1. Health Insurance and the Wage Gap Helen Levy University of Michigan May 18, 2007

2. Background Vast literature on gender wage gap. –There are actually studies of studies of the gender wage gap (Jarrell & Stanley 2004) Altonji and Blank (1999) review reports M/F gap of 27% in 1995; 24% after adjusting for covariates. Many refinements to basic method of running a regression. The bottom line is there remains an unexplained gap. The dependent variable is always earnings or wages.

3. What about fringe benefits?

4. Compensation in the private sector Source: National Compensation Survey, 2004

5. What about fringe benefits? Fringes are 29% of total compensation. Inequality in fringes could be bigger or smaller than wage inequality Therefore, inequality in total compensation could be greater or less than wage inequality. What does health insurance contribute to compensation inequality?

6. Background: Related studies Hersch and White-Means (1993): 1988 CPS data; for whites, M/F gap in wages is 30% and M/F gap in compensation is 29%. Solberg and Laughlin (1995): male/female wage gap is 16%; compensation gap is 11% (NLSY) Compensation inequality (90 th /10 th percentile gap) exceeds wage inequality (Pierce 2001; Chung 2003). Even and Macpherson (1994): two-thirds of male/female pension gap explained by observables Monheit and Vistnes (1999): Hispanic/white gap in health insurance among males mostly explained by observables; not true for other racial/ethnic/gender groups

7. Contributions of this paper Compare wage and health insurance gaps Look at changes in gaps over a long period of time (1980 – 2005) Define groups by race, ethnicity and gender Look at other outcomes related to health insurance: offering, spousal coverage.

8. Outline of results to be presented 1.Trends in wages and the wage gap. 2.Trends in health insurance and HI gaps. 3.Effect of adjusting for simple covariates 4.Refinements: 1.Single v. family coverage, employer contribution 2.Additional covariates (tenure, occupation, etc.) 5.What about health insurance from other sources?

9. Data: The Current Population Survey March 1981 – 2006 for main analysis of wage and health insurance inequality February supplements 1995, 1997, 1999, 2001, and 2005: –Additional covariates: citizenship, job tenure –Other outcomes: health insurance offering, other sources of coverage

10. March Current Population Survey, 1981 - 2006 Wage = average hourly earnings in previous calendar year (earnings/hours*weeks) Full-time, full-year workers (hours ≥ 35, weeks ≥ 50) Health insurance from own employer in previous calendar year About 30-50,000 observations per year

11. February Current Population Survey, 1995, 1997, 1999, 2001 and 2005 Supplements on “Contingent and Alternative Employment Arrangements” Wage = actual or average hourly earnings (based on usual earnings/hours per week) Full-time workers (hours ≥ 35) Health insurance from own employer at the time of the survey If not covered by own employer: –Does firm offer it? –Was worker eligible? –Does worker have coverage from some other source? About 20-28,000 observations per year

12. 1. Trends in wages and wage gaps.

15. 2. Trends in health insurance and HI gaps.

18. The point so far: health insurance gaps tend to be smaller than wage gaps.

22. 3. Effect of adjusting for simple covariates Estimate separate regressions for wages and health insurance in each year Covariates: –Age –Age 2 –Marital status –Education (4 categories) –Industry (8 categories) –State Plot coefficients on female dummies

24. Take-home points for whites: Covariates reduce the male/female gap in both wages and health insurance. Even after controlling for covariates, significant gaps remain. The wage gap is larger than the health insurance gap.

25. Results for blacks and Hispanics: Covariates don’t affect the male/female gaps much for blacks. Adjusted M/F health insurance gap is about zero. M/F wage gap is significant for both groups (adjusted or not)

28. Implications for M/F compensation inequality Compensation inequality is the weighted sum of wage inequality and health insurance inequality: where S 1 is the share of compensation that is devoted to health insurance. Since M/F health insurance gaps are smaller than wage gaps (or are zero), M/F compensation gap (wages + HI) would be smaller than the M/F wage gap.

