Employer responses to the Massachusetts mandate
The employer mandate in Massachusetts has a very weak penalty, just $295 per employee per year. That’s far below health insurance premiums and the ACA’s penalty. One might think that employers in Massachusetts would drop insurance coverage and pay the tiny penalty instead. Nope.
In their recent NBER paper, Colla, Dow, and Dube wrote a useful paragraph about employer responses to the Massachusetts employer mandate:
Based on a pre-post comparison from a Massachusetts household survey, Long and Masi (2008) found no evidence of dropped coverage or restricted eligibility, and no major changes in the scope of benefits, network of providers, cost to employees or quality of available care under health plans. They also found that employer sponsored coverage had expanded due to increased take up among employees. Gabel and colleagues surveyed Massachusetts employers, finding that the percentage of firms with 3 or more employees offering health benefits increased from 73 to 79 percent, that there was an increase in firms offering Section 125 plans, and that Massachusetts employers were less likely than other US firms to terminate coverage or restrict eligibility (Gabel et al. 2008, Gabel, Whitmore, Pickreign 2007). Furthermore, evidence from Massachusetts indicates that despite concerns about potential crowd out from new public options (Cutler and Gruber 1996, Gruber and Simon 2008), there was actually an expansion in private coverage. (© 2010 by Carrie Hoverman Colla, William H. Dow, and Arindrajit Dube.)
Despite incentives to the contrary, employer-based coverage is alive and well in the Bay State. Go figure.
References
Cutler, D. and J. Gruber (1996). “Does public health insurance crowd-out private insurance?” Quarterly Journal of Economics 111: 391–430.
Gabel, J.R. et al. (2008). “After The Mandates: Massachusetts Employers Continue To Support Health Reform As More Firms Offer Coverage.” Health Affairs 27 (6).
Gabel, J.R., H. Whitmore, J. Pickreign (2007). “Report From Massachusetts: Employers Largely Support Health Care Reform, And Few Signs Of Crowd-Out Appear.” Health Affairs 27(1).
Gruber, J. and K. Simon (2008). “Crowd-out 10 years later: Have recent public insurance expansions crowded out private health insurance?” Journal of Health Economics 27(2):201-217.
Long, S.K. and P.B. Masi (2008). “On the Road to Universal Coverage: Impacts of Reform in Massachusetts at One Year,” Health Affairs 27 (4).
Lit Review: Health Insurance Benefits Mandates
Jason Shafrin is the only other health economist I’m aware of who routinely blogs. He deserves some credit for reviewing literature and posting references. His post today on the effect on premiums of health insurance benefits mandates is a good example. Here’s an excerpt.*
A recent paper by the Pacific Research Institute summarizes the findings of various studies of the impact of mandates on health insurance premiums.
- CBO (2000): 4 to 9 percent of premiums, all mandates aggregated
- Graham (2008): 5 to 23 percent of premiums, all mandates aggregated
- Bunce and Wieske (2009): 20 to 50 percent of premiums, all mandates aggregated
- New (2006): 15 percent of premiums, all mandates aggregated
- Congdon et al. (2006): 0.3 to 0.7 percent of premiums, per mandate above 20
- Wisconsin OCI (2002): 1 to 3 percent of premiums, five specific mandates aggregated
- GAO (2003): 3 to 5 percent of premiums, all mandates aggregated
- Krohm and Grossman (1990): 0.2 percent of claims, specific mandated benefits
- Maryland HCC (2006): 2 percent of premiums, all mandates aggregated
- Maryland HCC (2008): 0.01 to 1 percent of premiums per each of five specific mandates
… What one can conclude from the above studies is that mandates do increase cost. The degree to which health insurance premiums increase, however, is not a settled matter.
Of course the notion that mandates increase costs and premiums cannot possibly be controversial except in the case of a small subset of services the increase use of which might offset other, more expensive, health care utilization. An advantage of mandates, or standardization, is that it can decrease complexity and search costs for the consumer, making the market function more efficiently.
