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date: 18 October 2019

Health Insurance and Labor Supply

Summary and Keywords

Health insurance depends on labor market activity more in the U.S. than in any other high-income country. A majority of the population are insured through an employer (known as employer-sponsored insurance or ESI), benefiting from the risk pooling and economies of scale available to group insurance plans. Some workers may therefore be reluctant to leave a job for fear of losing such low-cost insurance, a tendency known as “job lock,” or may switch jobs or work more hours merely to obtain it, known as “job push.” Others obtain insurance through government programs for which eligibility depends on income. They too may adapt their work effort to remain eligible for insurance. Those without access to ESI or who are too young or earn too much to qualify for public coverage (Medicare and Medicaid) can buy insurance only in the individual or non-group market, where prices are high and variable. Most studies using data from before the passage of the Patient Protection and Affordable Care Act (ACA) in 2010 support the prediction that ESI reduced job mobility, labor-force participation, retirement, and self-employment prior to the ACA, but find little effect on the labor supply of public insurance. The ACA profoundly changed the health insurance market in the U.S., removing restrictions on obtaining insurance from new employers or on the individual market and expanding Medicaid eligibility to previously ineligible adults. Research on the ACA, however, has not found substantial labor supply effects. These results may reflect that the reforms to the individual market mainly affected those who were previously uninsured rather than workers with ESI, that the theoretical labor market effects of expansions in public coverage are ambiguous, and that the effect would be found only among the relatively small number on the fringes of eligibility.

Keywords: health economics, health insurance, labor supply, work, employment, job lock, Medicaid, Medicare, employer-sponsored insurance, Affordable Care Act

Health Insurance in the U.S.

The U.S. is unique in the dependence of health insurance on employment, both directly and indirectly. Over half of the population obtains health insurance through an employer, either their own or that of a parent or domestic partner Berchick, Hood, and Barnett (2018). For those without employer-sponsored insurance (ESI), the availability of most public sources of insurance depends on income, and therefore also on employment. For example, Medicaid provides insurance generally only for persons whose income is below 138% of the Federal Poverty Limit (Mitchell, Baumrucker, & Herz, 2014; Gruber, 2003). Medicare, which insures most Americans over 65, becomes available only after someone has worked and paid Medicare taxes for 10 years (Davis et al., 2017). Those who are unable to obtain insurance through an employer, who earn too much to qualify for Medicaid, or who are too young to qualify for Medicare can obtain insurance through health insurance exchanges set up by the Patient Protection and Affordable Care Act (ACA) or outside the exchanges in the private market. The ACA provides subsidies to help individuals afford insurance purchased on the exchanges. Since these subsidies phase out as a family’s income rises, exchange-based insurance also depends indirectly on employment. This article reviews the theoretical reasons to expect and the empirical evidence to evaluate whether the dependence of insurance on employment affects labor supply. We first consider the employment–insurance connection before the passage of the ACA.

Employer-Sponsored Health Insurance

Pre-ACA

Cost Advantage of ESI

Figure 1 shows the share of persons under 65 years of age with different sources of health insurance. Individuals 65 years of age and over are excluded because 95% of them are insured through Medicare, though around half also have supplementary private insurance. The figure shows that of 2017 nearly 60% of non-elderly persons have employer-sponsored insurance (ESI), a type known as “group” insurance because almost all members of the group—the employees of a firm—are offered the same insurance plan; just over 20% have government insurance (Medicaid); 7% have coverage through individual or non-group insurance; and about 10% are uninsured.

Health Insurance and Labor SupplyClick to view larger

Figure 1. Sources of Health Insurance, 1976 to 2017 (Ages 65 and Below).

ESI is the main form of private insurance, largely because it is the least expensive, for several reasons (Gruber, 2011). One is that it allows insurers to spread the risk of having to pay medical bills among a large group of people whose average health is stable. This pooling of risk enables the insurer to calculate a premium that covers the expected medical expenditures of the group as well as the insurer’s administrative costs and profit. As first highlighted by Akerlof (1970), this arrangement will fail if employees who know they will spend less on medical care than the premium drop out or if people who know they will spend more than the premium join the firm just to get the insurance, a warping of the risk pool known as adverse selection. Insurers discourage this in a number of ways, including restricting enrollment to certain brief periods during the year, which reduces the ability of employees to sign up only when they are sick and drop out when they are better, as well as requiring the firms they insure to enroll a certain percentage of employees (Fernandez, 2012). Prior to the ACA, no such pooling mechanism existed in the individual or non-group market. Insurers in the individual market were allowed to charge each customer a different premium based on that customer’s age, health, and other characteristics, and could deny coverage altogether if a customer’s expected medical expenses were too high or too hard to forecast.

A second reason ESI premiums are lower is that employer plans—particularly among large firms—have lower administrative costs per covered person than plans in the individual market: costs such as management, sales, marketing, and processing claims. Because many of these costs are fixed, large employers can spread them over more enrollees and as a result such costs make up a smaller share of premiums in large firms than in small firms or in the non-group market. For example, in 2010, according to the National Association of Insurance Commissioners (NAIC), administrative costs made up about 9% of premiums paid by large employers (those with 50 or more employees), about 13% of small employers’ premiums, and 19% of premiums in the individual market (NAIC, 2011, p. 15). This disparity in costs explains why nearly all firms with 50 or more employees offered health insurance to their employees in 2017, compared with 30% of firms with fewer than 50 employees (MEPS-IC, 2017).

