Knowledge that Transforms

We document significant gaps in wealth across health status over the life cycle in Germany—a country with a universal healthcare system and negligible out‐of‐pocket medical expenses. To investigate the underlying sources of these wealth‐health gaps, we build a heterogeneous‐agent life‐cycle model in which health and wealth evolve endogenously. In the model, agents exert efforts to lead a healthy lifestyle, which helps maintain good health status in the future. Effort choices, or lifestyle behaviors, are subject to adjustment costs to capture their habitual nature in the data. We find that our estimated model generates the great majority of the empirical wealth gaps by health and quantify the role of earnings and savings channels through which health affects these gaps. We show that variations in individual health efforts account for around a quarter of the model‐generated wealth gaps by health, illustrating their role as an amplification mechanism behind the gaps.

We show that stricter grading policies in STEM courses reduce STEM enrollment, especially for women. We estimate a model of student demand for courses and optimal effort choices given professor grading policies. Grading policies are treated as equilibrium objects that in part depend on student demand for courses. Differences in demand for STEM and non‐STEM courses explain much of why STEM classes give lower grades. Restrictions on grading policies that equalize average grades across classes reduce the STEM gender gap and increase overall enrollment in STEM classes.

In markets with complicated products, individuals may choose how much time and effort to spend understanding and comparing alternatives. Focusing on insurance choice, we find evidence consistent with individuals acquiring more information when there are larger consequences from making an uninformed choice. Building on the rational inattention literature, we develop and estimate a parsimonious demand model in which individuals choose how much to research difficult‐to‐observe characteristics. We use our estimates to evaluate policies that simplify choice. Reducing the number of plans can raise welfare through improved choice as well as savings in information costs. Capping out‐of‐pocket costs generates larger welfare gains than standard models. The empirical model can be applied to other settings to examine the regulation of complex products.

We estimate and trace a credit demand curve for households that recently experienced damage to their homes from a natural disaster. Our administrative data include over one million applicants to a federal recovery loan program for households. We estimate extensive‐margin demand over a large range of interest rates. Our identification strategy exploits 24 natural experiments, leveraging exogenous, time‐based variation in the program's offered interest rate. Interest rates meaningfully affect consumer demand throughout the distribution of rates. On average, a 1 percentage point increase in the interest rate reduces loan take‐up by 26%. We find a large impact of applicants' credit quality on demand and evidence of monthly payment targeting. Using our estimated demand curve and information on program costs, we find that the program generates an average social surplus of $2900 per borrower.

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.