Note that costs are increased in two ways: (1) More benefits covered translates to higher insurer payout; (2) More benefits covered attracts enrollment from higher risk individuals. In a market with no standardization low-risk individuals could find less expensive insurance that covers fewer services. But such a market might segment risks so finely that the risk pooling mechanism of insurance ceases to function. That’s made all the more likely in a market with guaranteed issue and no pre-existing condition exclusion periods. Switching products to match needs to coverage is just an extension of the gaming problem I’ve been writing about lately.
Causality and Cost Shifting
In the health care cost shifting debate there are two hypotheses. One is that lower Medicare reimbursements motivate hospitals to seek higher payments from private payers. That’s the classic and pervasive notion of cost shifting. The other hypothesis is that hospitals with high degrees of power command high prices from private payers. This permits such well-paid hospitals to have weak cost controls, resulting in low or negative Medicare margins. That’s a somewhat counter-intuitive story that has been offered by MedPAC in reports to congress and explored in a new Health Affairs paper by three members of MedPAC’s staff, Jeffrey Stensland, Zachary Gaumer, and Mark Miller (summary on the Health Affairs blog).
Which hypothesis seems more likely to be correct? Do low Medicare prices cause high private payments, or do high private payments cause low Medicare margins (via relaxed cost controls)? There is no way to tell from descriptive analysis of observational data. The best evidence would come from a randomized trial. Go ahead and wait for it if you like, but it won’t happen. We can’t randomize hospitals to low and high payments any more than we can randomize them to low and high market clout.
The best we can do is look for natural experiments that can be exploited by well-designed observational studies. Sometimes there is exogenous (uncorrelated with private payment) variation in Medicare payment, such as that induced by the 1997 Balanced Budget Act. In a credible and well designed study, Vivian Wu exploited that phenomenon to deduce that, on average 21% of Medicare payment reductions are shifted to private payers. She also found that market concentration mattered, that in markets with the most dominant hospitals cost shifting rates were as low as 5%. (I reviewed Wu’s paper in a prior post.)
Wu’s results are consistent with other work, and I’m generally satisfied that cost shifting from public to private payers does occur, but at a level much lower than claimed by the hospital or insurance industries. However, that does not mean MedPAC’s hypothesis is incorrect. In fact Wu’s results actually strengthen it. In truth (or so I believe) payer-specific revenues, costs, and market power are, at least in part, simultaneously determined. There is no causal chain that runs only one way or another. Relatively dominant hospitals do cost shift (in the classic sense found by Wu, though at a relatively low rate) and they are also able to accommodate high cost structures and low/negative Medicare margins (as per the MedPAC interpretation). There’s really no disagreement between the two views.
Of course it would be very nice to see a convincing study that explores the issue explicitly from the MedPAC perspective. As the authors make clear themselves, the paper by Stensland, Gaumer, and Miller only illuminates the hypothesis and shows that it might plausibly be true. But it does not show evidence that is necessarily consistent with a causal connection between market power and costs. That ’s not a critique, just a fact.
Theory-minded economists might dismiss the notion that an organization would allow revenue to drive costs. Don’t all organizations minimize costs to maximize profit, independent of revenue? Stensland, Gaumer, and Miller think that nonprofit hospitals would not.
When nonprofit hospitals have more resources, they tend to spend those resources because nonprofit hospitals do not have shareholders to distribute profits to. The nonprofit hospital’s expenditures could be on service-line expansions, such as a new cardiac surgery wing; on acquiring physician practices; on patient amenities, such as larger rooms; or on other capital expenditures that help the hospital maintain and expand its market share of private-payer patients.
On the other hand, in theory for-profit hospitals should minimize costs irrespective of revenue and should maximize revenue over each payer independently. For such hospitals, neither cost shifting theory should hold. If costs don’t vary with revenue then they can’t explain Medicare margins. And a revenue maximizing firm cannot compensate for low Medicare payment with high private payment because, as for profit entities, they’re already maximizing private payment independent of other revenue sources, including Medicare.
