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Cluster sample inference using sensitivity analysis: the case with few groups

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Abstract

This paper re-examines inference for cluster samples. Sensitivity analysis is proposed as a new method to perform inference when the number of groups is small. Based on estimations using disaggregated data, the sensitivity of the standard errors with respect to the variance of the cluster effects can be examined in order to distinguish a causal effect from random shocks. The method even handles just-identified models. One important example of a just-identified model is the two groups and two time periods difference-in-differences setting. The method allows for different types of correlation over time and between groups in the cluster effects.

Suggested Citation

  • Vikström, Johan, 2009. "Cluster sample inference using sensitivity analysis: the case with few groups," Working Paper Series 2009:15, IFAU - Institute for Evaluation of Labour Market and Education Policy.
  • Handle: RePEc:hhs:ifauwp:2009_015
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    1. Meyer, Bruce D & Viscusi, W Kip & Durbin, David L, 1995. "Workers' Compensation and Injury Duration: Evidence from a Natural Experiment," American Economic Review, American Economic Association, vol. 85(3), pages 322-340, June.
    2. Gabor Kezdi, 2005. "Robus Standard Error Estimation in Fixed-Effects Panel Models," Econometrics 0508018, University Library of Munich, Germany.
    3. Card, David & Krueger, Alan B, 1994. "Minimum Wages and Employment: A Case Study of the Fast-Food Industry in New Jersey and Pennsylvania," American Economic Review, American Economic Association, vol. 84(4), pages 772-793, September.
    4. A. Colin Cameron & Jonah B. Gelbach & Douglas L. Miller, 2008. "Bootstrap-Based Improvements for Inference with Clustered Errors," The Review of Economics and Statistics, MIT Press, vol. 90(3), pages 414-427, August.
    5. Guildo W. Imbens, 2003. "Sensitivity to Exogeneity Assumptions in Program Evaluation," American Economic Review, American Economic Association, vol. 93(2), pages 126-132, May.
    6. Timothy G. Conley & Christopher R. Taber, 2011. "Inference with "Difference in Differences" with a Small Number of Policy Changes," The Review of Economics and Statistics, MIT Press, vol. 93(1), pages 113-125, February.
    7. A. Colin Cameron & Jonah B. Gelbach & Douglas L. Miller, 2011. "Robust Inference With Multiway Clustering," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(2), pages 238-249, April.
    8. Nada Eissa & Jeffrey B. Liebman, 1996. "Labor Supply Response to the Earned Income Tax Credit," The Quarterly Journal of Economics, Oxford University Press, vol. 111(2), pages 605-637.
    9. 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.
    10. Hansen, Christian B., 2007. "Asymptotic properties of a robust variance matrix estimator for panel data when T is large," Journal of Econometrics, Elsevier, vol. 141(2), pages 597-620, December.
    11. Alberto Abadie & Alexis Diamond & Jens Hainmueller, 2007. "Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California's Tobacco Control Program," NBER Technical Working Papers 0335, National Bureau of Economic Research, Inc.
    12. Hansen, Christian B., 2007. "Generalized least squares inference in panel and multilevel models with serial correlation and fixed effects," Journal of Econometrics, Elsevier, vol. 140(2), pages 670-694, October.
    13. Susan Athey & Guido W. Imbens, 2006. "Identification and Inference in Nonlinear Difference-in-Differences Models," Econometrica, Econometric Society, vol. 74(2), pages 431-497, March.
    14. Greenwald, Bruce C., 1983. "A general analysis of bias in the estimated standard errors of least squares coefficients," Journal of Econometrics, Elsevier, vol. 22(3), pages 323-338, August.
    15. Ashenfelter, Orley & Card, David, 1985. "Using the Longitudinal Structure of Earnings to Estimate the Effect of Training Programs," The Review of Economics and Statistics, MIT Press, vol. 67(4), pages 648-660, November.
    16. Moulton, Brent R., 1986. "Random group effects and the precision of regression estimates," Journal of Econometrics, Elsevier, vol. 32(3), pages 385-397, August.
    17. Kloek, T, 1981. "OLS Estimation in a Model Where a Microvariable Is Explained by Aggregates and Contemporaneous Disturbances Are Equicorrelated," Econometrica, Econometric Society, vol. 49(1), pages 205-207, January.
    18. Randall Eberts & Kevin Hollenbeck & Joe Stone, 2002. "Teacher Performance Incentives and Student Outcomes," Journal of Human Resources, University of Wisconsin Press, vol. 37(4), pages 913-927.
    19. Finkelstein, Amy, 2002. "The effect of tax subsidies to employer-provided supplementary health insurance: evidence from Canada," Journal of Public Economics, Elsevier, vol. 84(3), pages 305-339, June.
    20. Jeffrey M. Wooldridge, 2003. "Cluster-Sample Methods in Applied Econometrics," American Economic Review, American Economic Association, vol. 93(2), pages 133-138, May.
    21. John Copas & Shinto Eguchi, 2001. "Local sensitivity approximations for selectivity bias," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(4), pages 871-895.
    22. Moulton, Brent R, 1990. "An Illustration of a Pitfall in Estimating the Effects of Aggregate Variables on Micro Unit," The Review of Economics and Statistics, MIT Press, vol. 72(2), pages 334-338, May.
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    More about this item

    Keywords

    Cluster-correlation; difference-in-difference; sensitivity analysis;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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