A third reason that ESI has historically been cheaper than non-group insurance is that the employer contributions to group health insurance are untaxed. Wage and price controls during World War II constricted firms’ ability to compete for workers with higher money wages, but the 1942 Stabilization Act allowed firms to pay for a group health insurance plan for their employees (Thomasson, 2003). An Internal Revenue Service (IRS) ruling the following year exempted such employer contributions from employees’ taxable income, a ruling that was codified in 1954 in Section 106(a) of the Internal Revenue Code (The Yale Law Journal, 1954). Employer-paid premiums are excluded not only from federal income taxes, but from federal employment and state and local income taxes as well. Since employers typically contribute over 80% of the premium (KFF, 2017), the income exclusion creates a sizeable subsidy for ESI (Gruber, 2005). Firms responded quickly to the IRS ruling. Among manufacturing firms surveyed by the National Industrial Conference Board, the share offering group health insurance rose from 36% in 1940 to 68% in 1946 (Jacoby, 1985, p. 198). Since then a majority of employees have been offered health insurance in their places of employment.

The tax advantage of ESI has been reduced over the years. The Tax Reform Act of 1986 (Section 1161) allows the self-employed to exclude 25% of the cost of health insurance premiums from their adjusted gross income. As discussed in the following section “Labor Market Implications of ESI”, the Health Insurance Portability and Accountability Act (HIPAA) of 1996 raised the exclusion to 40% in 1997 and to 100% by 2003. In addition, people who purchase insurance on the individual market have long been able to deduct the cost of the premium above a certain percentage of their income, which stands at 7.5% for 2017 and 2018 (Mulvey, 2011). Further, the ACA offers tax rebates on the purchase of insurance for families whose income is below certain limits.

Labor Market Implications of ESI

A persistent concern about ESI is that it constrains worker mobility. Workers may be reluctant to leave their current job for a better one or for a more rewarding non-employment activity out of fear of losing their insurance. The concern became especially acute in the 1980s and early 1990s, when healthcare prices rose twice as fast as overall consumer prices (CBO, 1991). Since at least 1991, this reluctance to leave one’s current job has been known as “job lock” (The New York Times, 1991), but as early as 1958, Ross investigated whether fringe benefits were creating an “industrial feudalism,” in which “the worker is bound to his job by the chains of benevolence” (Ross, 1958, p. 912). The natural question is why workers would expect not to be able to obtain insurance at their new job or in their new activity. One reason could be that some might have developed chronic health conditions since starting their current jobs. Before the passage of the HIPAA, employers could refuse insurance coverage of conditions that “pre-existed” employment in the firm, regardless of whether the employee was covered prior to joining the firm. Pre-existing conditions would also make it less likely someone could obtain insurance in the non-group market. A second reason could be that health insurance at a prospective firm may be more expensive than the current firm or not offered to any of its workers because the cost of providing it is prohibitively high, perhaps because the risk pool is too small.

Mirroring the problem of job lock is “job push,” where a worker switches to a less rewarding job merely to obtain health insurance (Anderson, 1997; Hamersma & Kim, 2009). Job push could cause workers to choose full-time over part-time work as well as to choose large employers over small employers because of the former’s lower cost of providing health insurance.

Panel A of Figure 2 shows the choices available to a worker who is contemplating changing jobs or leaving full-time employment. The horizontal axis represents the fraction of a year a person spends on non-labor activities. The vertical axis measures consumption, which we assume equals post-tax income. Moving from right to left, each additional hour she works raises her consumption by her after-tax wage. We assume that the slope of the budget constraint below full employment equals the worker’s after-tax marginal product. When she reaches full-time employment, her consumption jumps up because inexpensive ESI becomes available. From that point, consumption rises more sharply than for part-time workers because the insurance is untaxed. Given a choice between working part-time, at point A, or working full-time, at point B, the worker depicted in the diagram would clearly prefer to work full-time because of the tax and price advantages of ESI. This is an example of job push. Now consider a worker at point B who is contemplating moving to a job where she is equally productive but which does not offer ESI. Her prospective budget line would then run from point C0 to 1 Year (x-intercept). Clearly she would not take this job because she would end up at point A. She will only switch jobs if her productivity in her new job raises her budget line at least to C2. Even though she would be more productive at any job that pays between C0 and C2, she will not move because she cannot afford the loss of insurance. This is the prototypical example of job lock.

Health Insurance and Labor SupplyClick to view larger

Figure 2. Labor Market Incentives faced by Workers under Employer Sponsored Insurance (ESI) and Public Insurance (Medicaid).

As alluded to by the incentives facing this worker, job lock and job push can take a number of forms, affecting movement from a full-time job to another full-time job, to self-employment, to part-time work, to non-employment activities, and to retirement. We consider each labor outcome in turn, starting with job-to-job mobility. For fuller reviews of research prior to the ACA, see Gruber and Madrian (2004) and the report by the Government Accountability Office (GAO) (2011). Among the earliest studies was Ross (1958). Using aggregate data on quit rates in manufacturing, he concluded that the declining turnover since World War II reflected mainly better matches between the workers and their jobs and not the attraction of employer-provided benefits. He emphasized the importance of controlling for tenure when analyzing job turnover. More recent studies have exploited longitudinal microdata to better control for the characteristics of workers and of jobs. The most common outcome is a dichotomous variable that equals one if the respondent moved to another job during some time period, usually between one and four years, and zero if she stayed in her current job. Another common dependent variable is the duration of the current job. These two models are closely related but not quite equivalent. The latter is equivalent to a hazard model, which is the probability that a person changes jobs in the next period conditional on the length of time she has been in her current job. The former is an unconditional probability. Buchmueller and Valletta (1996) argue that when modeling this probability one should control for the worker’s duration in the job because this is an indication of the quality of the match between the worker and the job. Doing so makes the models essentially equivalent.