Well, that’s theory. The world is often messier. In her empirical study Wu did not find a statistically significant relationship between cost shifting and hospital profit status writing that, “cost shifting is not determined solely by institutional characteristics.”
Market power, costs, private, and public payment are almost surely all related and nothing definitive can be learned without exogenous variation in at least one of these factors. Though work that exploits just that exists, the cost shifting debate will no doubt continue, due in part to the allure of descriptive work based on the weak assumption that costs are exogenous (i.e. outside the control of administrators). But one thing ought to be settled. When two things are simultaneously determined it cannot be said which causes which. Do low Medicare margins cause higher private payments or vice versa? The answer is yes (but to a small degree). And the key mediating factor is market power.
Employer-Based Health Insurance: Setting Employees’ Share
The 2009 Kaiser/HRET employer health benefits survey found that employees pay 17% of the $4,824 annual premium for single coverage and 27% of the $13,375 annual premium for family coverage (all average figures). What determines the employee proportion of the premium?
A plausible purpose of the employee contribution is to take advantage of employee price-sensitivity. All other things equal, the higher the premium faced by an employee, the less likely it is that employee will purchase coverage. A firm can reduce its health care costs to the extent it is successful using price signals to encourage its workers to drop coverage, shift coverage to a less expensive plan within the firm, or to shift coverage to a spouse’s plan from another firm.
Dranove, Spier, and Baker (2000) developed a theoretical model that explains employees’ contribution levels as a source of encouragement on the part of employers for their workers to obtain coverage from their spouses’ employer. The authors found confirming evidence for their model with empirical estimates using 1993-1994 establishment data from at least one employer in each of ten states. The employee proportion of total premium is explained by firm and work force characteristics plausibly related to likelihood of spousal coverage including firm size, proportion of work force that is female, age distribution, full/part time breakdown, union status, wage distribution, flexible spending account offer, and premium level.
Gruber and McKnight (2003) also found empirical evidence based on the 1982-1996 versions of the Current Population Survey to support the hypothesis that as employees’ outside options increase their share of contributions rise. With 1997-2001 MEPS-IC data, Vistnes, Morrisey, and Jensen (2006) found a positive relationship between the proportion of two-earner spouses in the local labor market and employee premium contributions.
Abraham, Vogt, and Gaynor (2006/2007) applied MEPS-IC (1996) data to the question of how households choose among their employer-based insurance options. They found that employee contribution, marital status, wealth, household size, sector (federal vs. non-federal government), number of offers, types of offers (i.e. degree of choice of providers), cost sharing, and income are relevant to the choice. Estimates of own-price elasticity revealed that households are more sensitive to changes in price of plans with the least provider network restrictions. The investigators also note that motivating workers to exit employer plans by increasing the employee contribution may also cause the employer’s risk pool to become more adverse. The resulting higher premiums will partially offset the savings from fewer covered lives.
Finally the authors also consider instances in which firms offer a financial incentive for employees not to enroll in offered coverage (according to the 2009 Kaiser/HRET employer health benefits survey 18% of firms offer incentives for employees to decline coverage). They found that providing a $1,000 payment to workers in one- (two-) offer households is associated with a reduction of 13.3% in the average probability of taking up coverage.
Employer-sponsored health insurance is a good deal for workers, due to the tax subsidy. But the association of health insurance with employment places yet another entity–the employer–between the individual and the health care they obtain. Employers’ interests therefore exert an influence on employee behavior through price signals. That provides an opportunity for another layer of distortion in the health care system, and one that is likely to be with us for a while.
References
J Abraham, W Vogt, M Gaynor. (2006/2007). How Do Households Choose Their Employer-Based Health Insurance. Inquiry 43:315-332.
D Dranove, K Spier, L Baker. (2000). ‘Competition’ Among Employers Offering Health Insurance. Journal of Health Economics 19:121-140.
J Gruber, R McKnight. (2003). Why Did Employee Health Insurance Contributions Rise? Journal of Health Economics 22: 1085-1104.
