Marital Status, Spousal Coverage, and the Gender Gap in Employer-Sponsored Health Insurance

Excellus Health Plan, Inc.

Marital Status, Spousal Coverage, and the Gender Gap in Employer-Sponsored Health InsuranceAuthor(s): Thomas C. BuchmuellerSource: Inquiry, Vol. 33, No. 4 (Winter 1996/97), pp. 308-316Published by: Excellus Health Plan, Inc.Stable URL: http://www.jstor.org/stable/29772648 .

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Thomas C. Buchmueller Marital Status, Spousal

Coverage, and the Gender Gap in Employer-Sponsored

Health Insurance

Not only do men who work full time earn more than women, but they are more likely to receive employer-sponsored health benefits. This paper provides evidence on the

gender gap in employer-sponsored health insurance. The results indicate that the gap is

driven largely by the tendency of married women to decline employer-sponsored insurance in favor of being covered through their husbands. Indeed, among single workers, women are more likely than men to be offered insurance. These findings call

into question the conclusion made by previous researchers that employers discriminate

against women in the provision of health insurance.

In the United States, women who work full time earn

between two-thirds and three-quarters as much as

men who work full time (O'Neill and Polachek

1993). In addition, women are less likely than men to

have employer-sponsored health insurance coverage

in their own name. Although the earnings gap has

been heavily scrutinized,1 less attention has been

given to male/female differences in fringe benefit

coverage.

There are two primary reasons why the gender gap in employer-sponsored health insurance is of inter?

est to economists and policymakers. First, health

benefits are an important component of total com?

pensation, representing roughly 7% of total com?

pensation, and roughly one-third of employer expen?

ditures on voluntarily provided employee benefits

(U.S. Bureau of Labor Statistics 1994). Hence, by

failing to account for the value of health benefits,

prior estimates of the gender gap in wages may understate the gap in total compensation. Second,

because the workplace is the primary source of

health insurance in the United States, workers who

lack access to employer-sponsored coverage will be

left to purchase insurance in the nongroup market

where the cost is substantially higher, coverage tends to be less comprehensive, and exclusionary under?

writing and marketing practices are more prevalent.2

Thus, a lack of access to employer-sponsored insur?

ance may mean reduced access to medical care and

greater exposure to large financial losses in the event

of a serious medical event.

The few studies that have examined the gender gap in health insurance benefits have attributed

much of the gap to discrimination by employers. However, because these studies share two important

shortcomings, their results are misleading. First, all

prior studies on the gender gap in fringe benefits measure access to health benefits by whether work?

ers are covered by an employer-sponsored plan, not

by whether they are offered coverage. Second, these

studies do not allow for the possibility that the

relationship between marital status and benefit cov?

erage differs for men and women, or for the possi?

bility of coordinated behavior by husbands and

wives.

This paper analyzes the gender gap in employer

sponsored health insurance using data from a sup?

plement to the Current Population Survey (CPS),

Thomas C. Buchmueller, Ph.D., is an assistant professor, Graduate School of Management, University of California, Irvine. Address correspondence to Dr. Buchmueller at UCI Graduate School of Management, Irvine, CA 92697-3125.

Inquiry 33: 308-316 (Winter 1996/97). ? 1996 Blue Cross and Blue Shield Association and

Finger Lakes Blue Cross and Blue Shield.

308 0046-9580/96/3304-0308$1.25



Gender Gap

which was conducted in April 1993 and provides detailed information on employer-sponsored health

insurance and other fringe benefits. Stratifying by marital status reveals that the gender gap in employ? er-sponsored health insurance coverage among full

time workers is driven by a large difference in cov?

erage rates between married men and married

women. Indeed, among single workers, women are

slightly more likely than men to receive employer sponsored health benefits. One important advantage

of the April CPS over data sets used in previous studies is that it provides information on whether

workers are offered employer-sponsored insurance.

Tabulations on the percentage of workers who are

offered health insurance reveal that much of the

gender gap in insurance coverage among married

workers is caused by the greater tendency of married women to decline benefits in favor of being covered on their husbands' policies.

