. · During the 2001 crisis, agriculture served as an important function of last resort for income generation for a large part of the population.The particular vulnerability of households that cannot use increased (rather than just unchanged) agricultural production as a coping mechanism may have to be taken into account in the design of social protection mechanisms. In addition, the relative success with which the sector could serve this role can perhaps be partly attributed to the public investments in rural infrastructure that have been made in re- cent years and also to a number of external factors, such as increased world rice prices. This assumption (that circumstances led to the suc- cess of the sector) implies that the extent to which the agricultural sector can again function as a safety net during future crises should not be overestimated. · As education and gender are the main determinants of acquiring good jobs, the government should continue to promote equal access to edu- cation across socioeconomic groups and geographic areas, as well as in- creased gender equality in the workplace. · The quality of labor analyses, among other factors, and the extent to which the findings can inform policy, could be greatly improved by re- solving a number of data issues, such as the enhanced reliability of de- mographic data through the conduct of a new census, and a consistent focus on levels and changes in the depth of poverty in addition to the poverty rate. Notes 1. For example, Kraay (2006) finds that in the short and medium terms, income growth accounts for 70 percent of the variation in headcount poverty rates, and in the long run for 97 percent. 2. See, for example, Bourguignon (2002); Kakwani, Neri, and Son (2006); Lucas and Timmer (2005); and Ravallion (2004) for evidence on heterogeneity in the Madag-txt-fin.qxd 5/22/08 8:17 PM Page 12 12 Making Work Pay in Madagascar: Employment, Growth, and Poverty Reduction poverty impact of growth. See Ravallion (2004) for a discussion of the rele- vance of this heterogeneity from the perspective of a country: a 1 percent increase in income levels could result in a reduction in poverty of as much as 4.3 percent or as little as 0.6 percent. 3. Owing to the data limitations of the 1999 Household Surveys (HHS) analyses concerning earnings data are restricted to the 2001 and 2005 surveys. Madag-txt-fin.qxd 5/22/08 8:17 PM Page 13 C H A P T E R 2 Definitions and Data This report adopts a broad concept of labor markets and earnings in order to capture, as fully as possible, the entire spectrum of income-generating individuals and activities in a low-income context. This chapter defines the terms used in the discussions (table 2.1), as well as the main sources of data for the report. This report is based on two main sources of information: national accounts data and household surveys. Two different sets of national accounts data were available for the out- put data. One was received directly from the Malagasy National Statistics Bureau (Institut National de la Statistique--INSTAT). This set provided GDP and sectoral and subsectoral output data in constant and current prices for the period 1995­2006. The second set also originated from INSTAT, but some of the data had been adjusted by the International Monetary Fund (IMF). This set contained estimates and projections for the period 1999­2026 of GDP and sectoral and subsectoral output in current prices; GDP and sectoral and subsectoral growth rates in constant prices; and sectoral and subsectoral shares of GDP in constant prices. A comparison of the two data sets revealed that for the period 1999­2006, GDP and sectoral growth rates were identical, but differ- ences in the subsectoral growth rates were quite substantial. On the basis 13 Madag-txt-fin.qxd 5/22/08 8:17 PM Page 14 14 Making Work Pay in Madagascar: Employment, Growth, and Poverty Reduction Table 2.1 Definitions Employment Labor market The place where labor services are bought, sold, and exchanged. The labor market comprises wage and salaried workers and their employers, but also nonwage family enterprise workers and the self-employed, who make up the largest share of workers in Madagascar. Labor force The sum of the working-age employed and unemployed. Employed An individual who performed market activities for at least one hour in the week prior to the survey, or who has a permanent job. Unemployed A working-age individual who is not employed but is actively look- ing for work. Inactive A person who is neither employed nor actively looking for work. Wage worker A worker who has declared being salaried for his/her work. Self-employed A self-declared self-employed person, living in a household in which there are no other self-employed or unpaid family enter- prise workers. Household enterprise A self-declared self-employed person living in a household with worker, family other self-employed or unpaid family enterprise workers. enterprise worker Formal employment : Strict definition Employment that provides paid leave, social protection, and pension contributions. Broad definition Employment that provides social protection or pension contributions. Working-age population The population between 15 and 64 years of age. Child labor A child between 6 and 14 years old, who performed market activities for at least one hour in the week prior to the survey, or who has a permanent job. Earnings Earnings, labor income All cash payments, payments in kind, and benefits received in exchange for labor services in wage and salaried employment, self-employment, and other forms of labor exchange. Earnings and labor income are used interchangeably, although the latter is more often used when referring to the labor income of a house- hold rather than of an individual. Depending on the context, earnings include only primary job earnings (e.g., when comparing earnings in the different sectors) or the sum of earnings in both the first and a possible second job (when total household labor income is relevant). Throughout this report, earnings are mostly expressed on a monthly basis (continued) Madag-txt-fin.qxd 5/22/08 8:17 PM Page 15 Definitions and Data 15 Table 2.1 (continued) Wage earnings Total cash and in-kind earnings as declared in the survey, regionally deflated. Earnings of the For nonagricultural work: sum of declared profits and value of self-employed household auto-consumption, net of taxes and deflated regionally. For agricultural work: owing to difficulties in the calculation of agricultural production, estimated as the residual of household expenditure minus received wages, nonfarm earnings, and transfers, regionally deflated. Household enterprise Earnings are derived in the same way as those for the self- earnings employed, but divided by the number of adult household members performing either nonagricultural or agricultural work. Low earner An employed individual whose earnings are below the national poverty line. Working poor Those employed who reside in households where average per capita expenditures are below the national poverty line. of this observation, this report uses the GDP and sectoral growth rates from the original INSTAT (covering 1995­2006 and corresponding to the IMF-adjusted data for the period 1999­2006), and the levels of GDP as well as sectoral and subsectoral output from the IMF-adjusted data set. The multipurpose household surveys (enquêtes periodiques auprès des ménages) were conducted by INSTAT in 1999, 2001, and 2005.The 1999 survey comprised 5,120 households, the 2001 survey comprised 5,080 households, and the 2005 survey comprised 11,781 households. All sur- veys were held in a single round between September and December of the relevant year.All questionnaires include sections on education, health, housing, agriculture, household expenditure, and employment. The 2001 and 2005 questionnaires include additional sections on assets and non- farm enterprises (among other subjects). For a measure of household well-being, this report uses the estimated household-level consumption aggregate as constructed by INSTAT. A number of concerns regarding the accuracy and comparability of the data sets have arisen. The three main issues and their implications are described below. First, the nationwide population levels and dependency rates that can be derived from the household surveys (HHS), as well as their changes Madag-txt-fin.qxd 5/22/08 8:17 PM Page 16 16 Making Work Pay in Madagascar: Employment, Growth, and Poverty Reduction over time, differ fairly significantly from those of other sources, such as INSTAT, the IMF, and the World Development Indicators (WDI), as shown in table 2.2. There is no certainty as to which source is the most reliable. Madagascar's last population census was in 1993, and population and dependency rate estimates for the more recent years lack reliability. Nevertheless, some apparent discrepancies in the HHS data, as well as some practical arguments, warranted the adjustment of the data to the IMF population estimates and WDI dependency rate numbers. With regard to the population data, the 3.1 million increase between 2001 and 2005, which follows from the HHS data and represents an annual growth rate of almost 5 percent, seems somewhat unrealistic. Throughout this report, population levels and the related variables have therefore been adjusted to match IMF estimates.This also improves com- parability between the microdata from the household surveys and the macrodata from the national accounts; also, the output data used in this report correspond to the data used (and adjusted) by the IMF. With regard to the dependency rates, the sharp fall and subsequent increase in dependency rates within a six-year period cast doubts on the reliability of (particularly) the 2001 dependency rate as derived from the HHS. Since the dependency rates are especially important for the macroanalysis in chapter 3, the dependency rates in that part of the report have been adjusted to match those from the WDI.As is shown in the last column of table 2.2, the WDI dependency rates for Madagascar are comparable, though slightly higher than the WDI dependency rate for Sub-Saharan Africa as a whole. No adjustments in dependency rates have been made in the other sections of this report. Table 2.2 Population and Dependency Rates in Madagascar, 1999­2005 Population (million) Dependency rate* WDI, Sub-Saharan HHS IMF HHS WDI Africa 1999 14.6 15.0 0.93 0.92 0.91 2001 15.7 15.9 0.86 0.91 0.90 2005 18.8 17.9 0.95 0.89 0.88 Sources: HHS; WDI; IMF. Note: * Dependency rate is defined as number of children and elderly per working-age person. Madag-txt-fin.qxd 5/22/08 8:17 PM Page 17 Definitions and Data 17 The second issue is that a number of differences exist between the 1999 survey and the surveys of 2001 and 2005.The main consequence of these differences is that no earnings data can be derived from the 1999 HHS that are comparable to those calculated from the later surveys.This is largely (although not solely) caused by the absence of a nonfarm enter- prise module in the 1999 survey.1 In the 2001 and 2005 surveys, both employment and earnings information can be derived from the employ- ment, agriculture, and nonfarm enterprise sections combined. Although the absence of a nonfarm enterprise section in the 1999 survey does not prevent the construction of employment data, it does present problems for the derivation of earnings data. Nonfarm enterprise earnings for 1999 can be estimated from "other sources of revenue," but their comparabili- ty with the 2001 and 2005 data on nonfarm enterprise is doubtful. Moreover, as nonwage agricultural earnings are defined as the residual of household expenditures and other earnings, doubts about nonfarm enter- prise earnings automatically translate into uncertainty about the reliabil- ity of agricultural earnings. For this reason, when analyzing earnings this report is limited to the years 2001 and 2005. Third, the poverty rates derived from the 1999 survey are not fully comparable with those from 2001 and 2005, among other reasons because the 1999 HHS used a different bundle of goods to construct consumption aggregates.2 Whereas in this report the 1999 poverty rate is calculated to be 71.3 percent, earlier publications have put it at 71.7 percent.3 This lat- ter poverty rate is arrived at by using a calculation method that attempts to ensure comparability with surveys that were conducted before 1999. Clearly, differences in the calculation method of the poverty rate can result in different values of indicators that are determined for the poor and non- poor subsections of the population, such as the unemployment rate of the poor, the share of wage workers among the nonpoor, and so forth. To test whether indicators for the poor and the nonpoor change significantly when they are based on the two different poverty rate calculation meth- ods, the main statistics used in this report were calculated using both poverty rates. The differences that were found were minimal. While the above-mentioned issues were those that posed (or initially seemed to pose) the most substantial risks to data comparability, they were not the only issues. In a number of cases questionnaires changed slightly, either by small modifications of questions or by addition or removal of possible answers. For example, the 2005 HHS asks for profits net of taxes in the nonfarm enterprise section, whereas the 2001 HHS Madag-txt-fin.qxd 5/22/08 8:17 PM Page 18 18 Making Work Pay in Madagascar: Employment, Growth, and Poverty Reduction does not specify whether profits should be net or gross of taxes. Or, in 2005,"always" (toujours) was added to the list of possible responses to the question "When did you look for a job?" A more detailed description of the comparability issues between the 2001 and 2005 household surveys is presented in Stifel, Rakotomanana, and Celada (2007). Notes 1. In addition to the absence of the nonfarm enterprise section, the 1999 survey does not provide information on time spent working (for example, days per week, hours per day), nor on outgoing transfers (remittances), both of which absences make it more difficult to arrive at reasonable earnings estimates. 2. In 2001, a new method to construct the national poverty line (NPL) was intro- duced. The 2001 NPL equaled a per capita annual consumption of 988,600 Malagasy francs, which corresponded to the sum of the price of minimum food (2,133 Kcal daily) and nonfood goods in Antananarivo prices.The poverty rate is determined using real per capita expenditure, rural/urban and province deflated. The NPL is adjusted annually to inflation (using the consumer price index) and, since 2005, converted into the Malagasy ariary. The composition of the basket used to construct the consumption aggregate has not changed since 2001. 3. See, for example, Dorosh et al. (2003). Madag-txt-fin.qxd 5/22/08 8:17 PM Page 19 C H A P T E R 3 Country Context As in most low-income countries, labor participation and employment rates in Madagascar are high, a large share of the population is active in agriculture, and formal and wage jobs are relatively rare. Although over- all and rural poverty rates have fallen since 1999, the urban population still feels the adverse impact of the severe crisis in 2002, despite the quick rebounding of economic growth. In the medium term, the economic out- look is positive, with projected annual growth rates of 8 percent. Large differences in employment structure exist between areas with different levels of urbanization. Population, Income, and Poverty Madagascar's population of 17.9 million continues to grow at a rapid pace. From 2000 to 2005, the population grew at an average annual rate of 2.9 percent, compared with 2.3 percent per year in Sub-Saharan Africa as a whole. Over a quarter of the population lives in urban areas, indicat- ing a steady pace of urbanization since the 1960s, when more than 85 percent of the population lived in rural areas. Nevertheless, the urbanized share of the population in Madagascar is still well below the Sub-Saharan Africa average of 35 percent. 19 Madag-txt-fin.qxd 5/22/08 8:17 PM Page 20 20 Making Work Pay in Madagascar: Employment, Growth, and Poverty Reduction Madagascar's strong population growth rate, even in the African con- text, appears in part to be due to the population's relatively high longevi- ty. At 55.8 years in 2005, the average life expectancy at birth in Madagascar was almost 20 percent higher than the average of 46.7 years in Sub-Saharan Africa. Although the pace of population growth contin- ued to increase in the last part of the 20th century, it began to decline in 2000 and reached 2.7 percent in 2005.This apparent reversal seems to be related to a steady decrease in birthrates, and may be interpreted cau- tiously as a sign that Madagascar has reached the next stage of demo- graphic transition.1 A continuing fall in population growth would be accompanied by a rise in the share of the working-age population (ages 15­64)--a share that has been fairly constant at around 52 percent since the mid-1960s, but that has been on the rise since 2000, reaching 52.9 percent in 2005 (table 3.1). The associated decrease in the dependency ratio, which according to U.S. Census Bureau estimates will fall from 83.6 percent in 2005 to 71.0 percent in 2025, would increase the scope for higher savings and investments.2 As the East Asian experience has shown, this could offer ample opportunities for accelerating economic growth and raising the living standard of the population.The extent to which the increase in the share of the working-age population leads to a reduction in poverty will depend critically on the increased availability of good jobs for the poor. Living standards in Madagascar are generally bleak. In 2005, the aver- age annual per capita income was US$233. Although this reflects an improvement compared with the previous few years, it is still substantial- ly lower than the average Malagasy income per capita in the 1990s. In 2005, 68.7 percent of the population lived below the national poverty line, with poverty in the countryside being substantially higher than in urban areas (73.5 percent and 52.0 percent, respectively). The 2005 poverty rate was lower than the 2001 rate. However, it is unlikely that there was a constant decline in poverty in the intermediate years, because the crisis that began at the end of 2001 is believed to have initially raised the share of the poor, particularly in urban areas. Although no poverty data for the period 2002­04 exist, one estimate puts the overall poverty rate at 73.0 percent directly after the crisis (World Bank 2003). In 2005, the effect of the crisis on poverty still appeared to be visible in the urban poverty rate. While urban poverty had fallen by an impres- sive 8 percentage points between 1999 and 2001, it returned to its 1999 level in 2005, and it is likely that urban poverty was even higher in the Madag-txt-fin.qxd Table 3.1 Population, Income, and Poverty in Madagascar, 1960­2005 1960 1970 1980 1990 1999 2000 2001 2002 2003 2004 2005 Population 5/22/08 Population growth (annual %) 2.5 2.6 2.8 2.9 3.0 2.9 2.9 2.8 2.8 2.7 2.7 Population ages 0­14 (% of total) 43.8 45.1 45.1 45.0 44.8 44.8 44.7 44.5 44.4 44.2 44.0 Population ages 15­64 (% of total) 53.4 52.0 51.9 52.0 52.1 52.2 52.3 52.4 52.5 52.7 52.9 8:17 Population ages 65 and above (% of total) 2.8 2.9 3.0 3.0 3.1 3.1 3.1 3.1 3.1 3.1 3.1 Age dependency ratio (dependents/working-age population) 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 Urban population (% of total) 10.6 14.1 18.5 23.6 25.9 26.0 26.2 26.3 26.5 26.6 26.8 PM Income Page GDP per capita (constant 2000 US$) 389 405 342 271 235 239 247 209 224 229 233 GDP per capita (constant local currency) 46,766 48,697 41,097 32,599 28,297 28,787 29,656 25,177 26,887 27,541 28,045 21 GDP per capita growth (annual %) 2.6 ­2.0 0.2 1.6 1.7 3.0 ­15.1 6.8 2.4 1.8 Povertya Poverty rate, at national poverty line (% of population) Total 71.3 69.7 73.0 68.7 Rural 76.7 77.3 73.5 Urban 52.1 44.2 52.0 Poverty rate, at US$1 a day poverty (% of population) Total 53.0 60.8 60.0 Rural 58.7 68.7 65.0 Urban 33.3 34.2 42.3 Sources: World Development Indicators (WDI); World Bank staff estimates using HHS. a. Poverty rate for 2002 is derived from a simulation (World Bank 2003); other poverty rates are derived from household surveys. 21 Madag-txt-fin.qxd 5/22/08 8:17 PM Page 22 22 Making Work Pay in Madagascar: Employment, Growth, and Poverty Reduction years between 2001 and 2005. Poverty in rural areas, where the lion's share of the Malagasy population lives, shows an opposite trend. Rural poverty rates increased slightly between 1999 and 2001, and although the crisis is assumed to have caused a deterioration in living conditions in rural areas, by 2005 the rural poverty rate was almost 4 percentage points lower than it had been in 2001. This reduction in rural poverty can be attributed in part to public investment in rural areas and in part to increases in world rice prices, in combination with a sharp depreciation of the local currency.3 Notwithstanding this impressive postcrisis rural rebound, almost three-quarters of the rural population continue to live in poverty (see table 3.1).4 Macroeconomic Context Madagascar's economy since independence in 1960 has been character- ized by periods of moderate to fairly high growth levels, interrupted by regular periods of brief but often severe crises, which tend to be caused by structural domestic imbalances that are either triggered or aggravated by external shocks (figure 3.1).The country's more recent past was taint- ed by a six-month political crisis, which started in December 2001 and was triggered by contested elections. Prior to the crisis, a period of eco- nomic growth averaging 4.6 percent between 1997 and 2001 had increased average living standards nationwide, although the benefits were largely confined to the urban areas. The crisis had a significant impact on Figure 3.1 Changes in Annual GDP Growth, 1960­2006 15 10 5 tnecrep 0 -5 -10 -15 1961 1966 1971 1976 1981 1986 1991 1996 2001 2006 Source: WDI. Madag-txt-fin.qxd 5/22/08 8:17 PM Page 23 Country Context 23 social and economic conditions. As net inflows of foreign direct invest- ment (FDI) were reduced to less than 10 percent of their previous level, exports faltered, and infrastructure was destroyed, GDP fell by almost 13 percent in 2002 compared with the year before. After the crisis the economy was relatively quick to rebound. With output growth rates of 9.8 percent and 5.3 percent, respectively, in the two years following the crisis, GDP had returned to its 2001 level by 2004. In 2005, investment levels were recovering toward their precrisis levels, and growth was originating largely from improved performance in the agricultural sector, higher tourism receipts, and continued public investment programs. Despite the adverse effects of high oil prices, the financial crisis of the electric parastatal JIRIMA, and the stagnation of growth in the textiles sector owing to the phasing out of the Most Favored Nation Status Agreement, growth still reached 4.6 percent in 2005 and 4.7 percent in 2006. The medium-term outlook is positive, and the government is committed to strengthening economic stability From 2007 to 2011 growth is expected to average 8.0 percent per year, with the main sources of this growth including mining, tourism, construc- tion, and more efficient agriculture.The economy in general, and the min- ing sector in particular, are expected to benefit significantly from a recent- ly confirmed nickel mining investment of about US$3 billion (or some 50 percent of GDP) over the next three years, which has led to a significant upward revision of estimated growth in the short and medium terms.5 Both tourism and agriculture are expected to gain from ongoing public or largely public investments. Exports are projected to grow at an average of almost 20 percent per year in the 2007­11 period, with the growth orig- inating from mining, tourism, and a more diverse range of agricultural products.Although the export processing zones (EPZs) are going through a consolidation phase, a hopeful sign that the sector can again become a source of growth in the future is that textile exports have been stronger than expected because the industry is diversifying and moving toward higher value-added products. Overall, the macroeconomic outlook is relatively stable. Nevertheless, problems in domestic revenue mobilization hamper the government's ability to implement its ambitious and recently published Madagascar Action Plan 2007­2010 (map), which serves as Madagascar's second poverty reduction strategy paper. Improving tax revenues, as well as Madag-txt-fin.qxd 5/22/08 8:17 PM Page 24 24 Making Work Pay in Madagascar: Employment, Growth, and Poverty Reduction managing the macroeconomic impacts of large investment flows and making further progress in the restructuring of JIRIMA, will remain the government's key priorities in the near future. Meanwhile, foreign assis- tance continues to play an important role in macromanagement and poverty reduction. The 2002 crisis temporarily increased the importance of the primary sector in total output Since the mid-1990s output growth in the manufacturing and services sectors has substantially exceeded growth in the primary sector in every year except during the crisis year 2002. Between 1996 and 2001, annual growth rates averaged 5.1 percent for the secondary sector and 4.7 per- cent for the tertiary sector, compared with only 2.5 percent for the pri- mary sector.As a result, the shares of the secondary and tertiary sectors in total output increased (to 13.4 and 52.6 percent, respectively, in 2002) at the expense of the primary sector's shares. Although the crisis temporar- ily interrupted this trend, the trend appears to have picked up again in recent years. In 2006, the tertiary sector generated an estimated 53.8 per- cent of output, the primary sector provided 33.5 percent, and the second- ary sector still produced a mere 12.7 percent (see table 3.2). The secondary and tertiary sectors were severely hit by the crisis. In 2002, output fell by 20.7 percent and 15.0 percent, respectively, reduc- ing the sectoral shares in total output to pre-1995 levels. The impact of the crisis on the primary sector was much more limited, as 2002 output fell by only 1.3 percent (see figure 3.2). Despite relatively modest growth rates in the years after the crisis, output in this sector had returned to its precrisis level by 2003. The secondary and tertiary sectors also managed to rebound from the crisis relatively quickly, with 2003 growth rates of 14.5 percent in the secondary sector and 10.6 percent in the tertiary sec- tor. By 2006, four years after the crisis, the output levels in all three sec- tors were well above their precrisis levels of 2001.6 Agriculture and cattle farming are the main subsectors, while the secondary sector is fragmented into numerous small industries Figure 3.3 depicts the output shares of the largest subsectors in each of the three main sectors in 2006. As the figure shows, the agriculture sub- sector and the cattle farming and fisheries subsector are the two largest subsectors in the economy.Together they make up more than 30 percent of total output, or 90 percent of primary sector output. The three next- largest subsectors--services, trade, and transport of goods--are all in the Madag-txt-fin.qxd Table 3.2 Macroeconomic Indicators, 1997­2006 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 Output 5/22/08 GDP (billion, in constant local currency) 409.1 425.2 445.1 466.2 494.3 431.6 473.9 498.8 521.8 546.5 GDP growth (annual %) 3.7 3.9 4.7 4.7 6.0 ­12.7 9.8 5.3 4.6 4.7 Primary sector (share of total output, %) 36.6 35.9 35.5 34.6 34.0 37.8 35.6 35.0 34.3 33.5 8:17 Secondary sector (share of total output, %) 12.6 12.8 12.7 13.1 13.4 11.9 12.7 12.9 12.7 12.7 Tertiary sector (share of total output, %) 50.8 51.3 51.8 52.3 52.6 50.3 51.7 52.2 53.0 53.8 PM Expenditures,inflation,lending,trade,and FDI Household consumption (% of GDP) 87.5 85.2 85.6 85.5 76.4 84.2 82.0 82.7 84.2 n.a. Page Total consumption (% of GDP) 95.3 93.0 92.8 92.3 84.7 92.3 91.1 92.2 92.3 n.a. Gross capital formation (% of GDP) 12.8 14.8 14.9 15.1 18.5 14.3 17.9 24.3 22.4 n.a. 25 Inflation (consumer prices, annual %) 4.5 6.2 9.9 12.0 6.9 15.9 ­1.2 13.8 18.5 n.a. Lending interest rate (%) 30.0 27.0 28.0 26.5 25.3 25.3 24.3 25.5 27.0 n.a. Trade (% of GDP) 51.8 50.8 56.7 68.7 61.4 38.6 55.2 81.8 66.0 n.a. Foreign direct investment (net inflows, % of GDP) 0.4 0.5 1.6 2.1 2.1 0.2 0.2 1.0 n.a. n.a. Foreign direct investment (net inflows, current US$) 14.0 17.0 58.0 83.0 93.0 8.0 13.0 45.0 n.a. n.a. Sources: Output data--MoF and IMF staff projections, World Bank calculations. Other data--World Development Indicators. Note: Sectoral output shares are expressed as share of total output from the three sectors, not taking into account indirect taxes and other items that are not directly attributed to sectoral output; n.a. = not available. 