A simple comparison shows that workers with ESI switch jobs less often than those without ESI. For example, using the Panel Study of Income Dynamics, Holtz-Eakin (1994) found that between 1984 and 1987 the probability of changing jobs was about 10 percentage points higher for workers without insurance. However, this cannot be interpreted as an estimate of job lock because ESI is not randomly distributed among jobs. Jobs with ESI may appeal to workers in other ways, such as better retirement benefits and a better match between the job and the worker. Or workers who seek out ESI may have a lower unobserved propensity to change jobs. A second difficulty is that the researcher does not observe the possible alternative jobs available to survey participants. The probability of switching must be positively related to the quality of available alternative jobs (Cooper & Monheit, 1993; Mitchell, 1982).

Two methods have been used to avoid the potential bias resulting from specific job-related and person-level characteristics. One is to control for as many of them as possible. The second is to difference them out, based on difference-in-differences (DD) or fixed-effects models. Using the first approach, Mitchell (1982) compared the probability of switching jobs among those with and without ESI, controlling for as many other characteristics of the jobs and workers as available in the dataset, finding no effect of ESI. Subsequent studies that attempted to control for both job characteristics and person-specific propensities of turnover have found ESI to be associated with job lock (Cooper & Monheit, 1993; Gilleskie & Lutz, 2002). Most studies have utilized the differencing method to minimize bias from unobserved selection into jobs offering ESI. These compare the mobility of workers who differ along two dimensions, first, in the opportunities of retaining insurance across jobs, and, second, in their preferences to do so (Madrian, 1994).

An example of using differences in opportunities is to compare the job mobility of married persons who can obtain insurance either through their own or their spouse’s employer with the job mobility of married persons who have access to insurance only through their own employer. This single difference strategy will work if people whose spouses have insurance do not differ systematically from other married people. To account for this possibility, Madrian (1994) suggests a DD model. The first difference is as just described. The second difference is between workers without ESI whose spouses have ESI, and workers without ESI whose spouses do not have ESI. This identifies an effect of ESI for a fairly large part of the population, married persons with employer-sponsored health insurance, who make up about 45% of persons aged between 25 and 54 (IPUMS CPS, 2000–2016, 2018). Such studies find that ESI reduces the probability of changing jobs by 20 to 40% (Buchmueller, 1996; Cooper, 1993; Madrian, 1994), though a few have found no effect (Berger, Black, & Scott, 2004). A potential problem with this strategy is that a person may seek out a job with health insurance because she knows her spouse may want to change jobs (Royalty & Abraham, 2006). Other changes in the opportunities of retaining insurance across jobs comes from expansions of government insurance. Hamersma and Kim (2009) finds that the expansion of Medicaid in the 1990s reduced job lock among unmarried women though not among men or married women. Bansak and Raphael (2008) find that the availability of the State Children’s Health Insurance Program (SCHIP) among married fathers without spousal insurance increases the likelihood of changing jobs by about 5% compared with married fathers with spousal insurance.

An example of using differences in preferences or perceived valuation for insurance is to compare job mobility among insured people with high and those with low expected health expenditures. Since the former need insurance more than the latter, they should be less likely to risk losing insurance by switching jobs. This will identify job lock so long as people with high expected expenditures do not change jobs less frequently than those with low expected expenditures independently of insurance. To avoid this potential bias, most studies again use a DD model, in which the first difference is among workers with insurance, and the second, among those without. Studies using this approach have found mixed results. Madrian (1994) found that pregnancy and large families, which tend to be associated with large medical expenditures, are associated with lower job mobility. Other studies (Holtz-Eakin, 1994; Kapur, 1998) have not found statistically significant effects. The weaker findings may reflect that the population with high expected medical expenditures is a much smaller share of the population than married persons with ESI (Gruber & Madrian, 2004).

The first substantial effort by Congress to alleviate job lock was the Consolidated Omnibus Budget Reconciliation Act of 1985 (COBRA), which allows former workers to continue to obtain coverage through their former employer for 18 months after leaving a job. A drawback is that former employees may be required to pay both their and their previous employer’s share of the premiums plus a 2% administrative cost (Kinzer, 2013). Gruber and Madrian (1994) found that similar laws at the state level passed before COBRA significantly reduced job lock, defined as changing jobs, becoming self-employed, or becoming unemployed.

The second attempt by Congress was the HIPAA of 1996. The Act restricts the length of time an employer may delay offering health insurance due to pre-existing conditions, depending on the person’s coverage status before starting the new job. If an employee was covered for 12 months prior to starting her new job, her new employer may not delay coverage due to pre-existing conditions. The Act, however, continued to allow employers to impose waiting periods before new employees were offered coverage so long as the waiting period was applied regardless of employees’ health status. In 1999, about 70% of firms imposed such waiting periods (Gabel et al., 2001). Also, firms could continue to deny coverage due to pre-existing conditions if the employee had been without coverage for over two months prior to starting at the firm. Thus, HIPAA could be expected to have only a moderate effect on job-to-job mobility. Indeed, studies using data collected after HIPAA was passed continued to find job lock of approximately the same magnitude as before (Kim & Philips, 2010; Okunade & Wunnava, 2002; Rashad & Sarpong, 2008).

