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 topics that are policy-relevant. Her current research focuses on the effects of health insurance reform in Massachusetts on hospital care, preventive care, labor market outcomes, and patient exposure to financial risk. 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. She holds a 2008 Ph.D. in economics from MIT and a 2003 A.B. in economics from Harvard. She spent a year before graduate school as a research assistant at the White House Council of Economic Advisers and a year after graduate school as a post-doctoral fellow in Health and Aging at the NBER. She is currently spending the 2011-2012 academic year on sabbatical from Yale as the Okun Model Fellow at the Brookings Institution in Washington, DC.
Publications
"Health Reform, Health Insurance, and Selection: Estimating
Selection into Health Insurance Using the Massachusetts Health
Reform" (with Martin B. Hackmann and Jonathan T. Kolstad). (Older Version: NBER Working Paper 17748. Cowles Foundation Discussion Paper 1841.) Forthcoming, American Economic Review Papers and Proceedings. May 2012. [PDF on AEA website]
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]
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.)
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.
Amanda E. Kowalski, William J. Congdon, and Mark H. Showalter (2008) "State Health Insurance Regulations and the Price of High-Deductible Policies," Forum for Health Economics & Policy: Vol. 11: Iss. 2 (Health Care Reform), Article 8. [PDF] http://www.bepress.com/fhep/11/2/8/
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
"Estimating the Tradeoff Between Risk Protection and
Moral Hazard with a Nonlinear Budget Set Model of
Health Insurance." Latest Version: April 2011. [PDF]
Insurance induces a well-known tradeoff between the welfare gains from risk protection and
the welfare losses from moral hazard. Empirical work traditionally estimates each side of this
tradeoff separately, potentially yielding mutually inconsistent results. I develop a nonlinear
budget set model of health insurance that allows me to estimate both sides of this tradeoff
jointly, allowing for a relationship between moral hazard and risk protection. An important
feature of this model is that it considers nonlinearities in the consumer budget set that arise
from deductibles, coinsurance rates, and stoplosses that lessen moral hazard as well as risk
protection relative to full insurance. Within my empirical context of health insurance plans
offered by a large firm, results suggest that on average, the deadweight losses from moral
hazard substantially outweigh the welfare gains from risk protection. There is considerable
variation in the estimated tradeoff across individuals.
"The Impact of Health Care Reform on Hospital and Preventive Care: Evidence from Massachusetts" (with Jonathan T. Kolstad) NBER Working Paper 16012. First Version: May 2010. Latest Version: October 2010. Revise & Resubmit, Journal of Public Economics [PDF][Slides]
In April 2006, the state of Massachusetts passed legislation aimed at achieving near universal health insurance coverage. A key provision of this legislation, and of the national legislation passed in March 2010, is an individual mandate to obtain health insurance. Although previous researchers have studied the impact of expansions in health insurance coverage among the indigent, children, and the elderly, the Massachusetts reform gives us a novel opportunity to examine the impact of expansion to near-universal health insurance coverage among the entire state population. In this paper, we are the first to use hospital data to examine the impact of this legislation on insurance coverage, utilization patterns, and patient outcomes in Massachusetts. We use a difference-in-difference strategy that compares outcomes in Massachusetts after the reform to outcomes in Massachusetts before the reform and to outcomes in other states. We embed this strategy in an instrumental variable framework to examine the effect of insurance coverage on utilization patterns. Using the Current Population Survey, we find that the reform increased insurance coverage among the general Massachusetts population. Our main source of data is a nationally-representative sample of approximately 20% of hospitals in the United States. Among the population of hospital discharges in Massachusetts, the reform decreased uninsurance by 36% relative to its initial level. We also find that the reform affected utilization patterns by decreasing length of stay and the number of inpatient admissions originating from the emergency room. Using new measures of preventive care, we find some evidence that hospitalizations for preventable conditions were reduced. The reform affected nearly all age, gender, income, and race categories. We also examine costs on the hospital level and find that hospital cost growth did not increase after the reform in Massachusetts relative to other states.
"Censored Quantile Instrumental Variable Estimates of the Price Elasticity of Expenditure on Medical Care" (Job Market Paper) NBER Working Paper 15085. Latest Version: June 2010. Under review. [PDF][Download Stata Command to Implement CQIV Below]
I estimate the price elasticity of expenditure on medical care using
recent, detailed data. With a new censored quantile instrumental
variable (CQIV) estimator, I go beyond the literature by allowing
estimates to vary across the expenditure distribution, relaxing the
distributional assumptions traditionally used to deal with
censoring, and addressing endogeneity with a rigorous identification
strategy. Across the .65 to .95 conditional quantiles of the
expenditure distribution, I find an expenditure elasticity of -2.3
with respect to the year-end consumer fraction of expenditure post
insurance. Several pieces of evidence support this estimate, which
is an order of magnitude larger than previous estimates.
"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: April 2011. Under review. [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 handles censoring semi-parametrically in the tradition of Powell (1986), and it generalizes standard censored quantile regression (CQR) methods to incorporate endogenous regressors in a manner that is computationally tractable. Our computational algorithm combines a control function approach with the CQR estimator developed by Chernozhukov and Hong (2002). Through Monte-Carlo simulation, we show that CQIV performs well relative to Tobit IV in terms of median bias and interquartile range in a model that satisfies the parametric assumptions required for Tobit IV to be efficient. Given the strong parametric assumptions required by Tobit IV, the gains to CQIV relative to Tobit IV are likely to be large in empirical applications. We present results from an empirical application of CQIV to the estimation of Engel curves for alcohol. This empirical application demonstrates the importance of accounting for censoring and endogeneity with CQIV.
Work in Progress
"Mandate Based Health Reform and the Labor Market: Evidence from the Massachusetts Health Insurance Reform" (with Jonathan T. Kolstad)
"Estimates of the Risk-Reducing Benefits of Health Insurance for the Uninsured" (with Jonathan T. Kolstad)
"Invincible or Uninsurable? An Empirical Estimate of Risk Preference and Risk Type in Health Insurance Markets" (with Jonathan T. Kolstad)
"How do Individual Medical Expenditures Evolve over Time? Evidence from Nonelderly Individuals in New York"
Stata Code
Stata Command to Implement CQIV. First Version: December 2010. Latest Version: August 27, 2011. (Developed with Victor Chernozhukov, Ivan Fernandez-Val, and Sukjin Han)
The simplest way to install this command is to type the following at the Stata command prompt:
net install cqiv, from("http://www.econ.yale.edu/~ak669/")
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.