Knowledge that Transforms

Freedman (2008a,b) showed that the linear regression estimator is biased for the analysis of randomized controlled trials under the randomization model. Under Freedman's assumptions, we derive exact closed‐form bias corrections for the linear regression estimator. We show that the limiting distribution of the bias corrected estimator is identical to the uncorrected estimator. Taken together with results from Lin (2013), our results show that Freedman's theoretical arguments against the use of regression adjustment can be resolved with minor modifications to practice.

This paper studies the effect of a restorative justice intervention targeted at 143 youth ages 13 to 17 facing felony charges of medium severity (e.g., burglary, assault). Eligible youths were randomly assigned to participate in the Make‐it‐Right (MIR) restorative justice program or a control group where they faced standard criminal prosecution. We estimate the effects of MIR on the likelihood that a youth will be rearrested in the four years following randomization. Assignment to MIR reduces the probability of a rearrest within six months by 19 percentage points, a 44 percent reduction relative to the control group. Moreover, the reduction in recidivism persists even four years after randomization. Thus, our estimates show that restorative justice conferencing can reduce recidivism among youth charged with relatively serious offenses and can be an effective alternative to traditional criminal justice practices.

We study a flexible class of trade models with international production networks and arbitrary wedge‐like distortions like markups, tariffs, or nominal rigidities. We characterize the general equilibrium response of variables to shocks in terms of microeconomic statistics. Our results are useful for decomposing the sources of real GDP and welfare growth, and for computing counterfactuals. Using the same set of microeconomic sufficient statistics, we also characterize societal losses from increases in tariffs and iceberg trade costs and dissect the qualitative and quantitative importance of accounting for disaggregated details. Our results, which can be used to compute approximate and exact counterfactuals, provide an analytical toolbox for studying large‐scale trade models and help to bridge the gap between computation and theory.

We exploit a natural experiment involving a randomization of votes across parties within coalitions in all local elections in Italy for over a decade. A lottery on the position of party symbols in the ballot papers allows estimating the causal effect of increasing votes to parties for coalition policies. A non‐marginal random boost of votes shifts budgetary spending towards the treated party's platform, but only for issues that are salient in that party's political manifesto. We study the chains of mechanisms mapping votes into policies and link it to an increase in bargaining power within legislative majorities. Parties leverage their higher electoral support to gain the appointment of politically affiliated cabinet members. Empowering different parties also leads to the selection of cabinets with different socio‐demographic characteristics. The unintentional experiment helps shed new light on mechanisms mapping votes to parties into coalition policies.

Measuring the effect of peers on individuals' outcomes is a challenging problem, in part because individuals often select peers who are similar in both observable and unobservable ways. Group formation experiments avoid this problem by randomly assigning individuals to groups and observing their responses; for example, do first‐year students have better grades when they are randomly assigned roommates who have stronger academic backgrounds? In this paper, we propose randomization‐based permutation tests for group formation experiments, extending classical Fisher Randomization Tests to this setting. The proposed tests are justified by the randomization itself, require relatively few assumptions, and are exact in finite samples. This approach can also complement existing strategies, such as linear‐in‐means models, by using a regression coefficient as the test statistic. We apply the proposed tests to two recent group formation experiments.

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.

A signal is privacy‐preserving with respect to a collection of privacy sets if the posterior probability assigned to every privacy set remains unchanged conditional on any signal realization. We characterize the privacy‐preserving signals for arbitrary state space and arbitrary privacy sets. A signal is privacy‐preserving if and only if it is a garbling of a reordered quantile signal . Furthermore, distributions of posterior means induced by privacy‐preserving signals are exactly mean‐preserving contractions of that induced by the quantile signal . We discuss the economic implications of our characterization for statistical discrimination, the revelation of sensitive information in auctions and price discrimination.