GENDER DIFFERENCES IN THE VALUATION OF EMPLOYER-PROVIDED HEALTH INSURANCE

Transcription

1 GENDER DIFFERENCES IN THE VALUATION OF EMPLOYER-PROVIDED HEALTH INSURANCE NASSER DANESHVARY and TERRENCE M. CLAURETIE* We present evidence that accurate estimates of the labor-earning/employerprovided health insurance trade-off must account for two different effects: the heterogeneity of jobs and the endogeneity of health insurance. The size of the trade-off depends on employees contribution to premiums, health-care needs, and valuation of insurance. We use Medical Expenditure Panel Survey data and instrumental variables/two-stage least squares. On average, workers accept about 16.5% to 20% lower earnings in return for insurance, and married women value insurance by about 3.5 percentage points more than married men, explaining about 3% of the gender-earning differentials. Health insurance does not contribute to the unexplained portion of the gender-pay gap. (JEL J3, J7, I1) I. INTRODUCTION Based on the seminal compensating wage differential work of Rosen (1986), a considerable number of empirical studies have analyzed the effect of employer-provided health insurance on various forms of potential job lock, such as decisions to retire, to change jobs, to participate in the labor market and the extent of participation (full time/part time), and to move from receiving public assistance to the workforce. 1 Though employer-provided health insurance is the most significant source of health *We wish to thank Bernard Malamud, Mary Riddel, Paul Thistle, and Bradley Wimmer for their comments and suggestions. Special thanks to an anonymous referee of the journal and to Stephen Miller for their extensive comments and suggestions. Daneshvary: Professor of Economics, Department of Economics, University of Nevada, Las Vegas, 4505 Maryland Parkway, Las Vegas, NV Phone , Fax , nasser. daneshvary@unlv.edu Clauretie: Professor of Finance, Department of Finance, University of Nevada, Las Vegas, 4505 Maryland Parkway, Las Vegas, NV Phone , Fax , mikec@ccmail.nevada.edu 1. For examples of the effect on retirement decision, see Blau and Gilleskie (2001). Simon (2001) considers the effect on job change. Examples of the health insurance effect on the labor force participation rate include Wellington and Cobb-Clark (2000) and Buchmueller and Valletta (1999). Meyer and Rosenbaum (2001) investigate the health insurance effects on transition from welfare to work of single mothers. Gruber and Madrian (2002) provide an excellent survey of the empirical literature on the relationship between health insurance and job change and labor supply, as well as overall welfare implications of such relationships. insurance in the United States, about 65% of all sources, its effects on wages have not received due attention and the empirical validity of such effects has not been established. As noted by Currie and Madrian (1999), estimates of the wage/employer-provided health insurance trade-off in most studies exhibit a positive sign, are statistically insignificant, or both. The most recent research has also generated diverse results. Simon (2001) investigated the trade-off by concentrating on displaced workers. She found a positive sign for the trade-off and concluded that unobserved worker effects do not cause bias and that the more important cause of the bias reflects job and the job match. That is, jobs are either good or bad jobs, with good jobs having higher pay and higher benefits at the same time. Kaestner and Simon (2002) examined the labor market consequences of state-mandated health insurance benefits and small-group health insurance reforms during the late 1990s. They found no evidence of a trade-off between wages and health insurance costs and suggested the ABBREVIATIONS 2SLS: Two-Stage Least Squares AHRQ: Agency for Healthcare Research and Quality HC: Household Component HMO: Health Maintenance Organization IV: Instrumental Variable MEPS: Medical Expenditure Panel Survey OLS: Ordinary Least Squares 800 Economic Inquiry doi: /j x (ISSN ) Online Early publication May 2, 2007 Vol. 45, No. 4, October 2007, Ó 2007 Western Economic Association International

2 DANESHVARY & CLAURETIE: GENDER DIFFERENCES IN THE VALUATION 801 possibility that increased insurance costs were passed on to workers through higher employee contributions to premiums. Olson (2002) concentrated on a sample of married women and accounted for the endogeneity of choice between jobs with and without health insurance but did not account for insurance cost sharing such as out-of-pocket premiums, health-care need variables, or job characteristics. He found a trade-off of about 20%. The existing literature also indicates a gender differential with respect to health-care utilization and expenditure (Manning and Mullahy, 2001) and employer-provided health insurance premiums (Jensen and Morrisey, 1990). In addition, there is a possibility of gender discrimination due to mandated health insurance provision (Gruber, 1994). Furthermore, health insurance can create labor market distortions, resulting in job locks, rent for some workers, and pay discrimination. These factors may contribute to gender differentials in earnings/health insurance trade-off and raise the possibility that Olson s finding may overestimate the trade-off for the entire labor force. This study makes three substantive contributions to the literature. First, we isolate the effect of job characteristics, insurance cost sharing as measured by out-of-pocket premiums, and health-care need variables that confound the effect of employer-provided health insurance on annual earnings. We do this by using the 2001 Medical Expenditure Panel Survey (MEPS) data and its recently released supplementary files. Second, by accounting for the endogeneity of choice between jobs with and without health insurance and by utilizing possession of family coverage policy as an alternative source to own employerprovided insurance, we expand Olson s model to separate samples of married men and married women and investigate potential differentials in valuation/cost of employer-provided health insurance between the genders. Third, we estimate the effect of employer-provided health insurance on the size and composition of the gender-earning differentials. Our results show that failure to control for heterogeneity as well as for endogeneity will understate the size and the statistical significance of the tradeoff. A gender-trade-off differential exists of about 3.5%, explaining about 3% of the gender-earning differentials. Health insurance does not contribute to the unexplained portion of the gender-earning gap. The following section examines the literature and discusses conceptual considerations in order to estimate the trade-off between earnings and health insurance. A more complete alternative model to estimate trade-offs for married men and married women appears in Section III. Section IV provides a discussion of the data set and presents the ordinary least squares (OLS) and instrumental variables/twostage least squares (IV/2SLS) estimations as well as the gender-earning gap decomposition results. The conclusion follows in Section V. II. THE TRADE-OFF BETWEEN WAGES AND EMPLOYER-PROVIDED HEALTH INSURANCE A. Literature Review Compensating wage differential theory predicts that in perfectly competitive labor markets with perfect information and perfect mobility of labor, a trade-off occurs between wages and the provision of health insurance. Employers buy insurance on a worker-byworker basis and shift the cost to workers in terms of reduced wages. The size of the wage reduction depends on the employer s costs of providing insurance and the employee s valuation of it. With no market distortions, workers evaluate their demand for the insurance and sort themselves across firms according to the package of wage and insurance benefits that best corresponds to their preferences. Empirical studies of wage/employer-provided health insurance have mostly utilized aggregate data at the firm, city, or state levels and reached dissimilar estimates and conclusions. These studies found a positive relationship, no significant relationship, or both. Based on data from the RAND Health Insurance Experiment, Leibowitz (1983) found a positive relationship between wages and employer health insurance expenditure. The findings were probably due to omitted productivity-related unobserved variables. Studies by Miller (1995) and Simon (2001) looked at job change to investigate the relationship between change in health insurance coverage and wage change. Miller reported a positive relationship between wages and the provision of health insurance. Simon (2001) investigated the trade-off by concentrating on displaced workers. Estimates from a sample of displaced workers, due to an exogenous shock such as plant closings and mass layoffs, reduce the