The Kaiser Family Foundation and Health Research & Educational Trust. (2009). Employer Health Benefits: Annual Survey.
J Vistnes, M Morrisey, G Jensen. (2006). Employer Choices of Family Premium Sharing. International Journal of Health Care Finance and Economics 6(1):25-47.
A Second Lit Reveiw on the Effect of Health Insurance on Mortality
Another literature review on the relationship between health insurance and mortality and health outcomes has been posted on Ezra Klien’s blog. This one is by Stan Dorn, the author of the Urban Institute study that estimated 18,000 deaths could be blamed on lack of insurance. It is a nice complement to the review provided by Michael McWilliams.
In particular, Dorn goes further than McWilliams in his critique of Richard Kronick’s study upon which McArdle’s conclusions are largely based. This may interest readers of this blog, some of whom have asked for more critique of Kronick.
[T]he main point of Kronick’s study is that some of the earlier research may have overstated the effect of insurance on mortality by omitting important variables. Kronick’s study had its own problems because, as his paper alludes, he was not able to address a critically important methodological issue—namely, that people in poor health are more likely to seek health insurance, which obscures any positive relationship between health insurance and health status. Studies that adjust for this factor have found a statistically and quantitatively significant relationship between lack of insurance and increased mortality risk.
Note what has transpired here. McArdle has characterized Kronick’s study as “what may be the largest and most comprehensive analysis yet done of the effect of insurance on mortality.” That sounds very convincing, as if the Kronick study is the definitive word on this matter. In fact, no single study can be. There is no such thing in social science. Every study, including Kronick’s has some limitations. Even a large and comprehensive analysis can suffer from an important methodological limitation, as Dorn believes Kronick’s does.
Therefore, one needs to base conclusions on a body of work. And as Dorn and McWilliams have both found, among recent studies in this area the evidence is greater than three-to-one in favor of an insurance-health outcome link, including mortality. To reach her conclusions, McArdle ignored the entirety of the research in favor of a small number of studies unrepresentative of the whole.
Letting Perfect be the Enemy of Good?
This is a guest post by J. Michael McWilliams, MD, PhD, assistant professor of health care policy and of medicine at Harvard Medical School and an associate physician in the Division of General Medicine at Brigham and Women’s Hospital. He is also author of the 2009 Milbank Quarterly paper “Health Consequences of Uninsurance among Adults in the United States: Recent Evidence and Implications.” (This post has been cited in the 18 February 2010 edition of Health Wonk Review.)
An Atlantic Monthly article by Megan McArdle questions whether health insurance coverage saves lives, drawing from a narrow slice of the literature to suggest the beneficial effects of insurance coverage on mortality might be negligible. While it is true these effects have been challenging for researchers to assess accurately, this question deserves more than a selective reading of the literature to inform the public and policymakers properly. Indeed, when reviewed comprehensively and with an understanding of key clinical and methodological nuances, the research to date provides consistent and compelling evidence that health insurance coverage significantly improves health outcomes, particularly for adults with treatable conditions (McWilliams 2009).
Studies on the health consequences of uninsurance can be broadly categorized as observational or quasi-experimental. Observational studies compare health outcomes between insured and uninsured adults and use statistical techniques to control for differences in other predictors of health between the two groups. These studies are fundamentally limited because it is usually impossible to control for all possible differences and some differences may be both causes and consequences of insurance coverage. Consequently, observational results may underestimate or overestimate the true effects of coverage. From the sizable observational literature, McArdle selects just one negative study to suggest insurance coverage may not affect mortality (Kronick 2009). Yet several other observational studies that controlled for an equally robust set of characteristics have consistently demonstrated a 35-43% greater risk of death within 8-10 years for adults who were uninsured at baseline and even higher relative risks for older uninsured adults with treatable chronic conditions such as diabetes and hypertension (Baker et al. 2006; McWilliams et al. 2004; Wilper et al. 2009).