25 Madag-txt-fin.qxd 5/22/08 8:17 PM Page 26 26 Making Work Pay in Madagascar: Employment, Growth, and Poverty Reduction Figure 3.2 Sectoral Output Growth, 1997­2006 primary sector secondary sector tertiary sector 20 10 tnecrep 0 -10 -20 -30 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 Source: MoF and IMF staff projections. tertiary sector. Together they make up less than two-thirds of total terti- ary sector output.With output shares varying between 10 percent and 13 percent of total output, they do not vary much in size. Almost one-fifth of total output is made up of other smaller subsectors in the tertiary sec- tor, including public administration and public works, which account for 5.1 and 3.8 percent of total output, respectively. The secondary sector is made up of a relatively large number of sub- sectors, all of which account for less than 2.5 percent of total output.The largest subsectors are the beverages and food industries, which together make up more than one-third of total secondary sector output. Other subsectors in the secondary sector include EPZs and the energy sector (1.7 percent and 1.5 percent, respectively), and electronic equipment, tobacco, metal, pharmaceuticals, and construction material. (In figure 3.3, these are all incorporated in the "secondary other" category.) Export processing zones and public works have been the fastest growing subsectors Table 3.3 depicts the growth trends of the largest subsectors in each of the three main sectors for the period 1999 to 2006. It depicts the sub- sectors' average annual growth rates for the precrisis, crisis, and post- crisis years as well as for the overall period. The table also shows--as an Madag-txt-fin.qxd 5/22/08 8:17 PM Page 27 Country Context 27 Figure 3.3 Subsectoral Output Shares, 2006 (%) tertiary other, 19.0 agriculture, 15.8 cattle farming and fisheries, 14.7 transport of goods, 10.6 primary other, 3.0 beverages industry, 2.3 trade, 11.3 food industry, 2.2 services, 12.9 secondary other, 8.2 Sources: INSTAT data and MoF and IMF staff estimates. Table 3.3 GrowthTrends in the Main Subsectors, 1999­2006 Average Crisis annual recovery: output output Average annual output growth, growth, overall relative subperiods (%) period (%) to 2001 (%) 1999­2001 2002 2003­06 1999­2006 2006 Primary sector 2.9 ­1.3 2.3 2.0 107.9 Agriculture 2.9 0.8 3.3 2.8 114.8 Cattle farming and fisheries 1.1 2.6 2.5 2.0 113.3 Secondary sector 6.3 ­20.7 7.2 3.4 104.3 Beverages industry 10.7 ­13.2 5.6 5.1 107.5 Food industry 1.4 ­10.3 5.3 1.9 109.7 EPZ 28.3 ­40.0 24.0 17.6 125.4 Energy 1.2 ­31.1 5.4 ­0.7 85.0 Tertiary sector 5.5 ­15.0 7.2 3.8 112.2 Services 10.4 ­19.3 7.1 5.0 106.2 Trade 3.6 ­7.4 4.3 2.6 109.5 Transport of goods 2.9 ­21.1 8.2 2.6 107.8 Administration 0.6 2.0 3.2 2.1 115.6 Public works 10.9 ­15.1 24.2 14.3 201.4 Sources: INSTAT data and MoF and IMF estimates. Madag-txt-fin.qxd 5/22/08 8:17 PM Page 28 28 Making Work Pay in Madagascar: Employment, Growth, and Poverty Reduction indicator of crisis recovery--the output for 2006 relative to the precrisis output levels for 2001. The fastest growing subsectors in the 1999­2006 period were the EPZ and the public works sector. For the EPZs, high overall growth occurred despite a substantial but temporary output contraction in 2002. Indeed, except for the textiles sector, no subsector in the Malagasy economy was as heavily affected by the crisis as the EPZs, as their situation was aggra- vated by their export focus and their location in the capital, which suf- fered from a blockade during the crisis.7 As a consequence of this dispro- portionately large decline in output, the share of the EPZs in overall output fell by one-third in 2002. However, the postcrisis recovery of the EPZ sector was quick and vast: with a growth rate of 76 percent in the year after the crisis, value added generated by the EPZs had returned to precrisis levels by the end of 2003. As a result of this quick rebound and the impressive growth rates before and after the crisis, the EPZ share in total value added increased between 1999 and 2004 from 1.0 to 1.8 per- cent.8 However, growth stagnated in 2005 and output even fell by 5 per- cent in 2006, by which year the EPZ share of total output had declined to less than 1.7 percent (see also box 3.1). The public works sector, particularly, experienced strong growth in the postcrisis period.9 From 2003 to 2006, the sector grew by an average of 24.2 percent per year. This growth reflects, among other things, the sub- stantial boost in public investments in an ambitious road rehabilitation and management program. By 2006, output from public works was more than double its level just prior to the crisis, and the sector is expected to continue experiencing annual growth rates of 14 percent and more until 2010. The energy industry experienced a substantial contraction during the crisis, which was not offset by strong growth rates in the surrounding years. Consequently, this industry is the only relatively substantial subsec- tor that did not see its output increase in the period under observation and had not yet recovered to the precrisis levels by 2006. Owing to the energy sector's weak performance relative to that of the other larger sub- sectors, its share in overall output gradually declined from 2.1 percent in 1999 to 1.5 percent in 2006. The economy's largest subsectors in terms of output--agriculture, and cattle farming and fisheries--showed a relatively modest growth both before and after the crisis (and a modest contraction in 2002). Agricultural growth in particular has picked up since 2003. This can be Madag-txt-fin.qxd 5/22/08 8:17 PM Page 29 Country Context 29 Box 3.1 Export Processing Zones: Are Drivers of Growth at Risk? In 1991, the government of Madagascar started to offer tax incentives and other benefits to companies intending to export more than 95 percent of their pro- duction. In the mid-1990s, these Zones Franches, or export processing zones (EPZs), had become the most dynamic sector of the Malagasy economy, having attracted 120 EPZ companies within five years. Apart from the favorable tax treat- ment, EPZ investors were attracted by the low labor costs of the Malagasy work- force and the country's unsaturated textile quota under the Multi-Fiber Arrange- ment and subsequent WTO Agreement on Textile and Clothing (MFA/ATC). Indeed, textile companies make up a large part of the EPZ companies. In 2001, according to the central bank, clothing accounted for 90 percent of EPZ produc- tion, with the remaining 10 percent divided among food processing, crafts, and services such as data processing. The strong export growth trends that Madagascar experienced in the 1990s can be attributed to a substantial extent to the success of the EPZs. From 1991 to 2001, the value of total exports almost tripled, the share of manufactured prod- ucts in total exports grew from 16 percent to 48 percent, and the share of cloth- ing in total exports rose from 5 percent to 43 percent. Although the 2002 crisis took a heavy toll on EPZ companies, they rebounded quickly, and by 2004 the 186 operational EPZ companies employed more than 100,000 workers. The year 2005, however, brought renewed threats to the EPZ sector, as the expiration of the MFA/ATC removed an important reason for investments in the Malagasy tex- tile industry. Output growth in the EPZ sector stagnated in 2005, and even shrank by 5 percent in 2006. Although Madagascar's export-oriented sectors continue to benefit from special tariffs (unlike their Asian competitors) under preferential agreements concluded with the United States and the European Union, the EPZ sector is not expected to continue to be a source of economic growth in the near future. Nevertheless, the textile industry is diversifying and moving toward higher value-added products, which may be cautiously interpreted as a sign that the sector could again be a source of growth in the medium to longer term. Meanwhile, according to Cling, Razafindrakoto, and Roubaud (2007), the wage premium that EPZs used to offer their employees (compared with employ- ees with similar characteristics in other formal secondary sector jobs in the mid- 1990s) has gradually evaporated and turned negative, particularly since the (continued) Madag-txt-fin.qxd 5/22/08 8:17 PM Page 30 30 Making Work Pay in Madagascar: Employment, Growth, and Poverty Reduction Box 3.1 (continued) beginning of the millennium.a Furthermore, the quality of labor standards in EPZs (including social security affiliation, paid holidays, job security, and working hours), which used to be relatively high, is declining toward the standards in the rest of the formal secondary sector (which are still much higher than in the informal sec- tor).The expiration of the MFA may be one of the causes of these trends; addition- al possible explanations could be related to the 2000 rise in the exchange rate, the aftermath of the 2002 crisis, and the EPZs'export orientation, which did not allow them to profit from the domestic boom as other industries did. Sources: Cling, Razafindrakoto, and Roubaud 2007; CRS, UNDP, and ILO (2005). a. Wages in this context do not include benefits such as paid holidays or bonuses. EPZs tend to provide relatively high benefits. Possibly, the observed trend of the wage premium would change if these bonuses were included. attributed in part to the increase in world prices for rice, in combination with the sharp depreciation of the currency in the first half of 2004, which is believed to have sparked production (see boxes 3.2 and 3.3). By 2006, both subsectors had recovered from the crisis, with output levels exceeding those of 2001 by more than 13 percent. Forestry, the only other subsector in the primary sector (not depicted in the table), did not fare as well. Owing largely to substantial output decreases in 2002 and 2003, and to only weak growth rates in the succeeding years, the output for 2006 was only 70 percent of its 2001 level. Unlike most of the primary sector, almost all of the major subsectors in the secondary and tertiary sectors experienced a fall in output in 2002. The impact of the crisis on the beverages and food industries was relative- ly weak compared with the impact on most other industries. In the terti- ary sector, the services and transport subsectors were especially hard hit, with 2002 output levels falling by 21.1 percent and 19.3 percent, respec- tively. The decrease in value added through trade was limited to 7.4 per- cent. By 2006, however, output levels of all major subsectors--except the previously mentioned energy industry--widely exceeded the precrisis output levels of 2001. Madag-txt-fin.qxd 5/22/08 8:17 PM Page 31 Country Context 31 Box 3.2 A Lagged Link between Growth of Rice Prices and Production Trends in the growth of the price and production of rice seem linked (figure a), particularly when the data assume a one-year time lag for production to respond to price changes (figure b). (a) 20 15 10 10 % 0 5 % 1999 2000 2001 2002 2003 2004 2005 -10 0 -20 -5 (b) 20 15 2000 2005 10 2004 2004 10 2003 1999 2005 % 0 5 % 2001 2002 2003 -10 0 2002 2000 2001 -20 -5 producer price growth production growth Source: FAOSTAT. http://faostat.fao.org/. Note: Producer prices are derived from annual producer prices in local currency, and production is from annual data, in 1,000 tons. Madag-txt-fin.qxd 5/22/08 8:17 PM Page 32 32 Making Work Pay in Madagascar: Employment, Growth, and Poverty Reduction Box 3.3 Integrated Growth Poles: New Motors of Growth? Nonagricultural private sector growth in Madagascar appears to be constrained by a deficient investment climate, poor and unreliable infrastructure, and high risks associated with the policy environment. Although wide-ranging reforms have been initiated addressing the structural impediments to private sector­led growth, results are expected only in the medium to long term. The government of Madagascar has identified three regions in which appropriate market condi- tions could be created to generate more immediate and higher private sector growth in the tourism, mining, and manufacturing sectors. The government has identified a number of constraints to business development, which are either general or specific to these pôles intégrés de croissance (integrated growth poles), and is assisted by the World Bank and other donors in addressing these con- straints. In particular, activities focus on supporting export-led growth in the An- tananarivo-Antsirabé region (for example, through technical assistance aimed at enhancing EPZ competitiveness and the creation of a business park for the infor- mation and communications technologies); tourism in the Nosy Be region (for example, infrastructure upgrading and the adoption of a tourism development master plan); and both mining and tourism in the area of Tolagnaro (the upgrad- ing of infrastructure and key public utilities services, and the adoption of tourism and urban development plans). Efforts aimed at strengthening the business environment in all three growth poles include the monitoring of governmental policy and regulatory changes, strengthening the investment promotion agency and the creation of registries, and building capacity and improving access to fi- nance for micro-, small, and medium enterprises. Source: World Bank 2005. Labor Market Outcomes As is typical of many low-income countries, labor force participation and employment rates in Madagascar are high, formal and wage employment rates are low, a large share of the population is active in agriculture, and there is a relatively high incidence of child labor (tables 3.4 and 3.5).10 Of the total working-age population in 2005, about 88 percent were either Madag-txt-fin.qxd 5/22/08 8:17 PM Page 33 Country Context 33 Table 3.4 Basic Labor Market Indicators for Madagascar, 2005, 2001, and 1999 Level Change Indicator 2005 2001 1999 Absolute Percent Employment and unemployment Labor force 88.1 83.5 80.2 4.6 6 Employment-to-population ratioa 85.8 82.5 79.2 3.3 4 Unemployment rate 2.6 1.2 1.3 1.4 113 Child labor rate 18.8 24.3 26.4 ­5.5 ­23 Women's employment rate 83.2 77.8 72.2 5.4 7 Poverty rate among unemployed 42.1 43.5 61.0 ­1.5 ­3 Wage and salaried workers Median monthly earnings (MGA x 1,000)b 71.5 88.1 ­16.6 ­19 Earnings inequality (Gini) 0.45 0.49 0.0 ­9 Low earnings ratec 18.6 15.8 2.9 ­18 Poverty rate 47.0 33.1 50.1 14.0 42 Nonwage workers Median monthly earnings (MGA x 1,000)b 32.2 25.3 6.9 27 Earnings inequality (Gini) 0.47 0.61 ­0.1 ­23 Low earnings ratec 36.6 50.9 ­14.3 ­28 Poverty rate 58.7 77.3 71.4 ­8.3 ­11 All workers Median monthly earnings (MGA x 1,000)b 35.3 30.8 4.5 15 Earnings inequality (Gini) 0.50 0.62 ­0.1 ­19 Low earnings ratec 33.8 44.1 ­10.3 ­23 Poverty rate 65.4 69.2 68.2 ­4.0 ­6 Source: HHS 2005, 2001, 1999. a. The individual is employed if he/she has a permanent job or has worked at least 1 hour in the week prior to the survey. b. Earnings levels for 2001 are expressed in thousands of MGA and divided by 0.6476 (= 197,720 / 305,300 = 2001 poverty line/2005 poverty line) to compare 2005 and 2001. c. Low earnings line: Official national poverty line, 305,300 MGA per year for 2005. Note: MGA = Malagasy ariary. working or actively looking for work, and nearly 86 percent held jobs.This represented an increase of 4 percent from 2001 and 8 percent from 1999. Although open unemployment more than doubled between 2001 and 2005 (there was little change from 1999 to 2001), it remains low at 2.6 percent of the adult labor force.This measure, however, sends mixed infor- mation for low-income countries such as Madagascar, where unemploy- ment can be viewed as a luxury afforded to those with the means to forgo Madag-txt-fin.qxd 34 Table 3.5 Hierarchical Decomposition of the Labor Market, 2005, 2001, and 1999 Population (millions) Change (%) Hierarchical rates (%) Change (%-points) 5/22/08 2005 2001 1999 2001­2005 1999­2001 2005 2001 1999 2001­2005 1999­2001 A. Total working population (6+ years) 14.44 12.84 12.13 12.5 5.8 100 100 100 B. Child population (ages 6­14) 4.78 3.85 3.89 24.3 -­1.0 33.1 30.0 32.1 3.1 ­2.06 8:17 B1. Child laborers 0.90 0.93 0.88 ­3.6 5.5 18.8 24.2 22.67 ­5.4 1.53 C. Elderly population (65+) 0.49 0.42 0.49 16.4 ­14.4 3.4 3.3 4.0 0.1 ­0.75 PM C1. Employed 0.31 0.26 0.27 20.8 ­5.0 63.7 61.3 55.8 2.4 5.53 Page D. Working-age population (ages 15­64) 9.17 8.57 7.75 7.0 10.6 63.5 66.8 63.9 ­3.3 2.91 D1. Inactive 1.09 1.41 1.53 ­22.8 ­7.9 11.9 16.5 19.8 ­4.6 ­3.26 a) Discouraged 0.07 0.11 ­40.9 6.1 7.9 ­1.9 7.90 34 D2. Active 8.08 7.15 6.22 12.9 14.9 88.1 83.5 80.2 4.6 3.26 b) Unemployed 0.21 0.09 0.08 140.8 9.8 2.6 1.2 1.3 1.4 ­0.12 c) Employed 7.87 7.07 6.14 11.3 15.2 97.4 98.8 98.7 ­1.4 0.12 c1) Waged and salaried 1.17 1.29 0.92 ­9.2 40.3 14.9 18.3 15.0 ­3.4 3.32 With low earnings 0.22 0.20 7.5 18.6 15.7 2.9 Management 0.12 0.22 ­45.7 10.1 17.0 ­6.8 Skilled workers 0.41 0.41 ­0.4 34.8 31.7 3.1 Unskilled workers 0.65 0.66 ­2.6 55.1 51.4 3.7 c2) Nonwage employed 6.69 5.77 5.22 15.9 10.6 85.1 81.7 85.0 3.4 ­3.29 With low earnings 2.33 2.70 ­13.7 34.4 46.6 ­12.2 ci) Primary 6.30 5.19 4.58 21.3 13.3 80.1 73.5 74.6 6.6 ­1.12 cii) Industry 0.20 0.48 0.34 ­58.8 40.7 2.5 6.8 5.6 ­4.3 1.24 ciii) Services 1.37 1.36 1.22 0.8 11.9 17.4 19.2 19.8 ­1.9 ­0.60 Source: INSTAT HHS 1999, 2001, 2005. Madag-txt-fin.qxd 5/22/08 8:17 PM Page 35 Country Context 35 income-earning employment while searching for good jobs.11 Thus, it is not surprising that the poverty rate among unemployed individuals is lower than for workers in general (42 percent and 65 percent, respectively). Employment is characterized predominantly by jobs that are either nonwage (85.1 percent) or agricultural (77.7 percent) or both (76.7 per- cent). Although nonwage employment rose by 3.4 percentage points between 2001 and 2005, the effect has been a return to the 1999 levels. In the precrisis period, wage employment was growing at a faster rate than nonwage employment.Agricultural employment, however, rose con- sistently over the entire period. The informal sector dominates the labor market. A conservative esti- mate places 64.5 percent of the 1.2 million wage laborers in the informal sector. Considering the total workforce, including nonwage workers, approximately 95 percent of the 7.9 million working adults are informal- ly employed. When a more restrictive measure of formality is used, 74.2 percent of workers are informally employed.12 (Informality figures are not included in the tables.) Although nearly one in five children between the ages of 6 and 14 were involved in some kind of income-earning activities in 2005, this con- stituted a fall in the child labor rate of 23 percent compared with 2001, and of 29 percent compared with 1999. Child labor is 42 percent higher among poor households than among nonpoor households. Children tend to be employed in the sectors with the lowest earnings and the highest low-earnings rates. On a brighter note, the percentage of working children who attend school more than tripled, from 15 percent to 46 percent between 2001 and 2005. (See box 6.1 in chapter 6 for a closer look at child labor rates across regions and household expenditure levels.) It is worthwhile to note that, unless presented otherwise, labor indica- tors in this report concern the working-age population, which is defined as people between 15 and 64 years of age. As a consequence, the produc- tion of children and the elderly is attributed to working adults, resulting in an upward bias of the working-age population's productivity. Another option would have been to not define a working age and to base labor indicators on the entire population.Assuming, however, that children and elderly are generally less productive than those who are currently defined as being of working age, this option would have created a downward bias of the productivity of working-age adults. Madag-txt-fin.qxd 5/22/08 8:17 PM Page 36 36 Making Work Pay in Madagascar: Employment, Growth, and Poverty Reduction There are large differences in employment structure between areas with different levels of urbanization In rural areas (where 80 percent of workers reside) nearly 9 out of 10 working adults were employed in primary sector activities in 2005; serv- ices accounted for most of the remainder. In urban areas, on the other hand, services accounted for 46.9 percent of primary jobs and industry for 7.9 percent. Even in urban areas, agriculture remained an important source of employment, providing 45.2 percent of urban jobs. By far the largest number of workers hold primary jobs in a family enterprise. In rural areas the share of (predominantly agricultural) family enterprise workers is as much as 86 percent. In urban areas more than half of all of the working-age employed are in family enterprises, although this share is much smaller if only the large urban centers (that is, the provincial capitals) are taken into account. In the large urban centers only 22 percent of the working adults are engaged in family enterprises, and wage work is the dominant type of employment, providing jobs to 66 percent of the employed (figure 3.4). In the precrisis period from 1999 to 2001, the number of wage jobs grew by 373,000, reflecting an increase of 31 percent. This growth rate was almost three times as high as the growth rate of nonwage jobs (11 percent). As a result, the share of wage jobs to total employment increased by 3.3 percentage points, to 18.3 percent. Most of the new wage jobs (239,000) were created in rural areas, raising the share of rural wage workers by 3.0 percentage points, to 12.2 percent of all rural work- ers. In the large urban centers, the number of generated wage jobs was smaller (96,000) but more substantial in relative terms, resulting in an increase in the share of wage workers of 6.4 percentage points, to 57.5 percent.The generation of wage jobs was most modest in secondary cities in both absolute and relative terms. There, the creation of 38,000 wage jobs raised the share of wage workers by 2.43 percentage points, to 30.2 percent. The period between 2001 and 2005 shows a different picture when comparing employment generation and the relative importance of wage jobs--both nationwide and when distinguishing between areas with dif- ferent levels of urbanization. First of all, the impact of the 2002 crisis is clearly visible in the total number of jobs created between 2001 and 2005, which were relatively limited.Whereas in the two-year period from 1999 to 2001 the number of jobs rose by 15 percent, net employment creation between 2001 and 2005 was only 11 percent (794,000 jobs). Madag-txt-fin.qxd 5/22/08 8:17 PM Page 37 Country Context 37 Figure 3.4 Employment Status by Level of Urbanization, 2005 large urban centers secondary urban centers (8.2% of workers,0.6 million) (11.8% of workers,0.9 million) 12% 7% 15% 3% 22% 75% 65% 1% rural areas (80% of workers,6.3 million) 6% 5% 3% nonwage,nonagriculture wage agriculture nonwage family labor self-employed 86% Source: HHS 2005. This increase can be completely attributed to the growth in nonwage jobs, as the level of wage jobs fell by 119,000 (9.2 percent). This devel- opment was in sharp contrast to the preceding period of 1999­2001, in which the growth of wage employment far exceeded that of nonwage jobs. As a result, the share of wage employment fell by 3.4 percentage points, bringing the share of wage workers in total employment back to about the 1999 level (14.9 percent). The fall in wage work can be attributed almost entirely to a reduction in nonagricultural jobs in secondary cities and rural areas (by 256,000 jobs). In large urban areas, nonagricultural wage jobs actually increased by 15 percent (53,000), which further increased the already high share of Madag-txt-fin.qxd 5/22/08 8:17 PM Page 38 38 Making Work Pay in Madagascar: Employment, Growth, and Poverty Reduction nonagricultural wage work by almost 9 percentage points, to 65.1 percent (see figure 3.4). Wage employment in agriculture increased as well, by as much as 60 percent (or 80,000 jobs) in rural areas. However, neither increase was sufficient to offset the negative impact of the fall in nonagri- cultural wage jobs outside of Madagascar's large cities. Differences in employment structure are also seen workers with different levels of education More than half of working-age adults have no formal education. Approximately 30 percent have primary education and 15 percent have secondary education. In addition, three-quarters of the new workforce entrants are uneducated. Employment rates are highest among those without any education (93.2 percent) and lowest among those with an upper secondary education (64.6). In addition, employment rates fall steadily as educational attainment levels increase, up through secondary education.As education levels increase, individuals shift out of agriculture and into industry and services (though much more rapidly into the lat- ter). The percentage of the workforce in agriculture decreases with edu- cation levels (89.8 percent to 14.6 percent), and the percentages in indus- try and services increase (1.0 percent to 12.8 percent for industry and 9.1 percent to 72.6 percent for services). Of the 14.9 percent of the workforce with wage and salaried jobs, 55.1 percent are employed in unskilled positions. In large urban areas this share is 42.8 percent, while the share of the unskilled among the wage workers is 48.8 percent in secondary urban areas. Not surprisingly, in rural areas, unskilled labor accounts for 65.6 percent of wage labor. Good jobs tend to be waged, nonagricultural, urban, and in the formal sector Access to employment does not necessarily translate into a path out of poverty for many workers and their families in Madagascar. Almost two- thirds of the working-age population that was gainfully employed in 2005 lived in poverty (the "working poor"). Despite improvements since 2001, job quality remains low, and median monthly earnings for all adult work- ers were MGA 35,600 (approximately US$17.80). The lowest returns to labor are found in agriculture. Unsurprisingly, the agricultural sector is also the sector in which the largest share of workers has earnings that fall below the poverty line. With 37.5 percent, the share of these low earners in the agricultural sector is over three times Madag-txt-fin.qxd 5/22/08 8:17 PM Page 39 Country Context 39 that of nonagricultural wage workers (11.8 percent). For each employ- ment category, earnings tend to be highest (and low earnings and pover- ty rates lowest) in the more urbanized areas (table 3.6). Wage workers in the private formal sector have median earnings that are 60 percent higher than those of informal wage workers. Further dis- aggregation of nonwage employment by formality is also revealing. Median earnings among employees in registered nonfarm enterprises are more than two and a half times higher than those for workers in unregis- tered enterprises. Table 3.6 Low Earnings and Poverty, by Employment Category, Region, and Gender, 2005 Median earnings Low earners Poverty rate (monthly, MGA 1,000) (%) (%) National Agriculture (wage and nonwage) 31.5 37.5 72.3 Nonwage nonagriculture 51.9 32.2 42.0 Wage nonagriculture 87.1 11.8 38.7 Large urban centers Agriculture (wage and nonwage) ... ... ... Nonwage nonagriculture 68.5 12.2 32.6 Wage nonagriculture 100.0 8.8 32.9 Secondary urban centers Agriculture (wage and nonwage) 31.9 37.6 68.6 Nonwage nonagriculture 58.4 27.7 41.1 Wage nonagriculture 89.5 13.2 43.3 Rural areas Agriculture (wage and nonwage) 31.3 37.6 72.9 Non-wage nonagriculture 37.6 43.6 46.7 Wage nonagriculture 77.8 14.7 43.6 Men Agriculture (wage and nonwage) 39.1 37.1 71.7 Nonwage nonagriculture 66.0 24.2 37.0 Wage nonagriculture 99.8 8.4 38.9 Women Agriculture (wage and nonwage) 38.3 37.9 72.9 Nonwage nonagriculture 44.0 37.9 45.5 Wage nonagriculture 67.5 17.8 38.4 Source: INSTAT HHS 2005. Note: ... = negligible number of agricultural workers in large urban centers. Madag-txt-fin.qxd 5/22/08 8:17 PM Page 40 40 Making Work Pay in Madagascar: Employment, Growth, and Poverty Reduction Good jobs are more likely to be held by educated males Access to higher quality jobs is positively associated with educational attainment. At the extreme, median monthly earnings of workers with an upper secondary education are almost 2.5 times as high as those of the workers without schooling (MGA 76,000 compared with MGA 30,900).12 The association between job quality and education applies with- in different worker types, but it is less strong. In agriculture, wage earnings for those with an upper secondary education are only 20 percent higher than for those with no schooling. Gains in earnings are larger across employment types within education categories than across education cat- egories for those up through upper secondary education (table 3.7). Men have greater access to well-paid jobs than women. Nearly two- thirds of wage-employed women hold unskilled positions, while less than half of men do so. Women tend to be employed more often in agri- culture and the informal sector, where earnings are relatively low, while men tend to have higher rates of employment in the formal sector where earnings are relatively high. In terms of earnings, men fare better than women in every employment category other than nonwage agriculture, and these differentials are not driven by differences in educational attainment.14 Differences range from 10 percent for formal nonfarm enterprise employment to 67 percent for informal nonfarm enterprise Table 3.7 Employment Status and Earnings by Education Level, 2005 Wage workers Nonwage workers Education level Agriculture Informal Formal Agriculture Informal Formal Distribution by employment category per education level (%) None 3.5 4.3 1.0 84.7 6.0 0.6 Primary 2.6 6.6 2.0 78.8 8.3 1.7 Low secondary 2.6 12.3 10.5 56.1 14.9 3.5 Upper secondary 2.6 17.9 28.2 30.8 12.8 7.7 Postsecondary 3.7 14.8 59.3 11.1 3.7 7.4 Monthly earnings by education level and employment category (MGA 1,000) None 37 56 83 29 39 ... Primary 42 68 98 33 37 82 Low secondary 39 70 116 40 63 111 Upper secondary 45 75 122 45 80 119 Postsecondary 160 105 166 40 100 261 Source: INSTAT HHS 2005. Note: ... = negligible number of workers. Madag-txt-fin.qxd 5/22/08 8:17 PM Page 41 Country Context 41 employment. Informal male wage workers earn 55 percent more than women (table 3.6). Notes 1. Before starting to fall from 2000, the population growth rate had increased from 2.5 percent annually in 1960 to 3.0 percent in 1999. Life expectancy at birth increased from 40.1 years in 1960 to 55.8 years in 2005. The birthrate has fallen steadily from 48.9 births per 1,000 population in 1960 to 38.0 births per 1,000 in 2004. 2. U.S. Census Bureau estimate, International Data Base (IDB)--Summary Demographic Data for Madagascar (http://www.census.gov/ipc/www/idb/). The dependency rate is defined as the sum of the number of those under 15 years and over 65 years of age, as a share of those ages 15­64. 3. It should be noted that many of the poor are subsistence farmers who do not trade large amounts of rice or do not trade any rice.The increase in rice prices increased the value of autoconsumption and thus reduced the incidence and depth of poverty and made rice farmers better off than those who had to pay market prices to obtain rice. However, they are unlikely to have considered themselves better off in 2005 than in 2001, owing to the increase in rice prices. 4. The $1-a-day poverty line is below the national poverty line. Compared with the national poverty line, the $1-a-day poverty line can therefore be viewed as a measure of deeper poverty. During the postcrisis period, those rates moved parallel to the official poverty rates at the national, urban, and rural levels. In the precrisis period, despite a fall in the percentage of rural inhabi- tants who were poor, those who remained poor were worse off, as indicated by the 10-percentage-point increase in rural $1-a-day poverty. 5. Previous estimates of GDP growth during the 2007­11 period amounted to an average of 5.6 percent per year, which is similar to the projections for 2007 and 2008 in the Organisation for Economic Co-operation and Development (OECD) African Economic Outlook. 6. In 2006, primary sector output was 7.9 percent higher than in 2001, second- ary sector output was 4.4 percent higher, and tertiary sector output was 12.2 percent higher. 7. National accounts data distinguish between the textile industry within and outside of the EPZ context. The textile industry output that is included in the EPZ data is not counted in the textile industry subsector. 