Knowledge that Transforms

We study the relationship between corporate income taxation and carbon dioxide (CO 2 ) emissions in the United States. We show CO 2 -intensive firms benefit more from the tax advantage of debt and pay lower income taxes on their capital income. Building on these new facts, we provide evidence that a cut in the corporate income tax rate leads to a larger expansion of clean firms. We develop a multisector general equilibrium model that accounts for our evidence and quantify the impact of corporate tax reforms on aggregate emissions. A policy that eliminates the tax advantage of debt could reduce aggregate emissions without affecting GDP.

An increasing number of decisions are guided by machine learning algorithms. But when consequential decisions are encoded in algorithms, individuals may strategically alter their behavior to achieve desired outcomes. This paper develops an empirical approach that adjusts decision algorithms to anticipate manipulation. By explicitly modeling incentives to manipulate, our approach produces decision rules that are stable under manipulation, even when the rules are fully transparent. We stress-test this approach through a large field experiment in Kenya. When implemented, linear strategy-robust decision rules outperform standard linear models such as LASSO.

We document how deepening resource scarcity results in rationing on the basis of race in a high-stakes setting: health care. Using detailed, time stamped data on 107,000 inpatient admissions to a large health system, we find that in-hospital mortality increases for Black but not White patients as hospitals reach capacity. These findings are not explained by differential patient selection. We identify rationing by wait times as a mechanism, documenting that sicker Black patients wait longer for care than healthier White patients at almost all capacity levels. Text analysis of unstructured clinical notes reveals rationing of provider effort as another mechanism.

Experimental and quasi-experimental studies show that childhood neighborhoods have substantial causal impacts on children's adult earnings and other long-term socioeconomic outcomes. Several market failures are likely to lead to excessive residential segregation by parental income and to an undersupply of mixed-income neighborhoods. Missing markets for opportunity originate in capital market imperfections for borrowing on children's future earnings, neighbor spillovers, behavioral biases, and housing search frictions. Customized housing mobility services combined with subsidized housing vouchers can help low-income families move to higher-opportunity areas. Place-based policies that create mixed-income neighborhoods appear to increase long-term outcomes for less-advantaged children.

The random utility model (RUM), a cornerstone in economics, is typically studied under the assumption that choice frequencies of all alternatives are observable. In practice, however, some alternatives have unobservable choice frequencies and are commonly aggregated into a single category called an outside option. We study RUM in such environments and derive a finite, nonredundant system of inequality constraints on observed choice frequencies that characterizes RU-rationalizability. We show that the conventional practice of aggregating unobserved alternatives can miss key information leading to incorrect conclusions such as that observed choices are rationalizable, even when no RUM is consistent with them.

Mechanisms that aim to reduce environmental degradation at low cost can be undermined when participants' conservation actions are not marginal to the incentive—or “additional”—as the lowest-cost participants may not be the highest social value. We investigate this challenge in the Conservation Reserve Program's auction mechanism for ecosystem services, linking bids to satellite-derived land use. Three-quarters of marginal auction winners are not additional. The heterogeneity in counterfactual land use introduces adverse selection. We develop a model of bidding and additionality to quantify welfare implications. Alternative auctions increase efficiency by using scoring rules that incorporate expected land use impacts.

We study the role of victim reporting in the production of public safety. We examine the Secure Communities program, a crime-reduction policy that involved police in detecting unauthorized immigrants and increased deportation fears in immigrant communities. We find that the policy reduced the likelihood that Hispanic victims report crimes to police and increased offending against Hispanics. The number of reported crimes is unchanged, masking these opposing effects. We show that reduced reporting drives the offending increase and provide the first elasticity of offending to victim reporting in the literature, calculating that a 10 percent decline in reporting increases offending by 7.9 percent.

Moretti (2021) reports a positive elasticity between technology cluster size and patenting, and uses an event study and instrumental variables regressions to justify a causal interpretation. The event study does not use the variation generated by inventors moving across cities, and the instrument is constructed incorrectly due to a coding error. I run a corrected event study and fix the coding error, and find null effects. The reported elasticity may not be causal.