Bridging Towards Medicare

Transcription

1 Bridging Towards Medicare: Evidence from Massachusetts Hongming Wang Department of Economics, University of Southern California October 31, 2015 Abstract There is extensive evidence in the health and labor literature that workers in their early 60s have incentive to stay employed so that they retain affordable coverage from employers until eligible for Medicare at age 65. The 2006 reform in Massachusetts, widely regarded as the precursor of the subsequent national reform of ACA, unties insurance affordability from employment, and partly offsets the incentive to bridge towards Medicare. Basing identification on insurance discontinuity at age 65, I document large pull-back from labor force among subsidy eligible workers in age 60-64, for whom retirement propensity is higher the closer to Medicare, and social security onset is more likely at age 62. I argue in a social insurance model that welfare gain of premium subsidy is limited by the degree of moral hazard distortion, which in turn is exacerbated by public program crowding out private insurance. Both effects I estimate using simulated subsidy rate as instrument. I find subsidy induces larger reduction in participation among near-retirement workers and longer unemployment duration among entering workers, whereas crowd-out is mostly concentrated at younger ages. Welfare calculation suggests current subsidy rate is higher than the optimal, especially for older workers. Keywords: Massachusetts Health Care Reform; Health Insurance; Health; Retirement; Labor Supply; Social Security. Address: KAP 300, 3620 South Vermont Ave., Los Angeles, CA 90007, USA, hongminw@usc.edu. 1

2 1 Introduction The Patient Protection and Affordable Care Act, signed into law by President Obama in March, 2010, is the most comprehensive national health care reform currently underway in the US. Central to the reform is the individual mandate that requires all legal residents to obtain eligible health insurance or face a tax penalty. The goal is to bring affordable coverage to the 50 million (2010, Census Bureau) uninsured Americans through a combination of policy instruments including Medicaid expansion, premium subsidy administered on the Exchanges, and regulation of the individual insurance market. The implication of the mandate is far-reaching. Most primarily, it reduces adverse selection and lowers the average coverage cost of enrollees. Using the precursor of ACA in Massachusetts in 2006, Hackmann et al.(2014) finds lower premium rate among unsubsidized individual plans in the first few years following the reform, and calculates a welfare improvement of 4.1% per person attributable to reduction in adverse selection. Less obvious is the general equilibrium consequences of the mandate on the labor market, and its implication for other social safety net programs such as Medicare and Social Security. Because most of the working age population is left out of the public insurance program and instead obtain coverage through employer group plans, economists have long been wary of the labor market implications of insurance availability. A large literature has documented the job-lock effect of employer plans (Madrian, 1994, Gruber & Madrian, 1994, 1997, 2004, Gilleskie & Lutz, 2002, and Dey & Flinn, 2005 for a theoretical motivation). This literature suggests there is high degree of self-selection into jobs of different insurance status, and those with insurance tied to their jobs are less likely to switch jobs. In the case of retirement, joblock is more appropriately termed as employment lock (Garthwaite et al., 2014), since transition is more likely to occur on the extensive margin rather than between jobs (Gruber & Madrian, 1995, Blau & Gilleskie, 2001, 2006, 2008). Gruber & Madrian (2004) gives an excellent survey on this topic. The incentive of retaining affordable source of insurance is also complicated by the type of coverage agent is entitled to. Coverage from a public source is probably more susceptible to moral hazard distortion than private coverage either from work or from spouse, especially among means-tested public programs. Utilizing state expansion in Medicaid, previous research generally finds reduced labor supply after public insurance expansion, although the magnitude seems to differ from experiment to experiment. Baicker et al.(2014) does not find economically significant effect on labor supply in the case of Oregon Medicaid expansion, whereas Dague et al.(2013) finds positive evidence of reduced labor supply among new Medicaid enrollees in Wisconsin Garthwaite et al.(2014) instead looks at Medicaid contraction in Tennessee that disenrolled childless adults from its Medicaid program in 2005, and finds swift substitution into employer group plans and increased labor supply among the disenrollees. Closely related is the question whether government in-kind transfers simply crowd out private transfers already in place in the economy. Cutler & Gruber(1996) pioneered and spawned an ever growing literature quantifying crowd-out in state insurance programs, with the findings critically assessed and updated in Gruber & Simon(2008). By and large, the crowd-out estimate seems to be robust at 0.5 across a wide variety of studies. Because the reduction in uninsurance tends to be lower than the increase in public coverage, the 2

3 presence of crowd-out limits the gain of public intervention, and may in fact worsen welfare if both crowd-out and labor supply disincentives are strong. Chetty & Saez(2010) shows theoretically how public transfer can exacerbate moral hazard in the private market and how omitting crowd-out can overstate the benefit of public expansions. Taking into account both effects, they argue that the current Medicaid program is very close to the optimal level. In light of the recent health care reform, the case of retirement combines elements from all three strands of literature. With rising health expenditures and uncertainty at older ages (Hartman et al., 2015), value of health insurance is higher among older workers, and the effect of job lock should be most salient among near-retirement workers. Medicare, on the other hand, creates sharp discontinuity in insurance coverage and large substitution into public coverage at age 65, with drastically different labor supply implications just below and above the threshold: whereas coverage is independent of employment status for the elderly, pre- Medicare workers have incentive to stay employed full-time to bridge towards Medicare. Finally, the reform smooths out the insurance discontinuity facing the old age group and as the previously insurance-constrained types substitute into publicly-subsidized plans and retire early, will likely generate large crowd-out of private insurance before Medicare, and eliminate the bunching in retirement at Medicare. The degree to which post reform transition into retirement is more likely to peak right before than after 65, more evenly spaced over the range 60-70, and more likely to occur at younger ages of 60-64, gives causal identification of insurance availability on retirement. This paper advances the previous literature in several ways. Unlike previous research on job lock, where identification often comes from selected subgroups such as workers enrolled in employer plans, covered by spousal insurance, or veterans, this paper utilizes a natural experiment that alters the insurance incentive for the entire population, and gives clear transition patterns into retirement at the population and subgroup levels that are not available in the previous literature. Moreover, I contribute to the understanding of one particular aspect of the reform, premium subsidy extended to the poor, and causally estimate the degree of crowd-out and moral hazard distortion traceable to the price variation induced by subsidy schemes. I also add to the literature on insurance demand by estimating the demand elasticity of premium subsidy. These estimates I argue are key to the welfare analysis of the social provision of insurance, and I quantify the welfare loss of one more dollar of premium subsidy at 0.12 dollar for the average population, and 0.30 dollar for nearretirement workers. Finally, to the best of my knowledge, this paper is also the first to note the policy complementarity between insurance reform and social security benefit claiming. Because most low income households may be dually eligible for both, a better understanding of how programs interact can inform policy design and budgeting. This paper share a similar topic as Heim & Lin(2014), but the content and motivation differ drastically. Heim & Lin(2014) focuses on the age group and estimates average treatment effect for this group in a standard before-after cross-state comparison. My paper centers around the hypothesis that agents might bridge towards Medicare and therefore more closely looks at transition behavior immediately before Medicare versus after. To do this I use much richer empirical strategies than simple differences, and extend the analysis to study welfare and crowd-out, elements that are absent in Heim & Lin(2014). With respect to the difference models employed in both papers, identification in Heim & Lin(2014) would require cross-state time trend for the given age group to be parallel, which I show below 3

