• Job lock: Entrepreneurship lock

    Links to all posts in the series to which this post belongs are in the introductory post

    While we’re on the subject of the effect of health insurance on job mobility, there’s a related literature focused on a particular type of job transition: from employment to self-employment. Though it’s now the case that the self-employed can deduct health insurance premiums from income, they still potentially face higher costs on the individual market than they might pay for a group-market product since the former (pre-ACA) could be risk (or experience) rated and also includes a higher loading fee than is typical for group-market products.*

    Gruber and Madrian included two papers on this subject in their literature review.

    The first paper on this topic, by Holtz-Eakin, Penrod and Rosen (1996), finds no effect of health insurance on the transition from employment to self-employment. However, they find no affect of most other variables on this transition either (e.g. income, race, education), so the lack of an effect for health insurance may speak more to the quality of the data than to the actual effect of health insurance. Madrian and Lefgren (1998) [unpublished] find some evidence that both continuation coverage and spousal health insurance increase transitions to self-employment.

    There are, however, other papers to consider, most published since Gruber and Madrian’s review. Wellington (2001) found that coverage through one’s spouse increased the probability of self-employment between 2.3 and 4.4 percentage points for men and 1.2 and 4.6 percentage points for women.

    Selden (2009) exploited the temporal variation in tax deductibility of health insurance for the self-employed (25% deductible in 1986, 30% in 1996, and rose in steps to 100% by 2003) to study rates of coverage for self-employed workers and their spouses. He found that the increase in tax subsidization of such coverage over 1996-2004 expanded private insurance by 1.1 to 1.7 million persons. Velamuri (2012) also exploited self-employment health insurance tax deduction policy to examine the rate of self-employment among women. Those with no spousal health insurance were about 10% more likely to be self-employed when the deductibility rate was higher, compared to women with spousal coverage. 

    DeCicca (2010) focused on New Jersey’s 1993 Individual Health Coverage Plan, aimed to facilitate non-employer coverage. He found that the New Jersey law increased self-employment by about 15-25%. Heim et al. (2010) found that the increase in deductibility of health insurance for the self-employed increased self-employment by 9.1-14.9%. More recently, Fairlie, Kapur, and Gates (2011) found a nearly 14% increase in business ownership attributed to turning 65 and going on Medicare. (More on that paper in this prior post.) Gurley-Calvez (2011) found that households claiming the self-employment health insurance deduction were less likely to exit self-employment.

    More recently, Heim and Lurie published two papers in 2013 on this subject. Overall, neither found evidence of an increase in self-employment in states that implemented guaranteed issue and community rating regulations. But one found differing effects by age: workers over 40 were more likely to be self-employed in states with these insurance regulations. Finally, an unpublished working paper by Nikpay (2013) examines the connection between the tax subsidization of premiums for the self-employment and insurance market underwriting reforms, finding that tax subsidies have no effect on households with preexisting conditions in states where health-based denial of coverage is permitted. In states where denials are not permitted, a self-employed individual’s household is more responsive to tax subsidies if a member of that household has pre-existing conditions.

    The preponderance of evidence from the literature is that “entrepreneurship lock” exists, though there are varying estimates of its extent.

    * It is possible that some people could pay less  for an individual market product if, for example, they’re substantially healthier than the group at the employer they left and/or they opt for a less generous plan than they had access to within their employer group.

    @afrakt

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  • How to do nothing with nobody

    Via this tumblr, is the book pictured below the definitive activity guide for the introverted child?

    how to do nothin

    This is a real book you can buy.

    @afrakt

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  • Should health system performance measures be adjusted for sociodemographic factors?

    Increasingly, policymakers and researchers who study disparities have raised the question of whether performance measures would be even more accurate if they were adjusted for sociodemographic factors as well. Indeed, there is a growing understanding that social determinants significantly influence a person’s health. Factors far outside the control of a doctor or hospital—patients’ income, housing, education, even race—can significantly affect patient health, healthcare, and providers’ performance scores.

