Knowledge that Transforms

We explore efficiency and optimal policy in decentralized transport markets, such as taxis, trucks, and bulk shipping. We show that in these markets, search frictions distort the transportation network and the dynamic allocation of carriers over space. We derive explicit and intuitive conditions for efficiency and show how they translate into efficient pricing rules, or optimal taxes and subsidies for the planner who cannot set prices directly. The results imply that destination-based pricing is essential to attain efficiency. Then, using data from dry bulk shipping, we demonstrate that search frictions lead to a sizable social loss and substantial misallocation of ships over space. Optimal policy can eliminate about half of the welfare loss. Can a centralizing platform, often arising as a market-based solution to search frictions, do better? Interestingly, the answer is no; although the platform eradicates frictions, it exerts market power, thus eroding the welfare gains. Finally, we use two recent interventions in the industry (China’s Belt and Road Initiative and the environmental initiative IMO 2020) to demonstrate that taking into account the efficiency properties of transport markets is germane to any proposed policy.

We present a unified treatment of how welfare responds to changes in budget sets or technologies with taste shocks and nonhomothetic preferences. We propose a welfare metric that ranks production possibility frontiers that differs from one that ranks budget sets and characterize it using a general equilibrium generalization of Hicksian demand. This extends Hulten’s theorem, the basis for constructing aggregate quantity indices, to environments with nonhomothetic and unstable preferences. We illustrate our results using both long- and short-run applications. In the long run, we show that if structural transformation is caused by income effects or changes in tastes, rather than substitution effects, then Baumol’s cost disease is twice as important for our preferred measure of welfare. In the short run, we show that standard chain-weighted deflators understate welfare-relevant inflation for current tastes. Finally, using the COVID-19 recession, we illustrate that chain-weighted real consumption and real GDP are unreliable metrics for measuring welfare or production when there are taste shocks.

Unemployment rates differ widely across local labor markets. I offer new empirical evidence that high local unemployment emerges because of elevated local job-losing rates. Local employers, rather than local workers or location-specific factors, account for most of the spatial gaps in job stability. I propose a theory in which spatial differences in job loss emerge in equilibrium because of systematic differences between employers across local labor markets. The spatial sorting decisions of employers in turn shape heterogeneity across locations. Labor market frictions induce productive employers to overvalue locating close to each other. The optimal policy incentivizes them to relocate toward areas with high job-losing rates, providing a rationale for commonly used place-based policies. I estimate the model using French administrative data. The estimated model accounts for over three-quarters of the cross-sectional dispersion in unemployment rates and for the respective contributions of job-losing and job-finding rates. Inefficient location choices by employers amplify spatial unemployment differentials fivefold. Both real-world and optimal place-based policies can yield sizable local and aggregate welfare gains.

We investigate biases in expectations across different settings through a large-scale randomized experiment where participants forecast stable stochastic processes. The experiment allows us to control forecasters’ information sets as well as the data-generating process, so we can cleanly measure biases in beliefs. We report three facts. First, forecasts display significant overreaction to the most recent observation. Second, overreaction is stronger for less persistent processes. Third, overreaction is also stronger for longer forecast horizons. We develop a tractable model of expectations formation with costly processing of past information, which closely fits the empirical facts. We also perform additional experiments to test the mechanism of the model.

The world has 1.4 billion passenger vehicles. How should governments regulate their air pollution emissions? A Pigouvian tax is technologically infeasible. Most countries instead rely on exhaust standards that limit air pollution emissions per mile for new vehicles. We assess the effectiveness and efficiency of these standards, which are the centerpiece of U.S. Clean Air Act regulation of transportation, and counterfactual policies. We show that the air pollution emissions per mile of new U.S. vehicles has fallen spectacularly, by over 99%, since standards began in 1967. Several research designs with a half century of data suggest that exhaust standards have caused most of this decline. Yet exhaust standards are not cost-effective in part because they fail to encourage scrap of older vehicles, which account for the majority of emissions. To study counterfactual policies, we develop an analytical and a quantitative model of the vehicle fleet. Analysis of these models suggests that tighter exhaust standards increase social welfare and increasing registration fees on dirty vehicles yields even larger gains by accelerating scrap, although both reforms have complex effects on inequality.