Because these observational studies are not sufficiently rigorous to support causal conclusions, we should look to studies that are more experimental in design for more definitive evidence. McArdle cites a principal finding of the RAND Health Insurance Experiment (HIE) that more generous coverage led to more health-care utilization but not better health outcomes on average. However, the set of findings from the RAND HIE that is arguably more salient to this discussion is that more generous coverage did lead to better blood pressure control and lower predicted mortality for low-income adults with hypertension — adults that resemble the uninsured population more closely than the average adult. Moreover, the RAND study was conducted in the 1970s, prior to numerous advances that have improved the effectiveness of medical care for many acute and chronic conditions.
From the quasi-experimental literature, McArdle cites evidence of a lack of immediate survival gains with near-universal Medicare coverage after age 65 in the general population (Card et al. 2004; Levy, and Meltzer 2008). From a clinical perspective, however, we should not expect immediate survival gains for most previously uninsured adults because mortality is such a distal outcome. Survival gains may not manifest for years after improved chronic disease control and cancer screening are established, suggesting much more complex improvements in mortality trends are likely to evolve after age 65 in response to universal coverage. Quasi-experiments that rely on abrupt discontinuities occurring with age are not well suited to capturing these complex but potentially large effects. Consequently, the absence of evidence suggested by these studies is not evidence of absence. In contrast to the general population, immediate mortality effects might be expected for acutely ill patients for whom coverage may improve access to life-saving procedures and therapies. Indeed, a more recent study found age-eligibility for Medicare was associated with a substantial and lasting reduction in mortality for patients who were hospitalized for a range of acute illnesses that were amenable to treatment (Card et al. 2009).
Because many quasi-experimental strategies are geared to capture effects of insurance coverage only if they occur in the short term, they are better suited to examining proximal or intermediate health outcomes. Therefore, perhaps more can be learned about the effects of insurance coverage on mortality from studies that rigorously examine effects on health outcomes that are highly predictive of mortality. To date, numerous studies have found consistently beneficial and often significant effects of insurance coverage on health across a comprehensive set of outcomes and a broad range of treatable chronic and acute conditions that affect many adults in the U.S., including hypertension, coronary artery disease, congestive heart failure, stroke, diabetes, HIV infection, depressive symptoms, acute myocardial infarction, acute respiratory illnesses, and traumatic injuries (McWilliams 2009). In particular, several studies have robustly demonstrated positive effects of near-universal Medicare coverage after age 65 on self-reported health outcomes and clinical measures of disease control, particular for adults with cardiovascular disease or diabetes who make up two-thirds of the near-elderly (Decker and Remler 2004; McWilliams et al. 2007, 2009). Thus, when rigorous study designs have been coupled with appropriate outcomes and applied to clinical populations for whom medical care is effective, the evidence that insurance coverage improves health and survival is consistent and convincing.
How many lives would universal coverage save each year? A rigorous body of research tells us the answer is many, probably thousands if not tens of thousands. Short of the perfect study, however, we will never know the exact number. In the meantime, we can let perfect be the enemy of good. Or we can recognize the evidence to date is sufficiently robust for policymakers to proceed confidently with health care reforms that promise substantial health and financial benefits for millions of uninsured Americans.
References
Baker, D. W., J. J. Sudano, R. Durazo-Arvizu, J. Feinglass, W. P. Witt, and J. Thompson. 2006. “Health insurance coverage and the risk of decline in overall health and death among the near elderly, 1992-2002.” Med Care 44:277-82.
Card, D., C. Dobkin, and N. Maestas. 2004. “The impact of nearly universal insurance coverage on health care utilization and health: evidence from Medicare”. NBER Working Paper Series. Cambridge, MA: National Bureau of Economic Research.
Card, D., C. Dobkin, and N. Maestas. 2009. “Does Medicare save lives?” Quart J Econ 124(2):531-96.
Decker, S. L. and D. K. Remler. 2004. “How much might universal health insurance reduce socioeconomic disparities in health? : A comparison of the US and Canada.” Appl Health Econ Health Policy 3:205-16.
