metadata: authors: 0: author: Scot,Thiago
1: author: Rodella,Aude-Sophie
count: Haiti
docna: 0: docna: Sifting through the Data : labor markets in
            Haiti through a turbulent decade (2001-2012)
docty: Policy Research Working Paper
subtopic: Labor Markets,Educational Sciences,Employment and Unemployment,Inequality,Rural Labor Markets
teratopic: Conflict and Development,Poverty Reduction,Social Protections and Labor,Rural Development
abstracts: cdata!: In Latin America, labor markets have
            been the main channel through which growth has reduced
            poverty, with higher labor income accounting for 49 percent
            of the reduction in poverty in 2008–13. Understanding labor
            markets is critical to designing policies and programs aimed
            at reducing poverty. With close to 70 percent of the
            population under age 30 years, labor markets are bound to be
            central to defining Haiti's future. Yet, labor analysis
            in Haiti has been constrained by the dearth of data and the
            focus on measuring the impact of the 2010 earthquake. This
            present paper contributes to filling this gap by providing
            an overview of Haiti's labor markets and the
            determinants of labor income over a decade, focusing on
            growing urban areas. The paper also contributes to the
            research on Haiti in general, as well as labor markets in
            fragile countries such as Haiti, in particular through an
            unprecedented effort to harmonize three household surveys
            conducted between 2001 and 2012. Building on this exercise,
            the study provides new insights into the development of
            labor markets in a particularly turbulent decade for Haiti,
            one that was marked by the political crisis of 2004 and the
            earthquake of 2010. In spite of the earthquake, the analysis
            shows that Haiti’s labor markets are characterized by
            continuity over the period. Somewhat surprisingly, the
            defining features remain overall unchanged in spite of the
            shock, pointing to heavy forces shaping economic and labor dynamics.
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repnme: repnme: Sifting through the Data : labor markets in
            Haiti through a turbulent decade (2001-2012)
display_title: Sifting through the Data : labor
            markets in Haiti through a turbulent decade (2001-2012)
listing_relative_url: /research/2016/02/25888078/sifting-through-data-labor-markets-haiti-through-turbulent-decade-2001-2012
topicv3: Labor Markets m1326488 855,Poverty m1326687 695,Education m1327048 470,Jobs m1503931 2670,Unemployment m1504051 2747
publishtoextweb_dt: 2016-02-09T00:00:00Z
historic_topic: Conflict and Development,Poverty Reduction,Social Protections and Labor,Rural Development
no_of_pages: 41
author_keys: 0: author_key: 656706
model_extractions: WPS7562 Policy Research Working Paper 7562

# Sifting through the Data

Labor Markets in Haiti through a Turbulent Decade (2001–2012) _Thiago Scot Aude-Sophie Rodella_ Poverty and Equity Global Practice Group February 2016
Policy Research Working Paper 7562

# **Abstract**

In Latin America, labor markets have been the main channel through which growth has reduced poverty, with higher labor income accounting for 49 percent of the reduction in poverty in 2008–13. Understanding labor markets is critical to designing policies and programs aimed at reducing poverty. With close to 70 percent of the population under age 30 years, labor markets are bound to be central to defining Haiti’s future. Yet, labor analysis in Haiti has been constrained by the dearth of data and the focus on - measuring the impact of the 2010 earthquake. This pres ent paper contributes to filling this gap by providing an overview of Haiti’s labor markets and the determinants of labor income over a decade, focusing on growing urban areas. The paper also contributes to the research on Haiti in general, as well as labor markets in fragile countries such as Haiti, in particular through an unprecedented effort to harmonize three household surveys conducted between 2001 and 2012. Building on this exercise, the study provides new insights into the development of labor markets in a particularly turbulent decade for Haiti, one that was marked by the political crisis of 2004 and the earthquake of 2010. In spite of the earthquake, the analysis shows that Haiti’s labor markets are characterized by continuity over the period. Somewhat surprisingly, the defining features remain overall unchanged in spite of the shock, pointing to heavy forces shaping economic and labor dynamics.

This paper is a product of the Poverty and Equity Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at arodella@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Sifting through the Data:**

**Labor Markets in Haiti through a Turbulent Decade (2001-2012)**

**Thiago Scot**

**Aude-Sophie Rodella**

**Working Paper**<sup>**1**</sup>

_Keywords: labor markets; poverty; female labor markets; youth employment; fragility, conflict and violence;_ _JEL codes: J01; J24; O17_

> 1 Poverty Global Practice. This paper builds on analysis conducted by the authors in the context of the Haiti Poverty Assessment (2015) and the Haiti Systematic Diagnostic (2015) in collaboration with Facundo Cuevas. The authors are grateful for the comments received from: Facundo Cuevas, Raju Singh, Oscar Calvo-Gonzalez, Bernardo Atuesta and the LAC Poverty team for comments received during the 2015 Summer Internal Research Seminar Program (SIRPIG).
# **Summary**

Labor markets are key drivers of poverty reduction. In Latin America, labor markets have been the main channel through which growth reduced poverty with higher labor income accounting for 49 percent of the reduction in poverty in the region in 2008-2013. Incidentally, understanding labor markets is critical to designing policies and programs aimed at reducing poverty. This is particularly true in a low-income fragile country such as Haiti. With close to close to 70 percent of the population under age 30 years, labor markets are bound to be central to defining Haiti’s future. Yet, labor analysis in Haiti has been constrained by the dearth of data and the focus on measuring the impact of the 2010 earthquake. However, while measuring the impact of the earthquake matters, over five years on, this question may be one more interesting to researchers than to inform policies and programs beyond the reconstruction.

