Amitabh Chandra of Harvard University reviews “We’ve Got You Covered: Rebooting American Health Care” by Liran Einav and Amy Finkelstein. The Econlit abstract of this book begins: “Considers the objective and key elements of universal health insurance, presenting a blueprint for overhauling the US health-care system to guarantee essential medical coverage for everyone.”

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%.

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.

Because the tax savings of having a child are a realized only if the birth takes place before midnight, January 1, the incentives for the "marginal" birth are substantial. Using a sample of children from the National Longitudinal Survey of Youth, we find that the probability that a child is born in the last week of December, rather than the first week of January, is positively correlated with tax benefits. We estimate that increasing the tax benefit of having a child by $500 raises the probability of having the child in the last week of December by 26.9 percent.

Regulatory review is often seen as a barrier to innovation, increasing costs and delaying new medicines. Yet approval may also expand markets by certifying quality and reducing uncertainty. We test this by studying FDA approval for follow-on indications—uses that physicians could already prescribe “offlabel” — and find approval raises use in newly approved diseases by 25 percent within a year, with larger increases in smaller off-label markets. Subsequent approvals in the same disease yield smaller gains. Our results suggest regulatory approval of medicines expands market size by increasing demand, rather than easing insurer-imposed restrictions, revealing limits to marketbased learning.

We estimate the effect of rising health insurance premiums on wages, employment, and the distribution of part-time and full-time work using variation in medical malpractice payments driven by the recent "medical malpractice crisis." We estimate that a 10% increase in health insurance premiums reduces the aggregate probability of being employed by 1.2 percentage points, reduces hours worked by 2.4%, and increases the likelihood that a worker is employed only part time by 1.9 percentage points. For workers covered by employer provided health insurance, this increase in premiums results in an offsetting decrease in wages of 2.3%.

In the United States, health care technology has contributed to rising survival rates, yet health care spending relative to GDP has also grown more rapidly than in any other country. We develop a model of patient demand and supplier behavior to explain these parallel trends in technology growth and cost growth. We show that health care productivity depends on the heterogeneity of treatment effects across patients, the shape of the health production function, and the cost structure of procedures such as MRIs with high fixed costs and low marginal costs. The model implies a typology of medical technology productivity: (I) highly cost-effective “home run” innovations with little chance of overuse, such as anti-retroviral therapy for HIV, (II) treatments highly effective for some but not for all (e.g., stents), and (III) “gray area” treatments with uncertain clinical value such as ICU days among chronically ill patients. Not surprisingly, countries adopting Category I and effective Category II treatments gain the greatest health improvements, while countries adopting ineffective Category II and Category III treatments experience the most rapid cost growth. Ultimately, economic and political resistance in the United States to ever-rising tax rates will likely slow cost growth, with uncertain effects on technology growth. (JEL H51, I11, I18, O31)

It is common practice to use sensible rules of thumb for cleaning data. Measurement error is often the justification for removing (trimming) or recoding (winsorizing) observations where the dependent variable has values that lie outside a specified range. We consider a general measurement error process that nests many plausible models. Analytic results demonstrate that winsorizing and trimming are solutions for a narrow class of error processes. Indeed such procedures can induce or exacerbate bias. Monte Carlo simulations and empirical results demonstrate the fragility of cleaning. Even on root mean square error criteria, we cannot find generalizable justifications for these procedures.