Faire Compter les Haïtiens Pauvres : La Pauvreté en Haïti Rurale et Urbaine Basée sur la Première Enquête auprès des Ménages pour Haïti

P olicy R esea R ch W o R king P a P e R 4571 Making Poor Haitians Count Poverty in Rural and Urban Haiti Based on the First Household Survey for Haiti Dorte Verner The World Bank Social Development Sustainable Development Division March 2008 WPS4571 Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Produced by the Research Support Team Abstract 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. P olicy R esea R ch W o R king P a P e R 4571 This paper analyzes poverty in Haiti based on the first Living Conditions Survey of 7,186 households covering the whole country and representative at the regional level. Using a US$1 a day extreme poverty line, the analysis reveals that 49 percent of Haitian households live in absolute poverty. Twenty, 56, and 58 percent of households in metropolitan, urban, and rural areas, respectively, are poor. At the regional level, poverty is especially extensive in the northeastern and northwestern regions. Access to assets such as education and infrastructure services is highly unequal and This paper—a product of the Sustainable Development Division, Social Development—is part of a larger effort in the department to reduce poverty and increase social inclusion. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at dverner@worldbank.org. strongly correlated with poverty. Moreover, children in indigent households attain less education than children in nonpoor households. Controlling for individual and household characteristics, location, and region, living in a rural area does not by itself affect the probability of being poor. But in rural areas female headed households are more likely to experience poverty than male headed households. Domestic migration and education are both key factors that reduce the likelihood of falling into poverty. Employment is essential to improve livelihoods and both the farm and nonfarm sector play a key role. Making Poor Haitians Count Poverty in Rural and Urban Haiti Based on the First Household Survey for Haiti By Dorte Verner World Bank 1 1 I am very grateful to Nadim Khouri for inviting me to join the Haiti Rural Development Team, Michael Justesen for excellent and invaluable research assistance, and the Haiti Rural Development Team for answering many questions. Moreover I am especially grateful to Willy Egset for data information and other support with the household data set. Without the help of all these people this paper would not have existed. The findings, interpretations, and conclusions expressed in this paper are entirely those of the author. 2 1. Introduction Haiti, with some 8 million people, is the poorest country in the Western Hemisphere and has been so for quite some time. It is also one of the Caribbean Community's most densely populated countries, with 306 people per sq. km in 2003. Haiti has experienced a tortuous development notable for political instability and structural and institutional weakness. This, paired with the country's historical, socio- economic, and agricultural development have caused adverse long-term effects in several areas such as food security, nutrition, education, and income poverty. In 2001, 49 percent of the Haitian households lived in absolute poverty with 20, 56, and 58 percent of the households in metropolitan, urban, and rural areas, respectively, being poor based on a US$1 a day extreme poverty line. Most of the approximately 4.3 million indigents live in rural areas (3.06 million) and others live in the metropolitan and other urban areas (1.27 million). Poverty is especially extensive in the northeastern and northwestern regions of Haiti. The analyses in this paper are based on a recent national household survey (which is still not released) and available data (see Section 3). Social indicators such as literacy, life expectancy, infant mortality, and child malnutrition also show that poverty is broad in Haiti. Around 4 out of 10 people cannot read or write; around 20 percent of children suffer malnutrition, nearly half the population has no health care and more than four-fifths have no clean drinking water. Although still very high, these indicators show that poverty in non-income terms decreased in the last decades. However, most of the social indicators do show that poverty has increased since the mid-late 1990s. Moreover, the gap between rich and poor people and between regions is still large, such as between the Northeast and West region. This paper analyzes metropolitan, rural, and other urban (henceforth called urban) poverty in a broad manner taking into account regional differences. The paper is organized in 9 sections and the majority of analyses are of metropolitan, rural, and urban areas and the nine regions. Section 2 presents demographics and economic growth trends. Section 3 presents the data and methodology used throughout the paper. Section 4 presents the sources of incomes for metropolitan, rural, and urban areas and Section 5 analyzes poverty and its depth in metropolitan, rural, and urban areas. Section 6 shows the poverty profile and Section 7 presents access to assets such as education and infrastructure services for the poor and nonpoor population. Section 8 presents analyses of rural and urban poverty correlates and compares rural living and characteristics to those of urban areas in order to reveal important factors to escape poverty. Finally, Section 9 concludes and gives policy recommendations. Before initiating the analyses, this section ends with a short background presentation of the current situation in Haiti. Haiti's 200-year history has been marked by political instability and weak institutional capacity, resulting in a debilitated economy and an impoverished population. The current complex emergency is rooted in a four-year political impasse. In 2000, Aristide's party, Lavalas Family, claimed an overall victory in disputed legislative and municipal elections, then later that year, the opposition boycotted the presidential election 3 that Aristide won unopposed with low voter turnout. As a result, in 2002 growing lawlessness, instability, and politically motivated violence began to overwhelm the country. On February 29, 2004 Aristide resigned from the presidency and on March 9, 2004 Haiti's seven-person advisory council selected Latortue, a former United Nations official and foreign minister, as Haiti's Prime Minister. Having determined that the situation in Haiti continued to constitute a threat to international peace and security in the region the Security Council decided to establish the United Nations Stabilization Mission in Haiti (MINUSTAH) and requested that authority be transferred from the Multinational Interim Force, authorized by the Security Council in February 2004, to MINUSTAH on June 1, 2004. 