Knowledge that Transforms

Over the latest monetary policy tightening cycle, capital has been flowing from low-inflation countries to high-inflation countries. This pattern of capital flows is consistent with the predictions of an open-economy model with nominal rigidities where cost-push shocks generate an inflationary episode and capital flows freely across countries. Yet, by raising demand for domestic non-tradable goods and services, capital inflows cause unwelcome upward pressure on firms’ costs in countries most severely hit by these shocks. We find that a reverse pattern of capital flows would have improved the output-inflation trade-off globally, hence requiring a less aggressive monetary tightening in the most severely hit countries and delivering overall welfare gains.

I examine the impact of patent term on R&D and innovation in the presence of policy anticipation, common in real-world settings. Using a difference-in-difference design, I exploit quasi-experimental variation in US patent term across technological fields due to the ratification of TRIPs agreements in 1995. Despite a general increase in average patent term, in most fields innovators faced a considerable probability of patent term reduction for future innovations. Three key findings emerge: (1) R&D and innovation accelerate more in fields with a higher probability of patent term reduction, i.e. a shorter average patent term extension, before implementation. (2) This heightened activity persists for at least 5 years postimplementation, driven by indirect effects where the news-related acceleration fosters further innovation through technological externalities linked to cumulative knowledge creation. (3) Conversely, the direct effect of a shorter extension in patent term would stimulate relatively less innovation, absent the indirect effects of anticipation.

We exploit a panel of city-level data with rich demographic information to estimate the distributional effects of Department of Defense spending and its effects on a range of social outcomes. The income and employment generated by defence spending accrue predominantly to households without a bachelor's degree. These households as well as Black and Hispanic households tend to disproportionately benefit from this spending. Defence spending also promotes a range of beneficial social outcomes that are often targeted by government programs, including reductions in poverty, divorce rates, disability rates, and mortality rates, as well as increases in homeownership rates, health insurance rates, and occupational prestige. We compare the effects of defence spending with the effects of general demand shocks and explore reasons for the differential effects of the shocks.

Auction models are convenient abstractions of informal price-formation processes that arise in markets for assets or services. These processes involve frictions like bidder recruitment costs for sellers, participation costs for bidders, and limitations on sellers’ commitment abilities. This paper develops an auction model that captures such frictions. We derive novel insights, notably that outcomes are often inefficient, that markets sometimes unravel, and that the observability of competition may have a large effect.

I study linear panel data models with predetermined regressors (such as lagged dependent variables) where coefficients are individual-specific, allowing for heterogeneity in the effects of the regressors on the dependent variable. I show that the model is not point-identified in a short panel context but rather partially identified, and I characterize the identified sets for the mean, variance, and CDF of the coefficient distribution. This characterization is general, accommodating discrete, continuous, and unbounded data, and it leads to computationally tractable estimation and inference procedures. I apply the method to study lifecycle earnings dynamics among U.S. households using the Panel Study of Income Dynamics (PSID) dataset. The results suggest the presence of unobserved heterogeneity in earnings persistence, implying that households face varying levels of earnings risk which, in turn, contribute to heterogeneity in their consumption and savings behaviours.

When a policy prioritizes one person over another, is it because they benefit more, or because they are preferred? This paper develops a method to uncover the values consistent with observed allocation decisions. We estimate how much each person benefits from an intervention, and then reconcile the allocation with (i) the welfare weights assigned to different people; (ii) heterogeneous treatment effects of the intervention; and (iii) weights on different outcomes. We demonstrate this approach by analyzing Mexico’s PROGRESA anti-poverty programme. The analysis reveals that while the programme prioritized certain subgroups—such as indigenous households—the fact that those groups benefited more implies that the programme did not actually assign them a higher welfare weight. We also find evidence that the policy valued outcomes differently from households. The PROGRESA case illustrates how the method makes it possible to audit existing policies, and to design future policies that better align with values.

We assess the relationship between the racial diversity of medical providers and racial health disparities in the use of preventive care and in patient outcomes. We use unique data from the Military Health System, where we observe providers as patients so that we can identify their race, and where moves across bases change exposure to provider race in a plausibly exogenous fashion. We consider patients with four chronic, deadly, but manageable illnesses, where the relationship with the provider may have the most direct impact on health. We find striking evidence that provider racial diversity leads to reduced disparities in maintenance of preventive care and mortality.

Why are job polarization and income inequality higher in large U.S. cities? I offer a new explanation: when house prices grow faster in large cities, middle-income households increasingly cannot afford to own a house there. They move to smaller cities and the middle of the income distribution in large cities hollows out, making them more polarized and unequal. I document that (1) cities with higher price growth experienced larger polarization and increase in inequality since 1980 and (2) middle-income households migrate more often to cheaper locations for housing-related reasons than low- or high-income households. Using a spatial equilibrium model with tenure choice and skill heterogeneity, I find that excess growth of prices relative to incomes and rents in large cities accounts for nearly all of the gap in polarization and almost one-half of the gap in inequality growth between large and small cities from 1980 to 2019.

Regulatory concerns over a tension between expanding financial access and limiting default have led to significant restrictions on contract terms in a number of countries, despite limited evidence on their effectiveness. We use a large nation-wide RCT to examine new borrower responses to changes in interest rates and minimum payments for a credit card that accounted for 15% of all first-time formal loans in Mexico. Default rates were 19% over the 26 month experiment and a 30 pp decrease in interest rates decreased default by 2.5 pp with no effects on the newest borrowers. Doubling minimum payments increased default by 0.8 pp during the experiment but reduced it by 1 pp afterwards, possibly by reducing debt. Matching the experimental sample to their formal employment histories we find that the effect of job separation—more common among new borrowers—on default is seven times larger than the effect of the 30 pp interest rate change. We provide a simple framework for interpreting the experimental results, and rationalize the smaller contract term effects by their limited effects on cash flow rather than by differences in per-peso impacts.

Human decision-makers frequently override the recommendations generated by predictive algorithms, but it is unclear whether these discretionary overrides add valuable private information or reintroduce human biases and mistakes. We develop new quasi-experimental tools to measure the impact of human discretion over an algorithm on the accuracy of decisions, even when the outcome of interest is only selectively observed, in the context of bail decisions. We find that 90% of the judges in our setting underperform the algorithm when they make a discretionary override, with most making override decisions that are no better than random. Yet the remaining 10% of judges outperform the algorithm in terms of both accuracy and fairness when they make a discretionary override. We provide suggestive evidence on the behaviour underlying these differences in judge performance, showing that the high-performing judges are more likely to use relevant private information and are less likely to overreact to highly salient events compared to the low-performing judges.