Knowledge that Transforms

What happens when patients suddenly stop their medications? We study the health consequences of drug interruptions caused by large, abrupt, and arbitrary changes in price. Medicare's prescription drug benefit as-if-randomly assigns 65-year-olds a drug budget as a function of their birth month, beyond which out-of-pocket costs suddenly increase. Those facing smaller budgets consume fewer drugs and die more: mortality increases 0.0164 percentage points per month (13.9%) for each $100 per month budget decrease (24.4%). This estimate is robust to a range of falsification checks and lies in the 97.8th percentile of 544 placebo estimates from similar populations that lack the same idiosyncratic budget policy. Several facts help make sense of this large effect. First, patients stop taking drugs that are both high value and suspected to cause life-threatening withdrawal syndromes when stopped. Second, using machine learning, we identify patients at the highest risk of drug-preventable adverse events. Contrary to the predictions of standard economic models, high-risk patients (e.g., those most likely to have a heart attack) cut back more than low-risk patients on exactly those drugs that would benefit them the most (e.g., statins). Finally, patients appear unaware of these risks. In a survey of 65-year-olds, only one-third believe that stopping their drugs for up to a month could have any serious consequences. We conclude that far from curbing waste, cost sharing is itself highly inefficient, resulting in missed opportunities to buy health at very low cost ($11,321 per life-year).

Abstract Firms facing complex objectives often decompose the problems they face, delegating different parts of the decision to distinct subunits. Using comprehensive data and internal models from a large U.S. airline, we establish that airline pricing is not well approximated by a model of the firm as a unitary decision maker. We show that observed prices, however, can be rationalized by accounting for organizational structure and for the decisions by departments that are tasked with supplying inputs to the observed pricing heuristic. Simulating the prices the firm would charge if it were a rational, unitary decision maker results in lower welfare than we estimate under observed practices. Finally, we discuss why counterfactual estimates of welfare and market power may be biased if prices are set through decomposition, but we instead assume that they are set by unitary decision makers.

Abstract Tax collection with limited enforcement capacity may be consistent with both high- and low-delinquency regimes: high delinquency reduces the effectiveness of threats, thereby reinforcing high delinquency. We explore the practical challenges of unraveling the high-delinquency equilibrium using a mechanism design insight known as divide-and-conquer. Our preferred mechanism takes the form of prioritized iterative enforcement (PIE). Taxpayers are ranked using the ratio of expected collection to capacity use. Collection threats are issued in small batches to ensure high credibility and induce high compliance. Following repayments, liberated capacity is used to issue the next round of threats. In collaboration with a district of Lima, we experimentally assess PIE in a sample of 13,432 property taxpayers. The data validate and refine our theoretical framework. A semi-structural model suggests that keeping collection actions fixed, PIE would increase tax revenue by roughly 10%.

Abstract Decision makers, such as doctors, judges, and managers, make consequential choices based on predictions of unknown outcomes. Do these decision makers make systematic prediction mistakes based on the available information? If so, in what ways are their predictions systematically biased? In this article, I characterize conditions under which systematic prediction mistakes can be identified in empirical settings such as hiring, medical diagnosis, and pretrial release. I derive a statistical test for whether the decision maker makes systematic prediction mistakes under these assumptions and provide methods for estimating the ways the decision maker’s predictions are systematically biased. I analyze the pretrial release decisions of judges in New York City, estimating that at least 20% of judges make systematic prediction mistakes about misconduct risk given defendant characteristics. Motivated by this analysis, I estimate the effects of replacing judges with algorithmic decision rules and find that replacing judges with algorithms where systematic prediction mistakes occur dominates the status quo.

This paper examines the tradeoffs of monitoring for wasteful public spending. By penalizing unnecessary spending, monitoring improves the quality of public expenditure and incentivizes firms to invest in compliance technology. I study a large Medicare program that monitored for unnecessary healthcare spending and consider its effect on government savings, provider behavior, and patient health. Every dollar Medicare spent on monitoring generated $24-29 in government savings. The majority of savings stem from the deterrence of future care, rather than reclaimed payments from prior care. I do not find evidence that the health of the marginal patient is harmed, indicating that monitoring primarily deters low-value care. Monitoring does increase provider administrative costs, but these costs are mostly incurred upfront and include investments in technology to assess the medical necessity of care.

