Measures, Distribution and Decomposition of Poverty: An Empirical Analysis in Nepal

Abstract

Background: Poverty has been in existence for many years and continues to exist in a large number of countries. Poverty is “pronounced in wellbeing” where wellbeing (and poverty) in broader term, focuses on the capability of the individual to function in society and poor people often lack key capabilities, they may have not adequate income, education, or be in poor health or feel powerless or lack of political freedoms. In Nepal, despite the decreasing trend in poverty incidence, still the current prevalence is very high with the comparison of other countries.

Objective: To identify, compare and decomposition of different poverty measures by rural urban area and ecological belt in Nepal.

Materials and Methods: Data set of Nepal Living Standard Survey (NLSS) conducted by Central Bureau of Statistics in 2011 consisting of various variables related to food, non-food consumption, income, demographic, socioeconomics, etc., have been used for analysis. In order to measure the poverty, different measures such as head count ratio, poverty gap, poverty severity, Watts index and Sen-Shorrocks-Thon index were used. The comparisons of different poverty measures across different variables were attempted including use of appropriate poverty curves. The decomposition of poverty indices by consumption components using the Shapley value along with Lump-Sum Targeting approach has been applied.

Results: Average per capita consumption is 34186.5, the head count index, poverty gap and poverty severity of Nepal are 0.2518, 0.0545 and 0.0182, respectively. The poverty measures of rural area are higher than the urban area, and the incidence of poverty is highest in mountain ecological belt. Food and non-food component allows to 46.39% & 28.42% of the total population to be non-poor of headcount index, 60.19% & 34.34% for poverty gap index and 59.96% & 38.20% for poverty severity, respectively.

Conclusion: For both within and overall population, rural area has the higher impact than urban area and each measure of poverty in mountain is significantly higher than hill and terai. To reduce within group headcount index and poverty gap, policymakers should give more focus to rural area and mountain region.