8. Although subsectoral output data for before 1999 are less reliable, they indi- cate that the share of EPZ output in value added tripled within a decade, from 0.6 percent in 1995 to 1.8 percent in 2004. Madag-txt-fin.qxd 5/22/08 8:17 PM Page 42 42 Making Work Pay in Madagascar: Employment, Growth, and Poverty Reduction 9. The public works subsector can be defined broadly as publicly funded proj- ects, constructed with highly labor-intensive methods, with the double objec- tive to provide public infrastructure as well as income through employment for vulnerable population groups. 10. Indeed, it is unclear whether an increase in unemployment in such a situation is a signal of deteriorating or of improving conditions. 11. The conservative estimate considers a worker to be employed in the formal sector if the worker or the worker's employer contributes to a pension fund, or if the worker receives social protection. The stricter definition identifies a worker as protected if the worker simultaneously has a pension fund, receives social protection, and is given paid leave. 12. Earnings for those with postsecondary education are more than double this amount. However, they make up only a small percentage of the workforce. 13. No meaningful comparison between male and female nonwage nonagricultur- al earnings can be derived from the data, owing to the household-based cal- culation of agricultural earnings in family enterprises. Madag-txt-fin.qxd 5/22/08 8:17 PM Page 43 C H A P T E R 4 Growth, Employment, and Labor Productivity This chapter uses macrodata to review the employment and labor pro- ductivity profile of growth for the period from 1999 to 2005. It first describes economic sectors and subsectors in terms of output and employment shares, and discusses which sectors are particularly impor- tant for the working poor. It then decomposes changes in per capita growth into changes in sectoral employment and labor productivity. Comparing the Output and Employment Shares of Sectors and Subsectors Substantial differences can be seen in the shares of output and employ- ment accounted for by each of the main economic sectors. For example, though it employed over 80 percent of the working-age population, the primary sector generated just over one-third of total output in 2005. Moreover, although most primary sector workers were active in agricul- ture, this subsector generated only 16.1 percent of total output. The ter- tiary sector, in contrast, produced more than half of output in 2005 but employed only 17.4 percent of the working population. Finally, the sec- ondary sector generated 12.7 percent of output while employing a mere 2.5 percent of the working population. 43 Madag-txt-fin.qxd 5/22/08 8:17 PM Page 44 44 Making Work Pay in Madagascar: Employment, Growth, and Poverty Reduction The substantial differences between the sectors in output shares rela- tive to employment shares seem to indicate that there may be substantial differences in labor productivity between the sectors. In particular, pri- mary sector (agricultural) labor productivity appears to be considerably lower than in the secondary and tertiary sectors. Indeed, if labor produc- tivity is defined as average output per worker, primary sector labor pro- ductivity was only 8 percent of secondary sector labor productivity and 14 percent of tertiary sector labor productivity in 2005. The 2002 crisis caused a massive labor inflow into agriculture and resulted in substantial changes in sectoral labor productivity Table 4.1 depicts the sectoral and subsectoral output and employment distribution for 1999, 2001, and 2005. Taking into account the 2002 cri- sis, the period from 1999 to 2001 can be considered a fairly regular pre- crisis period, whereas the differences in variables between 2001 and 2005 are likely to be at least partly due to the impact of the crisis. The changes in output and employment distribution that occurred in the precrisis period (1999­2001) were characteristic for a country in the early stages of industrialization, such as Madagascar: the primary sector declined, while the other sectors gained ground in terms of output (sec- ondary and tertiary sectors) and employment (secondary sector). It is interesting to note that although the overall employment share of the pri- mary sector fell, this was largely the result of a substantial fall in the employment share of the forestry sector; the employment share of agri- culture actually increased by 0.6 percentage points.1 The success of the secondary sector in this period was to a large extent driven by the textiles and leather industry, in combination with the strong growth in the out- put share of the mining sector and employment growth in the food and wood industries. In the tertiary sector, the increase in output share can largely be attributed to the growth of the services sector, which saw its share rise from 12.3 percent in 1999 to 13.5 percent of total value added in 2001.2 The most striking observation when comparing the output and employment data for the period 2001­05, which includes the crisis, is the substantial increase in the share of agricultural employment. This rise (8 percentage points) came at the expense of nearly all of the other subsec- tors in the primary, secondary, and tertiary sectors; only the public works sector saw its share in employment rise as well in this period. In particu- lar, the secondary sector's employment share was reduced significantly, Madag-txt-fin.qxd Table 4.1 Sector and Subsector Output and Employment Shares, 2005, 2001, and 1999 percent 2005 2001 1999 Output Employment Output Employment Output Employment 5/22/08 Primary sector 34.3 80.1 34.0 73.9 35.5 74.6 Agriculture 16.1 77.7 15.1 69.7 16.0 69.1 Cattle farming and fisheries 15.1 2.0 14.3 3.1 14.4 3.2 Forestry and other primary 3.0 0.4 4.7 1.1 5.2 2.3 8:17 Secondary sector 12.7 2.5 13.4 6.8 12.7 5.6 PM Agro and food industrya 5.0 0.3 5.2 1.0 5.3 0.8 Mining 0.2 0.2 0.3 0.3 0.1 0.3 Page Energy 1.5 0.2 2.0 1.1 2.1 0.2 Textile and leather 1.8 0.9 1.6 2.5 1.3 1.9 45 Wood industry 0.3 0.3 0.4 0.9 0.4 0.6 Construction materials 0.3 0.4 0.4 0.8 0.4 0.6 Other secondary 3.5 0.3 3.5 0.3 3.1 1.1 Tertiary sector 53.0 17.4 52.6 19.3 51.8 19.8 Public works 3.3 1.4 2.1 0.6 1.8 0.7 Transport 13.4 0.9 14.6 1.8 14.8 1.3 Trade 11.3 5.4 11.3 6.3 11.6 6.6 Bank and insurances 2.1 0.1 1.7 0.1 1.6 0.1 Administrationb 5.2 2.4 4.8 2.9 5.2 3.1 Other tertiaryb 17.6 7.2 18.0 7.6 16.8 8.0 Sources: Ministry of Finance (MoF) and IMF staff projections; World Bank calculations from HHS data. a. Export Processing Zones (EPZ) output is allocated to the agro and food (10 percent) and textiles and leather industries (90 percent), based on 2001 Central Bank data. EPZ output makes up between 70 percent (1999) and 88 percent (2005) of the output of the textiles and leather industry. 45 b. Owing to the incompatibility of data sources, public telecommunications and postal output is included in"Other tertiary,"while public workers in post and communications are included in "Administration"in the employment data. Madag-txt-fin.qxd 5/22/08 8:17 PM Page 46 46 Making Work Pay in Madagascar: Employment, Growth, and Poverty Reduction from 6.8 percent to 2.5 percent. The agro and food, energy, textiles and leather, and wood industries all saw their share in employment fall to about one-third of their 2001 levels. Nominal employment levels show that the number of persons active in the secondary sector declined by more than 50 percent in the period 2001­05, while the number of tertiary sector workers rose by 7 percent-- half the rate of growth of the working-age population. At the same time, the number of primary sector workers increased by almost one-third. These numbers suggest that the increase in the agricultural share in employment cannot be entirely attributed to new labor entrants who view agricultural work as an employment of last resort. Rather, it seems likely that the crisis caused a massive influx of labor from the secondary and tertiary sectors to agriculture, the effects of which were still clearly visible in 2005. Although the distribution of sectoral employment differed significant- ly between 2001 and 2005, the differences in output shares were much less pronounced, with the primary and tertiary sectors gaining few per- centage points at the expense of the secondary sector.3 Most remarkably, secondary sector output shares remained fairly stable despite the sharp reduction in employment. The share in agricultural output rose by half the rate of agriculture's share in employment. The different develop- ments in sectoral output and employment shares hint at fairly substantial changes in relative labor productivity of the sectors. This assumption is confirmed by nominal output and employment data. Compared with 2001, labor productivity--defined as average output per worker-- increased by 130 percent in the secondary sector and decreased in the pri- mary and tertiary sectors by 18 percent and 1 percent, respectively. As a result, whereas in 2001 it took 4 workers in agriculture to produce the same value added as 1 worker in the secondary sector, it took more than 12 workers in 2005. Although tertiary sector productivity still exceeded the average output per worker in the secondary sector by 30 percent in 2001, by 2005 it took three tertiary sector workers to produce the same level of output as two workers in the secondary sector. (Chapter 5 reviews how these changes in sectoral labor productivity could be recon- ciled with the observed changes in mean and median sectoral earnings over the same period.) Madag-txt-fin.qxd Table 4.2 Sectoral Shares of Output and Employment, AllWorkers and the Poorest Quintile, 2005, 2001, and 1999 Primary Secondary Tertiary 5/22/08 2005 2001 1999 2005 2001 1999 2005 2001 1999 Output 34.3 34.0 35.5 12.7 13.4 12.7 53.0 52.6 51.8 Employment 8:17 Total 80.1 73.9 74.6 2.5 6.8 5.6 17.4 19.3 19.8 Poorest quintile 92.0 95.1 83.1 0.7 1.7 3.5 7.3 3.2 13.4 PM Sources: MoF and IMF staff projections; World Bank calculations using INSTAT HHS data; poverty data based on the national poverty line. Page 47 47 Madag-txt-fin.qxd 5/22/08 8:17 PM Page 48 48 Making Work Pay in Madagascar: Employment, Growth, and Poverty Reduction The sectoral distribution of the employment of the poorest differs from that of all workers In addition to sectoral output and employment shares in the three years under observation, table 4.2 depicts the sectoral employment distribution of the poorest consumption quintile of workers. As described in chapter 3, the share of the poorest workers who are active in the primary sector is even larger than the share of all workers in this sector. In 2005, only 8.0 percent of the poorest workers were active in either the secondary (0.7 percent) or the tertiary (7.3 percent) sector, compared to almost 20 per- cent of all workers. Turning to the changes in the sectoral employment distribution of the poorest over time, it becomes clear that in the precrisis period from 1999 to 2001 the share of the poorest workers who were in the primary sector increased substantially. The share of these workers in the primary sector became as much as 21 percentage points higher than the share of all workers in this sector (compared to a difference of 8.5 percentage points in 1999). This development could be explained by primary sector work- ers becoming poorer or by workers in the poorest quintile moving into the primary sector. Considering that the total share of primary sector employment actually fell in this period, and that between 1999 and 2001 rural poverty increased whereas urban poverty declined, the former may be the more plausible explanation. However, in the absence of panel data, this assumption cannot be tested. The large gap between the share of all workers and the share of the poorest workers who were active in the primary sector was reduced to 12 percentage points by 2005. This was caused by a fall in the share of poor workers, and particularly by an increase in the share of all workers active in the primary sector. The former could cautiously be assumed to be at least partly the result of increased agricultural earnings and a relative shift of poverty from rural to urban areas. The latter can be attributed to the aforementioned influx of labor into the primary sector following the 2002 crisis. At the same time, the share of the poorest workers in the secondary sector was reduced at the same rate as the share of total workers; in both 2001 and 2005 the chance that a worker was active in the secondary sec- tor was about four times as high as the possibility that a worker in the poorest income quintile would be found in this sector. The share of the poorest working in the tertiary sector more than doubled between 2001 Madag-txt-fin.qxd 5/22/08 8:17 PM Page 49 Growth, Employment, and Labor Productivity 49 and 2005, even though the increase in the overall employment share of this sector was much less pronounced. Decomposing Per Capita Growth into Labor Productivity, Employment, and Demographic Changes In exploring the links between growth, labor, and poverty, the question arises as to what extent changes in per capita income growth are related to changes in employment, productivity, and dependency rates. A better understanding of these linkages can provide useful guidance to policy dis- cussions aimed at reducing poverty through labor, particularly at a time when a changing demographic structure is expected to substantially increase the pool of available labor.4 Changes in GDP per capita can be related to changes in employment, labor productivity, and demographics There are various ways in which the changes in these three compo- nents--employment, labor productivity, and population structure--can be disentangled and related to changes in per capita growth. This is accomplished using a Shapley approach to decompose and attribute to each component a share of total observed growth using the following identity: Y S A = i Ei * (Equation 4.1) N YE i =1 A N i in which Y is total output; Yi is the value added of sector i = 1...S; Ei is the number of adult workers in sector i; A is the working-age population; and N is the total population. Y/N is thus equal to GDP per capita, and Yi/Ei reflects productivity per worker in sector i. Ei/Ai equals the share of the working-age population employed in sector i and is interpreted as a measure of employment in sector i. A/N, finally, is the share of the pop- ulation that is of working age; therefore this variable is inversely related to the dependency rate. Just as per capita output can be described as a product of labor pro- ductivity, employment, and the inverse of the dependency rate, changes in output per capita can be described in terms of the changes in these vari- ables. Subsequently, a Shapley decomposition can be used to identify the Madag-txt-fin.qxd 5/22/08 8:17 PM Page 50 50 Making Work Pay in Madagascar: Employment, Growth, and Poverty Reduction marginal contribution of each of these variables to the observed changes in output per capita (see also annex 4A and Shorrocks [1999]). The fall in GDP per capita is associated with a substantial decrease in labor productivity The decomposition has been performed at the aggregate level and also when distinguishing between the three main economic sectors. Figure 4.1 depicts the changes in per capita output and the contributions of changes in aggregate employment and productivity, plus changes in dependency rates, for the period 1999­2005, as well as for the subperiods 1999­2001 and 2001­05 (figures 4.1a­4.1c, respectively). The figure illustrates the decrease in GDP per capita between 1999 and 2005 by approximately MGA 16,000, or 3.6 percent. This decline can be fully attributed to the fall in GDP per capita in the subperiod 2001­05, which includes the 2002 crisis. The decline in GDP per capita was accompanied by a strong fall in productivity, which took place main- ly in the latter part of the period under observation. If the productivity fall had not been partly offset by the positive contributions (in both sub- periods) of the increase in employment and the share of adults in the population, the decline in GDP per capita would have been similar to the negative contribution of the fall in productivity, equaling almost MGA 60,000, or 13 percent of GDP. Similarly, if output per worker had remained constant, then GDP per capita would have increased by more than MGA 41,000 (or 8.6 percent), mainly owing to the rise in the share of the employed among adults (MGA 35,000) and, to a lesser extent, to the growth in the share of adults in the population (MGA 6,000). Additional analysis of the observed decline in labor productivity indi- cates that it can be almost fully attributed to a fall in total factor produc- tivity, rather than to a sharp decrease in the capital-labor ratio. However, the conducted analysis proves quite sensitive to assumptions about the production function and to the data used (see annex 4B). All sectors contributed to the negative growth in the 2001­05 period, albeit for different reasons Table 4.3 depicts the contributions of sectoral employment and produc- tivity changes to changes in GDP per capita. The contribution of the pri- mary sector was negative in both subperiods, since a fall in labor produc- tivity was not offset by the positive contributions of employment growth. The contributions of the secondary and tertiary sectors were positive in Madag-txt-fin.qxd 5/22/08 8:17 PM Page 51 Growth, Employment, and Labor Productivity 51 Figure 4.1 Aggregate Contributions to Changes in GDP Per Capita, 1999­2005 (a)1999­2005 Y/N Y/E E/A A/N -80 -40 0 40 MGA1,000 (b)1999­2001 Y/N Y/E E/A A/N -80 -40 0 40 MGA1,000 (c)2001­2005 Y/N Y/E E/A A/N -80 -40 0 40 MGA1,000 Sources: MoF and IMF estimates; HHS. Madag-txt-fin.qxd 5/22/08 8:17 PM Page 52 52 Making Work Pay in Madagascar: Employment, Growth, and Poverty Reduction the precrisis period; in the secondary sector this was the result of the rel- atively large employment increase, while in the tertiary sector it was largely attributed to productivity increases. In the subsequent period of 2001­05, however, a fall in employment resulted in negative contribu- tions for both sectors. Although from 2001 to 2005 none of the sectors contributed positive- ly to GDP per capita growth, the underlying reasons for these negative contributions appear to have differed substantially by sector. In the pri- mary sector, the substantial influx of workers resulted in the considerable positive contribution of the employment variable, but very likely also contributed to the substantial fall in output per worker. The opposite occurred in the secondary sector, where a massive departure of workers is assumed to have been closely linked to the almost equally substantial pos- itive contribution of the increase in labor productivity. In the tertiary sec- tor both employment and labor productivity fell. Even though the con- tributions of both variables, and particularly productivity, were fairly modest, the fact that they were both negative implied that they enforced rather than offset each other, as was the case in the other sectors in this period. As a result, the overall negative contribution of the tertiary sector Table 4.3 Sectoral Contributions to Changes in GDP Per Capita, 1999­2005 (MGA 1,000) Y/E E/A Total Primary sector 1999­2005 ­37 24 ­13 1999­2001 ­7 5 ­2 2001­2005 ­30 19 ­11 Secondary sector 1999­2005 46 ­49 ­3 1999­2001 ­10 15 5 2001­2005 56 ­64 ­8 Tertiary sector 1999­2005 6 ­12 ­6 1999­2001 8 3 11 2001­2005 ­2 ­15 ­17 Sources: MoF and IMF estimates; INSTAT HHS. Note: Y/E is output per worker, E/A is share of employed in working-age population. Madag-txt-fin.qxd 5/22/08 8:17 PM Page 53 Growth, Employment, and Labor Productivity 53 was more significant than the contributions of the primary and secondary sectors, even though these latter sectors experienced much more pro- nounced changes in both employment and productivity. Notes 1. The rise in the agricultural employment share is possibly related to agricul- ture's serving as a "sector of last resort" for new entrants in the labor market, in combination with a high population growth, particularly in rural areas. 2. Services are not recognized as a separate subsector in table 4.1. It is distin- guished as a subsector of the tertiary sector in the national accounts (output), but the HHS does not allow the distinction of services in employment data. 3. The similarity of sectoral output shares in 2001 and 2005 conceals the crisis- related increase and subsequent decline in the importance of the primary sec- tor in terms of output in the intermediate years. 4. In this section, as in most of the remainder of the report, the term productivi- ty is sometimes used to describe labor productivity, which in this report is defined as average output per worker. Madag-txt-fin.qxd 5/22/08 8:17 PM Page 54 54 Making Work Pay in Madagascar: Employment, Growth, and Poverty Reduction Annex 4A The Shapley Decomposition There are various ways in which the changes in employment, labor pro- ductivity, and population structure can be disentangled and related to changes in per capita GDP growth. Chapter 4 uses a Shapley approach to decompose and attribute to each of these three components a share of total observed growth, using the following identity: Y S A = (Equation 4A.1) i Ei * N YE i =1 A N i in which Y is total output; Yi is the value added of sector i = 1...S; Ei is the number of adult workers in sector i; A is the working-age population; and N is the total population. Y/N is thus equal to GDP per capita, and Yi/Ei reflects productivity per worker in sector i. Ei/A equals the share of the working-age population employed in sector i, and is interpreted as a measure of employment in sector i. A/N, finally, is the share of the pop- ulation that is of working age; this variable is therefore inversely related to the dependency rate. From the equation 4A.1 it is possible to decompose changes in per capita output in two consecutive periods, Y/N, into the marginal contri- bution of each of its sectoral components using a Shapley decomposition. This approach is based on the marginal effect on a variable of the sequen- tial elimination of each of the contributory factors. The method then assigns to each factor the average of its marginal contributions in all pos- sible elimination sequences. For example, to calculate the contribution of employment growth in the manufacturing sector to per capita GDP growth, changes in GDP per capita are calculated assuming that, in the period under observation, all variables remained unchanged except employment in manufacturing. The difference between this counterfac- tual per capita GDP growth and the actual growth is labeled "the contri- bution of changes in employment in manufacturing to per capita GDP growth."As opposed to merely comparing growth rates of employment in manufacturing with per capita GDP growth, this approach has the advan- tage that the relative size of a sector is taken into account in the calcula- tion of the contribution (see also Shorrocks [1999]). The decomposition can also be performed at the aggregate level. In this case, the contribution of changes in employment to per capita growth can be interpreted as a measure of the economy-wide employment inten- sity of growth. Madag-txt-fin.qxd 5/22/08 8:17 PM Page 55 Growth, Employment, and Labor Productivity 55 Annex 4B The Sources of Change in Labor Productivity An understanding of the origin of changes in labor productivity (output per worker) can provide an important contribution to insights into the causes of changes in GDP per capita. Changes in labor productivity can be the result of two different sources: increases in the capital-labor ratio, and increases in total factor productivity (TFP).Therefore, in Madagascar, where labor productivity declined during the period under observation, the question arises whether this decline was mainly due to a decline in the average capital stock per worker, or to certain inefficiencies that caused a fall in TFP. Contributions of changes in the capital-labor ratio and TFP to changes in labor productivity The question can be answered by approximating the contributions of changes in the capital-labor ratio and of changes in TFP to changes in out- put per worker. For this purpose, the calculation assumes a Cobb-Douglas production function with constant returns to scale, Y Y = (K K E1- ) or (Equation 4B-e1) E = E where Y is total value added, is total factor productivity, K is capital stock, E is employment,Y/E is output per worker, K/E is the capital-labor ratio, and is the importance of physical capital in output. When an esti- mate of a is available, TFP can be calculated as follows: Y K = TFP (Equation 4B-e2) E / E Changes in labor productivity can then be disaggregated into the con- tribution of changes in capital per worker and of changes in TFP: + ) + ) (Equation 4B-e3) = k (TFPt = 0 TFPt =1 + TFP ( k t = 0 k t=1 2 2 where = Y/E is total output per worker, and k is the capital-labor ratio K/E.1 Madag-txt-fin.qxd 5/22/08 8:17 PM Page 56 56 Making Work Pay in Madagascar: Employment, Growth, and Poverty Reduction In Madagascar, the importance of physical capital in output has been estimated to be 0.46.2 Using output and capital formation data from the national accounts, and employment numbers derived from household surveys,3 the contributions of changes in the capital-labor ratio and in TFP for the period 1999­2005 are depicted in table 4B.1. The results show that the decline in output per worker of MGA 7,517 (that is, a fall of more than 10 percent) during this period was largely due to a fall in total factor productivity (MGA 7,355, or 98 percent of the total decrease in output per worker). In a review of the two subperiods 1999­2001 and 2001­05, it becomes clear that the large fall in TFP and the subsequent decline in average out- put per worker occurred between 2001 and 2005. From 1999 to 2001, labor productivity increased slightly (less than 0.3 percent), as a fall in the capital-labor ratio was more than offset by an increase in TFP. From 2001 to 2005, the capital stock increased at a somewhat faster pace than the number of workers, but the positive impact of this development on out- put per worker was extinguished by the very significant negative effects of the major deterioration of TFP. While the above analysis does not reveal the reasons for the substan- tial fall in TFP, one possibility may be that the fall was due to inefficien- cies caused by the substantial influx of labor into the low-productivity agricultural sector, particularly from workers that were previously employed in services or industry. Table 4B.1 The Contribution of Changes in Capital-Labor Ratio andTFP to Changes in Output perWorker, 1999­2005 MGA Changes in Contribution of changes in Output Capital­labor Total factor per worker ratio productivity 1999­2005 ­7,517 ­162 ­7,355 1999­2001 203 ­614 817 2001­05 ­7,720 420 ­8,140 Sources: IMF National Accounts; INSTAT HHS. Madag-txt-fin.qxd 5/22/08 8:17 PM Page 57 Growth, Employment, and Labor Productivity 57 Caveats to estimating the contributions of changes in the capital-labor ratio and TFP to changes in labor productivity4 There are several caveats to the above-described approach, relating to the data used, the sensitivity of the analysis to these data, and the assump- tions made about the production function. For instance, the analysis can be sensitive to the value of a (that is, the importance of physical capital in output). For a production function with constant returns to scale, it is common to assume values between 0.3 and 0.5. The value of 0.46, as used in the above analysis, is therefore relative- ly high. Moreover, it was derived by regressing output growth on input growth using an ordinary least squares estimation, which ignores the existing correlation between inputs and outputs. In addition, the estima- tion of was based on employment data, which are quite different from the data derived from the household surveys and used throughout this report. The combined application of the value of of 0.46 with the employment numbers of this report in an analysis may therefore cause inconsistencies. To assess the robustness of the value of in the above-described approach, the analysis has also been carried out using values of of 0.3 and 0.4.As table 4B.2 shows, the results for different values of are quite similar. Although the (negative) contribution of the capital-labor ratio diminishes and the (negative) contribution of TFP rises with lower levels of , the differences are not substantial, and the contribution of changes of the capital-labor ratio remains negligible compared with the contribu- tion of changes in TFP. Table 4B.2 Assessment of the Robustness of Approximations to Capital Stock and Changes in Contribution of changes in Output Capital­labor Total factor per worker ratio productivity Original analysis ( = 0.46) ­7,517 ­162 ­7,355 Alternative (where = 0.4) ­7,517 ­141 ­7,376 Alternative (where = 0.3) ­7,517 ­105 ­7,411 Alternative capital base ­7,515 4,319 ­11,836 Sources: Bank staff calculations based on IMF national accounts data, IMF staff calculations, and HHS. Madag-txt-fin.qxd 5/22/08 8:17 PM Page 58 58 Making Work Pay in Madagascar: Employment, Growth, and Poverty Reduction The analysis proves to be much more sensitive to assumptions con- cerning initial capital stock. The original analysis assumes an initial capi- tal stock in 1960 of 1.5 times the GDP in that same year. Another approach, assuming an initial capital stock in 1980 equal to capital forma- tion in that same year but using similar increases in annual gross capital formation and similar depreciation rates as the original analysis, provides substantially different results. In addition to sensitivity to the value of and the assumed capital base, there are other considerations that should be taken into account when assessing the reliability of the approach used above. For example, the choice of the sample period can have a considerable impact on the results, as does the assumption that parameters of the production func- tion remain constant over the sample period. Even more fundamental is the question: to what extent is it appropriate to use a production func- tion that assumes both constant returns to scale and perfect competition in developing countries? Note 1. In principle, this analysis can also be conducted for individual sectors. However, in the case of Madagascar, no sectoral data on capital were available. 2. Source: IMF, staff calculations. 3. Total employment data are derived from household survey employment rates, taking into account the population numbers and dependency rates as described in section B of chapter 2 of this report. 