Many applications involve a censored dependent variable and an endogenous independent variable. Chernozhukov et al. (2015) introduced a censored quantile instrumental variable estimator (CQIV) for use in those applications, which has been applied by Kowalski (2016), among others. In this article, we introduce a Stata command, cqiv, that simplifies application of the CQIV estimator in Stata. We summarize the CQIV estimator and algorithm, we describe the use of the cqiv command, and we provide empirical examples.
This paper examines the link between legislative politics, hospital behavior, and health care spending. When trying to pass sweeping legislation, congressional leaders can attract votes by adding targeted provisions that steer money toward the districts of reluctant legislators. This targeted spending provides tangible local benefits that legislators can highlight when fundraising or running for reelection. We study a provision - Section 508 – that was added to the 2003 Medicare Modernization Act (MMA). Section 508 created a pathway for hospitals to apply to get their Medicare payment rates increased. We find that hospitals represented by members of the House of Representatives who voted ‘Yea’ on the MMA were significantly more likely to receive a 508 waiver than hospitals represented by members who voted ‘Nay.’ Following the payment increase generated by the 508 program, recipient hospitals treated more patients, increased payroll, hired nurses, added new technology, raised CEO pay, and ultimately increased their spending by over $100 million annually. Section 508 recipient hospitals formed the Section 508 Hospital Coalition, which spent millions of dollars lobbying Congress to extend the program. After the vote on the MMA and before the vote to reauthorize the 508 program, members of Congress with a 508 hospital in their district received a 22% increase in total campaign contributions and a 65% increase in contributions from individuals working in the health care industry in the members’ home states. Our work demonstrates a pathway through which the link between politics and Medicare policy can dramatically affect US health spending.
We use administrative data from the IRS to examine the long-term impact of childhood Medicaid expansions. We use eligibility variation by cohort and state that we can relate to outcomes graphically. We find that children with greater Medicaid eligibility paid more in cumulative taxes by age 28. They collected less in EITC payments, and the women had higher cumulative wages. Our estimates imply that the government will recoup 56 cents of each dollar spent on childhood Medicaid by the time these children reach age 60. This return does not include estimated private gains from increased college attendance and decreased mortality.
We develop a model of selection that incorporates a key element of recent health reforms: an individual mandate. We identify a set of key parameters for welfare analysis, allowing us to model the welfare impact of the actual policy as well as to estimate the socially optimal penalty level. Using data from Massachusetts, we estimate the key parameters of the model. We compare health insurance coverage, premiums, and insurer average health claim expenditures between Massachusetts and other states in the periods before and after the passage of Massachusetts health reform. In the individual market for health insurance, we find that premiums and average costs decreased significantly in response to the individual mandate; consistent with an initially adversely selected insurance market. We are also able to recover an estimated willingness-to-pay for health insurance. Combining demand and cost estimates as sufficient statistics for welfare analysis, we find an annual welfare gain of $335 dollars per person or $71 million annually in Massachusetts as a result of the reduction in adverse selection. We also find evidence for smaller post-reform markups in the individual market, which increased welfare by another $107 dollars per person per year and about $23 million per year overall. To put this in perspective, the total welfare gains were 8.4% of medical expenditures paid by insurers. Our model and empirical estimates suggest an optimal mandate penalty of $2,190. A penalty of this magnitude would increase health insurance to near universal levels. Our estimated optimal penalty is higher than the individual mandate penalty adopted in Massachusetts but close to the penalty implemented under the ACA.
We model the labor market impact of the three key provisions of the recent Massachusetts and national “mandate-based” health reforms: individual and employer mandates and expansions in publicly-subsidized coverage. Using our model, we characterize the compensating differential for employer-sponsored health insurance (ESHI) — the causal change in wages associated with gaining ESHI. We also characterize the welfare impact of the labor market distortion induced by health reform. We show that the welfare impact depends on a small number of sufficient statistics” that can be recovered from labor market outcomes. Relying on the reform implemented in Massachusetts in 2006, we estimate the empirical analog of our model. We find that jobs with ESHI pay wages that are lower by an average of $6,058 annually, indicating that the compensating differential for ESHI is only slightly smaller in magnitude than the average cost of ESHI to employers. Because the newly-insured in Massachusetts valued ESHI, they were willing to accept lower wages, and the deadweight loss of mandate-based health reform was less than 5% of what it would have been if the government had instead provided health insurance by levying a tax on wages.
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.
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 semiparametrically 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. We illustrate the computation and applicability of the CQIV estimator with numerical examples and an empirical application on estimation of Engel curves for alcohol.