Publications

We study dynamic matching in exchange markets with easy- and hard-to-match agents. A greedy policy, which attempts to match agents upon arrival, ignores the positive externality that waiting agents provide by facilitating future matchings. We prove that the trade-off between a “thicker” market and faster matching vanishes in large markets; the greedy policy leads to shorter waiting times and more agents matched than any other policy. We empirically confirm these findings in data from the National Kidney Registry. Greedy matching achieves as many transplants as commonly used policies (1.8% more than monthly batching) and shorter waiting times (16 days faster than monthly batching).

We study the impact of changing choice set size on the quality of choices in health insurance markets. Using novel data on enrolment and medical claims for school district employees in the state of Oregon, we document that the average employee could save $600 by switching to a lower cost plan. Structural modelling reveals large “choice inconsistencies” such as non-equalization of the dollar spent on premiums and out of pocket, and a novel form of “approximate inertia” where enrolees are excessively likely to switch to other plans that are close to the current plan on the plan design spreadsheet. Variation in the number of plan choices across districts and over time shows that enrolees make lower-cost choices when the choice set is smaller. We show that a curated restriction of choice set size improves choices more than the best available information intervention, partly because approximate inertia lowers gains from new information. We explicitly test and reject the assumption that this is because individuals choose worse from larger choice sets, or “choice overload”. Rather, we show that this feature arises from the fact that larger choice sets feature worse choices on average that are not offset by individual re-optimization.

Addressing public health externalities often requires community-level collective action. Due to social norms, each person’s sanitation investment decisions may depend on the decisions of neighbors. We report on a cluster randomized controlled trial conducted with 19,000 households in rural Bangladesh where we grouped neighboring households and introduced (either financial or social recognition) rewards with a joint liability component for the group, or asked each group member to make a private or public pledge to maintain a hygienic latrine. The group financial reward has the strongest impact in the short term (3 months), inducing a 7.5–12.5 percentage point increase in hygienic latrine ownership, but this effect dissipates in the medium term (15 months). In contrast, the public commitment induced a 4.2–6.3 percentage point increase in hygienic latrine ownership in the short term, but this effect persists in the medium term. Non-financial social recognition or a private pledge has no detectable effect on sanitation investments.

We present a mechanism based on managerial incentives through which common ownership affects product market outcomes. Firm-level variation in common ownership causes variation in managerial incentives and productivity across firms, which leads to intraindustry and intrafirm cross-market variation in prices, output, markups, and market shares that is consistent with empirical evidence. The organizational structure of multiproduct firms and the passivity of common owners determine whether higher prices under common ownership result from higher costs or from higher markups. Using panel regressions and a difference-in-differences design, we document that managerial incentives are less performance sensitive in firms with more common ownership.

This paper studies the welfare effects of encouraging rural–urban migration in the developing world. To do so, we build and analyze a dynamic general-equilibrium model of migration that features a rich set of migration motives. We estimate the model to replicate the results of a field experiment that subsidized seasonal migration in rural Bangladesh, leading to significant increases in migration and consumption. We show that the welfare gains from migration subsidies come from providing better insurance for vulnerable rural households rather than from correcting spatial misallocation by relaxing credit constraints for those with high productivity in urban areas that are stuck in rural areas.

This paper uses data from the 2019 Annual Business Survey to document that firms adopting advanced technologies are larger in terms of employment than other firms in their same industry and cohort. Using data from the Longitudinal Business Survey, we show that adopters were already large and growing faster before artificial intelligence, robotics, cloud computing, and specialized software systems became broadly available. These findings support the view that adopters are large because of selection and not because adopting advanced technologies for automation causally expands their employment.

We develop a dynamic model of input-output networks that incorporates adjustment costs of changing inputs. Our closed-form solution for the dynamics of the economy shows that temporary shocks to upstream sectors, whose output travels through long supply chains, have disproportionately significant welfare impact compared to affected sectors’ Domar weights. We conduct a spectral analysis of the U.S. production network and reveal that the welfare impact of temporary sectoral shocks can be represented by a low-dimensional, 4-factor structure.

What do recent advances in economic geography teach us about the spatial distribution of economic activity? We show that the equilibrium distribution of economic activity can be determined simply by the intersection of labor supply and demand curves. We discuss how to estimate these curves and highlight the importance of global geography—the connections between locations through the trading network—in determining how various policy relevant changes to geography shape the spatial economy.

Do foreign students affect the likelihood that domestic students obtain a STEM degree and occupation? Using administrative student records from a US university, we exploit idiosyncratic variation in the share of foreign classmates in introductory math classes and find that foreign classmates displace domestic students from STEM majors and occupations. However, displaced students gravitate toward high-earning social science majors, so their expected earnings are not penalized. We explore several mechanisms. Results indicate that displacement is concentrated in classes where foreign classmates possess weak English language ability, suggesting that diminished in-class communication and social interactions might play an important role.

