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Detailed decompositions in nonlinear models

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  • Boris Kaiser

Abstract

We propose a new approach for performing detailed decompositions of average outcome differentials when outcome models are nonlinear. The method can be flexibly applied to all generalized linear models, which are widely used in empirical research. The advantage over other approaches in the literature is that the effects of group-specific differences in covariate distributions are taken into account. At the same time, desirable features such as path independence are still satisfied. A simulation exercise demonstrates that our decomposition method produces more convincing results than existing methods.

Suggested Citation

  • Boris Kaiser, 2015. "Detailed decompositions in nonlinear models," Applied Economics Letters, Taylor & Francis Journals, vol. 22(1), pages 25-29, January.
    • Handle: RePEc:taf:apeclt:v:22:y:2015:i:1:p:25-29
    DOI: 10.1080/13504851.2014.907469
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    Cited by:

    1. Doris A. Oberdabernig, 2018. "Determinants of IMF lending: How different is Sub-Saharan Africa?," Economics Bulletin, AccessEcon, vol. 38(1), pages 136-145.
    2. Boris Kaiser, 2016. "Decomposing differences in arithmetic means: a doubly robust estimation approach," Empirical Economics, Springer, vol. 50(3), pages 873-899, May.
    3. Boneva, Teodora & Golin, Marta & Rauh, Christopher, 2022. "Can perceived returns explain enrollment gaps in postgraduate education?," Labour Economics, Elsevier, vol. 77(C).
    4. Teodora Boneva & Christopher Rauh, 2017. "Socio-Economic Gaps in University Enrollment: The Role of Perceived Pecuniary and Non-Pecuniary Returns," CESifo Working Paper Series 6756, CESifo.

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