    -Christine Cassel on the Health Affairs blog, discussing new National Quality Forum draft recommendations. Go read the whole post and, if so moved, provide a comment on the draft report (public comments will be accepted until 6PM Eastern, April 16).

    (With respect to hospital readmission performance measures, I’ve blogged on this issue here and here, among other posts.)

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  • How much does health care contribute to health? [FAQ]

    This is a FAQ entry. See the main FAQ index for others.

    * It is in the comments to this post that, on June 6, 2012, Adrianna—previously anonymous to us—made her TIE debut. She followed up with a wonky and helpful email the next day (screenshot below). This is the TIE-Adrianna origin story, about which we marvel. It is astonishing how much can follow from an email.

    TIE-AM first contact

    (Also, one day before her blog comment, on June 5th, Adrianna, along with Karan Chhabra, launched Project Millennial. )

    @afrakt, with help from Adrianna (@onceuponA)

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  • Job lock: Job mobility

    Links to all posts in the series to which this post belongs are in the introductory post

    My last several posts described research relating health insurance to labor market participation. That’s one vector for job lock—health insurance incentivizing entering or staying in the labor force. But there’s another, and more commonly studied, vector for job lock—staying with a particular firm for the coverage it offers, stifling job mobility (aka, affecting job choice or job turnover).

    Gruber and Madrian surveyed 18 papers in this area, finding a mixed literature. Six studies found statistically significant results consistent with job lock. Six returned only statistically insignificant results. Results in six other studies were mixed or could not be evaluated.

    A principal challenge in the study of the effect of health insurance on job mobility is that it’s difficult to disentangle whether someone has declined to switch jobs because of health insurance or, instead, because of other job-related factors. Confounding arises because some job-related factors (e.g., other benefits) are correlated with the availability of employer-sponsored insurance. If an employee stays at a firm that offers ESI, is that because he prefers the other benefits of working at that firm? Or because of the ESI? How could you tell?

    The identification strategy pursued in almost all of the other analyses of job turnover has been to compare the probability of job departure or turnover of otherwise observationally equivalent employees who differ only in the value that they are likely to place on a current employer’s health insurance policy. Various measures of the value of health insurance have been used. These include: [1] Health insurance coverage from a source other than one’s current employer, most often through a spouse or some sort of continuation coverage such as COBRA; [2] Family size; [3] Health conditions; [4] Health status. [References to papers employing each of these measures omitted.]

    These approaches have their strengths, but no study is ideal; the authors discuss various limitations of work in this area (omitted here for brevity). However, one 1994 paper by the two authors is singled out as particularly strong because it “uses a completely exogenous source of non-own-employment based health insurance.” The study exploited variations in state laws that mandated continued access to employer-provided health insurance for the non-employed (state laws akin to COBRA). They found that continuation coverage increased turnover by 10% and interpret it as a lower bound because the high cost of continuation coverage policies make it unlikely that the state laws fully alleviated job lock.

    Gruber and Madrian summarize the disparate findings in this area by using them to bracket the likely size of job lock. Their own work based on continuation coverage policies provides a lower bound, while work based on spousal insurance coverage provides an upper bound.

    Our view is that the approach of using alternative sources of insurance is more credible. Both approaches suffer from potential endogeneity problems, but the health/expected expenditures approach has a host of additional difficulties that do not arise with the alternative insurance approach. Moreover, within this alternative insurance approach the research by Gruber and Madrian (1994) provides an estimate which is likely free of endogeneity bias, by using variation in state and federal continuation of coverage mandates. So a conservative approach to reading this literature would be to take the results of Gruber and Madrian (1994) identified from continuation of coverage laws as providing as lower bound 10% estimate of the magnitude of job-lock, and the results from the spousal insurance approach as providing an upper bound estimate of 25-35% (Madrian, 1994b; Buchmueller and Valletta, 1996). [Links added.]

