As COVID-19 continues to wreak havoc across the world, researchers are attempting to quantify the economic fallout from the pandemic as it continues to unfold. Estimating the economic impacts of a prevailing pandemic is fraught with uncertainties …
This paper revisits the role of investment in human capital in closing the productivity gap, boosting labor productivity growth, speeding the rate of structural transformation, and ultimately creating high-quality jobs in Africa. Analysis of detailed sector-level historical data on employment, value added, and human capital shows that investment in human capital is significantly and positively associated with the rate at which countries close the labor productivity gap between agriculture and the rest of the economy. Investment in human capital also significantly increases labor productivity within sectors and the speed at which labor is reallocated from low-productivity to high-productivity employment. In line with other research on this topic, the findings from this study underscore that Africa is ready to benefit significantly from improving human capital through investments in education, health care, and nutrition.
Africa has no shortage of labor supply. But it lacks high-productivity job opportunities in high- productivity nonagricultural sectors. Its relatively rapid and sustained economic growth over the past decade did not yield enough jobs for the growing wave of jobs seekers-mainly youth in urban areas. Nonagricultural employment continues to be dominated by the informal sector, where wages are low, benefits nonexistent, workplace safety absent, and labor exploitation common. With significant demographic change expected to bring pressure on African labor markets, the urgency of creating high-quality and remunerative jobs at a much faster pace is not only an economic issue but a political and social one. This report investigates the extent to which failure to remove business constraints hinders actual and potential job growth. In particular, using World Bank Enterprise Survey (ES) panel data, the report quantifies the number of actual jobs lost due to the impact of business obstacles on firm survival and employment growth.
Africa enjoyed relatively fast economic growth over the past decade and a half. The sustained growth undoubtedly kindled hopes for a prosperous Africa. However, poverty and inequality remained pervasive. In 2013, poverty was still widespread, and the rate was high in Sub-Saharan Africa-41 percent, compared with the world average of just 10.7 percent and the South Asia average of 15 percent. While the intensity of poverty, measured by the poverty gap, declined from 26 percent to 16 percent during the same period, it is still high compared with the world average of 3.2 percent. Moreover, the benefits of growth were not shared widely, and inequality was widespread and persistent. The median Gini coefficient measuring inequality in Africa was 0.36 in 2014, and 7 percent of total income goes to the bottom 20 percent of the income distribution.
In the absence of third party and prepayment systems such as health insurance and tax-based healthcare financing, households in many low-income countries are exposed to the financial risks of paying large medical bills from out-of-pocket. In recent years, community based health insurance schemes have become popular alternatives to fill such void in the healthcare financing systems. This paper investigates the impact of these schemes on out-of-pocket spending based on three rounds of nationally representative data from Rwanda. We estimate an Extended Two-Part Model to address endogeniety in insurance enrollment and censoring in healthcare expenditure data. We find that community based health insurance program has non-linear and mixed impacts on out-of-pocket expenditure. While the program significantly increases the probability of overall spending, it decreases the amount of per capita spending on healthcare. The program also significantly reduces spending on drug but increases outpatient spending with no detectable impact on inpatient services. Furthermore, we find notable heterogeneity in treatment effects in which households in the top income distribution realize the highest reduction in out-of-pocket spending.
In this paper, we implement a Bayesian potential outcomes model to evaluate the impact of program interventions using non-randomized data. The approach jointly addresses selection bias in program placement, heterogeneous treatment intensity among the treated, and heterogeneity in treatment effects. Using data from a non-randomized household survey, we evaluate the impact of Ethiopia's Health Extension Program on fertility and child mortality outcomes. We find that there is significant selection bias in both program placement and intensity of exposure to the program among the treated. On average, the program has significant impact on reducing fertility and child mortality. However, there is notable heterogeneity in the treatment effects ranging from negative impacts for some individuals to positive impacts for the majority in the sample. We recover individual-level treatment effects and present the distributions graphically.
The Ethiopian economy has witnessed a double-digit rate of inflation since 2003, culminating at 53% in June 2008. Particularly the significant rise in the relative prices of grain and other foodstuff such as sugar, edible oil and other necessities in recent period are very worrisome. Evidently such large changes in both absolute and relative prices in a space of few years can undermine the rebound in per capita incomes in the last decade and the poverty reduction effort of the government. The gravity of the problem has been well understood by policy makers, and efforts are underway to cushion vulnerable households from the consequences of the price surge. The potential role of such interventions can only be known if welfare effects of rising prices are understood. In addition, better measures of the key parameters that drive the demand for grain and other goods is a useful input to the analysis of the causes of relative price changes in Ethiopia.