There remains a small gap in the percentage of married men and women who are offered insurance

coverage. In the second part of the paper, I inves?

tigate whether this gap also can be explained by voluntary behavior. Other studies (Olson 1995b; Buchmueller and Valletta 1996; Wellington and Cobb-Clark 1995) have shown that women whose husbands have employer-sponsored insurance are

themselves less likely to be employed and, when

employed, less likely to work full time. I test whether

among women who work full time, those who have

the option of coverage through their husbands are more likely to choose jobs which do not offer insur? ance at all. Probit regression results indicate no

evidence of this type of sorting.

Background and Previous Literature

In 1993, 76% of the men and 68% of the women who worked full time had employment-based health in? surance coverage in their own names.3 The gender

gap in employer-sponsored health insurance cover?

age has declined slightly since the early 1980s,

though not due to gains in coverage by women.

Using data from the March 1983 CPS, Robinson

(1991) reports coverage rates of 81% and 71% for men and women, respectively, who work full time.

Thus, the gap has narrowed because coverage has

declined more for men than for women.4

For a variety of institutional reasons, the situation

facing an insurance-providing firm is similar to that of an insurer under a system of community rating.5

In both cases, there is an incentive to screen out

individuals who are more costly to insure. Previous

empirical studies provide some evidence of screen?

ing by insurance-providing employers. Olson (1993) and Buchmueller (1995) both find that workers in

fair or poor health are less likely to receive health

benefits than workers who are in good health, and

Scott, Berger, and Garen (1995) find that insurance

providing employers are less likely than other firms to hire older workers who are more costly to insure.

Higher average medical expenditures for women

(Sindelar 1983) would seem to suggest a reason why

insurance-providing employers might be reluctant to

hire women, though the theoretical case for em?

ployer screening against women is not so clear cut.

First, higher medical care use by women does not

necessarily imply that female employees are more

expensive to insure. Most employers that provide

insurance allow workers to extend coverage to their

dependents. If women have fewer covered depen?

dents or are more likely to decline coverage in favor

of being covered through their spouses' employers, they may in fact be less expensive to insure. In

addition, to the extent that employers can pass the

higher cost of covering any group of worker on to

those workers themselves, the financial incentive to

screen is reduced.6

Three prior studies examine the gender gap in

employer-sponsored health benefits, and interpret

the gap as evidence of discrimination by employers. Dalto (1988) argues that while the tax code's non

discrimination rules make it difficult for employers to offer tax-preferred fringe benefits to some em?

ployees and not others, employers can exclude

women from benefit plans by hiring workers in fe?

male-dominated occupations indirectly through sub?

contracting arrangements. He regresses the number

of fringe benefits a worker receives on a set of

controls including both the worker's gender and the

gender composition of his or her occupation. Find?

ing no significant effect of the former, but a negative and significant effect for the latter, he concludes that

there is no evidence of direct discrimination, but that

workers in female-dominated jobs are the victims of

"institutionalized discrimination."

Heywood (1989) and Robinson (1991) both use

the May 1983 CPS to examine male/female (and in

Heywood's case white/nonwhite) differences in

health insurance and pension plan participation. Heywood (1989) estimates probit models of plan participation with dummy variables for women and minorities. He finds that controlling for personal characteristics, occupation, and industry, women are

less likely to participate in employer-sponsored

309



Inquiry/Volume 33, Winter 1996/97

health insurance plans. When wage rates are con?

trolled for, however, women are no less likely to be

covered. Robinson (1991) estimates separate probit models for men and women and concludes that

between 67% and 70% of the gender gap in insur? ance coverage cannot be explained by differences in

human capital. He attributes the unexplained gap to discrimination.

These studies share two important shortcomings which have implications for their empirical results and the interpretation of those results. First, all

three measure access to employer-provided health

insurance by whether a worker is covered by em?

ployer-provided insurance in his or her own name.

Thus, no distinction is made between workers who are not offered insurance and those who are offered

coverage, but turn it down. Clearly, to the extent that

some workers decline coverage which they are of?

fered, differences in offers of insurance provide a

better test of the discrimination hypothesis than differences in actual coverage.

