Micro-level determinants of poverty reduction in Sri Lanka: a multivariate approach
Indunil De Silva
Purpose – The main purpose of this paper is to construct a poverty profile for Sri Lanka, and examine the micro-level determinants and correlates of poverty. Design/methodology/approach – The study is based on the latest Sri Lanka Integrated Survey commissioned by the World Bank. The unconditional poverty profile was constructed using three different poverty measures (poverty headcount, average poverty gap and squared poverty gap), nested in the Foster-Greer-Thorbecke index. The conditional poverty profile was constructed on the basis of a multivariate analysis of poverty correlates. Partial correlates of poverty are computed using two comparable methodologies. First, a logistic regression was estimated, with the probability of a household being in poverty as the dependent variable and a set of economic and demographic variables as correlates. Second, the quantile regression approach was utilized to examine the correlates of per capita consumption at different points on the distribution. Findings – The empirical findings are broadly encouraging. The estimation results show that the education of the household head, being salary employed and being engaged in business have a significant positive effect on the standard of living. The probability of being poor increases with the household size, household head being female, living in a rural area, and being a casual wage earner. These findings indicate the importance of a set of policies which are super pro-poor, namely increasing school enrolment and achievement, effective family planning programs to reduce the birth rate and dependency load within households, and granting priorities for specific cohorts (children-, elderly-, rural- and female-headed households) in targeted interventions. Originality/value – This is the first study that examines the probable determinants and correlates of Sri Lankan poverty in a multivariate framework employing both logit and quantile regressions.
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