"Mandate-Based Health Reform and the Labor Market: Evidence from the Massachusetts Reform" (with Jonathan T. Kolstad). NBER Working Paper 17933. Latest Version: January 2016. Forthcoming, Journal of Health Economics. [Pre-publication PDF][Slides][Download Problem Set on This Paper Below]
Press Coverage: [Yale Daily News] [Boston Herald] [Washington Examiner] [New York Times Economix Blog]
Video Presentations on Health Reform and the Labor Market:
[Federal Reserve Bank of Chicago Conference on the Affordable Care Act and the Labor Market, March 2014] [Slides]
We model the labor market impact of the key provisions of the national and Massachusetts "mandate-based" health reforms: individual mandates, employer mandates, and subsidies. We characterize the compensating differential for employer-sponsored health insurance (ESHI) and the welfare impact of reform in terms of "sufficient statistics." We compare welfare under mandate-based reform to welfare in a counterfactual world where individuals do not value ESHI. Relying on the Massachusetts reform, we find that jobs with ESHI pay $5,350 less annually, approximately the cost of ESHI to employers. Accordingly, the deadweight loss of mandate-based health reform was approximately 2% of its potential size.
"Censored Quantile Instrumental Variable Estimates of the Price Elasticity of Expenditure on Medical Care." Journal of Business and Economic Statistics. January 2016. Vol. 34, No. 1: 107-117. (Job Market Paper) (Older Version: NBER Working Paper 15085.) [Pre-publication PDF][Online Appendix] [Data Appendix][Download Stata Command to Implement CQIV Below]
Efforts to control medical care costs depend critically on how individuals respond to prices. I estimate the price elasticity of expenditure on medical care using a censored quantile instrumental variable (CQIV) estimator. CQIV allows estimates to vary across the conditional expenditure distribution, relaxes traditional censored model assumptions, and addresses endogeneity with an instrumental variable. My instrumental variable strategy uses a family member's injury to induce variation in an individual's own price. Across the conditional deciles of the expenditure distribution, I find elasticities that vary from -0.76 to -1.49, which are an order of magnitude larger than previous estimates.
"Estimating the Tradeoff Between Risk Protection and Moral Hazard with a Nonlinear Budget Set Model of Health Insurance." International Journal of Industrial Organization. November 2015. Vol. 43: 122-135. (Older Version: NBER Working Paper 18108.) [Pre-publication PDF][Slides][Data Appendix]
Insurance induces a tradeoff between the welfare gains from risk protection and the welfare losses from moral hazard. Empirical work traditionally estimates each side of the tradeoff separately, potentially yielding mutually inconsistent results. I develop a nonlinear budget set model of health insurance that allows for both simultaneously. Nonlinearities in the budget set arise from deductibles, coinsurance rates, and stoplosses that alter moral hazard as well as risk protection. I illustrate the properties of my model by estimating it using data on employer sponsored health insurance from a large firm. Within my empirical context, the average deadweight losses from moral hazard substantially outweigh the average welfare gains from risk protection. However, the welfare impact of moral hazard and risk protection are both small relative to transfers from the government through the tax preference for employer sponsored health insurance and transfers from some agents to other agents through a common premium.
"Quantile Regression with Censoring and Endogeneity" (with Victor Chernozhukov and Ivan Fernandez-Val). Journal of Econometrics. May 2015. Vol. 186: 201-221. (Older Versions: NBER Working Paper 16997. arXiv identifier 1104.4580. Boston University Department of Economics Working Paper 2009-012.) [Pre-publication PDF][Download Stata Command to Implement CQIV Below]
In this paper, we develop a new censored quantile instrumental variable (CQIV) estimator and describe its properties and computation. The CQIV estimator combines Powell (1986) censored quantile regression (CQR) to deal with censoring, with a control variable approach to incorporate endogenous regressors. The CQIV estimator is obtained in two stages that are nonadditive in the unobservables. The first stage estimates a nonadditive model with infinite dimensional parameters for the control variable, such as a quantile or distribution regression model. The second stage estimates a nonadditive censored quantile regression model for the response variable of interest, including the estimated control variable to deal with endogeneity. For computation, we extend the algorithm for CQR developed by Chernozhukov and Hong (2002) to incorporate the estimation of the control variable. We give generic regularity conditions for asymptotic normality of the CQIV estimator and for the validity of resampling methods to approximate its asymptotic distribution. We verify these conditions for quantile and distribution regression estimation of the control variable. Our analysis covers two-stage (uncensored) quantile regression with nonadditive first stage as an important special case. We illustrate the computation and applicability of the CQIV estimator with a Monte-Carlo numerical example and an empirical application on estimation of Engel curves for alcohol.