2. Economic and Demographic Trends This section outlines what can serve as a base for a poverty reduction strategy in Haiti. It covers demographics and a brief section on economic growth. Individual and household assets, in particular human capital, are other important poverty reducing factors (addressed in Section 7). DEMOGRAPHIC TRENDS Demographic factors have direct and indirect effects on prices, poverty, and conflict risks in Haiti. As the size and age composition of the population changes, so too do the relative size of the labor force and the number of dependents. This affects the dependency ratio of families and therefore their level of poverty. High population growth can also increase conflict risk by reducing per capita economic opportunities and creating a large pool of potential recruits (typically, young men below 25 years of age) for criminal and political violence. Some studies, moreover, find that population growth, density, and turnover contribute to increased crime rates and conflict risks by limiting economic opportunities and increasing the supply of potential victims who do not know the perpetrator (Kelly 2000; Collier 2000). Family instability and break-up have been identified as additional demographic risk factors for violence and crime because of the emotional disturbance suffered by children, 2 the subsequent lack of role models, and other effects such as worsened socioeconomic outcomes (Kelly 2000). Demographic changes affect quantities: number of children, size of the labor force, and number of elderly people. These changes in quantities will generally influence prices in the economy. In particular, changes in the population’s growth rate and age structure may have significant effects on the labor supply, savings, household production decisions, and migration. Consequently, demographic changes may have a substantial impact on wage levels and interest rates. Since these prices are important determinants of family income, they are bound to have a profound influence on the level of poverty. Hence demographic changes indirectly impact poverty through their effects on savings, wages, production decisions, and interest rates. 2 Including exposure to violence at a young age, which is a prime risk factor for violent behavior later in life (Buvinic and Morrison n.d.). 4 Changing demographics can also have significant effects on the demand for public sector investments and public services, incentives for private sector investments, social and political conflict, and labor markets. Thus it is important to look at recent changes in demographic patterns in Haiti’s rural and urban areas. The following overview describes demographic changes between rural and urban areas that have taken place from 1982 to 2003. Overview of Demographic Changes Haiti is slightly smaller than Wales and its population is growing rapidly (2.2 percent a year). In 1950, the population was estimated at just over 3 million. By 2001, the number had grown to nearly 8 million. With a surface area of just 27,797 square kilometers (km 2 ), Haiti is second only to Barbados as the most densely populated country (306 people per km 2 ) in the Americas. Considering the fact that parts of the vast mountain ranges that traverse the country remain completely uninhabitable, the actual population density is greater still. After expanding at an annual rate of about 1.5 percent between 1950 and 1982, Haiti’s population increased by 2.2 percent annually in the period 1982-2003 and reached 7.9 million in 2003 (Table 2.1). The current population growth rate of more than 2 percent a year suggests that the country’s inhabitants could total some 12.3 million by 2030. 3 The indications, however, are that the population growth rate is slowing. Overall, the proportion of the population aged below 15 years is gradually declining. This reflects the twin effects of urbanization (fertility rates are lower in urban areas than in the countryside) and gradually declining fertility rates overall (partly the result of increased educational attainment). The median age increased slightly from 18.5 to 18.9 years between 1994-95 and 2000, revealing the incremental pace of this demographic change (DHS 2000). During 1982-2003, data reveal that the poorest region, the Northeast, together with the West region where the capital, Port-au-Prince, is located, experienced a higher population growth rate than the country’s average of 2.2 percent. The Northeast region reached an annual population growth rate of 2.3 percent and in the West region the population increased at 3.4 percent. This compares to the Southeast and Gran-Anse regions where the population only expanded by around 1.0 percent annually during 1982- 2003 (Table 2.1 and Figure 2.1). 