3 802 ECONOMIC INQUIRY possible correlation between the health insurance coverage variable and unobserved productivity variables. She found a positive sign for the trade-off. Those who lost insurance due to job change also lost about 23% in wages with the new job relative to those who had insurance with both the old and the new jobs. Those who gained insurance also gained 15% in wages relative to those who never had insurance. Simon concluded that unobserved individual effects do not cause bias and that the more important cause of bias relates to the unobserved heterogeneity of job characteristics. In short, jobs are either good or bad jobs, with good jobs having higher pay and higher benefits at the same time. Some studies used legislation and state/citylevel data sets to see whether the cost of insurance benefits passes to workers in terms of reduced wages. Gruber (1994) looked at the effects of state and federal mandates that stipulated comprehensive coverage of childbirth during the late 1970s. He found that the wages of women of childbearing age (20 40 years old) were reduced in proportion to the costs of the mandates. The wages of husbands of childbearing-age women (20 40 years old) were also reduced. The percentage decline in women s wages was, however, much larger than the percentage decline in the wages of men (Gruber, 1994, tables 4 and 6). The differential decline of wages raises the possibilities of differential valuation of insurance by men and women, differential treatment by employers due to cost, and/or both. Kaestner and Simon (2002) examined the labor market consequences of state-mandated health insurance benefits and small-group health insurance reforms during the late 1990s. They found no evidence of a tradeoff between wages and the costs of providing health insurance and suggested that increased insurance costs were passed on to workers through higher employee contributions. Olson (2002) utilized the Current Population Survey data and husbands job characteristics, such as union membership, firm size, and health insurance coverage, to instrument wives health insurance coverage. He found a wage/health insurance trade-off of about 20% among married women. His theoretical model and empirical estimates advanced our understanding of the trade-off. His estimated models, due to lack of data, however, do not account for employees contribution to premiums, health-related variables, and type of insurance policy (single/family). The lack of data for health- and job-related variables raises the potential for biased estimates of the trade-off. Furthermore, Olson s work does not include estimates for men. If gender differences in the size of the trade-off exist, then the trade-off for the entire labor force may be different from his estimate. These issues will be discussed in the next section. B. Conceptual Discussion As stated earlier, compensating wage differential theory relies on perfectly competitive labor markets with perfect information by employers and employees and perfect mobility of labor. Gruber and Madrian (2002) show that under such conditions, the cost of employer-provided insurance for a particular worker-job match will equalize across firms. There will be a market-wide compensating differential. The compensating differential for such a worker equals to the employer s cost of providing the insurance. Workers will sort themselves to jobs. Under this scenario, there will not be a job lock and there will not be a mismatch of worker and job characteristics. Firms do not know individual worker s valuation of health insurance, however. By law, they cannot offer worker-specific benefit packages or practice worker-specific insurance cost shifting. In addition, there is a significant variation in insurance costs across firms. These factors create market distortions and give rise to job lock situations. 2 A worker may not receive comparable health insurance across jobs, thus he/she may not move to another job with higher productivity, higher wages, but no insurance. The job lock creates failure of the matching mechanism between worker and job characteristics. Furthermore, joblocked workers may earn rent at their current jobs if they value the insurance more than its cost (Gruber and Madrian, 2002). The above conceptual discussion implies that employer-provided insurance has the 2. Potential sources of cost variations include (1) heterogeneity of the workers pool with respect to gender and age (Jensen and Morrisey, 1990), (2) differential effects on small and large insured groups resulting from insurance companies practice of experience rating and loading factor (Cutler, 1994), and (3) unobserved heterogeneity of workers with respect to health status and heterogeneity of job characteristics (Currie and Madrian, 1999).

4 DANESHVARY & CLAURETIE: GENDER DIFFERENCES IN THE VALUATION 803 potential for market distortions, resulting job locks, and rent for some workers. It has important implications with respect to the estimated size of the trade-off. For example, good health positively correlates with productivity/earnings but negatively correlates with the valuation of health insurance. Outof-pocket premiums directly affect labor income. Along with out-of-pocket expenses, the premiums affect the estimated size of the trade-off. Estimating the trade-off without accounting for out-of-pocket premiums and health-related variables will produce biased results. Under a job lock environment, the sorting mechanism that matches worker characteristics with job characteristics does not work. As noted by Currie and Madrian (1999), health insurance is correlated with a variety of individual and job characteristics and that the potential for omitted variables bias is something that should be taken seriously (p. 3359). Thus, an unbiased estimate of the trade-off requires accounting for heterogeneity of jobs and workers. Theoretically, due to job locks, a firm can extract workers rent that is created by differential valuations of insurance across workers. The possibility and the extent of extractions depend on the firm s ability to practice wage discrimination. Thus, if there is a systematic gender differential with respect to the cost of providing insurance and/or insurance valuation (perhaps due to systematic risk tolerance differences), then the provision of health insurance may explain some of the genderpay gap. On the one hand, Gruber s (1994) results show there is a differential effect of health insurance mandates on married men and married women of the same age group. Studies of displaced workers, such as Simon s (2001), raise the question of good and bad jobs and, thus, the possibility of a differential trade-off. Differences in occupational distributions by gender, though declining, still exist and imply differential trade-offs between genders. As noted by Blau (1997), such differences may reflect workers preferences or labor market discrimination. On the other hand, the cost of providing group health insurance depends on the expected medical costs of the group. Premiums depend on the size and demographic composition of the insured group. With experience rating, which is a prevalent phenomenon of the insurance market, the premium depends on the most recent history of actual claims. The existing literature, such as Manning and Mullahy (2001), indicates that women have higher health-care utilization and expenditures than men. In addition, there is evidence that women up to age 60 have higher healthcare utilization and expenditures than men of the same age (see Appendix 1). Jensen and Morrisey (1990) found that premiums for employer-provided insurance rise with percentage of women in a firm s workforce. These factors raise the possibility of higher valuation of health insurance by women and/or more expensive employer-provided health insurance for women and, thus, the possibility of a gender differential in the size of the earning/health insurance trade-off. 3 In what follows, we hypothesize that controlling for various aspects of heterogeneity, discussed earlier, reduces the size of the wage/health insurance trade-off bias. Furthermore, earnings and health insurance are joint decisions, influenced by underlying unobserved variables causing endogeneity. We show that ignoring the endogeneity will bias the estimates. Finally, we expect that the provision of health insurance affects gender-pay differentials due to gender differences in its valuation and/or costs. We decompose gender-pay differentials and estimate the effect of employer-provided health insurance. III. ANALYTICAL BACKGROUND In this paper, we examine an empirical model that estimates the effect of a dichotomous variable representing employer-provided health insurance on annual earnings. 4 We define the natural logarithm of annual earnings of an individual, lne i, conditional on having employer-provided health insurance, 3. By individual s valuation, we mean valuation in respect to the entire family. 4. Given that the MEPS data sets provide annual figures for employees contribution to insurance premiums and health-care expenditures, the annual labor income is deemed appropriate. While we would prefer a wage variable, the hourly wages have missing values for a significant portion of the MEPS data sets. Furthermore, hourly wages are top coded and the information on the number of weeks worked is not provided, introducing problems associated with truncation.

