Knowledge that Transforms

The 2009 CARD Act limited credit card lenders' ability to raise borrowers' interest rates on the basis of new information. Pricing became less responsive to public and private signals of borrowers' risk and demand characteristics, and price dispersion fell by one‐third. I estimate the efficiency and distributional effects of this shift toward more pooled pricing. Prices fell for high‐risk and price‐inelastic consumers, but prices rose elsewhere in the market and newly exceeded willingness to pay for over 30% of the safest subprime borrowers. On net, average traded prices fell and consumer surplus rose at all credit scores. Higher consumer surplus was partly driven by a fall in lender profits, and partly by the Act's insurance value to borrowers who could retain favorable pricing after adverse changes to their default risk. The relatively high level of pre‐CARD‐Act markups was crucial for realizing these surplus gains.

We use the martingale construction of Luedtke and van der Laan (2016) to develop tests for the presence of treatment heterogeneity. The resulting sequential validation approach can be instantiated using various validation metrics, such as BLPs, GATES, QINI curves, etc., and provides an alternative to cross‐validation‐like cross‐fold application of these metrics. This note was prepared as a comment on the Fisher–Schultz paper by Chernozhukov, Demirer, Duflo, and Fernández‐Val, forthcoming in Econometrica.

Policymakers often choose a policy bundle that is a combination of different interventions in different dosages. We develop a new technique— treatment variant aggregation (TVA)—to select a policy from a large factorial design. TVA pools together policy variants that are not meaningfully different and prunes those deemed ineffective. This allows us to restrict attention to aggregated policy variants, consistently estimate their effects on the outcome, and estimate the best policy effect adjusting for the winner's curse. We apply TVA to a large randomized controlled trial that tests interventions to stimulate demand for immunization in Haryana, India. The policies under consideration include reminders, incentives, and local ambassadors for community mobilization. Cross‐randomizing these interventions, with different dosages or types of each intervention, yields 75 combinations. The policy with the largest impact (which combines incentives, ambassadors who are information hubs, and reminders) increases the number of immunizations by 44% relative to the status quo. The most cost‐effective policy (information hubs, ambassadors, and SMS reminders, but no incentives) increases the number of immunizations per dollar by 9.1% relative to the status quo.

We develop a theory of monotone comparative statics for models with adjustment costs. We show that comparative‐statics conclusions may be drawn under the usual ordinal complementarity assumptions on the objective function, assuming very little about costs: only a mild monotonicity condition is required. We use this insight to prove a general Le Chatelier principle: under the ordinal complementarity assumptions, if short‐run adjustment is subject to a monotone cost, then the long‐run response to a shock is greater than the short‐run response. We extend these results to a fully dynamic model of adjustment over time: the Le Chatelier principle remains valid, and under slightly stronger assumptions, optimal adjustment follows a monotone path. We apply our results to models of saving, production, pricing, labor supply, and investment.

We propose flexible Gaussian representations for conditional cumulative distribution functions and give a concave likelihood criterion for their estimation. Optimal representations satisfy the monotonicity property of conditional cumulative distribution functions, including in finite samples and under general misspecification. We use these representations to provide a unified framework for the flexible maximum likelihood estimation of conditional density, cumulative distribution, and quantile functions at parametric rate. Our formulation yields substantial simplifications and finite sample improvements over related methods. An empirical application to the gender wage gap in the United States illustrates our framework.

Trade policy is often cast as a solution to the free‐riding problem in international climate agreements. This paper examines the extent to which trade policy can deliver on this promise. We incorporate global supply chains of carbon and climate externalities into a multi‐country, multi‐industry general equilibrium trade model. By deriving theoretical formulas for optimal carbon and border taxes, we quantify the maximum efficacy of two trade policy solutions to the free‐riding problem. Adding optimal carbon border taxes to existing tariffs proves largely ineffective, delivering only 3.4% of what could be achieved under globally optimal carbon pricing. In contrast, Nordhaus's (2015) climate club framework, in which border taxes are used as contingent penalties to deter free‐riding, can achieve 33–68% of the globally optimal carbon reduction, depending on the initial coalition (EU, EU + US, or EU + US + China). In all cases, the climate club ensures universal compliance, thereby preserving free trade.

