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Fixed Effects Regression and Effect Heterogeneity

An Illustration Using a Causal Inference Perspective

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Zusammenfassung

Fixed effects regressions are commonly used by social scientists to identify causality. However, several criticisms against the fixed effects estimator emerged in recent years. In addition to confounding factors that are associated with time variant covariates, fixed effects can lead to an improper aggregation of heterogeneous effects. In the present chapter, we discuss the problem that pertains to the fixed effect estimator and show techniques that do not suffer from this source of bias. We also illustrate the problem with empirical analysis of Chilean students for the period time from 2007 to 2013. On the basis of the theoretical framework developed in the chapter and empirical findings, we suggest some implications for research in social sciences.

Luis Maldonado acknowledges support provided by FONDECYT REGULAR 1160921 and Centro Nacional de Investigación para la Gestion Integrada de Desastres Naturales (CIGIDEN). We thank Marco Giesselmann for helpful comments.

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Literatur

  • Andreß, Hans-Jürgen, K. Golsch, and A.W. Schmidt, A. W. 2013. Applied panel data analysis for economic and social surveys. Springer Science & Business Media.

    Google Scholar 

  • Angrist, Josh D., and J.-S. Pischke. 2008. Mostly harmless econometrics: An empiricist’s companion. Princeton University Press.

    Google Scholar 

  • Aronow, P. M., and C. Samii. 2013. Estimating average causal effects under interference between units. arXiv Preprint arXiv:1305.6156.

  • Aronow, P. M., and C. Samii. 2016. Does Regression Produce Representative Estimates of Causal Effects? American Journal of Political Science 60(1): 250-267.

    Google Scholar 

  • Blackwell, M. 2013. A framework for dynamic causal inference in political science. American Journal of Political Science 57(2): 504-520.

    Google Scholar 

  • Geraldo, P. 2015. El rol de la ensenanza media tecnico profesional en la reproduccion de la desigualdad educativa. Un estudio cuasi-experimental basado en el modelo de efectos primarios y secundarios del origen social. (Tesis de Magister). Pontificia Universidad Catolica de Chile, Santiago de Chile.

    Google Scholar 

  • Gerber, Alan S., and D.P. Green. 2012. Field experiments: Design, analysis, and interpretation. WW Norton.

    Google Scholar 

  • Hernan, Miguel A., and J.M. Robins. 2017. Causal inference. Chapman and Hall/CRC.

    Google Scholar 

  • Holland, P. W. (1986). Statistics and causal inference. Journal of the American Statistical Association 81(396): 945-960.

    Google Scholar 

  • Humphreys, M. 2009. Bounds on least squares estimates of causal effects in the presence of heterogeneous assignment probabilities. Manuscript, Columbia University.

    Google Scholar 

  • Imbens, Guido W., and D.B. Rubin. 2015. Causal inference in statistics, social, and biomedical sciences. Cambridge University Press.

    Google Scholar 

  • Larrañaga, O., G. Cabezas, and F. Dussaillant. 2013. Informe completo del Estudio de la Educacion Tecnico Profesional. Programa de Naciones Unidas para el Desarrollo.

    Google Scholar 

  • Lewis, D. (1974). Causation. The Journal of Philosophy 70(17): 556-567.

    Google Scholar 

  • Middleton, J. A., M.A. Scott, R. Diakow, and J.L. Hill. 2016. Bias amplification and bias unmasking. Political Analysis 24(3): 307-323.

    Google Scholar 

  • Wooldridge, Jeffrey M. 2010. Econometric analysis of cross section and panel data. MIT press.

    Google Scholar 

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Correspondence to Luis Maldonado .

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Maldonado, L., Geraldo, P. (2018). Fixed Effects Regression and Effect Heterogeneity. In: Giesselmann, M., Golsch, K., Lohmann, H., Schmidt-Catran, A. (eds) Lebensbedingungen in Deutschland in der Längsschnittperspektive. Springer VS, Wiesbaden. https://doi.org/10.1007/978-3-658-19206-8_17

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  • DOI: https://doi.org/10.1007/978-3-658-19206-8_17

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