The present paper contributes to answering this need by providing an overview of Haiti’s labor markets and the determinants of labor income over a decade, focusing on growing urban areas. The paper also contributes to the research on Haiti in general, as well as labor markets in fragile countries such as Haiti in particular through an unprecedented effort by the authors to harmonize three household surveys conducted between 2001 and 2012. Building on this exercise this paper provides new insights into the development of labor markets in a particularly turbulent decade for Haiti, one that was marked by the political crisis of 2004 and the earthquake of 2010.

In spite of the earthquake, the analysis shows that Haiti’s labor markets are characterized by continuity over the period. Somewhat surprisingly, its defining features remain overall unchanged in spite of the shock – pointing to heavy forces shaping economic and labor dynamics. Self-employment in low-productivity sectors such as commerce and construction remains the norm outside the farm sector. In a rapidly urbanizing Haiti, analysis of the determinants of labor income in urban settings echoes those findings, showing only modest improvement in workers’ endowments (such as education) or employment in more productive sectors. Additional analysis flags the important role of both the public and NGO sectors in the income increase, particularly for the lower end of the wage distribution.

The paper also finds a stable premium on education and an overall reduction of the gender wage gap, except in low productivity sectors, where this gap increased over the period to 60 percent. It also points to the persisting gender inequities – showing that on average, women in urban areas earned 23 percent less than men in 2012, a situation that had not changed since 2007.

Changes observed over the period are limited and those that stand out most carry no perspective for better labor market outcomes in the medium term. On the rural side, the significant rise in employment rates observed was due mainly to young people becoming unpaid workers in family businesses. On the urban side, income gains in the metropolitan area were driven mainly by the influx of foreign aid, raising questions of sustainability going forward in an environment where aid is already waning.

Overall, the analysis in this paper offers a new evidence-based analysis for the situation and highlights the remaining challenges and the fragility of the limited gains that Haiti has achieved. As such, the authors hope the paper will help in the crafting of policies that can directly address the country’s urgent difficulties and take advantage of its assets – and particularly its demographic dividend.

2
# Table of Contents

|Introduction .................................................................................................................................................. 4|
|---|
|I.<br>An Overview of Haiti’s Labor Market Trends from 2001 to 2012 ......................................................... 5|
|A.<br>Labor Force Participation .................................................................................................................. 6|
|B.<br>Employment Rates ............................................................................................................................ 9|
|C.<br>Unemployment Rates ..................................................................................................................... 11|
|D.<br>Quality of Jobs ................................................................................................................................. 12|
|II.<br>Shining Light on the Determinants of Labor Income in Urban Areas (2007-2012) ............................ 15|
|A.<br>Labor Income Increased between 2007 and 2012 with Important Sectoral Disparities. ............... 15|
|B.<br>Returns to Endowment Drove the Wage Increase between 2007-2012 ........................................ 16|
|C.<br>Drawing Lessons from Lower Productivity Sector Analysis ............................................................ 19|
|Conclusion ................................................................................................................................................... 23|
|Bibliography ................................................................................................................................................ 24|
|Annexes ....................................................................................................................................................... 26|

3
# **Introduction**

Though it endured traumatic economic, political, and natural shocks during the first decade of the century, Haiti managed to achieve modest success in poverty reduction: extreme poverty fell from 31 percent to 23.8 percent at the national level between 2000 and 2012.<sup>2</sup> Nonetheless, the country remains deeply impoverished, with an overall poverty headcount of 58.5 percent, the highest in the Latin America and Caribbean region. Though extreme poverty is found mainly in rural areas, where 80 percent of the extreme poor reside, more than 40 percent of urban dwellers are below the moderate poverty line.

Preliminary evidence suggests that the country’s small gains in poverty reduction in urban areas were linked to higher non-farm labor income, which rose in share of total income for all households except those in the poorest quintile. By contrast, households in rural areas saw income increases mainly from agricultural work and production for home consumption, reflecting a growing reliance on the historically low-performing agricultural sector (World Bank, 2014a).

Human capital is typically the most abundant asset of poor households and labor income their main source of income (Attanasio and Székely, 1999, and Bussolo and Lopez-Calva, 2014). Furthermore, mounting empirical evidence suggests that the main driver of poverty and inequality reduction in the world during the last decade was rising labor income, with more and better-paying jobs explaining most of the gains achieved across the developing world, particularly in Latin America and the Caribbean (Azevedo et al., 2013a, Azevedo et al., 2013b, and World Bank, 2014b). Labor markets have been the main channel through which growth reduced poverty in LAC since 2003. Higher labor income accounted for 58 percent of the overall LAC reduction in poverty in 2003-2008 and 49 percent in 2008-2013.<sup>3</sup> For that reason, if Haiti is to reach its goal of becoming an emerging country by 2030, it would do well to work toward significant improvements in its labor market and income generation for its citizens.