Kronick, R. 2009. “Health insurance coverage and mortality revisited.” Health Serv Res 44:1211-31.
Levy, H. and D. Meltzer. 2008. “The impact of health insurance on health.” Annu Rev Public Health 29:399-409.
McWilliams, J. M. 2009. “Health consequences of uninsurance among adults in the United States: recent evidence and implications.” Milbank Q 87:443-94.
McWilliams, J. M., E. Meara, A. M. Zaslavsky, and J. Z. Ayanian. 2007. “Health of previously uninsured adults after acquiring Medicare coverage.” JAMA 298:2886-94.
McWilliams, J. M., E. Meara, A. M. Zaslavsky, and J. Z. Ayanian. 2009. “Differences in control of cardiovascular disease and diabetes by race, ethnicity, and education: U.S. trends from 1999 to 2006 and effects of Medicare coverage.” Ann Intern Med 150:505-15.
McWilliams, J. M., A. M. Zaslavsky, E. Meara, and J. Z. Ayanian. 2004. “Health insurance coverage and mortality among the near-elderly.” Health Aff (Millwood) 23:223-33.
Wilper, A. P., S. Woolhandler, K. E. Lasser, D. McCormick, D. H. Bor, and D. U. Himmelstein. 2009. “Health insurance and mortality in US adults.” Am J Public Health 99(12):2289-95.
Read the Literature
Not only do I believe McArdle has badly misread (or not read) the literature on the relationship between health insurance and health outcomes, including mortality, Monday I will publish on this blog a guest post that includes a literature review that illustrates it. Of course, one already exists, it’s just not accessible to everyone. In addition, I’ve already posted a very brief one.
If one knows that literature, McArdle’s statements continue to baffle. In her latest post on the matter she writes,
I think it is possible that the lack of insurance has no effect on aggregate mortality statistics. I do not think that this is likely, but I think it’s possible.
… Mostly what I think is that the statistics are really, really flawed.
And even more stunning,
… The mortality question is really important, but it doesn’t touch non-mortality outcomes, which are even harder to measure comprehensively.
Not only are the statistics on mortality and it’s relationship to health insurance not flawed (and certainly not “really, really flawed”), but the connection between insurance and non-mortality health outcomes is extremely well established. I cannot fathom how it could be missed by anyone examining the literature. Measuring the effect of insurance on non-mortality health outcomes is not “even harder,” it is far easier. That’s why health services researchers and health economists do it all the time, and publish the results.
This is incredibly important. People really do suffer and die due to lack of insurance. The empirical evidence bears that out. Meanwhile, policymakers debate (and debate, and debate) what to do. McArdle advises a go slow and/or go small approach based on a misreading of the evidence. If there is one thing I would hope we could agree on it is that that’s a very poor basis for policy prescriptions. My recommendation: read the literature or a credible literature review before claiming to know what it says or what it implies we should do.
Consequences of Uninsurance
Apparently Megan McArdle is not convinced that health insurance promotes health. I assume she (and any reasonable minded individual) would agree that death can be caused by lack of sufficiently good health. It is, therefore, only a trivial bit of logic to conclude that if insurance promotes health it can also be life preserving. Or, turning it around, if uninsurance leads to bad health outcomes it can also increase mortality.
That uninsurance is bad for you is easy to defend if you know the research. There is a large body of health services and health economics literature that documents the negative effects on health due to lack of insurance. My own work with Steve Pizer and Lisa Iezzoni, published in Health Affairs, reviews some of that literature as it pertains to individuals with chronic health conditions.