There are three prevailing theories of mortgage default: strategic default (driven by negative equity), cash flow default (driven by negative life events), and double-trigger default (where both negative triggers are necessary). It has been difficult to compare these theories in part because negative life events are measured with error. We address this measurement error using a comparison group of borrowers with no strategic-default motive. Our central finding is that only 6% of underwater defaults are caused exclusively by negative equity, an order of magnitude lower than previously thought. We then analyze the remaining defaults. We find that 70% are driven solely by negative life events (i.e., cash flow defaults), while 24% are driven by the interaction between negative life events and negative equity (i.e., double-trigger defaults). Together, the results provide a full decomposition of the theories underlying borrower default and suggest that negative life events play a central role.

We provide evidence for a bias that we call “representative signal distortion” (RSD), which is particularly relevant to settings of statistical discrimination. Experimental subjects distort their evaluation of new evidence on individual group members and interpret such information to be more representative of the group to which the individual belongs (relative to a reference group) than it really is. This produces a discriminatory gap in the evaluation of members of the two groups. Because it is driven by representativeness, the bias (and the discriminatory gap) disappears when subjects are prevented from contrasting different groups; because it is a bias in the interpretation of information, it disappears when subjects receive information before learning of the individual’s group. We show that this bias can be easily estimated from appropriately constructed data sets and can be distinguished from previously documented inferential biases in the literature. Importantly, we document how removing the bias produces a kind of free lunch in reducing discrimination, making it possible to significantly reduce discrimination without lowering accuracy of inferences.

Scholars have argued that powerful individuals can exert influence on the path of a nation’s development. Yet the process through which people can have an effect on macro-level political economy outcomes remains unclear. This study uses the deadliest civil war in modern history, the Taiping Rebellion (1850–1864), to elucidate how one person—Zeng Guofan—used his personal elite networks to organize an army to suppress the rebellion, and how these networks would affect the nation’s power distribution. Two findings stand out: (i) counties that already had more prewar elites in Zeng’s networks experienced an increase in soldier deaths after he took power; and (ii) postwar political power shifted significantly toward the home counties of these elites, creating a less balanced national-level power distribution. Our findings highlight how micro-level elite networks can influence national politics and societal power distribution, shedding new light on the relationship between elites, war, and the state.

Children’s indirect exposure to the justice system through biological parents or coresident adults is both a marker of their own vulnerability and a measure of the justice system’s expansive reach in society. Estimating the size of this population for the United States has historically been hampered by inadequate data resources, including the inability to observe nonincarceration events, follow children throughout their childhood, and measure adult nonbiological parent cohabitants. To overcome these challenges, we leverage billions of restricted administrative and survey records linked with Criminal Justice Administrative Records System data and find substantially larger exposure rates than previously reported: prison, 9% of children born between 1999–2005; felony conviction, 18%; and any criminal charge, 39%. Charge exposure rates exceed 60% for Black, American Indian, and low-income children. While broader definitions reach a more expansive population, strong and consistently negative correlations with childhood well-being suggest that these remain valuable predictors of vulnerability. Finally, we document substantial geographic variation in exposure, which we leverage in a movers design to estimate the effect of living in a high-exposure county during childhood. We find that children moving into high-exposure counties are more likely to experience postmove exposure events and exhibit significantly worse outcomes by age 26 on multiple dimensions (earnings, criminal activity, teen parenthood, mortality); effects are strongest for those who moved at earlier ages.

This article studies the macroeconomic implications of imperfect risk sharing implied by a class of New Keynesian models with heterogeneous agents. The models in this class can be equivalently represented as a representative-agent economy with wedges. These wedges are functions of households’ consumption shares and relative wages, and they identify the key cross-sectional moments that govern the impact of households’ heterogeneity on aggregate variables. We measure the wedges using U.S. household-level data and combine them with a representative-agent economy to perform counterfactuals. We find that deviations from perfect risk sharing implied by this class of models account for only 7% of output volatility on average but can have sizable output effects when nominal interest rates reach their lower bound.