5 804 ECONOMIC INQUIRY H i, as well as a vector of other factors, X i, such that: ð1þ ln E i 5 b 0 þ b 1 H i þ X i d þ e i ; where b 1 estimates the earnings/health insurance trade-off, d is a vector of other estimated coefficients, and e i is the unobserved variation. Compensating wage differential theory implies that under a perfectly specified model b 1, 0. Given the wage offer distribution and thus the budget constraint, a worker considers his/her marginal utility of health insurance and makes a choice about earning and health insurance combinations. This implies endogeneity between health insurance coverage and earning decisions. Thus, H i and e i in Equation (1) are correlated, and OLS estimates of b 1 will produce biased results. 5 Empirical validation of the compensating wage differential theory requires that the estimated equation is free from omitted-variable and endogeneity biases. In the case of health insurance, a worker s preference and thus marginal utility of health insurance determine the trade-off and hence the welfare and health of the entire family. The preferences are influenced by whether alternative sources of health insurance are available and the marginal utility of additional coverage. For married individuals, the best alternative is the availability of insurance through the spouse s job. This implies that in the case of married people, the probability of accepting a job that provides health insurance, H i in Equation (1), depends on the probability that the spouse has health insurance through his/her job. 6 That is, ð2þ H i 5 a 0 þ a 1 H s þ X i c þ m i ; where H s is equal to 1 if the spouse has health insurance through his/her employment, 0 otherwise, a 1 is the associated coefficient, expected to be negative, and m i represents unobserved variation. There are two potential problems with estimating Equation (2). First, there is an endogeneity/simultaneity problem in the sense that 5. Our discussion of the empirical model and the endogeneity issues that follow draws significantly on the discussion developed by Olson (2002). 6. Due to the lack of an alternative insurance source variable for singles, this paper concentrates on married men and women. acceptance of health insurance by one spouse, H i, may cause the other spouse, H s, to accept a job without health insurance. Thus, H s and m i in Equation (2) are correlated, and the OLS estimates of Equation (2) produce biased results. That is, the estimated coefficient will overstate the negative effect of the spouse s health insurance on the probability that the other spouse has health insurance, a 1, through his/her job. 7 The second problem is associated with assortive mating. 8 If the estimated equation suffers from unobserved variables that affect compensation packages of both spouses, then assortive mating will cause a positive correlation between H s and the error terms in Equation (2), m i, and Equation (1), e i. Thus, a 1 understates a negative causal relationship between the two spouses demand for health insurance and will produce biased estimates of the trade-off by understating the negative effect of b 1. 9 To overcome the problem, our strategy is to identify and include one or more instruments in Equation (2). The instruments must correlate with the spousal health insurance variable but not correlate with the own health insurance variable. We use two variables as such instruments the spouse s firm size (an indicator for firms with fewer than 100 employees) and whether the spouse s insurance plan provides family coverage. The former instruments H s and thus H i ; the latter directly instruments H i. As in Currie and Madrian (1999), small firms are less likely than larger firms to provide health insurance. Again, firm size, the same as spousal health insurance, may produce biased results due to assortive mating. In this case, if couples with low unobserved productivity factors take low-paying jobs with small firms that do not offer health insurance, then there will 7. According to Gruber and Madrian (2002), and based on empirical studies of job turnover, the joint determination of the health insurance decision by husbands and wives can perhaps be treated as exogenous. Empirical studies of the wage/health insurance trade-off, however, suggest the possibility of endogeneity. We examine this issue in the empirical section below. 8. Assuming the existence of a matching process by which men and women sort themselves out and marry alike person, a positive correlation with respect to ability, skill, and education between married couples is expected. This would cause dual or overlapping coverage and no coverage for high-income and low-income couples, respectively. 9. In fact, Olson (2002) found that husbands health insurance as an instrument produces a lower bound estimate of the trade-off for the wives wage equation.

