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What Are We Weighting For?

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  • Gary Solon
  • Steven J. Haider
  • Jeffrey Wooldridge

Abstract

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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Handle: RePEc:nbr:nberwo:18859

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  1. Justin Wolfers, 2006. "Did Unilateral Divorce Laws Raise Divorce Rates? A Reconciliation and New Results," American Economic Review, American Economic Association, American Economic Association, vol. 96(5), pages 1802-1820, December.
  2. John Fitzgerald & Peter Gottschalk & Robert Moffitt, 1998. "An Analysis of Sample Attrition in Panel Data: The Michigan Panel Study of Income Dynamics," NBER Technical Working Papers 0220, National Bureau of Economic Research, Inc.
  3. Steven D. Levitt, 1997. "Juvenile Crime and Punishment," NBER Working Papers 6191, National Bureau of Economic Research, Inc.
  4. Jeffrey M. Wooldridge, 1999. "Asymptotic Properties of Weighted M-Estimators for Variable Probability Samples," Econometrica, Econometric Society, Econometric Society, vol. 67(6), pages 1385-1406, November.
  5. 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.
  6. 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, De Gruyter, vol. 11(1), pages 1-44, June.
  7. Jeffrey M. Wooldridge, 2004. "Inverse probability weighted estimation for general missing data problems," CeMMAP working papers, Centre for Microdata Methods and Practice, Institute for Fiscal Studies CWP05/04, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  8. Card, David & Krueger, Alan B, 1992. "Does School Quality Matter? Returns to Education and the Characteristics of Public Schools in the United States," Journal of Political Economy, University of Chicago Press, University of Chicago Press, vol. 100(1), pages 1-40, February.
  9. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, Econometric Society, vol. 48(4), pages 817-38, May.
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As found by EconAcademics.org, the blog aggregator for Economics research:
  1. Weights in Regressions
    by René Böheim in Econ Tidbits on 2013-03-25 13:29:00
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  3. Muller, Sean, 2014. "Randomised trials for policy: a review of the external validity of treatment effects," SALDRU Working Papers, Southern Africa Labour and Development Research Unit, University of Cape Town 127, Southern Africa Labour and Development Research Unit, University of Cape Town.
  4. Li, Qing & Sweetman, Arthur, 2014. "The quality of immigrant source country educational outcomes: Do they matter in the receiving country?," Labour Economics, Elsevier, Elsevier, vol. 26(C), pages 81-93.
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  9. Jonas Helth Lønborg & Ole Dahl Rasmussen, 2013. "Can Microfinance Reach the Poorest: Evidence from a Community-Managed Microfinance Intervention," Study Papers, Rockwool Foundation Research Unit 55, Rockwool Foundation Research Unit.
  10. Cadena, Brian C., 2014. "Recent immigrants as labor market arbitrageurs: Evidence from the minimum wage," Journal of Urban Economics, Elsevier, vol. 80(C), pages 1-12.
  11. Terence C. Cheng & Pravin K. Trivedi, 2014. "Attrition Bias in Panel Data: A Sheep in Wolf’s Clothing? A Case Study Based on the MABEL Survey," Melbourne Institute Working Paper Series, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne wp2014n14, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
  12. Prashant Bharadwaj & Leah K. Lakdawala & Nicholas Li, 2013. "Perverse Consequences of Well Intentioned Regulation: Evidence from India's Child Labor Ban," NBER Working Papers 19602, National Bureau of Economic Research, Inc.

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