Gender wage difference estimation at quantile levels using sample survey data

Mihaela-Cătălina Anastasiade-Guinand (), Alina Matei () and Yves Tillé ()
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Mihaela-Cătălina Anastasiade-Guinand: Swiss Federal Statistical Office
Alina Matei: University of Neuchâtel
Yves Tillé: University of Neuchâtel

TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, 2023, vol. 32, issue 4, No 14, 1392-1433

Abstract: Abstract This paper is motivated by the growing interest in estimating gender wage differences in official statistics. The wage of an employee is hypothetically a reflection of her or his characteristics, such as education level or work experience. It is possible that men and women with the same characteristics earn different wages. Our goal is to estimate the differences between wages at different quantiles, using sample survey data within a superpopulation framework. To do this, we use a parametric approach based on conditional distributions of the wages in function of some auxiliary information, as well as a counterfactual distribution. We show in our simulation studies that the use of auxiliary information well correlated with the wages reduces the variance of the counterfactual quantile estimates compared to those of the competitors. Since, in general, wage distributions are heavy-tailed, the interest is to model wages by using heavy-tailed distributions like the GB2 distribution. We illustrate the approach using this distribution and the wages for men and women using simulated and real data from the Swiss Federal Statistical Office.

Keywords: Gender gap statistics; Quantile estimation; Counterfactual distribution; GB2 distribution; Survey weights; 62D05 (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11749-023-00885-8

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