Using a unique panel data set for rural households in Zimbabwe we estimate a microeconomic model of growth under uncertainty, a stochastic version of the Ramsey model with livestock as the single asset. We use the estimation results in simulation experiments (over a 20-year period) to quantify...

Using a unique panel data set for rural households in Zimbabwe we estimate amicroeconomic model of growth under uncertainty, a stochastic version of the Ramsey modelwith livestock as the single asset. We use the estimation results in simulation experiments(over a 20-year period) to quantify the...

Most measures of vulnerability are a-theoretic and essentially static. In this paper we use a stochastic Ramsey model to find a household's optimal welfare and we measure vulnerability as the shortfall from the welfare attained if the household consumed permanently at the poverty line. The...

Globalization provokes both excitement and fear. This comprehensive collection, which brings together some of the most important published work on the subject, addresses a core issue of contention: the implications of globalization for poverty and inequality. While the debate is highly...

This paper proposes a method for estimating distribution functions that are associated with the nested errors in linear mixed models. The estimator incorporates Empirical Bayes prediction while making minimal assumptions about the shape of the error distributions. The application presented in...

We construct and derive the properties of an estimator of welfarewhich takes advantage of the detailed nature of information about living standards available in small household surveys and the comprehensive coverage of a census. By combining the strengths of each, our estimator can be used at a...

This paper examines the performance small area of welfare estimation. The method combines census and survey data to produce spatially disaggregated poverty and inequality estimates. To test the method, predicted welfare indicators for a set of target populations are compared with their true...