This paper shows the endogeneity of amenities plays a crucial role in determining the welfare distribution of a city's residents. We quantify this mechanism by building a dynamic model of residential choice with heterogeneous households, where consumption amenities are the equilibrium outcome of a market for non‐tradables. We estimate our model using Dutch microdata and leveraging variation in Amsterdam's spatial distribution of tourists as a demand shifter, finding significant heterogeneity in residents' preferences over amenities and in the supply responses of amenities to changes in demand composition. This two‐way heterogeneity dictates the degree of horizontal differentiation across neighborhoods, residential sorting, and inequality. Finally, we show the distributional effects of mass tourism depend on this heterogeneity: following rent increases due to growing tourist demand for housing, younger residents—whose amenity preferences are closest to tourists—are compensated by amenities tilting in their favor, while the losses of older residents are amplified.

We propose strategies to estimate and make inference on key features of heterogeneous effects in randomized experiments. These key features include best linear predictors of the effects using machine learning proxies, average effects sorted by impact groups , and average characteristics of most and least impacted units . The approach is valid in high‐dimensional settings, where the effects are proxied (but not necessarily consistently estimated) by predictive and causal machine learning methods. We post‐process these proxies into estimates of the key features. Our approach is generic; it can be used in conjunction with penalized methods, neural networks, random forests, boosted trees, and ensemble methods, both predictive and causal. Estimation and inference are based on repeated data splitting to avoid overfitting and achieve validity. We use quantile aggregation of the results across many potential splits, in particular taking medians of p ‐values and medians and other quantiles of confidence intervals. We show that quantile aggregation lowers estimation risks over a single split procedure, and establish its principal inferential properties. Finally, our analysis reveals ways to build provably better machine learning proxies through causal learning: we can use the objective functions that we develop to construct the best linear predictors of the effects, to obtain better machine learning proxies in the initial step. We illustrate the use of both inferential tools and causal learners with a randomized field experiment that evaluates a combination of nudges to stimulate demand for immunization in India.

We propose a double robust Bayesian inference procedure on the average treatment effect (ATE) under unconfoundedness. For our new Bayesian approach, we first adjust the prior distributions of the conditional mean functions, and then correct the posterior distribution of the resulting ATE. Both adjustments make use of pilot estimators motivated by the semiparametric influence function for ATE estimation. We prove asymptotic equivalence of our Bayesian procedure and efficient frequentist ATE estimators by establishing a new semiparametric Bernstein–von Mises theorem under double robustness; that is, the lack of smoothness of conditional mean functions can be compensated by high regularity of the propensity score and vice versa. Consequently, the resulting Bayesian credible sets form confidence intervals with asymptotically exact coverage probability. In simulations, our method provides precise point estimates of the ATE through the posterior mean and delivers credible intervals that closely align with the nominal coverage probability. Furthermore, our approach achieves a shorter interval length in comparison to existing methods. We illustrate our method in an application to the National Supported Work Demonstration following LaLonde (1986) and Dehejia and Wahba (1999).

Landmines affect the lives of millions in many conflict‐ridden communities long after the end of hostilities. However, there is little research on the role of demining. We examine the economic consequences of landmine removal in Mozambique, the only country to transition from heavily contaminated in 1992 to mine‐free in 2015. First, we present the self‐assembled georeferenced catalog of areas suspected of contamination, along with a detailed record of demining operations. Second, the event‐study analysis reveals a robust association between demining activities and subsequent local economic performance, reflected in luminosity. Economic activity does not pick up in the years leading up to clearance, nor does it increase when operators investigate areas mistakenly marked as contaminated in prior surveys. Third, recognizing that landmine removal reshapes transportation access, we use a market‐access approach to explore direct and indirect effects. To advance on identification, we isolate changes in market access caused by removing landmines in previously considered safe areas, far from earlier nationwide surveys. Fourth, policy simulations reveal the substantial economywide dividends of clearance, but only when factoring in market‐access effects, which dwarf direct productivity links. Additionally, policy counterfactuals uncover significant aggregate costs when demining does not prioritize the unblocking of transportation routes. These results offer insights into the design of demining programs in Ukraine and elsewhere, highlighting the need for centralized coordination and prioritization of areas facilitating commerce.