3 World Bank: http://genderstats.worldbank.org/hnpstats/HNPDemographic/total.pdf. 5 Table 2.1: Population Size and Growth and Household Size in Urban and Rural Haiti, 1982-2003 Urban Rural Total Region Population Avg. House- hold Size Male to Female Ratio (%) Population Avg. House -hold Size Male to Female Ratio (%) Population Avg. House- hold Size Annual Population Growth 1982-2003 (%) Pop. Density 2003 (pop/km 2 ) Artibonite 278,290 4.58 85.6 792,107 4.21 96.0 1,070,397 4.30 1.9 215 Center 90,843 4.65 90.7 474,200 4.51 100.8 565,043 4.53 2.2 154 Grand-Anse 90,095 4.47 93.2 513,799 4.45 105.7 603,894 4.46 1.0 182 North 295,624 5.20 85.1 477,922 5.07 96.9 773,546 5.12 1.6 367 Northeast 112,782 4.76 88.5 187,711 5.13 99.6 300,493 4.99 2.3 166 Northwest 102,338 5.30 84.9 342,742 5.00 96.0 445,080 5.07 2.1 205 West 2,070,799 4.72 86.5 1,022,900 4.39 95.9 3,093,699 4.61 3.4 641 South 98,506 4.96 89.3 528,805 4.80 105.3 627,311 4.82 1.1 225 Southeast 65,688 4.51 88.3 383,897 4.40 93.9 449,585 4.42 1.0 222 Haiti 3,204,965 4.76 86.7 4,724,083 4.55 98.5 7,929,048 4.63 2.2 286 Source: IHSI 2003. The West and Artibonite regions have the largest population shares of Haiti’s 9 regions: 39.0 and 13.5 percent, respectively. In the West region, the people mainly reside in Port-au-Prince. The other regions have each between 3.8 (Northeast) and 9.8 (North) percent of the total population. The West also has the largest share of total urban and total rural populations, 65 and 22 percent respectively (Figure 2.1). Figure 2.1: Rural and Urban Population and Total Population Growth Rates Haiti and its Regions, 2001 0 500,000 1,000,000 1,500,000 2,000,000 2,500,000 Southeast Grand-Anse South North Artibonite Northwest Center Northeast West Population 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 Urban population Rural population Yearly pop. growth 1982-2003 Source: IHSI 2003 6 Haiti has become far more urbanized in the last two decades because the highest population growth has been in urban areas. In 2003, 40.4 percent of Haitians lived in urban areas, up from 24.5 percent in 1982. Rural Haiti is now home to some 4.7 million people (59.6 percent of the population). The urban population increased from 1.2 million to 3.2 million between 1982 and 2003. In other words, 115,000 people have been added to Haiti’s cities every year for the past 21 years. Among the 1.97 million people added to urban areas between 1982 and 2003, 1.3 million (or two-thirds) went to the West region. The metropolitan area has received an average of 75,000 migrants a year in the past 20 years, in addition to a natural growth of nearly 40,000 people a year, bringing its total annual population growth to 115,000 people. The rural population represented 59.6 percent of the total population in 2003; down from 75.5 percent in 1982. Hence, rural Haiti is currently home to around 4.7 million dwellers (Figure 2.1 and Table 2.1). The regions that have the largest share of rural population – all have close to 85 percent – are Center, Grand-Anse, South and Southeast. The regions with the lowest share of rural-dwellers are the North, West, and Northeast with 61.8, 33.1, and 62.5 percent respectively. Moreover, demographic developments in rural areas have not been homogeneous in the last decade. Table 2.2: Degree of urbanization in Haiti and its regions, 1982 and 2003 (percent) Region 1982 2003 Artibonite 15.6 26.0 Center 10.7 16.1 Grand-Anse 10.6 14.9 North 21.1 38.2 Northeast 18.2 37.5 Northwest 11.3 23.0 West 49.1 66.9 South 11.7 15.7 Southeast 7.2 14.6 Haiti (whole country) 24.5 40.4 Source: IHSI 2003. During 1982-2003 the rural population increased by 910,000, but it lost ground to urban Haiti. The rural population share fell from 75 to 60 percent in this period. Not only did the country as a whole experience relative population loss to urban areas and emigration, but all the nine regions followed the national trend (Figure 2.2). The West region that was already the region with the lowest share of rural-dwellers (51 percent) in 1982 experienced the largest relative reduction of rural population during 1982-2003, reaching 33 percent in 2003. However, in 2003, the West was still the region with the largest rural population in absolute terms of around 1.0 million people, more than the second most populous region, Artibonite, which had 792,107 people in rural areas in 2003. The South, Center, and Grand-Anse regions lost the least population. The easier access to Port-au-Prince and the imports and other goods and markets it provides the country may explain this. 7 Figure 2.2 : Rural Population Share of Total Population in Haiti, 1982 and 2003 0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0 100.0 Artibonite Center Grand-Anse North Northeast Northwest West South Southwest Haiti Percent Rural pop. share 1982 Rural pop. share 2003 Source: WDI, World Bank 2004. What is driving the population growth pattern Haiti is experiencing? There are various reasons for the demographically changing pattern and many relate to economic opportunities. It is clear, for example, that living conditions in rural Northeast are inferior to West Haiti. Rural areas in the West region are close to Port-au-Prince and the rural population therefore has easy access to goods and services that are produced, imported, or provided by the capital. Moreover, rural farmers in the West have easier access to a large market for their produce than do other regions such as the Northeast, as roads and other infrastructure is limited in the poor regions (see Section 7). Table 2.3: Degree of Urbanization (percent), 1982-2003 Urban Rural Region 1982 2003 Growth 1982 2003 Artibonite 15.6 26.0 66.7 84.4 74.0 Center 10.7 16.1 50.5 89.3 83.9 Grand-Anse 10.6 14.9 40.6 89.4 85.1 North 21.1 38.2 81.0 78.9 61.8 Northeast 18.2 37.5 106.0 81.8 62.5 Northwest 11.3 23.0 103.5 88.7 77.0 West 49.1 66.9 36.3 50.9 33.1 South 11.7 15.7 34.2 88.3 84.3 Southeast 7.2 14.6 102.8 92.8 85.4 Haiti 24.5 40.4 64.9 75.5 59.6 Source: IHSI 2003. 