Economic impacts of professional training in the informal sector : the case of the labor force training program in Cote d'Ivoire

The authors address the economic impact of the labor force training program (PAFPA) developed for the informal sector in Côte d'Ivoire. The data contain a subsample of the participants in the agricultural sector, tailoring sector, and the electronics sector, and a comparable control group of nonparticipants. The data have been analyzed using standard program evaluation tools, namely difference-in-difference estimators, in order to detect potential program impacts. The authors find positive economic impacts as a result of training received for some groups, namely women, the agricultural and electronics sectors, firms employing 1-3 individuals, and firms with 10 or more employees.

Labor markets in rural and urban Haiti--based on the first household survey for Haiti

This paper addresses labor markets in Haiti, including farm and nonfarm employment and income generation. The analyses are based on the first Living Conditions Survey of 7,186 households covering the whole country and representative at the regional level. The findings suggest that four key determinants of employment and productivity in nonfarm activities are education, gender, location, and migration status. This is emphasized when nonfarm activities are divided into low-return and high-return activities. The wage and producer income analyses reveal that education is key to earning higher wages and incomes. Moreover, producer incomes increase with farm size, land title, and access to tools, electricity, roads, irrigation, and other farm inputs.Rural Poverty Reduction,Population Policies,,Rural Development Knowledge&Information Systems

Rural poor in rich rural areas : poverty in rural Argentina

Rural poverty remains a crucial part of the poverty picture in Argentina. This paper used a rural dataset collected by the World Bank in 2003. Findings show that extreme income poverty in rural areas reached 39 percent of the people or 200,000-250,000 indigent families. These families tend to: be large, and young, and to escape from poverty as they mature and children leave the household (life cycle); live largely in dispersed areas where basic service provision is often weak and delivery is difficult (in particular school attendance beyond 11 years of age falls off very rapidly compared with grouped rural or urban areas); and be more likely to be small landholders than landless laborers. The structure of poverty in rural Argentina shows that larger households are poorer than smaller households, female-headed households are poorer than male-headed households, young households/household heads are poorer than older households/household heads, the poor tend to work more in the informal sector, and a greater share of those engaged in agriculture are poor. However, poverty is by no means strictly an agricultural problem. Furthermore, the deepest poverty is among the poorly educated and young household heads with children. Without interventions to improve their opportunities and assets, their plight is likely to worsen.Rural Poverty Reduction,Population Policies,Achieving Shared Growth,Services&Transfers to Poor

Poverty in rural and semi-urban Mexico during 1992-2002

This paper analyzes poverty in rural and semi-urban areas of Mexico (localities with less than 2,500 and 15,000 inhabitants, respectively) and provides guidance on a social agenda and poverty alleviation strategy for rural Mexico. The analyses are based on INIGH and ENE data sets for 1992-2002. Monetary extreme poverty affected 42 percent of the rural dwellers in dispersed rural areas and 21 percent in semi-urban areas in 2002, slightly less than one decade earlier. Most of the rural poor live in dispersed rural areas and 13.2 million people live in poverty in rural Mexico with less than 15,000 inhabitants. It is disproportionately a feature of households whose main job is in the agricultural sector, as self-employed farmers or rural laborers, and that have at most a primary education. However, the incidence of extreme rural poverty has declined since 1996 but at a slower pace than the decline in urban poverty. Hence, the rural-urban poverty gap increased in recent years and in some places extreme poverty is at least four times higher in rural than in urban areas. Moreover, not only is the income gap in urban areas increasing, but also the gap between richer and poorer segments of the population in the rural areas is growing. Finally, the gap between rich and poor regions is still large.Poverty Assessment,Achieving Shared Growth,Environmental Economics&Policies,Health Economics&Finance,Services&Transfers to Poor

