Econometrica最新文献

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.

India's Industrial Disputes Act (IDA) requires large manufacturing plants to pay substantial costs if they wish to shrink their workforce. Since the early 2000s, these large plants have dramatically increased their use of contract workers who are not subject to these regulatory constraints. Between 2000 and 2015, the contract labor share in non-managerial employment nearly doubled at establishments with more than 100 workers (from 21 to 40 percentage points), while it only increased from 14 to 17 percentage points at establishments with less than 50 workers. Over the same period, the thickness of the right tail of the establishment size distribution in formal Indian manufacturing plants increased, the average product of labor at large plants declined, the job creation rate for large plants increased, and the probability that large plants introduced new products rose. We argue that these changes were caused by the increased adoption of contract labor. In a model of establishment growth subject to firing costs, we show that easing access to contract labor increased TFP in Indian manufacturing by 7.3% since the early 2000s, occurring all through a one-time reduction in misallocation between large and small plants with negligible change in the long-run growth rate.

Milgrom and Shannon (1994) provide necessary and sufficient conditions on parameterized optimization problems for their solution sets to be globally monotone in the parameter. We establish that their conditions may be significantly relaxed when focusing on discrete, binary comparisons between solution sets. Such binary comparisons are ubiquitous in economics and may involve comparing the same decision maker across two distinct regimes or two distinct decision makers with related objectives (e.g., a monopolist firm versus a social planner). While the single-crossing property remains prominent in the theory, quasisupermodularity of the objective functions of interest is not needed. Our approach relies upon a novel method of embedding a new optimization problem with a quasisupermodular objective function “between” the two original problems of interest. In smooth problems, sufficient conditions for our new assumptions may be verified by elementary differential comparisons, making them well suited for applied work. We illustrate the relevance of this novel approach with several economic applications.

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.

Fiduciary duty aims to solve principal-agent problems, and the United States is in the middle of a protracted debate surrounding the merits of extending it to all financial advisers. Leveraging a transaction-level data set of deferred annuities and state-level variation in common law fiduciary duty, we find that it raises risk-adjusted returns by 25 bp and leads to a 16% decline in the entry of affected firms. Through the lens of a model of entry and advice provision, we show that this effect can be due to both an increase in fixed costs and an increase in the cost of providing low-quality advice. We show how to disentangle these channels and find that both are empirically relevant. Counterfactual simulations show that further increases in the stringency of fiduciary duty monotonically improve advice quality.

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.