The second problem with these studies is that they do not allow for the possibility that the effect of marital status on benefit coverage is not the same for

men and women. This exacerbates the problems caused by using coverage in one's own name to

measure access to insurance. The distinction be?

tween having coverage and being offered coverage is

not a major issue for single workers who typically lack an alternative source of insurance, and thus are

unlikely to turn down coverage offered to them. This distinction is significant, however, in the case of married workers. As will be shown, many married

workers with the option of being covered through their spouse's policy decline offered benefits, rather than pay a monthly premium contribution for cov?

erage which is largely redundant.

Marital Status and the Gender Gap in

Employer-Sponsored Insurance

The data used in this paper come from the Employee Benefits Supplement to the April 1993 Current Pop? ulation Survey. The supplement, which was admin?

istered to 27,000 workers, contains detailed ques?

tions on employer-provided health insurance,

pension plans, and other fringe benefits. The most

important advantage of the supplement for the pur? pose of this study is that respondents are asked whether their employer provides a plan, whether

they are eligible for the plan, and whether they choose to participate.7 In contrast, most other na

Table 1. Percentage of full-time wage and

salary workers with employer-sponsored insurance in own name: by gender, age, and

marital status

Percentage offered and accepting

coverage Percentage offered

coverage

(i) Women

(2) Men

(3) Women

(4) Men

A. All workers

Age 18-29 30-39 40-49 50-64

18-64

B. Single workers

60.9 68.0 69.5 72.0

67.6

Age 18-29 30-39 40-49 50-64

60.9 77.3 80.9

2***

18-64 73.8**

C. Married workers

Age 18-29 30-39 40-49 50-64

18-64

61.0 62.6 63.0 66.6

63.1

60.9 78.3*** 83.1*** 78.3***

75.8***

57.4 74.7 83.0 70.0

64.8* 7g 4*** 83.2*** 80.0***

78.0***

70.6 80.4 81.4 82.5

78.9

66.4* 81.0** 83.6 84.2***

78 l***

75.6 80.1 80.1 81.2

79.5

67.3 83.7*** gy y*** 84.3

81.3***

62.7 76.8 85.2 72.2

72.4

72.6

gg 4*** 86.6***

84.5***

Notes: Author's calculations using the April 1993 Employee Benefit Supplement to the Current Population Survey. ***Gender difference statistically significant at the .01 level. **

Gender difference statistically significant at the .05 level.

* Gender difference statistically significant at the .10

level.

tional surveys that have been used to address ques?

tions concerning employer-sponsored health insur?

ance contain information on coverage only. Table 1 presents the percentage of full-time wage

and salary workers (30 or more weekly hours) with

employer-sponsored health insurance in their own

name (columns 1 and 2) and the percentage offered

coverage (columns 3 and 4).8 The tabulations on insurance coverage for all workers (panel A) show that 75.8% of men who work full time receive health insurance from their employers, compared to 67.6%

of women. Perhaps the most striking result in the table is that among single workers, women are

roughly five percentage points more likely than men to have employer-sponsored insurance. The break?

down by age reveals a large differential favoring women among single workers between the ages of 50

310



Gender Gap

and 64 and smaller, statistically insignificant, differ? ences for younger single workers.

The figures in panel C indicate that being married raises the probability of men having their own health insurance coverage, and lowers it for women. As a

result, married men between the ages of 18 and 64 are 15 percentage points more likely to receive health benefits than are married women. The gap is

largest (roughly 20 percentage points) for married workers between the ages of 40 and 49 and smallest for those under age 30 (less than four percentage

points). Columns 3 and 4 present evidence on the percent?

age of workers who are offered insurance by their

employer. This is a better measure of access to

health insurance, particularly for assessing the hy?

pothesis of employer discrimination. The figures in

panel A indicate that, overall, the gender gap in the

percentage of workers offered health insurance is

less than one-third as large as the gap in coverage in

one's own name (2.4 percentage points compared to

8.2 percentage points). For the youngest and oldest

age categories, the difference in the percentage of married men and women offered insurance is not

significantly different from zero.

Differences between eligibility and coverage rates

arise from workers declining benefits that are offered to them. Because a similarly small fraction of single

men and women decline coverage, the distinction

between having coverage and being offered coverage does not have material implications for the gender differences among single workers. The distinction

between actual coverage and eligibility for coverage is important for married workers, however. While

married women are 15 percentage points less likely to have employer-sponsored insurance in their own

names, they are only five percentage points less likely to be offered coverage. As with the gap in coverage, the difference in offer rates is greatest for workers between the ages of 40 and 49, for whom there is an

eight-percentage point differential favoring married men. For workers under age 30, married women are

slightly more likely to be offered insurance, though the difference is not statistically significant.

