Research

How well can organizations allocate tasks when individual performance is only imperfectly observed? We develop a simple framework that exploits quasi-random personnel exits to infer how much organizations attend to differences in worker quality when assigning tasks. We apply this framework to health care, a setting with life-and-death stakes where quality is particularly difficult to assess. Using rich administrative data from Brazil, we show that hospitals react to the unexpected death of higher-performing physicians by shifting patients toward higher-quality physicians. This adjustment is sharply asymmetric: losses from high-performer exits are largely offset, whereas exits of low performers lead to nearly random reassignment. Quantitatively, targeted reassignment prevents a 20 percent increase in mortality that would otherwise arise under random reassignment after the exit of a top physician. The pattern is driven almost entirely by shifts among incumbent physicians rather than external hiring, becomes stronger with the deceased physician’s tenure, and is most pronounced in hospitals with formal oversight structures and labor slack. These findings suggest that organizations can learn about differences in worker quality, but that effective task allocation also requires the organizational capacity to act on that information.

This paper provides the first causal evidence on the effects of patient--physician racial concordance in childbirth care, drawing on administrative data linking nearly 15 million deliveries to the attending physician in Brazil. Exploiting the quasi-random assignment of physicians to expectant mothers, we find that racial concordance shapes welfare-relevant margins of childbirth care, including pain management, reproductive autonomy, and preventive care. Race-matched encounters increase analgesia use during vaginal delivery and prophylactic medication, and reduce tubal ligation. Concordance also reduces diagnostic testing and length of stay, concentrated in lower-risk deliveries, suggesting more efficient care. These effects are particularly pronounced among mothers of African descent background, who face substantial baseline disparities in childbirth care that are meaningfully attenuated under concordant encounters.  By contrast, we find no detectable effects on health outcomes, including maternal readmission, ICU admission, maternal mortality, or infant Apgar scores. More broadly, our results   show that provider identity systematically shapes discretionary medical decision-making, with implications for the design of policies aimed at reducing persistent racial disparities.

Does environmental regulation constrain or catalyze economic development? We study São Paulo’s 2002 ban on pre-harvest burning in sugarcane, which forced rapid adoption of mechanical harvesting. Mechanical harvesters were available before the ban, but adoption remained limited. The ban effectively raised the cost of manual harvesting, shifting demand toward modern technology. Using land slope as an instrument for mechanization costs, we find that regulation-induced technology adoption triggered structural transformation in local labor markets. Mechanization reduced agricultural employment and increased manufacturing employment, accounting for approximately three-quarters of observed sectoral shifts between 2000 and 2010. Critically, labor reallocation was selective: employment gains occurred only in agro-linked manufacturing, such as biofuel production and sugar processing, rather than across all industries. This selectivity explains the smooth transition despite the displacement of workers in the agricultural sector. Mechanization also generated economy-wide gains: household incomes rose by approximately 5.8% above baseline levels, unemployment declined, and poverty fell. Our findings show that when regulation forces the substitution of an obsolete technology with a more productive one, environmental policy becomes economic policy: aligning pollution reduction and development rather than trading them off.

A core challenge for healthcare systems is ensuring timely care for critical conditions while efficiently managing lower-complexity cases. Hospitals, often overburdened by both, struggle to balance these demands and allocate resources effectively. Many countries have responded by introducing alternative 24/7 facilities to relieve hospital strain and improve patient outcomes, yet evidence on their impact remains limited. We evaluate the introduction of freestanding Emergency Care Centers (UPAs) within Brazil’s publicly funded health system, leveraging rich administrative data. We find that UPAs reduced hospital outpatient procedures by 30% and hospital admissions for ambulatory-sensitive conditions by 24–37%, enabling hospitals to focus on more complex cases, such as surgeries and obstetric admissions, which increased by 25%. We observe a 13% reduction in inpatient mortality, particularly in intensive care and for conditions best suited to hospital treatment. While some deaths were displaced to UPAs, there was a decline in population-level mortality of 1.8%, although this is not statistically significant. Our findings show how an intermediate tier of emergency care reshapes patient sorting, alleviates hospital congestion, and improves hospital performance in an overstretched public health system. 

Deaths caused by or connected to exposure to Brazilian prisons are widely acknowledged as frequent and preventable, yet official data fails to capture their scale, causes, and circumstances. To circumvent what official administrative datasets miss, this article examines an original dataset of 1077 criminal case files from 27 Brazilian state courts involving individuals who died between 2017 and 2021 after having been incarcerated. Drawing on the systematic document review of these cases, we analyze sociodemographic characteristics, health information, causes of death, and judicial responses, distinguishing between deaths occurring in custody (“internal”) and after release (“external”). Our findings reveal pervasive omissions in basic demographic and medical information, extensive use of ill-defined causes of death, and a striking absence of investigation in most cases, including deaths under direct state custody. We identify instances of obfuscation and judicial inaction that, together with the absence of reliable administrative data, are likely to sustain institutional ignorance and normalize preventable deaths. This study advances debates on incarceration and health, state accountability, and proposes the concept of prison lethality: the capacity of carceral spaces to increase people’s exposure to health risks and harms, combined with the epistemic practices that shed light on or obfuscate this capacity. 

