A Simple Solution to the Identification Problem in Detailed Wage Decompositions

By Myeong-Su Yun. Social Science Research Network, 2005

506 citations recorded by the source corpus.

Oaxaca and Ransom (1999) show that a detailed decomposition of the coefficients effect is destined to suffer from an identification problem since the detailed coefficients effect attributed to a dummy variable is not invariant to the choice of reference groups. It turns out that the identification problem in the decomposition equation is a disguised identification problem of constant and dummy variables in a regression equation. This paper proposes a simple and natural remedy for this problem by utilizing “normalized” regressions which enable us to identify the constant and estimates of each dummy variable. The identification problem is automatically resolved once we obtain “normalized” regression equations for two comparison groups.

Subfields: Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/7b51dbec25df685e83ed5c6011cc37bcad4f19f2

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In Economics and Econometrics

Citations in the corpus

Identification problem and detailed Oaxaca decomposition: A general solution and inference (2008, unknown): “2 Intuitively, the identiﬁcation problem can be resolved by averaging the coefﬁcients effects of a set of dummy variables while permuting the reference groups [9].”
Poverty In Rural India: Caste And Tribe (2008, methodology): “The covariance of b/s is, in turn, used for hypothesis testing (Yun, 2005a).”
When Do Covariates Matter? And Which Ones, and How Much? (2016, methodology): “Gardeazabal and Ugidos (2004), Yun (2005), and Fortin (2008) suggest various approaches to dealing with the issue raised in Oaxaca and Ransom (1999). My own view, as I argue in Gelbach (2002a), is that the issue itself is not really an identification problem, as that term is used in contemporary eco”
7. Multivariate Decomposition for Hazard Rate Models (2009, methodology): “Regression decomposition has been extended to nonlinear models including probit (Gomulka and Stern 1990), logit (Even and Macpherson 1993; Fairlie 2005; Nielsen 1998; Yun 2005a), and count models (e.g., see Bauer et al. 2007; Heitmueller 2004; Park and Lohr 2008).”