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

Empirical Bayes When Estimation Precision Predicts Parameters

原刊和作者：

Econometrica, Volume 94, Issue 2

Jiafeng Chen （Stanford University）

Abstract

Gaussian empirical Bayes methods usually maintain a precision independence assumption: The unknown parameters of interest are independent from the known standard errors of the estimates. This assumption is often theoretically questionable and empirically rejected. This paper proposes to model the conditional distribution of the parameter given the standard errors as a flexibly parameterized location-scale family of distributions, leading to a family of methods that we call close. The close framework unifies and generalizes several proposals under precision dependence. We argue that the most flexible member of the close family is a minimalist and computationally efficient default for accounting for precision dependence. We analyze this method and show that it is competitive in terms of the regret of subsequent decision rules. Empirically, using close leads to sizable gains for selecting high-mobility Census tracts.

Link: https://doi.org/10.3982/ECTA22935

Firm Accommodation After Workplace Disability: Labor Market Impacts and Implications for Subsidy Design

原刊和作者：

Econometrica, Volume 94, Issue 2

Naoki Aizawa （University of Wisconsin-Madison）

Corina Mommaerts （University of Wisconsin-Madison）

Stephanie Rennane （RAND Corporation）

Abstract

This paper studies the labor market impacts of firm accommodation decisions after workplace disability and assesses implications for the design of firm subsidies. We leverage a workers' compensation (WC) program in Oregon that provides wage subsidies to firms for accommodating workers with workplace disabilities. Leveraging rich administrative data and a policy change to the wage subsidy, we show that accommodation rates respond to the subsidy rate and that receipt of accommodation leads to a significant increase in employment and earnings a year later. To explore welfare implications, we develop and estimate a frictional labor market model of accommodation as a form of human capital investment. Worker turnover and imperfect experience rating in WC lead to underaccommodation and inefficient labor market outcomes after workplace disability. Counterfactual simulations show that subsidizing accommodation not only improves long-run labor market outcomes of workers experiencing work-related disability but also yields welfare gains for most workers.

Link: https://doi.org/10.3982/ECTA22565

Dynamic Incentives in Incompletely Specified Environments

原刊和作者：

Econometrica, Volume 94, Issue 2

Gabriel Carroll （University of Toronto）

Abstract

Consider a repeated interaction where it is unknown which of various stage games will be played each period. This framework separates the basic logic of intertemporal incentives from the requirement that any given strategy profile yields a well-defined payoff vector. A natural solution concept is ex post perfect equilibrium: strategies must form a subgame-perfect equilibrium for any realization of the sequence of stage games. When there is one long-run player and others are short-run, and public randomization is available, we can adapt the standard recursive approach to determine the maximum feasible gap between reward and punishment for the long-run player. This allows us to identify which actions can be played in equilibrium and, assuming perfect monitoring, to fully characterize what outcome paths can arise. With multiple long-run players or no public randomization, the approach fails; a diagnostic of this failure is that optimal penal codes may no longer exist.

Link: https://doi.org/10.3982/ECTA23373

Spatial Economics for Granular Settings

原刊和作者：

Econometrica, Volume 94, Issue 2

Jonathan I. Dingel （Columbia University）

Felix Tintelnot （Duke University）

Abstract

We examine the application of quantitative spatial models to the growing body of fine spatial data used to study local economic outcomes. In granular settings in which people choose from a large set of potential residence-workplace pairs, observed outcomes in part reflect idiosyncratic choices. Using analytical examples, Monte Carlo simulations, and event studies of neighborhood employment booms, we demonstrate that calibration procedures that equate observed shares and modeled probabilities perform very poorly in these high-dimensional settings. Parsimonious specifications of spatial linkages deliver better counterfactual predictions. To quantify the uncertainty about counterfactual outcomes induced by the idiosyncratic component of individuals' decisions, we introduce a quantitative spatial model with a finite number of individuals. Applying this model to Amazon's proposed second headquarters in New York City reveals that its predicted consequences for most neighborhoods vary substantially across realizations of the individual idiosyncrasies.

Link: https://doi.org/10.3982/ECTA19350

The Complexity of Multidimensional Learning in Agriculture

Econometrica, Volume 94, Issue 2

Rachid Laajaj （Universidad de Los Andes）

Karen Macours （Paris School of Economics）

Abstract

Studies on agricultural technology adoption often focus on one input, practice, or package, which is analytically useful, but may overlook the complexities involved with multidimensional learning needed for a lot of agricultural decisions. In Kenya, we study farmers' dynamic learning (from oneself and others) and adoption decisions over six seasons after randomly inviting them to participate in agronomic research trials, comparing different combinations of inputs during three consecutive seasons. As a response to the trials, adoption increases steadily despite the absence of positive profits multiple seasons after exposure to the trials. Know-how increases rapidly and faster for high skill farmers who experiment the most, at the cost of making new mistakes. The findings are consistent with a theoretical model with multidimensionality of input and practice decisions and differential learning from one's own experience by skills, where complementarities imply that adoption of an input requires finding how to re-optimize other dimensions, which adds to the cost of adoption.

Link: https://doi.org/10.3982/ECTA22974