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Minimum Wages and the Health of Hispanic Women

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Abstract

States are increasingly resorting to raising the minimum wage to boost the earnings of those at the bottom of the income distribution. Several policymakers have also claimed such increases may be health improving. In this paper, we examine the effects of minimum wage increases on the health of low-educated Hispanic women, who constitute a growing part of the US labor force, are disproportionately represented in minimum wage jobs, and typically have less access to health care. Using a difference-in-differences identification strategy and data drawn from the Behavior Risk Factor Surveillance Survey and the Current Population Survey from the years 1994 to 2015, we find little evidence that low-educated Hispanic women likely affected by minimum wage increases experience any changes in health status, access to care, or use of preventive care. We conclude that efforts to improve the health of low-educated Hispanic women are not likely to occur through increases in the minimum wage.

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Notes

  1. We use the term Hispanic as that is the term used by the BRFSS which is our first data source. They do ask respondents if they are Hispanic or Latino/a; thus, we adopt their convention recognizing that some Hispanic individuals are actually Latina in our sample. We recognize that Hispanics and Latinos may not see these terms as interchangeable (e.g., Duncan and Trejo 2011).

  2. https://www.dol.gov/wb/media/Hispanic_Women_Infographic_Final_508.pdf.

  3. Based on the authors’ calculations from the 2016 American Community Survey for women aged 25–64 years.

  4. Other scholars have focused on Hispanic women’s health-related outcomes when exploring the effects of other policies (e.g., welfare reform, title X funding) (see, e.g., Slusky 2018; Amuedo-Dorantes et al. 2016).

  5. See http://nces.ed.gov/pubs2014/2014391.pdf for data on high school graduation rates by race/ethnicity and gender.

  6. Our focus in this paper on Hispanic women does not mean that it is not important to look at the effects of increases in minimum wages on other racial/ethnic and gender groups, e.g., black women, especially given the documented race/ethnicity and gender differences in the effects of increases in minimum wages. However, we decide to focus on Hispanic women here for reasons above and leave other groups for future research.

  7. See the Living Wage Calculator run by MIT professor Amy Glasmeier at http://livingwage.mit.edu/articles/15-minimum-wage-can-an-individual-or-a-family-live-on-it.

  8. The American Association of University Women is among those who have advocated this: http://www.aauw.org/2014/08/07/raise-the-wage/.

  9. We also recognize that higher minimum wages could mean lower employment rates (the extensive margin) or fewer hours worked (the intensive margin) for low-income workers. For each hour worked, low-income workers who retain their jobs after a minimum wage increase receive higher wages, which means they could potentially earn higher incomes unless their employer responds by cutting their hours. Here, we focus on those who retain their jobs, thus largely leaving those issues for future work.

  10. It has also been established that income influences health among low-educated US workers (e.g., Averett and Wang 2013; Evans and Garthwaite 2014).

  11. Meaningful state variation in minimum wages did not start until the end of the 1980s (Simon and Kaestner 2004).

  12. Appendix Table 16, available from the authors upon request, details these changes in minimum wages.

  13. See Appendix Table 17, available from the authors upon request, for dates of indexation.

  14. See our Appendix on the details of the minimum wage for a full list of states that changed their minimum wage as well as other details regarding minimum wage workers. This is available from the authors upon request.

  15. Technically, this change was implemented in two steps which is reflected in our data which is from the BLS.

  16. There are many states that do not have any minimum wage changes other than those that occurred due to changes in the federal minimum wage.

  17. Appendix, available from authors upon request, details characteristics of minimum wage workers.

  18. See https://webapps.dol.gov/elaws/whd/flsa/screen75.asp for a discussion of exemptions to the minimum wage.

  19. This variable takes into consideration the relationship among the predominance of the Democrats in state governments, minimum wages, and the health status of Hispanic women. For example, Democratic governors have typically been more active in passing minimum wage legislation (e.g., Bjørnskov and Potrafke 2013; Compton et al. 2017). In addition, labor market outcomes of immigrants are better under Democratic governors (e.g., Beland and Unel 2018).