8 The proportion of children and youth is slightly larger in the countryside than in urban areas. A higher share of the working age population live in urban areas, so urban households should be better able to feed their children than those in rural areas. Hence the overall dependency ratio is larger in rural and non-metropolitan urban areas than in Port- au-Prince. The average household is slightly larger in the former areas (4.6 and 4.7, respectively) than in the capital (4.5). Table 2.4: Age distribution, 2003 Age Urban (%) Rural (%) Total (%) 0-4 10.21 12.86 11.79 5-17 33.31 35.45 34.59 18+ 56.47 51.69 53.62 Haiti 100 100 100 Source: IHSI 2003 . Demographic trends have so far not lowered the dependency ratio, and it has therefore contributed negatively to poverty reduction in Haiti. This trend is likely to deepen further in the future if Haiti does not actively implement reproductive health programs. Table 2.5: Average Household Size by Income Group and Place of Residence, 2001 Region Location Artibonite Center Grand- Anse North North- east North- west West South South- east Metro- politan Urban Rural Total Haiti Indigent 5.0 5.2 5.2 5.4 5.2 4.9 4.9 5.1 5.2 4.8 5.3 5.2 5.1 (2.4) (2.4) (2.5) (2.6) (2.7) (2.3) (2.4) (2.6) (2.6) (2.1) (2.6) (2.5) (2.5) Poor 4.8 4.9 5.0 5.2 5.1 4.8 4.7 4.9 4.9 4.9 5.1 4.9 4.9 (2.4) (2.4) (2.5) (2.6) (2.7) (2.3) (2.3) (2.5) (2.6) (2.2) (2.6) (2.5) (2.5) Nonpoor 2.6 3.5 3.4 4.2 3.8 3.1 3.9 3.8 3.2 4.2 3.6 3.3 3.6 (2.0) (2.2) (2.1) (2.7) (2.1) (1.8) (2.8) (2.3) (2.0) (2.5) (2.4) (2.1) (2.3) Total 4.4 4.7 4.7 5.0 5.0 4.6 4.4 4.7 4.5 4.5 4.7 4.6 4.6 (2.5) (2.4) (2.5) (2.7) (2.7) (2.3) (2.4) (2.5) (2.6) (2.4) (2.6) (2.5) (2.5) Note: Standard deviations in parentheses. Source: Own calculations based on HLCS 2001. The typical extremely poor or poor household has more young members than does a nonpoor household. In Haiti, extremely poor households in rural and metropolitan areas have on average 2.2 and 1.7 household members below 15 years of age, respectively (see Section 3 for a definition of indigent, poor, and nonpoor). This compares to the average nonpoor household, in which only 0.9 and 1.2 members are below the age of 15. (Table 2.6) Extremely poor households therefore have about twice as many children as do 9 nonpoor households. Most Haitians lack pensions, social security and savings, and thus children are often the only security for old age. An older Haitian woman expressed the matter this way: “It costs a lot to educate a child in Haiti; you have to work very hard. When I helped them with their education, I considered it like putting money into a savings account. My children are my bank account.” 4 The fertility rate has fallen rapidly in recent decades. During the three decades leading to the 1990s the fertility rate fell from 6.3 children per woman in 1960 to 5.4 in 1990, and then to 4.7 in 2000. (Table 2.7). Women’s increased participation in the labor market is an important factor in the decline in the fertility rate. Moreover, as education attainment increases, the fertility rate drops. Total desired fertility rate is lower than the actual fertility rate. This would indicate that there is still a substantial unmet demand for high quality and reliable family planning services, information, and resources. 5 Table 2.6: Average Number of Household Members Aged Less than 15 Years, 2001 Region Area Artibonite Center Grand- Anse North North- east North- west West South South- east Metro- politan Urban Rural Total Haiti Indigent 2.1 2.5 2.3 2.3 2.4 2.1 1.9 2.2 2.6 1.7 2.3 2.2 2.2 (1.8) (1.9) (1.9) (1.9) (1.9) (1.8) (1.7) (1.9) (1.9) (1.5) (1.9) (1.9) (1.9) Poor 2.0 2.3 2.2 2.1 2.3 2.0 1.7 2.1 2.3 1.9 2.1 2.1 2.0 (1.7) (1.9) (1.9) (1.9) (1.9) (1.8) (1.6) (1.8) (1.9) (1.8) (1.8) (1.8) (1.8) Nonpoor 0.6 1.1 1.1 1.3 1.5 0.9 1.0 1.4 1.0 1.2 1.1 0.9 1.0 (1.1) (1.4) (1.4) (1.6) (1.6) (1.2) (1.4) (1.6) (1.3) (1.4) (1.4) (1.3) (1.4) Total 1.7 2.1 2.0 2.0 2.3 1.8 1.4 1.9 2.0 1.4 1.9 1.9 1.8 (1.7) (1.9) (1.9) (1.9) (1.9) (1.7) (1.6) (1.8) (1.8) (1.5) (1.8) (1.8) (1.8) Note: Standard deviations in parentheses. Source: Own calculations based on HLCS, 2001 Net migration has been larger than population growth rate since at least 1985 (Table 2.8). Large numbers of Haitians continue to flow over the 275 km border with the Dominican Republic to find work as sugar cane cutters, coffee pickers, and construction laborers. Many Haitians have settled in the Dominican Republic, and today there are an estimated 500,000 Haitians and Dominicans of Haitian descent living in the Dominican Republic. 6 There are also significant Haitian populations in the French Caribbean. Moreover, around one million Haitians live legally in North America. 4 Source: http://www.philly.com/mld/inquirer/9102135.htm. 5 Unfortunately, fertility rate micro-data are not available, therefore the analysis cannot be taken further 6 The Center and Southeast regions are by far the largest suppliers of labor to the Dominican Republic (and seasonal migration is negligible). The other regions appear to be far less affected by this labor pull. 10 Table 2.7: Fertility Rate in Haiti, 1960-2002 1960 1980 1990 1995 2000 2002 Fertility rate, (births per woman) 6.30 5.88 5.42 4.93 4.39 4.20 Source: WDI, World Bank, 2004. Members of the Haitian diaspora send cash transfers and other resources back to Haiti, which has been identified as the world’s most remittance-dependent country. It is hard to estimate the exact amount of remittances to Haiti because of poor statistical information and informal channels of exchange, but the estimates currently available suggest that expatriates send home about US$700-900 million per year—about a quarter of the country’s GDP and about three times the foreign aid Haiti receives annually. 