4 is hardly satisfied in pre-reform years, let alone in post-reform years complicated by the recession. My identification instead stems from the differential behavioral patterns of the below age group (60-64) versus the above age group (65-69) within any state-year pair. In my main triple difference specification, this requires that recession does not differentially affect age groups after controlling for all two-way fixed effects, which is much weaker than what Heim & Lin(2014) needs to invoke in a single age group differences-in-differences setup. In terms of outcome, I find much larger increase in early retirement rate, 3.9% as opposed to 0.6% in Heim & Lin(2014). The magnitude differs hugely enough that they carry completely different policy weights, and suggests proper defense against confounds and other threats to identification can be crucial for studies using Massachusetts experience as crystal ball that prophets the national impact of ACA (Gruber, 2013). I begin with a difference-in-differences specification comparing within-state age group, applied to Massachusetts and all other Northeastern states. Estimates from the treatment state is potentially biased due to neglected regional shocks and common demographic shifters, and estimates from the control states constitute natural placebo tests for an erroneous treatment effect. I pay special attention to the complication of the recession: if the concurrent recession affects Massachusetts age group differentially, then the effect of the insurance reform cannot be identified in difference-in-differences. Using other Northeastern states as proxies, I present clear graphical evidence that below and above age groups exhibit identical trends over the period for my main outcome variables. More rigorously, I open a two year window around the onset of recession (December 2007, NBER) and apply the same differences-in-differences strategy to pooled control states. The result shows that conditional on state-year fixed effects, recession does not appear to affect retirement transition differentially across age groups. I then progress to the triple difference model that additionally compares the age group differential across treatment and control states. This extra layer of difference sweeps out any common regional shocks and changes in cohort-specific demographics, sources of biases in the within-state analysis. Inclusion of all two-way fixed effects accounts for differential trending that does not vary at the state-year-age-group level, and importantly absorbs any confounding effect by recession. The remaining variation in the triple interaction term is attributed to the insurance reform. I find strong disincentivizing effect on labor supply of the below group compared to the above group, and the effect is larger among low income households eligible for premium subsidy. The reduction in labor supply is mostly on the extensive margin, in particular direct transition into retirement from full-time work, with little evidence of reduced hours on the intensive margin. The main findings are robust to using only aggregated variables, different computations of clustered standard error, and a spatial discontinuity design where I compare age group differentials within paired contiguous border areas. Disaggregating by age, I show larger effects at policy cut-off ages of 62 (social security early retirement) and 65 (Medicare). To get at the treatment effect at precisely the cut-off, I extend the basic regression discontinuity design and nest it within a difference-in-differences setup. Essentially I compare the discrete change in retirement behavior at cut-off ages across states and over time. Similar to the triple difference design, the double difference takes care of both time-invariant and time-varying confounds that may contribute to retirement discontinuity at the cut-off, including, for instance, recession and cohort-specific variables such as formal retirement age. 4

5 I prove that coefficient before the triple interaction term in the modified design identifies the average treatment effect at the cut-off among the treated states. Applying the specification at early retirement and Medicare age, I show the insurance reform eliminates the bunching in retirement in the first year of Medicare, and sees larger decline in participation right before Medicare rather than after. At early retirement age, workers are more likely to quit working, reduce hours, and initiate old age benefit. In both cases the effect is overwhelmingly driven by the low income group eligible for premium subsidy. The overall age profile of retirement transition differs starkly by income. Effect on higher income workers is modest and peaks at early retirement age for most outcomes. For low income workers eligible for subsidy, however, effect on transition is emphatically larger, nearly monotone over age, and culminates right before Medicare at age 65. In other words, the necessity of bridging towards Medicare is especially relevant for the low income population for whom the employment lock binds more tightly the closer to Medicare. Interestingly, in contrast to their progressive retirement transition, social security onset among the lower income group bunches heavily at age 62. The policy message is that, while most low income workers do not quit working altogether at early retirement age, they do seem to start benefit the moment it becomes available. For future generations fully anticipating and internalizing the policy incentives, early retirement rate may well exhibit the same hump-shaped pattern for the low income group. As a robustness check, I apply the synthetic control approach proposed by Abadie & Gardeazabal(2003) to my setting. This method does not rely on the common trend assumption underlying differencing, but instead constructs a weighted average of control states that best mimics the treatment state in terms of pre-treatment outcomes and key characteristics. The method is equivalent to a differences-in-differences model with time varying fixed effects in the form of multiplicative factors (Abadie et al., 2010). Since my triple difference strategy allows for two-way fixed effects that similarly capture these time varying shocks, the result from the two approaches are comparable. One additional insight is the machine selected set of covariates that best predicts pre-treatment outcome. It is reassuring to find measure of job lock, next to macroeconomic variables, receives high weight in predicting age group differentials in retirement rate. Welfare analysis of the Massachusetts reform benefits from the readiness of post reform data. Previous work includes Hackmann et al.(2014), who focuses on the efficiency gain of mandate in minimizing adverse selection, and Kolstad & Kowalski(2012), who argues employer mandate reduces welfare loss relative to an alternative insurance program funded completely by tax. The second point partially justifies the long tradition of employmentbased insurance system in the US, and the deviation from that paradigm towards a more tax-based, publicly financed insurance system raises natural question as to the labor market repercussions of the reform and the associated welfare loss. However, few paper has directly linked the two together in the insurance context, and hence it remains unknown to what extent is the reduction in labor supply induced by the public provision of insurance, and how does welfare correspond with different level of subsidies. Such knowledge is valuable not only because premium assistance is what practically makes insurance affordable to the otherwise uninsured, but also because it is a policy instrument directly controllable by the policy maker. To get at these questions, I turn to post-reform Massachusetts for an empirical characterization of the mandate economy in practice. 5

