Knowledge that Transforms

This paper investigates fairness perceptions of extreme income inequality generated in winner-take-all competitions. Two large-scale experiments with more than 7,000 participants from the general population of the U.S. show that extreme earnings inequality is widely accepted, even when the winner only slightly outperforms the runner-up. The effect of the winning margin on inequality acceptance is modest compared to the effect of shifting the source of inequality from luck to winning by the smallest possible margin. The experimental choices are systematically associated with broader fairness attitudes and policy preferences, including support for higher taxation of top earners and redistributive economic policy.

This paper examines how politically connected firms shape housing supply in U.S. cities. Using new data on campaign donations to U.S. mayors and a regression discontinuity design, I present three findings. First, developers connected to the mayor sell more new housing units. Second, more sales of new housing by connected developers coincide with higher local housing supply: cities where mayors received more developer donations issue nearly 70 percent more permits for new housing units. Third, differences in mayors’ pre-existing policy stances—rather than connections to developers—is a quantitatively larger determinant of local housing supply.

Can workplace contact with outgroup members affect attitudes towards that group? If so, can this spread to others within a social network? We randomly assigned Hindu job applicants in India either a Hindu or Muslim partner for training and placement in call center jobs. One year later, those applicants assigned Muslim partners expressed more positive attitudes towards Muslims, were more likely to say Muslims faced discrimination and had greater knowledge of Islamic practices. Close friends and family members of those workers also experience similar, though smaller, changes, despite reporting no additional direct contact with Muslims.

We study a panel data model with heterogeneous effects, allowing slopes to vary across individuals and time. To reduce dimensionality, we assume these slopes follow a factor structure, so slope matrices can be estimated via low-rank regularized regression. We propose a multi-step estimation procedure incorporating sample splitting and partialing-out to enable valid inference after penalized estimation. We establish the asymptotic normality of the resulting estimator, facilitating inference for individualtime- specific effects and their cross-sectional averages. The method’s performance is illustrated through simulations and an empirical application.

We analyze the labor market impact of CVS Health’s acquisition of Target’s pharmacy business in December 2015 using Lightcast job postings data. Using differencein- differences and triple-difference designs, we find meaningful negative effects of the merger on posted pay. In our preferred specification, we estimate that the acquisition reduced posted pay in affected labor markets by 2.9%. We test for heterogeneous merger effects by occupational characteristics, finding that the merger caused pay to fall by more in lower-paid occupations than in higher-paid occupations.

The production approach to markup estimation has recently attracted the attention of scholars and policymakers for its straightforward implementation and limited data requirements. Criticism has also been directed at the approach and validation of the approach is called for. This paper provides a novel empirical assessment of the approach using rich panel data on the complete population of Swedish firms across four sectors (1998-2019). I first estimate the markup of each firm using the production approach. Using two sources of identification, I then show that these estimates match the stylized fact that markups are higher on more concentrated local markets.

This paper exploits the exogenous variation of Mexico’s Oportunidades conditional cash transfer program on urban households’ time and consumption allocations to identify and structurally estimate a collective labor supply model with home production. I use my structural estimates to show that participation in Oportunidades increased maternal intrahousehold bargaining power by almost 13%, which is associated with an increase of approximately 14% in the production of a child-related public good in dual-earner beneficiary households. Counterfactual exercises show that Oportunidades is as effective as alternative cash transfer programs and wage subsidies at increasing mothers’ bargaining power, control over household monetary resources, and domestic output.

This paper proposes a new method to estimate the welfare impact of conflicts and remedy common data constraints in conflict-affected environments. The method first estimates how agents regard spatial welfare differentials by voting with their feet, using pre-conflict data. Then, it infers a lower-bound estimate for the conflict-driven welfare shock from partially observed post-conflict migration patterns. Results for the conflict in Eastern Ukraine between 2014 and 2019 show a large lower-bound welfare loss for Donetsk residents equivalent to 8–32 percent of lifetime income depending on agents’ time preference and risk aversion parameters.

We study how temporary subsidies for adoption of modern foreign technology drove South Korea’s industrialization in the 1970s. Leveraging unique historical data, we provide causal evidence consistent with coordination failures: adoption improved adopters’ performance and generated local spillovers, with firms more likely to adopt when other local firmshad already adopted.We incorporate these findings into a quantitative model, where the potential for multiple steady states depends on parameters mapped to the causal estimates. In our calibrated model, South Korea’s temporary subsidies shifted its economy to a more industrialized steady state, increasing heavy manufacturing’s GDP share by 27% and export intensity by 39%. Larger market access and lower idiosyncratic distortions amplified the effects of these subsidies, as the gains from adoption increase with firm scale.

We study how various zoning regulations combine to affect housing supply, prices, and rents of single- and multifamily homes using novel lot-level zoning data from Greater Boston and a cross-sectional boundary discontinuity design at regulation boundaries. Looser density restrictions, alone or with other less restrictive regulations, are most effective in increasing supply and reducing per-housing-unit rents and prices. We theoretically and empirically show that restrictive zoning regulations shift housing stock towards larger units, increasing prices per housing unit. Counterfactuals imply that a recent Massachusetts law increasing building density near transit can reduce long-run rents and prices, particularly in suburbs.