What Are We Weighting For?
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 are grateful for helpful comments from David Autor, Raj Chetty, John DiNardo, Todd Elder, Mike Elsby, Osborne Jackson, Fabian Lange, Jason Lindo, Jim Ziliak, and seminar participants at the University of Kentucky. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.
Gary Solon & Steven J. Haider & Jeffrey M. Wooldridge, 2015. "What Are We Weighting For?," Journal of Human Resources, University of Wisconsin Press, vol. 50(2), pages 301-316. citation courtesy of