Knowledge that Transforms

Abstract We develop a model in which specialized bond investors must absorb shocks to the supply and demand for long-term bonds in two currencies. Since long-term bonds and foreign exchange are both exposed to unexpected movements in short-term interest rates, a shift in the supply of long-term bonds in one currency influences the foreign exchange rate between the two currencies, as well as bond term premia in both currencies. Our model matches several important empirical patterns, including the comovement between exchange rates and term premia, and the finding that central banks’ quantitative easing policies affect exchange rates. An extension of our model links spot exchange rates to the persistent deviations from covered interest rate parity that have emerged since 2008.

Abstract By constraining an individual’s choice during a search, housing discrimination distorts sorting decisions away from true preferences and results in a ceteris paribus reduction in welfare. This study combines a large-scale field experiment with a residential sorting model to derive utility-theoretic measures of renter welfare loss associated with the constraints imposed by discrimination in the rental housing market. Results from experiments conducted in five cities show that key neighborhood amenities are associated with higher levels of discrimination. Counterfactual simulations based on the sorting model suggest that discrimination imposes damages equivalent to 4.4% and 3.5% of the annual incomes for African American and Hispanic/Latinx renters, respectively. Damages are increasing in income for African American renters, such that effects become stronger for economically mobile households. Renters of color must make substantial investments in additional search to mitigate the costs of these constraints. We find that a naive model ignoring discrimination constraints yields biased estimates of willingness to pay for key neighborhood amenities.

Abstract 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.

Abstract 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.

Abstract 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.

Abstract 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.

Abstract 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.

Abstract 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.

Abstract 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.

Abstract 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.