"Adverse Selection and an Individual Mandate: When Theory Meets Practice" (with Martin Hackmann and Jonathan T. Kolstad). American Economic Review. March 2015. Vol. 105, No. 3: 1030-66. (Older Version: NBER Working Paper 19149.) [Pre-publication PDF] [Slides] [Data and Programs] [Online Appendix][Download Problem Set on This Paper Below]
Press Coverage: [SHADAC Issue Brief] [SHARE Webinar with Slides] [Politico]
We develop a model of selection that incorporates a key element of recent health reforms: an individual mandate. Using data from Massachusetts, we estimate the parameters of the model. In the individual market for health insurance, we find that premiums and average costs decreased significantly in response to the individual mandate. We find an annual welfare gain of 4.1% per person or $51.1 million annually in Massachusetts as a result of the reduction in adverse selection. We also find smaller post-reform markups.
"The Early Impact of the Affordable Care Act State-by-State" Brookings Papers on Economic Activity, Fall 2014:277-333. (Older Version: NBER Working Paper 20597.) [Pre-publication PDF] [BPEA Slides] [Full Slides] [Video Summary by Justin Wolfers] [Interactive Map] [Data and Programs] [Online Appendix]
Press Coverage: [Washington Post Blog] [Wall St Journal Blog] [Forbes] [Motley Fool]
Radio Interview on Early Impact of the ACA:
I examine the impact of state policy decisions on the early impact of the ACA using data through the first half of 2014. I focus on the individual health insurance market, which includes plans purchased through exchanges as well as plans purchased directly from insurers. In this market, at least 13.2 million people were covered in the second quarter of 2014, representing an increase of at least 4.2 million beyond pre-ACA state-level trends. I use data on coverage, premiums, and costs and a model developed by Hackmann, Kolstad, and Kowalski (2013) to calculate changes in selection and markups, which allow me to estimate the welfare impact of the ACA on participants in the individual health insurance market in each state. I then focus on comparisons across groups of states. The estimates from my model imply that market participants in the five direct enforcement states that ceded all enforcement of the ACA to the federal government are experiencing welfare losses of approximately $245 per participant on an annualized basis, relative to participants in all other states. They also imply that the impact of setting up a state exchange depends meaningfully on how well it functions. Market participants in the six states that had severe exchange glitches are experiencing welfare losses of approximately $750 per participant on an annualized basis, relative to participants in other states with their own exchanges. Although the national impact of the ACA is likely to change over the course of 2014 as coverage, costs, and premiums evolve, I expect that the differential impacts that we observe across states will persist through the rest of 2014.
"The Impact of Health Care Reform on Hospital and Preventive Care: Evidence from Massachusetts" (with Jonathan T. Kolstad). Journal of Public Economics. December 2012. Vol. 96. 909-929. (Older Version: NBER Working Paper 16012.) [Pre-publication PDF][Slides]
Press Coverage: [NBER Digest] [Knowledge@Wharton] [Newsweek] [The Economist Online] [NPR WBUR CommonHealth]
Video Presentations of Massachusetts Research:
In April 2006, Massachusetts passed legislation aimed at achieving near-universal health insurance coverage. The key features of this legislation were a model for national health reform, passed in March 2010. The reform gives us a novel opportunity to examine the impact of expansion to near-universal coverage state-wide. Among hospital discharges in Massachusetts, we find that the reform decreased uninsurance by 36% relative to its initial level and to other states. Reform affected utilization by decreasing length of stay and the number of inpatient admissions originating from the emergency room. When we control for patient severity, we find evidence that preventable admissions decreased. At the same time, hospital cost growth did not increase.