Abstract We answer three core questions about the hypothesized role of newly emerging job categories (“new work”) in counterbalancing the erosive effect of task-displacing automation on labor demand: what is the substantive content of new work, where does it come from, and what effect does it have on labor demand? We construct a novel database spanning eight decades of new job titles linked to U.S. Census microdata and to patent-based measures of occupations’ exposure to labor-augmenting and labor-automating innovations. The majority of current employment is in new job specialties introduced since 1940, but the locus of new-work creation has shifted from middle-paid production and clerical occupations over 1940–1980 to high-paid professional occupations and secondarily to low-paid services since 1980. New work emerges in response to technological innovations that complement the outputs of occupations and demand shocks that raise occupational demand. Innovations that automate tasks or reduce occupational demand slow new-work emergence. Although the flow of augmentation and automation innovations is positively correlated across occupations, the former boosts occupational labor demand while the latter depresses it. The demand-eroding effects of automation innovations have intensified in the past four decades while the demand-increasing effects of augmentation innovations have not.

We study the lifetime effects of the first and largest American youth employment and training program in the United States-the Civilian Conservation Corps (CCC), 1933-1942. We match newly digitized enrollee records to census, World War II enlistment, Social Security, and death records. We find that longer service in the CCC led to improvements in height, health status, longevity, geographic mobility, and lifetime earnings but did not improve short-term labor market outcomes, including employment and wages. We address potential selection into CCC duration using several approaches, most importantly two newly developed control-function approaches that leverage unbiased estimates of the short-term effects of a randomized controlled trial of Job Corps (the modern version of the CCC). Our findings suggest that short- and medium-term evaluations of employment programs underestimate effects because they fail to capture lifetime effects and often ignore or underestimate health and longevity benefits that increase in magnitude at later ages.

Abstract We link every police report in Finland to administrative data to identify violence between colleagues and the economic consequences for victims, perpetrators, and firms. This new approach to observe when one colleague attacks another overcomes previous data constraints limiting evidence on this phenomenon to self-reported surveys that do not identify perpetrators. We document large, persistent labor market effects of between-colleague violence on victims and perpetrators. Male perpetrators experience substantially weaker consequences after attacking female colleagues. Perpetrators’ relative economic power in male-female violence partly explains this asymmetry. Turning to broader implications for firm recruitment and retention, we find that male-female violence causes a decline in the proportion of women at the firm, both because fewer new women are hired and current female employees leave. Management plays a key role in mediating the effects on the wider workforce. Only male-managed firms lose women. Female-managed firms exhibit a key difference relative to male-managed firms: male perpetrators are less likely to remain employed after attacking their female colleagues.

Abstract Does a school district that expands school choice provide better outcomes for students than a neighborhood-based assignment system? This article studies the Zones of Choice (ZOC) program, a school choice initiative of the Los Angeles Unified School District (LAUSD) that created small high school markets in some neighborhoods but left attendance zone boundaries in place throughout the rest of the district. We study market-level effects of choice on student achievement and college enrollment using a difference-in-differences design. Student outcomes in ZOC markets increased markedly, narrowing achievement and college enrollment gaps between ZOC neighborhoods and the rest of the district. The effects of ZOC are larger for schools exposed to more competition, supporting the notion that competition is a key channel. Demand estimates suggest families place substantial weight on schools’ academic quality, providing schools with competition-induced incentives to improve their effectiveness. The evidence demonstrates that public school choice programs have the potential to improve school quality and reduce neighborhood-based disparities in educational opportunity.

Abstract Who bears the consequences of administrative problems in health care? We use data on repeated interactions between a large sample of U.S. physicians and many different insurers to document the complexity of health care billing, and estimate its economic costs for doctors and consequences for patients. Observing the back-and-forth sequences of claim denials and resubmissions for past visits, we can estimate physicians’ costs of haggling with insurers to collect payments. Combining these costs with the revenue never collected, we estimate that physicians lose 18% of Medicaid revenue to billing problems, compared with 4.7% for Medicare and 2.4% for commercial insurers. Identifying off of physician movers and practices that span state boundaries, we find that physicians respond to billing problems by refusing to accept Medicaid patients in states with more severe billing hurdles. These hurdles are quantitatively just as important as payment rates for explaining variation in physicians’ willingness to treat Medicaid patients. We conclude that administrative frictions have first-order costs for doctors, patients, and equality of access to health care. We quantify the potential economic gains—in terms of reduced public spending or increased access to physicians—if these frictions could be reduced and find them to be sizable.