To control for other factors that influence access

to employer-sponsored health insurance, I estimated

several probit regression models. The main results

from those regressions are summarized in Table 2.

As in Table 1, I stratify the CPS sample by marital status and examine the impact of gender on two

outcomes: the probability of having employer-spon? sored insurance coverage and the probability of

being offered such coverage. For each outcome, two

specifications are reported. The first controls only for individual demographic characteristics (age, ed?

ucation, race/ethnicity, number of children, and geo?

graphic location); the second specification also con?

trols for the type of job each individual holds by adding the number of usual weekly hours and a set

of industry and occupation dummies.

The regression results tell essentially the same

story as the cross-tabulations. For single workers,

there is little difference between the models in which the dependent variable is insurance coverage (col

Table 2. Probit regression results: gender differences in insurance coverage and eligibility after controlling for individual and job characteristics

Has coverage in own name Offered coverage in own name

(1) (2) (3) (4) Women Men Women Men

Prob(Ins I Male) ?

Prob(Ins I Female) A. Single workers -.027** -.005 -.035*** -.012

(n = 5,843) (.013) (.014) (.012) (.013)

B. Married workers .153*** .138*** .053*** .053***

(n = 11,028) (.009) (.024) (.007) (.009)

Controlling for .. .

Individual characteristics Yes Yes Yes Yes

Job characteristics No Yes No Yes

Notes: Percentage-point differentials are based on probit regression models. The standard error for the differential is in

parentheses. Individual characteristics are: age, age squared education (six dummy variables), number of children under

age 18, presence of children under age 6 (dummy variable), race/ethnicity (three dummy variables), geographic region

(three dummy variables), MSA resident dummy variable. Job characteristics are: occupation (six dummy variables),

industry (eight dummy variables), and usual weekly hours.

**Statistically significant at the .05 level.

***Statistically significant at the .01 level.

311



Inquiry/Volume 33, Winter 1996197

Table 3. Percentage of full-time workers offered health insurance who accept or decline

Percentage who accept

Workers the offer

Single men 95.2 4.8

Single women 94.5 5.5 Married men

All 92.4 7.6 Whose spouse also is

offered coverage 83.2 16.8 Whose spouse is not

offered coverage 96.7 3.3 Married women

All 79.7 20.3 Whose spouse also is

offered coverage 72.5 27.5 Whose spouse is not

offered coverage 93.0 7.0

umns 1 and 2) and those in which the dependent variable is the offer of insurance coverage (columns 3 and 4). A comparison of the models that do and do not control for job characteristics indicates that the small gender gap favoring single women is explained by differences in the types of jobs single men and women hold, rather than differences in personal characteristics. For married workers, the distinction

between observed coverage and eligibility for cover?

age remains substantial. Controlling for individual

and job characteristics, married men are 15 percent?

age points more likely to have employer-sponsored insurance coverage in their own name, but only five

percentage points more likely to be offered coverage.

Spousal Coverage and the Decision to Decline Health Insurance Coverage

An important reason workers decline offered health

insurance is that they have an alternative source of

coverage, the most common of which is a spouse's

policy.9 Table 3 provides some descriptive evidence of the effect of spousal coverage on the decision to

accept or decline insurance in one's own name. The

first two rows of the table pertain to single men and women who work full time and are offered insurance

by their employers. As could be inferred from the

figures in Table 1, the vast majority of single workers who are offered coverage accept that offer.

The sample of married workers who are offered

insurance is stratified by whether the worker's

spouse also is offered coverage. The figures show

that married men (women) who do not have the

option of being covered through their spouse's em

ployer are slightly more (less) likely than single workers to accept coverage offered to them. In con?

trast, when spousal coverage is available, a signifi? cant fraction of married workers decline coverage in

their own name. The effect of spousal coverage

appears to be slightly stronger for women than for men. Among married women who are offered insur?

ance coverage in their own name, the availability of

insurance through their husband's employer in?

creases the probability of declining coverage from 7% to 27.5%. Among married men who are offered

insurance, the option of coverage through their

wife's employer raises the probability of declining coverage from 3.3% to 16.8%.