6 I build up a social insurance model (Baily, 1978, Chetty, 2006b, 2009) that incorporates key institutional elements of the reform: individual mandate and premium subsidy on the Exchange applied to those not offered employer sponsored insurance. Subsidy is funded by lump-sum tax on the working, creating moral hazard disincentive on labor supply. Higher subsidy rate lowers participation and crowds out private insurance, worsening the moral hazard distortion (Chetty & Saez, 2010). Optimal subsidy rate would balance the consumption smoothing gain of social transfer with the compounded moral hazard inefficiency arising from reduced labor supply and crowd-out. Corresponding sufficient statistics are those quantifying the rate of crowd-out and the subsidy elasticity of labor supply, which I estimate empirically from post reform Massachusetts. An alternative approach to quantify these mechanism would be a dynamic job search models where workers match with firms offering different bundles of wage and insurance, as in Aizawa & Fang(2015) and Aizawa(2015). The advantage of the approach is greater flexibility in counterfactuals and a more general equilibrium scope, whereas the limitation in this particular application is the lack of empirical support for some of their findings, as ex post data are not yet available. In particular, simulation from Aizawa(2015) suggests older workers tend to sort into lower productivity jobs and obtain insurance from the public source, and to prevent the loss in productivity, optimal subsidy rate should be substantially declining in age. As the two reforms bear major similarity, the reduced form estimates in Massachusetts can be thought of as empirical counterparts to the mechanism suggested in Aizawa(2015). Estimating these statistics can be challenging because observed subsidy is endogenous. To isolate exogenous variation of subsidy schedule, I follow Currie & Gruber(1996) and simulate subsidy from a reference national sample. To increase the variation of the instrumental variable, I follow Mahoney(2015) and simulate subsidy for a total of 1008 demographic cells from the national sample. It turns out that crowd-out in Massachusetts is modest: one percentage point increase in subsidy rate induces the marginal enrollee to drop private insurance by an extra 0.36 percentage point, lower than the range found in previous estimates. Most of the crowd-out occurs among the younger workers, and appears to be result of endogenous job sorting as employer offer rate remains high during this period. This stands in sharp contrast to the proposition in Aizawa(2015), where crowd-out is more likely among the older workers. In terms of labor supply, I find consistent evidence across different measures that older workers are most likely to exit labor market at higher subsidy rates, and the effect is not to be confounded with longer unemployment duration or job search frictions that seem to disproportionately affect entering workers. The combined evidence seems to suggest that in near retirement age, insurance subsidy induces workers to leave the labor market altogether rather than sort into differential jobs and insurance types, and the welfare loss of the reform is probably more substantial than the reduction in productivity due to sub-optimal matching. The paper is organized as follows. Section II introduces the Massachusetts reform and its relation to ACA. Section III describes the data. Section IV contains main results from a variety of empirical strategies and robustness checks. Section V formulates a welfare model and estimates sufficient statistics. Section VI concludes. 6

7 2 Massachusetts Health Care Reform The Massachusetts comprehensive health reform is signed into law by the then-governor Mitt Romney in As evident in Figure 1, Massachusetts has higher pre-reform insurance rate than the nation s average, and quickly achieves near universal coverage within a few years of the reform. Central to the law is the individual mandate requiring all Massachusetts residents over age 17 to acquire affordable health insurance that meets a set of minimum creditable coverage standards. Failure to obtain coverage will result in a tax penalty, unless the individual is able to demonstrate unaffordability (for instance, below 150% of FPL) or religious exemption. Figure 1: Non-Elderly Insurance Rate, MA USA Northeastern states except MA Note: I derive aggregate insurance coverage rate from CPS using the insurance sample weight. Census Bureau defines the Northeastern region as including the states of Connecticut, Maine, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, and Vermont. Following a 1950 proposal, I also include Delaware, Maryland and Washington, D.C. throughout the analysis. The law builds upon a sequence of pre-2006 regulations of the state insurance market. Massachusetts merged its small-group and non-group plans into a single risk pool, mandated community rating and guaranteed issue, and formulated a set of coverage and benefit criteria that became the basis of minimum creditable coverage standards in 2006 and the minimal essential coverage standards under ACA. Plans are categorized into bronze, silver and gold tiers according to actuarial values, and similar classification is adopted in ACA. The readiness of a relatively good infrastructure ex-ante is believed to have fostered the quick penetration of the reform and its immediate impact on insurance take-up. To encourage enrollment among the low income population, the reform expands the Massachusetts Medicaid program (MassHealth) and institutes a publicly-subsidized Exchange 7

