How do income and education affect health inequality: evidence from four developing countries

Using household survey data from four countries ? Albania, Nepal, Tajikistan and Tanzania ? this article calculates income-related inequality in health care utilization. We measure health disparity separately for generally and chronically ill individuals by constructing two models: one for the probability of a visit to a physician and another for the number of visits. Following model-based measurements, we decompose inequality into two major parts: one accounted for by identity-related factors and another by socioeconomic and other factors such as education, geography and distance to a clinic. We propose a new method to quantify the effect of changes in income and education on health disparity. One of our important findings suggests that health disparity is pro-rich in all our sample countries. The pro-rich disparity is prevalent among generally ill as well as chronically ill patients, in both visit probability and visit frequency models. Health inequality seems primarily driven by income differences followed by nonidentity factors. Further, the principle of equal treatment for equal need is not fulfilled in any of our countries. Among policy implications, increasing average income and education in a way that also reduces disparity in income and education, respectively, will substantially shrink inequality in health care utilization.     

Agricultural Productivity and Poverty Reduction in Nepal

This paper provides for the first time a clear quantitative link between agricultural productivity and poverty among rural households in Nepal. Using data from a nationwide Nepal Living Standard Survey 2004, we first estimate household‐specific productivity per worker under both Cobb–Douglas and translog production functions. Second, the paper identifies the determinants of productivity. Third, we explore a theoretical link between productivity and poverty using Sen's poverty index and find empirically that productivity growth substantially helps poverty reduction. Finally, the integrated effects of changes in productivity determinants are found to be stronger than the outcomes of sectoral policies taken in isolation.     

Calculation and decomposition of income inequality in low- and middle-income countries: a survey data analysis

This article estimates income inequality in a sample of four low- and middle-income (LMI) countries namely; Albania, Nepal, Tajikistan and Tanzania using the household survey data – Nepal Living Standard Measurement Survey Second. First, we estimate the income generation function for each country and calculate the income inequality using Gini index (GI). Second, we decompose the income Gini into the determinants of income generation functions. Based on the decomposition result, socio-economic factors are the most important determinants of income inequality followed by geographic factors. Demographic factors have the least effect on income inequality in all four countries. Third, we propose a new method to quantify the effect of change in each covariate of income generation function on income Gini. That allows us to quantify the effects of change in specific policy such as increase in investment in schooling or public health to specific group of the population in society on income inequality. A carefully chosen, integrated policy can significantly reduce inequality in all four countries under study.