The lack of physicians in poorer areas is a matter of concern in developed and developing countries. This paper exploits location choices and individual characteristics of all generalist physicians who graduated in Brazil between 2001 and 2013 to study policies that aim at increasing the supply of physicians in underserved areas. We estimate physicians’ locational preferences using a random coefficients discrete choice model. We find that physicians have substantial utility gains if they work close to the region they were born or from where they graduated. We show that wages and health infrastructure, though relevant, are not the main drivers of physicians’ location choices. Simulations from the model indicate that quotas in medical schools for students born in underserved areas and the opening of vacancies in medical schools in deprived areas improve the spatial distribution of physicians at lower costs than financial incentives or investments in health infrastructure.

This study assess socioeconomic-related inequalities in health and healthcare utilization among the elderly in Brazil.  We evaluated the Brazilian National Health Survey data collected in 2019 and computed the prevalence of measures of health conditions and healthcare utilization by age-bracket and markers of socioeconomic status—income, educational attainment, and race/ethnicity—among individuals aged 60 or older. We further employed logistic regression models, adjusted for a wide set of covariates, to estimate the relationship between socioeconomic status and those outcomes. Results from regression models showed strong associations with income and educational attainment for most health conditions: health status, physical activity, difficulties with activities of daily living and instrumental activities of daily living, and depression. For most conditions, weaker or no associations with race/ethnicity were found. Individuals in the highest income quintile and that completed higher education also had higher odds of having consulted a physician, while high-income individuals had lower odds of having received emergency care at home. The findings of this study highlight the significant socioeconomic inequalities in the health of the elderly population in Brazil. 

The Brazilian private health insurance market is the second largest in the world, covering approximately 47 million people in 2019. This paper documents how physicians' inpatient reimbursement fees vary in Brazil and analyzes their relationship with health providers and health insurance market concentrations. We implement a fixed-effects panel regression and take advantage of an unprecedented database that contains information on national administrative records of inpatient procedures paid by health insurance companies in 2016. We find a positive correlation between reimbursement for ICU procedures and providers' market share and a negative correlation for insurers. We also document substantial variation in procedure prices between and within Brazilian states and that more competitive markets are more populous and richer in terms of GDP. These results suggest that more concentrated providers' markets tend to practice higher procedure fees, but that can be compensated by the insurers' bargain power. The study contributes to a better understanding of these dynamics in a developing country context and may help policymakers implement adequate regulation to guarantee access to healthcare.

Power is an important factor in assessing the likely validity of a statistical estimate. An analysis with low power is unlikely to produce convincing evidence of a treatment effect even when one exists. Of greater concern, a statistically significant estimate from a low-powered analysis is likely to misstate the true effect size, including finding estimates of the wrong sign or that are several times too large. Yet statistical power is rarely reported in published economics work. This is in part because many modern research designs are complex enough that power cannot be easily ascertained using simple formulae. Power can also be difficult to estimate in observational settings. Using an applied example–the link between gaining health insurance and mortality–we conduct a simulated power analysis to outline the importance of power and ways to estimate power in complex research settings. We find that standard difference-in-differences and triple differences analyses of Medicaid expansions using county or state mortality data would need to induce reductions in population mortality of at least 2% to be well powered. While there is no single, correct method for conducting a simulated power analysis, our manuscript outlines how applied researchers can conduct simulations appropriate to their settings. 

In this Article, we present a comprehensive analysis of health-system preparedness and response to COVID-19 in Brazil. We examine the relationship between the availability of health-care resources in different parts of the country, the socioeconomic characteristics of the population (eg, income, housing, and employment status), risk factors for adverse COVID-19 outcomes (age and burden of chronic disease), and socioeconomic vulnerability, with the pattern of spread, response, and outcomes of the epidemic, as measured by the number of deaths by state and municipality.  Our results show that the initial spread of COVID-19 was mostly affected by patterns of socioeconomic vulnerability as measured by the SVI rather than population age structure and prevalence of health risk factors. The states with a high (greater than median) SVI were able to expand hospital capacity, to enact stringent COVID-19-related legislation, and to increase physical distancing adherence in the population, although not sufficiently to prevent higher COVID-19 mortality during the initial phase of the epidemic compared with states with a low SVI. Death rates accelerated until June, 2020, particularly in municipalities with the highest socioeconomic vulnerability. Throughout the following months, however, differences in policy response converged in municipalities with lower and higher SVIs, while physical distancing remained relatively higher and death rates became relatively lower in the municipalities with the highest SVIs compared with those with lower SVIs.