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Sample Selection Models for Panel Data: Application to Labor Force Participation in India

In: Applied Econometric Analysis Using Cross Section and Panel Data

Author

Listed:
  • Sutirtha Bandyopadhyay

    (Indian Institute of Management Indore)

  • Soham Sahoo

    (Indian Institute of Management Bangalore)

Abstract

This chapter explores existing empirical methods to tackle sample selection bias in a panel data framework. In the first part of the chapter, we illustrate the theoretical foundations for estimating regression models where sample selection can potentially lead to bias and inconsistency in the estimates, with a special focus on the Heckman model. We explain the challenges of applying a standard selection model in the panel data framework, especially when fixed effects are used to aid in causal inference. We discuss the incidental parameters problem that arises in this context, as the selection model relies on a maximum likelihood estimation. We then discuss the solutions to this problem. In the second part of the chapter, we demonstrate a practical application of the method using data from India. We focus on the estimation of a standard wage regression when labor force participation is an endogenous decision. By applying the techniques covered in the first section of the chapter, we extend the study utilizing panel data.

Suggested Citation

  • Sutirtha Bandyopadhyay & Soham Sahoo, 2023. "Sample Selection Models for Panel Data: Application to Labor Force Participation in India," Contributions to Economics, in: Deep Mukherjee (ed.), Applied Econometric Analysis Using Cross Section and Panel Data, chapter 0, pages 391-413, Springer.
    • Handle: RePEc:spr:conchp:978-981-99-4902-1_13
    DOI: 10.1007/978-981-99-4902-1_13
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