Social Implications of Climate Change in Latin America and the Caribbean

Climate change is the defining development challenge of our time. More than a global environmental issue, climate change is also a threat to poverty reduction and economic growth and may unravel many of the development gains made in recent decades. Latin America and the Caribbean account for a relatively modest 12 percent of the world’s greenhouse gas (GHG) emissions,1 but communities across the region are already suffering adverse consequences from climate change and variability (De la Torre, Fajnzylber, and Nash 2009). As highlighted in “Reducing Poverty, Protecting Livelihoods, and Building Assets in a Changing Climate” (Verner 2010), climate change is likely to have unprecedented social, economic, environmental, and political repercussions.climate change, latin america, weather, mitigation, adaptation, climate policy, developing countries, world bank, flood, drought, temperature

What factors influence world literacy? is Africa different?

Ninety-five percent of the world’s illiterate people live in developing countries, and about 70 percent are women. Female illiteracy rates are particularly high in Sub-Saharan Africa. In Niger and Burkina Faso, for example, more than 90 percent of women are illiterate. This paper presents a model of literacy. It shows that the main determinants of worldwide literacy are enrollment rates, average years of schooling of adults, and life expectancy at birth. Income has a weak nonlinear effect, negatively affecting literacy until a threshold level of per-capita income of about $2200 a year is reached and positively affecting literacy thereafter. Finally, African countries do not have a significantly higher literacy rate when controlling for other factors.Public Health Promotion,Education Reform and Management,Nonformal Education,PrimaryEducation,Curriculum&Instruction,Primary Education,Gender and Education,Curriculum&Instruction,Education Reform and Management,Nonformal Education

Poverty in the Brazilian Amazon: an assessment of poverty focused on the State of Para

The states in the Brazilian Amazon have made progress in reducing poverty and improving social indicators in the last decade. Despite this progress, the poverty rate in the Amazon is among the highest in Brazil. As of 2000, rural poverty is the greatest challenge. In Par?, not only is the headcount poverty rate of 58.4 percent in rural areas more than 55 percent higher than headcount poverty in urban areas, but also poverty is much deeper in rural areas. The fall in infant mortality and adult illiteracy corroborate the improvement in measured income poverty. Census data from 2000 and 1991 reveal that more people left Par? than came to live in the state during the 1970s, the opposite of the 1980s. In 2000, the Gini coefficient for Par?, as in the Amazon as a whole, was 0.60. The poverty profile reveals that indigenous peoples experience a higher poverty incidence than other groups. Census 2000 data reveal that living in rural areas in Par? does not by itself affect the probability of being poor. Individual and household characteristics are more important than geographical location. The largest statistical differences in poverty reduction between rural and urban areas are found in the effect of education, sector of employment, gender, and family size. PNAD data from 2001 reveal that living in urban areas in Par? does not by itself affect the probability of falling below the poverty line in urban areas in Brazil. The strongest poverty correlates are education, experience, race, rural location, gender, and labor market association.Health Economics&Finance,Public Health Promotion,Health Monitoring&Evaluation,Environmental Economics&Policies,Health Indicators,Achieving Shared Growth,Environmental Economics&Policies,Health Economics&Finance,Health Monitoring&Evaluation,Poverty Assessment

Making the poor count takes more than counting the poor : A quick poverty assessment of the state of Bahia, Brazil

