Heterogeneity in the US gender wage gap

Abstract

As a measure of gender inequality, the gender wage gap has come to play an important role both in academic research and the public debate. In 2016, the majority of full-time employed women in the United States earned significantly less than comparable men. The extent to which women were affected by gender inequality in earnings, however, depended greatly on socio-economic characteristics, such as marital status or educational attainment. In this paper, we analyse data from the 2016 American Community Survey using a high-dimensional wage regression and applying double lasso to quantify heterogeneity in the gender wage gap. We find that the wage gap varied substantially across women and that the magnitude of the gap varied primarily by marital status, having children at home, race, occupation, industry, and educational attainment. These insights are helpful in designing policies that can reduce discrimination and unequal pay more effectively.

What really matters for global intergenerational mobility?

“…However, the issue of gender effects on mobility in question Q2 cannot be fully resolved by the aforementioned techniques due to the inherent challenge of disentangling the effects of X and D on Y , particularly when gender bias factors are intertwined with socioeconomic factors [ 15 ]. In a manner akin to the research conducted by Philipp Bach et al [ 18 ] on the multidimensional gender effects with socio-economic characteristics on the gender wage gap in the US, a rigorous methodology is employed in this instance to tackle this problem. Specifically, the Partialling-out Lasso (with interactions) [ 40 ] and the Interactive Model Regression (IRM) [ 32 ] (as alternatives to the PLR) are utilized.…”

“…In research on the impact on intergenerational mobility, the gender effect through social inequality typically manifests through differential treatment in terms of equal opportunities for education and employment between men and women [ 16 , 17 ]. Many studies have investigated this dimension of the gender effect, notably Philipp Bach et al [ 18 ] in the US on the gender wage gap and Grace Chisamya et al [ 19 ] in Bangladesh and Malawi on gender inequities in schools and communities. On the other hand, the gender effect through parental dependency manifests through how parents treat their sons and daughters within the family.…”

ddml: Double/Debiased Machine Learning in Stata

“…Several recent articles by Bonaccolto-Töpfer and Briel (2022), Strittmatter and Wunsch (2021), Böheim and Stöllinger (2021) and Bach, Chernozhukov, and Spindler (2023), among others, focus instead on semi-parametric decompositions of the wage gap leveraging more flexible machine learning algorithms. Much of this literature focuses, however, on lasso-based approaches, even though there is no apparent reason to favor sparsitybased approaches over learners relying on other regularization assumptions.…”

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“…However, the issue of gender effects on mobility in question Q2 cannot be fully resolved by the aforementioned techniques due to the inherent challenge of disentangling the effects of X and D on Y, particularly when gender bias factors are intertwined with socioeconomic factors [15]. In a manner akin to the research conducted by Philipp Bach et al [18] on the multidimensional gender effects with socio-economic characteristics on the gender wage gap in the US, a rigorous methodology is employed in this instance to tackle this problem. Specifically, the Partialling-out Lasso (with interactions) [40] and the Interactive Model Regression (IRM) [32] (as alternatives to the PLR) are utilized.…”