Panel B of Figure 2 shows the options available to someone who at first obtains group insurance through her job (facing the same kinked budget constraint as the worker with access to ESI in Panel A) and then obtains it from some other source. Her budget line in the latter case is straight because her insurance does not depend on how much she works, but it is higher than that of someone without group insurance because individual insurance is more expensive. When she could obtain insurance only through her work, she chose point A, but the income effect of other group insurance induces her to work less, at point B. Studies of the effect of ESI on transitions into part-time employment or non-employment activities have generally focused on the effect of the availability of spousal health insurance on the labor market behavior of married women. Whether the studies model the probability of working full-time (Bradley, Neumark, Luo, & Bednarek, 2007; Bradley, Neumark, & Barkowski,2013), or multinomial outcomes such as full-time, part-time, or not in the labor force (NLF) (Cebi & Wang, 2013; Murasko, 2008; Olson, 1998; Schone & Vistnes, 2000; Tunceli et al., 2009, or the number of hours worked (Buchmueller & Valletta, 1999; Kapinos, 2009; Perry, Kenney, & Tereshchenko, 2009), the consistent result is that spousal health insurance reduces hours worked among married women.

The literature specifically on job push is considerably smaller than that on job lock, partly because, according to Anderson (1997), studies on job lock often conflate the two situations. Anderson (1997) compares two DD strategies to clarify the relationship between them. The first DD compares job mobility solely among persons with health insurance. The first difference is between people with high and low expected medical expenditures. The second is between people with and without portable health insurance, such as that of a spouse or from the government. This identifies job lock, because lack of insurance is not causing anyone to change jobs. The second DD compares job mobility solely among people without ESI. The first difference is between people with and without low expected medical expenditures among those without any coverage, while the second is the same comparison among only those with portable health insurance. This identifies job push, as the excess job mobility reflects the needs of some workers to obtain health insurance. Using the National Longitudinal Survey of Youth (NLSY) of 1979, Anderson (1997) finds that about half of the usual estimate of job lock is actually job push. Using the Survey of Income and Program Participation (SIPP), Hamersma and Kim (2009) examine the effect of the expansion of Medicaid in the late 1990s and early 2000s on both job lock and job push, finding strong evidence of job lock among certain groups, but only weak evidence of job push.

Research on the effect of ESI on the decision to retire has focused on workers aged between 50 and 64, before a worker becomes eligible for Medicare. The effect has been identified largely by comparing the likelihood of retiring among workers in this age range with and without retiree health insurance (RHI), which is coverage that is paid for or subsidized by a person’s previous employer. Even if they have to pay the full premium themselves, such coverage is a good deal for the retiree. McDevitt et al. (2010) estimates that in 2007 a 55-year-old worker in average health would have to pay a third more in the individual market to obtain the insurance equivalent to ESI. The availability of this benefit has declined considerably since the 1980s. In 1988 more than two-thirds of large firms (those with 200 or more employees) offered retiree health benefits; by 2017, that share had shrunk to one-quarter (KFF, 2017; also authors’ calculations using data from the Current Population Survey (CPS) Annual Social and Economic Supplement (ASEC)). Thus the composition of those offered the benefit has likely changed over time toward higher-paid workers in larger companies. Nonetheless, studies have consistently found that the availability of RHI significantly increases the probability that a person retires before age 65 (Blau & Gilleskie, 2001; French & Jones, 2011; Gustman & Steinmeier, 1994; Headen, Clark, & Ghent, 1997; Johnson, Davidoff, & Perese, 2003; Karoly & Rogowski, 1994; Madrian, 1994; Marton & Woodbury, 2007; Rogowski & Karoly, 2000; Rust & Phelan, 1997; Zissimopoulos, Maestas, & Karoly 2007).

Another possible consequence of job lock is to reduce self-employment. This concerns dates at least to 1986, when the Tax Reform Bill of that year allowed self-employed workers to deduct 25% of the cost of premiums, a portion that was raised to 100% by 2003, as described. The earliest study of the possible effect of ESI on self-employment was that by Holtz-Eakin, Penrod, and Rosen (1996), which used a DD approach similar to that in Madrian (1994) and Holtz-Eakin (1994) to assess if the availability of spousal health insurance increases the probability that someone leaves a job providing ESI to open his or her own business; the authors found no effect. Most subsequent studies, however, have found that ESI does reduce self-employment. Using a similar strategy to Holtz-Eakin, Penrod, & Rosen (1996), Wellington (2001) and Fairlie, Kapur, & Gates (2011) find that spousal health insurance significantly raises the probability of becoming self-employed. DeCicca (2010) looked at the effect of the New Jersey-created Individual Health Coverage Plan (IHCP) in 1993, which lowered premiums in the non-group market. Comparing the difference in self-employment in New Jersey before and after the IHCP with the differences before and after 1993 in a number of comparison states, the study found that IHCP raised self-employment by between 14 to 20%. Heim and Lurie (2010) find that the increase in the deductibility of health insurance premiums from zero in 1985 to 100% in 2003 for self-employed workers has raised the likelihood of self-employment from 22 to 27%. Velamuri (2012) compared the difference in the likelihood of being self-employed between persons with and without spousal insurance before and after the passage of the 1986 Tax Reform Act, which allowed self-employed persons to deduct 25% of insurance premiums, finding that self-employment rose by about 10%.