In Haiti today, jobs are both scarce and of low quality. Unemployment is high across the country. Having a job, however, is no guarantee of escaping poverty: even though the majority of the poor work, their earnings are insufficient to lift them out of poverty, particularly if they are working in the primary sector. Almost 70 percent of the heads of poor households have jobs (compared to 73 percent of the non-poor). Opportunities and outcomes are unequally distributed: unemployment is particularly high among women and young people, and women earn over 30 percent less than men even after controlling for observable characteristics (World Bank, 2014a) such as educational attainment and sector of employment.

While an important amount of the research on Haiti since 2010 has focused on the impact of the earthquake, five years on this question may be one more interesting to researchers than to inform policies and programs beyond the reconstruction. Attempting to contribute to the latter, the paper takes a longer term perspective

> 2 Poverty numbers for 2012 are based on per capita consumption and were calculated using the 2012 official moderate and extreme poverty lines of  HTG 81.7 per capita per day ($2.41 PPP of 2005) and HTG 41.6 per capita per day ($1.23 PPP of 2005), respectively. The 2000 poverty rates are from the Fafo Institute for Applied International Studies (2001), based on the IHSI _Enquête Budget et Consommation des Ménages_ 1999/2000 (EBCM 1999/2000). For details on methodology and comparability of estimates, see World Bank (2014a).

> 3 Using SEDLAC. The estimation uses the methodology introduced by Barros et al. (2006) and Azevedo, Sanfelice, and Cong Nguyen (2012), it is possible to isolate the role played by different income sources in reducing poverty

4
to better understand the characteristics of Haiti’s labor markets from the household and individual perspective, a current gap in the literature.

This paper has two main sections. First, it provides an overview of labor market development in Haiti during the last decade thanks to an unprecedented harmonization of three different surveys, including the latest 2012 ECVMAS household survey. This first section presents the main trends in employment and participation in Haiti and explores more in-depth the characteristics of non-farm jobs in Haiti. In the paper’s second part, the analysis focuses on the determinants of labor income in the country.

## **Box 1: Of data and surveys in Haiti**

This paper relies on data from three surveys conducted in Haiti between 2001 and 2012: the 2001 _Enquête des Conditions de Vie des Menages (ECVH),_ or Survey on Households’ Living Conditions; the 2007 _Enquête sur L`Emploi et l`Economie Informelle (EEEI),_ or Survey on Employment and Informal Economy; and the most recent _Enquête sur les conditions de Vie des Ménages Aprés Seisme (ECVMAS),_ or Survey on Households’ Living Conditions after the Earthquake.

The 2001 ECVH is a household survey covering 7,186 households and including modules on health, mortality, migration, education, labor, and income. ECVH income data have been previously used to estimate poverty rates in Haiti (Sletten and Egset, 2004) and are, to some degree, comparable to ECVMAS’ income data. The 2007 EEEI is a labor survey comprising 6,620 households with some information at the household level and more detailed data on labor relations, as well as labor and nonlabor income. Finally, the 2012 ECVMAS is a household survey including both income and expenditure information for 4,930 households, detailed data at the household and individual level, and a labor module that encompasses separate sections on agriculture and non-agriculture enterprises for company owners and self-employed workers.

It should be noted that the three surveys do not use an exactly similar questionnaire, and therefore are not perfectly comparable. Annex 1 discusses the harmonization effort undertaken to render the interest variables as comparable as possible.

# **I. Overview of Haiti’s Labor Market Trends from 2001 to 2012**

As of 2012, Haiti’s population was estimated at 10.8 million people,<sup>4</sup> 7 million of whom were of working age (Figure 1). Among those, 74 percent were active in the labor market, meaning that they were either employed or willing to start a job on short notice if offered one.<sup>5</sup> Two-thirds of the 1.4 million people defined as unemployed were discouraged by the tough labor conditions and were not actually searching for work, while the remaining 0.5 million were actively looking. Unemployment as defined by the International Labour Organization, which considers only people looking for a job, stood at 12 percent, high for Latin America, but in line with other countries in the Caribbean.

> 4 Haiti’s last Census dates to 2003. Total population for 2012 refers to estimates from the 2012 household survey, based on projections by Haiti’s statistical office, the _Institut Haitien de Statistiques et Informatique_ (IHSI).

> 5 This number refers to an extended definition of unemployment, which considers not only those currently looking for a job (the ILO’s definition), but also those who are not looking due to discouragement by labor market conditions but who would be willing to take a job if offered one. For more details on definitions used in this paper, see Box 2.