  20. Because of these sample criteria, we test whether minimum wages affect the probability of being in our sample by regressing the probability sample inclusion on the minimum wage and the covariates described later. We find no evidence of such conditional on positive (COP) bias.

  21. Both of these binary variables are created from the respondent’s self-report of their own health measured on a Likert scale where 1 = excellent, 2 = very good, 3 = good, 4 = fair, and 5 = poor.

  22. Mental health is an important consideration because of the prevalence and high costs of mental health problems. About 25% of adults in the USA suffer from a mental health disorder in a given year, with about 6% suffering from a serious mental illness. Mental health disorders were also one of the five most costly conditions in the USA in 2006, with care expenditures rising from $35.2 billion in 1996 to $57.5 billion in 2006. Despite the prevalence and the high costs of mental health disorders, access to mental health care is still problematic. For example, 4% of young adults, who self-reported mental health needs, did not seek mental health care in the past year (AHRQ 2009). Mental health disorders are also particularly prevalent among low-income households (Sareen et al. 2011). Our measure of mental health is self-reported and not diagnosed.

  23. See https://www.census.gov/programs-surveys/cps.html for a detailed description of the survey.

  24. See Pascale et al. (2016) for a discussion of the new health insurance question.

  25. Both of these binary variables are created from the respondent’s self-report of their own health measured on a Likert scale where 1 = excellent, 2 = very good, 3 = good, 4 = fair, and 5 = poor.

  26. While it is difficult to compare these numbers to those of other studies given our sample restrictions, it is well documented in the literature that Hispanic women are less likely to be insured, to use preventive care, and tend to report higher rates of fair/poor health (e.g., Rodríguez et al. 2009; Bustamante et al. 2010). When we compare these numbers to non-Hispanic women in the BRFSS, we do find that Hispanic women are more likely to report fair/poor health and less likely to report blood pressure checks or having received a flu shot.

  27. See Nelson et al. (2003) for further discussion of these differences.

  28. Our results are essentially unchanged if we run unweighted regressions.

  29. We use the log of the nominal hourly wage as our dependent variable.

  30. The sample sizes for the lagged models are smaller since we lose a year of data.

  31. For example, in 2014, Massachusetts has one of the highest minimum wages, yet it has the lowest ratio, and in 2015, Colorado has one of the highest minimum wages, yet it has the lowest ratio.

  32. One might wonder if any positive effects of the minimum wage on health could be a function of individuals’ migrating to states with higher minimum wages. We address this possibility by regressing the share of Hispanics in a state and share of healthy Hispanic women in a state on the minimum wage, and we find no evidence that this is the case. These results are in Appendix Table 17.

  33. Not surprisingly, our results are essentially unchanged when we drop states with below 100 observations for the full sample. These include Vermont, Maine, West Virginia, North Dakota, Mississippi, Louisiana, and Montana. When we limit our sample to the four states (New York, California, Texas, and Florida) in our sample with the largest numbers of Hispanic women, we also find similar results.

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Acknowledgements

The authors thank Katim Woldemariam and Tamerlane Asher for helpful research assistance. We are grateful to Dhaval Dave, Mary Beth Walker, Andrew Freidson, and session participants at the Eastern Economic Association 2016, the Population Association of American 2016, the Southern Economic Association 2016, and the Midwest Economics Association 2017 annual meetings as well as seminar participants at AHRQ, the University of Missouri at Columbia and the University of Kansas for helpful comments.

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Correspondence to Julie K. Smith.

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Appendix

Appendix

Table 16 Effects of minimum wage increases on hourly earnings for Hispanic women
Table 17 Endogeneity of location using CPS ASEC

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Averett, S.L., Smith, J.K. & Wang, Y. Minimum Wages and the Health of Hispanic Women. J Econ Race Policy 1, 217–239 (2018). https://doi.org/10.1007/s41996-018-0019-3

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