本文精选了经济学国际顶刊《Econometrica》近期发表的论文，提供经济学研究领域最新学术动态。

The Effect of Job Loss and Unemployment Insurance on Crime in Brazil

原刊和作者：

Econometrica 2022年 7月

Diogo G. C. Britto （Bocconi University）

Paolo Pinotti （Bocconi University）

Breno Sampaio （Federal University of Pernambuco）

Abstract

We investigate the impact of job loss on crime and the mitigating role of unemployment benefits, exploiting detailed individual-level data linking employment careers, criminal records, and welfare registries for the universe of male workers in Brazil. The probability of committing crimes increases on average by 23% for workers displaced by mass layoffs, and by slightly less for their cohabiting sons. Using causal forests, we show that the effect is entirely driven by young and low-tenure workers, while there is no heterogeneity by education and income. Regression discontinuity estimates indicate that unemployment benefit eligibility completely offsets potential crime increases upon job loss, but this effect vanishes completely immediately after benefit expiration. Our findings point to liquidity constraints and psychological stress as the main drivers of criminal behavior upon job loss, while substitution between time on the job and leisure does not seem to play an important role.

Link: https://doi.org/10.3982/ECTA18984

Firm and Worker Dynamics in a Frictional Labor Market

原刊和作者：

Econometrica 2022年 7月

Adrien Bilal （Harvard University）

Niklas Engbom （New York University）

Simon Mongey （University of Chicago）

Giovanni L. Violante （Princeton University）

Abstract

This paper integrates the classic theory of firm boundaries, through span of control or taste for variety, into a model of the labor market with random matching and on-the-job search. Firms choose when to enter and exit, whether to create vacancies or destroy jobs in response to shocks, and Bertrand-compete to hire and retain workers. Tractability is obtained by proving that, under a parsimonious set of assumptions, all worker and firm decisions are characterized by their joint surplus, which in turn only depends on firm productivity and size. The job ladder in marginal surplus that emerges in equilibrium determines net poaching patterns by firm characteristics that are in line with the data. As frictions vanish, the model converges to a standard competitive model of firm dynamics. The combination of firm dynamics and search frictions allows the model to: (i) quantify the misallocation cost of frictions; (ii) replicate elusive life-cycle growth profiles of superstar firms; and (iii) make sense of the failure of the job ladder around the Great Recession as a result of the collapse of firm entry.

Link: https://doi.org/10.3982/ECTA17955

Mechanism Design With Limited Commitment

原刊和作者：

Econometrica 2022年 7月

Laura Doval （Columbia University）

Vasiliki Skreta （University of Texas at Austin）

Abstract

We develop a tool akin to the revelation principle for dynamic mechanism-selection games in which the designer can only commit to short-term mechanisms. We identify a canonical class of mechanisms rich enough to replicate the outcomes of any equilibrium in a mechanism-selection game between an uninformed designer and a privately informed agent. A cornerstone of our methodology is the idea that a mechanism should encode not only the rules that determine the allocation, but also the information the designer obtains from the interaction with the agent. Therefore, how much the designer learns, which is the key tension in design with limited commitment, becomes an explicit part of the design. Our result simplifies the search for the designer-optimal outcome by reducing the agent's behavior to a series of participation, truth telling, and Bayes' plausibility constraints the mechanisms must satisfy.

Link: https://doi.org/10.3982/ECTA16846

Locally Robust Semiparametric Estimation

原刊和作者：

Econometrica 2022年 7月

Victor Chernozhukov （MIT）

Juan Carlos Escanciano （Universidad Carlos III de Madrid）

Hidehiko Ichimura （University of Arizona）

Whitney K. Newey （MIT）

James M. Robins （Harvard University）

Abstract

Many economic and causal parameters depend on nonparametric or high dimensional first steps. We give a general construction of locally robust/orthogonal moment functions for GMM, where first steps have no effect, locally, on average moment functions. Using these orthogonal moments reduces model selection and regularization bias, as is important in many applications, especially for machine learning first steps. Also, associated standard errors are robust to misspecification when there is the same number of moment functions as parameters of interest.

We use these orthogonal moments and cross-fitting to construct debiased machine learning estimators of functions of high dimensional conditional quantiles and of dynamic discrete choice parameters with high dimensional state variables. We show that additional first steps needed for the orthogonal moment functions have no effect, globally, on average orthogonal moment functions. We give a general approach to estimating those additional first steps. We characterize double robustness and give a variety of new doubly robust moment functions. We give general and simple regularity conditions for asymptotic theory.

Link: https://doi.org/10.3982/ECTA16294

Optimal Dynamic Information Acquisition

Econometrica 2022年 7月

Weijie Zhong （Stanford University）

Abstract

I study a dynamic model in which a decision-maker (DM) acquires information about the payoffs of different alternatives prior to making a decision. The model's key feature is the flexibility of information: the DM can choose any dynamic signal process as an information source, subject to a flow cost that depends on the informativeness of the signal. Under the optimal policy, the DM acquires a signal that arrives according to a Poisson process. The optimal Poisson signal confirms the DM's prior belief and is sufficiently precise to warrant immediate action. Over time, given the absence of the arrival of a Poisson signal, the DM continues seeking an increasingly precise but less frequent Poisson signal.

Link: https://doi.org/10.3982/ECTA17787