5
<!-- Start of picture text --> Haitian population<br>(2012)<br>10.8m<br>Working Age Population Younger ‘nan 15 years-<br>6.96m (64%) 3 ol (36%)<br>Unpaid workers Paid workers Unemployed Inactive<br>(aide-famitiale) 3.1m (44%) 1.4m (20%)<br>0.7m (10%) 1.8m (26%)<br>por Sta fasun salt orem — Unemployed Discouraged<br>sector employment ee Wage work (ILO)<br>1.2m (39%) 0.8m (28%) o2m(TM) 0.8m (26%) 0.5m (35%) 0.9m (65%)<br>Informal Formal<br>0.4m (52%) 0.4m (48%)<br>Private NGO, family and Public<br>other<br>0.22m (56%) 0.07m (16%) 0.11m (27%)<br><!-- End of picture text -->
<!-- Start of picture text --> Survey Year<br>-0.030<br>2012 —e—<br>-0.097<br>2007 4 —*—<br>Region<br>o}o03<br>Other urban<br>0.042<br>Rural —_—+—<br>Individual characteristics<br>0.024<br>Higher education —_<br>-0.015<br>Secondary Education<br>0.029<br>Primary Education —_——<br>0.096<br>Male —+—<br>-0.100 -0.050 0.000 0.050 0.100<br>Point estimate and 95% Cl<br><!-- End of picture text -->
close to the regional level (51 vs. 52.6 percent in LAC) and significantly above those of other countries with similar overall participation rates.

**Figure 3 – Labor Force Participation (%) in LAC**

<!-- Start of picture text --> Female labor force participation<br>Labor force participation<br>90<br>Male labor force participation<br>85<br>79.06<br>80<br>75 73<br>70 65.3<br>65 61.4<br>60<br>55 52.62<br>50.9<br>50<br>45<br>40<br><!-- End of picture text --> Source: ECVMAS 2012 and LAC Equity Lab.

# **Box 2 : Jobs and Labor Market Definition**

As highlighted by the World Development Report on Jobs (World Bank, 2013), the definition of a “job” can vary greatly across countries and regions. Globally, almost half of the workforce does not work for a salary, and this share is even higher in developing countries. A job, therefore, should be understood as a broad concept of “activities that generate actual or imputed income, monetary or in kind, formal or informal” (World Bank, 2013). This box defines some of the labor market concepts discussed in this paper (for a detailed version see Annex 2). _Working-age population_ : Population 15 years of age or older. _Employed or occupied_ : People in the working-age population who worked for at least an hour in the week before the survey or who did not work that week but have a job that will resume in less than a month. _Unemployed_ : <u>Definition of the International Labour Organization</u> (ILO): People in the working-age population who do not have a job but are looking for one and are immediately available to work if they find one; Extended: Contains all unemployed people under the ILO definition, plus those individuals who have stopped looking for a job due to discouragement but are willing to work if offered one. _Informal sector_ : Unincorporated enterprises (household businesses) that are not registered or do not keep formal accounts and are not in the primary sector (agriculture). _Informal employment_ : All contributing family workers, all independent workers in the informal sector, and all employees without written contracts and not benefiting from social protection. These jobs are not in the primary sector (agriculture).

8
<!-- Start of picture text --> Survey Year<br>0.122<br>2012 +<br>0.021<br>2007 +<br>Region<br>0.027<br>Other urban ——<br>——<br>Rural 0.119<br>Individual characteristics<br>-0.020<br>Higher education<br>-0.064<br>Secondary Education —_—+—<br>0.016<br>Primary Education —+—<br>—<br>Male 0.183<br>-0.100 0.000 0.100 0.200<br>Point estimate and 95% Cl<br><!-- End of picture text -->
<!-- Start of picture text --> sf<br>zo -0.017<br>c 2007<br>2<br>a 0.047<br>pgao 2» —_—<br>=<br>c<br>& sor -0.037<br>s .<br>Z 2012 0.43<br>=m a<br>é 186<br>2012 —<br>0.050 0.000 Point0.050 estimate and 95%0.100Cl 0.150 0.200<br><!-- End of picture text --> <!-- Start of picture text --> 61 or more |<br>56 to 60<br>51 to 55<br>46 to 50<br>41 to 45<br>36 to 40<br>31 to 35<br>26 to 30<br>21 to 25<br>15 to 20<br>-2 -15 -1 -05 0 05 1 16 2 265 3 35 4<br>Point estimates and 95% Ci<br>——————— MetropolitanRural Area 19———— Other urban<br><!-- End of picture text -->

<!-- Start of picture text --> =D =D =D =D<br><!-- End of picture text -->
Yet, despite the large increase in employment rates for young people in rural areas, there is no evidence that school enrollment rates have decreased—although the “double burden” of work and school can lower the amount of learning. Overall enrollment rates among people aged 15-20 did not change from 2007 to 2012, including for groups where the employment expansion was most significant: both men and women aged 15-20 years-old in rural areas maintained stable enrollment rates in the period. Furthermore, family workers are much more similar to the not employed than to those in paid jobs when it comes to school enrollment: 71 percent of young family workers in rural areas are enrolled in school vs. 76 percent for people not employed and only 31 percent of those employed in paid professions (Figure A.1). This suggests that young people are not dropping out of school to enter the labor market, but are helping in their family business while in school. Of course, this “double burden” can affect the quality of education, such as reducing time devoted to studying or the amount actually learnt while in class (Heady, 2000, Emerson, Ponczeck, and Portela Souza, 2013, Bezerra, Kassoufm and Arends-Kuenning, 2009). There is some evidence that people engaged in helping their families are less committed to school. In 2012, for example, only 23 percent of family aids aged 15-20 in rural areas reported spending any time studying vs. 45 percent of those not employed. In a more general way, a much smaller share of people aged 15-20 in rural areas report having spent time studying in 2012 as compared to 2007, be they working as family aids or not. On the upside, conditional on spending any time studying, young people in rural areas actually reported a higher number of hours in 2012 compared to 2007.

