The New Palgrave Dictionary of Economics

2018 Edition
| Editors: Macmillan Publishers Ltd

Stratified and Cluster Sampling

  • Jeffrey M. Wooldridge
Reference work entry


The random sampling paradigm, typically introduced in basic statistics courses, ensures that a sample of data is, loosely speaking, ‘representative’ of the underlying population. When the population parameters are identified, many common estimation techniques, including least squares, maximum likelihood, and instrumental variables, have desirable statistical properties under random sampling. Unfortunately, while random sampling is convenient, it can be, and often intentionally is, violated when cross-sectional data and panel data are collected. Two important deviations from random sampling are stratified sampling and cluster sampling, or perhaps a combination.


Cluster correlation Cluster sampling Exogenous sampling Heteroskedasticity Multinomial sampling Probability sampling Sampling Stratified sampling Survey sampling Two-stage sampling Unbiased estimators Variable probability sampling Variance Weighted least squares 

JEL Classifications

This is a preview of subscription content, log in to check access.


  1. Cosslett, S.R. 1993. Estimation from endogenously stratified samples. In Handbook of statistics, ed. G.S. Maddala, C.R. Rao, and H.D. Vinod, Vol. 11. Amsterdam: Elsevier.Google Scholar
  2. Imbens, G.W., and T. Lancaster. 1996. Efficient estimation and stratified sampling. Journal of Econometrics 74: 289–318.CrossRefGoogle Scholar
  3. Jewell, N.P. 1985. Least squares regression with data arising from stratified samples of the dependent variable. Biometrika 72: 11–21.CrossRefGoogle Scholar
  4. Manski, C.F., and S. Lerman. 1977. The estimation of choice probabilities from choice-based samples. Econometrica 45: 1977–1988.CrossRefGoogle Scholar
  5. Scott, A.J., and D. Holt. 1982. The effect of two-state sampling on ordinary least squares methods. Journal of the American Statistical Association 77: 848–854.CrossRefGoogle Scholar
  6. White, H. 1982. Maximum likelihood estimation of misspecified models. Econometrica 50: 1–25.CrossRefGoogle Scholar
  7. Wooldridge, J.M. 1999. Asymptotic properties of weighted M-estimators for variable probability samples. Econometrica 67: 1385–1406.CrossRefGoogle Scholar
  8. Wooldridge, J.M. 2001. Asymptotic properties of weighted M-estimators for standard stratified samples. Econometric Theory 17: 451–470.CrossRefGoogle Scholar
  9. Wooldridge, J.M. 2002. Econometric analysis of cross section and panel data. Cambridge, MA: MIT Press.Google Scholar
  10. Wooldridge, J.M. 2003. Cluster sample methods in applied econometrics. American Economic Review 93: 133–138.CrossRefGoogle Scholar

Copyright information

© Macmillan Publishers Ltd. 2018

Authors and Affiliations

  • Jeffrey M. Wooldridge
    • 1
  1. 1.