4. This section draws largely on World Bank (2000). "Measuring Growth in Total Factor Productivity." Madag-txt-fin.qxd 5/22/08 8:17 PM Page 59 C H A P T E R 5 Relating Aggregate and Sectoral Labor Productivity with Earnings This chapter discusses the relationship between some of the aggregate indicators of economic performance described in the previous chapter and individual labor earnings. The relationship between these indicators is relevant because earnings are a close individual-level analog to labor productivity as measured in the national accounts data. Economic theory suggests a link between earnings and labor productivity, although the strength of the link would depend on the prevailing wage mechanism(s). Review of Earnings Data After having explored changes in aggregate and average variables in the previous chapter, this chapter starts by reviewing the household survey earnings data.1 Unlike the macrodata, these data permit the analysis to also take distributional issues into account."Looking beyond the averages" in this way will help to reconcile some of the messages on labor produc- tivity changes from the previous chapter with the individual-level labor earnings data.2 The remainder of this chapter compares the information on labor productivity from the aggregate data with the earnings data obtained from the household surveys. 59 Madag-txt-fin.qxd 5/22/08 8:17 PM Page 60 60 Making Work Pay in Madagascar: Employment, Growth, and Poverty Reduction However, as shown below, the comparisons that are made are sur- rounded by uncertainties, rendering this chapter necessarily largely descriptive. First of all, it is unclear to what extent changes in earnings do indeed reflect changes in labor productivity. Although labor productivity is defined as average output per worker in this report, the marginal prod- uct of labor may be a more appropriate determinant of earnings (Temple 2005). Furthermore, at times the comparison merely provides insights into the nature and extent of compatibility issues between the microda- ta and macrodata, rather than into the links between labor productivity and earnings. Finally, as labor income is described in terms of monthly earnings, the analysis does not take into account changes in the number of hours worked per month that could help explain potential discrepan- cies between changes in monthly earnings and in labor productivity. Changes in hours worked, and their effect on household income, are addressed in chapter 6. Additional data are given in the annex to this chapter. Income inequality has fallen Table 5.1 presents the mean and median monthly labor earnings for all workers and for workers in each of the economic sectors. In 2005, the median monthly earnings (MGA 35,300, or approximately US$17.60, per month) were even lower than the mean monthly earnings (MGA 55,500, or about US$27.70).The earnings distribution is thus skewed, with medi- an earnings representing the earnings of the "middle" worker, while mean earnings reflect the earnings of workers in about the 70th percentile of the 2005 earnings distribution. Earnings inequality in 2005 was less pro- nounced than in 2001, a development that is also illustrated by the falling Gini coefficient (measuring earnings inequality) in table 3.4. The fall in Table 5.1 Monthly Labor Earnings by Sector, 2005 and 2001 Mean Median MGA 1,000 (2005) 2005 2001 % Diff 2005 2001 % Diff Primary 39.7 33.8 17.5 31.6 24.2 30.6 Secondary 103.2 148.1 ­30.3 80.0 83.6 ­4.4 Tertiary 118.4 177.6 ­33.3 72.3 92.6 ­22.0 Total 55.5 69.4 ­20.0 35.3 30.8 14.6 Source: INSTAT HHS 2001, 2005. Madag-txt-fin.qxd 5/22/08 8:17 PM Page 61 Relating Aggregate and Sectoral Labor Productivity with Earnings 61 earnings inequality reflects the fact that the distribution of earnings is becoming less dispersed, which in this case is consistent with the lower and middle portions of the distribution that are experiencing increases in earnings (for example, rising median earnings) while the earnings of those at the upper end of the distribution fall (for example, falling mean earn- ings). As is discussed later in this chapter, factors that contribute to the compression of the earnings distribution seem to have been (i) an increase in primary sector earnings, (ii) the fact that the highest-paid secondary sector workers have seen their wages fall or their jobs disappear, and (iii) a pull-down effect through which the higher earnings in the tertiary sec- tor converged with those in the secondary sector. The decrease in labor earnings inequality is further illustrated by the compression of the 2005 distribution of earnings compared with the 2001 distribution shown in figure 5.1. Monthly earnings rose for those workers up to approximately the 67th percentile (the 2005 distribution is to the right of the 2001 distribution up to this point), while they fell for the 33 percent of the workers with the highest earnings.3 Figure 5.1 Distributions of Monthly Earnings, 2001 and 2005 1.0 0.8 srekro 0.6 wfo erahs 0.4 2001 2005 Poverty line 0.2 0.0 0 50 100 150 200 250 300 MGA 1,000 Sources: HHS 2001, 2005. Note: 0 = 0 workers, 1 = all workers. Madag-txt-fin.qxd 5/22/08 8:17 PM Page 62 62 Making Work Pay in Madagascar: Employment, Growth, and Poverty Reduction When distinguishing between the three economic sectors, table 5.1 shows that in the primary sector both the mean and median earnings increased between 2001 and 2005 while the secondary and tertiary sec- tors experienced a fall in the mean and median earnings levels. Thus, the compression of the earnings distribution seems to have been driven by a fall in earnings among the higher-paid workers in the secondary and ter- tiary sectors and a rise in the earnings of the lower-paid workers in the primary sector in combination with the sectoral shift of workers into the primary sector. The magnitudes of the changes in sectoral earnings (a greater fall for mean than median secondary and tertiary earnings and a larger increase for median than for mean primary earnings) are consistent with an overall decrease in inequality in the earnings distribution. In the primary sector, data and data compatibility problems prevent a meaningful comparison of labor productivity and earnings data In the comparison of macro productivity data and micro earnings data, and starting from the competitive market assumption that earnings are a direct reflection of labor productivity, the fall in average monthly earnings by 20 percent between 2001 and 2005 is consistent with the 11 percent fall in aggregate productivity in the same period. However, the changes in sectoral earnings do not appear to be consistent with those in sectoral labor productivity. Primary sector productivity, for example, fell during this period, while earnings in this sector rose, and the very substantial increase in secondary sector productivity was not reflected in the changes in earnings, as mean secondary sector earnings fell by almost one-third. How can the increase in mean primary sector earnings (17.5 percent) be reconciled with the fall in aggregate labor productivity of an approxi- mately equal extent in this sector? Unfortunately, in the case of the pri- mary sector, a number of data and data compatibility issues appear to be at play that exacerbate the reduction in output per worker and the increase in earnings. As these data concerns seem substantial enough to severely complicate a meaningful comparison of labor productivity and earnings data, this section concerning the primary sector is necessarily limited to a description of the data issues. First, the price increases of the main crop are partly reflected in earn- ings but not in output. To reflect quantity rather than value, output is expressed in constant prices and is thus not expected to capture the rel- atively large price increases of agriculture's main crop, rice. Earnings data are also adjusted to reflect changes in real, rather than nominal, Madag-txt-fin.qxd 5/22/08 8:17 PM Page 63 Relating Aggregate and Sectoral Labor Productivity with Earnings 63 earnings. However, the index used to correct the earnings data is based on economy-wide price changes. When applied to the primary sector only, the index likely did not fully capture the price increases that occurred in this sector. Second, the primary sector output may have been underestimated in 2005. One reason for this assumption is that the estimation of the aggre- gate output of the primary sector is based, among other things, on projec- tions that tend to smooth annual fluctuations. The increase in production between 2001 and 2005 may therefore have been larger than is assumed in the output data.This assumption is reinforced when taking into account the fact that, in particular, production changes in the more remote areas may not be well captured in the national accounts; however, there are rea- sons to assume that production in those areas has been relatively substan- tial: (i) public investments in rural infrastructure are assumed to have improved market access in the more remote areas, increasing production incentives; and (ii) increases in primary sector earnings have been more substantial among the lowest earners than among the best earners (figure 5.2a). Assuming that the lowest earners are located mostly in the more remote areas, the observation that primary sector earnings increased rela- tively substantially may indicate that the output increases in those areas were more substantial than in the less remote areas.4 Third, agricultural earnings for 2005 may have been overestimated. Owing to difficulties in estimating the value of agricultural production, agricultural nonwage earnings have been estimated as a residual between household consumption on the one hand, and nonagricultural nonwage earnings, wage earnings, nonlabor earnings, and net transfers on the other. A strong assumption of zero savings is made in using this residual as an estimate of agricultural nonwage earnings. It is possible that these earnings have been overestimated for 2005 because the political crisis in 2002 and the ensuing economic disruptions forced households to use their savings or accumulated stocks of produce to maintain consumption levels. In that case, the amount of savings used for consumption would have been incor- rectly attributed to agricultural earnings.The fact that the 2005 survey was conducted more than three years after the crisis, however, reduces the strength of this concern. Finally, differences between microdata and macrodata occur because earnings as derived from household surveys reflect distributional issues that do not occur in the national accounts output data, in which changes in output across subsectors are averaged. In the case of Madagascar, Madag-txt-fin.qxd 5/22/08 8:17 PM Page 64 64 Making Work Pay in Madagascar: Employment, Growth, and Poverty Reduction Figure 5.2 Distribution of Monthly Earnings by Sector, 2001 and 2005 (a) primary sector (b) secondary sector 1.0 1.0 srekro 0.8 0.8 0.6 srekro 0.6 wfo 0.4 wfo 0.4 erahs 0.2 erahs0.2 0.0 0.0 0 50 100 150 200 250 300 0 50 100 150 200 250 300 MGA 1,000 MGA 1,000 (c) tertiary sector 1.0 srekro 0.8 2001 0.6 2005 wfo poverty line erahs0.4 0.2 0.0 0 50 100 150 200 250 300 MGA 1,000 Source: INSTAT HHS 2001, 2005. modest growth in the primary sector output may be a result of the 42 percent fall in high-value vanilla production, partly offsetting the 28 per- cent increase in rice production during this period (FAOSTAT 2007). Since a small portion of agricultural workers are involved in vanilla pro- duction (less than 2.5 percent), and a large portion produce rice (over 85 percent), the distribution of agricultural earnings in the household survey data is determined largely by the outcomes for rice producers, not by vanilla producers. In addition, because rice workers account for the majority of agricultural workers, the household survey is more likely to be representative of rice workers (only 200 households out of a sample Madag-txt-fin.qxd 5/22/08 8:17 PM Page 65 Relating Aggregate and Sectoral Labor Productivity with Earnings 65 of 11,781 reported producing vanilla). Thus, the increases in both mean and median labor earnings--and the entire distribution--measured in the household survey are likely to represent increases in the production of rice (and other crops for which there were increases in production during this period, such as maize, cassava, and fruit). Changes in primary sector output per worker in the national accounts, on the other hand, average the changes in output across all of the subsectors. Sector Comparability Although the comparability of micro- and macrodata for the secondary sector is possibly also compromised by data compatibility issues, the pro- ductivity and earnings data for this sector seem to allow a meaningful comparison and a cautious interpretation. The more unproductive workers seem to have left the secondary sector While secondary sector mean wages fell by almost one-third between 2001 and 2005, average output per worker more than doubled. At the same time, the number of people working in the sector decreased by more than half. The notion that a large share of workers could apparent- ly depart without significantly affecting overall sector output suggests that the sector had shed large numbers of unproductive workers. As shown in table 5.2, in 2005 wage workers made up a substantially larger share of all secondary sector workers than in 2001 (77 percent compared with 59 percent), which might suggest that it was largely the self- employed and household enterprise workers who left the sector in this period.That they could exit from the sector without causing a fall in total output could be a sign of disguised unemployment among these workers.v The assumption that the less productive workers left the sector is strengthened by the earnings data for the self-employed. Although the Table 5.2 Employment Distribution in the Secondary Sector, 2005 and 2001 (percent) 2005 2001 Wage employment 77.4 59.0 Self-employed 5.6 14.1 Family labor 17.0 27.0 Source: INSTAT HHS 2001, 2005. Madag-txt-fin.qxd 5/22/08 8:17 PM Page 66 66 Making Work Pay in Madagascar: Employment, Growth, and Poverty Reduction share of the self-employed in secondary sector employment dropped from 14 percent to 6 percent, the median earnings of this category were almost 70 percent higher in 2005 than in 2001.This leads to the hypoth- esis that the most productive (highest earning) self-employed remained in the sector while others moved, probably to the primary sector. At the same time, the highest wage earners seem to have seen their wages fall or their jobs disappear Although these observations provide an explanation for the sharp increase in labor productivity, the question remains as to why higher pro- ductivity was not accompanied by higher earnings, except, presumably, in the case of the self-employed. The answer can perhaps be found in the difference between mean and median earnings (table 5.1). Mean earnings in the secondary sector dropped by more than 30 percent, but median earnings fell less than 5 percent. This reflects the fact that it was largely those at the top of the secondary sector income distribution who saw their earnings fall. As can be seen in figure 5.2b, changes in median earn- ings for the lowest-earning quintiles in the secondary sector were ambigu- ous and much less pronounced. Considering the importance of the share of wage workers in this sector, and taking into account that the median earnings of wage workers fell by only 8 percent, it also seems likely that, in the wage sector, the highest-paid workers experienced a relatively sub- stantial loss of earnings, if not their jobs. The latter seems to be support- ed by the observation that, for the economy as a whole, the decline in wage employment was largely driven by the fall in (presumably higher paid) management-level positions. An additional explanation for this trend could be that the only enterprises that weathered the crisis were those that either paid lower wages in the first place, or reduced them dur- ing the period under observation. The relatively high wages in the tertiary sector converged with secondary sector levels The tertiary sector experienced a sharp reduction in mean earnings, even though the average output per worker barely changed. Unlike in the sec- ondary sector, there were no significant differences in earnings changes between the earnings quintiles in the tertiary sector (hence, changes in mean and median earnings were fairly similar), nor were there substantial shifts in the shares of wage, self-employed, and household enterprise workers. Developments in the tertiary sector can thus be summarized as Madag-txt-fin.qxd 5/22/08 8:17 PM Page 67 Relating Aggregate and Sectoral Labor Productivity with Earnings 67 a shedding of workers, and unchanged labor productivity, in combination with a substantial fall in average earnings. The rationale behind these developments is as yet unclear. One possi- ble, but only partial, explanation could be that the crisis-related fall in the demand for tertiary sector labor eliminated or reduced a barrier that had kept tertiary sector wage earnings artificially high prior to the crisis. Because wage workers made up 55 to 60 percent of tertiary sector work- ers, changes in their earnings would be reflected in a change in median earnings, just as the absence of wage workers' productivity would limit the observed changes in average output per worker. As reflected in table 5.3, tertiary sector wages exceeded those in the secondary sector by 12.6 percent. By 2005, median wage earnings had fallen in both sectors and the difference had been reduced to 4.8 percent. Though this clearly indi- cates a convergence of the sectors' wages, the assumption that wage workers' productivity did not change (or hardly changed) while earnings fell remains untested and thus unconfirmed. An analysis of possible rea- sons for the initial difference in wage earnings between the secondary and tertiary sectors, as well as for their convergence, could shed more light on whether the crisis helped to "unleash market forces" where tertiary sector workers had previously been sheltered from competition, or whether other factors were at play. At the same time that tertiary sector wages converged downward to the secondary sector level, family enterprise workers in both sectors saw their earnings fall by around 30 percent, thereby maintaining the original earnings difference of about 3 percent. It has been suggested that this similarity in earnings levels and changes might imply that no barriers between the secondary and tertiary sectors exist for family workers, and that perhaps this openness might improve the extent to which earnings reflect labor productivity. Table 5.3 Median Earnings by Sector and Employment Status, 2005 and 2001 (MGA 1,000) 2005 2001 Primary Secondary Tertiary Primary Secondary Tertiary Wage employment 39.2 84.8 88.9 49.4 91.9 103.5 Self-employed 50.1 118.2 70.2 38.3 70.0 111.2 Family labor 30.6 45.0 46.8 23.0 65.6 67.6 Source: INSTAT HHS 2001, 2005. Madag-txt-fin.qxd 5/22/08 8:17 PM Page 68 68 Making Work Pay in Madagascar: Employment, Growth, and Poverty Reduction For the self-employed (10­15 percent of tertiary sector workers), ter- tiary sector earnings fell drastically as earnings in the secondary sector rose. As a result, in 2005 tertiary sector earnings were only 60 percent of the earnings of the secondary sector self-employed, even though in 2001 they had been 60 percent higher. It is unclear what assumptions can be drawn from these developments, concerning the relation between labor productivity and wages. Notes 1. This chapter's analysis is limited to the years 2001 and 2005 because of the lack of comparable earnings data for 1999. 2. Ravallion (2001) notes that,"The poor typically do share in the benefits of ris- ing aggregate affluence, and they typically do suffer from economic contrac- tion. However, there is a sizable variance around the `typical' outcomes for the poor. One source of variance is that `economic growth,' as measured in the national accounts, is not always reflected in average household living standards as measured in surveys, at least in the short run." 3. This does not imply that all individuals with higher earnings were necessarily worse off in 2005 than in 2001, nor that those with low earnings were better off. Since these distributions treat individuals anonymously, some switching in the order of individuals has probably occurred. Furthermore, the distributions are estimated using two cross-sectional data sets, the latter of which represents a larger number of workers owing to population growth. Note that the pover- ty line in figure 5.1 illustrates the low earnings rates reported in table 3.4. 4. See Stifel, Minten, and Dorosh (2003) on the correlation between individual earnings and household consumption and between household consumption and remoteness. 5. Disguised unemployment occurs when the marginal labor product is positive but the removal of a worker does not reduce output (see 1966). Madag-txt-fin.qxd 5/22/08 8:17 PM Page 69 C H A P T E R 6 Linking Employment and Earnings with Poverty This chapter examines the links between employment and labor earnings on the one hand and poverty on the other. Although the multi-faceted nature of poverty is recognized, for the purpose of this analysis poverty is defined in terms of expenditure levels, since these are relatively objective measures of poverty that can be derived from household surveys and are strongly linked to labor income. Although the individual has been the focal point of the previous chap- ters, this chapter measures poverty at the household level. Therefore, after a brief description of the labor market conditions of the poor and the nonpoor at the individual level, the unit of analysis in this chapter moves to the household level.1 The chapter starts by comparing the employment and income profiles of the poor with those of the better-off segments of the population. It then moves to an examination of the effect of changes in the sectoral structure of household earnings and employ- ment on poverty. Finally, it examines the components that make up household labor and assesses the impact of changes in each of these com- ponents on poverty. First of all, however, it is worth briefly focusing on the impact of the level of the selected poverty line that is used in the subsequent analysis. As described in chapter 3, the national poverty line, which is used to 69 Madag-txt-fin.qxd 5/22/08 8:17 PM Page 70 70 Making Work Pay in Madagascar: Employment, Growth, and Poverty Reduction determine poverty-based indicators in this report, puts the headcount poverty rate in Madagascar at 68.7 percent in 2005. This has two impli- cations. First, when the poor are discussed in this context, the term actu- ally refers to the large majority of the Malagasy population. If the desired focus is a smaller share of the population that is poor even in relative terms, it may be preferable to focus on the one or two poorest expendi- ture quintiles of the population rather than on all of the poor. To allow both, the following analysis tends to reflect the results of the distinction between "poor"-and-"nonpoor" as well as the distinction between the var- ious expenditure quintiles. Second, the high headcount poverty rate implies that improvements in the conditions of the poorest of the poor are not reflected in changes in the headcount poverty rate unless they are so substantial that they pull households out of poverty. The headcount poverty rate therefore may be a less appropriate indicator for capturing changes in the living conditions of a large share of the poor than, for example, the depth of poverty. This is taken into account in later sections B and C of this chapter, which ana- lyze the effects of sectoral changes and earnings determinants not only on poverty incidence but also on poverty depth. Employment and Earnings Profiles of the Population Table 6.1 depicts the employment status of the working-age population by poverty level for the years 1999, 2001, and 2005. There has been a clear trend of decreasing inactivity, which is combined with a rise in employment but also with an increase in unemployment. Not surprising- ly, the poor had higher levels of employment and lower levels of unem- ployment and inactivity in each of these years. Developments differ in the employment status of the poor and the nonpoor The patterns of change for these variables over time have been quite dif- ferent for the two groups. Between 1999 and 2001, the share of the non- poor who worked decreased. Their level of employment fell, and the share of unemployed and inactive adults increased. In the same period, the poor started to work more frequently, as evidenced by the rise in employment and the fall in both unemployment and inactivity. It is pos- sible that the economic circumstances in Madagascar in this period were such that they allowed some of the better-off to remain idle, while at the Madag-txt-fin.qxd 5/22/08 8:17 PM Page 71 Linking Employment and Earnings With Poverty 71 Table 6.1 Employment Status of theWorking-Age Population by Poverty Level 2005, 2001, and 1999 Employed Unemployed Inactive 2005 85.8 2.6 11.9 Poor 88.7 1.7 9.8 Nonpoor 80.8 4.4 15.5 2001 82.5 1.2 16.5 Poor 88.4 0.8 10.9 Nonpoor 71.7 2.2 26.7 1999 79.2 1.3 19.8 Poor 80.7 1.2 18.3 Nonpoor 76.1 1.6 22.7 Sources: INSTAT HHS 1999, 2001, 2005. same time they provided increased job opportunities for those poor indi- viduals who wanted to work. The situation was reversed from 2001 to 2005. By 2005, nonpoor inactivity had decreased substantially. Although a significant share of the formerly inactive were working by 2005 (employment increased by 9.1 percent), the doubling of the unemployment rate would seem to indicate that not all of those who wanted to work had found jobs. The nonpoor may have attempted to deal with the adverse effects of the 2002 crisis by raising the share of workers in their households (an assumption that is strengthened later in this chapter). The poor, on the other hand, experienced only minor changes during the same period. The fall in inactivity of the poor by 1.1 percentage points appears to have resulted mainly in an increase in the unemployment rate of approximately equal extent.2 Of those who worked, four out of five were in household enterprises, while 15 percent were wage employed and the remaining 6 percent were self-employed (2005 data). For the poor, family labor was even more important (85 percent), while a larger than average share of the nonpoor were in wage employment or were self-employed (see table 6A.2, and annex 6C.1 for the same table by expenditure quintiles). As mentioned in chapter 3, the period from 1999 to 2001 saw the share of wage workers increase by 3.3 percentage points, at the expense of the shares of both the self-employed and family workers. However, the share of the poor with wage work fell by 2.3 percentage points in this period, as the shares of the poor in self-employment and family labor rose. Madag-txt-fin.qxd 5/22/08 8:17 PM Page 72 72 Making Work Pay in Madagascar: Employment, Growth, and Poverty Reduction Table 6.2 Employment Status of theWorking-Age Employed Population by Poverty Level 2005, 2001, and 1999 Weekly working hours Wage Self- Family 1st and employed employed labor Total 1st job 2nd job 2005 14.9 5.9 79.2 100 37.0 45.4 Poor 10.8 4.1 85.2 100 35.6 44.4 Nonpoor 22.7 9.2 68.2 100 39.7 47.1 2001 18.3 8.2 73.6 100 42.3 46.9 Poor 8.7 5.7 85.6 100 40.9 46.1 Nonpoor 39.8 13.7 46.5 100 45.3 48.7 1999 15.0 9.7 75.3 100 .. .. Poor 11.0 7.8 81.2 100 .. .. Nonpoor 23.5 13.9 62.5 100 .. .. Source: INSTAT HHS 1999, 2001, 2005. Note: n.a. = not available. In 2005, the share of workers in family labor was 5.6 percentage points higher than it had been in 2001. This change in the distribution of work- ers across employment categories was largely due to changes in the distri- bution of the nonpoor: the share of the nonpoor in family labor rose by almost 22 percentage points, to 68.2 percent, at the expense of both wage employment and self-employment. In the same period, changes in the distribution of the poor across employment categories were much less pronounced. The increase in the share of family workers can be assumed to be closely related to the rise in primary sector workers (by 6.2 percent- age points) in the same period. In addition, the large increase in the share of the nonpoor in family labor mirrors the sectoral shifts as observed in chapter 4 (table 4.2), where it was noted that despite the increased importance of the primary sector as a source of employment, the share of the poor in this sector had actually gone down. On average, the employed poor work fewer hours per week than the nonpoor, particularly when only the workers' primary jobs are taken into account. In 2005 the poor spent 10.3 percent less time in their primary jobs than the nonpoor (35.6 hours compared with 39.7 hours per week). This difference is reduced to 5.7 percent when the hours worked in sec- ond jobs are also taken into account. These data may reflect that poverty Madag-txt-fin.qxd 5/22/08 8:17 PM Page 73 Linking Employment and Earnings With Poverty 73 among the working poor is caused at least partly by their inability to work enough hours to help them escape poverty, even after having taken on a second job. Indeed, looking at poor workers in wage jobs, it appears that in 2005, 27.2 percent were poor owing to short working hours only (less than 40 hours per week), while another 30 percent of poor wage work- ers were poor owing to a combination of both short working hours and low productivity (Stifel, Rakotomanana, and Celada 2007). Between 2001 and 2005, the average number of hours worked by employed persons in their primary jobs fell substantially, by 13 percent (5.3 hours per week). The workforce seems to have made up for these lost hours by working more hours in second jobs. As a result, the decline in the average number of hours worked in primary and secondary jobs combined was limited to 3 percent (1.5 hours).3 This increase in hours worked in second jobs was solely due to the sharp increase in the share of workers who held second jobs (from 13.3 percent to 28.9 percent). The number of hours spent by workers in second jobs actually decreased (from 23.7 to 21.7 hours per week). The decline in hours worked in the primary job, which was partly offset by a rise in hours spent on a second job, occurred among both poor and nonpoor workers. Earnings differences between employment categories and between the poor and the nonpoor became smaller In an analysis of earnings, rather than of employment status, the median