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

Sutirtha Bandyopadhyay () and Soham Sahoo
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Sutirtha Bandyopadhyay: Indian Institute of Management Indore

Chapter Chapter 13 in Applied Econometric Analysis Using Cross Section and Panel Data, 2023, pp 391-413 from Springer

Abstract: 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.

Keywords: Sample selection bias; Heckman model; Panel data; Female labor force participation; MGNREGS (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:conchp:978-981-99-4902-1_13

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DOI: 10.1007/978-981-99-4902-1_13

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