# C. Unemployment Rates

National unemployment rates have declined steadily since 2001, falling from 43 percent to 27 percent in 2012, but remain higher than the LAC average (Table A.1 in Annex 4). A large part of this remarkable decrease is explained by an even faster fall in rural areas, from 41 to 18 percent between 2001 and 2012. The rise in numbers of unpaid workers observed in 2012 also played a role; nonetheless, the largest reduction in the unemployment rate actually dates to 2007, and is thus unrelated to the unpaid workers phenomenon. In the Metropolitan area, the reduction was smoother, falling from 51 percent in 2001 to 45 percent in 2007 and 38 percent in 2012. Rates held fairly stable at around 40 percent in other urban areas. The trends are similar when using other measures of unemployment. Considering only the ILO’s stricter definition of unemployment, national unemployment fell from 27 percent in 2001 to 12 percent in 2012, again with important reductions in rural parts of the country and the Metropolitan area but very little change in other urban areas. At present levels, unemployment in Haiti is much higher than LAC’s average of 6 percent, but comparable to other nations in the Caribbean region (Bahamas: 14 percent; Belize: 14 percent; Barbados: 12 percent).<sup>6</sup> Women continue to face special barriers to employment in Haiti. At 36 percent at the national level, the female unemployment rate is 17 p.p. higher than the male rate, highlighting gender differences in endowments and use of assets in the labor market (World Bank, 2014). This gap has remained unchanged across the decade (see Figure A.2 in Annex 4). Women face particularly challenging labor markets in urban areas, where their unemployment rate is close to 50 percent against about 30 percent for men. In rural areas, overall unemployment rates are much lower, at 27 percent, but the gender gap is as high as in urban areas

> 6 All unemployment rates for LAC and other countries are from the World Development Indicators (WDI).

11
<!-- Start of picture text --> Survey Year<br>0.179<br>2012 —}—<br>-0.106<br>2007 ——<br>Region<br>-0.028<br>Other urban —_—e<br>-0.124<br>Rural —+—<br>Individual characteristics<br>0.045<br>Higher education ——_<br>0.077<br>Secondary Education —_—e<br>oloo4<br>Primary Education<br>-0.168<br>Male +<br>-0.200 -0.100 0.000 0.100<br>Point estimate and 95% Cl<br><!-- End of picture text -->
<!-- Start of picture text -->
-0.0)5<br>Metropolitan Area<br>0.120<br>Other Urban —>——_<br>0.395<br>Rural i<br>0.000 0.100 0.200 0.300 0.400<br>Point estimate and 95% Cl<br><!-- End of picture text -->
<!-- Start of picture text --> Construction Wholesale and retail trade<br>Metropolitan Area Area -023023 0.0<br>other Urban Urban 020 024<br>Rural 0.035~_~_ 0.043<br>. . .<br>Other low-end low-end services Tradables and and High-end services<br>Metropolitan Area Area aa0.1380.138 -0.120==<br>Other Urban: Urban: —_0.0860.086 ——0.1220.122<br>Rural 0.114__ _—-0.2110.211<br>0.200 ° Paint etry ana Paint etry ana etry ana ana 02% c 200 c 200 200 0.000 0.200<br>mest<br><!-- End of picture text --> <!-- Start of picture text --> Public & NGOs Private Informal Construction Wholesale and retail trade<br>Metropolitan Area ——0.085 ——-0.095 Metropolitan Area Area -023023 0.0<br>Other Urban —+0.086 —+——_.-0.078 other Urban Urban 020 024<br>Rural —_—+—0.076 —_+—-0.078 Rural 0.035~_~_ 0.043<br>0.100 = -0.050 0.000 0.050 0.100 . . .<br>|<br>Private Formal Other low-end low-end services Tradables and and High-end services<br>Metropolitan Area 007 Metropolitan Area Area aa0.1380.138 -0.120==<br>Other Urban -0.007 Other Urban: Urban: —_0.0860.086 ——0.1220.122<br>Rural -0.g02 Rural 0.114__ _—-0.2110.211<br>0.100 = -0.050 0.000 0.050 0.100 0.200 ° Paint etry ana Paint etry ana etry ana ana 02% c 200 c 200 200 0.000 0.200<br>Point estimate and 95% Cl mest<br><!-- End of picture text -->
Source: ECVH (2001), EEEI (2007) and ECVMAS (2012) – authors’ calculations. Note: The graph presents the marginal changes (point estimate and 95 percent confidence interval) in underemployment in 2012, using 2007 as a baseline, on a probit model. Regression also controls for age (quadratic form) and household size.

Ultimately, and in spite of the cataclysmic shock experienced in 2010, the analysis of the evolution of labor markets in Haiti finds few changes. Those changes are primarily due to the dynamics of the post-earthquake reconstruction, such as a temporary expansion of the labor demand in the construction and NGO sectors. Overall, Haitian labor markets are characterized by long-term stagnation. Acute challenges continue to impede Haitian labor markets’ ability to improve the welfare of the country’s people. With labor income a key vector of poverty reduction, the next section investigates its determinants in urban settings.

