This dissertation is a collection of three essays that cover issues in poverty analysis. The first essay (Partial Identification of Poverty Measures with Contaminated and Corrupted Data) applies a partial identification approach to poverty measurement when data errors are non-classical in the sense that it is not assumed that the error is statistically independent of the outcome of interest, and the error distribution has a mass point at zero. This paper shows that it is possible to find non-parametric bounds for the class of additively separable poverty measures. A methodology to draw statistical inference on partially identified parameters is extended and applied to the setting of poverty measurement. The methodology developed in this essay is applied to the estimation of poverty treatment effects of an anti-poverty program in the presence of contaminated data.

The second essay (On the Design of an Optimal Transfer Schedule with Time Inconsistent Preferences) addresses a very recent literature that studies public policy and its connection to behavioral economics. It incorporates the phenomenon of time inconsistency into the problem of designing an optimal transfer schedule. It is shown that if program beneficiaries are time inconsistent and receive all of the resources in just one payment, then the equilibrium allocation is always inefficient. In the spirit of the second welfare theorem, I also show that any efficient allocation can be obtained in equilibrium when the policymaker has full information. This assumption is relaxed by introducing uncertainty and asymmetric information into the model. The optimal solution reflects the dilemma that a policymaker has to face when playing the roles of commitment enforcer and insurance provider simultaneously.

The third essay (Does Conditionality Generate Heterogeneity and Regressivity in Program Impacts? The Progresa Experience) studies both empirically and theoretically the consequences of introducing a conditional cash transfer scheme for the distribution of program impacts. Intuitively, if the conditioned-on good is normal, then better-off households tend to receive a larger positive impact. I formalize this insight by means of a simple model of child labor, applying the Nash-Bargaining approach as the solution concept. A series of tests for heterogeneity in program impacts are developed and applied to Progresa, an anti-poverty program in Mexico. It can be concluded that this program exhibits a lot of heterogeneity in treatment effects. Consistent with the model, and under the assumption of rank preservation, program impacts are distributionally regressive, although positive, within the treated population.