Amanda E. Kowalski

Research

Brief Biography

Amanda Kowalski, Assistant Professor of Economics at Yale University and Faculty Research Fellow at the National Bureau of Economic Research (NBER), is a health economist who specializes in applying econometric techniques to answer questions that inform current debates in health policy.

Professor Kowalski's recent research explores the impact of the 2006 Massachusetts health reform with an eye toward the likely impact of the 2010 national health reform, focusing specifically on hospital care, labor market outcomes, and adverse selection in the individual health insurance market. She has also researched the price elasticity of expenditure on medical care and the marginal returns to medical spending on at-risk newborns using new estimation techniques. Her research has received the Zellner Thesis Award, the HCUP Outstanding Article of the Year Award, and the Garfield Economic Impact Award.

In 2014, Professor Kowalski was honored with a CAREER Award from the National Science Foundation. The National Institutes of Health, the Robert Wood Johnson Foundation, and the W.E. Upjohn Institute have also supported her research, which has been published in peer-reviewed journals, including the Quarterly Journal of Economics and the Journal of Public Economics. Her research has also been featured in the popular press, including The Wall Street Journal and Newsweek.

Professor Kowalski holds a PhD in economics from MIT and an AB in economics from Harvard. Before joining Yale, she held a post-doctoral fellowship in Health and Aging at the NBER in Cambridge, MA. Her interest in health policy has led her to spend two years in Washington, DC, one as a research assistant in health and labor at the White House Council of Economic Advisers, and another as the Okun Model Fellow at the Brookings Institution.

Publications

"Censored Quantile Instrumental Variable Estimation via Control Functions" (with Victor Chernozhukov and Ivan Fernandez-Val). NBER Working Paper 16997. Cowles Foundation Discussion Paper 1797. arXiv identifier 1104.4580. (Older Version: Boston University Department of Economics Working Paper 2009-012.) Latest Version: March 2014. Forthcoming, The Journal of Econometrics. [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.

"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.) [PDF][Slides]

Press Coverage: [NBER Digest] [Knowledge@Wharton] [Newsweek] [The Economist Online] [NPR WBUR CommonHealth]

Video Presentations of Massachusetts Research:

[Yale Panel on the Economy and the Election, November 2012] [Slides]

[ISPS Health At Yale, September 2013] [Slides]

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 B. 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.) [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. [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. [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. [PDF] http://www.bepress.com/fhep/11/2/8/

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.

Completed Working Papers

"Adverse Selection and an Individual Mandate: When Theory Meets Practice" (with Martin Hackmann and Jonathan T. Kolstad). NBER Working Paper 19149. Latest Version: May 2014. Revised & Resubmitted, American Economic Review. [PDF] [Slides]

Press Coverage: [SHADAC Issue Brief]

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.

"Mandate-Based Health Reform and the Labor Market: Evidence from the Massachusetts Reform" (with Jonathan T. Kolstad). NBER Working Paper 17933. Latest Version: July 2014. Under review. [PDF][Slides][Download Problem Set on This Paper Below]

Press Coverage: [Yale Daily News] [Boston Herald] [Washington Examiner] [New York Times Economix Blog]

Video Presentation on Health Reform and the Labor Market:

[Brookings Event on The Future of U.S. Health Care Spending, April 2014] [Slides]

[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.

"Estimating the Tradeoff Between Risk Protection and Moral Hazard with a Nonlinear Budget Set Model of Health Insurance." NBER Working Paper 18108. Latest Version: December 2013. Under review. [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.

"Censored Quantile Instrumental Variable Estimates of the Price Elasticity of Expenditure on Medical Care" (Job Market Paper) NBER Working Paper 15085. Latest Version: June 2014. Revised & Resubmitted, Journal of Business and Economic Statistics. [PDF][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.

Selected Work in Progress

"Medicaid as an Investment in Children: What is the Long-Term Impact on Tax Receipts?" (with David Brown and Ithai Lurie).

"The Value of Public Health Insurance to the Young, Healthy, and Privately Insured."

"Returns to Medicare Spending: Evidence From Variation Across Physicians." (with Joseph J. Doyle and Heidi Williams)

"ACA: A Coverage Analysis"

Stata Code

"CQIV: Stata Module to Perform Censored Quantile Instrumental Variable Regression." First Version: December 2010. Latest Version: June 2012. (with Victor Chernozhukov, Ivan Fernandez-Val, and Sukjin Han). [Link to CQIV at RePEc]

The simplest way to install this command is to type the following at the Stata command prompt:

ssc install cqiv

You can also download the .ado and .sthlp files [Download CQIV Stata ado file][Download CQIV Stata help file], and then copy them into your personal ado directory [How to find your personal ado directory].

If you are updating from a previous version, type "net uninstall cqiv" at the Stata prompt before installing.

Problem Sets

Problem Set on "Mandate-Based Health Reform and the Labor Market: Evidence from the Massachusetts Reform." First Version: May 2012. Latest Version: October 2012. (Developed with Toby Chaiken and Jonathan Kolstad).

This problem set is designed for graduate students and advanced undergraduates. It requires Stata and Excel.

Download Stata, Excel, and Word Files as a zip file. The Word file contains instructions.

Answer key available, instructors only please. Email Amanda Kowalski.

Problem Set on "Estimating Marginal Returns to Medical Care: Evidence from At-risk Newborns" First Version: February 2012. Latest Version: February 2012. (Developed with Tiffany Fan and Michael Cunetta).

This problem set is designed for undergraduates. It requires Stata.

Download Stata and Word Files as a zip file. The Word file contains instructions.

Answer key available, instructors only please. Email Amanda Kowalski.

Research Team

Summer 2013 Research Team (from left): William Bishop, Martin Hackmann, Kate Archibald, Tiffany Fan, Amanda Kowalski, Gerardo Ruiz Sanchez.

Last Updated July 2014| Email Amanda Kowalski