7 Hence remittances provide essential support to thousands of families living in Haiti (in Port-au-Prince, nearly every block contains an office of a money-transfer agent, underscoring the central role remittances play in the economy – see Section 4). Many Haitians living abroad return regularly, for example for carnival and vacations. Although Haiti is still predominantly a society of peasant farmers, it is changing. The very slow improvements in telecommunications and urbanization are creating a population that is more closely linked to the global system. Moreover, Haitian expatriates conduct a large variety of micro-level and charitable activities in their towns and villages of origin. These activities span small public work projects, school canteens, school programs, health clinics, and library construction. Although these activities contribute greatly to social development, they do not make a significant contribution to the economic growth of the localities they serve. The challenges facing Haiti today are multiple and multidimensional, and meeting them requires a critical mass of educated people. Haitian expatriates, therefore, are not only important because they have projects in towns and villages, and send back remittances, but because Haiti’s future development is dependent on their human capital. Table 2.8: Migration from Haiti during 1985-2005 1985-1990 1990-1995 1995-2000 2000-2005 Net migration (per 1000) 2.80 3.40 2.60 2.30 Source: IHSI, 2003 ECONOMIC GROWTH Haiti is one of the world’s poorest countries and in the last decades the country’s real income or GDP has decreased. Between 1980 and 2003, the Haitian economy declined at a real average annual rate of -0.82 percent (GDP in constant 1995 USD based on WDI 2004). In 2003, the GDP of Haiti amounted to around US$2.8 billion. Poor economic performance is mainly due to political instability and lack of investments across all sectors. 7 The value is in 1995 dollars. 11 Haiti’s per capita GDP performed very poorly in the period 1980-2003, relative to both the country’s own historical experience and to other countries in the Latin American and Caribbean region. In 1980 Haiti’s per capita GDP stood at $632, and by 2003 it had fallen by about half to $332 (Figure 2.3). In the same period, Jamaica’s per capita income increased by around 17 percent, and the Dominican Republic’s by 57 percent. Figure 2.3 : GDP Per Capita in Selected Countries and Haiti, 1980-2003 0.00 500.00 1,000.00 1,500.00 2,000.00 2,500.00 3,000.00 3,500.00 4,000.00 4,500.00 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 Constant 1995 USD Costa Rica Dominican Republic Haiti Jamaica Mexico Source: WBI 2003. Fundamental to lack of growth in Haiti is the country’s long history of political instability, lack of governance, distortions at the macroeconomic level, and inadequate levels of private investment (see Sections 3 and 4). Macroeconomic stability and a lessening of distortions, so as to encourage private sector investment are essential to increased productivity. Sectorial GDP In recent years Haiti’s agricultural sector has done less well than services and manufacturing sectors. During 1996-2002, the share of agriculture in total GDP fell while the share of services and industry increased (Table 2.7). In 2002, Haiti remained very much a dual economy where, on one hand, agriculture contributes 27.1 percent of GDP, accounting for around 50 percent of employment, and industry on the other hand, contributes 16.3 percent of GDP, but only around 10 percent of jobs. Agricultural output has suffered from a growing population farming a finite area of land. The result has been the division of cultivated land into smaller and smaller plots, so that by the 1990s, 78 percent of Haiti's farms had an average size of less than two 12 hectares. 8 On these tiny farms, the soil has become progressively exhausted and less productive. This problem has been compounded by the extensive deforestation of the country which, in turn, has led to severe erosion of the fertile topsoil. As yields have declined, Haitian peasants have found themselves locked into a self-destructive cycle in which the cutting of trees for charcoal production, and the farming of land higher up the mountainsides, can stave off short term financial disaster, but only create greater problems for the sector as a whole in the long term. Moreover, the agricultural sector is characterized by scarcity of physical capital including tools, machines, fertilizers, transportation, and infrastructure (Egset 2004). Especially, poor households with low incomes pose a key obstacle to innovations and technological changes in agriculture. Table 2.9: GDP by Sector (percent), 1996-2002 1996 1998 2000 2002 Agriculture 32.9 30.9 28.5 27.1 Industry 15.4 16 16.6 16.3 Services 51.7 53.1 54.9 56.5 Source: WDI, World Bank 2004 Agricultural production for export has undergone a significant decline, partly because farmers have been obliged to shift to growing food crops to avoid starvation and partly because of changes in international markets. At the same time, food production has failed to keep pace with population growth. Today, the country must import more than half of the food that is consumed, creating a further pressure on the balance of payments. Potential Growth Areas Three potential areas of growth in the medium term are crafts, selected agricultural products, and tourism. Below each will be addressed in turn. It would be important to have involvement from Haitian expatriates as well as other foreign investors in order to give the economy a push forward. In the beginning of the 1990s around 400,000 people were working in craft production such as basketry, embroidery and needlework, leather goods, and pottery. Most worked in family enterprises or small workshops, but some were employed in urban factories. The bulk of sales were to the tourist market in the Caribbean, but even so, craft accounted for more than 10 percent of exports in 1990. The recent economic and social collapse has brought the craft sector to its knees. Haiti's artisans need access to credit, and support for marketing and distribution of their products. More attention has to be given to tourism. Like the decline in crafts, the political turmoil that followed the 1991 military coup and lack of infrastructure made most tourists stay away. If tourism is to bring real benefits to the poor, more needs to be done to promote eco-tourism and other sustainable development initiatives. In 1979, a peak year for Haiti’s tourism sector, over 173,000 8 See Verner (2007), Labor Markets in Rural and Urban Haiti. 