The Effect of Husbands' Coverage on Wives' Job Choice Decisions

Previous researchers have noted similarities be?

tween factors that influence whether a worker is

offered insurance and those influencing whether she

will accept coverage when it is offered (Long and

Marquis 1993). This suggests that the availability of

spousal coverage may influence not only the decision to accept coverage when offered, but also the choice

between jobs that do and do not offer insurance in the first place.

Several other recent studies find evidence of this.

Using data from three different national surveys,

Olson (1993, 1995b) finds that working women whose husbands have employer-sponsored health

insurance are less likely to have coverage in their

own names. However, this result is difficult to inter?

pret because it combines three distinct decisions.

First, because the data sets that Olson uses provide information on coverage only, his results reflect the

tendency of women with spousal coverage to decline

health benefits, which is documented in Table 3.

Second, because Olson's samples include women

who work both full time and part time, his results also reflect the effect of husbands' insurance on

married female labor supply. Because employers

typically limit insurance eligibility to full-time work?

ers, women whose husbands lack insurance may

work longer hours in order to qualify for health benefits. Olson's (1995b) own research and work by Buchmueller and Valletta (1996) and Wellington and Cobb-Clark (1995) find substantial evidence that is consistent with this type of behavior.

The third possible effect is that conditional on the hours decision, women with insured husbands may

be more likely to choose jobs not offering insurance. This type of sorting will occur if jobs that lack

312



Gender Gap

insurance provide higher wages or some other com?

pensating amenities. To investigate the prevalence of such behavior, I estimate a probit regression model in which a married woman's choice between

full-time jobs that do and do not offer health insur? ance depends on whether insurance is available

through her husband. The model has the form:

Prob(OFFERw = 1) = <$>{Xw? + yOFFERH) (1)

where <E represents a standard normal distribution

function, OFFER is a dichotomous variable that

equals one if an individual works in a job that offers insurance and the subscripts W and H refer to hus?

bands and wives, respectively. The tabulations in

Table 3 show that while married men are less likely than married women to decline insurance, a non

trivial portion of men whose wives are offered in?

surance turn down coverage. This means that hus?

bands' actual insurance coverage is endogenous with

respect to their wives' insurance status. In contrast,

OFFERH is exogenous under the assumption that husbands' job choices are independent of wives'

choices, while wives may condition their job choices on the type of jobs their husbands hold.10

Since the availability of insurance increases with a worker's skill level, the vector X includes standard

human capital control variables such as education

and (potential) labor market experience (age -

years of schooling -

6). Because the presence of

dependent children is likely to affect a woman's job preferences, I also include the number of children under age 19 and a dummy variable for women with children under age 7. Regional dummies and a

dummy variable for residence in a Metropolitan Statistical Area (MSA) are included as rough con? trols for regional variation in labor and health in?

surance market conditions. X also includes controls

for race/ethnicity.

Nonwage income is an important variable in mod?

els of female labor supply. Since my estimation

sample is limited to women who work 30 or more hours per week (so that the results are not con?

founded by the effect of husbands' insurance on married female labor supply found in other studies), the rationale for controlling for nonwage income is

somewhat less clear. Nonetheless, I include (the natural log of) husbands' earnings and family non

wage income. The results do not change qualitatively when I exclude these two variables.

There are theoretical arguments for and against also including controls for industry and occupation.

On one hand, industry and occupation can be viewed

as endogenous job characteristics. On the other

hand, in many cases an individual's industry and

occupation are determined by her educational back?

ground and prior job choices. The model reported in this paper excludes industry and occupation con?

trols. Adding these variables has no impact on the coefficients of main interest.

The probit regression results are presented in Table 4. The coefficients on the control variables are

not surprising. The probability of being offered in? surance increases with potential labor market expe?

rience and education. The ethnic/racial differences

are statistically insignificant. All else equal, women

living in the northeast (the omitted region) and in

metropolitan areas are most likely to work in jobs that offer insurance.