6 DANESHVARY & CLAURETIE: GENDER DIFFERENCES IN THE VALUATION 805 be a positive correlation between spousal firm size and the error term in the earnings equation. This overstates the size of the tradeoff, b 1. Given our samples of married men and married women, family coverage is a more accurate measure of an available alternative source of insurance than simply whether the spouse has insurance. The provision of family insurance coverage will add extra value from the employer-provided health insurance. Firms, in general, do not provide a choice between insurance and higher wages to a given individual worker, but many firms provide at least a partially subsidized family coverage option. The subsidized family coverage is an additional benefit, and it is a better instrument for a spouse s decision to accept a job with insurance and lower pay or to accept a job without insurance and higher pay. While, as discussed above, the decision to take a job with or without insurance is subject to assortive-mating bias, it is less likely for family coverage to be subject to the same bias. That is, it is less likely that both spouses search for and accept family coverage offers. Family plans usually require an employee s contribution to the premiums. The extra out-of-pocket premium reduces the likelihood of taking additional or overlapping family plans. Thus, family coverage provides the opportunity to properly instrument employer-provided health insurance. Now, we return to other variables in the earnings equation. 10. Information on premiums and types of coverage was released for public use for the first time in the summer of In this study, medical benefit is defined as total medical expenditures minus out-of-pocket expenses, not including premiums. The MEPS files report total health-care expenses and out-of-pocket expenses for each individual, including children. These expenses and, thus, benefits received are appropriately reassigned to the spouse who holds the single or family policy. The policyholder refers to an employee holding an insurance policy, not employer. Total expenditures include payments made for health-care services, other than over-the-counter drugs, regardless of the payment source. They include annual expenditures for doctor visits, hospital inpatient stays, hospital outpatient visits, emergency room visits, dental visits, home health care, vision aids, and prescribed medicine. The expenditure data are not self-reported. They are collected from the medical provider component. Whereas dental and vision expenditures were collected from the HC, the rest of the expenditures, such as physician visits, home-base care, in- and outpatient events, and emergency room visits, were collected from the provider. A series of logical edits for consistency were made by the AHRQ. The vector X i in Equation (1) includes (1) variables representing health insurance and health-care needs of workers and their family, (2) job characteristics to include occupation and industry, and (3) a set of variables representing personal characteristics that traditionally assume to augment human capital. In addition to the variable representing whether the job provides health insurance, the vector of health insurance includes the employee s annual contribution to health insurance premiums. 10 A positive effect on annual earning is expected for this out-ofpocket variable. The amount of health-care benefit paid by insurance for the entire family is also included. 11 This variable may be considered as a proxy for across-firm variations in the generosity of the health plan. It is expected to have a negative effect on wages. 12 An additional proxy for the quality generosity of the plan includes an indicator for whether the plan is a Health Maintenance Organization (HMO). HMO plans, in general, have lower premiums, deductibles, and coinsurance payments. HMOs attract healthier populations, offer fewer choices, and have fewer benefit features than other insurance plans. Thus, a smaller earnings/health insurance trade-off is expected among the holders of HMO plans, implying a positive sign. Four additional variables proxy for the health status of the employee/family. One dichotomous variable indicates whether the individual has work-limiting disabilities. Additional continuous variables include the total dollar amount of health-care expenses on self (not for the entire family) regardless of the payment sources, total number of half-days missed work due to own sickness, and total number of half-days missed work to care for others. 13 All included variables are expected to reduce wages. 12. As noted by an anonymous referee, benefit is determined by the amount of health care consumed, and thus, it may be an endogenous variable. We tested for the endogeneity. For the models that the possession of health insurance does not enter as endogenous variable (OLS), the null hypothesis of benefit s exogeneity is rejected. However, when the health insurance is modeled as an endogenous variable, as is the case in this paper, the null hypothesis of the benefit being exogenous could not be rejected. 13. Wellington (1993) used the last two variables to measure one s attachment to work. Both interpretations are expected to have the same effect on earnings.

7 806 ECONOMIC INQUIRY The job characteristics vector includes indicators for industry and occupation, categorical variables for federal and state/local government employment, union membership, and firm size (firms with less than 100 employees are the omitted category). Hours worked per week and a categorical variable for whether worked part time or full time are also included. 14 To capture the quality of jobs, two additional indicators for whether the job provides paid sick leave and provides paid vacation leave are included. As discussed earlier, the notion of good versus bad jobs, with good jobs having higher benefits and higher wages, implies a positive sign for these variables. Inclusion of these variables reduces the possibility of confounding the effect of health insurance on wages with the effects of other job characteristics. 15 These measures together will also reduce the possible correlation between health insurance and the error term in the wage equation. The human capital vector includes potential labor market experience and its square, tenure with current firm, and education. Previous studies of wages have found differences in the valuation of prior potential experiences and tenure (experience) with current employer. We divide the total potential labor market experiences to experience prior to working for current employer and tenure with current employer. Then, given the shape of the earning/labor market experience profile, we include prior work experience and its square, tenure and its square, and interaction between prior experience and tenure in the estimated models. One interpretation of the interaction term is that the effect of tenure on wages depends on prior experience. 14. In general, the likelihood of part-time workers not having health insurance is higher than that for full-time workers. Omitting such workers from the sample, however, will most likely cause selectivity biases similar to the studies of discouraged workers and labor force participation. Part-time work is a decision that individuals might make based on the availability of health insurance through a job or through a spouse s job. The inclusion of such individuals in the sample provides a more complete picture of the wage/health insurance trade-offs. 15. It is plausible that paid vacation and sick leave, as the provision of health insurance, are endogenous in Equation (1). We performed chi-squared tests for endogeneity. The null hypothesis of exogeneity could not be rejected for either the paid vacation or the sick leave variables. On the other hand, the exogeneity of the insurance variable was rejected at p values below.01. These results were robust across all specifications that are reported in the empirical section. Finally, based on the past literature, several control variables are included in the model. These include the number of children under age 6, number of children of ages 6 18, and dichotomous variables for black workers, Hispanic workers, workers residing in four regions of the United States, and workers residing in metropolitan areas. Other control variables include spouse s education and wage income. Inclusion of these variables controls for variation in the own health insurance variable that might otherwise correlate with the error term in the spouse s wage equation. They control for assortive-mating effects and reduce the possibility/size of the bias in the coefficient of health insurance. IV. EMPIRICAL RESULTS A. Data The data used in this study come from the Agency for Healthcare Research and Quality (AHRQ). Since 1996, AHRQ has been conducting the MEPS. MEPS files include three survey components. The core survey is the household component (HC), which forms the basis for the medical provider component and the insurance component. The MEPS-HC uses an overlapping panel of six rounds over a period of 2.5 years. It also produces annual end-of-the-year information. The 2001 MEPS-HC contains approximately 12,700 civilian noninstitutionalized families and 33,553 individuals. Though the HC was released earlier, the supplementary file that contains information about persons with private insurance, including the health insurance premiums paid by the policyholder, entitled 2001 Person Round Plan Public Use File, was released to the public for the first time during the summer of Previous years files do not provide the premium information. Evans, Levy, and Simon (2000) provide an excellent survey and description of available data sets, including MEPS, that can be used in health economics Private insurance means that the insurance is purchased versus free provision of health care by the public sector. Private insurance may be obtained from an employer, a union, an insurance agent, an insurance company, or a professional association. 17. MEPS and its predecessor, the National Medical Expenditure Survey, files have been used by a few researchers to investigate issues such as employer-provided insurance and job lock and health-care use. See Evans, Levy, and Simon (2000) for details.