    GAO (2011) reviewed studies from 2001-2011 and found most consistent with job lock including Adams (2004) and Gilleskie et al. (2002). The former found that among 25-55 year old, married men, ESI reduces job mobility by 22.5% for those without alternative coverage. The latter, that among 24-35 year old, married males, ESI reduces job mobility by 10-15%.

    Despite their potential methodological weaknesses, many (though a minority) of studies of the effect of health insurance on job mobility did not find consistent, statistically significant results indicating job lock. If one was motivated to argue against job lock, this is where one should look, though it requires willfully ignoring the majority of studies that do find a statistically significant job lock effect. Of course, it’s important to keep in mind that the effect of health insurance on job mobility is only one kind of job lock. It tells you nothing about its effect on labor force participation, covered earlier in the series.

    @afrakt

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  • Job lock: Labor force participation (prime-aged workers)

    Links to all posts in the series to which this post belongs are in the introductory post

    Older adults aren’t the only ones considering health insurance options when making labor force participation decisions. Younger adults do so as well. And, it should not be terribly surprising that spouses’ access to coverage can affect those decisions too. A wife or husband may be less likely to work or work less if her or his spouse has secured coverage for the family. And an unhealthy worker who has a greater relative need for coverage than a healthy worker may be more likely to work if her retention of health benefits depends on it.

    Gruber and Madrian found seven studies of the labor force participation of “prime-aged workers who are not single mothers.”* All seven reported statistically significant evidence consistent with the notion that employer-sponsored insurance (ESI) affects labor force participation decisions among married couples and results consistent with job lock for men.

    Four studies—Buchmueller and Valleta (1999)Olson (1998)Schone and Vistnes (1997), and Wellington and Cobb-Clark (2000)—examined the labor force participation of married women. As Gruber and Madrian explain, they all found “strong evidence that the employment and hours decisions of married women do in fact depend on whether or not health insurance is available through a spouse’s employment.”  GAO (2011) reviewed studies from 2001-2011 and found many consistent with job lock. Kapinos (2009) and Murasko (2008), for example, both found that married women worked less if they had coverage through their spouses. And Royalty and Abraham (2006) found that workers with spousal coverage were less likely to work full-time.

    One might be concerned, however, that a married man may be more likely to work and obtain employer-sponsored insurance (ESI) if his spouse has a distaste for market work. In other words, causality could run the other way. Gruber and Madrian read the evidence to suggest that this is unlikely.

    First, Buchmueller and Valletta (1999) find that the effect of husbands’ health insurance on wives’ labor supply is strongest in larger families, which is consistent with the notion that it is the value of health insurance that is driving the results and not simply tastes for market work. Second, Buchmueller and Valletta (1999) find that wives employed in jobs without health insurance work longer, rather than shorter hours, if their husbands have health insurance. In addition, Olson (1998) shows that conditional on working at least 40 hours per week, wives have a very similar distribution of hours regardless of whether or not their husbands have health insurance. Finally, both Buchmueller and Valletta (1999) and Olson (1998) find that husband’s health insurance reduces the probability of full-time employment for their wives quite substantially, but has only small effects on the probability of part-time employment. These findings taken together provide support for a causal explanation for the effect of husbands health insurance on the labor force participation of their wives, rather than a story based on unobserved correlations with tastes for market work.

    Of course, the conclusion that married women are less likely to work if their spouse has ESI coverage doesn’t say much about job lock. Such women are not in any sense “locked” into work. More broadly, however, the studies lend support for the intuition that the presence of health coverage affects the labor market.

