Health Insurance and the Wage Gap

Helen Levy

University of Michigan

May 18, 2007

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.

What about fringe benefits?

Compensation in the private sectorSource: National Compensation Survey, 2004

Wages/salaries71%

Legally required benefits

9%

Health insurance7%

Paid leave6%

Retirement & savings4%

Other3%

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?

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 (90th/10th 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

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.

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?

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

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

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

1. Trends in wages and wage gaps.

Trends in ln(real wage) by race, ethnicity and sexFull-time, full-year workers, CPS 1980 - 2005

2.0

2.1

2.2

2.3

2.4

2.5

2.6

2.7

2.8

2.9

3.0

1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

White men White women Black men Black women Hispanic men Hispanic women

Female-male gap in ln(real wages) by race/ethnicityFull-time, full-year workers; CPS 1980 - 2005

-0.50

-0.40

-0.30

-0.20

-0.10

0.00

0.10

0.20

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

White Black Hispanic

2. Trends in health insurance and HI gaps.

Trends in P(own-employer health insurance) by race, ethnicity and sexFull-time, full-year workers, CPS 1980 - 2005

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

White men White women Black men Black women Hispanic men Hispanic women

Female-male gap in P(own-employer health insurance) by race/ethnicityFull-time, full-year workers; CPS 1980 - 2005

-0.50

-0.40

-0.30

-0.20

-0.10

0.00

0.10

0.20

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

White Black Hispanic

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

wage gaps.

Wage and health insurance gender gaps for whitesFull-time, full-year workers; CPS 1980 - 2005

-0.60

-0.50

-0.40

-0.30

-0.20

-0.10

0.00

0.10

0.20

0.30

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

Wages

Own EHI

Wage and health insurance gender gaps for blacksFull-time, full-year workers; CPS 1980 - 2005

-0.60

-0.50

-0.40

-0.30

-0.20

-0.10

0.00

0.10

0.20

0.30

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

Wages

Own EHI

Wage and health insurance gender gaps for HispanicsFull-time, full-year workers; CPS 1980 - 2005

-0.60

-0.50

-0.40

-0.30

-0.20

-0.10

0.00

0.10

0.20

0.30

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

Wages

Own EHI

3. Effect of adjusting for simple covariates

• Estimate separate regressions for wages and health insurance in each year

• Covariates: – Age– Age2

– Marital status– Education (4 categories)– Industry (8 categories)– State

• Plot coefficients on female dummies

Wage and health insurance gender gaps for whites: effect of adjusting for covariatesFull-time, full-year workers; CPS 1980 - 2005

-0.60

-0.50

-0.40

-0.30

-0.20

-0.10

0.00

0.10

0.20

0.30

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

Unadjusted w

Adjusted w

Unadjusted HI

Adjusted HI

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.

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)

Wage and health insurance gender gaps for blacks: effect of adjusting for covariatesFull-time, full-year workers; CPS 1980 - 2005

-0.60

-0.50

-0.40

-0.30

-0.20

-0.10

0.00

0.10

0.20

0.30

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

Unadjusted w

Adjusted w

Unadjusted HI

Adjusted HI

Wage and health insurance gender gaps for Hispanics: adjusting for covariatesFull-time, full-year workers; CPS 1980 - 2005

-0.60

-0.50

-0.40

-0.30

-0.20

-0.10

0.00

0.10

0.20

0.30

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

Unadjusted w

Adjusted w

Unadjusted HI

Adjusted HI

Implications for M/F compensation inequality

• Compensation inequality is the weighted sum of wage inequality and health insurance inequality:

where S1 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.

HISWSC 111

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?

Trends in P(family coverage) for insured workers by race, ethnicity and sexFull-time, full-year workers, CPS 1980 - 2005

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

White males White females Black males Black females Hispanic males Hispanic females

Trends in P(employer pays all) for insured workers by race, ethnicity and sexFull-time, full-year workers, CPS 1980 - 2005

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

White males White females Black males Black females Hispanic males Hispanic females

Trends in P(employer pays some) for insured workers by race, ethnicity and sex

Full-time, full-year workers, CPS 1980 - 2005

0.30

0.35

0.40

0.45

0.50

0.55

0.60

0.65

0.70

0.75

0.80

1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

White males White females Black males Black females Hispanic males Hispanic females

Trends in P(employer pays none) for insured workers by race, ethnicity and sex

Full-time, full-year workers, CPS 1980 - 2005

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

White males White females Black males Black females Hispanic males Hispanic females

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.

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

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)

Gender wage gaps vs. gender compensation gaps for whitesFull-time, full-year workers; CPS 1980 - 2005

-0.60

-0.50

-0.40

-0.30

-0.20

-0.10

0.00

0.10

0.20

0.30

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

Adjusted w

Adjusted c

Gender wage gaps vs. gender compensation gaps for blacksFull-time, full-year workers; CPS 1980 - 2005

-0.60

-0.50

-0.40

-0.30

-0.20

-0.10

0.00

0.10

0.20

0.30

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

Adjusted w

Adjusted c

Gender wage gaps vs. gender compensation gaps for HispanicsFull-time, full-year workers; CPS 1980 - 2005

-0.60

-0.50

-0.40

-0.30

-0.20

-0.10

0.00

0.10

0.20

0.30

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

Adjusted w

Adjusted c

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.

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

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?

Component of coverage: Offering, eligibility and takeupFull-time workers, CPS February 1995, 1997, 1999, 2001 and 2005

0.500

0.550

0.600

0.650

0.700

0.750

0.800

0.850

0.900

0.950

1.000

White men White women Black men Black women Hispanic men Hispanic women

Own EHI

Offering

Eligibility

Takeup

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.

What about coverage from other sources?

Other sources of health insurance coverageFull time workers, CPS February 1995, 1997, 1999, 2001 and 2005

0.775

0.692 0.708 0.694

0.552 0.567

0.081

0.173

0.065 0.093

0.047

0.103

0.036 0.032

0.0280.040

0.023

0.035

0.108 0.103

0.199 0.173

0.378

0.295

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

White men White women Black men Black women Hispanic men Hispanic women

Uninsured

Other HI

Spouse EHI

Own EHI

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.

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.

Limitations

• Full-time, full-year workers only

• Don’t have good data on value of employer contribution

• Other fringe benefits?

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.

Backup slides start here.

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.