8 DANESHVARY & CLAURETIE: GENDER DIFFERENCES IN THE VALUATION The MEPS questionnaire also asked participants whether or not they were offered health insurance through their current job. Due to a skip pattern of the question and inconsistencies in responses, a significant portion of the data set includes missing values for the offer variable, making it unreliable, if not impossible, to use. Limiting the sample to observations with reliable values for the offer variable reduces its size significantly. The 2001 MEPS-HC includes information on an individual s age, race, employment status, marital status, wages, family size, healthcare total and out-of-pocket expenditures, and other demographic data. 18 There are 20,092 civilian noninstitutionalized men and women of ages The private insurance supplementary file contains 11,260 policyholders, covering 22,069 individuals, for which outof-pocket premiums, including zero dollar amounts, and type of policy (single versus family) were reported. When the two files are merged by person identifiers, 19,971 individuals have records in both files. Restricting the merged sample to ages reduced the sample size to 13,143 individuals and 8,595 policyholders. The data set provides annual total medical expenditures, total out-of-pocket expenditures paid by the user or user s family, and thus, the total medical benefit, and the premium paid by the policyholder. Appendix 1 shows, by age group and by gender, the average amount of expenditures (Panel A), the percentage of the sample with nonzero expenditures (Panel B), the average amount of medical benefits received (Panel C), and the medical benefit as a percentage of expenses (Panel D). Information on these four panels is based on the sample of 20,092 individuals in MEPS-HC. Appendix 1 also shows insurance premiums paid by policyholders by the type of coverage (single/family) and by gender, based on the 8,595 policyholders (Panel E). Information from Appendix 1 suggests that women, in general, experience higher health-care expenses, receive higher benefit payments from insurance, exhibit a higher probability of utilizing health-care services, and exhibit a higher probability of receiving insurance benefits. These results raise questions regarding relative costs of employerprovided health insurance, effects on wages of men and women, and effects on genderwage differentials. To estimate the model outlined earlier using spouse s variables as instruments, we concentrate on two separate samples of married men and married women. We use family identifiers to obtain spousal variables. We restrict samples to ages of working men and women, excluding self-employed individuals, individuals attending college, noncivilian labor-force workers, and farm workers. Further, deleting records with missing values for needed variables produced samples of 3,723 working married men and 3,042 working married women. We report sample means and standard deviations in Appendix 2. The personal and job characteristics variables seem reasonable and consistent with other nationally representative data sets. The means of the health-related variables, such as personal annual health expenditure and number of days missed work, are lower for men than for women. A slightly larger percentage of men, relative to women, hold jobs that provide vacation leave. As expected, a larger portion of the men sample has employer-provided health insurance and a family plan policy than the women s sample. Thus, men pay a higher premium and have larger total health-care benefits for the entire family. B. Estimation of Earning Equations To disentangle the effects of heterogeneity of jobs, the insurance cost sharing, and healthrelated variables and to check for the severity of potential endogeneity of the employer-provided health insurance variable, the earnings model outlined above was first estimated for married men and married women, separately, using the OLS. The OLS results do not account for endogeneity of the health insurance variable. They provide a baseline for comparison and allow us to investigate the endogeneity issue. Table 1 reports the results. Table 1 also reports the results of four specifications applied to each of the two samples. Column 1, for each sample, reports results from a baseline model that includes the human capital and other control variables as well as the variable representing employer-provided health insurance. They exclude job characteristics and other health- and health insurance related variables. Column 2 adds variables related to jobs. Column 3 includes other health and health insurance variables but not job variables. Finally, Column 4 reports results of a complete model and includes all variables.

9 808 ECONOMIC INQUIRY TABLE 1 Heteroskedasticity-Corrected OLS Estimates of Natural Logarithm of Annual Earnings: Married Men and Married Women Married Men Alternative Specifications Married Women Alternative Specifications Variables (1) (2) (3) (4) (1) (2) (3) (4) Own employer-provided.103* * * *.026 health insurance Job characteristics quality Job has paid.056*.052*.101*.100* sick leave Job has paid.145*.149*.140*.139* vacation leave Health status and health insurance variables Annual health expenditure (self,.000) Work-limiting disability.179**.175** No. of half-days.002*.002* missed work (self) No. of half-days missed work (others) Contribution to premium (.000) Total annual health-care benefits (.000) Plan is a HMO Notes: In addition to the variables shown in the table, each model includes own experience, tenure, and their squares and interaction; own education, spouse s education and wage income, as well as indicator variables for race, occupation, industry, other job characteristics, union membership, firm size, Metropolitan Statistical Area residency, region of residency, number of children under age 6, and number of children of ages See the text and Appendix 1 for details. * and **Significant at.01 and.05 levels, respectively. Estimated coefficients of human capital and other control variables, not reported in Table 1 but available upon request, are reasonable and have the correct signs. These coefficients are robust across all specifications for both samples. In addition, the coefficients of variables that are reported in Table 1 are stable across specifications. Coefficients of the health insurance variable are positive in all specifications, contrary to the prediction of the compensating wage differential theory but consistent with the findings of most previous research. Coefficients are statistically significant in the baseline models and in models that exclude job characteristics but include other health- and health insurance related variables. When the job characteristic variables are included, Columns 2 and 4, the positive coefficients for the health insurance variable fall and prove insignificant. These results provide evidence of the job heterogeneity s confounding effect on the estimation of the trade-off between wages and health insurance. In addition, the results in Table 1 strongly suggest biased estimates resulting from the use of OLS. To obtain an accurate estimate of the tradeoff requires that the measured insurance variable does not correlate with the error term in the earnings equation. This can be accomplished by identifying IVs for H i in Equation (1). To be potentially valid, each IV must correlate with the likelihood of having own health insurance, H s in Equation (2), which, in turn, affects the likelihood of the spouse receiving his/her health insurance, H i in Equation (2) and, thus, affecting earnings in Equation (1). For the reasons discussed earlier, we consider spouse s firm size and whether spouse s insurance plan includes family coverage as potential instruments. Table 2, Panel A, shows the first-stage heteroskedasticity-robust conditional linear probability estimates of Equation (2), where

