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Mean and quantile regression Oaxaca-Blinder decompositions with an application to caste discrimination


This paper extends the Oaxaca-Blinder decomposition method to the quantile regression random-coefficients framework. Mean-based decompositions are obtained as the integration of the quantile regression decomposition process. This method allows identifying if the observed differences between two groups differ across quantiles, and if so, what is the contribution to the mean-based Oaxaca-Blinder decomposition. The proposed methodology is applied to the analysis of caste discrimination in Nepal. The results indicate that much of the discrimination occurs at the lowest quantiles, which implies that disadvantaged groups are the ones who suffer the most caste discrimination.

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We are grateful to the Associate Editor Prof. V. Dardanoni and to an anonymous reviewer for constructive comments and suggestions. This research has been partially supported by grants ECO2013-46516-C4-1-R (Equity and Poverty: Methods and Implications, Ministerio de Economía y Competitividad, Gobierno de España) and SGR2014-1279 (Equity and Development Research Group, Generalitat de Catalunya).

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Correspondence to Gabriel Montes-Rojas.

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Montes-Rojas, G., Siga, L. & Mainali, R. Mean and quantile regression Oaxaca-Blinder decompositions with an application to caste discrimination. J Econ Inequal 15, 245–255 (2017).

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  • Quantile regression
  • Oaxaca-Blinder decomposition