earnings of wage workers were higher than those of the self-employed, and in turn, the median earnings of the self-employed were higher than those of family laborers. This was the case not only for the working pop- ulation as a whole, but also for the different employment categories, whether by expenditure quintile or by poverty status, in 2001 and 2005. For example, the median earnings of the poorest 20 percent of the wage workers were higher than those of the poorest 20 percent of the self- employed. The difference for the poorest 20 percent of the family labor- ers was even more pronounced. And the difference between wage work- ers and household enterprise workers was so substantial that the median earnings of the poorest quintile of wage workers corresponded to those of the fourth expenditure quintile of family laborers in 2005. In other words, in 2005 only about 30 percent of family laborers had median earn- ings that were above those of the poorest wage workers (table 6.3). Between 2001 and 2005 the differences in median earnings between all employment categories were reduced, as the earnings of wage workers Madag-txt-fin.qxd 5/22/08 8:17 PM Page 74 74 Making Work Pay in Madagascar: Employment, Growth, and Poverty Reduction Table 6.3 Median Monthly Earnings by Employment Status, by Quintile and Poverty Level, 2005 and 2001 (MGA 1,000) Wage workers Self-employed Family labor 2005 71.50 57.00 31.10 Poorest 41.90 32.10 17.00 Q2 50.00 44.10 25.80 Q3 51.30 46.80 31.30 Q4 73.00 64.80 39.00 Richest 110.00 71.80 60.20 Poor 48.90 44.10 26.00 Nonpoor 100.00 76.10 49.60 2001 88.10 51.50 24.30 Poorest 24.70 22.50 12.90 Q2 42.30 37.20 19.50 Q3 60.10 35.00 25.90 Q4 77.20 44.20 42.00 Richest 120.00 116.80 70.30 Poor 58.70 33.50 21.00 Nonpoor 108.70 99.90 60.50 Source: HHS 2005, 2001, 1999. fell by almost a quarter and the earnings of the self-employed and family laborers increased (by 11 percent and 20 percent, respectively). The increase in earnings of family laborers was related in particular to the rise in agricultural earnings.An interesting pattern emerges when the changes in earnings over time are compared between the poor and the nonpoor. For wage workers, the earnings of both the poor and the nonpoor were lower in 2005 than in 2001. For the self-employed and household enter- prise workers, however, the median earnings of the nonpoor fell, while those of the poor increased, which resulted in a narrower gap between the earnings of the poor and of the nonpoor. The importance of agricultural earnings for better-off households increased substantially Because poverty is measured at the household level, table 6.4 moves indi- vidual employment and earnings information to the household level. It does this by showing the various sources from which household income is derived and the shares of household income that those sources provided Madag-txt-fin.qxd 5/22/08 8:17 PM Page 75 Linking Employment and Earnings With Poverty 75 Table 6.4 Structure of Household Income by Quintile and Poverty Level, 2005 and 2001 percent Wage Nonfarm Net Agriculturea employmentb enterprise transfersc Total 2005 68.6 14.6 13.1 3.7 100 Poorest 80.1 7.1 8.7 4.1 100 Q2 75.3 11.8 10.4 2.5 100 Q3 74.6 10.6 11.8 3.0 100 Q4 67.1 15.7 13.5 3.6 100 Richest 45.7 27.8 21.0 5.4 100 Poor 75.9 10.1 10.8 3.2 100 Nonpoor 52.5 24.4 18.3 4.9 100 2001 62.1 20.0 13.3 4.6 100 Poorest 84.5 4.9 6.4 4.3 100 Q2 80.1 7.5 8.7 3.7 100 Q3 70.3 14.4 11.3 3.9 100 Q4 53.4 25.9 16.3 4.3 100 Richest 21.7 47.9 23.8 6.6 100 Poor 75.6 10.8 9.6 4.0 100 Nonpoor 30.9 41.5 21.8 5.8 100 Source: HHS 2001, 2005. a. Residual plus agricultural wages. b. Excluding agricultural wages. c. Family remittances and public transfers. in 2001 and 2005. By far the largest share of household income is derived from agriculture (69 percent in 2005), with smaller shares from nonagri- cultural wage employment (15 percent), nonfarm enterprise income (13 percent), and nonlabor income transfers (4 percent). Compared with 2001, the 2005 composition of sources of household income reflects a 7- percentage-point increase in agricultural earnings that occurred mainly at the expense of a fall in nonagricultural wages. The changes in the composition of household income for the poor in this period differed substantially from those of the nonpoor. There were also large differences between the various expenditure quintiles. Not sur- prisingly, the poor derived a larger share of their income from agriculture than the nonpoor in both 2001 and 2005. However, there was a large increase in the share of agricultural income for the nonpoor, from 31 per- cent in 2001 to 53 percent in 2005. This rise in the agricultural share Madag-txt-fin.qxd 5/22/08 8:17 PM Page 76 76 Making Work Pay in Madagascar: Employment, Growth, and Poverty Reduction came largely at the expense of income from wage employment, which was the main source of income for the nonpoor in 2001 but saw its share reduced to 24 percent in 2005. These data appear to be consistent with earlier findings that earnings increased in the primary sector and fell in industry and services, and that there has been a major crisis-related influx of labor into agriculture. A comparison between expenditure quintiles completes this picture. In the two richest quintiles, the portions of income derived from nonfarm enterprise and, particularly, nonagricultural wage work, fell between 2001 and 2005, while that from agricultural income increased. The rise in the importance of agricultural income was especially pronounced in the rich- est population quintile; there, the share of agricultural income more than doubled, from 22 percent to 47 percent. An opposite trend was seen in the poorer expenditure quintiles, where a modest decrease in the share of agricultural income of around 5 percentage points occurred, which was picked up by both nonagricultural wage employment and nonfarm enter- prise employment. In the absence of panel data, it is not possible to deter- mine whether these changes occurred through changes in the income structure of households that remained within the same quintile (that is, the poorest households remaining the poorest while becoming less reliant on agricultural income) or as a result of the reranking of households across the distribution (for example, more agriculture-dependent house- holds moving to higher-expenditure quintiles). Public transfers and remittances appear to have had a positive impact on poverty reduction An examination of the composition of household earnings by sectoral ori- gin should be preceded by a brief discussion of the effects of nonlabor income transfers on poverty. As is clear from table 6.5, nonlabor income transfers, defined as the sum of family remittances and public transfers received by the household, make up a larger share of income for the bet- ter-off households in the population. This was the case in 2001 as well as in 2005, although in 2005 the importance of labor-income transfers had declined. The impact of remittances and public transfers on both the poverty rate and the poverty gap is depicted in table 6.5. In both 2001 and 2005, these transfers helped reduce the poverty rate by 1.7 percentage points, with the larger part of these positive effects being due to remittances Madag-txt-fin.qxd 5/22/08 8:17 PM Page 77 Linking Employment and Earnings With Poverty 77 Table 6.5 Impact of Nonlabor Transfers on Poverty, 2005 and 2001 2005 2001 Headcount poverty,based on: Total income 69.2 69.5 Total income minus remittances and public transfers 70.9 71.2 Total income minus public transfers 69.4 69.9 Poverty gap,based on: Total income 28 36 Total income minus remittances and public transfers 30 38 Total income minus public transfers 29 36 Source: HHS 1999, 2001, 2005. Note: For this table, poverty is measured through income. Hence, the poverty data in the table are not similar to the poverty data used throughout the rest of this report, which are based on expenditures. (public transfers accounted for a reduction in poverty of 0.4 percentage points in 2001 and 0.2 percentage points in 2005). Also in both years, nonlabor income transfers are estimated to have reduced the poverty gap in 2001 and 2005 by 2 percentage points. Sectoral Employment, Earnings, and Poverty Like table 6.4, table 6.6 depicts the sources of household income, but this time the table distinguishes between earnings from the three economic sectors. In addition to reflecting the findings on agricultural earnings from from the first section of this chapter (by showing the important role of the primary sector for the poor in particular and, increasingly, for the bet- ter-off), the table also shows the relative importance of the secondary and tertiary sectors in the provision of household income. The importance of tertiary sector income increased substantially for the poorer households After the primary sector, the tertiary sector was the second largest source of earnings in 2005 (21.4 percent). Secondary sector activities, on the other hand, accounted for only 3.5 percent of household income in 2005. Owing to the substantial increase in the importance of the primary sec- tor for the better-off households, the share of primary sector earnings in Madag-txt-fin.qxd 5/22/08 8:17 PM Page 78 78 Making Work Pay in Madagascar: Employment, Growth, and Poverty Reduction Table 6.6 Structure of Household Income by Sector and Quintile, 2005 and 2001 percent Nonlabor Income Primary Secondary Tertiary earnings transfers Total 2005 70.7 3.5 21.4 0.7 3.7 100 Poorest 81.02 1.9 12.6 0.5 4.0 100 Q2 77.1 2.1 17.8 0.5 2.5 100 Q3 76.2 3.0 16.9 1.0 2.9 100 Q4 68.8 3.6 23.0 1.0 3.7 100 Richest 50.6 6.8 36.6 0.4 5.5 100 2001 64.8 7.9 21.9 1.0 4.5 100 Poorest 87.2 3.0 5.4 0.2 4.3 100 Q2 82.4 4.3 8.4 1.1 3.7 100 Q3 73.3 5.0 16.0 1.9 3.8 100 Q4 56.7 10.7 27.0 1.2 4.3 100 Richest 23.7 16.5 52.9 0.4 6.5 100 Source: HHS 1999, 2001, 2005. household income for the nation as a whole went up by 6.0 percentage points. The shares of all other identified sources of income declined between 2001 and 2005, particularly that of the secondary sector, which saw its share reduced by more than half.The reduction in the tertiary sec- tor's share between 2001 and 2005 was limited to 0.5 percentage points. As in the primary sector, the average changes in the secondary and ter- tiary sector shares mask important differences in distributional levels and changes in the composition of household income. For example, those in the richest quintile earned considerably more from secondary and terti- ary sector activities (6.8 percent and 36.6 percent of earnings, respective- ly) than the poorest (1.9 percent and 12.6 percent, respectively). As the importance of the primary sector declined for the poorest households (despite the increase in median earnings observed earlier for primary sec- tor workers), the share of income originating from the tertiary sector more than doubled for the two poorest quintiles. Conversely, richer households became substantially more dependent on primary activities as earnings from both the secondary and tertiary sectors fell. Madag-txt-fin.qxd 5/22/08 8:17 PM Page 79 Linking Employment and Earnings With Poverty 79 The relatively strong fall in the importance of the secondary sector for the better-off households corresponds with findings that the crisis affected the high earners in this sector particularly The relative importance of the secondary sector as a source of household income declined for all expenditure quintiles (that was not the case for the primary and tertiary sectors). The decline confirms the decrease in both employment and earnings in this sector. The relatively large fall in the secondary sector income share for the two richest quintiles further corresponds with the assumption posited in chapter 5, that owing to the crisis, the highest wage earners (and other sources), in particular those in the secondary sector, experienced either a fall in income or the loss of their job. There are various possible explanations for the observed increase in the share of tertiary sector income of the poor in combination with the rise in the importance of primary sector earnings of the better-off in the popula- tion. One possibility is that a portion of the poor moved into tertiary sec- tor activities and a portion of the rich moved into agriculture (the latter option seeming particularly plausible considering the effects of the crisis). Another option is that there have been changes in the geographic compo- sition of households in the various income quintiles. As rural poverty fell and urban poverty increased between 2001 and 2005, the poorest quin- tiles in 2005 probably contained more urban households that would be less dependent on primary sector income than in 2001. Similarly, there would be more primary sector dependent rural households in the better- off quintiles in 2005 than there were in 2001.Although it may appear that the actual changes in household income structure by quintile have been the result of both possible developments, the absence of panel data does not allow the verification of this assumption. Decomposition provides a better understanding of changes in poverty in relation to changes within and between sectors A better understanding of the link between changes in household sectoral earnings and poverty can be achieved by decomposing changes in pover- ty into those components that can be attributed to changes in poverty within the sectors and to movement among the sectors, as proposed by Ravallion and Huppi (1991). By applying an additively separable poverty measure, P, to two distributions of household consumption over time (years 1 and 2), the analysis can break down the difference in national poverty for this period into three general components:4 Madag-txt-fin.qxd 5/22/08 8:17 PM Page 80 80 Making Work Pay in Madagascar: Employment, Growth, and Poverty Reduction 3 Intrasectoral effects: P 2 ­ P1 = (P 2 + s - Ps 1 )n1s Change in poverty arising from s =1 within-sector poverty changes 3 Intersectoral effects: (n 2 1 s - n1s )Ps + Change in poverty arising from s=1 changes and population shifts 3 Interaction between sectoral (P 2 s - Ps )(ns 1 2- n1s ) employment/earning shifts s =1 (Equation6.1) where Ps is the poverty measured in sector s at time t, and ns is the pop- t t ulation share of sector s at time t. The first component, the intrasectoral effects, shows how changes in poverty within each sector contribute to the aggregate change in poverty. The second component is the contribu- tion of changes in the distribution of the population across the sectors. The final component, the residual, can be interpreted as a measure of the correlation between population shifts and changes in poverty within the sectors. Because the decomposition is performed at the household level, households must be assigned to a sector. Households have multiple sources of income, so this assignment is not straightforward, and this sec- tion adopts two approaches to allocate households to a sector. In the first approach, households are assigned to a sector if more than half of the workers in the household were employed in that sector. The second approach assigns households to a sector if more than half of the total household labor income was derived from that sector. In both cases there are households for which either employment or income is distributed across all three sectors in such a way that they are not associated with any sectors.These households, along with those with no labor income, are cat- egorized as "other." The results of the decompositions are shown in table 6.7. The upper half of the table depicts the results of the decomposition using the approach that categorizes households by number of workers in each sec- tor; the lower half depicts the results from the approach that classifies households by sectoral income.The interpretation of the results is similar to the results of the Shapley decomposition discussed in chapter 4. For example, if the classification were by number of workers, the headcount Madag-txt-fin.qxd Table 6.7 Decomposition of Changes in Poverty into Intrasectoral and Intersectoral Effects, 2001­05 Intrasectoral effects Intersectoral effects 5/22/08 2005 2001 Change Prim. Sec. Tert. Other Prim. Sec. Tert. Other Residual Households categorized by shares of workers Levels Incidence (P0) 68.7 69.7 ­1.0 ­6.8 ­0.4 3.1 0.1 7.0 ­1.5 ­0.5 ­1.0 ­0.9 8:17 Depth (P1) 26.8 34.9 ­8.7 ­10.5 ­0.3 1.2 0.1 3.7 ­0.6 ­0.2 ­0.3 ­1.2 Share of total population PM 2005 75.5 1.6 15.6 7.4 Page 2001 67.4 5.9 17.5 9.3 Households categorized by shares of income 81 Levels Incidence (P0) 68.7 69.7 ­1.0 ­6.5 0.8 3.3 ­0.2 5.9 ­1.6 ­0.3 ­1.3 ­1.1 Depth (P1) 26.8 34.9 ­8.7 ­9.9 0.0 1.2 ­0.1 3.1 ­0.6 ­0.1 ­0.6 ­1.0 Share of total population 2005 66.5 7.5 21.8 4.2 2001 73.4 3.5 21.0 2.1 Sources: HHS 1999, 2001, 2005. 81 Madag-txt-fin.qxd 5/22/08 8:17 PM Page 82 82 Making Work Pay in Madagascar: Employment, Growth, and Poverty Reduction poverty rate would fall by 6.8 percentage points owing to the fall in poverty in the primary sector, provided that there had been no movement of workers between sectors. Likewise, the movement of workers into the primary sector would have caused poverty to increase by 7.0 percentage points if the intersectoral poverty reduction (that is, the increases in pri- mary sector earnings) had not occurred at the same time. As is clear from table 6.7, the two classification methods produce sim- ilar results except for the intrasectoral effects attributed to the secondary sector. Furthermore, the directions of the various effects are similar for both the incidence (headcount ratio) and the depth of poverty measures, although the relative magnitude of the contributions is always higher for the incidence. The fall in poverty can be largely attributed to a decrease in poverty within the primary sector The decrease in poverty between 2001 and 2005 appears to be largely due to the fall in poverty within the primary sector (6.8 percentage points in the sectoral categorization by household workers and 6.5 percentage points in the categorization by household earnings). The employment shift out of the secondary and tertiary sectors into the primary sector following the 2002 crisis is reflected in the rising shares of the population living in primary sector households. For exam- ple, the percentage of the population living in households with more than half of the household workers employed in the primary sector rose from 67.4 to 75.5 percent. Similarly, the share living in households in which more than half of the earnings came from this sector rose from 66.5 to 73.4 percent. As more households became more dependent on primary sector employment and earnings, the sector accounted for a greater per- centage of the poor. This is illustrated by the positive and large intersec- toral effects (7.0 percentage points in the upper half of the table and 5.9 percentage points in the lower half).With poverty rates highest in the pri- mary sector, it is not surprising that national poverty would rise substan- tially because more workers are employed in this sector and sectoral poverty rates are assumed to be held constant. The opposite is observed for the tertiary sector, where poverty within the sector has risen (positive intrasectoral effect). However, employment in this sector fell (15.6 percent lived in households with more than half of the workers in the tertiary sector in 2005 compared with 17.5 percent Madag-txt-fin.qxd 5/22/08 8:17 PM Page 83 Linking Employment and Earnings With Poverty 83 in 2001), and, more important, a portion of the poor left; as a result, the sector contributed marginally to a fall in national poverty, as seen in the small positive intersectoral effect. Similar to the tertiary sector, the secondary sector is attributed a neg- ative intersectoral effect, because the fall in employment lowered the share of the poor. As has been mentioned, the sign of the intrasectoral effect in the secondary sector is ambiguous. It is worth noting that although this decomposition is informative, it suffers from a weakness in that it cannot fully differentiate the sources of income changes. For example, the decreased reliance on primary sector income for the poorest households does not lead to a shift in household category, since well over 50 percent of their household income continues to come from the primary sector. Among households in this category, the fall in the depth of poverty that may be due to increases in tertiary sec- tor income is attributed to improvements in the primary sector.With this in mind, the next section moves away from the sectoral sources of house- hold earnings and turns to a different breakdown of household income. TheVarious Sources of Labor Income andTheir Links with Poverty Another way of analyzing how labor income is linked to poverty is to dis- entangle those sources of household per capita labor income that are responsible for the observed changes in income. This approach, based on a methodology from Kakwani, Neri, and Son (2006), decomposes changes in household labor income into components such as household average hourly earnings, hours worked, and employment. This decompo- sition can then be used as a basis for simulating changes in poverty. Decomposition illustrates poverty changes in relation to changes in sources of income The starting point is to note that the average weekly labor income of household, j, can be written as: I j = I j H j Ej Lj (Equation 6.2) N j H j Ej Lj N j Madag-txt-fin.qxd 5/22/08 8:17 PM Page 84 84 Making Work Pay in Madagascar: Employment, Growth, and Poverty Reduction where Ij is the total weekly labor income of the household, Nj is the num- ber of household members, Hj is the total number of hours worked per week by household members, Ej is the number of employed household members, and Lj is the number of household members participating in the labor force. Because an important fraction of labor stems from chil- dren and, to a lesser extent, elder workers, hours worked (Hj), household employment (Ej), and household labor force (Lj) are determined by tak- ing into account all household members, not just those of working age. Using equation 6.2 as a basis, one can determine the portions of the average change in average household labor income that are due to changes in household hourly earnings, hours worked, unemployment, and participation. These results appear in the bottom panel of table 6.8 (see annex 6B for a more elaborate description of this approach). Results of decomposition show that the rise in per capita household labor income was due to increased hourly earnings The results of the decomposition appear in the bottom panel of table 6.8 and show that the rise in per capita household labor income was due to increased hourly earnings. The two upper panels depict the value of the various sources of labor income for 2005 and 2001; the third panel shows the changes in these values between the two years. This third panel illus- trates that between 2001 and 2005 there was an increase in the average hourly earnings and the household participation rate, both of which would be expected to have affected per capita household income posi- tively. On the other hand, in the same period the unemployment rate increased and the number of average hours worked per week per employed person fell, and these developments are likely to have had a negative effect on household income. As the table shows, the overall impact of these changes has been positive, as average household per capi- ta weekly labor income increased by 15 percent between 2001 and 2005. The fourth panel attributes weights and directions to the various earn- ings sources' contributions to this 15 percent increase in labor income.As expected, changes in the household participation rate and, in particular, changes in average hourly earnings, affected income growth positively. Indeed, 138 percent of the increase is explained by the rise in hourly earnings, offsetting both the fall in average hours worked and the rise in household unemployment. Madag-txt-fin.qxd Table 6.8 Household Labor Income Profile, 2005 and 2001 Expenditure quintile Geometric mean across households Poorest Q2 Q3 Q4 Richest Poor Nonpoor Total 2005 Average hourly earnings in MGA 152.5 224.3 258.1 312.6 506.2 216.0 432.6 267.2 5/22/08 Average hours worked per week by the employed 37.6 40.6 42.3 42.8 44.1 40.4 43.8 41.4 Household unemployment rate (percent) 1.5 1.4 1.9 2.3 4.7 1.6 4.1 2.4 Household participation ratea 36.7 38.6 41.9 45.0 48.5 39.6 47.5 41.9 Total household per capital weekly labor income 2,090 3,551 4,578 6,039 10,345 3,465 8,727 4,599 8:17 2001 Average hourly earnings in MGA 86.6 146.6 203.3 331.2 680.0 152.2 546.3 222.3 PM Average hours worked per week by the employed 41.2 43.4 43.8 45.5 43.1 45.6 43.8 Household unemployment rate (percent) 0.3 0.8 0.7 1.2 2.7 0.7 2.2 1.1 Page Household participation ratea 38.1 40.4 43.3 41.5 42.8 40.9 41.9 41.2 Total household per capital weekly labor income 1,366 2,581 3,876 6,196 12,924 2,681 10,297 3,995 85 Percent change Average hourly earnings 76.0 53.0 26.9 ­5.6 ­25.6 41.9 ­20.8 20.2 Average hours worked per week by the employed ­8.9 ­6.5 ­3.4 ­5.8 ­3.1 ­6.3 ­3.9 ­5.5 Household unemployment rate 484.5 63.1 177.3 90.3 71.5 122.6 89.2 105.7 Household participation ratea ­3.6 ­4.4 ­3.1 8.5 13.3 ­3.0 13.6 1.8 Total household per capital weekly labor income 53.0 37.6 18.1 ­2.5 ­20.0 29.3 ­15.2 15.1 Sources of change in labor income (percent) Average hourly earnings 133.7 138.4 149.3 124.9 133.3 142.8 140.6 138.3 Average hours worked per week by the employed ­21.9 ­21.9 ­21.6 128.4 14.1 ­26.7 24.1 ­42.8 Household unemployment rate ­3.0 ­1.8 ­7.8 23.8 9.1 ­3.6 11.9 ­9.3 Household participation ratea ­8.8 ­14.8 ­19.9 ­177.1 ­56.5 ­12.5 ­76.6 13.8 Total household per capital weekly labor incomeb + + + - - + - + Source: HHS 2001, 2005. a. Share of adult household membes who are working or looking for work. 85 b. A"+"indicates that average labor income rose, while a"-"indicates that it fell between 2001 and 2005. Madag-txt-fin.qxd 5/22/08 8:17 PM Page 86 86 Making Work Pay in Madagascar: Employment, Growth, and Poverty Reduction Changes in hourly earnings increased the income of the poor and reduced that of the better-off Changes in per capita household labor income have not been uniform across the population: the labor income of the poor increased by 29 per- cent while that of the nonpoor fell by 15 percent.A review of the changes per expenditure quintile shows that the poorer quintiles had the greatest gain. Average household earnings rose in each of the three poorest quin- tiles by 53.0 percent, 37.6 percent, and 18.1 percent, respectively. At the same time they fell by 2.5 percent and 20 percent, respectively, in the two richest quintiles. Changes in hourly earnings explain both the increase in total earnings among the poorer households and the fall in total earnings among the better-off. In the analysis the rate of growth of hourly earnings falls from 76 percent for the poorest quintile to 26.9 percent for the middle quin- tile. It then turns negative for the fourth quintile and the richest quintile (­5.6 percent and ­25.6 percent, respectively). Thus, changes in hourly earnings account for between 133.3 percent and 149.3 percent of the changes in total household per capita labor income. For the poorer households this increase in hourly earnings tended to be the only source of labor income that contributed positively to the rise in earnings. Its impact was substantial enough to easily offset the negative impacts of the fall in the number of hours worked, the fall in participa- tion rate, and the increase in unemployment. In the better-off households, the negative effect on labor income of the fall in hourly earnings was compounded by the reduction in the number of hours worked as well as by the increase in unemployment. The only component that dampened these effects was the participation rate. The increase in hourly earnings reduced the depth of poverty, though not its level Increasing the understanding of the linkages among poverty, employment, and earnings involves simulating the effect of changes in the sources of labor income on poverty. The following approach is taken in this chapter. To isolate the effect of changes in hourly earnings, the average hourly earn- ings for all households are scaled in the 2001 data, so that the mean of each expenditure quintile equals the mean of the 2005 data.The resulting changes in total labor earnings are added to household expenditures, on the basis of which new poverty levels are calculated.The difference in the simulated poverty level and the original poverty level is then attributed to Madag-txt-fin.qxd 5/22/08 8:17 PM Page 87 Linking Employment and Earnings With Poverty 87 changes in hourly earnings.The same approach is taken to derive the con- tribution of changes in the number of average household hours worked, and the percentage of household members who are employed. The results of this simulation, summarized in table 6.9, are consistent with the previous analysis. They also illustrate that the effects of earnings and employment on poverty depend on the type of poverty measure used (incidence or depth), which highlights the importance of an emphasis on distribution, as in earlier parts of this report. The results of the simulation with regard to the change in the inci- dence of poverty can be explained as follows. Because a large percentage of the population falls below the poverty line (68.7 percent in 2005), factors that affect the headcount ratio will necessarily affect the relative- ly better-off households (that is, those households around the 70th per- centile). As noted in table 6.8, average hourly earnings fell for those households in the two richest quintiles, which explains why the simulat- ed changes in hourly earnings alone resulted in an increase in poverty of 1.0 percentage point. Changes for the poorer parts of the population, on the other hand, influenced the depth rather than the incidence of poverty. For example, the increase in hourly earnings for those at the lower end of the distri- bution has had little effect on the incidence of poverty, as this measure is insensitive to changes in the earnings of those who remain poor. But it does affect the depth of poverty, as this measure represents the average consumption shortfall in the population (that is, the average of the poverty gaps). Thus, the substantial increases in hourly earnings for households in the lowest three quintiles resulted in a 6.9 percentage point decrease in the depth of poverty. The resulting increases in labor earnings for these households more than offset the increase in the share of households that became poor owing to the fall in earnings among the better-off