Econometrica Volume 90, Issue 4 (July 2022) is now online
JOURNAL OF THE ECONOMETRIC SOCIETY
Volume 90, Issue 4 (July 2022) has just been published. The full content of the journal is accessible at
The Effect of Job Loss and Unemployment Insurance on Crime in Brazil
Diogo G. C. Britto, Paolo Pinotti, Breno Sampaio
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.
Firm and Worker Dynamics in a Frictional Labor Market
Adrien Bilal, Niklas Engbom, Simon Mongey, Giovanni L. Violante
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.
Mechanism Design with Limited Commitment
Laura Doval, Vasiliki Skreta
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.
Locally Robust Semiparametric Estimation
Victor Chernozhukov, Juan Carlos Escanciano, Hidehiko Ichimura, Whitney K. Newey, James M. Robins
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.
Optimal Dynamic Information Acquisition
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.
Robust Incentives for Teams
Tianjiao Dai, Juuso Toikka
We show that demanding team incentives to be robust to nonquantifiable uncertainty about the game played by the agents leads to contracts that align the agents' interests. Such contracts have a natural interpretation as team‐based compensation. Under budget balance they reduce to linear contracts, thus identifying profit‐sharing, or equity, as an optimal contract absent a sink or a source of funds. A linear contract also gives the best profit guarantee to an outside residual claimant. These contracts still suffer from the free‐rider problem, but a positive guarantee obtains if and only if the technology known to the contract designer is sufficiently productive.
From Imitation to Innovation: Where Is all that Chinese R&D Going?
Michael König, Kjetil Storesletten, Zheng Song, Fabrizio Zilibotti
We construct an endogenous growth model with random interactions where firms are subject to distortions. The TFP distribution evolves endogenously as firms seek to upgrade their technology over time either by innovating or by imitating other firms. We use the model to quantify the effects of misallocation on TFP growth in emerging economies. We structurally estimate the stationary state of the dynamic model targeting moments of the empirical distribution of R&D and TFP growth in China during the period 2007–2012. The estimated model fits the Chinese data well. We compare the estimates with those obtained using data for Taiwan and perform counterfactuals to study the effect of alternative policies. R&D misallocation has a large effect on TFP growth.
The Analytic Theory of a Monetary Shock
Fernando Alvarez, Francesco Lippi
We propose an analytical method to analyze the propagation of an aggregate shock in a broad class of sticky‐price models. The method is based on the eigenvalue‐eigenfunction representation of the dynamics of the cross‐sectional distribution of firms' desired adjustments. A key novelty is that we can approximate the whole profile of the impulse response for any moment of interest in response to an aggregate shock (any displacement of the invariant distribution). We present several applications for an economy with low inflation and idiosyncratic shocks. We show that the shape of the impulse response of the canonical menu cost model is fully encoded by a single parameter, just like the Calvo model, although the shapes are very different. A model with a quadratic hazard function, arguably a good fit to the micro data on price setting, yields an impulse response that is close to the canonical menu cost model.
Maximum Likelihood Estimation in Markov Regime-Switching Models with Covariate-Dependent Transition Probabilities
Demian Pouzo, Zacharias Psaradakis, Martin Sola
This paper considers maximum likelihood (ML) estimation in a large class of models with hidden Markov regimes. We investigate consistency of the ML estimator and local asymptotic normality for the models under general conditions, which allow for autoregressive dynamics in the observable process, Markov regime sequences with covariate‐dependent transition matrices, and possible model misspecification. A Monte Carlo study examines the finite‐sample properties of the ML estimator in correctly specified and misspecified models. An empirical application is also discussed.
Terrorism Financing, Recruitment, and Attacks
This paper investigates the effect of terrorism financing and recruitment on attacks. I exploit a Sharia‐compliant institution in Pakistan, which induces unintended and quasi‐experimental variation in the funding of terrorist groups through their religious affiliation. The results indicate that higher terrorism financing, in a given location and period, generate more attacks in the same location and period. Financing exhibits a complementarity in producing attacks with terrorist recruitment, measured through data from Jihadist‐friendly online fora and machine learning. A higher supply of terror is responsible for the increase in attacks and is identified by studying groups with different affiliations operating in multiple cities. These findings are consistent with terrorist organizations facing financial frictions to their internal capital market.
Job Search Behavior among the Employed and Non-Employed
R. Jason Faberman, Andreas I. Mueller, Ayşegül Şahin, Giorgio Topa
We develop a unique survey that focuses on the job search behavior of individuals regardless of their labor force status and field it annually starting in 2013. We use our survey to study the relationship between search effort and outcomes for the employed and non‐employed. Three important facts stand out: (1) on‐the‐job search is pervasive, and is more intense at the lower rungs of the job ladder; (2) the employed are at least three times more effective than the unemployed in job search; and (3) the employed receive better job offers than the unemployed. We set up a general equilibrium model of on‐the‐job search with endogenous search effort, calibrate it to fit our new facts, and find that the search effort of the employed is highly elastic. We show that search effort substantially amplifies labor market responses to productivity shocks over the business cycle.