# **II. Shining Light on the Determinants of Labor Income in Urban Areas (2007-2012)**

While the definition of urban areas can be hard to reconcile, there is little denying that Haiti is rapidly urbanizing.<sup>10</sup> The World Bank’s World Development Indicators (WDI) for Haiti, show an urbanization rate of over 57 percent by 2014 (World Bank, 2015) and the UN demographic forecasts that Haiti’s urbanization rate will expand to 76 percent by 2050, when cities will host nearly 11 of the 14.3 million inhabitants (United Nations, 2014). While rural areas continue to remain important – especially for poverty reduction – urban areas are sure to play a critical role in Haiti’s future and its capacity to cash in on its demographic dividend given that almost 70 percent of Haiti population is less than 30 years old. The following section focuses on the labor income determinants in urban areas in 2007 and 2012; data limitations prevent the extension of the analysis to 2001.<sup>11</sup>

- A. Labor Income Increased between 2007 and 2012, but with Important Sectoral Disparities.

Both monthly and hourly labor income increased by about 11 percent annually between 2007 and 2012 in urban areas. Monthly income in the Metropolitan area grew at an annualized rate of 11 percent while other urban areas saw an increase of 7 percent. A more uniform increase of 11 to 13 percent occurred across other urban areas in the case of hourly incomes. In those areas, this increase was most notable in the private informal sector, where 50 percent of the workforce is employed. In Metropolitan areas, a wage expansion of close to 60 percent hints at the role played by labor dynamics linked to the reconstruction.

Hourly wages in the public and NGO sectors are higher than those in the private formal sector. The wage distribution in 2012 shows that average wages in the public and NGO sectors are significantly higher than

> 10 The _Haiti Poverty Assessment_ (World Bank, 2014a) found that 52 percent of Haitians lived in a rural setting in 2012, while UN data used by Glaeser (2013) refer to a 53 percent of the population as urban in 2011, with urban areas overtaking rural ones around 2008/9.

> 11 In addition to the Haiti Poverty Assessment (World Bank, 2014a), analysis focused on rural areas can be found in Coello, Oseni, Savrimootoo and Weiss, 2014.

15
<!-- Start of picture text --> +.<br>oQ<br>2<br>@<br>5 “0<br>“=.<br>oO<br>-2 0 2 4 6<br>Log hourly wages in October 2012 prices<br>— Informal —— Formal<br>——— Public ———_ NGO<br><!-- End of picture text --> <!-- Start of picture text --> Sectors of work<br>Agriculture ea<br>Public Sector & NGO —<br>Private Informal —e— °<br>GenderMale oe<br>Education. groups<br>Primary Education —e—e<br>Secondary Education —<br>Higher education —e—<br>4 -5 t) 5 1 15<br>Point estimate and 95% Cl<br>———_ 2007 —— 2012<br><!-- End of picture text -->
component, suggesting either changes in unobservable characteristics or increases in returns to endowments.

**Figure 15 - Oaxaca-Blinder Decomposition of Change in Average Wages (2007-2012)**

<!-- Start of picture text --> Total gap 0.57<br>Explained<br>Unexplained<br>Decomposition -0.02 0.59<br>-0.10 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70<br><!-- End of picture text --> Source: EEEI (2007) and ECVMAS (2012) – authors’ calculations. Note: Total gap refers to the total difference in mean real log wages between 2007 and 2012. The decomposition bar presents the results of a two-fold decomposition using a pooled model for the reference coefficients. The estimated model includes explanatory variables dummies for region of residence, type of work (agriculture, public sector and NGO, private informal, and private formal), household size, gender, age group, level of education, and 10 economic sectors of employment.

Understanding how the increase in labor income played out across the income distribution requires looking beyond the mean. Using the simple OB decomposition allows one to decompose the gap between two groups on a specific parameter of the distribution, i.e. the mean. This imposes a slight limitation to the interpretation of results since one cannot make any inference about quantiles or other distributional parameters.<sup>13</sup> There are several reasons why going beyond the mean may help the analysis. The main one is that there may be significant differences in the composition of the wage increase between the rich and poor. This is an important consideration because these differences may lead to different policy decisions.

Based on the recentered influence function (RIF) methodology proposed by Firpo et al. (2009) and Fortin et al. (2011), RIF regressions are used to extend the analysis beyond the mean. This methodology allows measurement of the contribution of each factor to changes in labor income, and also decomposition of whether the effect of each factor is due to changes in endowments (“composition effect”) or changes in returns (“pay structure effect”). Furthermore, one can analyze changes for each quantile of the labor income distribution. The changes in hourly labor income between 2007 and 2012 are thus decomposed along three dimensions: factors, endowment versus returns, and quantiles.<sup>14</sup> Looking across the distribution shows that hourly wages grew faster for lower income individuals, with returns to observable characteristics explaining almost all of this wage increase between 2007 and 2012. Quantile decomposition of change in hourly income shown in Figure 16 shows the predominant role played by returns to observable (i.e. endowment or explained effects) and unobservable characteristics of workers. Changes in observable characteristics did play a small part (10 percent) for individuals at the lower end of

> 13 As detailed in Firpo et al. (2010), going beyond the mean for the detailed decomposition is not as straightforward: while the mean can be decomposed using standard regressions, one cannot decompose quantiles using simple quantile regressions. See Annex 4 for further details on the RIF methodology.