households. Table 6.9 Simulated Changes in Poverty Due to Changes in Household Labor Income Profile (reference year 2001) Hourly Hours Percent 2005 2001 Diff earnings worked employed Other Incidence (P0) 68.7 69.7 ­1.0 1.0 1.5 ­1.9 ­1.7 Depth (P1) 26.8 34.9 ­8.1 ­6.9 1.3 0.4 ­3.0 Source: HHS 2001, 2005. Madag-txt-fin.qxd 5/22/08 8:17 PM Page 88 88 Making Work Pay in Madagascar: Employment, Growth, and Poverty Reduction Box 6.1 A Closer Look at Child Labor A closer look at child labor rates at different points in time (2005, 2001), and across regions with different levels of urbanization, produces the following find- ings (see table on next page): · Child labor rates decreased among the poorest households and increased in the better-off households. This occurred across the country, in large urban centers, secondary cities, and rural areas alike. · Child labor rates in large urban centers were consistently and considerably lower than in secondary cities and rural areas. · In large urban centers and rural areas, the child labor rate fell between 2001 and 2005. In secondary cities, the child labor rate increased. These observations allow a number of assumptions: · The differences in changes in child labor rates between the expenditure quintiles may be linked to the condensation of the income and expenditure distributions. Poorer households became better off, reducing their need for child labor, while at the same time better-off households coped with a fall in earnings and expenditures by increasing household employment, including of their children. · The relatively high earnings and the large supply of"good jobs"in large urban centers offer an explanation for the low child labor rates in these areas. Addi- tional reasons could include better access to education, fewer opportunities for children to work (most child labor involves farm activities) and thus also lower opportunity costs of education, and the higher importance attached to education in large urban centers. · The increase in child labor in secondary cities may reflect that these cities were particularly hard-hit by the crisis. As mentioned earlier, unlike house- holds in the larger cities, where wage work increased, households in second- ary cities experienced a reduction in both the number and the share of wage jobs (by 13.2 percentage points). Although wage employment also declined in rural areas, the share of rural wage work fell by only 2.8 percentage points. An additional explanation of the relative high impact of the crisis on second- ary cities is that it may have been more difficult for households in these areas to benefit optimally from the increased earnings opportunities in agriculture. (continued) Madag-txt-fin.qxd 5/22/08 8:17 PM Page 89 Linking Employment and Earnings With Poverty 89 Box 6.1 (continued) Although a large share of workers was engaged in agriculture, access to (good) land may have been limited relative to access for households in rural areas. · The large decline in rural child labor rates may have not only been related to improvements in the economic well-being of rural households (recall that rural poverty fell by 3.8 percentage points). An additional explanation may be that the large increase in the supply of adult labor due to the crisis re- duced the demand for child labor. Indeed, the labor inflow was so substan- tial that it is assumed to have reduced even the average weekly working hours of adults. These--as yet untested--findings not only endorse the link between poverty and child labor, but also reflect a high sensitivity of child labor rates to short-term changes in the economic well-being of households. In other words, child labor is used as a mechanism to cope with the risk of immediate household income loss. Furthermore, the use of child labor as a coping mechanism occurs in all layers of the population, even among the better-off households. Particularly taking into account the possible long-term negative effects of child labor--which keeps children from going to school and thus limits their future earnings potential-- improved access to alternative household coping mechanisms may not only re- duce child labor rates in the short term, but also are likely to generate benefits that will help to structurally reduce poverty (particularly when combined with ef- forts to improve the access to and the quality of education, such as those that are ongoing under the Education for All initiative). Child Labor Rates by Expenditure Quintile and Level of Urbanization, 2005 and 2001 Total Poorest Q2 Q3 Q4 Richest National 2005 18.8 24.3 18.3 18.6 17.4 12.6 2001 24.3 36.6 26.3 24.3 18.2 7.1 Large urban 2005 2.9 0.0 7.9 0.0 0.9 3.1 2001 5.1 16.9 20.3 5.0 4.5 2.0 Secondary cities 2005 18.5 25.3 18.2 20.6 15.4 12.1 2001 15.7 39.4 24.6 15.3 7.1 5.6 Rural 2005 20.5 24.9 19.3 19.7 19.8 15.6 2001 27.7 36.8 26.7 27.3 23.1 10.6 Madag-txt-fin.qxd 5/22/08 8:17 PM Page 90 90 Making Work Pay in Madagascar: Employment, Growth, and Poverty Reduction The opposite can be observed for employment. The fall in employ- ment among poorer households contributes to an increase in the depth of poverty, while a rise in employment (an increase in household participa- tion means an increase in household employment) among households in the top two quintiles translates into a decrease in the percentage of households that are poor. The combination of the sectoral decomposition of poverty and the simulated changes in poverty based on the components of household labor income helps to formulate a larger picture of how changes in employment and earnings affected individuals in different types of house- holds, and how these changes manifested themselves in changes in pover- ty. For example, the fall in the poverty rate (headcount ratio) appears to be driven by households in the upper portion of the income distribution who rely more on agriculture for their incomes, and who escape poverty through having more household members who work. Although the poorest 40 percent of the population remain poor, the depth of their poverty has fallen as a result of higher earnings.These high- er earnings appear to come from earnings in the tertiary sector, as they rely more on this sector as a source of household income. Although earn- ings in the tertiary sector have fallen overall, households with members who switch from low-paying agricultural employment to higher-paying nonfarm employment will see a rise in household income even if they do not escape poverty entirely. See annex 6A for a comparison of the changes in weekly household per capita labor income and the sources of these changes between rural and urban areas, and between Madagascar's six provinces. Notes 1. Also, because many labor allocation decisions in low-income countries such as Madagascar are at the household level, any effort to establish a link between labor market outcomes and poverty must view those outcomes in the context of the household (see, for example, Behrman [1999] and Singh, Squire, and Strauss [1986]). Note that this report does not touch upon intrahousehold dis- tribution issues. 2. See annex 6A­3 for a replication of table 6.1 by quintiles, which reveals some additional insights. For example, in 2005 the poorest quintile had lower employment and higher inactivity than the subsequent two quintiles.This may Madag-txt-fin.qxd 5/22/08 8:17 PM Page 91 Linking Employment and Earnings With Poverty 91 imply that although it may be mainly the better off who are unemployed or inactive, these can also be characteristics of the poorest of the poor who are in that situation because they are not able to find work. 3. The household surveys do not contain information on employment in addition to first and second jobs. 4. The Foster, Greer, and Thorbecke (1984) measures (P ) are a class of such additively separable poverty measures. This analysis uses the headcount ratio (P0) and the depth of poverty (P1). Madag-txt-fin.qxd 5/22/08 8:17 PM Page 92 92 Making Work Pay in Madagascar: Employment, Growth, and Poverty Reduction Annex 6A Employment and PopulationTables Table 6A.1 Employment Status of theWorking-Age Population by Quintile and Poverty Level, 2005, 2001, and 1999 Employed Unemployed Inactive 2005 85.8 2.6 11.9 Q1 87.9 1.6 10.6 Q2 89.0 1.7 9.5 Q3 89.0 1.9 9.3 Q4 86.4 2.3 11.7 Q5 79.0 5.1 16.8 Poor 88.7 1.7 9.8 Nonpoor 80.8 4.4 15.5 2001 82.5 1.2 16.5 Q1 91.3 0.2 8.5 Q2 89.2 0.9 9.9 Q3 87.8 1.1 11.3 Q4 80.2 0.9 19.1 Q5 69.2 2.9 28.8 Poor 88.4 0.8 10.9 Nonpoor 71.7 2.2 26.7 1999 79.2 1.3 19.8 Q1 81.8 1.7 16.8 Q2 80.3 0.8 19.1 Q3 80.9 1.1 18.2 Q4 79.8 1.2 19.3 Q5 74.5 1.8 24.1 Poor 80.7 1.2 18.3 Nonpoor 76.1 1.6 22.7 Sources: INSTAT HHS 1999, 2001, 2005. Madag-txt-fin.qxd 5/22/08 8:17 PM Page 93 Linking Employment and Earnings With Poverty 93 Table 6A.2 Employment Status of theWorking-Age Employed Population by Quintile and Poverty Level, 2005, 2001, and 1999 Weekly working hours Wage Self- Family 1st and employed employed labor Total 1st job 2nd job 2005 14.9 5.9 79.2 100 37.0 45.4 Q1 8.9 2.8 88.2 100 33.9 41.6 Q2 12.4 3.8 83.8 100 35.7 44.9 Q3 10.7 5.0 84.3 100 36.6 46.0 Q4 13.9 5.5 80.6 100 37.6 46.4 Q5 26.4 10.9 62.7 100 40.5 47.2 Poor 10.8 4.1 85.2 100 35.6 44.4 Nonpoor 22.7 9.2 68.2 100 39.7 47.1 2001 18.3 8.2 73.6 100 42.3 46.9 Q1 4.5 3.3 92.2 100 39.1 43.8 Q2 6.2 4.4 89.4 100 39.8 45.9 Q3 10.1 6.8 83.0 100 42.1 47.5 Q4 22.2 10.6 67.2 100 44.7 48.5 Q5 46.7 15.0 38.4 100 45.2 48.3 Poor 8.7 5.7 85.6 100 40.9 46.1 Nonpoor 39.8 13.7 46.5 100 45.3 48.7 1999 15.0 9.7 75.3 100 .. .. Q1 9.2 5.4 85.4 100 .. .. Q2 9.8 7.5 82.8 100 .. .. Q3 12.6 8.6 78.7 100 .. .. Q4 14.2 9.9 75.9 100 .. .. Q5 26.7 15.9 57.4 100 .. .. Poor 11.0 7.8 81.2 100 .. .. Nonpoor 23.5 13.9 62.5 100 .. .. Source: INSTAT HHS 1999, 2001, 2005. Madag-txt-fin.qxd 5/22/08 8:17 PM Page 94 94 Making Work Pay in Madagascar: Employment, Growth, and Poverty Reduction Annex 6B The Kakwani, Neri, and Son Decomposition of Household Labor Income The average labor income of household j can be written as: I j = I j H j Ej Lj (Equation 6B.1) N j H j Ej Lj N j where Ij is the total weekly labor income of the household, Nj is the num- ber of household members, Hj is the total number of hours worked per week by household members, Ej is the number of employed household members, and Lj is the number of household members participating in the labor force.1 One can then define ij = Ij/ Nj as average weekly household labor income (averaged over all household members). In the same way wj = Ij/Hj is the average earnings per hour worked, hj = Hj/Ej is the average hours worked per week by those employed, Ej/Lj is the household employment rate, and lj = Lj/Nj is the household participation rate. For simplicity, equation 6B.1 can thus be written as: ij = wj hj (1­uj) lj (Equation 6B.2) where (1­uj) corresponds to the household employment rate, which is rewritten as one minus the household unemployment rate (uj). By aver- aging each of the components of the per capita household labor income over population sub-groups, a more complete profile of labor market characteristics by, for example, income quintiles, can be obtained. To analyze which sources of labor income are responsible for observed changes in total labor income, the logs are taken and each com- ponent is averaged. The temporal differences in these averages then are the following: 1 N 1 N 1 N 1 N 1 N ln i = ln w + ln h + l n(1 - u j ) + ln l N j j j j j =1 N j =1 N j =1 N j =1 N j =1 (Equation 6B.3) Dividing this equation by the left-hand side provides the portions of the average change in average household labor income which are due to changes in household hourly earnings, hours worked, unemployment, and participation. Madag-txt-fin.qxd 5/22/08 8:17 PM Page 95 Linking Employment and Earnings With Poverty 95 A next possible step (not performed in this report) would be to fur- ther decompose average hourly earnings into, for example, hourly earn- ings from agricultural employment, nonagricultural wage employment, and nonagricultural nonwage employment. In this case, however, log-lin- earization is no longer possible and one would have to perform Shapley decompositions to analyze income changes. A comparison of the sources of changes in household labor income between population subgroups can shed light on the different channels that have affected the income of, for example, the poor and the nonpoor. However, there may have been considerable heterogeneity in the changes in labor income sources between various employment types or economic sectors. In those cases it may be useful to further disaggregate households according to other characteristics, such as main source of household income. Note 1. If only a small fraction of labor originates from children and/or older workers, a variable for the number of working-age household members (Aj) can be included in the equation, allowing the participation rate to be calculated for working-age adults only, as well as the addition of a "household dependency rate" variable (Aj/Nj). Madag-txt-fin.qxd 5/22/08 8:17 PM Page 96 96 Making Work Pay in Madagascar: Employment, Growth, and Poverty Reduction Annex 6C Changes in Labor Income and Sources of Change in Rural and Urban Areas and by Province This annex compares changes in labor income in rural and urban areas and between Madagascar's provinces and different geographical regions. Changes in labor income and sources of change in rural and urban areas Throughout this report, various differences in labor market conditions and their changes over time have been observed between rural and urban areas. They include, for example, poverty rates, dependency on the pri- mary sector, earnings levels, and the importance of wage work. It is thus not surprising that there also exist differences between rural and urban areas when looking at the changes in household labor income, and the sources of these changes using Kakwani, Neri, and Son decompositions. Between 2001 and 2005, the average per capita weekly labor income of rural households increased by 25 percent. In the same period, urban households experienced a fall in per capita labor income of 11.3 percent. Nevertheless, even in 2005, per capita weekly labor income of urban households was still around one-third higher than that of rural house- holds. In 2001, urban labor income had still been 88 percent higher (table 6C.1 and figure 6C.1.) In 2001, particularly the urban poor were better off than the poor in rural areas: household per capita labor income for the two poorest quin- tiles in urban areas was about 11 percent and 16 percent higher than labor income of the two poorest quintiles in rural areas. The average income of those in the upper 40 percent of the rural population hardly differed from that of the two richest quintiles in urban areas. Between 2001 and 2005, the lower three quintiles in both urban and rural areas experienced an increase in labor income. As the increase was higher in rural areas, the difference in labor income between the poorer rural and urban households decreased. The opposite development occurred for the upper two quintiles in urban and rural areas. Both saw their labor income fall, but the fall was more substantial in rural than in urban areas. As a result, the difference in labor income between the better-off in urban areas and the better-off in rural areas increased. Figure 6C.2 depicts the sources of the changes in average weekly per capita household labor income for the entire urban and rural populations, as well as for the poor and the nonpoor population groups in rural and Madag-txt-fin.qxd 5/22/08 8:17 PM Page 97 Linking Employment and Earnings With Poverty 97 Table 6C.1 Weekly Household Per Capita Labor Income (MGA) in Urban and Rural Areas by Expenditure Quintile, 2005, 2001, and Percentage Difference Poorest Q2 Q3 Q4 Richest Poor Nonpoor Total 2005 Urban 2,256 3,717 4,543 6,190 10,951 3,699 9,607 5,807 Rural 2,064 3,511 4,588 6,017 10,320 3,424 8,496 4,348 Difference (%) 9.3 5.9 ­1.0 2.9 6.1 8.0 13.1 33.6 2001 Urban 1,507 2,946 4,156 6,229 12,910 3,508 10,807 6,543 Rural 1,355 2,536 3,820 6,184 12,936 2,567 9,958 3,479 Difference (%) 11.2 16.2 8.8 0.7 ­0.2 36.6 8.5 88.1 Source: HHS 2005, 2001. urban areas. The figures reflect the outcomes of Kakwani, Neri, and Son decompositions that are described in chapter 6. Unlike table 6.8, howev- er, a positive value of a variable in figure 6C.2 implies that the change in this variable between 2001 and 2005 had contributed positively to the growth of weekly household per capita labor income, regardless of whether the overall change in labor income was positive or negative. Similarly, a negative value in all cases implies that the change in the vari- able has had a downward effect on labor income. For example, in figure 6C.2a, the value of minus 120 for urban hourly earnings shows that, if none of the other variables had changes, the average labor income of the urban population would have fallen by 120 percent. Figure A6C.1 Changes inWeekly Household Per Capita Labor Income in Rural and Urban Areas by Expenditure Quintile, 2001­05 (percent) rural urban 60 tnecrep 30 0 -30 poorest Q2 Q3 Q4 richest Source: HHS 2005, 2001. Madag-txt-fin.qxd 5/22/08 8:17 PM Page 98 98 Making Work Pay in Madagascar: Employment, Growth, and Poverty Reduction Figure A6­c.2 Sources of Change inWeekly Household Per Capita Labor Income in Rural and Urban Areas for the Overall Populations, the Poor, and the Nonpoor, 2001­05 percent (a) overall rural and urban population (b) poor rural and urban population hourly earnings hourly earnings weekly hours weekly hours unemployment unemployment participation participation -250 -150 -50 50 150 250 -250 -150 -50 50 150 250 (c) nonpoor rural and urban population hourly earnings weekly hours rural urban unemployment participation -250 -150 -50 50 150 250 Source: INSTAT HHS 2001, 2005. Figure 6C.2 allows a number of observations concerning the differ- ences in the sources of change in labor income between rural and urban areas. First, for the rural population, the increase in average hourly earn- ings was the main source of the (positive) change in labor income. In urban areas, the fall in hourly earnings was the major source of the decrease of labor income, and the effect was compounded by the impact of increased unemployment and the fall in hours worked. For urban households, only the increase in participation helped to mitigate the neg- ative effects of the changes in the other variables (see figure 6C.2a). Second, as discussed earlier in this annex, labor income of the poor increased in both rural and urban areas, but figure 6C.2b shows that the sources of this increase were different in both areas. In rural areas, hourly earnings were the only source of change with an upward impact on the poor's labor income. In urban areas, increased hourly earnings also had a substantial positive impact on labor income of the poor, but a participa- tion increase had an almost equally high impact. It seems plausible to assume that, unlike in rural areas, urban households felt forced to increase participation to mitigate the negative effects of the fall in hours worked. Madag-txt-fin.qxd 5/22/08 8:17 PM Page 99 Linking Employment and Earnings With Poverty 99 Urban households also experienced the negative impact of increased unemployment. It is not clear whether increased unemployment trig- gered higher participation rates, or whether the higher unemployment is due to the fact that not all extra workers who entered the labor force were able to find work. Third, with regard to the nonpoor populations in urban and rural areas, the most striking observation is that the impact of the decrease in hourly earnings was much more substantial in urban households, and that this might be the reason why the urban nonpoor increased their participation rate by almost 17 percent, while in rural areas the increase was about 9 percent (figure 6C.c). Changes in labor income and sources of change in Madagascar's provinces In addition to the distinction between rural and urban areas, changes in labor income can be compared for different geographical regions. Table 6C.2 shows the average changes in per capita weekly labor income by province for the whole population, as well as for the poorest quintile, the poor, and the nonpoor. Two out of six provinces, Antananarivo and Anstiranana, experienced a fall in labor income between 2001 and 2005. Other provinces saw per capita household labor income increase, up to as much as 51 percent in Fianarantsoa. (See figure 6C.3 for a map of Madagascar and its provinces.) In all provinces, the poorest experienced an increase in labor income. Even in Antananarivo, despite overall labor income falling by 6.4 percent, labor income in the poorest expenditure quintile rose by more than 50 percent. Labor income of the poor increased in all provinces except Anstiranana (­4.5 percent).The poor gained, particularly in Fianarantsoa. There, labor income of the poor increased by 59 percent, more than 2.5 times as much as any of the other provinces. The nonpoor lost labor income in all provinces. Interestingly, the variation of the fall in labor income of the nonpoor across provinces was relatively small, with the smallest decrease in Anstiranana (­5.9 percent) and the largest in Fianarantsoa (­18.5 percent). Moving to the sources of the changes in labor income (table 6C.3), a diverse picture arises of the explanations of the changes in labor income in the various provinces. For instance, in Fianarantsoa, the positive impact of the increase in average hourly wages was relatively modest compared to most other provinces. However, since none of the other identified sources of income affected labor income negatively, Fianarantsoa experi- Madag-txt-fin.qxd 100 Table 6C.2 Changes in Household Per CapitaWeekly Labor Income by Province, 2001­05 (percent) Fianarantsoa Toamasina Mahajanga Toliara Anstiranana Antananarivo 5/22/08 Share of employed in 2005 18.1 12.4 11.4 12.7 3.4 41.9 Overall 50.8 28.4 16.8 12 ­1.1 ­6.4 Poorest 79.9 45.1 26.9 53.8 19.6 50.5 8:17 Poor 59.2 22.9 22.9 19.4 ­4.5 19.8 Nonpoor ­18.5 ­12.2 ­6.5 ­15.5 ­5.9 ­14.4 PM Sources: INSTAT HHS 2001, 2005. Page 100 Table 6C.3 Sources of Changes inWeekly Household Per Capita Labor Income by Province, 2001­05 (percent) Fianarantsoa Toamasina Mahajanga Toliara Anstiranana Antananarivo Average hourly earnings 90 110 180 260 1,750 ­100 Average weekly hours 20 ­20 ­80 ­120 ­1,520 ­70 Household unemployment 0 ­10 ­10 ­20 ­160 ­10 Household participation 0 20 10 ­30 ­170 70 Sources: INSTAT HHS 2001, 2005. Madag-txt-fin.qxd 5/22/08 8:17 PM Page 101 Linking Employment and Earnings With Poverty 101 Figure A6C.3 The Provinces of Madagascar 2 4 5 1 3 1. Antananarivo 6 2. Anstiranana 3. Fianarantsoa 4. Mahajanga 5. Toamasina 6. Toliara enced the highest increase in labor income of all provinces. In Toliara, on the other hand, the rise in hourly earnings alone would have increased labor income by 260 percent, but the impact of the change in hourly earnings was all but offset by the negative impact of simultaneous changes in hours worked, unemployment, and participation, limiting the increase in labor income to 12 percent. More specifically, the following observations can be made with regard to the sources of changes in labor income across Madagascar's provinces: · Changes in average hourly earnings were the main source of change in each province. Except in Antananarivo, average hourly earnings in- creased everywhere (28­44 percent), thus affecting labor income sub- stantially and positively. In Antananarivo, the 8.3 percent fall in average hourly earnings had a downward effect on labor income. · Fianarantsoa is the only province where the number of hours worked per employed person increased (by 6.4 percent, to 44.2 hours per week). This development will have reinforced the positive impact of the simultaneous rise in average hourly earnings, and may have been an important reason why the province experienced the highest increase in household per capita labor income. Madag-txt-fin.qxd 5/22/08 8:17 PM Page 102 102 Making Work Pay in Madagascar: Employment, Growth, and Poverty Reduction · Fianarantsoa is also the only province where changes in the household unemployment rate did not affect labor income; even though the un- employment rate increased from 0.4 percent to 1.5 percent, the over- all impact was negligible. In all other provinces, the rise in the unem- ployment rate had a modest, negative effect on labor income of between 10 and 20 percent. · The impact of changes in the household participation rate on labor in- come varied across the provinces. In Antananarivo,Toamasina, and Ma- hajanga the impact was positive,although only inAntananarivo was the effect large enough compared with changes in other variables to make a substantial difference in the changes in labor income. In Toliara and Anstiranana, falling participation had a negative impact on labor in- come, while in Fianarantsoa the effect on labor income of the 1.2 per- cent fall in the participation rate was negligible. Madag-txt-fin.qxd 5/22/08 8:17 PM Page 103 C H A P T E R 7 Good Jobs, Bad Jobs As indicated in chapter 3, good jobs--measured in terms of earnings-- tend to be wage, nonagricultural, and urban, and in the formal sector, and they are more likely to be held by the higher educated and by men. This chapter* further explores the individual and household characteristics that influence the probability that an individual will acquire a good job, as well as the determinants of earnings. It also reviews whether earnings differences between good and bad jobs may be due to labor market seg- mentation. The Probability of Getting a Good Job To estimate how individual and household characteristics affect the prob- ability that an individual will be in a certain employment category, a multinominal logit model has been applied on the 2005 household sur- vey (HHS) data. The employment categories that have been distin- guished are "nonagricultural formal,""nonagricultural informal," and "agri- cultural," where the first is considered as providing the best type of * This chapter is largely derived from Stifel, Rakotomanana, and Celada (2007). 103 Madag-txt-fin.qxd 5/22/08 8:17 PM Page 104 104 Making Work Pay in Madagascar: Employment, Growth, and Poverty Reduction employment and the last is assumed to provide the worst jobs.1 In addi- tion, the category "not working" is included, covering both unemploy- ment and inactivity. The possible determinants of employment that are tested by the model include age, educational attainment, household struc- ture, a number of measures of nonlabor income and assets, and migration and capital city dummies. The model has been run separately for work- ing-age men and women in both rural and urban areas, and the results are summarized in annex 7A. The main findings are described below. The probability of being in an agricultural ("bad") job is reduced mainly by educational attainment The probability of working in any of the various employment categories is most substantially and consistently influenced by educational attain- ment. Regardless of gender or area of residence, a higher level of educa- tion is associated with a smaller probability of being employed in agricul- ture. In rural areas, for example, women are 6 percent more likely to be employed in nonagricultural sectors or not be employed at all if they completed only primary education. Those with a lower secondary, upper secondary, and postsecondary education are, respectively, 26 percent, 39 percent, and 53 percent less likely to be employed in agriculture. The effects of education on the likelihood of being employed in agriculture are even greater for men in rural areas. The likelihood of agricultural employment is 8 percent lower for those with only a primary education, and decreases by as much as 59 percent for those with postsecondary education. Also, in urban areas the probability of working in agriculture (both genders) decreases with educational attainment--by about 8 per- cent for those with only a primary education, and as much as 37 percent for those with postsecondary education. The probability of being employed in the formal sector increases sub- stantially with higher levels of education, consistent with the assumption that formal sector jobs are more accessible to those with skills or to those who can be trained more easily. For upper secondary education, the increases in the probability of formal sector employment vary from 20 percent (urban men) to 26 percent (rural men). For postsecondary edu- cation, the increases in probability range from 33 percent (rural women and urban men) to 45 percent (urban women). There are no systematic differences in these marginal effects by gender or region. Madag-txt-fin.qxd 5/22/08 8:17 PM Page 105 Good Jobs, Bad Jobs 105 The effect of education on the probability of informal nonagricultural employment is mixed. In urban areas, men with more education are 3 percent to 13 percent less likely to be employed in the informal sector. Women in urban areas who completed primary education, on the other hand, are 4 percent more likely to be in this sector. In rural areas, lower levels of education are associated with a greater probability of informal employment for both men and women. Given the supposition that those with higher educational attainment tend to reside in better-off households, the findings on the probability of nonemployment, based on education level, are consistent with the assumption posed in chapter 3, that in the Malagasy context, unemploy- ment--or inactivity--is a luxury that only the better-off can afford.Thus, the probability of nonemployment is higher for those with an education than for those with no education. Unlike the relationship between edu- cation and formal sector employment, the effect of education on nonem- ployment is not monotonic (except for women in rural areas). Indeed, the effect does increase monotonically from primary to lower secondary to upper secondary, but it drops off for postsecondary.This may follow from the availability of relatively more lucrative employment opportunities for the limited number of individuals with postsecondary education, thus raising the opportunity cost of remaining either unemployed or out of the labor force. A word of caution on the above interpretation is warranted, however. The findings of the analysis do not automatically imply that educational attainment improves the chances of acquiring good, high-earning employment. There seems to be, for instance, a strong intergenerational correlation of schooling levels, as youths who live in households with rel- atively more educated household members are more likely to attend school. Because this implies that schooling is not randomly distributed across the sample used, the estimates in the