ACA Reforms Affecting Job Lock

The ACA changed the insurance market in a number of ways that would be expected to reduce job lock (Baughman, Dave, & Dills, 2018; GAO, 2011). Some of the changes are aimed at increasing health insurance availability, others, at improving its quality, and still others at reducing its price. The ACA bars insurers from refusing to sell coverage in the group or non-group market because of a customer’s pre-existing conditions, regardless of how long a person may have been uninsured, a requirement known as “guaranteed issue.” While employers may still impose waiting periods before new employees may obtain health insurance, such waiting periods cannot exceed 90 days. A group plan that covers the children of the group’s members must continue to cover those children until they turn 26, regardless of whether the child is married, a student, lives with the parent, or is claimed as a dependent by the parent. This requirement is known as the “dependent coverage mandate” (DCM). Coverage is standardized by requiring all plans to offer 10 categories of “essential benefits.” Other changes are meant to reduce the price of health insurance, mainly but not exclusively in the non-group market. The Act has established Health Insurance Exchanges (or Marketplaces), websites where individuals can compare and purchase health insurance and where they can also obtain tax credits to partially offset the cost if their income is between 100% and 400% of the Federal Poverty Level (FPL). The tax credits are adjusted each year to keep the cost of the plans within a certain percentage of enrollees’ incomes. For example, a family whose income is between 100% and 133% of the FPL will have to pay no more than 2% of its income for a specified level of insurance, technically, the second-cheapest silver plan offered on the Exchange. Thus, if premiums rise, so do the tax credits. With some exceptions, insurance plans can no longer be tailored to the health of the individual applicant, but may vary only by state of residence, age, tobacco use, and whether the insurance is for an individual or a family. To help facilitate the purchase of insurance for employers with 50 or fewer employees the ACA has established the Small Business Health Options Program (SHOP) Exchange. Firms with 25 or fewer employees that buy insurance through SHOP qualify for tax credits that pay for up to half of the premiums. Finally, to avoid adverse selection, almost everyone is required to obtain health insurance, though in 2017 the tax penalty for failing to do so was set to zero.

As a result of the ACA, between 2010 and 2017 the number of uninsured dropped by about 20 million and the rate of uninsurance dropped from 16% to 9% (Cohen, Zammitti, & Martinez, 2018). Frean, Gruber, and Summers (2017) find that about 40% of the decline in uninsurance is due to the subsidies to help people afford policies in the individual insurance market, which grew by 50% to about 15 million people (Semanskee, Cox, & Levitt, 2018). They also found that the increase in non-group insurance did not crowd out group insurance, suggesting that the workers have not used the Exchanges to facilitate transitions to other jobs or other activities. This conforms Sachs’s (2013) findings: that by lowering premiums in the individual market, health reform in Massachusetts (which predated the ACA) increased coverage but did not affect job mobility.

Studies of the effect of the ACA on job mobility, whether from job to job or to non-employment activities, have found mixed results. Most identify the effect using the ACA’s DCM because it went into effect almost immediately after the law passed in 2010, in contrast to most of the changes to the individual market and to Medicaid, which took effect in 2014. Before the ACA, dependent coverage generally ended when the child was 19 or at 24 if the child was a full-time student (Goldman, 2013). Because the DCM extends coverage to age 26, studies have generally defined the treatment group as ages 19 to 25 and the control group as ages 27 to 29, or have used narrower ranges around 26 if the dataset is large enough. Data on 26-year-olds are generally excluded because they are a mixture of treated and control groups.

Using the SIPP, Colman and Dave (2018) estimate that by the end of 2013 the DCM increased dependent coverage by about 10 percentage points, offset by a decline in ESI by about 5 percentage points. Matching this change, they find, using the American Time Use Survey, that the DCM reduced employment by 5 percentage points at the extensive margin and had no effect on employment at the intensive margin. Using the SIPP, Antwi, Moriya, and Simon (2013) also find that the DCM reduces dependents’ labor supply, though they find a smaller effect than Colman and Dave (2018), possibly because their data extends only through 2011. Similarly, Lenhart and Shrestha (2017) provide evidence for reductions in work time for young adults, switching from full- to part-time employment as well as increases in time allocated toward leisure activities following the DCM implementation. Bailey and Chorniy (2016), however, using the CPS from 2008 to 2013, find no effect of the DCM on job mobility, defined as whether the respondent changed jobs within four months of his or her initial interview in the survey. Similarly, using the American Community Survey (ACS), Bailey (2017) finds no effect of the DCM on the likelihood of being self-employed. Heim, Lurie, and Simon (2017) analyze federal tax returns on about 360,000 persons who were listed as dependents in 1997 and who were between the ages of 24 and 29 in the 2008–2013 period. They limit the treatment group to ages 24 to 25 and the control group to ages 27 to 29, and define “employed” as equal to one if the person filed a W-2 tax form, and “self-employed” if the person filed a Schedule SE or 1099-MISC form. The percent employed varies from 79% for 24-year-olds to 81% for 29-year-olds, and from 81% in 2008 to 79% in 2013. The study finds no effect of the DCM on either employment or entrepreneurship, even when limiting the sample to parents whose jobs provide retirement benefits, a proxy for health insurance. It is difficult to compare the results from the studies described to those undertaken prior to the ACA because the treatment groups do not overlap. The DCM by its nature can only reveal the effect on young adults aged 19 to 25 with access to parental coverage, most of whom are not married, while the treatment groups in most pre-ACA studies of job mobility were married persons between 25 and 54.