    Those labor-market effects can manifest in job lock for prime-aged men, especially for those who have spouses or dependents who rely on that coverage  Two studies have examined the effect of health insurance on the labor force participation of prime-aged men—Wellington and Cobb-Clark (2000) (mentioned above) and Gruber and Madrian (1997). They include the following statistically significant results:

    • Among 25-54 year old men, continuation coverage (i.e., COBRA) increases the probability of exiting and the time out of the labor force by 15%.
    • Among working-age, married women, spousal health insurance reduces labor force participation by 6-12 percentage points, increases part time work by 1.6-3 percentage points, decreases full time work by 7-13.8 percentage points, and reduces hours worked per week by 15-36%.
    • Among married couples with both partners 24-62 years old, spousal health insurance reduces labor force participation by 23% for women and between 4-10% for men. It reduces annual hours worked between 8-17% for women and 4% for white men.

    More recent work by has focused on the effect of health shocks on employment for workers with and without ESI coverage Bradley et al. (2007) examined breast cancer-diagnosed, married women in Detroit. Those with ESI were ten percentage points more likely to remain with their jobs six months after diagnosis than those with coverage from another source; after 18 months, they were 17 percentage points more likely to stay in their jobs. Tunceli et al. (2009) examined cancer survivors 2-6 years after diagnosis, compared to a non-cancer sample. Those with ESI had a higher employment rate after diagnosis, compared to those with another source of coverage or no coverage. Bradley, Neumark, and Barkowski (2013) found evidence that women with own-job ESI reduce their labor supply by 8 to 11% less after a diagnosis of breast cancer compared to women less dependent on own-job ESI for coverage. All these results are consistent with job lock.

    * Single mothers are covered separately, usually in the context of “welfare lock.”

    @afrakt

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  • Will this paragraph make you want to read about diagnosis-related groups?

    Imagine a government initiative that was supported by Republicans and Democrats alike, saved billions of dollars, improved health care, and was adopted around the world. It happened in 1983, and it continues today. 1 October 2013 marked 30 years since Medicare began paying hospitals by diagnosis-related group (DRG), arguably the most influential innovation in the history of health care financing.

    -Kevin Quinn, Annals of Internal Medicine

    Whether you agree with the assertions or not, that’s a very good opening paragraph, far better than just about anything you’ll read in an academic journal. (Stylistically, the “1 October 2013″ leading off a sentence isn’t so good. Editorial convention, I gather.)

    @afrakt

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  • Job lock: Labor force participation (retirement decisions)

    Links to all posts in the series to which this post belongs are in the introductory post

    Theory tells us that job lock can affect labor force participation, but not its extent. For that we need evidence.

    It’s natural to hypothesize that job lock might be more pronounced for older workers who might otherwise consider retirement. Medical issues and costs increase with age, making employer-sponsored insurance (ESI) more valuable to, say, a 60 year old than, say, a 30 year old, on average. Poorer health at an older age makes working more difficult and retirement more attractive. However, before the age of Medicare eligibility, ESI generally provides the best route to affordable coverage.*

    Even after the age of Medicare eligibility, the existence of more generous ESI could influence participation in the workforce. Consequently, workers who might prefer retirement might continue to work exclusively for ESI benefits. That’s a form of job lock. A corollary is that offers of employer-sponsored retiree health insurance (RHI) might reduce job lock. Older workers not yet eligible for Medicare but who place high value on health insurance might be more likely to retire early if they can do so and still be covered by RHI.

    Gruber and Madrian found 16 papers on the effect of health insurance on retirement. The studies considered one or several of the situations described above. Based on them, Gruber and Madrian concluded,

    Despite using a variety of estimation techniques and several different types of datasets, almost every examination of the topic has found an economically and statistically significant impact of health insurance on retirement.