preceding description will be biased (also see Behrman [1999]). Moreover, analyses such as the one described above do not reveal the direction of any causality. Though this unspecified causality does not pose a problem in the case of educational attainment and good jobs (as educational attainment precedes employ- ment), in other instances the direction of causality may be less obvious, such as when considering the impact of credit availability on the possibil- ity of having a good job (or vice versa), as described below. Madag-txt-fin.qxd 5/22/08 8:17 PM Page 106 106 Making Work Pay in Madagascar: Employment, Growth, and Poverty Reduction The influence of the other determinants is generally less substantial or less straightforward Antananarivo. Since Antananarivo is the manufacturing and industrial center of the country and the seat of most central government offices, it is not surprising that in the capital men are 84 percent more likely and women are 88 percent more likely than in other urban areas to be employed in the formal sector. They are also least likely to be employed in agriculture. Age. Older individuals are more likely to be employed in one form or another. For urban women this employment is primarily in nonagricultur- al labor (0.3 percent more likely with each year of age), whereas urban men are more likely to find formal sector jobs (0.5 percent more likely with each year of age). In rural areas the default sector is agriculture (0.5 percent and 0.2 percent more likely with each year of age for men and women, respectively). Migration. Migration reduces the probability of working in agriculture, and in most cases increases the probability of informal sector work. Rural migrant women are the exception, as they are no more likely than non- migrant women to be employed in the informal sector. Migrants are gen- erally not more likely than nonmigrants to be unemployed or out of the labor force, except for urban migrant women, who are 4 percent more likely not to be employed. Nonlabor income and assets. A number of other interesting results can be derived from the inclusion of determinants that capture nonlabor income and assets. For example, individuals in urban households that have successfully obtained credit are more likely to be employed in the formal sector and less likely to be employed in agriculture. Urban men are 10 per- cent more likely to have a formal sector job if they have access to credit. Only part of these results can be explained by access to credit's improving the prospects of family nonfarm enterprises. From similar sets of models that were estimated for wage and nonwage nonagricultural employment choices (not included in this report), it appears that access to credit increases the probability of wage employment more than it increases the probability of nonwage employment. The 5 percent greater likelihood of formal sector employment among women, however, can plausibly be attributed to credit affecting nonfarm enterprise profitability. Although nonlabor income appears to have no effect on employment choices, individuals in households who have accumulated agricultural Madag-txt-fin.qxd 5/22/08 8:17 PM Page 107 Good Jobs, Bad Jobs 107 assets are more likely to remain in the agricultural sector than in any other employment option. The direction of causality may go the other way with regard to these assets, however. Individuals living in agricultur- al households who expect to remain in agriculture are more likely to accumulate agriculture-specific assets than those who expect to work in some other sector. Thus, it is the employment in agriculture that leads to the accumulation of agricultural assets, not the accumulation of those assets, that affects the probability of employment in agriculture. Household structure. The effects of household structure on employ- ment differ by gender and by area of residence. For example, the num- ber of children under the age of five in rural households decreases the probability of nonemployment for both men and women in rural areas by 1.7 percent, while in urban areas this percentage varies from 3.1 per- cent for women to 3.9 percent for men. A higher number of adults in a household increases the probability of nonemployment, particularly in urban areas. Finally, the number of older persons (over 65 years of age) in a household affects employment status quite significantly. Except for urban males, the presence of older males in a household decreases the probability of informal employment by up to 11 percent (for urban females). It increases the probability of females being employed in agri- culture by 5 percent (urban) to 9 percent (rural), and raises the proba- bility of nonemployment for both genders and for geographic areas (although not significantly for urban males). The presence of older women, on the other hand, has a significant (negative) effect on the probability of nonemployment for rural women. The Determinants of Earnings This section moves from exploring the influence of individual and house- hold characteristics on employment status to reviewing the determinants of earnings. For this purpose, earnings functions are estimated separately for those employed in nonagricultural wage jobs, nonagricultural non- wage jobs, and agriculture.2 An advantage of the estimation of earnings functions over the comparisons that were made in chapter 3 among employment types, gender, education levels, and so forth is that earnings functions estimate the impact of various characteristics on individual earnings while controlling for the effects of other possible determinants. The earnings functions thus provide a clearer view of the impact of each of these characteristics. Madag-txt-fin.qxd 5/22/08 8:17 PM Page 108 108 Making Work Pay in Madagascar: Employment, Growth, and Poverty Reduction The dependent variable used in the earnings functions is the log of real daily earnings, which means that the estimated coefficients represent the percentage change in daily earnings for a one-unit change in the associat- ed explanatory variable. The explanatory variables used are those typical- ly found in standard Mincerian earnings functions and include hours worked per day, work experience, education level, and gender.3 Selection bias is controlled for by a correction method in which selectivity is mod- eled as a multinominal logit.4 The results are shown in table 7.1 for the years 2005 and 2001 and the difference between those years. Increases in employment category earnings by level of education As in the previous section, when the level of education is related to the probability of ending up in a good or a bad employment category, the effects of education on earnings were positive and significant, and increased monotonically with the level of schooling.5 However, the corre- lation between education and earnings does not necessarily represent cau- sation, as adolescents who reside in households with more education are more likely to attend school, and thus schooling is not distributed ran- domly among the individuals in the sample. In other words, the returns to schooling in table 7.1 are likely to be overestimated and should be interpreted with caution. Education. The returns to education were largest for wage workers. For example, while earnings were 23 percent higher for wage workers with a primary education than for those with no education, the returns to primary schooling were 12 percent for nonwage workers and 8 percent for agricultural workers. Wage workers with an upper secondary level of education earned 69 percent more than those without schooling, while those with a postsecondary education earned 105 percent more on aver- age. For primary and lower secondary education, the returns were greater for nonwage workers than for agricultural workers. For upper secondary and postsecondary education, the returns were higher for agricultural labor than for nonwage labor. This latter finding, however, should be treated with care, since the sample is small (these workers represent less than 2 percent of the entire workforce). In the agricultural sector, the returns to education in 2005 were signif- icantly lower than in 2001 for primary, lower secondary, and upper sec- ondary education levels (17, 15, and 24 percentage points, respectively). In nonagricultural jobs the only significant fall in returns was in the wage sector for lower secondary education (11 percentage points). Madag-txt-fin.qxd 5/22/08 8:17 PM Page 109 Good Jobs, Bad Jobs 109 Table 7.1 Determinants of Daily Earnings, 2005 and 2001 2005 2001 Difference Coeff. t-value Coeff. t-value Coeff. t-value Wage employed (nonagriculture) Hours worked per day 0.027 5.23** 0.017 3.53** 0.011 1.514 Experience 0.035 5.70** 0.017 2.60** 0.019 2.08* Experience squared 0.000 ­4.05** 0.000 ­0.95 0.000 ­2.02* Education Primary 0.232 5.65** 0.280 5.94** ­0.049 ­0.78 Lower secondary 0.480 11.03** 0.591 11.59** ­0.111 ­1.66+ Upper secondary 0.693 14.23** 0.720 13.20** ­0.026 ­0.36 Post-secondary 1.054 21.18** 1.170 21.44** ­0.115 ­1.56 Female dummy ­0.320 ­10.89** ­0.285 ­9.77** ­0.035 ­0.84 Constant 7.572 22.31** 7.799 24.41** ­0.227 ­0.49 Number of observations 2,993 2,558 R-squared 0.29 0.32 Nonfarm nonwage Hours worked per day 0.025 2.73** 0.034 3.71** ­0.009 ­0.70 Experience 0.012 1.26 ­0.001 ­0.08 0.013 0.78 Experience squared ­0.0003 ­1.86+ 0.0000 ­0.12 0.000 ­0.92 Education Primary 0.116 2.02* 0.200 2.55* ­0.084 ­0.87 Lower secondary 0.260 3.83** 0.255 2.78** 0.0005 0.04 Upper secondary 0.428 4.71** 0.579 5.33** ­0.151 ­1.07 Post-secondary 0.715 5.29** 0.758 5.75** ­0.043 ­0.23 Female dummy ­0.323 ­6.77** ­0.313 ­5.35** ­0.009 ­0.12 Constant 7.370 18.50** 8.142 13.19** ­0.771 ­1.05 Number of observations 2,432 1,229 R-squared 0.09 0.17 Agriculture Hours worked per day 0.002 0.54 0.053 9.18** ­0.051 ­7.61** Experience ­0.009 ­2.69** ­0.017 ­3.11** 0.0008 1.19 Experience squared 0.000 3.39** 0.000 2.79** 0.000 ­0.59 Education Primary 0.084 5.60** 0.251 9.39** ­0.167 ­5.45** Lower secondary 0.218 8.70** 0.371 7.23** ­0.153 ­2.69** Upper secondary 0.438 8.75** 0.674 8.24** ­0.236 ­2.46* Post-secondary 0.877 10.64** 1.114 8.24** ­0.237 ­1.50 Female dummy ­0.023 ­1.78+ 0.002 0.08 ­0.025 ­0.99 Constant 7.932 101.57** 6.942 57.64** 0.990 6.90** Number of observations 17,266 5,077 R-squared 0.09 0.18 Sources: HHS 2001, 2005. Note: Regional dummies included but not shown. The model corrects for selection bias as proposed by Bour- guignon, Fournier, and Gurgand (2004).*p < .05 **p < .01 Madag-txt-fin.qxd 5/22/08 8:17 PM Page 110 110 Making Work Pay in Madagascar: Employment, Growth, and Poverty Reduction Experience. Work experience contributed positively to wage earnings. The significance of the quadratic term for experience in the wage earn- ings equation for 2005 indicates that experience was associated with increases in wage earnings up to the age of 40, after which earnings fell. This was considerably lower than the turning point of 78 in the 2001 model, which may have been a consequence of the deterioration in over- all wage earnings, which may have affected older workers more than younger workers. In agriculture, experience affected earnings negatively up until the age of 25, after which point the returns increased. In 2001 the turning point was 10 years higher (35 years of age). Experience did not have a discernable effect on nonwage earnings. Hours worked. As the 2005 data shows, earnings increased with the number of hours worked for nonagricultural workers (both wage work and nonfarm enterprises) but not significantly for agricultural workers. This may be a sign of disguised employment in agriculture. In 2001 agri- cultural daily earnings increased significantly for each extra hour worked, by 5 percent, which was more than the increases in nonagriculture in the same year from an extra hour worked. This change in the effect on earn- ings of extra hours worked in agriculture may be closely related to the large influx of labor and the associated fall in labor productivity in this sector (described in chapter 4). Differences in education and experience do not fully explain gender differences in wages Gender. In both wage and nonwage nonagricultural employment, women earned 32 percent less than men when the analysis is controlled for edu- cation, experience, and other factors that determine employment selec- tion.6 Although the earnings estimate does not produce a significant dif- ference in male and female agricultural earnings, this finding does not imply that the agricultural earnings were equal between the sexes, because the calculation of these earnings is based on the equal allocation of the larger share of agricultural income among working household members. Table 7.2 provides some further insights into the earnings differences between the genders for the wage employed. For this group of workers, the results of the estimation of the earnings determinants for men and women separately confirm that the returns to both experience and edu- cation differed by gender, although only the returns to primary sector education (14 percentage points higher for men than for women) help Madag-txt-fin.qxd 5/22/08 8:17 PM Page 111 Good Jobs, Bad Jobs 111 Table 7.2 Determinants of DailyWage Earnings by Gender, 2005 Male Female Difference Coeff. t-value Coeff. t-value Coeff. t-value Hours worked per day 0.026 4.63** 0.039 4.69** 0.013 1.32 Experience 0.025 3.63** 0.055 6.56** 0.030 2.78** Experience squared ­0.0002 ­1.96* ­0.0008 ­5.52** ­0.0006 ­3.03** Education Primary 0.306 7.33** 0.167 3.21** ­0.139 ­2.08* Lower secondary 0.514 11.32** 0.485 8.52** ­0.029 ­0.40 Upper secondary 0.713 14.04** 0.833 11.86** 0.120 1.38 Postsecondary 1.072 20.81** 1.264 18.20** 0.192 2.22* Constant 7.857 27.21** 6.257 19.49** ­1.600 ­3.70** Number of observations 2,325 R-squared 0.31 Source: HHS 2005. Note: Provincial dummies included but not shown. Corrected for selection bias as proposed by Bourguignon, Fournier, and Gurgand (2004). *p < .05 **p < .01 explain the gender gap in wage earnings. Postsecondary education returns were higher for women (by 19 percentage points), but this concerns only a small share of workers. In 2005, only 2.6 percent of working adults had a postsecondary education.7 The returns to experience for women were initially greater than for men but diminished at a faster rate. An additional year of experience for a woman with five years of experience resulted in a 4.7 percent earnings increase compared with 2.3 percent for men, but by the time men and women attained 26 years of experience the returns were similar. The significant difference in the constant term (160 percent) indicates that a part of the gap remains unexplained by the model. In other words, the lower wage earnings for women compared with men are only partly explained by differences in the returns of education and experience. Segmentation in theWage Labor Market Differences in wages for workers with similar characteristics can signal labor market segmentation Much of the theoretical literature on labor markets in developing coun- tries emphasizes the fragmentation of labor into different segments. Although it is hard to prove empirically, there appears to be a consensus Madag-txt-fin.qxd 5/22/08 8:17 PM Page 112 112 Making Work Pay in Madagascar: Employment, Growth, and Poverty Reduction that in some contexts, different parts of the labor market follow their own dynamic, particularly in terms of wage determination and employment policy, and that movement between the different segments is limited. While numerous different fragments can exist in reality, the literature reg- ularly stylizes the labor market into a dual system, consisting of a so- called good segment and bad segment.8 There seems to be no consensus about what exactly distinguishes the two segments. In the literature, the good segment is alternatively called the formal, urban, modern, or indus- trial sector; the bad segment is referred to as informal, rural, traditional, or agricultural (Fields 2004).Which parts of the labor market are considered to be the good and the bad segments can differ between countries and over time. Although there may be various reasons for the existence of labor mar- ket segmentation, the general consequence is that workers with equal characteristics do not receive equal returns to labor across the segments. There are barriers to labor mobility, and the labor demand for the good segment exceeds the labor supply. In the context of this report, labor mar- ket segmentation is important in that it may lead to the greater incidence and depth of poverty by preventing workers from earning a higher income by moving to the good segment of the labor market. Segmentation models can be used to explain certain labor market developments that do not make sense under a unique labor market, such as why labor productivity can increase simultaneously with a fall in wages, why a firm can add workers without raising wages, or why urban employment creation may increase unemployment. Indeed, in chapter 5 it was suggested that segmentation could provide an explanation for observed changes in tertiary sector earnings and labor productivity, which seemed hard to reconcile with a competitive market model. Because labor market segmentation implies that comparable workers receive different earnings, an analysis of whether workers with similar characteristics receive different returns to labor in different segments could shed light on the question of whether segmentation exists. The results of such an analysis should be interpreted with caution, however, as there may be explanations other than segmentation. For example, wage differentials could be caused by differing nonpecuniary job characteristics or unobserved worker characteristics, monopsonistic power in one (or more) of the segments, or transaction costs involved with moving from one segment to the other. Thus, although it remains useful to determine whether different labor market segments reward observable worker char- Madag-txt-fin.qxd 5/22/08 8:17 PM Page 113 Good Jobs, Bad Jobs 113 acteristics differently, the existence of such differences does not necessar- ily imply that the labor market is segmented. Returns to secondary education in the public sector are higher than in the formal private sector The examination of Madagascar's labor market for signs of segmentation involved estimating separate earnings functions for nonagricultural wage earnings in the public, formal private,9 and informal private sectors for the year 2005.Although, unfortunately, the restriction to nonagricultural wage workers excludes a large share of the Malagasy workers, it has the advan- tages that wage data are believed to be more reliable than data on nonwage workers, and that all earnings that are grouped together and compared are calculated in a similar fashion. The parameter estimates in the earnings functions will therefore more accurately reflect differences in the returns to labor rather than differences in the definitions of earnings. Table 7.3 shows the results of tests of whether the differences between the various determinants across these three sectors were significant.10 The model finds significant differences in the returns to lower and upper sec- ondary education between wage workers in the formal public and private sectors (at the 10 percent significance level). All other identified charac- teristics generated similar--or at least not statistically different--returns in each sector. The returns to both lower secondary and upper secondary education were about twice as high for public sector wage workers than for formal private sector wage workers (46 percent compared with 21 percent for lower secondary, and 53 percent compared with 27 percent for upper secondary education). From the simulation of the earnings of public sector workers undertaken to find out whether their returns to education were equal to those of formal private sector workers, it becomes clear that 30 percent of the earnings difference can be attributed to this difference in returns to education. Furthermore, from the rejection of the existence of different constants in both models, it can be inferred that 70 percent of the earnings differences between formal public and private sector wage workers are explained by the characteristics that are identified in the model. The higher returns to secondary education for public sector workers compared with formal private sector workers could be a sign of labor seg- mentation between the two sectors. However, as previously noted, there could be other explanations. The determination as to whether the differ- ences in returns to labor are indeed the results of labor segmentation Madag-txt-fin.qxd 114 Table 7.3 Testing for Segmentation--Determinants of DailyWage Earnings, 2005 (1) (2) (3) Difference (1­2) Difference (2­3) M ak Public sector Formal private sector Informal private sector Public minus formal Formal minus informal ing 5/22/08 Coeff. t-value Coeff. t-value Coeff. t-value Coeff. t-value Coeff. t-value W or Hours worked per day 0.024 2.86 0.010 1.00 0.028 4.28** 0.013 1.00 ­0.018 ­1.46 k Experience 0.011 1.09 0.018 1.61 0.025 4.26** ­0.007 ­0.5 ­0.007 ­0.55 Pay 8:17 in Experience squared 0.000 0.24 0.000 ­1.45 0.000 ­4.29** 0.000 1.25 0.000 0.59 M Education adagasc PM Primary 0.208 1.87 0.134 1.37 0.146 3.64** 0.074 0.50 ­0.012 ­0.12 Lower secondary 0.459 4.27 0.205 2.05* 0.283 6.01** 0.254 1.73+ ­0.078 ­0.71 ar Page Upper secondary 0.532 5.00* 0.271 2.40* 0.454 7.05** 0.261 1.68+ ­0.183 ­1.40 :Emplo Postsecondary 0.814 7.35** 0.735 6.62** 0.669 7.56** 0.079 0.51 0.066 0.47 114 Female dummy ­0.165 ­3.24** ­0.259 ­4.29** ­0.402 ­12.37** 0.094 1.19 0.143 2.08* ymen Constant 8.145 14.84** 7.990 13.26** 7.289 42.11** 0.155 0.19 0.701 1.12 Number of observations 860 937 3,652 t,G R-squared 0.20 0.14 0.13 rowth, Source: HHS 2005. and Note: Estimates are corrected for selection bias by using a correction method as proposed by Bourguignon, Fournier, and Gurgand (2004). *p < .05 ** Pover ty Reduc tion Madag-txt-fin.qxd 5/22/08 8:17 PM Page 115 Good Jobs, Bad Jobs 115 would require a more detailed review of the wage determinants and employment policies of both sectors, and of the dynamics between the two sectors. The gender wage gap is significantly higher in the informal sector than in the formal private sector The only variable that generates different returns when the formal private sector is compared with the informal private sector is gender. Whereas in the formal sector women on average earned 26 percent less than men with otherwise similar characteristics, this gender gap was as high as 40 percent in the informal sector. Separate estimates of earnings functions for men and women by formality status (not depicted here) further indi- cate that the earnings differences are not explained by differences in returns to experience or education. In addition, simulations by gender (similar to table 7.2) for the formal and informal sectors show large dif- ferences between the constant terms, which seem to indicate an unob- served form of gender discrimination in both the formal private and the informal private wage sectors. These findings are supported by Cling, Razafindrakoto, and Roubaud (2007). Using labor force surveys that were conducted in Antananarivo since 1995, they conclude that gender dis- crimination in remuneration is twice as high in the capital's economy as a whole (including both formal and informal and wage and nonwage workers) as in the formal secondary sectors. Notes 1. Wage employment is considered formal if the employer contributes to a pen- sion fund or provides social protection. Nonwage employment is considered formal if the enterprise is registered with the authorities. 2. The types of employment that are distinguished differ somewhat from those in the previous section, where the two nonagricultural categories were formal and informal rather than wage and nonwage jobs. 3. Experience is difficult to measure, as it is unknown when individuals started working. Experience here is calculated as the individual's age minus the num- ber of years of schooling (plus five years). It is important to account for expe- rience, as it is negatively correlated to education. Since experience is likely to contribute positively to earnings, the error terms in the model are likely to be negatively correlated with education without the inclusion of the experience variable. Madag-txt-fin.qxd 5/22/08 8:17 PM Page 116 116 Making Work Pay in Madagascar: Employment, Growth, and Poverty Reduction 4. This method is proposed by Bourguignon, Fournier, and Gurgand (2004) and is an extension of the method as proposed by Lee (1983), which deviates from probit-based selectivity models. 5. This differs from Glick (1999), who found no statistically significant effect of primary education in Madagascar. 6. In calculating work experience similarly for both sexes, experience for women may be overestimated compared with experience for men, as women are more likely to have had maternity-related nonworking periods. 7. These findings are supported by a simulation of the effects of education alone on the wage earnings of men and women (World Bank 2007a). The gender gap for those without education is found to be 16 percent. For those with pri- mary education, the gap increases to 25 percent and then becomes smaller again for lower secondary (18 percent), upper secondary (10 percent) and postsecondary (8 percent) education. 8. See Magnac (1991) and Heckman and Sedlacek (1985) for discussions about testing labor market dualism. 9. A worker is considered to be employed in the formal private sector if the worker or the employer contributes to a pension fund, or if the worker receives social protection. 10. Funkhouser (1998) points out that the allocation of workers across sectors is determined by the marginal worker and not the mean worker. This analysis, along with those of others (for example, Dickens and Lang [1985]), is admit- tedly based on the latter. Madag-txt-fin.qxd 5/22/08 8:17 PM Page 117 Good Jobs, Bad Jobs 117 Annex 7A Determinants of Male and Female, Urban, and Rural Employment--Tables Tables 7A.1 through 7A.4 show the marginal effects, which are interpret- ed as the average change in the probability of an individual finding him- self or herself in an employment category as a result of a one-unit change in the independent variables. Because the average marginal effects are shown instead of the estimated coefficients, all identified employment categories (including the omitted category) can be shown. The marginal effects sum to zero across the categories. Madag-txt-fin.qxd 118 Table 7A.1 Determinants of Male Rural Employment, 2005 Formal Informal Agricultural Not employed 5/22/08 Marginal Marginal Marginal Marginal effect t-value effect t-value effect t-value effect t-value Age 0.001 8.73 ** 0.000 0.86 0.005 11.65 ** ­0.007 ­16.74 ** Migrant 0.028 3.62 ** 0.043 2.85 ** ­0.064 ­3.35 ** ­0.007 ­0.50 8:17 Education dummies Primary 0.023 2.42 * 0.010 1.03 ­0.081 ­6.15 ** 0.048 4.77 ** PM Lower secondary 0.128 4.68 ** 0.034 2.05 * ­0.335 ­13.46 ** 0.173 8.88 ** Upper secondary 0.263 6.29 ** 0.005 0.25 ­0.472 ­14.93 ** 0.204 6.66 ** Page Post-secondary 0.407 7.55 ** ­0.008 ­0.31 ­0.593 ­15.91 ** 0.194 3.85 ** Nonlabor income (log) 0.0000 0.00 0.0000 ­0.01 0.0000 ­0.03 0.0000 0.06 118 Value of agricultural assets (log) ­0.001 ­1.06 ­0.009 ­3.71 ** 0.011 3.86 ** ­0.001 ­0.37 Obtained credit 0.017 1.13 ­0.041 ­1.55 0.049 1.47 ­0.025 ­1.38 Household structure No. children under 5 ­0.001 ­0.24 0.008 1.81 0.009 1.58 ­0.017 ­4.12 ** No. children 5­14 0.001 0.68 ­0.006 ­2.15 * ­0.003 ­1.00 0.008 4.06 ** No. men 15­64 ­0.005 ­2.19 * 0.000 ­0.10 ­0.012 ­2.41 * 0.017 6.20 ** No. women 15­64 0.003 1.12 ­0.006 ­1.18 ­0.007 ­1.06 0.010 2.79 ** No. men 65+ 0.018 1.45 ­0.083 ­2.73 ** 0.027 0.88 0.038 3.20 ** No. women 65+ ­0.004 ­0.29 0.004 0.15 ­0.004 ­0.15 0.005 0.27 Antananarivo city dummy (n.a.) Percentage in each category 3.5 6.6 81.1 8.8 Number of observations 6,930 Pseudo R-squared 0.23 Source: Bank calculations using HHS data. Note: The analysis included the incorporation of regional dummies, which are not shown in this table. n.a. = not applicable. *p < .05 **p < .01 Madag-txt-fin.qxd Table 7A.2 Determinants of Female Rural Employment, 2005 Formal Informal Agricultural Not employed Marginal Marginal Marginal Marginal effect t-value effect t-value effect t-value effect t-value 5/22/08 Age 0.001 6.10 ** 0.001 3.07 ** 0.002 5.24 ** ­0.004 ­11.83 ** Migrant 0.012 2.04 * 0.000 0.01 ­0.034 ­1.87 + 0.021 1.43 Education dummies Primary 0.017 2.35 * 0.028 2.90 ** ­0.061 ­4.99 ** 0.015 1.64 8:17 Lower secondary 0.096 4.47 ** 0.054 3.10 ** ­0.260 ­11.47 ** 0.110 5.94 ** Upper secondary 0.244 5.45 ** 0.001 0.05 ­0.388 ­9.65 ** 0.143 3.89 ** PM Post-secondary 0.332 5.47 ** 0.056 1.21 ­0.534 ­9.66 ** 0.146 2.60 ** Nonlabor income (log) 0.0000 ­0.01 0.0000 ­0.01 0.0000 ­0.04 0.0001 0.07 Page Value of agricultural assets (log) ­0.001 ­0.98 ­0.009 ­3.52 ** 0.010 3.33 ** 0.000 ­0.01 Obtained credit 0.028 1.74 + ­0.016 ­0.56 ­0.047 ­1.17 0.035 1.09 119 Household structure No. children < 5 ­0.007 ­2.49 * 0.005 1.20 0.019 3.14 ** ­0.017 ­3.82 ** No. children 5­14 0.000 ­0.14 0.000 ­0.12 0.002 0.68 ­0.002 ­0.70 No. men 15­64 ­0.005 ­2.44 * ­0.021 ­4.81 ** 0.017 3.08 ** 0.010 2.73 ** No. women 15­64 ­0.002 ­1.00 ­0.002 ­0.46 ­0.023 ­4.20 ** 0.027 7.03 ** No. men 65+ ­0.074 ­1.52 ­0.058 ­2.40 * 0.092 2.46 * 0.040 2.60 ** No. women 65+ ­0.009 ­1.07 0.018 0.82 0.027 1.03 ­0.036 ­2.11 * Antananarivo city dummy (NA) Percentage in each category 2.2 7.7 79.0 11.1 Number of observations 7,258 Pseudo R-squared 0.15 Source: HHS 2005. Note: The analysis included the incorporation of regional dummies, which are not shown in this table. n.a. = not applicable. *p < .05 **p < .01 119 Madag-txt-fin.qxd 120 Table 7A.3 Determinants of Male Urban Employment, 2005 Formal Informal Agricultural Not employed 5/22/08 Marginal Marginal Marginal Marginal effect t-value effect t-value effect t-value effect t-value Age 0.005 19.18 ** 0.002 6.72 ** 0.003 8.43 ** ­0.010 ­26.98 ** Migrant 8:17 0.048 4.84 ** 0.077 5.18 ** ­0.125 ­9.03 ** 0.000 0.03 Education dummies PM Primary 0.039 2.57 ** ­0.029 ­2.09 * ­0.074 ­6.05 ** 0.065 4.24 ** Lower secondary 0.132 5.97 ** ­0.043 ­2.60 ** ­0.260 ­18.27 ** 0.171 8.42 ** Page Upper secondary 0.198 7.54 ** ­0.099 ­6.09 ** ­0.349 ­26.10 ** 0.250 10.38 ** Post-secondary 0.329 10.59 ** ­0.131 ­8.14 ** ­0.365 ­24.70 ** 0.168 6.09 ** 120 Nonlabor income (log) ­0.0005 ­0.62 0.0000 ­0.01 0.0000 0.02 0.0004 0.65 Value of agricultural assets (log) ­0.005 ­1.90 + ­0.007 ­2.00 * 0.021 7.23 ** ­0.009 ­3.65 ** Obtained credit 0.103 3.94 ** ­0.023 ­0.71 ­0.103 ­2.95 ** 0.023 1.03 Household structure No. children under 5 0.003 0.71 0.017 2.72 ** 0.019 3.08 ** ­0.039 ­7.21 ** No. children 5­14 ­0.003 ­1.24 ­0.009 ­2.46 * 0.001 0.43 0.011 3.99 ** No. men 15­64 ­0.011 ­2.72 ** ­0.005 ­1.01 ­0.012 ­2.33 * 0.027 7.99 ** No. women 15­64 0.006 1.38 ­0.022 ­3.38 ** ­0.006 ­1.00 0.022 5.29 ** No. men 65+ ­0.015 ­0.70 ­0.006 ­0.21 0.019 0.71 0.002 0.10 No. women 65+ ­0.027 ­1.31 0.015 0.60 ­0.020 ­0.80 0.032 2.01 * Antananarivo city dummy 0.840 180.37 ** ­0.200 ­26.93 ** ­0.474 ­84.55 ** ­0.167 ­37.59 ** Percentage in each category 17.8 28.2 34.0 20.0 Number of observations 6,810 Pseudo R-squared 0.33 Source: HHS 2005. Note: The analysis included the incorporation of regional dummies, which are not shown in this table. *p < .05 **p < .01 Madag-txt-fin.qxd Table 7A.4 Determinants of Female Urban Employment, 2005 Formal Informal Agricultural Not employed Marginal Marginal Marginal Marginal effect t-value effect t-value effect t-value effect t-value Age 0.003 13.15 ** 0.003 7.17 ** 0.001 3.72 ** ­0.007 ­18.73 ** 5/22/08 Migrant 0.025 3.22 ** 0.035 2.62 ** ­0.101 ­7.76 ** 0.041 3.01 ** Education dummies 8:17 Primary 0.029 2.21 * 0.036 2.59 ** ­0.088 ­8.04 ** 0.023 1.59 Lower secondary 0.117 5.57 ** 0.022 1.29 ­0.230 ­18.43 ** 0.092 4.89 ** Upper secondary 0.218 6.98 ** ­0.019 ­0.92 ­0.326 ­24.14 ** 0.126 4.83 ** PM Post-secondary 0.451 11.12 ** ­0.068 ­2.94 ** ­0.381 ­25.53 ** ­0.002 ­0.05 Page Nonlabor income (log) 0.0000 0.00 ­0.0003 ­0.30 0.0000 0.02 0.0002 0.28 Value of agricultural assets (log) ­0.004 ­1.63 ­0.015 ­4.37 ** 0.039 13.62 ** ­0.020 ­5.82 ** 121 Obtained credit 0.049 2.48 * 0.020 0.58 ­0.130 ­3.79 ** 0.060 1.81 + Household structure No. children under 5 ­0.004 ­0.93 0.003 0.54 0.031 5.53 ** ­0.031 ­5.07 ** No. children 5­14 0.000 ­0.23 ­0.004 ­1.28 0.007 2.07 * ­0.002 ­0.54 No. men 15­64 0.001 0.27 ­0.045 ­8.48 ** 0.013 2.56 * 0.032 6.45 ** No. women 15­64 0.001 0.24 ­0.007 ­1.40 ­0.034 ­6.59 ** 0.041 8.54 ** No. men 65+ ­0.038 ­2.31 * ­0.107 ­4.40 ** 0.050 2.42 * 0.096 4.68 ** No. women 65+ ­0.008 ­0.59 ­0.003 ­0.15 0.015 0.67 ­0.004 ­0.17 Antananarivo city dummy 0.883 278.43 ** ­0.187 ­25.30 ** ­0.450 ­86.65 ** ­0.246 ­38.03 ** Percentage in each category 10.0 24.0 31.4 34.6 Number of observations 7,522 Pseudo R-squared 0.25 Source: HHS 2005. 