Since ESI may deter workers from leaving jobs that provide health insurance to start a business of their own, Bailey and Dave (2018) study whether the main provisions of the ACA eased such “entrepreneurship lock,” a consequence of job lock. They focus on older adults, a population for whom the higher average healthcare costs and premiums would make the availability of coverage particularly salient to their labor supply decisions. Using Medicare eligibles (older adults ages 65+), who have access to public coverage under Medicare, as a control group, they find that the ACA raised self-employment by between 3 to 4%. The study identifies the combined effects of the main ACA provisions that went into effect in 2014 (including community rating, guaranteed issue, subsidized exchanges, individual mandate, and the expansions in Medicaid) and is unable to parse out which of these provision are driving the response in self-employment.

ACA Reforms Affecting Retirement

The effect of the ACA’s individual market reforms on retirement would be expected to vary by income, age, the availability of ESI, and of RHI. Among both low- and high-wage workers with ESI but not RHI, the ACA has reduced the cost of insurance in retirement, though for different reasons. As described, low-wage workers can obtain subsidized insurance through the Exchanges. High-wage workers, though not eligible for Exchange subsidies, benefit from the ACA’s community rating requirements. The ACA requires that premiums for people aged 64 and over be no more than three times the premium for those aged between 21 and 24 (Federal Register, 2013). This represents a sizeable subsidy for the older workers, since they consume more than five times as much medical care as the younger group (authors’ calculations using data from the 2000–2015 Medical Expenditure Surveys). One study estimates that in 2017 this subsidy ranged from $400 for a 50-year-old to about $2,100 for a 64-year-old, a reduction of about 20% compared with premiums before the ACA (Saltzman & Eibner, 2015). Thus, workers with ESI but not RHI are more likely to retire after the ACA than before. The effect of the ACA on retirement decisions of workers with RHI is less straightforward. RHI premiums reflect the average consumption of healthcare by the firm’s covered workers, whose average age is around 36 (based on data from the 2010–2013 CPS ASEC). Because potential retirees tend to be considerably older than the average worker, RHI premiums are likely much cheaper than those available to them on the Exchanges, even with the age subsidy required by the ACA. Nonetheless some low-wage workers with RHI may prefer to buy insurance on an Exchange if their share of the cost of RHI is higher than the Exchange-subsidized premium. Hence, some workers with RHI may retire earlier after the ACA. Low-wage workers without either ESI or RHI can also obtain insurance more cheaply after the ACA, but this availability is unrelated to whether the worker is retired, except for workers whose earnings push them just above the income cut-off for subsidies.

Levy, Buchmueller, and Nikpay (2016) use the CPS from January 2005 through June 2015 to analyze trends in part-time work and retirement among people aged 50 to 64 in states that expanded Medicaid and states that did not. The study finds no break in the linear trend in either variable starting in January, 2014. Extending the data through 2016 (Levy, Buchmueller, & Nikpay, 2018), a follow-up study confirms the earlier results. In contrast, Aslim (2018), using data from 2010–2016 ACS and a similar DD strategy comparing trends in Medicaid expansion versus non-expansions states pre- and post-ACA, finds that the expansions increased the probability of early retirement for women but not for men. Gustman, Steinmeier, and Tabatabai (2018) use the Health and Retirement Study to compare the change in the probability of retirement between 2010 and 2014 among workers whose cost of retirement insurance changed as a result of the ACA, with similar workers whose cost of retirement was unchanged. The former include workers with ESI but without RHI, while the latter include workers with both ESI and RHI, and workers with neither. The study also compares changes in retirement expectations among the three groups. It finds the ACA had no effect on either outcome.

Medicaid

Created in 1965, Medicaid is the primary source of health insurance coverage for low-income persons in the U.S., covering over 20% of the population in 2017. The program is funded jointly by the federal and state governments. In 2016 the federal government contributed 63% of the $566 billion of Medicaid spending. A state’s participation in Medicaid is voluntary, but if a state accepts the funding, it must abide by federal Medicaid rules and regulations. All states as well as DC and the Territories have chosen to participate. Within federal guidelines, each state administers its own Medicaid program, setting eligibility requirements, benefits, and reimbursement rates. As a result, these characteristics of the program vary widely from state to state. For example, in 2009, the maximum income a parent could make and still qualify for Medicaid varied from 25% of the FPL in Alabama to 275% in Minnesota. Originally, eligibility depended on receipt of Aid to Families with Dependent Children (AFDC; a federal financial assistance program to low-income families with children) or Supplemental Security Income (SSI; a government means-tested welfare program for low-income elderly, blind, or disabled) and hence the program covered mainly the same beneficiaries: low-income persons who are aged, blind, disabled, or members of families with dependent children. In addition, Medicaid covered persons deemed “medically needy.” Subsequent federal legislation has expanded the groups that states must cover and disconnected Medicaid eligibility from receipt of cash assistance. For example, in 1989 Congress required states to extend eligibility to pregnant women and children under 5 whose family income was less than 133% of the FPL, regardless of whether the family received AFDC or SSI. In 1990, states were required to extend eligibility to all children under 19 whose family income was under 100% of the FPL. Further changes have increased eligibility for children, pregnant women, and victims of various illnesses. By 2009, the median income thresholds among the states were 200% of the FPL for children, 185% for pregnant women, 68% for working parents, and 41% for jobless parents (KFF, 2009). Non-elderly adults without dependent children were rarely eligible (Davidoff, Yemane, & Adams, 2005).