10 DANESHVARY & CLAURETIE: GENDER DIFFERENCES IN THE VALUATION 809 TABLE 2 Estimates of the Effects of Spousal Firm Size and Spousal Family Policy on the Probability of Having Own Employer-Provided Health Insurance and on the Natural Logarithm of Annual Earnings Married Men Married Women Variable (1) (2) (3) (1) (2) (3) Panel A: Probability of having own employer-provided health insurance Spouse works.099*.033*.100*.030** for a small firm Spouse s policy.346*.338*.330*.322* includes family plan Panel B: Natural logarithm of annual earnings Spouse works.052**.045** for a small firm Spouse s policy includes family plan.050**.041***.053*.049** Notes: Estimates in both panels are corrected for heteroskedasticity. Panel A estimates are conditional (first-stage) linear probabilities. In addition to the variables shown in the table, each model includes own experience, tenure, and their squares and interaction; own education, spouse s education and wage income, as well as indicator variables for race, occupation, industry, other job characteristics, union membership, firm size, Metropolitan Statistical Area residency, region of residency, number of children under age 6, and number of children of ages See the text and Appendix 1 for details. *, **, and ***Significant at.01,.05, and.10 levels, respectively. spousal health insurance is captured by one or two instruments. Panel B provides estimates of the relationship between the spousal instruments (firm size and family coverage) and own annual earnings. Estimates are provided for each instrument separately, Columns 1 and 2, and for the combination of the two instruments, Column 3. Each specification in Table 2 includes all the explanatory variables that are included in estimating the earnings equations. The results in Table 2, Panel A, indicate that even after accounting for the list of exogenous variables, individually and jointly, both instruments strongly relate to spouse receiving health insurance and exhibit the expected signs. The results are consistent and similar across gender. A spouse working for a small firm increases the likelihood of the other spouse having health insurance by 10 percentage points. A spouse having a family insurance policy reduces the probability of having own employer-provided insurance by about percentage points. As shown in Column 3 of both samples, when both instruments are included in the model, the coefficients have the expected sign and are significant at the 1% level. These results show that the two instruments, individually and jointly, are good predictors of the probability of spousal health insurance, H i in Equation (2). Panel B of Table 2 shows that coefficients of both instruments, separately or jointly, have the expected signs. Men married to women who work for small firms (whose wives have a family health insurance policy) earn about 4% 5% less (more). This perhaps indicates that men whose wives work for larger firms and have family insurance do not have a need for employer-provided health insurance and seek higher paying jobs without insurance. For the sample of women, the coefficients of husband working for a small firm have the expected sign but are insignificant. Women whose husbands have a family insurance policy earn about 5% more. Table 3 reports heteroskedasticity-robust IV/2SLS estimates of wage/health insurance coverage trade-offs for working married men and married women, separately. The results are presented according to the identification exclusion restrictions. Row A presents the estimated trade-offs when spousal firm size is the excluded variable (the instrument). Row B reports the trade-off results when spousal family coverage is the instrument. Row C includes both instruments. Coefficients of job- and health-related variables are also reported for the specification in Row C. Column arrangements are the same as in Table 1, each column representing a different model

11 810 ECONOMIC INQUIRY TABLE 3 IV/2SLS Estimates of the Effects of Health Insurance Coverage on the Natural Logarithm of Annual Earnings: Married Men and Married Women Married Men Alternative Specifications Married Women Alternative Specifications Variables (1) (2) (3) (4) (1) (2) (3) (4) Own employer-provided health insurance A. Instrument: **.515**.529** Spouse s firm size B. Instrument: ** ** *.122***.198** Spouse has family coverage C. Instrument: ** ** ** * Spouse s firm size and family coverage (2SLS) Job characteristics quality Job has paid sick leave.067**.066**.138*.128* Job has paid vacation leave.191*.199*.171*.177* Health status and health insurance variables Annual health expenditure (self, 10 3 ) Work-limiting disability.207*.199*.143***.132*** No. of half-days missed.002*.002* work (self) No. of half-days missed work (others) Contribution to premium **.022*.020***.020*** Total annual health-care benefits 10 3 Plan is a HMO.040** p-values chi-squared test of overidentification (orthogonality) of both instruments Notes: In addition to the variables shown in the table, each model includes own experience, tenure, and their squares and interaction; own education, spouse s education and wage income, as well as indicator variables for race, occupation, industry, other job characteristics, union membership, firm size, Metropolitan Statistical Area residency, region of residency, number of children under age 6, and number of children of ages See the text and Appendix 1 for details. *, **, and ***Significant at.01,.05, and.10 levels, respectively. specification. All specifications in Columns 1 4, for each gender, include human capital and other control variables discussed before. Estimated coefficients of these variables, not reported in Table 3 but available upon request, are reasonable and have the correct signs. These coefficients are robust across all specifications. In addition, all statistically significant variables in Columns 1 4 have their expected signs. The results of all three instrumental models, Rows A C and Columns 1 4, show that IV/ 2SLS estimates of the trade-off are negative, suggesting that accounting for the endogeneity of the health insurance variable corrects for OLS-biased estimates. The results of the models that use spousal firm size as the only instrument, Row A, are relatively large and significant only at the 10% level in the men sample and insignificant in the women sample. As predicted by our earlier discussion, these estimates probably overestimate the tradeoff resulting from assortive mating. When spousal family coverage, Row B, or spousal family coverage and spousal firm size, Row C, are included as instruments, the estimates of the trade-off are of the correct sign and are significant, except in Specification 2. The estimated trade-offs are similar between the two models that use family coverage alone or with firm size. For example, in Specification

