Knowledge that Transforms

Abstract Bridges are critical but sparse links in land transport networks. I exploit quasi-experimental variation in bridge construction over major rivers in the U.S. to measure the causal effects of land transport infrastructure. Bridges are more often built upstream than downstream of tributary confluences—where smaller rivers join larger rivers—generating local differences in connectivity. Local connectivity advantages have negative effects on per capita income. In contrast, more substantial changes in connectivity arising from the opening of major bridges increase per capita economic activity. A narrative explanation that can reconcile both results is that land transport infrastructure creates productivity advantages that drive economic growth, structural transformation, and urbanization over large spatial scales, but local sorting within the cities that form around early transport routes then reverses this gradient over smaller spatial scales.

Abstract The information banks produce drives their lending decisions and macroeconomic outcomes, but this information is inherently difficult to analyse because it is private. We construct a novel measure of bank information quality from confidential regulatory data that include banks’ private risk assessments for US corporate loans. Information quality improves as local economic conditions deteriorate, particularly for new loans, large loans, and loans with higher expected losses. Information quality also declines during periods of rapid local house price appreciation. Our results provide empirical support for theories of countercyclical information production in credit markets.

Abstract Complementarities between partners’ characteristics are often held responsible for the patterns of assortative mating observed in marriage markets along different dimensions, such as race and education. However, when the marriage market is segmented into racially and educationally homogeneous clusters, people naturally have more match opportunities with their likes. In this paper, we build an empirically tractable dynamic matching model with endogenous separation and remarriage. In every period, agents participate in a competitive matching game with transferable utility, where mating strategies depend on both the expected match gains and search frictions in the form of meeting costs. We leverage panel data on the duration of both non-cohabiting and cohabiting relationships to jointly estimate both determinants of assortative mating with a nationally representative sample of the U.S. population. We show that, in the absence of search frictions, the share of matches between people of the same race (education) would decrease from 88.2% (49.2%) to 55.5% (40.8%), as opposed to 53.3% (33.5%) if singles were randomly matched. As a result, search frictions explain nearly all the racial homogamy observed in the data, but only approximately half of the observed educational homogamy, with the other half attributed to match complementarities. In a counterfactual exercise, we show that minority groups experiencing an unfavourable gender ratio when marriage markets are segmented, such as Hispanic men and Black women, would benefit from access to a broader and more diverse pool of partners.

Abstract We develop a test for instrument strength based on the bias of two-stage least squares (2SLS) that (1) generalizes Stock and Yogo’s and Sanderson and Windmeijer’s tests to be robust to heteroskedasticity and autocorrelation, and (2) extends Montiel Olea and Pflueger’s robust test for models with a single endogenous regressor to multiple endogenous regressors. Our test can be based either on an absolute bias criterion or on the 2SLS bias relative to a worst-case benchmark. We also develop extensions to test whether weak instruments cause bias in individual 2SLS coefficients. In simulations, our test controls size and is powerful, and we provide efficient code packages for its practical implementation. We demonstrate our testing procedures in the context of the estimation of state-dependent fiscal multipliers, following recent leading estimates.

Abstract This paper documents that production networks play an essential role in the job search and matching process. We document five facts about worker mobility in production networks using employer–employee data matched with the universe of firm-to-firm transactions for the Dominican Republic: (1) workers move between buyers and suppliers almost twice as much as predicted by standard labour market characteristics, (2) movers between buyers and suppliers experience larger earnings increases than other movers, (3) incumbent workers earnings increase when their firm hires from its buyers or suppliers, (4) firm-to-firm trade increases following supply chain hires, and (5) hiring from buyers or suppliers is associated with stronger firm growth. Survey evidence points to supply chain-specific human capital and better information about job applicants as the main reasons for hiring within the supply chain. These results reveal a new channel through which factors affecting the supply chain, such as international outsourcing or contracting frictions, impact labour markets.

Abstract Wealthy households disproportionately invest in equity, causing equity returns to generate large and persistent fluctuations in top wealth inequality. Motivated by this observation, I study the joint dynamics of asset prices and wealth inequality in a model where a subset of agents (entrepreneurs) hold levered positions on the economy. In the model, as in the data, the wealth distribution is stochastic and it exhibits a Pareto tail, with a tail index that depends on the logarithmic average return of top households. The model features a feedback loop between asset prices and wealth inequality, which amplifies the effect of aggregate shocks on the economy. The model, calibrated to the U.S. data, can account for a substantial portion of the fluctuations in asset prices and top wealth shares over the 20th century.

Abstract I introduce a generic method for inference about a scalar parameter in research designs with a finite number of heterogeneous clusters where only a single cluster received treatment. This situation is commonplace in difference-in-differences estimation, but the test developed here applies more broadly. I show that the test controls size and has power under asymptotics where the number of observations within each cluster is large but the number of clusters is fixed. The test combines weighted, approximately Gaussian parameter estimates with a rearrangement procedure to obtain its critical values. The weights needed for most empirically relevant situations are tabulated in the paper. Calculation of the critical values is computationally simple and does not require simulation or resampling. The rearrangement test is highly robust to situations where some clusters are much more variable than others. Examples and an empirical application are provided.

Abstract This paper proposes that strong institutions provide comparative advantage in clean industries, and thereby improve a country’s environmental quality. I study financial, judicial, and labour market institutions. Five complementary tests evaluate and assess implications of this hypothesis. First, industries that depend on institutions are clean. Second, strong institutions increase relative exports in clean industries. Third, an industry’s complexity helps explain the link between institutions and clean goods. Fourth, cross-country differences in the composition of output between clean and dirty industries explain an important share of the global distribution of emissions. Fifth, a quantitative general equilibrium model indicates that strengthening a country’s institutions decreases its pollution through relocating dirty industries abroad, though increases pollution in other countries. The comparative advantage that strong institutions provide in clean industries gives one under-explored reason why developing countries have relatively high pollution levels.

Abstract We estimate impacts of male job loss, female job loss, and male unemployment benefits on domestic violence (DV) in Brazil. We merge individual-level employment and welfare registers with different measures of DV: judicial cases brought to criminal courts, the use of public shelters by victims, and mandatory DV notifications by health providers. Leveraging mass layoffs for identification, we first show that both male and female job loss, independently, lead to large, and pervasive increases in DV. Using a regression discontinuity design, we then show that access to unemployment benefits does not reduce DV while benefits are being paid, and it leads to higher DV risk once benefits expire. Our findings can be explained by the negative income shock brought by job loss and by increased exposure of victims to perpetrators, as partners tend to spend more time together after displacement. Although unemployment benefits partially offset the income drop following job loss, they reinforce the exposure shock as they increase unemployment duration. Since our results cannot be explained by prominent DV theories, we propose a simple model formalizing these mechanisms.

Abstract We develop a new class of random graph models for the statistical estimation of network formation—subgraph generated models (SUGMs). Various subgraphs—e.g. links, triangles, cliques, stars—are generated and their union results in a network. We show that SUGMs are identified and establish the consistency and asymptotic distribution of parameter estimators in empirically relevant cases. We show that a simple four-parameter SUGM matches basic patterns in empirical networks more closely than four standard models (with many more dimensions): (1) stochastic block models; (2) models with node-level unobserved heterogeneity; (3) latent space models; and (4) exponential random graphs. We illustrate the framework’s value via several applications using networks from rural India. We study whether network structure helps enforce risk-sharing and whether cross-caste interactions are more likely to be private. We also develop a new central limit theorem for correlated random variables, which is required to prove our results and is of independent interest.