The regression results provide no evidence that

women whose husbands are offered employer-spon? sored insurance are less likely to be offered in?

surance themselves?the estimated coefficient on

OFFERH is not significantly different from zero. This result is consistent with the conventional wisdom

that jobs which offer insurance are better in many dimensions than jobs not offering insurance. The

underlying explanation for this is likely related to the fact that most full-time jobs that do not offer health

Table 4. Probit regression results: effect of husbands' insurance on wives' eligibility for

employer-provided insurance

Standard

Independent variables Coefficient error

Husband is oifered insurance .036 (.057) Potential experience .029** (.009) Experience2/100 -.059** (.019) High school graduate .339** (.086) Some college, no degree .432** (.096) A. A. degree .524** (.112) B. A./B.S. degree .526** (.102) Advanced degree .903** (.127)

Black -.023 (.145) Asian -.021 (.149)

Hispanic -.134 (.102) Any children < 6 years old .200** (.068) Number of children

- .075*

* (.026)

Midwest -.102 (.068) South -.162** (.066)

West -.173** (.072) MSA resident .117** (.047) ln(husband's earnings) ?.010 (.013) ln(family non-wage income) .025** (.007)

Intercept_.166_(.141) N 4,132

Log-likelihood -2,009.7 **

Statistically significant at the .05 level.

313



Inquiry/Volume 33, Winter 1996/97

insurance are with small firms, and small firms tend

also to pay lower wages and offer fewer fringe ben?

efits than larger firms (Brown, Hamilton, and Medoff

1990). Thus, while some workers with an alternative source of insurance coverage may be willing to trade

health benefits for higher wages or some other fringe benefits, most jobs that do not offer insurance have

relatively low wages and few other benefits.

Sensitivity of the Results

These results are robust to the inclusion or exclusion

of various controls. As noted, adding controls for

industry and occupation do not change the results,

nor does dropping the unearned income variables. I

also considered alternative criteria for determining

the estimation sample. For example, I excluded cou?

ples in which the husband was not employed at the time of the survey on the basis that the wife's role as

sole earner might be temporary. Also, I used infor?

mation available on insurance coverage for the prior

year to exclude couples in which the husband or wife had been covered by Medicaid or Medicare in the

previous year. These modifications also had no ma?

terial impact on the results.

One possible criticism of this model is that hus? bands' insurance eligibility may not, in fact, be ex?

ogenous. There are two potential (opposing) sources

of bias. First, to the extent that husbands and wives

make job choices simultaneously rather than sequen?

tially, some husbands may be in non-insurance-pro?

viding jobs because their wives are offered insurance in their jobs. This will produce a negative bias on y.

Alternatively, if unmeasured labor market and in?

surance conditions affect the job/insurance options of husbands and wives in similar ways, y may pick up the effect of these unobservables and thus suffer from a positive bias.

To account for the possible endogeneity of hus? bands' insurance eligibility, I estimated models in which OFFERH is replaced by fitted values from a

first-stage regression of that variable on all the vari?

ables in equation 1 plus variables representing the husband's characteristics (e.g., education, experi?

ence) and the characteristics of his job (e.g., firm

size, industry, occupation, pension coverage). This

instrumental variables approach will provide consis?

tent estimates of y if the variables that are included in the first-stage (husbands' insurance) regression? but do not directly enter the second-stage (wives' insurance) equation?predict OFFERH, but are un

correlated with the error term in equation 1. Since

the two-stage model is over-identified and theory

provides limited guidance as to which husbands' variables can or should be used as instruments, I

estimated several alternative versions of this model.

The point estimates from these second-stage re?

gressions were somewhat sensitive to the choice

of excluded instruments (ranging from negative and insignificant to positive and insignificant), though the qualitative results were fairly consistent.

None of these two-stage models provides support

for the hypothesis that among women who work full time, those whose husbands are offered insur?

ance are more likely to choose non-insurance

providing jobs.

Concluding Comments

Previous studies have interpreted the gender gap in health benefits as evidence of employer discrimina?

tion. However, these studies have two important flaws. First, to the extent that they control for marital

status, they do not account for the fact that the

relationship between marital status and benefit cov?

erage may differ for men and women. Second, they measure access to health benefits by whether a

worker has coverage in his or her own name. A

better measure for the purposes of testing the dis?

crimination hypothesis is whether a worker is offered employer-sponsored health insurance.

