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What Are We Weighting For?

  • Gary Solon
  • Steven J. Haider
  • Jeffrey Wooldridge

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.

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Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 18859.

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Date of creation: Feb 2013
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Publication status: published as 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.
Handle: RePEc:nbr:nberwo:18859
Note: CH DEV ED HC HE LS PE TWP
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  1. Steven D. Levitt, 1998. "Juvenile Crime and Punishment," Journal of Political Economy, University of Chicago Press, vol. 106(6), pages 1156-1185, December.
  2. Justin Wolfers, 2006. "Did Unilateral Divorce Laws Raise Divorce Rates? A Reconciliation and New Results," American Economic Review, American Economic Association, vol. 96(5), pages 1802-1820, December.
  3. O'Connell, Philip J. & Russell, Helen & FitzGerald, John, 2006. "Human Resources," Book Chapters, in: Morgenroth, Edgar (ed.), Ex-Ante Evaluation of the Investment Priorities for the National Development Plan 2007-2013 Economic and Social Research Institute (ESRI).
  4. John Fitzgerald & Peter Gottschalk & Robert Moffitt, 1998. "An Analysis of Sample Attrition in Panel Data: The Michigan Panel Study of Income Dynamics," Journal of Human Resources, University of Wisconsin Press, vol. 33(2), pages 251-299.
  5. David Card & Alan Krueger, 1990. "Does School Quality Matter? Returns to Education and the Characteristics of Public Schools in the United States," Working Papers 645, Princeton University, Department of Economics, Industrial Relations Section..
  6. Stephen G. Donald & Kevin Lang, 2007. "Inference with Difference-in-Differences and Other Panel Data," The Review of Economics and Statistics, MIT Press, vol. 89(2), pages 221-233, May.
  7. Elder Todd E & Goddeeris John H & Haider Steven J, 2011. "A Deadly Disparity: A Unified Assessment of the Black-White Infant Mortality Gap," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 11(1), pages 1-44, June.
  8. Dickins, William T, 1990. "Error Components in Grouped Data: Is It Ever Worth Weighting?," The Review of Economics and Statistics, MIT Press, vol. 72(2), pages 328-33, May.
  9. Wooldridge, Jeffrey M., 2007. "Inverse probability weighted estimation for general missing data problems," Journal of Econometrics, Elsevier, vol. 141(2), pages 1281-1301, December.
  10. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-38, May.
  11. Jeffrey M. Wooldridge, 1999. "Asymptotic Properties of Weighted M-Estimators for Variable Probability Samples," Econometrica, Econometric Society, vol. 67(6), pages 1385-1406, November.
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