Knowledge that Transforms

This paper shows that matching frictions and a thick market externality in firm‐to‐firm trade shape the agglomeration of economic activity. Using panel data of firm‐to‐firm trade in Japan, I demonstrate that firms gradually match with alternative suppliers following an unanticipated supplier bankruptcy, and that the rate of rematching increases in the geographic density of alternative suppliers. Motivated by these empirical findings, I develop a general equilibrium model of firm‐to‐firm matching in input trade across space. The model reveals that the thick market externality gives rise to an agglomeration externality affecting regional production and welfare. Using the calibrated model to the reduced‐form patterns of firm‐to‐firm matching, I estimate that the elasticity of a region's real wage with respect to population density due to the thick market externality is approximately 0.02. This finding highlights the substantial impact of the thick market externality on the overall agglomeration benefit.

The government budget constraint ties the market value of government debt to the expected present discounted value of fiscal surpluses. We find evidence that U.S. Treasury investors fail to impose this no‐arbitrage restriction in the United States. Both cyclical and long‐run dynamics of tax revenues and government spending make the surplus claim risky. In a realistic asset pricing model, this risk in surpluses creates a large gap between the market value of debt and its fundamental value, the PDV of surpluses, suggesting that U.S. Treasuries may be overpriced.

This paper considers a moral hazard problem where the agent can choose any output distribution with a support in a given compact set. The agent's effort‐cost is smooth and increasing in first‐order stochastic dominance. To analyze this model, we develop a generalized notion of the first‐order approach applicable to optimization problems over measures. We demonstrate each output distribution can be implemented and identify those contracts that implement that distribution. These contracts are characterized by a simple first‐order condition for each output that equates the agent's marginal cost of changing the implemented distribution around that output with its marginal benefit. Furthermore, the agent's wage is shown to be increasing in output. Finally, we consider the problem of a profit‐maximizing principal and provide a first‐order characterization of principal‐optimal distributions.

The gains from agglomeration economies are believed to be highly localized. Using confidential Census plant‐level data, we show that large industrial plant openings raise the productivity not only of local plants but also of distant plants hundreds of miles away, which belong to large multi‐plant, multi‐region firms that are exposed to the local productivity spillover through one of their plants. This “global” productivity spillover does not decay with distance and is stronger if plants are in industries that share knowledge with each other. To quantify the significance of firms' plant‐level networks for the propagation and amplification of local productivity shocks, we estimate a quantitative spatial model in which plants of multi‐region firms are linked through shared knowledge. Counterfactual exercises show that while large industrial plant openings have a greater local impact in less developed regions, the aggregate gains are greatest when the plants locate in well‐developed regions, which are connected to other regions through firms' plant‐level (knowledge‐sharing) networks.

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.