13 travelers were put ashore by cruise liners, but in the early 1980s, this achievement was wiped out by media coverage of political violence. Albeit not in the short term, the potential for expanding tourism is large given the beauty, culture, and beaches of the country and not to forget Haiti’s proximity to the United States. Further research is needed in order to have a growth strategy. Coffee is the primary peasant export and is well integrated in traditional agriculture. If production of Haitian Bleu (high quality coffee) can be extended to all parts impact could be substantial (Lundahl 2004). Coffee production has received some attention from European NGOs, which have promoted fair trade arrangements that bypass monopolies to the benefit of cooperative producers. Newer export crops like avocados and mangoes have been produced in recent years, and there is potential for crops such as spices. With investment, more efforts could be undertaken taking advantage of Haiti's climate and its close geographical location to the U.S. market. Mangoes have advanced to become the most traditional agricultural product next to coffee. As many farmers all over the country produce mangoes there is a potential for increased exports. Export firms are investing in different kinds of facilities that will allow them to develop new varieties of mango products (Lundahl 2004). This section on demographic trends and economic growth showed that the population growth rate increased in the last two decades although the fertility rate fell. In the last two decades the rural population has flocked to urban Haiti, especially to the metropolitan area. In 2003, 40 percent of the Haitians lived in urban areas up from 25 percent in 1982. The rural areas experienced a fall in the total population share. Rural indigent households have a smaller household size than metropolitan indigent households. In nonpoor households the average number of children under age 15 is 50 percent lower than those of extremely poor households. Economic growth has been poor in the last decade, and GDP per capita was reduced by roughly 50 percent, with agriculture being the hardest hit. 3. Data and Methodology This section presents data sources and the methodologies used in the paper to analyze poverty and labor markets in Haiti. Data Haiti is completing the first comprehensive household survey that covers both rural and urban areas. National household data are critical for making informed decisions on alleviating urban and rural poverty in Haiti. The analyses in this paper are based on the national households survey ( l’Enqueête sur les Conditions de Vie Haïti — the Haiti Living Conditions Survey (HLCS )) from 2001 (still unreleased). Population data are from publications produced by the statistical office ( Institut Haitien de Statistique et d’Informatique— IHSI) . The survey was undertaken in all nine regions ( department) and is 14 representative at the regional level in Haiti. The dataset includes 7,186 households. It is the first time in Haiti’s history that a survey of this magnitude has been conducted. 9 The household survey consists of 15 SPSS files (these files are dated 10.06.2004 and named Base de Données Mar). The Bank obtained them directly from the Haitian statistical agency via FAFO the Norwegian institution that has worked with the statistical office. We have discovered a number of serious flaws. The most important flaw relates to the variable describing the metropolitan-urban-rural status of a household/individual, which is different in the different files. After discussions with the IHSI the only reliable data for metropolitan, urban, and rural levels are those based on the file with household information, therefore this data is used throughout the paper. To calculate poverty, income including self-consumption has been used. Income is for the past 12 months based on a number of individual income sources and self- consumption is estimated value of consumption (and barter) of household production of crops, meat, and fish during the last week. First, respondents answered questions on consumption of own production, and the market value of it. Second, an average unit price of each type of good was calculated for the whole sample and multiplied by the quantity consumed last week and multiplied by 52 weeks. Methodology The income-poverty measures are designed to count the poor and to diagnose the extent and distribution of poverty. The income-poverty measures proposed by Foster, Geer, and Thorbecke (1984) are used throughout the paper. These are the headcount rate (P0), poverty gap (P1), and squared poverty gap (P2) measures. The former measures the magnitude of poverty and the latter two poverty measures assess both poverty magnitude and intensity. The headcount rate is defined as the proportion of household heads (not the whole population) below the poverty line. One concern applying the P0 measure is that each individual below the poverty line is weighted equally and, therefore, the principle of transfers is violated. A limitation of the measure is illustrated by the fact that it would be possible to reduce the P0 measure of poverty by transferring money from the very poor to lift some richer poor out of poverty, hence increasing social welfare according to the measure. P0 takes no account of the degree of poverty and it is unaltered by policies that lead to the poor becoming even poorer. One measure of poverty that takes this latter point into account (at least in weak form) is the poverty gap measure (P1). P1 is the product of incidence and the average distance between the incomes of the poor and the poverty line. It can be interpreted as a per capita measure of the total economic shortfall relative to population. P1 distinguishes the poor from the not-so-poor and corresponds to the average distance to the poverty line of the poor. One problem with the poverty gap, as an indicator of welfare is that, poverty 9 See FAFO for more information. www.fafo.no. 15 will increase by transfers of money from extreme poor to less poor (who become non- poor), and from poor to non-poor. Furthermore, transfers among the poor have no effect on the poverty gap measure. The P2 measure of poverty is sensitive to the distribution among the poor as more weight is given to the poorest below the poverty line. P2 corresponds to the squared distance of income of the poor to the poverty line. Hence, moving from P0 towards P2 gives more weight to the poorest in the population. This paper sets its poverty bar very low. To define “extreme poverty” or indigence it uses a US$1 a day poverty line, which is annually 2,681 gourdes. 