Using data from the National Health Interview Survey, a recent report found that 46.0 million nonelderly U.S. adults (ages 18–64) reported having at least one of seven major chronic conditions in 1997; by 2006, that number had risen to 57.7 million.[1] This and other studies document much lower access to care among uninsured people with chronic conditions compared with insured people. Adverse access markers include lower rates of having a usual source of care, fewer primary care and specialist visits, more frequent use of emergency departments (EDs) for primary care, and difficulties affording services.[2] Such studies complement a growing body of research documenting poorer health outcomes among uninsured people with chronic conditions. [3-6] Acquiring health insurance can improve people’s health and change downward trajectories of functional declines.[7]
(Bold mine.) Since health outcomes pertaining to the transition to Medicare is one focus of Megan McArdle’s Atlantic Monthly piece (see also her related blog post; h/t Tyler Cowen), let’s focus on that for a moment. In Health of Previously Uninsured Adults after Acquiring Medicare Coverage [7] McWilliams, et al. find that
eligibility for Medicare coverage at age 65 years was associated with significant improvements in self reported health trends for previously uninsured adults relative to previously insured adults. … our findings suggest long-term benefits of gaining insurance on the health of previously uninsured Medicare beneficiaries, particularly those with cardiovascular disease or diabetes.
(Again, bold mine.) The evidence that insurance and the access to care it facilitates improves health, particularly for vulnerable populations (due to age or chronic illness, or both) is as close to an incontrovertible truth as one can find in social science.
References
[1] Hoffman C, Schwartz K. Eroding access among nonelderly U.S. adults with chronic conditions: ten years of change. Health Aff (Millwood). 2008;27(5):w340–8.
[2] Wilper AP, Woolhandler S, Lasser KE, McCormick D, Bor DH, Himmelstein DU. A national study of chronic disease prevalence and access to care in uninsured U.S. adults. Ann Intern Med. 2008;149(3):170–6.
[3] Ayanian JZ, Kohler BA, Abe T, Epstein AM. The relation between health insurance coverage and clinical outcomes among women with breast cancer. New Engl JMed. 1993;329(5):326–31.
[4] McWilliams JM, Zaslavsky AM, Meara E, Ayanian JZ. Health insurance coverage and mortality among the near-elderly. Health Aff (Millwood). 2004;23(4):223–33.
[5] Ayanian JZ, Zaslavsky AM,Weissman JS, Schneider EC, Ginsburg JA. Undiagnosed hypertension and hypercholesterolemia among uninsured and insured adults in the third National Health and Nutrition Examination Survey. Am J Public Health. 2003;93(12):2051–4.
[6] Fowler-Brown A, Corbie-Smith G, Garrett J, Lurie N. Risk of cardiovascular events and death—does insurance matter? J Gen InternMed. 2007;22(4):502–7.
[7] McWilliams JM, Meara E, Zaslavsky AM, Ayanian JZ. Health of previously uninsured adults after acquiring Medicare coverage. JAMA. 2007;298(24):2886–94.
Do Premiums Affect Wages?
There seems to be a bit of a debate going about the extent to which health insurance premiums relate to wage levels. This question has current salience as it relates to the predicted effects of the Cadillac tax on high premium insurance plans. Will reductions in premiums translate into higher wages? In a prior post I argued, as many economists have, that they will since employees really pay the full cost of all benefits through lower wages.
Yet, in the Washington Post Alec MacGillis writes, “Some economists also doubt that employers would shift savings from health care into wages, given how slack the labor market is likely to be for the foreseeable future.” And in an EPI issue brief Lawrence Mishel argues that the notion that premium decreases cause wage increases is faulty (*). He writes,
The recent claims that trends in employer health care expenditures explain the beneficial wage growth of the late 1990s and the disappointing wage growth since 2000 does not hold up to any careful scrutiny. Health care expenditures are relatively small compared to overall wages, and an examination of the actual trends shows that health care cost increases do not correspond to the major movements in wages or compensation.
But neither MacGillis nor Mishel cite evidence from peer-reviewed studies about the connection between premiums and wages. (I wouldn’t expect MacGillis to do so in a Washington Post article, but it would be customary in Mishel’s medium.) Let’s take a look at what some of that literature says.