In 2010, the ACA required states to extend eligibility to all persons under 65 years old whose family income was less than 138% of the FPL. In 2012, however, the Supreme Court ruled that this requirement is unconstitutionally coercive, effectively letting states opt-out of expanding eligibility. Thus far 34 states (including DC) have opted to participate in the Medicaid expansion. Between 2013 and 2018, Medicaid enrollment in the expansion states grew by about 13.6 million, an increase of 36% (MACPAC, 2018). Of this change, about 12.6 million are newly eligible adults. The rest of the increase was among people who had been eligible before the ACA but had not enrolled. In non-expansion states, enrollment has also increased by 2 million, or by 11%. Some of the increase among those already eligible may be due to the increased publicity that Medicaid has received because of the ACA, an effect known as the “woodwork” or “welcome-mat” effect.

Expanding Medicaid is likely to affect labor supply differently depending on different preferences toward labor. This can be visualized with reference to Panel C of Figure 2, adapted from Yelowitz (1995). The budget constraint connecting point C0 to the x-intercept shows the person’s marginal product and wage. If Medicaid is not available, the person represented in the diagram will choose to work the fraction of the year shown at point A. The introduction of Medicaid shifts the budget line up until earnings reach the Medicaid income eligibility threshold. This induces the worker to work less in order to reach point B. The expansion of Medicaid under the ACA increases the incentive to work so as to reach point C. Someone with the same marginal product who earns more than the new income threshold will face the same incentives after the expansion as the person at point A, and will therefore have an incentive to work less. If Medicaid is expanded on a large scale, it is also possible that the extra dollars of spending on healthcare might increase employment in general (Kaestner, Garrett, Chen, Gangopadhyaya, & Fleming, 2017). Thus the effect of the ACA expansion on labor supply is ambiguous, and would affect only workers close to the eligibility thresholds.

Economists examining the relationship between Medicaid and private health insurance coverage prior to the ACA have established that Medicaid crowds out private coverage (Cutler & Gruber, 1996; Dave et al., 2010; Dubay & Kenney, 1997; Gruber & Simon, 2008; Lo Sasso & Buchmueller, 2004). While researchers agree that crowding out occurs, there is less clarity about the mechanisms behind the reduction in private coverage following the earlier Medicaid expansions. Given the ongoing debate on how to effectively reduce healthcare spending and lower the budget deficit, it is important for policymakers to have a better understanding of the mechanisms underlying potential switches from private to public insurance coverage.

Dave, Decker, Kaestner, and Simon (2015) suggest that crowd-out works through two potential channels: (a) changes in labor supply choices by individuals, and (b) changes in health insurance offers and take-up by employers and employees, respectively. Previous work on the latter mechanisms provided some evidence that employers did not stop offering health insurance to their workers following Medicaid expansions (Buchmueller et al., 2005; Cutler & Gruber, 1996; Shore-Sheppard et al., 2000), while finding only small declines in take-up of insurance coverage (Buchmueller et al., 2005). Following Dave et al. (2015), this leaves changes in labor supply choices as a plausible explanation for why public insurance may partly crowd out private coverage following Medicaid expansions. Theoretical economic predictions based on the eligibility criteria of Medicaid confirm that this channel might be driving the observed crowd-out effects in the literature. Previous work has examined evidence for labor market effects of Medicaid expansions for both the initial introduction of the program as well as for later expansions, with very mixed findings.

Among the studies reviewed by Gruber and Madrian (2004), a few found positive effects of Medicaid availability (e.g., Ham & Shore-Sheppard, 2001; Yelowitz, 1995); some, negative (Moffitt & Wolfe, 1992; Montgomery & Navin, 2000; Winkler, 1991); and some no effect at all (Meyer & Rosenbaum, 2000; Yazici, 1997). One of the more compelling studies since then focused on the Oregon Medicaid lottery (Baicker, Finkelstein, Song, & Taubman, 2014). In 2008 Oregon expanded Medicaid eligibility, resulting in 90,000 applications. Unable to accommodate so many, the state held a lottery, offering coverage to 30,000 applicants chosen at random. Comparing those who did and did not win the lottery, the study found that enrollment in Medicaid reduced labor force participation by an insignificant 1.6 percentage points, not out of line with prior research. A few recent studies have found significant negative effects. Dague, DeLeire, and Leininger (2017) looked at the effects of Wisconsin’s expansion of Medicaid to childless adults in 2009, finding a significant decline in labor force participation. Using data from the CPS from 2001 to 2008, Garthwaite, Gross, and Notowidigdo (2014) found that Tennessee’s contraction of Medicaid eligibility in 2005 led to a large and significant increase in labor force participation in comparison with nearby states, where Medicaid eligibility had not changed. DeLeire (2018), however, using the SIPP, found no effect of the Tennessee disenrollment on employment.

