Poor stays poor - Household Asset Poverty Traps in Rural Semi-arid India. Revise and resubmit at World Development
Abstract:Although identifying household-level poverty dynamics would have important implications for poverty reduction policies empirical evidence is still scant. This paper employs a novel semiparametric panel data estimator that combines the advantages of estimation methods in the existing literature and applies it to a uniquely long panel data set to examine poverty dynamics in three villages in rural semi-arid India. Structural immobility is pervasive. The currently poor are likely to remain poor, suggesting a structural poverty trap. While all households face static asset holdings, higher castes, larger landholders and more educated households are significantly less likely to be poor.
Measuring Poverty over Time - Accounting for Welfare Variability. To be submitted to Journal of Economic Inequality.
Abstract: Standard poverty measures focus on static, single-period, snapshot view of poverty. This paper proposes two classes of dynamic poverty measures that extend the static Foster-Greer-Thorbecke poverty indices to account for intertemporal fluctuations in household welfare. The applications to panel data from rural Pakistan show that both methods for accounting for household income variability over time substantially increase estimates of intertemporal poverty incidence.
Targeting maps: An asset-based approach to geographic targeting.
Abstract: Proper targeting of policy interventions requires reasonable estimates of the benefits of the various alternative interventions. In order to inform such decisions, we develop an integrated approach that estimates the marginal returns to a range of assets across geographically defined subpopulations allowing returns to vary by household and by geography. We then create a series of maps illustrating the estimated marginal returns to specific assets and the proportion of an area\u2019s population that would benefit from increased holdings of a specific asset. These maps can then be overlaid with traditional poverty maps to identify areas that are strong candidates for a particular development intervention. We develop a general method and demonstrate its potential with an application using Ugandan data.