Programming Console

November 17 2022, 5pm Paris time / 11am NY time

Ilse Lindenlaub

(Yale University)

Firm Sorting and Spatial Inequality (joint work with Ryungha Oh and Michael Peters)

Abstract: We study the importance of firm sorting for spatial inequality. If productive locations are able to attract the most productive firms, then firm sorting acts as an amplifier of spatial inequality. We develop a novel model of spatial firm sorting, in which heterogeneous firms first choose a location and then hire workers in a frictional local labor market. Firms’ location choices are guided by a fundamental trade-off: Operating in productive locations increases output per worker, but sharing a labor market with other productive firms makes it hard to poach and retain workers, and hence limits firm size. We show that sorting between firms and locations is positive—i.e., more productive firms settle in more productive locations—if firm and location productivity are complements and labor market frictions are sufficiently large. We estimate our model using administrative data from Germany and find that highly productive firms indeed sort into the most productive locations. In our main application, we quantify the role of firm sorting for wage differences between East and West Germany, which reveals that firm sorting accounts for 17%-27% of the West-East wage gap.

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September 29 2022, 5pm Paris time / 11am NY time

Alexander Wolitzky 

(Massachusetts Institute of Technology)

Persuasion as Matching (joint with Anton Kolotilin and Roberto Corrao)

Abstract: In persuasion problems where the receiver’s action is one-dimensional and his utility is single-peaked, optimal signals are characterized by duality, based on a first-order approach to the receiver’s problem. A signal is optimal if and only if the induced joint distribution over states and actions is supported on a compact set (the contact set) where the dual constraint binds. A signal that pools at most two states in each realization is always optimal, and such pairwise signals are the only solutions under a non-singularity condition on utilities (the twist condition). We provide conditions under which higher actions are induced at more or less extreme pairs of states. Finally, we provide conditions for the optimality of either full disclosure or negative assortative disclosure, where signal realizations can be ordered from least to most extreme. Optimal negative assortative disclosure is characterized as thesolution to a pair of ordinary differential equations.

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Past seminars

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August 25 2022, 5pm Paris time / 11am NY time

Susanne M. Schennach 

(Brown University)

Optimally-Transported Generalized Method of Moments (joint with Vincent Starck)

Abstract: We propose a novel optimal transport-based version of the Generalized Method of Moment (GMM). Instead of handling overidentified models by reweighting the data until all moment conditions are satisfied (as in Generalized Empirical Likelihood methods), this method proceeds by introducing measurement error of the least mean square magnitude necessary to simultaneously satisfy all moment conditions. This approach, based on the notion of optimal transport, aims to address the problem of assigning a logical interpretation to GMM results even when overidentification tests reject the null, a relatively common situation in empirical applications. We discuss the implementation of the method as well as its asymptotic properties and illustrate its  usefulness through examples.

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April 7 2022, 5pm Paris time / 11am NY time

Jacob Leshno 

(University of Chicago Booth School of Business)

Price Discovery in Waiting Lists:
A Connection to Stochastic Gradient Descent (joint with Itai Ashlagi, Pengyu Qian and Amin Saberi)

Abstract: Waiting lists offer agents a choice among types of items and associated non-monetary prices given by required waiting times. These non-monetary prices are endogenously determined by a simple tâtonnement-like price discovery process: an item’s price increases when an agent queues for it, and decreases when an item arrives and a queuing agent is assigned. By drawing a connection between price adjustments in waiting lists and the stochastic gradient descent optimization algorithm, we show that the waiting list mechanism achieves the optimal allocative efficiency minus a loss due to price fluctuations that is bounded by the granularity of price changes. We further consider a price discovery process inspired by the waiting list mechanism and show that this sim- ple price discovery process performs well if the granularity of price changes is chosen to appropriately trade-off the speed of price adaptation and loss from price fluctuations.

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Programming Console
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March 31 2022, 5pm Paris time / 11am NY time

Sergio Ocampo

(Western University)

A Task-Based Theory of Occupations with Multidimensional Heterogeneity

Abstract: I develop an assignment model of occupations with multidimensional heterogeneity in production tasks and worker skills. Tasks are distributed continuously in the skill space, whereas workers have a discrete distribution with a finite number of types. Occupations arise endogenously as bundles of tasks optimally assigned to a type of worker. The model allows us to study how occupations respond to changes in the economic environment, making it useful for analyzing the implications of automation, skill-biased technical change, offshoring, and workers’ training. I characterize how wages, the marginal product of workers, the substitutability between worker types, and the labor share depend on the assignment of tasks to workers. I introduce automation as a choice of the optimal size and location of a mass of identical robots in the task space. Automation displaces workers by replacing them in the performance of tasks. This generates a cascading effect on other workers as the boundaries of occupations are redrawn.