121 Note: The analysis included the incorporation of regional dummies, which are not shown in this table. *p < .05 **p < .01 Madag-txt-fin.qxd 5/22/08 8:17 PM Page 122 Madag-txt-fin.qxd 5/22/08 8:17 PM Page 123 C H A P T E R 8 Conclusions and Suggestions for a Way Forward This report aims to increase the understanding of how employment and earnings help to translate economic growth (or the lack thereof) into poverty reduction in Madagascar. It does not aim to provide definitive guidance on the concrete policy measures to improve the effectiveness of labor markets as such a transmission mechanism. That lies outside the scope of this report. However, the report's conclusions do provide a num- ber of broad policy directions that can be used as a basis for further research and initial policy discussions.The main conclusions, based on the findings of this report, are described below. To reduce poverty through employment, policies should focus on creating more high-earning jobs, rather than merely creating more jobs. One important policy question is whether the way labor markets translate growth into poverty reduction is improved more by (i) increasing the employment intensity of growth or (ii) by increasing earnings, expect- edly through raised labor productivity. Employment rates in Madagascar are high, with 88 percent of the adult population engaged in some kind 123 Madag-txt-fin.qxd 5/22/08 8:17 PM Page 124 124 Making Work Pay in Madagascar: Employment, Growth, and Poverty Reduction of employment. The unemployment rate is a mere 2.6 percent. Further- more, even in the period from 2001 to 2005, when GDP per capita de- clined, the number of new jobs generated (0.8 million) exceeded the growth of the working-age population (0.6 million). Although a large pro- portion of adults are somehow employed, a large share of those are work- ing poor: of all those who work, two-thirds live in poor households, and one-third do not earn enough even to keep one individual out of poverty. This leads to the conclusion that the creation of better jobs is more of an issue in Madagascar than the creation of more jobs.Although employ- ment is a necessary condition for poverty alleviation--helping to reduce the depth, if not always the incidence, of poverty--a large share of adults has been able to find some kind of gainful employment even in times of economic adversity. The fact that many of them remain poor, however, demonstrates that government policies should focus on increasing earn- ings by increasing labor productivity, particularly of those at the bottom of the earnings distribution. In that light, the reduction of the employ- ment shares of the relatively high-earnings, high-productivity secondary and tertiary sectors in favor of the low-earnings, low-productivity primary sector that occurred between 2001 and 2005 should be a serious concern. Further cost-benefit (or similar) analyses of potential policy interven- tions would help the government determine the most appropriate pol- icy mix, balancing the support of expanding labor-intensive output of the secondary and tertiary sectors with increasing agricultural pro- ductivity. A logical follow-up question is whether policies should aim to increase earnings and labor productivity in the work that is currently be- ing carried out by the poor, or whether they should concentrate on ex- panding labor-intensive output in the higher-earnings, higher-productivity sectors, which employ few poor, so that more of the poor can move into those sectors. In Madagascar, this question translates into determining whether policies should focus on increasing agricultural productivity (where the poor are), on generating more employment in the secondary and tertiary sectors where productivity and earnings are higher (so that the poor can move to those sectors), or a combination of both. Currently, in its Madagascar Action Plan 2007­2012, the government of Madagascar (2007) is committed both to agriculture-oriented rural development pro- grams and a "Green Revolution" and supporting for a high-growth econo- my that is geared more toward industry and services, without explicitly prioritizing one objective over the other. Madag-txt-fin.qxd 5/22/08 8:17 PM Page 125 Conclusions and Suggestions for a Way Forward 125 The answer to the above question depends on two factors: first, the very high labor productivity and earnings in the secondary and tertiary sectors compared with those in the primary sector, and second, the very small number of people employed in the secondary and tertiary sectors com- pared with the number working in agriculture. In 2005, median monthly earnings in the primary sector were less than half of those in the other sec- tors. Primary sector labor productivity, defined as average output per worker, was only 8 percent and 14 percent of productivity in the second- ary and tertiary sectors, respectively. Considering the very substantial dif- ferences in both earnings and labor productivity between the primary sec- tor and the other sectors, it seems unlikely that the primary sector will be able to catch up with the other sectors in the medium to short term. This implies that efforts to alleviate poverty through labor should focus on moving workers from the low-earnings, low-productivity primary sector to the higher-earnings, higher-productivity secondary and tertiary sectors. The secondary and tertiary sectors, however, start from a small employment base. The tertiary sector employs 17.4 percent of the popu- lation, the secondary only 2.5 percent. The agricultural sector employs the rest, almost 8 out of every 10 working adults.Two conclusions can be drawn from this observation. First, as the tertiary sector employs almost seven times as many workers as the secondary sector, employment-inten- sive growth in the tertiary sector would probably generate more higher- earnings jobs than it would in the secondary sector. Policy interventions might therefore in general better focus on employment-intensive growth in services than in industry (which is not to say that good opportunities for boosting secondary sector growth, such as currently exist in the min- ing sector, should not be taken full advantage of). Second, even when the secondary and tertiary sectors succeed in generating employment well above the rate at which the labor force increases, the share of agricultur- al workers that these sectors can absorb will be relatively limited. Thus the agricultural sector will remain, in the foreseeable future, the main sec- tor of employment for the poor. This continuation of the importance of the agricultural sector as an employer of the poor gives rise to the question whether, in addition to policies to increase employment in the secondary and tertiary sectors, the government should not also continue its efforts to increase primary sec- tor productivity. Particularly in the face of existing capacity and financial constraints, there is a trade-off between, on the one hand, investing in significantly improving the living standards of a relatively limited number Madag-txt-fin.qxd 5/22/08 8:17 PM Page 126 126 Making Work Pay in Madagascar: Employment, Growth, and Poverty Reduction of households by facilitating the growth of secondary and tertiary jobs, and, on the other hand, modestly improving the living standards of the larger share of the population through increasing primary sector produc- tivity and earnings. The answer to the question of whether the government should also aim to increase primary sector labor productivity depends on, among other things, an assessment of the costs and benefits of interventions that would improve agricultural productivity compared with those that would increase secondary and tertiary sector employment. Further analysis to help answer this question would therefore involve a review of the follow- ing: (i) which policy interventions can best boost employment-intensive growth opportunities for the poor in industry and, particularly, services, the costs of these policies, and the expected impact on poverty reduction; (ii) which policy interventions can best raise agricultural productivity, their costs, and their impact on poverty; (iii) which policies, such as well- targeted infrastructure projects, could at the same time help increase both agricultural productivity and employment creation in the sectors where earnings and productivity are higher; and (iv) what the indirect impact of policy interventions on other sectors would be, for example, policies that help move workers from agriculture to the tertiary sector and also raise the average output per worker in the primary sector. Understanding of the crisis-related departure of less productive work- ers from the secondary sector needs to be improved. The observed impact of the 2002 crisis on employment and earnings allows a number of observations related to, among other aspects, the exit of less-productive workers from the secondary sector and the importance of agriculture as a coping mechanism. Compared with 2001, the number of workers in the secondary sector in 2005 was reduced by half, while there were no major differences in output level (real output in 2005 was only 0.4 percent lower than in 2001). The crisis seems to have caused the departure of relatively unpro- ductive workers, who seem to have been mainly self-employed and fam- ily workers, rather than wage workers. This raises a number of yet unan- swered questions, including whether any rigidities existed prior to the crisis that kept these apparently less-productive workers attached to the secondary sector; why a similar development did not occur in the tertiary sector (where employment and output increased at more or less similar rates between 2001 and 2005); why the crisis triggered their departure Madag-txt-fin.qxd 5/22/08 8:17 PM Page 127 Conclusions and Suggestions for a Way Forward 127 from the sector; and why, three years after the crisis, they have not all returned to the secondary sector. Particularly in light of the suggested pol- icy focus on increased employment-intensive growth in the secondary and tertiary sectors, further analyses that help answer these questions may increase understanding of how to better support the creation of high- labor-productivity jobs in the secondary and tertiary sectors. The importance of the agricultural sector as a safety net should be taken into account in the design and targeting of social protection mechanisms. The massive influx of labor into agriculture reflects the importance of agriculture as a last resort for income generation in times of crises. Particularly in a country like Madagascar, which over the past decades has experienced its fair share of economic crises, this likely has implications for the value that the Malagasy population attaches to the opportunity to engage in agriculture. In other words, agriculture is not just an important economic sector, but also seems to function as a crucial safety net for a large share of the population. This is likely to add an addi- tional perspective to any possible policy issues that would affect, for ex- ample, access to and ownership of agricultural land. Also, as most agricul- tural activities take place in the context of family enterprises, households without access to land lack the coping mechanism of agricultural produc- tion; they may therefore be particularly vulnerable when confronted with economic (or other) adversity. Thus, the particular vulnerability of house- holds that cannot use agricultural production or increase their agricultural production as a coping mechanism could be taken into account in the de- sign of social protection mechanisms. A final observation regarding the impact of the crisis concerns the coinciding of the labor influx into agriculture with the global price increase of Madagascar's main crop, rice.Although a more detailed analy- sis of the impact of the changes in rice prices on poverty is forthcoming, it is likely that the agricultural sector would have been much less able to generate earnings for the substantial number of workers who moved into agriculture if the increase in rice prices had not occurred at the same time. In other words, the functioning of the agricultural sector as a cop- ing mechanism during and after the 2002 crisis may have been substan- tially influenced by external factors. Thus, the extent to which the agri- cultural sector can function as a safety net during future crises should not be overestimated. Madag-txt-fin.qxd 5/22/08 8:17 PM Page 128 128 Making Work Pay in Madagascar: Employment, Growth, and Poverty Reduction Because education and gender are the main determinants of acquir- ing good jobs, the government should continue promoting equal access to education across socioeconomic groups and geographic areas, as well as gender equality in the workplace. The poverty-reduc- ing impact of the increased availability of good jobs can be enhanced by improvements in access to these jobs by individuals in poor and disad- vantaged groups. Educational attainment and gender equality, as the main determinants of acquiring good jobs, are important vehicles for im- proving the equality of access to good jobs. Educational attainment has been shown to have a positive impact on the probability of obtaining nonagricultural, formal employment and higher earnings. This fact highlights the importance of the Malagasy gov- ernment's continuing engagement in educational reforms. As reflected in the Madagascar Action Plan (MAP), the commitment to achieve an "edu- cational transformation" includes the objective to increase access, quality, and effectiveness of education, including for underprivileged and vulner- able population groups. The achievement of these goals is expected to positively affect the poor's access to the available good jobs. In the field of education, the government is paying particular attention to the education of girls.While this is a welcome effort to promote gender equality, the analyses in this report suggest that the existing gender gap in earnings cannot be fully explained by the difference in education between male and female workers, nor by other observed factors that determine employment selection and earnings. The government's recognition of the need for additional steps to diminish gender inequality is reflected in the MAP, which includes the promotion of "gender equality and empower- ment of women" as one of the country's challenges. One of the indicators of the country's meeting this challenge is a reduction of the gender salary gap in the private sector by 50 percent by 2012.The minister in charge of women's affairs is leading governmental efforts to promote female partic- ipation in economic, social, and civic affairs, as well as efforts to intensive- ly recruit more women in the public sector at all levels. Considering the importance of schooling and gender differences for obtaining good jobs, the government should be encouraged to continue its efforts to improve educational attainment for the most vulnerable and to reduce gender inequality. To ensure an optimal impact of these initia- tives, policies would greatly benefit from rigorous monitoring and evalu- ation exercises that would allow the continuous improvement of their efficiency and effectiveness. Madag-txt-fin.qxd 5/22/08 8:17 PM Page 129 Conclusions and Suggestions for a Way Forward 129 Several data issues need to be resolved or taken into account: demographics, household surveys and national accounts, and the national poverty line. The analyses that were conducted for the prepa- ration of this report revealed a number of data-related issues. First, there is an urgent need for updated demographic information. The last popu- lation census in Madagascar was held in 1993 and no longer serves as a reliable basis for demographic data. A comprehensive census is needed on which the selection of participants and the weights of future house- hold surveys can be based. Second, a problem that is not confined to the Malagasy context is the difficulty of reconciling information from household surveys with aggre- gate data from the national accounts. In the context of this report, micro- data on earnings are compared with average output per worker to review the linkages between returns to labor. The usefulness of the comparison is limited, however, by a number of compatibility issues between the micro- and the macrodata.1 As a result, the review allows only some cau- tious assumptions to be made on the linkages between earnings, on one hand, and labor productivity on the other. Improvements in the consis- tency between household surveys and national accounts data would greatly expand the information that could be drawn, for many purposes, from a combined analysis of the two data sources. Given the technical requirements of this recommendation, and considering that household surveys are regularly conducted with the technical and financial assis- tance of donor agencies, this recommendation can likely not be carried out without explicit donor recognition of the importance of improving the consistency between the two data sources. Third, with the national poverty line of MGA 305,300 per year in 2005, almost 70 percent of the Malagasy population lives in poverty. (Based on the $1-a-day poverty line, the 2005 poverty rate was 61 per- cent.) As a result, changes in the headcount poverty rate tend to occur with a change in living standards of the relatively better-off households (households placed at about the 70th percentile of the expenditures dis- tribution).This result implies that, in addition to the poverty rate, both the depth of poverty and inequality measures are particularly relevant indica- tors of poverty and of changes in poverty in Madagascar. The depth of poverty is important because changes in the situation of the poorest quin- tiles are not reflected in changes in the poverty rate, unless they concern such major improvements in expenditures that these poorest households manage to cross the poverty line and leap from being very poor to being Madag-txt-fin.qxd 5/22/08 8:17 PM Page 130 130 Making Work Pay in Madagascar: Employment, Growth, and Poverty Reduction nonpoor. A measure of inequality helps determine whether or not a change in headcount poverty is due to redistribution. For example, a fall in headcount poverty due to redistribution from the poorest to those around the 70th percentile is unlikely to be considered a positive development. Note 1. See Robilliard and Robinson (2003) for an example of a method to reconcile HHS data with national accounts data for Madagascar. Madag-txt-fin.qxd 5/22/08 8:17 PM Page 131 References Behrman, Jere. 1999. "Labor Markets in Developing Countries." 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Madag-txt-fin.qxd 5/22/08 8:17 PM Page 135 Index A B age, job-getting, 106 birthrate, 20, 41n.1 agricultural sector, 4, 6, 11, 23­28, 30, 43, 44, 46, 53n.1 C earnings, 63, 64­65, 74­76, 108 capital-labor ratio, 55­58 determinants of, 109 capital stock, 56, 57, 58, 58n.1 education, 108 cattle farming, 28, 30 employment, 35, 36, 37­38 child labor, 4, 14, 32, 34, 35, 88­89 income, 7, 10 urbanization and, 89 labor, 35, 38­39 conclusions, 123­130 productivity, 124­126 cost-benefit analyses, 124­126 as safety net, 127 country context, 19­42 Anstiranana, 101 crisis, political, 5, 36­37 household income per capita, 100 agriculture and, 127 labor income changes, 99, 102 economic growth and, 22­23 Antananarivo, 101 labor and, 44­47 household income per capita, 100 poverty rate and, 20 labor income changes, 99, 101, 102 primary sector and, 24 residence and job-getting, 106 worker exit, 11, 126­127 assets, job-getting and, 106­107 currency depreciation, 30 local, 5 135 Madag-txt-fin.qxd 5/22/08 8:17 PM Page 136 136 Making Work Pay in Madagascar: Employment, Growth, and Poverty Reduction D distribution, 44, 118n.10 earnings and, 69­77 data, 11, 13, 15­18 growth per capita and, 49­50 issues, 129­130 poverty and, 70­73, 90, 90­91n.2 quality, 16­18 rate, 32, 123 definitions, 13, 14­15 sectoral distribution, 45, 46, 47, 48­49, demographics 65, 82 data issues, 129 status, 70­73, 92­93 growth per capita and, 49­50 structure, 36 demographic transition, 20 types, 117n.2 dependency rate, 16­17, 20, 41n.2 working-age population, 92­93 employment-intensive growth, 126 E energy industry, 28 earnings, 1, 4­7 enterprise income, 75 capacity, 2 experience, 117n.3, 118n.6 changes, 86­87 earnings and, 110­111 daily, 109, 111, 116 gender, 110­111 data, 12n.3, 59­65, 68nn.1, 3 returns to for women, 111, 118n.6 determinants of, 107­111, 116 export processing zones (EPZ), 23, 26­32, distribution, 60­61 41nn.7,8 education and, 41 exports, 23 employment and, 69­77 growth trends, 29 employment category, 39, 73­74 employment status, 67 F gender, 39, 40­41, 42n.13, 111 family enterprise worker, 14 hourly, 84, 98­99 family workers, 71­72 inequality, 7­9 earnings, 73­74 labor income, 14 farming sector, 24­25, 27 monthly, 60, 61, 62­65 Fianarantsoa, 101 nonfarm enterprise, 17 household income per capita, 100 nonwage agricultural, 17 labor income changes, 99, 102 poverty and, 69­77 fisheries sector, 24­25, 27, 28, 30 primary sector, 62­65 foreign assistance, 24 region, 39 foreign direct investment (FDI), 23 sector and, 10, 64, 67 forestry, 30 economic growth, 22, 68n.2 formal sector employment, 14, 32, 38­39, expected, 23, 41n.5 42n.11, 117n.1, 118n.9 economic stability, medium-term, 23­24 education G earnings and, 9­10, 11, 41, 42n.12, 108­110 GDP, 5­6, 7­9, 15 employment category and, 108­110 annual growth, 22, 23 employment structure and, 38, 40­41 gender gender gap, 118n.7 earnings and, 10, 11, 39, 40­41, 42n.13, job-getting and, 104­105, 128 110­111 returns to, 9, 113­115 determinants of rural employment elderly population, 34 female, 119 employed, 14 male 118 employment, 1­2, 4, 5, 33 determinants of urban employment data, 58n.3 female, 121 determinants of, 117 male, 120 Madag-txt-fin.qxd 5/22/08 8:17 PM Page 137 Index 137 experience, 110­111, 118n.6 Institut National de la Statistique job-getting, 104, 128 (INSTAT), 13, 15 gender gap, 117, 118n.7 integrated growth poles, 32 education and, 118n.7 International Monetary Fund, 15 informal vs formal sector, 117 investments, 5 job-getting, 128 GNP per capita J contributions to change, 51 JIRIMA, 23, 24 primary sector, 52 job creation, 126 productivity and, 50­52 better vs more, 123­124 secondary sector, 52 high-earning, 10 tertiary sector, 52 jobless growth, 2 Green Revolution, 124 jobs growth location, 4­5 demographics, 49­50 nonagricultural and wage-earning, employment, 49­50 103­121 labor productivity, 50 probability of getting, 103­107 per capita, 49­53 poverty impact, 11-12n.2 K sectoral vs subsectoral, 15 Kakwani, Neri, and Son decomposition, H 94­95 hours worked, 18n.1, 72­73, 98, 101 L earnings and, 110 household dependency rate, 95n.1 labor force, 4, 14, 49 household enterprises, 71 labor income earnings, 15 household participation, 102 worker, 14 rural vs urban areas, 96­99 household labor income, 75, 90n.1 sources, 83­90 composition, 75­76 sources of change, 96­102 Kakwani, Neri, and Son decomposition, by province, 99­102 94­95 labor market, 1­2, 14, 32­41 per capita, by province, 100 hierarchical decomposition, 34 poverty changes and, 87 indicators, 33 profile, 85 segmentation, 111­113 structure, 78 structure, 4­5 household structure, job-getting, 107 transmission mechanism, 2 labor productivity, 2, 5­6, 46, 49, 53n.4, I 123 aggregate and sectoral, 59­68 inactive, 14 assessments, 55­58 poverty and, 71, 90­91n.2 earnings and, 59­68 income, 21 growth per capita and, 49­50 growth, 1, 11n.1 primary sector, 62­65 hourly earnings, 84 sector and, 10, 43­44 inequality, 60­62 sources of change, 55­58 per capita, 20, 84, 96 life expectancy, 20, 41n.1 transfers, nonlabor, 75 living standards, 20 informal sector, 35 low earners, 15 infrastructure, rural, 11, 63 Madag-txt-fin.qxd 5/22/08 8:17 PM Page 138 138 Making Work Pay in Madagascar: Employment, Growth, and Poverty Reduction M intra- vs intersectoral effects, 81 rural, 20­22, 41n.3 macroeconomics urban, 20­22 context, 22­32 poverty rate, 8, 11, 11n.1, 17­18, 18n.2, indicators, 25 20, 21, 70 Madagascar Action Plan 2007­2010, 23­24 decrease, 82­83 Mahajanga, 101 rural vs urban, 7­8 household income per capita, 100, 102 prices, earnings and, 62­63 manufacturing sector, 24, 32, 106 primary sector, 30, 41n.6, 43, 44, 53n.3 marginal workers, 118n.10 earnings, 61­62, 63 market forces, 67 employment, 45, 47 microdata, macrodata vs, 63­64 GNP and, 52 migration, job-getting, 106 growth, 64 mining, 32 income, 7, 8, 10, 78, 82 Most Favored Nation Status Agreement, 23 labor productivity, 62­65, 126 output, 45, 47, 63 N political crisis and, 24 national accounts, data issues, 129 the poor, 48 national poverty line (NPL), 18n.2, 20, private sector 41n.4 education, returns to, 113­115 data issues, 129­130 nonagricultural, growth, 32 nonemployment, education and, 105 public transfers, 76­77 nonfarm nonwage workers, determinants of public works sector, 26­32, 42n.9 daily earnings, 109 nonlabor income, job-getting, 106­107 R nonlabor transfers, 77 remittances, 18n.1, 76­77 nonwage earnings, 63 report objectives and structure, 2­3 nonwage work, 35 residence, job-getting, 106 nonwage workers, 33 rice education and earnings, 108 prices, 5­7, 30,31 production, 5­7, 31, 64­65 O rural areas oil, 23 development programs, 124 output earnings, 39 data, 7 infrastructure, 11, 63 sectoral, 45, 46, 47 labor income sources of change, 96­99 labor market, 4 P rural employment, determinants of female, 119 pension funds, 42n.11, 118n.9 male, 118 policy mix, determining, 124­126 population, 16, 19, 21 S growth, 4, 20, 41n.1 poverty, 23, 41n.4 safety net, agriculture as, 127 changes within, decomposition, 79­82 salary workers, 33 depth, 8­9, 11, 86­87, 90 savings, 63 earnings and, 69­77 secondary sector, 24­25, 26, 30, 41n.6, 43, employment and, 69­77, 90­91n.2 44, 46 employment status, 70­73 earnings, 61­62 incidence, 7­9 employment, 45, 47 income and, decomposition, 83­84 employment distribution, 65 Madag-txt-fin.qxd 5/22/08 8:17 PM Page 139 Index 139 GNP and, 52 total productivity factor (TPF), 55­58 importance, 79 tourism, 23, 32 income, 8, 10, 78, 82 transport of goods, 27 labor-intensive output, 124­126 less productive workers, 126­127 U output, 45, 47 unemployed, 14 the poor, 48­49 unemployment, 9, 33, 35, 42n.10, 65, unproductive workers, 65­66 68n.5, 99, 102 wages, 65 rate, 71 worker exit, 11, 126­127 unproductive workers, 65­66 sectoral employment urban areas, 36, 38­39 distribution, 48­49 child labor and, 89 earnings, 77­83 earnings, 39 sectors employment determinants comparability, 65­68 female, 121 output growth, 26 male, 120 productivity, 44 employment status and, 37 self-employed, 14, 65­66, 68, 71­72 labor income, sources of change, 96­99 earnings, 73­74 labor market, 4 services sector, 24, 27 vanilla production, 64­65 Shapley Decomposition, 54 subsistence farmers, 41n.3 W surveys, household, 59, 92n.3 data issues, 129 wage employment, 32, 35, 36, 37­38, multipurpose, 15­18 42n.11, 71­72, 75 earnings, 15, 73­74, 76, 108 T determinants of daily earnings, 109 education and, 38, 108 taxes, 29 labor market segmentation, 111­115 tertiary sector, 24, 26, 27, 30, 41n.6, 44, wages 46, 53n.2 EPZ, 29, 30 earnings, 61­62 hourly, 101 employment, 45, 47 secondary sector, 65­68 GNP and, 52 tertiary sector, 66­68 importance, 79 wage workers, 9­10, 14, 33, 39 income, 7, 8, 10, 77­78, 82­83 working poor, 4, 15, 38 labor-intensive output, 124­126 working-age population, 14, 20, 32­33, 34, output, 45, 47 35, 43 wages, 66­68 employment status, 92­93 textiles sector, 23 trade, 41n.7 Z Toamasina, 101 household income per capita, 100 Zones Franches. See export processing labor income changes, 102 zones Toliara, 101 household income per capita, 100 labor income changes, 102 Madag-txt-fin.qxd 5/22/08 8:17 PM Page 140 ECO-AUDIT Environmental Benefits Statement The World Bank is committed to pre- Wood Saved:1 ton serving endangered forests and natural resources. The Office of the Publisher Total Energy: 2 million has chosen to print Making Work Pay in Madagascar: Employment, Growth, and Greenhouse Gases: 320 lbs Poverty Reduction on 50% recycled paper including 30% post-consumer recycled Wastewater: 1,326 gallons fiber in accordance with the recom- mended standards for paper usage set by Solid Waste: 171 pounds the Green Press Initiative, a nonprofit program supporting publishers in using fiber that is not sourced from endan- gered forests. For more information, visit www.greenpressinitiative.org.