Knowledge that Transforms

This article studies the impact of the arbitrator selection process on consumer outcomes. Using data from consumer arbitration cases in the securities industry over the past two decades, where we observe detailed information on case characteristics, the randomly generated list of potential arbitrators presented to both parties, the selected arbitrator, and case outcomes, we establish several motivating facts. These facts suggest that firms hold an informational advantage over consumers in selecting arbitrators, resulting in industry-friendly arbitration outcomes. We then develop and calibrate a quantitative model of arbitrator selection in which firms hold an informational advantage in selecting arbitrators. Arbitrators, who are compensated only if chosen, compete with each other to be selected. The model allows us to decompose the firms’ advantage into two components: the advantage of choosing pro-industry arbitrators from a given pool and the equilibrium pro-industry tilt in the arbitration pool that arises because of arbitrator competition. Selecting arbitrators without the input of firms and consumers would increase consumer awards by $60,000 on average relative to the current system. Forty percent of this effect arises because the pool of arbitrators skews pro-industry due to competition. Even an informed consumer cannot avoid this pro-industry equilibrium effect. Counterfactuals suggest that redesigning the arbitrator selection mechanism for the benefit of consumers hinges on whether consumers are informed. Policies intended to benefit consumers, such as increasing arbitrator compensation or giving parties more choice, would benefit informed consumers but hurt the uninformed.

Quality provision is often low in many developing markets, and firms commonly lack a reputation for quality. This article examines this issue both theoretically and empirically in the context of retail watermelon markets in China. I first demonstrate the existence of significant asymmetric information on quality between sellers and buyers, as well as the absence of a quality premium at baseline. To explain this, I develop a theoretical model that highlights the role of consumer beliefs and costly signalling in influencing sellers’ reputation incentives. I then conduct an experiment by randomly introducing two signalling technologies into different markets: a cheap sticker label and a more expensive laser-cut label. Consistent with the theoretical predictions, the laser label induces sellers to offer higher quality, resulting in increased sales and profits, while the sticker label fails to achieve the same effect. Using the experimental variation, I estimate an empirical model of consumer learning to uncover the underlying evolution of beliefs. The results show that pessimistic beliefs under the sticker label can hinder reputation building, whereas the laser label enhances consumer learning and strengthens sellers’ reputation incentives.

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.

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.

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.

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.

Industrial policies are widely used across the world. In practice, designing and implementing these policies is a complicated task. In this paper, we assess the long-term performance of different industrial policy instruments, which include production subsidies, investment subsidies, entry subsidies, and consolidation policies. To do so, we examine a recent industrial policy in China aiming to propel the country’s shipbuilding industry to the largest globally. Using firm-level data from 1998 to 2014 and a dynamic model of firm entry, exit, investment, and production, we find that (i) the policy boosted China’s domestic investment, entry, and international market share dramatically, but delivered low returns and led to fragmentation, idle capacity, as well as depressed world ship prices; (ii) the effectiveness of different policy instruments is mixed: production and investment subsidies can be justified by market share considerations, while entry subsidies are wasteful; (iii) counter-cyclical policies, firm-targeting, and shortening the intervention horizon can substantially reduce distortions. Our results highlight the critical role of firm heterogeneity, business cycles, and firms’ cost structure in policy design. Finally, when exploring potential rationales, we find support for nonclassical considerations, such as reducing freight rates to boost Chinese trade.

This article studies the problem of optimally allocating treatments in the presence of spillover effects, using information from a (quasi-)experiment. I introduce a method that maximizes the sample analogue of average social welfare when spillovers occur. I construct semi-parametric welfare estimators with known and unknown propensity scores and cast the optimization problem into a mixed-integer linear program, which can be solved using off-the-shelf algorithms. I derive a strong set of guarantees on regret, i.e. the difference between the maximum attainable welfare and the welfare evaluated at the estimated policy. The proposed method presents attractive features for applications: (i) it does not require network information of the target population; (ii) it exploits heterogeneity in treatment effects for targeting individuals; (iii) it does not rely on the correct specification of a particular structural model; and (iv) it accommodates constraints on the policy function. An application for targeting information on social networks illustrates the advantages of the method.

We develop a method of solving rational expectations models with dispersed information and dynamic strategic complementarities. In these types of models, the equilibrium outcome hinges on an infinite number of higher-order expectations which require an increasing number of state variables to keep track of. Despite this complication, we prove that the equilibrium outcome always admits a finite-state representation when the signals follow finite ARMA processes. We also show that such a finite-state result may not hold with endogenous information aggregation. We further illustrate how to use the method to derive comparative statics, characterize equilibrium outcomes in HANK-type network games, reconcile with empirical evidence on expectations, and integrate incomplete information with bounded rationality in general equilibrium.

We develop a theory of information spillovers in sovereign bond markets in which investors can learn about default risk before trading in primary and secondary markets. If primary markets are structured as multi-unit discriminatory-price auctions, an endogenous winner’s curse leads to strategic complementarities in information acquisition. Shocks to default risk in one country may trigger crisis episodes with widespread information acquisition, sharp increases in the level and volatility of yields in risky countries, low and stable yields in safe countries, market segmentation, and arbitrage profits between primary and secondary markets. These predictions are consistent with the dynamics of auction informativeness during the Eurozone Sovereign Debt Crisis, which we measure using the reaction of secondary market yields to primary market yields.