Healthcare and education exhibit wide variation in spending that is loosely associated with outcomes. We study supply-side explanations for such variation in in healthcare, and extend this discussion to how it might apply to education. In both sectors, variation in risk-adjusted rates could arise from some providers or educators doing too much (overuse) or others are using too little (underuse). Alternatively, the production function varies across providers and educators, so that hospitals and educators with higher returns to treatment deliver more because of comparative advantage. We discuss how a prototypical Roy model can separate these explanations.

In medicine, the reasons for variation in treatment rates across hospitals serving similar patients are not well understood. Some interpret this variation as unwarranted, and push standardization of care as a way of reducing allocative inefficiency. An alternative interpretation is that hospitals with greater expertise in a treatment use it more because of their comparative advantage, suggesting that standardization is misguided. A simple economic model provides an empirical framework to separate these explanations. Estimating this model with data for heart attack patients, we find evidence of substantial variation across hospitals in both allocative inefficiency and comparative advantage, with most hospitals overusing treatment in part because of incorrect beliefs about their comparative advantage. A stylized welfare-calculation suggests that eliminating allocative inefficiency would increase the total benefits from the treatment that we study by 44%.

This paper develops asymptotic distribution theory for instrumental variable regression when the partial correlation between the instruments and a single included endogenous variable is weak, here modeled as local to zero. Asymptotic representations are provided for various instrumental variable statistics, including the two-stage least squares (TSLS) and limited information maximum- likelihood (LIML) estimators and their t-statistics. The asymptotic distributions are found to provide good approximations to sampling distributions with just 20 observations per instrument. Even in large samples, TSLS can be badly biased, but LIML is, in many cases, approximately median unbiased. The theory suggests concrete quantitative guidelines for applied work. These guidelines help to interpret Angrist and Krueger's (1991) estimates of the returns to education: whereas TSLS estimates with many instruments approach the OLS estimate of 6%, the more reliable LIML and TSLS estimates with fewer instruments fall between 8% and 10%, with a typical confidence interval of (6%, 14%).

A large literature in medicine documents variation across areas in the use of surgical treatments that is unrelated to outcomes. Observers of this phenomena have invoked "flat of the curve medicine" to explain these facts, and have advocated for reductions in spending in high-use areas. In contrast, we develop a simple Roy model of patient treatment choice with productivity spillovers that can generate the empirical facts. Our model predicts that high-use areas will have higher returns to surgery, better outcomes among patients most appropriate for surgery, and worse outcomes among patients least appropriate for surgery, while displaying no relationship between treatment intensity and overall outcomes. Using data on treatments for heart attacks, we find strong empirical support for these and other predictions of our model, and reject alternative explanations such as waste or supplier induced demand, for geographic variation in medical care.

The federal government spends billions of dollars each year on programs designed to increase the resources available to hospitals that serve the poor. This paper explores the intended and unintended effects of such targeted funds. First, how do these funds distort the behavior of state and local governments who wish to appropriate the funds for other uses? Second, to the extent that these funds do increase resources in the targeted hospitals, do patients benefit? We use the rapid and uneven growth in Medicaid Disproportionate Share Hospital (DSH) payments across states and hospitals to answer these questions. We identify states that were most able to appropriate DSH funds and show that, while DSH payments to public hospitals in these states were systematically diverted, DSH payments to other hospitals and in other states were not diverted. Additional resources that were made available to hospitals (rather than appropriated by the state) were associated with significant declines in infant and post-heart attack mortality. A range of evidence suggests that these improvements were due to better hospital care. Overall, our analysis implies that public subsidies can be an effective mechanism for improving medical care and outcomes for the poor, but that the impact is limited by the ability of state and local government to divert the targeted funds.

We draw on macroeconomic models of diffusion and productivity to explain empirical patterns of survival gains in heart attacks. Using Medicare data for 2.8 million patients during 1986-2004, we find that hospitals rapidly adopting cost-effective innovations such as beta blockers, aspirin, and reperfusion, had substantially better outcomes for their patients. Holding technology adoption constant, the marginal returns to spending were relatively modest. Hospitals increasing the pace of technology diffusion ("tigers") experienced triple the survival gains compared to those with diminished rates ("tortoises"). In sum, small differences in the propensity to adopt effective technology lead to wide productivity differences across hospitals.

Recent theoretical and empirical advances have renewed interest in monopsonistic models of the labor market. However, there is little direct empirical support for these models. We use an exogenous change in wages at Department of Veterans Affairs (VA) hospitals as a natural experiment to investigate the extent of monopsony in the nurse labor market. We estimate that labor supply to individual hospitals is quite inelastic, with short‐run elasticity around 0.1. We also find that non‐VA hospitals responded to the VA wage change by changing their own wages.

We examine how employers learn about worker productivity in a randomized pilot experiment which provided objective estimates of teacher performance to school principals. We test several hypotheses that support a simple Bayesian learning model with imperfect information. First, the correlation between performance estimates and prior beliefs rises with more precise objective estimates and more precise subjective priors. Second, new information exerts greater influence on posterior beliefs when it is more precise and when priors are less precise. Employer learning affects job separation and productivity in schools, increasing turnover for teachers with low performance estimates and producing small test score improvements.

Gender and Performance: Evidence from School Assignment by Randomized Lottery by Justine S. Hastings, Thomas J. Kane and Douglas O. Staiger. Published in volume 96, issue 2, pages 232-236 of American Economic Review, May 2006

Abortion legalization in the early 1970s led to dramatic changes in fertility. Some research has suggested that it altered cohort outcomes, but this literature has been limited and controversial. In this paper, we provide a framework for understanding selection mechanisms and use that framework to both address inconsistent past methodological approaches and provide evidence on the long-run impact on cohort characteristics. Our results indicate that lower-cost abortion brought about by legalization altered young adult outcomes through selection. In particular, it increased likelihood of college graduation, lower rates of welfare use, and lower odds of being a single parent.