"Health Reform, Health Insurance, and Selection: Estimating Selection into Health Insurance Using the Massachusetts Health Reform" (with Martin Hackmann and Jonathan T. Kolstad). American Economic Review: Papers & Proceedings. May 2012. Vol. 102, No. 3: 498-501. (Older Version: NBER Working Paper 17748. Cowles Foundation Discussion Paper 1841.) [Pre-publication PDF]
We implement an empirical test for selection into health insurance using changes in coverage induced by the introduction of mandated health insurance in Massachusetts. Our test examines changes in the cost of the newly insured relative to those who were insured prior to the reform. We find that counties with larger increases in insurance coverage over the reform period face the smallest increase in average hospital costs for the insured population, consistent with adverse selection into insurance before the reform. Additional results, incorporating cross-state variation and data on health measures, provide further evidence for adverse selection.
"The Role of Hospital Heterogeneity in Measuring Marginal Returns to Medical Care: A Reply to Barreca, Guldi, Lindo, and Waddell" (with Douglas Almond, Joseph J. Doyle, and Heidi Williams). Quarterly Journal of Economics. November 2011. Vol. 126, No. 4: 2125-2131. [Pre-publication PDF]
Response to comment on "Estimating Marginal Returns to Medical Care: Evidence from At-Risk Newborns"
In Almond, Doyle, Kowalski and Williams (2010), we describe how marginal returns to medical care can be estimated by comparing patients on either side of diagnostic thresholds. Our application examines at-risk newborns near the very low birth weight threshold at 1500 grams. We estimate large discontinuities in medical care and mortality at this threshold, with effects concentrated at "low-quality" hospitals. While our preferred estimates retain newborns near the threshold, when they are excluded the estimated marginal returns decline, although they remain large. In low-quality hospitals, our estimates are similar in magnitude regardless of whether these newborns are included or excluded.
"Estimating Marginal Returns to Medical Care: Evidence from At-risk Newborns" (with Douglas Almond, Joseph J. Doyle, and Heidi Williams).Quarterly Journal of Economics. May 2010. Vol. 125, No. 2: 591-634. [Pre-publication PDF] [Online Appendix] (Older Version: NBER Working Paper 14522.)[Download Problem Set on This Paper Below]
Awarded the 2010 HCUP Outstanding Article of the Year Award
Awarded the 2011 Garfield Economic Impact Award
We estimate marginal returns to medical care for at-risk newborns by comparing health outcomes and medical treatment provision on either side of common risk classifications, most notably the "very low birth weight" threshold at 1500 grams. First, using data on the census of US births in available years from 1983-2002, we find evidence that newborns with birth weights just below 1500 grams have lower one-year mortality rates than do newborns with birth weights just above this cutoff, even though mortality risk tends to decrease with birth weight. One-year mortality falls by approximately one percentage point as birth weight crosses 1500 grams from above, which is large relative to mean one-year mortality of 5.5% just above 1500 grams. Second, using hospital discharge records for births in five states in available years from 1991-2006, we find evidence that newborns with birth weights just below 1500 grams have discontinuously higher costs and frequencies of specific medical inputs. We estimate a $4,000 increase in hospital costs as birth weight approaches 1500 grams from above, relative to mean hospital costs of $40,000 just above 1500 grams. Taken together, these estimates suggest that the cost of saving a statistical life of a newborn with birth weight near 1500 grams is on the order of $550,000 in 2006 dollars.
"State Health Insurance Regulations and the Price of High-Deductible Policies" (with William J. Congdon and Mark H. Showalter). Forum for Health Economics & Policy. 2008. Vol. 11: Iss. 2 (Health Care Reform), Article 8. [Pre-publication PDF]
Press Coverage: [Wall Street Journal]
This study examines the impact of state health insurance regulations on the price of high-deductible family and individual polices in the nongroup market. We use a unique and rich data set on actual insurance policies sold through a large Internet health insurance distributor to examine the impact of various regulations on policy prices, controlling for policy characteristics, demographic characteristics of the purchasers, and state-level demographics. We also use data from a single major insurance firm that provided offer prices for a family policy from a set of randomly selected zip codes. Both datasets suggest a strong statistical relationship between regulation and insurance prices.