The purpose of this paper is to help empirical economists think through when and how to weight the data used in estimation. We start by distinguishing two purposes of estimation: to estimate population descriptive statistics and to estimate causal effects. In the former type of research, weighting is called for when it is needed to make the analysis sample representative of the target population. In the latter type, the weighting issue is more nuanced. We discuss three distinct potential motives for weighting when estimating causal effects: (1) to achieve precise estimates by correcting for heteroskedasticity, (2) to achieve consistent estimates by correcting for endogenous sampling, and (3) to identify average partial effects in the presence of unmodeled heterogeneity of effects. In each case, we find that the motive sometimes does not apply in situations where practitioners often assume it does. We recommend diagnostics for assessing the advisability of weighting, and we suggest methods for appropriate inference.
We analyze the economic returns to different postsecondary degrees in Chile. We posit a schooling decision model with unobserved ability, observed test scores and labor market outcomes. We benefit from administrative records to carry out our empirical strategy. Our results show positive average returns to postsecondary degrees, especially for five-year degrees. However, we also uncover a large fraction of individuals with realized negative net returns. Although psychic benefits of postsecondary education could rationalize this result, we argue this might also suggest that individuals lack information at the time schooling decisions are made. Finally, our findings illustrate the importance of allowing for heterogeneous treatment effects when making policy recommendations.