29. Outline of results to be presented 1.Trends in wages and the wage gap. 2.Trends in health insurance and HI gaps. 3.Effect of adjusting for simple covariates 4.Refinements: 1.Single v. family coverage, employer contribution 2.Additional covariates (tenure, occupation, etc.) 5.What about health insurance from other sources?

34. Value of employer contribution White men are more likely than white women to have family coverage Men and women are about equally likely to have an employer who pays for all of their insurance.

35. Imputing value of employer contribution Assign value of health insurance to each worker and calculate real total compensation (wages plus health insurance) Assume that: –When employer pays some, employee’s share is 14% for single coverage and 26% for family coverage (Kaiser/HRET 2000 Survey) –Average monthly premium is $308 for single coverage, $829 for family (Kaiser/HRET 2004 survey) –Premiums have grown with CPI for medical care since 1980 –Premiums spread across 2000 annual hours of work

36. Imputing value of employer contribution How well does this imputation work? –Not too badly: value of health insurance is estimated as 10 – 12% of the value of wages, compared to 8 – 10% using BLS data on employer costs. Alternative approaches: –Assign premiums using historical data on ratio of wage costs to health insurance costs. –Use MEPS linked household-insurance plan data (restricted)

40. Compensation gap vs. wage gap Incorporating health insurance makes the M/F compensation gaps lightly smaller than the M/F wage gap Change is probably not significant Additional covariates (job tenure, occupation) don’t change the story that much.

41. Why are women less likely to have health insurance coverage from their own employers? Use February CPS data to analyze: Offering/eligibility/takeup Coverage from other sources

42. Offering/Eligibility/Takeup Coverage from one’s own employer is the product of offering, eligibility and takeup: P(Own EHI) = P(Offer) * P(Eligible | Offer) * P(Takeup| Eligible) Which of these explain(s) the M/F gap in coverage?

44. Offering/Eligibility/Takeup White women have lower takeup rates than white men. Black or Hispanic women have lower takeup rates than black or Hispanic men, but higher offer rates.

45. What about coverage from other sources?

47. Other sources of coverage White women are much more likely than white men or nonwhite women to have coverage from a husband’s employer. Men are more likely than women to be uninsured (small gap for whites, bigger for nonwhites). In other words, other sources of coverage make up for women’s lower rates of own- employer coverage.

48. Recap of main findings White women are less likely to have health insurance coverage from their own employers than are white men. Black or Hispanic women have their own coverage at about the same rate as Black or Hispanic men, respectively, adjusting for observable characteristics. For all three racial/ethnic groups, the male-female gap in health insurance is smaller than the wage gap. As a result, incorporating health insurance into measures of compensation inequality very slightly reduces measured male/female inequality. Nonetheless, M/F compensation inequality remains significant. Lower rates of own-employer coverage for white women are “explained” by lower takeup rates. White women are likely to have coverage from their spouses. Black and Hispanic men are at highest risk of being uninsured.

49. Limitations Full-time, full-year workers only Don’t have good data on value of employer contribution Other fringe benefits?

50. Implications What you take away from this depends on who you are. –White women are less likely than white men to have own-employer coverage even after controlling for covariates => labor market disparity –Black and Hispanic male workers are at elevated risk of being uninsured, and we can identify risk factors.

52. Backup slides start here.

53. Picky but important detail Percentage point versus percent gaps. Wage gaps are presented as differences in log wages. These translate roughly into percent differences. Eg. men make $15/hr, women make $12. In exact terms, women’s wages are 20% lower than mens (3/15 =.20) Ln(15)=2.48, ln(12) = 2.71, difference is.23 Health insurance is 0/1 => can’t take log. Why not just use simple gap in p(own ehi): eg 60% of women and 75% of men have ownehi = 15 ppt diff but 20 percent diff (.15/.75). I will need the difference in percentage terms so I convert health insurance gaps to percentage terms. The difference between the trend lines is percentage point; the chart with gaps uses percent gaps.