How does the distribution of power within the household affect the nutrition of its members? In 1998, the largest social program in rural Mexico (PROGRESA) designed a random experiment for the purpose of evaluation. Exploiting the experimental nature of the data, I estimate calorie demand equations based on the predictions from different models of household behavior. There are three main findings in the first chapter. First, I reject the income pooling restriction and the Pareto-efficiency assumption for the nutrition decisions of families assuming that only the head of household and his spouse participate in decision-making. Second, I reject the income pooling restriction in the context of the extended family, for which all income earners contribute to decision-making. Third, I show that changing the wife's non-labor income has little effect on the levels of food consumption in households with two decision-makers. In the extended family setting, I find that, for a given level of household income, an increase in the number of income earners is associated with a decrease in calorie consumption. Yet, when a female household member starts earning income, family calorie consumption increases. When it is a male household member who starts earning income, family calorie consumption decreases. In the second chapter, we investigate heterogeneity in program impact for the Mexican social program PROGRESA, which is a means-tested conditional cash transfer program implemented in rural regions of the country. The "common effect" model in program evaluation assumes that all treated individuals have the same impact from a program. Does the program have the same effect on everyone? Will some groups benefit more from the program than others? The design of PROGRESA provides a theoretical motivation for exploring heterogeneity in program impacts. We examine the program targeting mechanism and find heterogeneity in the eligible population along the criteria used for beneficiary selection. We also investigate the overall heterogeneity of program impacts, which includes both observed and unobserved heterogeneity. Experimental data are sufficient to identify mean program impacts or impacts on subgroups, but do not identify unobserved heterogeneity in impacts. Using a non-parametric technique, we find evidence against the "common effect" model. This result does not rely on any assumption and thus is particularly strong evidence of heterogeneous treatment effects. Additional assumptions allow us to further analyze the distribution of impacts.