I present tabulations and probit regression results from the April 1993 CPS which indicate that the overall gender gap in employer-sponsored health insurance coverage is driven by relatively high rates of coverage for married men, and relatively low rates

for married women. Indeed, among single workers,

women actually have slightly higher coverage rates than men. A comparison of the percentage of work?

ers who are offered insurance reveals that differ?

ences in coverage rates among married workers are

largely explained by the tendency of married women to decline offered coverage. While married women

who work full time are roughly 15 percentage points less likely than married men to have employer

sponsored insurance in their own name, they are

only five percentage points less likely to be offered insurance coverage. Among married workers under

the age of 30, there is no gender gap in insurance offers.

I estimate several probit regressions to determine

whether the small gap between married men and women in the probability of being offered insurance also could be explained by voluntary behavior. That

is, do women who have the option of insurance

coverage through their husbands tend to take jobs

314



Gender Gap

which do not offer insurance, but provide some other

compensating advantage? I find no evidence of this

type of sorting. This result may be explained by the fact that full-time jobs that do not offer insurance tend to be with small firms, and small firms tend to

pay lower wages and offer fewer other fringe bene?

fits.

Overall, these results contradict the hypothesis of

employer discrimination against women in the pro?

vision of health insurance. One obvious implication is that gender differences in labor market outcomes should be interpreted thoughtfully and cautiously. The importance of accounting for the percentage of workers who decline coverage also has potentially

important implications for understanding recent trends in employment-based health insurance. Sev?

eral recent studies (Kronick 1991; Acs 1995; Long and Rodgers 1995; and Olson 1995a) have docu?

mented a decline in employer-sponsored health in?

surance coverage over the past decade or so. The

results of this paper indicate, that for single workers, coverage rates and offer rates are quite similar; for

married workers, particularly married women, the

two measures diverge. It would be useful to deter?

mine what fraction of the decline in insurance cov?

erage over recent years can be attributed to employ? ees turning down offered coverage in response to

rising premium contribution requirements.

Notes

1 See Cain (1986) for a good review. More recent studies include Blau and Beller (1992) and O'Neill and Pola chek (1993).

2 The cost advantage of employment-based group insur? ance comes from preferential tax treatment, economies of scale, and risk pooling. See Phelps (1992) for a good discussion of these various factors. Farley (1985) pro? vides evidence on differences in coverage between

group and nongroup insurance. 3 These figures are based on the April 1993 Current

Population Survey. Among all workers (e.g., including part-time workers), the male and female coverage rates are 62% and 53%, respectively (EBRI 1994).

4 The decline in employer-sponsored insurance coverage over the 1980s and early 1990s has been extensively documented and analyzed (Kronick 1991; Acs 1995;

Long and Rodgers 1995; Olson 1995a). 5 See Buchmueller (1995) for a fuller discussion. 6 Gruber's (1994) analysis of mandated maternity bene?

fits indicates that the cost of these mandates was borne

largely by the group which they were intended to ben? efit: married women of child-bearing age. However, more generally, it is unclear to what extent employees with greater medical care use bear the cost of that use

(Pauly 1986).

7 See EBRI (1994) for a full set of tabulations on em?

ployment-based health insurance using this data set.

8 Part-time workers are excluded because they are rarely eligible for benefits. Thus, if they were included, gender differences in health plan participation rates would be

confounded by differences in usual weekly hours be? tween men and women.

9 The April 1993 CPS directly asks workers who have declined coverage their reason for doing so. The most common response (given by 78% of all workers declin?

ing coverage and by more than 90% of married workers who decline coverage and whose spouses are offered

coverage) is that the worker is covered by another

policy. The second most common reason is that the

insurance the worker's employer offers is too expensive

(18% of all workers declining coverage). 10 This "traditional family" assumption is fairly common

in the literature on married female labor supply. Mroz's

(1987) finding that husbands' earnings and hours are

exogenous to wives' hours decisions provides some

support for the assumption that husbands "go first."

Olson (1993, 1995b) also assumes that husbands' insur? ance outcomes affect wives' job choice decisions, but not vice versa.

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Documents

Marital Status, Spousal Coverage, and the Gender Gap in Employer-Sponsored Health Insurance