In a 2006 article in the Journal of Labor Economics titled The Labor Market Effects of Rising Health Insurance Premiums, Katherine Baicker and Amitabh Chandra
estimate that a 10% increase in health insurance premiums reduces the aggregate probability of being employed by 1.2 percentage points, reduces hours worked by 2.4%, and increases the likelihood that a worker is employed only part time by 1.9 percentage points. For workers covered by employer provided health insurance, this increase in premiums results in an offsetting decrease in wages of 2.3%.
Since health insurance premiums are plausibly a factor of five or so less than wages (annualized), the 10% increase in the former leading to a 2.3% decrease of the latter is close to a one-to-one trade-off.
But we don’t have to take just Baicker’s and Chandra’s word for it. Others cite similar findings. In a 2008 article in JAMA (link to a full access, low resolution version) Ezekiel Emanuel (yes that one) and Victor Fuchs write that “the health care cost–wage trade-off is confirmed by many economic studies.” In support of this claim they cite the following (extracted from their references):
- Eberts R, Stone J. Wages, fringe benefits, and working conditions: an analysis of compensating differentials. South Econ J. 1985;52:274-280.
- Sheiner L. Health Care Costs, Wages, and Aging. Washington, DC: Federal Reserve Board of Governors; April 1999. http://www.federalreserve.gov/pubs/feds/1999/199919/199919pap.pdf. Accessed February 6, 2008.
- Royalty AB. A Discrete Choice Approach to Estimating Workers’ Marginal Valuation of Fringe Benefits. Indianapolis: Indiana University–Purdue University; June 2003. http://liberalarts.iupui.edu/~anroyalt/wfdiscch_j03.pdf. Accessed February 6, 2008.
- Madrian BC. The US Health Care System and Labor Markets. Cambridge, MA: National Bureau of Economic Research; January 2006. NBER Working Paper No. 11980. http://www.nber.org/papers/w11980. Accessed February 6, 2008.
- Gruber J. The incidence of mandated maternity benefits. Am Econ Rev. 1994; 84(3):622-641.
- Miller RD. Estimating the compensating differential for employer-provided health insurance. Int J Health Care Finance Econ. 2004;4(1):27-41.
- Gruber J. Health insurance and the labor market. In: Culyer AJ, Newhouse JP, eds. Handbook of Health Economics. Vol 1. New York, NY: Elsevier Science; 2000.
Clearly the notion that premiums and wages offset one another has an impressive pedigree. One would have to do far more than MacGillis or Mishel did to convince me (and I would suspect most health or labor economists) to set it aside.
(*) An important update: Mishel’s EPI paper does not contradict the notion supported by the literature I cite in this post. It is about a different though related issue about the extent to which premium changes caused wage changes. He finds that they cannot explain all of the changes seen in the 1990s and 2000s, as some have claimed. See the comments to this post, Kevin Drum’s post, and Paul Krugman’s. Or, simpler yet, just see my follow-up.
Health Care Prices: From the Literature
One of Ezra Klein’s posts today makes the point that the U.S. health care cost issue is largely about prices, as opposed to volume. He writes, “There is a simple explanation for why American health care costs so much more than health care in any other country: because we pay so much more for each unit of care.” That’s not all he writes, so go read the whole post. But before you do, stick around here to learn about the academic literature that is right on this point (from my own blog archives).
The high U.S. spending on health care relative to other OECD nations must be due to relatively higher prices or greater health care consumption or both. The consensus in the academic literature is that price, not quantity, is to blame. In a thorough review of the literature, a 2007 Congressional Research Service report concludes
“[T]he United States has far fewer doctor visits per person compared with the OECD average; for hospitalizations, the United States ranks well below the OECD and is roughly comparable in terms of length of hospital stays…U.S. prices for medical care commodities and services are significantly higher than in other countries…”
Uwe Reinhardt and colleagues simply conclude “it’s the prices, stupid.” In another paper the same authors attribute higher prices to higher provider wages, lower consumer purchasing power (single payer systems in other nations can negotiate lower prices), greater supply constraints, higher administrative costs, and consumer demand for care at any cost.
What do you call a transaction in which you pay more than others for the same product? I call it a rip-off.
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