Beyond Health: Nonhealth Risk and the Value of Disability Insurance
Manasi Deshpande, Lee M. Lockwood
The public debate over disability insurance has centered on concerns about individuals without severe health conditions receiving benefits. We go beyond health risk alone to quantify the overall insurance value of U.S. disability programs, including value from insuring nonhealth risk. We find that disability recipients, especially those with less‐severe health conditions, are much more likely to have experienced a wide variety of nonhealth shocks than nonrecipients. Selection into disability receipt on the basis of nonhealth shocks is so strong among individuals with less‐severe health conditions that by many measures less‐severe recipients are worse off than more‐severe recipients. As a result, under baseline assumptions, benefits to less‐severe recipients have an annual surplus value (insurance benefit less efficiency cost) over cost‐equivalent tax cuts of $7700 per recipient, about three‐fourths that of benefits to more‐severe recipients ($9900). Insurance against nonhealth risk accounts for about one‐half of the value of U.S. disability programs.
Determination of Pareto exponents in economic models driven by Markov multiplicative processes
Brendan K. Beare, Alexis Akira Toda
This article contains new tools for studying the shape of the stationary distribution of sizes in a dynamic economic system in which units experience random multiplicative shocks and are occasionally reset. Each unit has a Markov‐switching type, which influences their growth rate and reset probability. We show that the size distribution has a Pareto upper tail, with exponent equal to the unique positive solution to an equation involving the spectral radius of a certain matrix‐valued function. Under a nonlattice condition on growth rates, an eigenvector associated with the Pareto exponent provides the distribution of types in the upper tail of the size distribution.
Making a Narco: Childhood Exposure to Illegal Labor Markets and Criminal Life Paths
Maria Micaela Sviatschi
This paper provides evidence that exposure to illegal labor markets during childhood leads to the formation of industry‐specific human capital at an early age, putting children on a criminal life path. Using the timing of U.S. antidrug policies, I show that when the return to illegal activities increases in coca suitable areas in Peru, parents increase the use of child labor for coca farming, putting children on a criminal life path. Using administrative records, I show that affected children are about 30% more likely to be incarcerated for violent and drug‐related crimes as adults. No effect in criminality is found for individuals that grow up working in places where the coca produced goes primarily to the legal sector, suggesting that it is the accumulation of human capital specific to the illegal industry that fosters criminal careers. However, the rollout of a conditional cash transfer program that encourages schooling mitigates the effects of exposure to illegal industries, providing further evidence on the mechanisms.
From Population Growth to Firm Demographics: Implications for Concentration, Entrepreneurship and the Labor Share
Hugo Hopenhayn, Julian Neira, Rish Singhania
In the U.S., large firms now account for a greater share of economic activity, new firms are being created at slower rates, and workers are receiving a smaller share of GDP. Changes in population growth provide a unified quantitative explanation. A decrease in population growth lowers firm entry rates, shifting the firm‐age distribution toward older firms. Firm aging accounts for (i) the concentration of employment in large firms, (ii) and trends in average firm size and exit rates, key determinants of firm entry rates. Feedback effects from firm demographics generate two‐thirds of the effect. Prior to the decrease, entry rates rose steadily reflecting the earlier baby boom. The glut of firms due to the baby boom lead to rich transitional dynamics within the feedback effects, accounting for more than half the total change. Baby boom induced changes in the firm‐age distribution provide a driving force for the post‐WWII rise and fall in the aggregate labor share. Ignoring changes in population growth attributes all the long run decline in entry rates to a decrease in firm exit rates, which in reality have been only one‐third as large.
A Comment on “Using Randomization to Break the Curse of Dimensionality”
Robert L. Bray
Rust (1997b) discovered a class of dynamic programs that can be solved in polynomial time with a randomized algorithm. For these dynamic programs, the optimal values of a polynomially large sample of states are sufficient statistics for the (near) optimal values everywhere, and the values of this random sample can be bootstrapped from the sample itself. However, I show that this class is limited, as it requires all but a vanishingly small fraction of state variables to behave arbitrarily similarly to i.i.d. uniform random variables.
Comment on Andrews (1991) “Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation”
This comment includes a solution to a problem in Section 8 in Andrews (1991) and points out a method to generalize the mean‐squared error (MSE) bounds appearing in Andrews (1988) and Andrews (1991).
Comment on Jackson and Sonnenschein (2007) “Overcoming Incentive Constraints by Linking Decisions”
Ian Ball, Matthew O. Jackson, Deniz Kattwinkel