10 Those that earn a per-capita income above US$1 are above the indigence line and therefore not extremely poor. The poverty lines used for rural, urban, and metropolitan areas are identical, as consumer price index data do not exist for different regions or locations in Haiti. This may overestimate poverty in rural areas slightly. Quantile Regressions Model The underlying economic model used in the analysis will simply follow Mincer’s (1974) human capital earnings function extended to control for a number of other variables that relate to location. In particular, we apply a semi-logarithmic framework that has the form: ln y i = φ (xi , z i ) + ui (1) where ln y i is the log of earnings or wages for an individual, i; x i is a measure of a number of personal characteristics including human capital variables, etc.; and z i represents location specific variables. The functional form is left unspecified in equation (1). The empirical work makes extensive use of dummy variables in order to catch nonlinearities in returns to years of schooling, tenure, and other quantitative variables. The last component, u i , is a random disturbance term that captures unobserved characteristics. Quantile regressions Labor market studies usually make use of conditional mean regression estimators, such as OLS. This technique is subject to criticism because of several, usually, heroic assumptions underlying the approach. One is the assumption of homoskedasticity in the distribution of error terms. If the sample is not completely homogenous, this approach, by forcing the parameters to be the same across the entire distribution of individuals may be too restrictive and may hide important information. 10 The conversion is based on the 2000 PPP. The questionnaire asks for information about income in the last 12 months and self-consumption in the last week (which is multiplied by 52 to obtain the annual self- consumption). 16 The method applied in this paper is quantile regressions. The idea is that one can choose any quantile and thus obtain many different parameter estimates on the same variable. In this manner, the entire conditional distribution can be explored. By testing, whether coefficients for a given variable across different quantiles are significantly different, one implicitly also tests for conditional heteroskedasticity across the wage distribution. This is particularly interesting for developing countries such as Haiti where wage disparities are huge and returns to, for example, human capital may vary across the distribution. The method has many other virtues apart from being robust to heteroskedasticity. When the error term is nonnormal, for instance, quantile regression estimators may be more efficient than least squares estimators. Furthermore, since the quantile regression objective function is a weighted sum of absolute deviations, one obtains a robust measure of location in the distribution, and as a consequence the estimated coefficient vector is not sensitive to outlier observations on the dependent variable. 11 The main advantage of quantile regressions is the semi-parametric nature of the approach, which relaxes the restrictions on the parameters to be fixed across the entire distribution. Intuitively, quantile regression estimates convey information on wage differentials arising from nonobservable characteristics among individuals otherwise observationally equivalent. In other words, by using quantile regressions, we can determine if individuals that rank in different positions in the conditional distribution (i.e., individuals that have higher or lower wages than predicted by observable characteristics) receive different premiums to education, tenure, or to other relevant observable variables. Formally, the method, first developed by Koenker and Basset (1978), can be formulated as 12 yi = x i ′ β θ + u θ i = Quant θ (yi | x i ) = xi ′ β θ (2) where Quant θ (yi | x i ) denotes the θ th conditional quantile of y given x, and i denotes an index over all individuals, i = 1,…,n. In general, the θ th sample quantile (0 < θ < 1) of y solves 11 That is, if 0 ˆ > ′ − θ β i i x y then y i can be increasing towards + ∞ , or if 0 ˆ < ′ − θ β i i x y , y i can be decreasing towards - ∞ , without altering the solution θ β ˆ . In other words, it is not the magnitude of the dependent variable that matters, but on which side of the estimated hyper plane the observation is. This is most easily seen by considering the first-order-condition, which can be shown to be given as (see Buchinsky 1998) ∑ = = ′ − + − n i i i i n x x y 1 2 1 2 1 1 . 0 )) ˆ sgn( ( θ β θ This can be seen both as a strength and weakness of the method. To the extent that a given outlier represents a feature of “the true” distribution of the population, one would prefer the estimator to be sensitive, at least to a certain degree, to such an outlier. 12 See Buchinsky (1998). 17 ⎭ ⎬ ⎫ ⎩ ⎨ ⎧ ′ − − + ′ − = ∑ ∑ ′ < ′ ≥ β β β β θ β θ i i i i x y i i i x y i i i x y x y n : : | | ) 1 ( | | 1 min . (3) Buchinsky (1998) examines various estimators for the asymptotic covariance matrix and concludes that the design matrix bootstrap performs the best. In this paper, the standard errors are obtained by bootstrapping using 200 repetitions. This is in line with the literature. 