February 17 2022, 5pm Paris time / 11am NY time

Florian Gunsilius

(University of Michigan)

 

Matching for causal effects via multimarginal optimal transport (joint with Yuliang Xu)

Abstract: Matching on covariates is a well-established framework for estimating causal effects in observational studies. The principal challenge in these settings stems from the often high-dimensional structure of the problem. Many methods have been introduced to deal with this challenge, with different advantages and drawbacks in computational and statistical performance and interpretability. Moreover, the methodological focus has been on matching two samples in binary treatment scenarios, but a dedicated method that can optimally balance samples across multiple treatments has so far been unavailable. This article introduces a natural optimal matching method based on entropy-regularized multimarginal optimal transport that possesses many useful properties to address these challenges. It provides interpretable weights of matched individuals that converge at the parametric rate to the optimal weights in the population, can be efficiently implemented via the classical iterative proportional fitting procedure, and can even match several treatment arms simultaneously. It also possesses demonstrably excellent finite sample properties.

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January 6 2022, 5pm Paris time / 11am NY time

Pauline Corblet

Education Expansion, Sorting, and the Decreasing Education Wage Premium
(job market paper)

Abstract: This paper studies the interplay between worker supply and firm demand, and their effect on sorting and wages in the labor market. I build a model of one-to-many matching with multidimensional types in which several workers are employed by a single firm. Matching is dictated by worker preferences, their relative productivity in the firm, and substitution patterns with other workers. Using tools from the optimal transport literature, I solve the model and structurally estimate it on Portuguese matched employer-employee data. The Portuguese labor market is characterized by an increase in the relative supply of high school graduates, an increasingly unbalanced distribution of high school graduates versus non-graduates across industries, and a decreasing high school wage premium between 1987 and 2017. Counterfactual exercises suggest that both changes in worker preferences and the increasing relative productivity of high school graduates over non-graduates act as a mitigating force on the decreasing high school wage premium, but do not fully compensate for high school graduates’ rise in relative supply.

Programming Console

December 2 2021, 5pm Paris time / 11am NY time

Job Boerma

Sorting with Team Formation
(with Aleh Tsyvinski and Alexander P. Zimin)

Abstract: We fully solve an assignment problem with heterogeneous firms and multiple heterogeneous workers whose skills are imperfect substitutes, that is, when production is submodular. We show that sorting is neither positive nor negative and is characterized sufficiently by two regions. In the first region, mediocre firms sort with mediocre workers and coworkers such that output losses are equal across all these pairings (complete mixing). In the second region, high skill workers sort with a low skill coworker and a high productivity firm, while high productivity firms employ a low skill worker and a high skill coworker (pairwise countermonotonicity). The equilibrium assignment is also necessarily characterized by product countermonotonicity, meaning that sorting is negative for each dimension of heterogeneity with the product of heterogeneity in the other dimensions. The equilibrium assignment as well as wages and firm values are completely characterized in closed form. We illustrate our theory with an application to show that our model is consistent with the observed dispersion of earnings within and across U.S. firms. Our counterfactual analysis gives evidence that the change in the firm project distribution between 1981 and 2013 has a larger effect on the observed change in earnings dispersion than the change in the worker skill distribution.

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Coding Station
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September 23 2021, 1pm Paris time / 7am NY time

Nicola Rosaia

Duality and Estimation of Undiscounted Markov Decision Processes

Abstract: This paper studies estimation of undiscounted Markov decision processes (MDPs). Exploiting convex analytic methods, it argues that undiscounted MDPs can be treated as static discrete choice models over state-action frequencies, leveraging this idea to derive a conjugate duality framework for studying this type of models. It then exploits this framework to draw implications in several dimensions. First, it characterizes the empirical content of undiscounted MDPs, analyzing how exclusion or parametric restrictions can produce identification of agents’ payoffs, and providing an axiomatic characterization of the undiscounted dynamic logit model; second, it proves convergence of simple inversion algorithms based on progressive Tâtonnements, and investigates novel estimation strategies based on these. Finally, it shows that the dual framework extends to models with persistent fixed effects and to models where certain actions or states are unobserved.