The state of Bahia, Brazil has made progress in reducing poverty and improving social indicators in the past decade. Despite this progress, Bahia's poverty is among the highest and its social indicators are among the lowest in Brazil. Currently, 41 percent of Bahia's population live in households below the poverty level, a drop of 14 percentage points since 1993. Moreover, poverty is less deep than in 1993, but deeper than in 1981. The fall in Bahia's social indicators, such as infant mortality and adult illiteracy, corroborate the improvement in measured income poverty. Part of the reason why the poverty indicators of Bahia are worse than in other countries with similar per-capita income is because of income inequality. In 2000 the Gini coefficient for Bahia was 0.61. The National Household Survey Data, PNAD, from 1981-2001 reveal that living in Bahia does not by itself affect the probability of falling below the poverty line in Brazil. Hence, other characteristics are more important for poverty reduction than geographical location. The strongest poverty correlates are education, experience, race, rural location, gender, and labor market association. Analyses reveal that the probability of being poor is decreasing with increasing educational attainment. The gender of the household head does not matter for poverty according to the poverty profile, but when we control for education and other individual characteristics, female-headed households have a much larger likelihood of being poor than do male-headed households. Household size also matters for poverty. Larger households are more likely to experience poverty than smaller households, and the effect is concave. Moreover, households with members under age five appear more likely to fall below the poverty line than families with no children below five years old. The presence of old-aged people (above 65 years of age) in the household is an important factor contributing to poverty reduction.Health Monitoring&Evaluation,Services&Transfers to Poor,Health Economics&Finance,Public Health Promotion,Environmental Economics&Policies,Poverty Assessment,Environmental Economics&Policies,Health Monitoring&Evaluation,Achieving Shared Growth,Rural Poverty Reduction

Labor markets and income generation in rural Argentina

This paper addresses three areas of the rural labor market-employment, labor wages, and agriculture producer incomes. Findings show that the poor allocate a lower share of their labor to farm sectors than the nonpoor do, but still around 70 percent work in agriculture, and the vast majority of rural workers are engaged in the informal sector. When examining nonfarm employment in rural Argentina, findings suggest that key determinants of access to employment and productivity in nonfarm activities are education, skills, land access, location, and gender. Employment analyses show that women have higher probability than men to participate in rural nonfarm activities and they are not confined to low-return employment. Moreover, workers living in poorer regions with land access are less likely to be employed in the nonfarm sector. There is strong evidence that educated people have better prospects in both the farm and nonfarm sectors, and that education is an important determinant of employment in the better-paid nonfarm activities. Labor wage analyses reveal that labor markets pay lower returns to poorer than to richer women and returns to education are increasing with increased level of completed education and income level. And nonfarm income and employment are highly correlated with gender, skills, household size, and education. This analysis also shows a rather heterogeneous impact pattern of individual characteristics across the income distribution, but education is important for all levels of income. Agricultural producer income analyses reveal that producers'income monotonically increases with land size and with completed education level, and positively correlates with road access and use of electricity, fertilizer, and irrigation. Finally, farms operated by women are slightly more productive than farms operated by men.Rural Poverty Reduction,Labor Markets,Population Policies,Work&Working Conditions

Youth at risk, social exclusion, and intergenerational poverty dynamics : A new survey instrument with application to Brazil

This paper addresses the underlying causes of problems and risks faced by poor and excluded youth of 10-24 years of age. The authors develop a survey instrument that addresses poverty in a broad sense, including hunger, early pregnancy and fatherhood, violence, crime, drug use, low levels of social capital, and low educational attainment. The authors also shed light on intergenerational transfer of risks that are considered to induce poverty. They document findings based on the survey data gathered in three poor urban neighborhoods in Fortaleza in Northeast Brazil. Their main findings show that: (i) Poor youth are at considerable risk of growing up without their father. Only 7 percent grow up with their father present in the household. (ii) The intergenerational transmission of low education attainment is at play, but it is diminishing. (iii) The risk of early pregnancy and fatherhood is large among poor and excluded youth-31 percent of the youth had their first child before age 16, triple that of the adult population. (iv) The risk of sexual abuse and violence within the household exists-6 percent of the youth answered that they had their first sexual relationship with a family member, and 13 percent grow up in households with violence. (v) The social capital levels are low-only 5 percent of the youth and 9 percent of the adults have measurable social capital. (vi) The risk of growing up in a violent neighborhood is large-59 percent of the youth claim that they live in a violent neighborhood, 80 percent feel unsafe in their neighborhood, and 50 percent feel unsafe at home.Health Monitoring&Evaluation,Housing&Human Habitats,Children and Youth,Public Health Promotion,Gender and Social Development,Health Monitoring&Evaluation,Housing&Human Habitats,Gender and Social Development,Adolescent Health,Youth and Governance