> 14 When reading the results of RIF decompositions, it is important to keep in mind that changes in coefficient interpreted as _returns_ may also be picking up changes in omitted variables rather than solely changes in the returns to endowments.

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the wage distribution, but this part was null for most workers and even negative for workers at the top of the distribution.

**Figure 16: Quantile Decomposition of Change in Hourly Income between 2007 and 2012**

<!-- Start of picture text --> 70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>-10%<br>Decil 1 Decil 2 Decil 3 Decil 4 Decil 5 Decil 6 Decil 7 Decil 8 Decil 9<br>Total Explained Unexplained<br><!-- End of picture text --> Source: EEEI (2007) and ECVMAS (2012)—authors’ calculations. Note: Total gap refers to the total difference in mean real log wages between 2007 and 2012. The OB decomposition is performed for each quantile using RIF-regressions. The estimated model includes explanatory variables dummies for region of residence, type of work (agriculture, public sector and NGOs, private informal, and private formal), household size, gender, age group, level of education, and 10 economic sectors of employment.

Higher returns for individuals in their prime working age and for informal workers were the main drivers of labor income increase in the period. Figure 17 provides a more detailed breakdown of the unexplained or returns component. Across the distribution, the two driving forces of higher income are the demographic component, which reflects higher returns for individuals in their prime age (especially in their 30s), and the type of work, with sizeable increase in returns of individuals working informally. The returns to specific economic sectors/industries are smaller on average, but reinforce the message of a reconstruction-driven labor market: the small gains observed in the lower end of the distribution are associated with increasing returns in the construction and hotels/restaurants sectors. The negative effects for the higher end, on the other hand, relate to decreases in returns for the wholesale/retail trade and education/health sectors. As presented above, the effects of changes in endowments are very small—a detailed breakdown of sectors for the explained component is presented at Table A.3 in Annex 4.

**Figure 17: Decomposition of Changes in Hourly Income (2007-2012)—Unexplained Components**

<!-- Start of picture text --> 2.00<br>1.50<br>1.00<br>0.50<br>0.00<br>-0.50<br>-1.00<br>Quantile 1 Quantile 2 Quantile 3 Quantile 4 Quantile 5 Quantile 6 Quantile 7 Quantile 8 Quantile 9<br>Geographical Type of Work Demographic Education Economic Sector<br><!-- End of picture text --> Source: EEEI (2007) and ECVMAS (2012) – authors’ calculations. Note: Total gap refers to the total difference in mean real log wages between 2007 and 2012. The OB decomposition is performed by each quantile using RIF-regressions. The estimated model includes explanatory variables dummies for region of residence, type of work (agriculture, public sector and NGOs, private informal, and private formal), household size, gender, age group, level of education, and 10 economic sectors of employment.

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<!-- Start of picture text --> 0.33<br>Male °0.46—e—e<br>-0.96<br>Co) Informal Employees ° -0.39..<br>£<br>an<br>fa] Education groups groups<br>0.30<br>= Primaryii Education"" 02see<br>0.36<br>Secondary Education i —*oa7<br>© 0.72<br>4 -2 Log0 hourly labour income2 4 6 Higher education education —e— 1g9<br>Formal 2007 Informal 2007 -1.00 -0.50 Point estimate0.00 estimate0.000.00 and 95%0.50 95%0.500.50 Cl 1.00 1.50<br>Formal 2012 Informal 2012<br>———_ 2007 ——_ 2012<br><!-- End of picture text --> <!-- Start of picture text --> 0.33<br>Male °0.46—e—e<br>-0.96<br>Informal Employees ° -0.39..<br>Education groups groups<br>0.30<br>Primaryii Education"" 02see<br>0.36<br>Secondary Education i —*oa7<br>0.72<br>Higher education education —e— 1g9<br>-1.00 -0.50 Point estimate0.00 estimate0.000.00 and 95%0.50 95%0.500.50 Cl 1.00 1.50<br>———_ 2007 ——_ 2012<br><!-- End of picture text -->
On the other hand, for this sub-sectoral group, the gender wage gap markedly increased, particularly for female workers at the lower end of the distribution. As seen in Figure A.7, the gender gap is actually larger within the low-productivity service sector than in the economy as a whole. Men earn on average 35-45 percent more than women, whereas the gap is 25 percent when looking at the entire economy. Estimating the gender gap across the distribution for this sector shows that women in the lower quantiles received particularly less than their male counterparts in 2012, with a 60 percent wage gap compared to 2007.

The realities of this sector point to the larger issues of Haiti’s labor market and its challenges to become more inclusive and productive. While informality continues to define the lower-productivity jobs, a reduction in the gaps between formal and informal could be encouraging. However, the growing gender gap for poor female workers of this sub-sector testifies to the persisting hurdles that the poor and vulnerable face in getting returns to their labor that will allow them to exit poverty within a reasonable horizon.