8 market known as the Commonwealth Health Insurance Connector. The new MassHealth covers children with family income no greater than 300% of FPL, up from the previous 200% cap. Low income population ineligible for Medicaid (for example, non-elderly, non-disabled, childless adults with income above 133% FPL) can obtain coverage from the Connector, a state clearing house bringing together consumers and state-certified individual plans. The Connector has a subsidized Commonwealth Care program and an unsubsidized Commonwealth Choice program. Commonwealth Care is open to eligible individuals whose family income is no greater than 300% of FPL and who are not offered health insurance from their employers. Those not eligible for subsidy can buy from the Commonwealth Choice program. Affordability and premium subsidy schedules are released in the middle of the previous year. In determining the tax penalty, the Connector sets the maximum monthly premium a person in a given income bracket needs to pay towards her coverage, or the affordability. The amount is zero for individuals with family income below 150% FPL. In 2010, for example, out-of-pocket premium cap for an individual plan is $ 39 per month for the % bracket, $ 77 per month for the % bracket, and $ 119 for the % bracket. Individuals are not held accountable for failing to enroll in plans with premium contribution exceeding their affordability threshold. Subsidy schedule works closely with affordability to ensure most of the low-income population are eligible for a subsidized Commonwealth Care plan. Applying subsidy, enrollee contribution falls to 0 for those below 150% FPL, roughly 10% for the % bracket, 20% for % and 30% for %. Alongside the individual mandate, Massachusetts also implements an employer mandate which requires employers with more than 11 full-time equivalent workers make fair and reasonable contribution towards the premium cost of full-time employees, or pay a fine of up to $ 295 per worker. Employers must also provide its employees a section 125 plan that allows premium payment on a pre-tax basis. Failing to do so will incur a free rider surcharge. Additionally, employers have Health Insurance Responsibility Disclosure (HIRD) obligation, and must collect signed HIRD forms from employees who refuse to enroll in employer-sponsored plans. These measures are meant to maintain the central role of employers in insuring workers and their families, and to minimize the crowd-out from private insurance. Due to concerns over administrative costs to firms, and in anticipation of the federal employer mandate phasing in, Massachusetts repealed its employer provisions effective Jul. 1st, Figure 2 depicts trend of employer-sponsored health insurance (ESHI) in Massachusetts. ESHI measures for are from May 2011 edition of Key Indicators, a quarterly report on the Massachusetts health care industry, and data for 2011 are CHIA 1 calculation from Massachusetts Health Insurance Survey. Disaggregation by firm size is derived from CPS by the author. All numbers exclude Medicare enrollees. Since 2007, the year when Massachusetts reaches near universal coverage, the proportion of ESHI among all insurance types has been declining over subsequent years, with nearly a one-to-one increase in the fraction covered from a public source (not shown). Comparing the trend with state employment 1 CHIA, Center for Health Information and Analysis, is an independent agency in Massachusetts that collects information on the performance of the state healthcare system. It administers and analyzes surveys tailored to answer specific questions of the reform. For example, insurance rate estimated from the Massachusetts Health Insurance Survey is around 97%-98%, 2-3 percentage point higher than census estimates. Data from the Employer Health Insurance Survey suggests employer offer rate is stable since the reform at about 70%. 8

9 Figure 2: ESHI Coverage Among Insured Massachusetts Residents, Excluding Medicare ESHI ESHI, firm size 50+ ESHI, firm size<50 Employment Rate Source: ESHI data for are from May, 2010 Key Indicators; for 2011 it is from CHIA. ESHI by firm size is author calculated from CPS. Employment rate is 100% minus unemployment rate, obtained from BLS. rate over the same period, it appears that ESHI percentage drops further despite improving economic conditions in recent years, especially among small businesses with fewer than 50 employees 2. At the same time, there is no evidence that employers are dropping insurance offers as CHIA estimates a relatively stable employer offer rate during this period. Therefore, at least part of the decrease in aggregate private insurance is potentially attributable to workers sorting into public insurance, possibly through matching with jobs not mandated to offer employee coverage, and the sorting is not completely an expediency in the time of recession. This motivates a closer look at the extent of crowd-out in Massachusetts, an issue I analyze further in Section V. Compared to Commonwealth Care, the ACA Exchange market covers families with income up to 400% FPL, up from 300% in Massachusetts, but average cost sharing among the subsidized is also higher. Qualified plans must meet a set of minimal essential coverage criteria, and are subject to similar regulations such as fair pricing based on single risk pool and guaranteed issue already in place in Massachusetts. ACA raises Medicaid income limit to 133% FPL, and encourages states to expand the program further. Starting 2016, the ACA employer mandate requires businesses with more than 50 full-time equivalent employees to cover at least 95% of full-time workers and their dependents below age 26, or pay a fine. Due to the obvious similarities between the two reforms, understanding the incentives and disincentives of the Massachusetts reform, as well as the behavior and motivations driving its 2 In all other Northeastern states, the decline in ESHI coverage rate among small businesses over this period is less than 8%, from 84.17% in 2006 to 76.80% in

10 outcomes, contributes important insight to the prescience of ACA and the normative design of optimal policy. 3 Data My primary data are the annual releases of American Community Survey (ACS), ACS is the most comprehensive census data currently collected in the US. The newest 2013 release samples over 3.5 million addresses, and all annual releases are representative at the state level, and at sub-state level for geographical areas with a minimum population of 65,000. Response to ACS is mandatory by law, giving ACS the lowest non-response rate (around 3%) among commonly used census data such as CPS and SIPP. Since my identification depends critically on within-state variation between narrowly defined age bands, and in some cases sub-state geographical areas, ACS is most suited for my purpose. However, in the appendix I show the same result holds using either March CPS over the same study period, or individual panels in SIPP despite significantly smaller sample size. ACS interviews are conducted continuously throughout the year. Respondents answer questions regarding labor supply status, income, and demographics. Starting 2008, ACS also asks insurance coverage and type. Age in ACS is rounded down to the nearest integer. Labor supply over the past 12 months and last week is recorded. If a person reports having worked in the past 12 months, I assign participation to this person at the prior age. Nonparticipation at a given age means that the person started sometime in that age a spell of non-employment that lasted at least 12 months. This is roughly in line with the notion that she retired in that age, so long as average unemployment duration is less than 12 months over the study period 3. Full-time work is also retrospectively defined at the prior age for jobs requiring more than 35 hours per week. Alternatively, I define current participation status if the person reports active on the labor market last week. Results are similar using the two measures for near-retirement workers. Following previous literature, I define transition into retirement as having worked in the past 12 months but not in labor force last week. Retirement from full-time work is similarly defined. I focus on civilian labor force over the years , excluding those working in the military. I also exclude group quarter individuals because they do not enter ACS until I proxy premium eligibility using reported family income from last year, which I transform into FPL percentages using information on family size and yearly poverty guidelines published by the Department of Health and Human Services. For subsidy, I rely on information contained in yearly HC Schedule Worksheets that help Massachusetts residents determine affordability and penalty when filing tax returns. The worksheets tabulate affordability thresholds by income brackets for that year, and give the premium of the cheapest individual plan by five year age bands available in a given region of residence. A region is a collection of counties with similar premium rates. Subsidy as a percentage is defined as one minus the ratio of affordability limit over the lowest cost premium for a given age-income-region cell as laid out in the worksheets. I map public use micro area (PUMA), the smallest geographical 3 According to BLS, average unemployment duration in Massachusetts 2009 is 23.9 weeks, and the median is 14.4 weeks 4 Group quarter residents constitute less than 1.5% of the age group in Massachusetts 10