12 DANESHVARY & CLAURETIE: GENDER DIFFERENCES IN THE VALUATION 811 4, the estimates are.158 and.165 for married men and.198 and.200 for married women. In addition, the estimated coefficients of other variables are similar in size and in level of significance across the two models. These findings suggest, as argued earlier, that the spousal family health insurance coverage proves robust as an appropriate instrument for the spousal health insurance variable in the estimation of the earnings equations. Referring back to Table 2, spousal firm size, however, has a small, but significant, effect on the probability of having employerprovided health insurance and thus earnings, even after accounting for the family coverage variable. We performed two additional tests with respect to the inclusion of firm size along with family coverage. First, the chi-square test of Hall and Peixe (2003) rejected the null hypothesis of redundancy/irrelevance of this instrument at the 5% level or better. Second, the chi-square tests of overidentification/ orthogonality (Hansen, 1982) that are reported in Table 3 show that we cannot reject the hypothesis that both variables are orthogonal to the error term of the earnings equation and/or are valid instruments. Thus, in what follows, we further discuss the results of the model that include both instruments. Column 1, in Table 3, shows estimates of the basic model that do not include job characteristics and other health- and health insurance related variables. The estimates of trade-offs now have the expected sign but are insignificant for both genders. Adding quality of job variables to the basic model, Column 2, has significant effects on the size and significance level of the estimated tradeoffs. The trade-offs are 11.7%, though marginally significant, and 14.6% for men and women, respectively. The change in results directly reflects to accounting for job characteristics. That is, when we account for the fact that good jobs have higher benefits and higher pay at the same time, we are purging most of the bias resulting from the correlation between the health insurance variable and the error term in the earnings equation. Adding health status- and health insurance related variables to the baseline models, Column 3, changes the size of the estimated trade-offs. The estimates are statistically insignificant. Comparing Specifications 2 and 4, however, shows a contribution of about 5 percentage points to the estimates of trade-offs due to the inclusion of health status- and health insurance related variables. Column 4 reports the result of complete models that include all variables. First, the trade-off between earnings and health insurance equals 16.5% and 20.0% for married men and married women, respectively, a difference of 3.5 percent points. Based on the minimum chi-square test of Newey and West (1987), the difference is statistically significant at 1% level. The coefficients of the other variables, except that of HMO, are similar in the Columns 2, 3, and 4 specifications. The coefficient of the out-of-pocket premium variable is significant and of a reasonable magnitude for both samples. Married men s and married women s wages increase by 2.2% and 2.0%, respectively, due to a $1,000 out-of-pocket premium. Considering the mean annual wages from the samples, these results translate to about $920 for men and $660 for women. The estimates of premiums and earnings trade-offs taken together translate to annual values of health care of about $7,200 for women and to $7,800 for men in 2001 dollars. These estimates seem reasonable for a typical family of three to four. Unlike the results of OLS, differential trade-offs between the men and the women samples are present in all 2SLS specifications. 19 What are plausible explanations for the 3.5 percentage point differential trade-offs between married men and married women, after accounting for health-care expenditures, health-care benefits, and out-of-pocket premium, among other variables? One possibility might be that the higher average health-care costs of women relative to men, due to pregnancy, are covered by firms in terms of lower wages and are not passed on in terms of higher employees contribution to premiums. We investigated this possibility by performing 19. Finally, if our IVs are themselves endogenous, then the reported results are also biased. Though there is not a prior reason to be concerned with the endogeneity of the spousal family coverage and firm size variables, a formal chi-square test of exogeneity/orthogonality of each variable can be performed (Hayashi, 2000). The test requires at least three excluded variables. Using the spousal union membership variable as the third instrument, we performed the test for each of the three variables. The hypothesis of orthogonality, and/or that the variable is a proper instrument, cannot be rejected at the 5% level or better. It is also interesting that, with the exception of the endogenous health insurance variable, OLS and IV/2SLS estimates of all the other variables are very similar (Tables 1 and 3).

13 812 ECONOMIC INQUIRY two tests. We reestimated the models for men and women by adding an indicator variable for reproductive ages. We also performed the estimations using a sample of women in nonproductive ages, 41 years and older, and a sample of their husbands. We still found estimated differential trade-offs of about 3 4 percentage points. These findings are, in general, consistent with those of Gruber (1994). A second and perhaps more plausible explanation is that women have a higher valuation of health insurance and are willing to give up a higher percentage of their potential earnings for it than do men. This would be consistent with the notion that, on average, women are more risk averse than men. Recall that under market distortions the differential valuation of health insurance has the potential of creating rents for some workers, which can potentially be extracted by employers and, thus, raises the possibility of gender-earning differentials. Then, the question is whether the estimated trade-off differentials are solely a result of workers preferences or labor market discrimination or a combination of the two. Next, we will investigate this issue by applying decomposition methodology. C. Decomposition of Earning Differentials We proceed by presenting standard decompositions of married men and married women earnings. The decomposition methodology compares the annual earning structure of two groups by dividing the observed annual earning gap into two components: a portion attributed to differences in the level of productivity-related factors (explained differentials) and the remainder attributed to differences in returns to endowments (unexplained differentials). Following the approach of Neumark (1988) and Oaxaca and Ransom (1994, 1998), we write the decomposition equation as follows: ð3þ ln W i ln W j 5 ½ðX i X j Þ # b Š þ½ X i # ð^b i b Þ X j # ð^b j b ÞŠ where i indexes the preferred group, typically the group with relatively high earnings, and j (i 6¼ j) indexes a disadvantaged group, ln W equals the natural log of earnings, X equals a vector of independent variables, ^b i and ^b j equal vectors of coefficients from group i and j wage regressions, and b equals the vector of coefficients associated with the nondiscriminatory wage structure. 20 The left-hand side of Equation (3) expresses the gross wage differential, while the first and second bracketed terms on the right-hand side capture the explained and unexplained differentials, respectively. Table 4 provides the results, in log points, from earning decompositions. Column 1 reports the result from a baseline OLS model that includes the human capital variables as well as the variable representing employerprovided health insurance. Columns 2 5 show the results that are based on the 2SLS estimates of Specifications 1 4 in Table 3, Panel C. 21 The cross differential is.259 or about 29.5%. Explained and unexplained differentials are, respectively, about 80% and 20% of the cross differentials and are statistically significant in all specifications. The OLS column in Table 4 shows that the employer-provided health insurance generates about 2% explained differentials in favor of men. The estimated positive coefficient of the trade-off, however, is biased (as shown in Table 1, Specification 1). The 2SLS Specification 1 that does not include either jobrelated or health-related variables produces statistically insignificant estimates of the explained differentials due to health insurance. Specifications 2 4, on the other hand, which include job- or health-related variables or both, produce negative and significant explained differentials for the employer-provided health insurance variable. Given the negative sign of the trade-off coefficient, this implies explained differentials in favor of women (3% in Specification 4). These findings are consistent with the findings of the previous section that show women value health insurance more than men. The estimated contribution of the health insurance trade-off to the unexplained differentials is statistically 20. Oaxaca and Ransom (1994, 1998) propose and test a generalized form of the nondiscriminatory wage structure by pooling the two samples and using the crossproduct matrices of the explanatory variables as weights for the parameters of separately estimated earning structures of the two groups. The approach eliminates the assumption that the nondiscriminatory structure equals a convex, linear combination of separately estimated structures of the two groups. 21. We thank Michael R. Ransom for kindly providing a computer program that estimates Equation (3) along with the standard deviation of each component.

