Knowledge that Transforms

I study regulation of noncompete employment contracts, assessing the trade‐off between restricting worker mobility and encouraging firm investment. I develop an on‐the‐job search model in which firms and workers sign dynamic wage contracts with noncompete clauses and firms invest in their workers' general human capital. Employers use noncompete clauses to enforce buyout payments when their workers depart, ultimately extracting rent from future employers. This rent extraction is socially excessive, and restrictions on these clauses can improve efficiency. The optimal regulation policy is characterized. In an application to the managerial labor market using a novel contract data set, I find the optimal policy to be quantitatively close to a ban.

Nearest neighbor (NN) matching is widely used in observational studies for causal effects. Abadie and Imbens (2006) provided the first large‐sample analysis of NN matching. Their theory focuses on the case with the number of NNs, M fixed. We reveal something new out of their study and show that once allowing M to diverge with the sample size an intrinsic statistic in their analysis constitutes a consistent estimator of the density ratio with regard to covariates across the treated and control groups. Consequently, with a diverging M , the NN matching with Abadie and Imbens' (2011) bias correction yields a doubly robust estimator of the average treatment effect and is semiparametrically efficient if the density functions are sufficiently smooth and the outcome model is consistently estimated. It can thus be viewed as a precursor of the double machine learning estimators.

This paper describes the response of the economy to large shocks in a nonlinear production network. A sector's tail centrality measures how a large negative shock transmits to GDP, that is, the systemic risk of the sector. Tail centrality is theoretically and empirically very different from local centrality measures such as sales share—in a benchmark case, it is measured as a sector's average downstream closeness to final production. It also measures how large differences in sector productivity can generate cross‐country income differences. The paper also uses the results to analyze the determinants of total tail risk in the economy. Increases in interconnectedness can simultaneously reduce the sensitivity of the economy to small shocks while increasing the sensitivity to large shocks. Tail risk is related to conditional granularity , where some sectors become highly influential following negative shocks.

Job loss comes with large present value earnings losses which elude workhorse models of unemployment and labor market policy. I propose a parsimonious model of a frictional labor market in which jobs differ in terms of unemployment risk and workers search off‐ and on‐the‐job. This gives rise to a job ladder with slippery bottom rungs where unemployment spells beget unemployment spells. I allow for human capital to respond to time spent out of work and estimate the framework on German Social Security data. The model captures the joint response of wages, employment, and unemployment risk to job loss which I measure empirically. The key driver of the “unemployment scar” is the loss in job security and its interaction with the evolution of human capital and, in particular, the search for better employment.

We study a regulation in Chile that mandates warning labels on products whose sugar or caloric concentration exceeds certain thresholds. We show that consumers substitute from labeled to unlabeled products—a pattern mostly driven by products that consumers mistakenly believe to be healthy. On the supply side, we find substantial reformulation of products and bunching at the thresholds. We develop and estimate an equilibrium model of demand for food and firms' pricing and nutritional choices. We find that food labels increase consumer welfare by 1.8% of total expenditure, and that these effects are enhanced by firms' responses. We then use the model to study alternative policy designs. Under optimal policy thresholds, food labels and sugar taxes generate similar gains in consumer welfare, but food labels benefit the poor relatively more.

What is the role of credit scores in credit markets? We argue that it is, in part, the market's assessment of a person's unobservable type, which here we take to be patience. We postulate a model of persistent hidden types where observable actions shape the public assessment of a person's type via Bayesian updating. We show how dynamic reputation can incentivize repayment. Importantly, we show how an economy with credit scores implements the same equilibrium allocation. We estimate the model using both credit market data and the evolution of individuals' credit scores. We conduct counterfactuals to assess how more or less information used in scoring individuals affects outcomes and welfare. If tracking of individual credit actions is outlawed, poor young adults of low type benefit from subsidization by high types despite facing higher interest rates arising from lower dynamic incentives to repay.

The discourse around pay transparency has focused on partial equilibrium effects: how workers rectify pay inequities through informed renegotiation. We investigate how employers respond in equilibrium. We study a model of bargaining under two‐sided incomplete information. Our model predicts that transparency reduces the individual bargaining power of workers, leading to lower average wages. A key insight is that employers credibly refuse to pay high wages to any one worker to avoid costly renegotiations with others. When workers have low individual bargaining power, pay transparency has a muted effect. We test our model with an event‐study analysis of U.S. state‐level laws protecting the right of private sector workers to communicate salary information with their coworkers. Consistent with our theoretical predictions, transparency laws empirically lead wages to decline by approximately 2%, and wage declines are smallest in magnitude when workers have low individual bargaining power.

Public employment programs may affect poverty both directly through the income they provide and indirectly through general equilibrium effects. We estimate both effects, exploiting a reform that improved the implementation of India's National Rural Employment Guarantee Scheme (NREGS) and whose rollout was randomized at a large (sub‐district) scale. The reform raised beneficiary households' earnings by 14%, and reduced poverty by 26%. Importantly, 86% of income gains came from non‐program earnings, driven by higher private‐sector (real) wages and employment. This pattern appears to reflect imperfectly competitive labor markets more than productivity gains: worker's reservation wages increased, land returns fell, and employment gains were higher in villages with more concentrated landholdings. Non‐agricultural enterprise counts and employment grew rapidly despite higher wages, consistent with a role for local demand in structural transformation. These results suggest that public employment programs can effectively reduce poverty in developing countries, and may also improve economic efficiency.