    Indeed, of the 16 papers surveyed, 12 found a statistically significant result consistent with job lock, one found a statistically significant result inconsistent with job lock, and the three others included results that were mixed or that could not be evaluated. In summary, findings consistent with job lock include:

    • RHI delays retirement until age of eligibility for it and accelerates retirement thereafter.
    • RHI reduces the age of retirement by 3.9-18 months, depending on data source.
    • RHI increases the probability of retirement before age 65 by 4.3-15 percentage points, depending on data source.
    • RHI decreases the probability of working past age 62 by 5.3%.
    • Medicare increases the probability of retirement by 280%.
    • Each year of continuation coverage (i.e., COBRA) increases retirement hazard by 30 percent, increases probability of self-reported retirement by 5.4%, and increases probability of not being in the labor force by 2.8%.
    • EHI increases the probability of working past age 65 by 5.3%
    • Poor health increases probability of retirement by 88% and decreases full time work by 5.1-6.3%, depending on age.
    • Private health insurance, RHI, and Medicaid decrease full-time work by 12-25%, depending on age.
    • A 10% decrease in the cost of health insurance in retirement increases retirement hazard by 1.1-1.4% for men and 1.4-1.9% for women.
    • The reduction in retirement health insurance cost associated with RHI increases retirement hazard by 25% for men and 28% for women.

    (The 2011 survey of the literature by GAO found six more studies all consistent with these results.)

    That’s a lot of results, and some cover a wide range of effect sizes. A formal meta-analysis would be handy, but none has been done and one is beyond the scope of this series. It’s worth mentioning, however, that all studies have limitations. Gruber and Madrian discuss some potential pitfalls. For example,

    Many companies have pension plans that encourage retirement at ages before individuals are eligible for Medicare. These companies, however, are also more likely to offer retiree health insurance benefits. Thus, the pension-related incentives for early retirement are correlated with the health insurance incentives for early retirement. [...] If pension-related early retirement incentives are positively correlated with retiree health insurance provision, it is likely that [] reduced form estimates of the effect of retiree health insurance on retirement are too large. Similarly, the selection of individuals with strong preferences for leisure into jobs that offer retiree health insurance will also lead to an upward bias in the reduced form estimates of the effect of retiree health insurance on retirement.

    This critique applies to studies by Madrian (1994)Karoly and Rogowski (1994), Headen, Clark and Ghent (1997) [unpublished], Hurd and McGarry (1996) [also unpublished], and  Rogowki and Karoly (2000). Some, though not all, of the mid-range and higher estimates listed above are from these studies.

    Before concluding, it’s worth mentioning that delaying retirement due to ESI is, from one point of view, not different from delaying it due to wages. That is, ESI, like wages, is a form of compensation. People like working for compensation, and would otherwise—wait for it—not work. The difference in the case of insurance is that, in principle, people could demand additional wages instead of ESI and then buy individual-market coverage. Were that practical, one could divorce labor market participation decisions from health insurance. In practice, that’s very hard for some older workers given the per-ACA dysfunctions of the individual-market. It’s on this very margin that “job lock” is meaningful with respect to labor force participation. That is, we want to know to what extent people continue working because that’s the only practical way to obtain coverage, not for the compensation effects of ESI. It’s not immediately clear to me to what extent this distinction is made in the literature.

    So far, I have considered the effect of health insurance on retirement decisions. Another strand of the literature addresses the effect of health insurance on the pre-retirement labor supply of married couples, which I consider in the next post.

    * Here and throughout, we’re considering a pre-ACA world unless otherwise specified. In a future post, we will consider how the ACA changes the landscape.

    @afrakt

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  • Citation analysis, in one “sobering fact”

    It is a sobering fact that some 90% of papers that have been published in academic journals are never cited. Indeed, as many as 50% of papers are never read by anyone other than their authors, referees and journal editors. We know this thanks to citation analysis, a branch of information science in which researchers study the way articles in a scholarly field are accessed and referenced by others.

    Sadly and ironically, this sobering fact from Lokman Meho is not associated with a citation. Not that I necessarily doubt its veracity, but I would love to be made aware of the body of work that supports it. Anybody know? (Comments open for one week for leads only. Email/Twitter fine too. I have also emailed the author.)

    @afrakt

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  • Pirate-themed CT scanner

    At a New York children’s hospital and via Powerful Pictures:

    pirate CT

    @afrakt

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