Gezici Armagan, Ozay Ozge
Department of Economics & Political Science, Keene State College, Keene, NH USA.
Department of Economics, History, & Political Science, Fitchburg State University, Fitchburg, MA USA.
J Econ Race Policy. 2020;3(4):270-281. doi: 10.1007/s41996-020-00075-w. Epub 2020 Dec 15.
Using the April 2020 Current Population Survey (CPS) micro dataset, we explore the racialized and gendered effects of the COVID-19 pandemic on the probability of being unemployed. The distribution of the pandemic-induced job losses for women and men or for different racial/ethnic categories has been studied in the recent literature. We contribute to this literature by providing an intersectional analysis of unemployment under COVID-19, where we examine the differences in the likelihood of unemployment across groups of White men, White women, Black men, Black women, Hispanic men, and Hispanic women. As a case of study of the COVID-19 recession, our work engages with the broader empirical literature testing the discrimination theories based on the unexplained gap after accounting for observable characteristics of women, men, and different races/ethnicities and their labor market positions. Controlling for individual characteristics such as education and age, as well as industry and occupation effects, we show that women of all three racial/ethnic categories are more likely to be unemployed compared to men, yet there are substantial differences across these groups based on different unemployment measures. Hispanic women have the highest likelihood of being unemployed, followed by Black women, who are still more likely to be unemployed than White women. We also examine if the ability to work from home has benefited any particular group in terms of lowering their likelihood of unemployment during the pandemic. We find that in industries with a high degree of teleworkable jobs, White women, Black men, and Hispanic men are no longer more likely to be unemployed relative to White men. However, Black women and Hispanic Women still experience a significantly higher probability of job loss compared to White men even if they are employed in industries with highly teleworkable jobs. As we control for both individual and aggregate factors, our results suggest that these differences are not simply the result of overrepresentation of women of color in certain industries and occupations; rather, unobservable factors such as discrimination could be at work.
利用2020年4月当前人口调查（CPS）微观数据集，我们探讨了新冠疫情对失业概率的种族化和性别化影响。近期文献研究了疫情导致的女性和男性或不同种族/族裔类别的失业分布情况。我们通过对新冠疫情下的失业情况进行交叉性分析，为这一文献做出了贡献，在此分析中，我们考察了白人男性、白人女性、黑人男性、黑人女性、西班牙裔男性和西班牙裔女性群体在失业可能性上的差异。作为对新冠疫情衰退的一个研究案例，我们的工作与更广泛的实证文献相关，这些文献在考虑了女性、男性以及不同种族/族裔的可观察特征及其劳动力市场地位后，基于无法解释的差距来检验歧视理论。在控制了教育和年龄等个人特征以及行业和职业影响后，我们发现，与男性相比，所有三个种族/族裔类别的女性失业可能性都更高，但基于不同的失业衡量标准，这些群体之间存在很大差异。西班牙裔女性失业可能性最高，其次是黑人女性，她们仍然比白人女性更有可能失业。我们还研究了在家工作的能力在降低疫情期间失业可能性方面是否使任何特定群体受益。我们发现，在远程工作岗位比例较高的行业中，白人女性、黑人男性和西班牙裔男性相对于白人男性不再更有可能失业。然而，即使黑人女性和西班牙裔女性受雇于远程工作岗位比例很高的行业，她们失业的可能性仍然比白人男性高得多。由于我们同时控制了个体和总体因素，我们的结果表明，这些差异不仅仅是有色人种女性在某些行业和职业中占比过高的结果；相反，歧视等不可观察因素可能在起作用。