Studies of the effects of the Medicaid expansion under the ACA have found similarly small effects. Comparing changes in employment among low-income childless adults in expansion and non-expansion states, Leung and Mas (2016) found no difference in employment. Gooptu, Moriya, Simon, and Sommers (2016), comparing rates of employment, job switching, and movement from full-time to part-time among people who were Medicaid eligible and ineligible across expansion and non-expansion states, also find no effect on any of their outcomes. Using the ACS and the CPS, Kaestner et al. (2017) compared labor market outcomes among 22 to 64-year-old non-disabled adults with a high school degree or less in expansion states with those of a synthetic control aggregation of non-expansion states, finding no effect on employment rates.

International Evidence

While the bulk of prior research on the relationship between health insurance and labor market outcomes is conducted in the U.S., there are a number of studies examining the association in other countries. The majority of these studies look at policy changes in Asian countries, whereas the evidence on labor market effects is rather mixed. Exploring labor market effects of the Vietnamese Health Care Fund for the Poor (HCFP), a program that aims to provide poor people and disadvantaged minority groups with free health insurance, Lê, Groot, Tomini, and Tomini (2017) find positive labor supply effects in the short run, but negative effects on work time and employment in the longer run. The authors suggest that income effects of the policy drive the observed negative impacts. Using the staggered rollout of universal healthcare in four waves over a 12-month period in Thailand, Wagstaff and Manachotphong (2012) find that universal coverage encouraged employment among married women, while reducing formal-sector employment among married men.

Two studies have examined the labor market effects of the introduction of national health insurance in Taiwan in 1995. Chou and Staiger (2001) provide evidence that the availability of insurance was associated with a 4 percentage point decline in labor force participation among married women, with the observed changes being larger for low-income households. Similarly, Kan and Lin (2009) find that the policy change led to reductions in work hours for private sector employees. In a country-level analysis of 28 nations in Central and Eastern Europe over a period of 15 years, Wagstaff and Moreno-Serra (2009) exploit changes from tax-funded to social health insurance systems. Estimating both DD and instrumental variables (IV) specifications, they provide evidence that social health insurance systems reduce employment by 10% and raise self-employment by 17%. These effects, however, mostly reflect a reduction in labor demand due to the increase in the non-wage component of labor costs from the social health insurance.

Discussion

The U.S. is the only developed country without universal guaranteed insurance for all citizens. Instead, it has a unique insurance system in place, where most individuals obtain coverage through either employer-based insurance, Medicare, or Medicaid. Given the tight link between employment and insurance coverage, the insurance system leaves many Americans uninsured. These individuals are not covered through their own or a family member’s employment, are not old or disabled enough to qualify for Medicare, are ineligible for Medicaid, and cannot afford to purchase health insurance in the private market. Policymakers have repeatedly attempted to reduce the number of people without insurance coverage. There have been several expansions to Medicaid in recent decades, with the 2010 ACA being one of the largest overhauls of the U.S. healthcare system since the creation of Medicaid in 1965.

The expansion of public insurance programs has generated interest among researchers in studying the effects of these programs on insurance coverage, with several studies examining how the Medicaid expansions have impacted the share of persons without insurance coverage and the extent of substitution of public for private, ESI coverage (crowd-out). Given the existing link between employment and insurance coverage in the U.S., a comprehensive analysis of how changes to public insurance programs affect society should also examine its potential effects on labor market outcomes. The evidence surveyed in this article indicates that the availability of insurance that is not tied to employment generally does impact labor supply. The sometimes mixed findings with respect to public insurance, however, relate to the specific populations being impacted, the type of coverage being studied, and the heterogeneity in the margins being impacted. For instance, recent research on the ACA has not found substantial labor supply effects. These results may reflect that the reforms to the individual market have affected mainly those who were previously uninsured rather than workers with ESI, that the theoretical labor market effects of an expansion of public insurance are ambiguous, and that the effect would be found only among the relatively small number of people on the fringes of eligibility.

Any behavioral changes in labor supply and job mobility can have implications for worker productivity and efficiency. For instance, if ESI in the current job impedes mobility of workers to self-employment or other jobs (that do not offer insurance) where they have a higher marginal product, then expansions of public coverage that break this inefficient “job lock” would enhance productivity and labor market functioning. However, not all job mobility is efficient (Bersak, 2018). For instance, some workers who place a low valuation on health insurance would have an incentive to switch from a more productive job with health insurance to a less productive job that does not offer insurance or offers it at lower cost (or if the worker is able to access coverage in the individual market at cheaper than the compensative wage differential of their current job with ESI); however, this move would by definition be inefficient as the worker is switching from work where their marginal product is higher to where their marginal product is lower. Conversely, a worker who places a high value on health insurance may switch to a less productive job merely to obtain health insurance, a drawback of job push (Anderson, 1997; Hamersma & Kim, 2009). Berskak (2018) shows that much of the empirical job-lock literature conflates these various types of labor market responses, and it is therefore difficult to derive unambiguous welfare implications. Currie and Madrian (1999) also raised the possibility that health insurance can in turn impact worker productivity by improving health outcomes and by changing the utility associated with leisure. A potentially fruitful direction for further research would be to empirically bound the welfare and efficiency implications of the shifts in labor supply associated with the form of health insurance and to build in potential dynamic effects from health insurance to productivity.

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