11 area identifiable in ACS, to regions. For PUMAs straddling more than one region, I weight subsidy rate by the relative population size in each region. By definition, subsidy is 0 for individuals with family income over 300% FPL, and 1 for those below 150%. Table 1 compares baseline characteristics of the age group in Massachusetts and other Northeastern states, pooled over pre-reform years. Insurance variables are aggregated from March CPS using health insurance weights over the entire non-elderly population. All other variables are aggregated from ACS for the age group, further stratified into five year age bands if noted. Compared to other Northeastern states, Massachusetts has an old age population that is better educated, with higher income, healthier, and more likely to remain active in the labor force. Baseline insurance rate is higher in Massachusetts, with more insured obtaining coverage from a private source. Retirement rate is lower in Massachusetts, and fewer people are drawing social security retirement compared to other Northeastern states. These pre-existing discrepancies tend to lower the external validity of the results. For internal validity, identification of the difference-in-differences model relies on parallel trends, and difference in baseline characteristics does not in and of itself invalidate the approach. Nevertheless, I correct for baseline imbalance using Abadie weights, and find similar results. 4 Empirical Strategies and Findings This section presents the main estimation methods and results. In section 4.1, I first present graphical evidence from the aggregated data documenting differential transition patterns between the below age group (60-64) and above (65-69), for both the treatment state and control states. I show how comparing neighboring age groups around Medicare better isolates the effect of health insurance and better fends off the potential confound of recession. Section 4.2 presents triple difference estimates, and various robustness checks including a spatial discontinuity design. Section 4.3 proposes a double differenced regression discontinuity design to quantify the local policy impact at Medicare and early retirement age. I complete the characterization of retirement transition in each integer age between 60 and 65, and discuss implications for subgroups stratified by subsidy eligibility. I also note the program complementarity with social security old age. Section 4.4 finds additional support for the motivation and estimates in the main regression using the synthetic cohort method. 4.1 Differences-in-Differences I start with a within-state analysis that compares neighboring age groups differentially affected by the reform. With near universal coverage by Medicare beyond age 65, the joblock matters less for the elderly workers who are arguably less affected by the reform. I hence open a five year band at Medicare age: individuals aged when sampled belong to the below group, and those aged are in the above group. Differential retirement patterns across groups before and after the reform suggest the role of health insurance in retirement transitions at the population level. Note that the comparison does not require the reform to have nil effect on the above age group at all. There are a few reasons to believe that the above group might also be affected. 11

12 Table 1: Baseline Characteristics, Years Old, Massachusetts Other Northeastern States High School *** Some College *** Male Married * Child Present Family Size White *** Black *** Hispanic *** Any Insurance *** Private Insurance *** Difficulty Limiting Work * *** % FPL ** % FPL *** *** Social Security Retirement *** *** In Labor Force *** *** Work Last Year *** *** Full-Timer Last Year *** Part-Timer Last Year *** *** Hours Worked * Retirement from Full Time Work ** Part Time Work * ** Any Work ** Demographic variables and labor market variables are aggregated from ACS over for the age group unless otherwise noted; sampling weights are applied. Insurance variables are aggregated from CPS over the entire non-elderly population, adjusted by insurance sampling weights. Asterisks next to Massachusetts summary statistics indicate significance of difference from corresponding measures from Northeastern states. *** 0.01 ** 0.05 *

13 For instance, for the working head to provide insurance for the entire family, one may have incentive to retain employer sponsored insurance even after Medicare age. Subsidy on the public plan tends to counteract such necessity, and indeed I find evidence that non-working individuals are much more likely to drop private insurance, presumably from spouses, than working individuals. Since part of the control group is also treated, it tends to bias the estimate downward. Alternatively, if insurance coverage leads to more health care utilization which ultimately brings about better health, the reform may induce higher participation rate among the above group. In that case, the estimate can be biased upward. Figure 3 plots the time trend of aggregate labor outcomes of the two age groups, separately for the treatment state and the collection of control states. The vertical line indicates the first observation of the post-treatment period. I look at both the retrospective and the point-in-time measure of participation. There is rising labor force participation among older workers in this period, but the increase for Massachusetts below group in post-reform years is either slower or stays flat relative to the above group. The difference is most striking for retirement from full-time work, with the age group gap nearly vanishing in a post-reform period overlapping with recession. Retirement from part-time work clearly fails the parallel trend condition in the first few years of the survey, and overall retirement seems mostly driven by transition from full-time work. I hence focus primarily on full-time retirement rate for transition outcomes. Importantly, group differentials in the control states are remarkably stable for the main outcomes over the study period: neither the onset of the insurance reform nor the recession seems to break the parallelism of the trends. This is expected as workers in the two age groups tend to have similar human capital but differ in health stock. The latter may explain the difference in average participation rate, but similarity in the former implies common trending in face of any economy-wide productivity shocks. Loosely treating pooled Northeastern states as the counterfactual for Massachusetts, Figure 3 lends graphical support to the identifying assumption of difference-in-differences. I estimate the following difference-in-differences model in the treatment state: y it = β 0 below post + β 1 below + β 2 post + ϵ it, where below is an indicator variable that takes value 1 if age is between 60-64, and post takes value 1 if year is 2007 or after. I take out transition year 2006 from my sample 5. β 1 captures the rising labor force participation for older workers in this period, and β 2 shows age group differential in participation, or the transition effect. β 0 gives the differential transition effect before and after the reform, or the treatment effect the insurance reform. Because I do not utilize cross-state variation in the difference-in-differences model, I cluster standard error by year. In light of the small number of years covered, I block bootstrap as in Duflo et al.(2004). Panel A of Table 2 shows the results. Retrospective participation drops by 2.9 percentage points after the reform among below age group, and full-time retirement rate increases by 3.6 percentage points, a 40% increase from a baseline of 8.8%. Decrease in current participation status and past full-time work is smaller in magnitude and insignificant. Panel B reports 5 Absorbing 2006 into the pre-reform period does not significantly alter my point estimates. 13

