Distributional Differences and the Native American Gender Wage Gap

Jeffrey Burnette,Weiwei Zhang

doi:10.3390/economies7020046

Jeffrey Burnette, Weiwei Zhang

Open Access

https://doi.org/10.3390/economies7020046

Copy DOI

Abstract

We use the Theil index and data from the 2012–2016, American Community Survey 5-Year Sample to document and analyze gender wage inequality for American Indian and Alaska Native (AIAN) women across single, multiracial and ethnic identity groups. Mean differences in hourly wages by gender contribute little to measured wage inequality when individuals are separated based upon their proximity to tribal homeland areas. Instead, we find between-group wage inequality is a function of glass-ceiling effects that differ by AIAN identification and homeland area. Differences in glass-ceiling effects across AIAN identity groups suggest the need to disaggregate data by AIAN ethnic identity. Furthermore, under certain circumstances, it may be appropriate to combine some racial AIAN identity groups into a single population even if the focus is to study policy impacts on citizens of federally recognized AIAN nations for those using government survey data.

Highlights

Scholars have utilized feminist and intersectional methodologies to criticize conventional quantitative methods that seek to document discrimination and understand labor market inequalities for decades (Baca Zinn and Dill 1996; Figart 1997; Cho et al 2013; Harnois and Ifatunji 2011; Ifatunji and Harnois 2016)
Median and the hourly wage for the top 10% of women and men as well as the female-male wage ratio is displayed by AIAN identity group in Public Use Microdata Areas (PUMAs) with and without an AIAN homeland in
The white AIAN groups, regardless of the ethnicity or homeland residence, reported relatively large gender wage gaps with the smallest being 2.1 and the largest gap of 2.9 between non-Hispanic white AIAN men and women living in areas with a homeland area

Summary

Introduction

Scholars have utilized feminist and intersectional methodologies to criticize conventional quantitative methods that seek to document discrimination and understand labor market inequalities for decades (Baca Zinn and Dill 1996; Figart 1997; Cho et al 2013; Harnois and Ifatunji 2011; Ifatunji and Harnois 2016). We contribute to the current discussion concerning the appropriate use of race and ethnicity categories by combining the results from intersectional analysis and conventional econometric methods. Together they have demonstrated that gender and racial inequalities in the labor market stem from and create differential observable characteristics often associated with higher wages, the documented differences in the gender gap across AIAN identity groups can be used to inform researcher’s ability to combine certain AIAN subpopulations

Methods

Results

Conclusion