14 DANESHVARY & CLAURETIE: GENDER DIFFERENCES IN THE VALUATION 813 TABLE 4 Decomposition of Married Men and Married Women Earning Differentials 2SLS Specification OLS (1) (2) (3) (4) Explained differentials.211 (.014)*.204 (.015)*.198 (.015)*.208 (.015)*.203 (.015)* Contributed to Health insurance.018 (.004)*.008 (.007).021 (.008)*.016 (.009)**.030 (.010)* Job characteristics.006 (.003)**.006 (.004)** Health-related variables.015 (.006)**.016 (.005)* Human capital variables.193 (.014)*.212 (.015)*.213 (.015)*.210 (.015)*.211 (.015)* Unexplained differentials.048 (.016)*.056 (.012)*.061 (.012)*.051 (.011)*.056 (.011)* Contributed to Health insurance.014 (.016).011 (.034).013 (.037).016 (.040).014 (.048) Job characteristics.018 (.026).016 (.027) Health-related variables.004 (.015).005 (.015) Human capital variables.432 (.135)*.474 (.135)*.446 (.134)*.489 (.134)*.463 (.099)* Constant term.465 (.134)*.518 (.138)*.572 (.135)*.528 (.138)*.523 (.102)* Notes: Estimates are based on the nondiscriminatory wage structure methodology developed by Oaxaca and Ransom (1994, 1998). See Table 3 and the text for a detailed description of the estimated wage equations and the applied decomposition method. Standard errors of estimates are in parentheses. The natural logarithm of gross differentials equals is.259 or about 29.5%. * and **Significant at.01, and.05, levels, respectively. insignificant in all specifications, indicating that employer-provided health insurance does not contribute to statistical discrimination against women. The explained differentials contributable to health-related and job characteristics variables have positive signs and are statistically significant. Together, these variables explain about 2.3% of the gender-earning differentials. They do not, however, contribute to the unexplained portion of the differentials, thus to the statistical discrimination. We conclude that health-care needs and the preference for employer-provided health insurance explain about 5% of the gender-earning differentials between married men and married women and do not contribute to the estimates of statistical discrimination. V. CONCLUSION Employer-provided health insurance is a significant part of workers well-being and their overall compensation package. The cost of group health insurance coverage to employers varies across firms and jobs and depends on the size and composition/characteristics of the covered group. The size of the tradeoff predicted by the compensating wage differential theory depends on the cost-sharing features of the plan and leads to labor market implications in terms of participation, job mobility, and earnings. With increasing medical costs over the past few decades, employers have aggressively redesigned their group health insurance benefits with respect to types of coverage (product attributes), delivery mechanisms, employee premium contributions, and deductible/co-payment amounts. An accurate estimate of the trade-off recognizes that workers make joint decisions about pay and health insurance. It requires accounting for employees contribution to premiums, health-care expenditures, utilized health-care benefits, and employees health status. It also requires controlling for the heterogeneity of jobs and job match. In addition, trade-off differentials by gender might exist due to potential differences in the valuation of health insurance and/or differences in the costs of providing health insurance. In this study, we examined the effect of employer-provided health insurance on annual earnings of married men and married women, separately. In contrast to most of the previous research, this paper accounts for the endogeneity of the health insurance decision and, at the same time, specifies models that

15 814 ECONOMIC INQUIRY adjust for heterogeneity of jobs, variations in health status, and variations among other health insurance related measures, such as family plan and cost sharing. We find results consistent with the prediction of the compensating differential theory. We identified and tested four different models. Our overall estimated average trade-offs equal 16.5 and 20.0 percentage points for married men and married women, respectively. Out-of-pocket premiums increase annual earnings by 2.2% and 2.0% for men and women, respectively. The estimated gender-trade-off differential is about 3.5 percentage points. Decomposition of the gender-pay gap shows that about 3% of the explained differentials can be attributed to employer-provided health insurance. Health insurance does not appear to contribute to the unexplained differentials or statistical discrimination. APPENDIX 1 Medical Expenditures and Benefits: Amounts and Proportions by Gender and Age Group, Ages Age group Male Female Difference Male Female A. Total expenditures ($) No. of observations , * ,031 1,048* 4,562 5, ,914 2, * 2,254 2, ,453 3, ,683 1, ,571 4, ,890 2, * 9,532 10,560 B. Percentage of the nonzero medical expenses * * * * * * C. Total benefits ($) , * , * ,544 1, * ,843 2, ,714 3, ,532 2, * D. Medical benefit as a percent of medical expenses * * * * * E. Insurance premium by type of coverage and gender of the policyholder ($) No. of observations Single coverage $30 1,949 2,108 Family coverage 1,673 1, * 2,776 1,762 Notes: Source: U.S. Department of Health and Human Resources, Agency for Healthcare Research and Quality, Household 2001 Full-Year Consolidated Data File, MEPS HC-060 and HC-057. *Signifies differences between male and female are significant at.05 or less.

16 DANESHVARY & CLAURETIE: GENDER DIFFERENCES IN THE VALUATION 815 APPENDIX 2 Sample Means and Standard Deviations: Married Men and Married Women Male Female Variables Mean Standard Deviation Mean Standard Deviation Annual wage income 41,797 31,105 32,807 24,162 Personal characteristics Prior experience Experience with current job Education Black 0.09 n/a 0.10 n/a Hispanic 0.17 n/a 0.15 n/a No. of children under No. of children Residence Metropolitan area 0.79 n/a 0.78 n/a Northeast 0.16 n/a 0.16 n/a Midwest 0.24 n/a 0.25 n/a South 0.35 n/a 0.36 n/a West 0.24 n/a 0.23 n/a Job characteristics Federal government 0.04 n/a 0.03 n/a State/local government 0.11 n/a 0.21 n/a Union member 0.16 n/a 0.11 n/a Small-size firm 0.57 n/a 0.60 n/a Medium- and large-size firm 0.43 n/a 0.40 n/a Part-time workers 0.02 n/a 0.10 n/a Hours worked per week Job characteristics ( quality of job) Paid sick leave 0.64 n/a 0.65 n/a Paid vacation leave 0.74 n/a 0.70 n/a Health status and health insurance variables Annual health expenditure 4,502 2,525 4,591 (self) 1,712 Work-limiting disability 0.02 n/a 0.03 n/a No. of half-days missed work (self) No. of 1/2-days missed work (others) Employer-provided 0.75 n/a 0.57 n/a health insurance Family plan 0.61 n/a 0.40 n/a HMO plan 0.47 n/a 0.48 n/a Contribution to health 971 1, ,238 insurance premiums Total health-care benefits 2,720 5,929 1,931 4,928 No. of observations 3,723 3,042 Notes: Industry and occupation variables are not shown. Source: U.S. Department of Health and Human Resources, Agency for Healthcare Research and Quality, Household 2001 Full-Year Consolidated Data File, MEPS HC-060 and HC-057. n/a, not applicable.

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