14 Figure 3: Age Group Differentials, Massachusetts and Northeastern Controls Participation (Retrospective), Massachusetts Participation (Retrospective), Other NE States Below, Above, Below, Above, Participation (Current), Massachusetts Participation (Current), Other NE States Below, Above, Below, Above, Full Time Work, Massachusetts Full Time Work, Other NE States Below, Above, Below, Above, Ret. from Full Time Work, Massachusetts Ret. from Full Time Work, Other NE States Below, Above, Below, Above, Ret. from Part Time Work, Massachusetts Ret. from Part Time Work, Other NE States Below, Above, Below, Above, Ret. from Any Work, Massachusetts Ret. from Any Work, Other NE States Below, Above, Below, Above, 65 69

15 estimates from a richer specification y it = β 0 below post + α a + α t + γ X it + ϵ it, where α a and α t are age and year fixed effects, respectively. Covariates in X it includes basic demographics such as gender, race, education, marital status and presence of own child under age 21. To account for any cohort specifics that may differentially affect the age groups over time, and most notably, normal retirement age (NRA), I include dummy variable of formal retirement age being 66 versus 65 for individuals in my sample 6 For outcomes conditional on past year participation, such as hours worked and transition, I also control for industry and occupation fixed effects. I additionally include family income as percentage of FPL and log personal income in transition specifications. I run the same regression over different Northeastern states, and present results for each state in Panel B. Including individual covariates produces larger estimates for participation and full-time retirement, both significant at 5%. To the extent that industries have different retirement age as a norm, and occupation sorting may be correlated with the worker s ability of securing insurance outside of job-lock, controlling for industry and occupation fixed effects tend to alleviate the biases from omitted variables and selection. Compared with placebo experiments in control Northeastern states, the effect on both measures of participation and full-time retirement is strongest in Massachusetts. In the pooled regression in Panel B, I essentially test for the parallel trend assumption in the absence of insurance reform, using other Northeastern states as proxies for Massachusetts. I control for state and state-year fixed effects in the pooled sample. For the main outcomes in the first five columns, most deviation from the common trend appears to be small and economically insignificant, except for point-in-time participation showing a 1.3 percentage point increase over the same period. To investigate if such increase is reflecting any differential impact of recession by age group, I open a two-year window around the onset of recession (December 2007, NBER), and compare age groups before ( ) and during ( ) the recession. Due to the extremely small number of years (4), I cluster standard error by household. Results in Panel C show negligible impact of recession on my main outcomes, including point-in-time participation. This suggests the slight deviation from common trend in control states is more likely due to unaccounted cohort differences than recession, and sweeping out such difference in the triple difference model will only strengthen the results. This careful defense against potential confounds in not possible in an alternative difference-in-differences model where only the below group is compared across state and time (Heim & Lin, 2014). Figure 3 shows that the pre-treatment trend for most outcomes of either age group differs perceptibly across states, invalidating the common trend assumption. Estimated effect is then a mixture of true insurance effect and any other factor varying by state and year, including heterogeneous local labor market shocks that tend to dominate the insurance effect in such models. This might explain why the increase in retirement rate is much smaller (0.6%) in Heim & Lin(2014), 6 I define two cohorts for my sample: the first cohort, born in , has NRA at 65, and the later cohort, , has NRA roughly at 66. Controlling for finer cohorts gives similar results, but introduces higher degree of multicollinearity with the full set of year and age fixed effects already in the model. Unaccounted cohort specifics may bias the difference-in-differences estimates, but will be differenced out across states in the triple difference model. 15