# **Box 3 - Making the Most of Existing Data in Haiti: The Way Forward**

Analyzing labor markets in Haiti has obvious limitations due to the paucity of data. The release of the 2012 ECVMAS considerably improved this situation and enabled the definition of a new baseline analysis of labor markets (and poverty) following the 2010 earthquake, and five years after the 2007 EEEI survey.

The recent release of the 2012 survey of Haitian firms opens further possibilities to refine the analysis, particularly through the use of a spatial lens. Both the ECVMAS and the firm census are geo-referenced, which enables expansion of the analysis to take into account the economic and labor context—both formal and informal.<sup>16</sup> This analysis could further be enhanced by including infrastructure data to identify patterns of firm locations, typologies, and synergies (such as sectors, size, status, and agglomeration effect).

Such analysis could also improve understanding of both job creation and self-employment. Regarding the latter, a separate draft (Cuevas and Rodella, 2014) that the authors prepared for the Haiti Systematic Country Diagnostic (SCD) looks into the different types of self-employment (one-person firm, family firm, and micro-enterprise). The draft’s goal is to provide a finer lens on the differences between survival and growth entrepreneurs.

With the caveat that correlations rather than causal relations are being found, the analysis of correlates of successful self-employment uses three measures: revenues per worker (in log), revenues per worker that are in the top 30 of the distribution, and profits per worker that are in the top 30 of the distribution. These help identify the characteristics and inputs that are significantly more relevant for entrepreneurial success. This type of study, combined with evidence-based analysis like the one conducted for the present paper, can help improve understanding of what it will take to elevate productivity and generate jobs in the self-employed and small business sector. The five main findings of the draft’s analysis are:

> 16 Although the coordinates have not been shared at this point and remain with the respective national agencies (IHSI, ONPES, MCI and MEF).

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**_Experience_** : More experienced entrepreneurs obtain higher returns. Every additional year of experience (proxy by age) increases the probability of the firm being in the top performance distribution.

**_Skills_** : Educational attainment of the entrepreneur is the single most important characteristic for revenue and profit maximization. Returns to education are high and increase with each additional level. Completing the basic level is associated with 40 percent higher revenues, while completing the highest level is linked to 67 percent higher revenues per worker.

**_Inputs_** : Availability of electricity and, to a lesser extent, water is important for better firm performance. Access to power is associated with higher revenues per capita of about 27 percent, and a higher chance of being among the top performers.

**_Finance_** : In the absence of information on access to finance or credit by firms, the analysis uses access to domestic and foreign private transfers as a proxy. There is no clear or significant association between revenues or profits with being the recipient of private transfers.

**_Gender_** : Although the self-employed are mostly women, firms run by men perform better in terms of revenues and profits per worker. The correlation remains when controlling for type of firm, being household head, and sector of focus. Men attain 40 percent higher revenues and are 13 percent more likely to make it to the top 30 performing businesses.

The authors also note that the dominance of survival entrepreneurs puts a limit on the analysis of growth entrepreneurs.<sup>17</sup>

> 17 Another challenge to the analysis is the presence of outliers among the “growth entrepreneurs.” Given the small sample of this category of highly performing entrepreneurs, their exclusion (the option elected by the team) further reduces the sample while their inclusion leads to results highly biased by those few outliers. This issue further speaks to the difficulty of labor surveys capturing the full spectrum of employers in settings such as Haiti.

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# **Conclusion**

This analysis makes possible an evidence-based discussion of labor markets and jobs in Haiti. Using three harmonized household surveys from 2001 to 2012, the paper provides insights into the evolution of labor markets in a particularly turbulent decade for Haiti marked by the political crisis of 2004 and the earthquake of 2010. The focus is the determinants of labor income, providing a household-level perspective on the larger dynamics of labor markets.

The main message of the analysis is that in spite of the shocks of the period, labor markets in Haiti presented more continuity than change. Self-employment in low-productivity sectors remains the norm outside of the farm sector, a cause for worry. While the share of workers in non-primary informal activities has decreased, this has not translated into an expansion of the formal private sector, but into the swelling of the public sector and NGOs. This puts in question the sustainability of poverty reduction in urban areas, especially in the Metropolitan area, in an environment of decreasing aid. Furthermore, the significant rise in employment rates in recent years was driven mainly by young people taking unpaid work in family businesses.

The analysis of the determinants of labor income in urban areas further echoes those results, showing no signs of sustained structural transformations in the endowments of the labor force or the quality of jobs. Additional analysis details the role of the public and NGO sectors in the wage increase observed, particularly for the lower end of the wage distribution. The analysis also found a stable premium on education and an overall reduction of the wage gap, except in low-productivity sectors where this gap increased over the period to 60 percent.

Generating more and better jobs largely depends on macro-economic factors that are largely outside of households’ control. But the analysis points to persisting structural challenges weighing down the returns workers get from their labor. For that reason, the obvious role that the private sector will have to play in generating growth and employment will need important help from the Haitian state in helping households improve their assets and use them in the labor markets (World Bank, 2015). It is the authors’ hope that this paper will provide a data-informed basis for discussion among policy makers and stakeholders who all acknowledge the critical role of labor markets and jobs to reduce poverty and foster shared prosperity in Haiti.

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