We present an approach to analyse learning outcomes in a broad class of misspecified environments, spanning both single-agent and social learning. We introduce a novel “prediction accuracy” order over subjective models and observe that this makes it possible to partially restore standard martingale convergence arguments that apply under correctly specified learning. Based on this, we derive general conditions to determine when beliefs in a given environment converge to some long-run belief either locally or globally (i.e. from some or all initial beliefs). We show that these conditions can be applied, first, to unify and generalize various convergence results in previously studied settings. Second, they enable us to analyse environments where learning is “slow”, such as costly information acquisition and sequential social learning. In such environments, we illustrate that even if agents learn the truth when they are correctly specified, vanishingly small amounts of misspecification can generate extreme failures of learning.

We argue that noisy aggregation of dispersed information provides a unified explanation for several prominent cross-sectional return anomalies such as returns to skewness, returns to disagreement and corporate credit spreads. We characterize asset returns with noisy information aggregation by means of a risk-neutral probability measure that features excess weight on tail risks, and link the latter to observable moments of earnings forecasts, in particular forecast dispersion and accuracy. We calibrate our model to match these moments and show that it accounts for a large fraction of the empirical return premia. We further develop asset pricing tools for noisy information aggregation models that do not impose strong parametric restrictions on economic primitives such as preferences, information, or return distributions.

This paper studies the relation between volatility and informativeness in financial markets. We identify two channels (noise-reduction and equilibrium-learning) that determine the volatility-informativeness relation. When informativeness is sufficiently high (low), volatility and informativeness positively (negatively) comove in equilibrium. We identify conditions on primitives that guarantee that volatility and informativeness comove positively or negatively. We introduce the comovement score, a statistic that measures the distance of a given asset to the positive/negative comovement regions. Empirically, comovement scores (i) have trended downwards over the last decades, (ii) are positively related to value and idiosyncratic volatility and negatively to size and institutional ownership.

This paper studies high-dimensional vector autoregressions (VARs) augmented with common factors that allow for strong cross-sectional dependence. Models of this type provide a convenient mechanism for accommodating the interconnectedness and temporal co-variability that are often present in large dimensional systems. We propose an ℓ1-nuclear-norm regularized estimator and derive the non-asymptotic upper bounds for the estimation errors as well as large sample asymptotics for the estimates. A singular value thresholding procedure is used to determine the correct number of factors with probability approaching one. Both the LASSO estimator and the conservative LASSO estimator are employed to improve estimation precision. The conservative LASSO estimates of the non-zero coefficients are shown to be asymptotically equivalent to the oracle least squares estimates. Simulations demonstrate that our estimators perform reasonably well in finite samples given the complex high-dimensional nature of the model. In an empirical illustration we apply the methodology to explore dynamic connectedness in the volatilities of financial asset prices and the transmission of ‘investor fear’. The findings reveal that a large proportion of connectedness is due to the common factors. Conditional on the presence of these common factors, the results still document remarkable connectedness due to the interactions between the individual variables, thereby supporting a common factor augmented VAR specification.

This paper studies control function (CF) approaches in endogenous threshold regression where the threshold variable is allowed to be endogenous. We first use a simple example to show that the structural threshold regression (STR) estimator of the threshold point in Kourtellos, Stengos and Tan (2016, Econometric Theory 32, 827–860) is inconsistent unless the endogeneity level of the threshold variable is low compared to the threshold effect. We correct the CF in the STR estimator to generate our first CF estimator using a method that extends the two-stage least squares procedure in Caner and Hansen (2004, Econometric Theory 20, 813–843). We develop our second CF estimator which can be treated as an extension of the classical CF approach in endogenous linear regression. Both these approaches embody threshold effect information in the conditional variance beyond that in the conditional mean. Given the threshold point estimates, we propose new estimates for the slope parameters. The first is a by-product of the CF approach, and the second type employs generalized method of moment (GMM) procedures based on two new sets of moment conditions. Simulation studies, in conjunction with the limit theory, show that our second CF estimator and confidence interval for the threshold point together with the associated second GMM estimator and confidence interval for the slope parameter dominate the other methods. We further apply the new estimation methodology to an empirical application from international trade to illustrate its usefulness in practice.

We offer an approach to cooperation in repeated games of private monitoring in which players construct models of their opponents' behavior by observing the frequencies of play in a record of past plays of the game in which actions but not signals are recorded. Players construct models of their opponent's behavior by grouping the histories in the record into a relatively small number of analogy classes for which they estimate probabilities of cooperation. The incomplete record and the limited number of analogy classes lead to misspecified models that provide the incentives to cooperate. We provide conditions for the existence of equilibria supporting cooperation and equilibria supporting high payoffs for some nontrivial analogy partitions.