16 Table 2: Differences-in-Differences, Massachusetts (1) (2) (3) (4) (5) (6) (7) Participation Participation Ret. From Ret. From Ret. From (Retro.) (Current) Full Time Log Hours Full-Time Part-Time Any Work Panel A: Massachusetts below post ** * (0.0142) (0.0172) (0.0151) (0.0475) (0.0184) (0.0297) (0.0181) below *** *** *** *** *** *** (0.0113) (0.0154) (0.0126) (0.0379) (0.0171) (0.0266) (0.0171) post *** *** *** *** *** (0.0140) (0.0173) (0.0113) (0.0430) (0.0193) (0.0208) (0.0150) R N Panel B: Northeastern States Massachusetts ** ** (0.0162) (0.0185) (0.0173) (0.0504) (0.0210) (0.0328) (0.0222) Connecticut (0.0196) (0.0213) (0.0197) (0.0747) (0.0295) (0.0547) (0.0226) Delaware (0.0253) (0.0240) (0.0248) (0.1017) (0.0477) (0.0590) (0.0369) D.C (0.0369) (0.0423) (0.0351) (0.1291) (0.0463) (0.0999) (0.0489) Maine * (0.0265) (0.0302) (0.0228) (0.1041) (0.0446) (0.0622) (0.0397) Maryland (0.0152) (0.0187) (0.0137) (0.0519) (0.0314) (0.0330) (0.0236) New Hampshire (0.0255) (0.0300) (0.0282) (0.0861) (0.0318) (0.0507) (0.0292) New Jersey (0.0130) (0.0193) (0.0123) (0.0177) (0.0230) (0.0340) (0.0177) New York (0.0097) (0.0136) (0.0086) (0.0343) (0.0178) (0.0351) (0.0168) Pennsylvania (0.0134) (0.0121) (0.0087) (0.0349) (0.0157) (0.0237) (0.0146) Rhode Island (0.0312) (0.0255) (0.0284) (0.0925) (0.0450) (0.0668) (0.0313) Vermont ** (0.0357) (0.0481) (0.0291) (0.1218) (0.0441) (0.0795) (0.0420) Pooled Control States ** ** ** *** (0.0063) (0.0064) (0.0044) (0.0154) (0.0084) (0.0123) (0.0060) Panel C: Recession below post * * ** (0.0053) (0.0053) (0.0047) (0.0168) (0.0062) (0.0101) (0.0053) Specifications for log hours worked and retirement transition are estimated from respective conditional samples of past year participants, full-time workers, part-time workers, etc. Panel A estimates treatment effects on Massachusetts below age group with no individual controls. Panel B shows estimated coefficient before the interactive term in a specification with year and age fixed effects as well as individual controls, for each single Northeastern state and pooled control states. In the latter case I also include state and state-year fixed effects. Individual controls include gender, race, education and family composition for all outcomes. For conditional outcomes, industry and occupation fixed effects are included. For transition outcomes, family income as percentage of FPL and log personal income are included. Panel C compares age group differential before ( ) and after ( ) the onset of recession. The regression pools over Northeastern control states and controls for age, year, state, and state-year fixed effects, in addition to the same set of individual controls as in Panel B. All regressions are weighted by ACS sampling weights. Standard errors in parentheses in Panel A and B are clustered by year, block bootstrapped from 500 repetitions; in Panel C clustered by households. *** 0.01 ** 0.5 *

17 whereas models based on insurance discontinuity at Medicare show an effect as high as 4.5%. 4.2 Triple Difference Further comparing age group differentials across states leads to the triple difference model. Inclusion of state-year fixed effects should address the confound introduced by the recession, as suggested by pooled regression over Northeastern control states. Year-age fixed effects flexibly capture any cohort-year differences that may bias the difference-in-differences estimates. State-age fixed effects in addition control for state-specific retirement age gradients correlated with factors extraneous to insurance availability. The remaining source of variation in the triple interaction term is the differential retirement behavior between age groups that persist even after controlling for all two-way fixed effects, or in this case, due to the Massachusetts health insurance reform in The main triple difference specification takes the form y ist = β 0 below post treat + α a + α s + α t + α st + α at + α as + γ X ist + ϵ ist, where treat is indicator of Massachusetts. I include a fully disaggregated set of state, year, and age fixed effects and their two-way interactions. Covariates in X ist are the same as in the case of double difference. Standard errors are clustered by states to allow for general serial correlation patterns, and block bootstrapped due to small number of cluster units (12). Table 3 show both triple difference estimates and previous difference-in-differences estimates. Correcting for the background increase in point-in-time participation, triple difference finds similar decline in both measures of participation by 3 percentage points, of which 2.2 percentage points is decrease in full-time work, significant at 5%. There is no evidence of reduced hours among workers, and the effect occurs mostly along the extensive margin where full-time workers transit directly into retirement, and the rate of transition increases by 4 percentage points. Lower personal earning is associated with earlier exit from the labor force, and lower education is associated with lower participation rate. Absence of co-residing young children is also a strong predictor of early retirement. Retirement from part-time work does not appear to be affected once compared against control states, and retirement from any work seems mainly driven by retirement from full-time work. However, since the common trend assumption in the last two outcomes is less tenable, triple difference estimates might still be biased, and I hereafter exclude them from the analysis. In Table 4 I show a similar triple difference model using only aggregate retirement rates. In the upper panel, I aggregate state-year-group level participation rate, full-time rate, log hours and full-time retirement rate, and regress the outcomes on triple interaction and all two-way interactions. To tease out the effect of differential labor market condition facing age groups, I control for unemployment rate at the state-year-group level obtained from BLS. I experiment with different methods to cluster standard errors. The square bracket contains 90% confidence interval from a randomization inference procedure by Conley & Taber(2011). They note that difference-in-differences estimates with only a few treated units are asymptotically biased, and the distribution of the bias term can be recovered from the residuals of placebo experiments in control states assuming random program placement. I permute the policy intervention in 11 Northeastern control states, and derive the 90% 17

18 Table 3: Triple Difference Estimates of Insurance Reform on Retirement (1) (2) (3) (4) (5) (6) (7) Participation Participation Ret. From Ret. From Ret. From (Retro.) (Current) Full Time Log Hours Full-Time Part-Time Any Work Difference-in-Differences below post ** ** (0.0162) (0.0185) (0.0173) (0.0504) (0.0210) (0.0328) (0.0222) R N Triple Difference treat below post *** *** ** *** (0.0101) (0.0108) (0.0096) (0.0291) (0.0138) (0.0196) (0.0113) Male *** *** *** *** ** * (0.0043) (0.0040) (0.0049) (0.0087) (0.0025) (0.0061) (0.0027) Race:Black ** *** (0.0143) (0.0157) (0.0174) (0.0335) (0.0054) (0.0087) (0.0066) Race: Other *** ** * *** * ** *** (0.0071) (0.0079) (0.0065) (0.0156) (0.0044) (0.0093) (0.0043) Hispanic Origin ** *** ** *** *** (0.0134) (0.0148) (0.0109) (0.0190) (0.0078) (0.0126) (0.0059) High School *** *** *** ** (0.0042) (0.0044) (0.0037) (0.0138) (0.0039) (0.0080) (0.0038) Some College *** *** *** *** ** *** * (0.0063) (0.0071) (0.0061) (0.0162) (0.0044) (0.0099) (0.0052) Married * *** *** *** *** *** (0.0030) (0.0030) (0.0027) (0.0086) (0.0020) (0.0040) (0.0021) Child Present *** *** *** *** *** * *** (0.0070) (0.0053) (0.0059) (0.0121) (0.0035) (0.0120) (0.0034) Percentage FPL (0.0002) (0.0006) (0.0003) Log Personal Income *** *** *** (0.0021) (0.0047) (0.0026) R N Difference-in-differences estimates are the same as in Panel B, Table 2; triple difference estimates are from a specification with fully disaggregated state, year, age fixed effects and two-way fixed effects. All regressions are weighted by ACS sampling weights. Standard errors are clustered by state, block bootstrapped from 500 repetitions. Transition variables additionally include industry and occupation fixed effects, not shown in the table. *** 0.01 ** 0.05 *