4. Sources of Income Except in the metropolitan area, most Haitian households have low annual per capita incomes. In 2001, the median income per capita of extremely poor households (1,080 gourdes) was around one-tenth of the median income of the nonpoor (10,304 gourdes). Median income varies greatly across regions and locations. In 2001, median household income per capita in the metropolitan area (7,293 gourdes) was far higher than elsewhere in Haiti. The households with the lowest median incomes per capita are located in the Northeast region, where they stand at 617 and 804 gourdes in urban and rural areas, respectively (Table 4.1). In the West region, by contrast, the median income per capita of households is 5-6 times higher in rural and urban areas (excluding the metropolitan area) than in the Northeast. These figures reveal how access to imported goods and to a large market like Port-au-Prince can make a difference in people’s well-being. Table 4.1: Median Annual Income per capita, 2001 (gourdes) Region Metropolitan Urban Rural Total Haiti Artibonite NA 1,723.0 2,134.8 2,000.0 Center NA 2,316.7 2,430.0 2,389.7 Grand-Anse NA 1,654.2 1,900.0 1,829.2 North NA 3,304.2 1,585.0 1,900.0 Northeast NA 804.2 616.7 671.7 Northwest NA 2,470.7 1,500.0 1,734.2 West 7,292.5 4,014.6 3,100.0 4,366.7 South NA 2,761.0 1,696.2 1,921.6 Southeast NA 3,240.0 2,371.7 2,507.5 Haiti 7,292.5 2,264.7 2,035.0 2,403.1 Source: Own calculations based on HLCS 2001 . There are significant differences in the distribution of per capita household income (PCHI) by geographic locality. The PCHI of a household in the first decile of the income distribution is 199, 166, and 910 gourdes in rural, urban, and metropolitan areas, respectively (Table 4.2). Hence households on the low end of the income distribution in metropolitan Haiti have much better incomes than those in other urban or rural areas. Rural households are better off than urban households on the low end of the income distribution. The per capita income difference between households in rural and metropolitan areas is fairly constant across the income distribution. At a given location in the distribution of income, metropolitan households earn roughly four times more than households in rural areas. One important explanatory factor may be the greater number of 18 opportunities in metropolitan Haiti. People living in the metropolitan area have access to more jobs, and the self-employed have access to domestically produced and imported goods that they can resell in the metropolitan market or to other urban and rural markets. This is significantly different from conditions in other areas in Haiti, where very few goods originate (see Section 5 for more on income inequality). Rural households are better off than urban households in the low end of the income distribution. This is a major difference to other countries in the region in the same location in the income distribution where rural dwellers always fare worse in terms of per-capita household income than urban households. The PCHI of rural households is higher than that of urban households for the first 4 deciles in Haiti (Figure 4.1). From the fifth quintile it changes and the average PCHI becomes higher in urban areas than rural areas. In the top deciles (9th and 10th) the PCHI in urban areas is 34 and 66 percent higher than in rural areas respectively. Table 4.2: Average Income Per Capita by Decile, 2001(gourdes) Decile Metropolitan Urban Rural 1 910 166 199 2 1,435 473 524 3 3,074 862 879 4 4,360 1,301 1,306 5 6,177 1,888 1,787 6 8,690 2,638 2,352 7 12,312 3,570 3,100 8 16,915 5,112 4,177 9 25,624 8,469 6,196 10 73,430 28,522 17,177 Source: Own calculations based on HLCS 2001 . Self-consumption is part of the explanation for the poorest being better off in rural than in urban areas, as shown in Figure 4.2. Given the difference between PCHI in urban and rural areas for the poorest in the population, it makes one wonder why people move to the urban areas except of course to gain access to public services. (See Section 7 for more on public service provision.) 19 Figure 4.1: Income per Capita by Decile, 2001 (gourdes) 0 10,000 20,000 30,000 40,000 50,000 60,000 70,000 80,000 1 2 3 4 5 6 7 8 9 10 Decile Gourdes per capita Metro Urban Rural Source: Own calculations based on HLCS 2001 . The poorest in rural areas get a marginally higher share of total income than in urban areas. The share of income going to the lowest 40 percent of the income pyramid amounts to 8, 7, and 6 percent in the rural, metropolitan and urban area respectively. This compares to the income share going to the top 10 percent of the income distribution that receive 43, 43, and 54 percent in the rural, metropolitan, and urban areas respectively. Table 4.3: Income Source for Households per Capita 2001 (percent) Decile Self- employed Salary Transfer Property Self- consumption Barter Other 1 46.7 5.0 26.4 3.8 4.1 0.0 14.1 2 48.4 5.9 22.6 3.0 10.8 0.0 9.4 3 43.2 5.4 22.1 2.8 17.9 0.1 8.5 4 39.5 8.7 22.6 2.7 21.2 0.3 5.2 5 40.9 5.7 20.8 3.2 23.9 0.4 5.1 6 38.2 7.5 22.4 2.6 24.1 0.0 5.1 7 40.2 10.4 23.8 2.4 19.5 0.1 3.5 8 38.6 14.2 25.0 2.2 16.5 0.2 3.3 9 37.2 19.3 28.8 2.6 9.2 0.2 2.8 10 30.9 30.7 29.8 3.0 1.3 0.1 4.3 Source: Own calculations based on HLCS 2001 . 20 Figure 4.2: Income Source for Households per Capita, 2001 (percent) 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 1 2 3 4 5 6 7 8 9 10 Decile Proportion of Income Self-emp Salary Transfer Property Self-cons Barter Other Source: Own calculations based on HLCS 2001. Self-employment income, wages, and transfers are crucial to reducing poverty in Haiti. Self-employment is the most important income source for all income levels, although it accounts for more of the total income of the poorest 10 percent of the population (46.7 percent of their total income) than of the richest 10 percent (30.9 percent of their income). Private transfers, mostly remittances, are generally the second most important income source, accounting for 26 and 30 percent of the total income of the poorest 10 percent and the richest 10 perce