19 confidence interval containing the range of true policy effect that cannot be rejected at size 10%. The curly bracket shows the 95% confidence interval from 500 repetitions of wild bootstrap as in Cameron et al.(2008). Similar to Duflo et al.(2004), wild bootstrap works well for small number of clusters. Both Conley-Taber and the wild bootstrap intervals show significant effect of the reform on aggregate outcomes. Estimated treatment effect is very similar with or without controlling for unemployment rate, and when included, coefficient before unemployment rate is statistically indistinguishable from zero. Table 4: Triple Difference Estimates of Insurance Reform on Retirement, Aggregated Variables (1) (2) (3) (4) Participation Participation Ret. From (Retro.) (Current) Full Time Full-Time Aggregated by State: treat below post [-.0476,-.0153] [-.0415,-.0109] [-.0659,-.0020] [.0199,.0775] {-.0420,-.0229} {-.0390,-.0193} {-.0433,-.0144} {.0319,.0595} unemployment rate {-.0022,.0020} {-.0046,.0026} {-.0063,-.0001} {-.0063,.0075} N R Aggregated by PUMA: treat below post [-.0681,.0049] [-.0556,.0027] [-.0681,.0049] [.0329,.0866] {-.0297,-.0074} {-.0348,-.0181} {-.0295,-.0096} {.0403,.0585} unemployment rate {-.0059,.0009} {-.0028,.0023} {-.0033,.0016} {-.0034,.0029} R N Upper panel shows triple difference estimates using aggregated variables at the state-year-group level. Lower panel shows triple difference estimates where outcomes are aggregated by PUMA-year-group and the regression includes PUMA fixed effects and uses only observations in year Unemployment rate is obtained from BLS at the state-year-group level. Standard errors are clustered by states. The square bracket contains 90% confidence interval from the Conley-Taber randomization procedure. The curly bracket contains 95% confidence interval from 500 repetitions of wild bootstrap. *** 0.01 ** 0.05 * 0.10 One unique advantage of ACS over other census data is its rich geographical information at the sub-state level. This allows me to aggregate labor market outcomes over public use micro area (PUMA), the smallest geographical unit identifiable in ACS. The Census Bureau revises PUMA demarcation following each decennial census. To retain consistent PUMA coding linkable over year, I restrict my sample to year A total of 52 PUMAs are superimposed on 14 counties in Massachusetts over this period, with population in each PUMA ranging from 100,000 to 200,000. PUMAs do not cross state borders, lending natural definition to treated and control units. The lower panel of Table 3 reports estimates from 19

20 the model y pt = β 0 treat below post+β 1 treat below+β 2 below after+β 3 below+γ UE st +α p +α t +α st +ϵ pt, where α p is PUMA fixed effects, and α s and α st are state and state-year fixed effects. I drop the interaction treat post having controlled α st. Accounting for smaller area variations, estimated treatment effect of retrospective outcomes (participation and full-time work) is smaller in magnitude, and Conley-Taber intervals extend to the positive domain for participation outcomes, although not by much. Inference by wild bootstrap still suggests significant policy impact on all outcomes, and insignificance of unemployment rate in each case. Overall, the tests seem to indicate that identification by age group differentials is fairly robust to unobserved labor market heterogeneity, and the magnitude does not appear sensitive to the unit of analysis or the particular study period chosen. Table 5: Triple Difference Estimates of Insurance Reform on Retirement, Spatial Discontinuity (1) (2) (3) (4) Participation Participation Ret. From (Retro.) (Current) Full Time Full-Time Border Segment: treat below post {-.0843,-.0072} {-.0693,-.0164} {-.1060,-.0548} {.0013,.0624} N R Contiguous PUMA Pairs: treat below post {-.1125,-.0306} {-.0773,-.0267} {-.1082,-.0526} {-.0457,.0275} R N Upper panel restricts to PUMA-level triple difference to 31 border PUMAs. Lower panel shows estimates using with PUMA-pair variation from a total of 30 contiguous PUMA pairs that straddle Massachusetts border. Standard errors are clustered by state in the upper panel; in the lower panel they are clustered by state and PUMA. In both cases I wild bootstrap from 500 repetitions and show 95% confidence intervals in the curly bracket. *** 0.01 ** 0.05 * 0.10 Upper panel in Table 5 estimates the same regression but restricted only to the border segment comprised of 31 PUMAs from Massachusetts and 5 neighboring states 7. If border area has more homogeneous labor market conditions, estimated effect is closer to the true effect of insurance reform on retirement transition. On the other hand, if there is significant degree of spatial heterogeneity within state and locational sorting by either firms or households, the effect by the border may lack external validity if interest is on average treatment effect in the population. It turns out there is larger decrease in participation along the border, but smaller increase in retirement transition. Most noticeably, the decline in full-time work is significant at 7.6 percentage point, nearly three times larger than the state-level estimates. The lower panel further groups PUMAs into contiguous pairs straddling Massachusetts borders. If a Massachusetts PUMA is contiguous with multiple PUMAs 7 New Hampshire and Vermont borders Massachusetts from the north, New York to the west, Connecticut and Rhode Island to the south. 20