Knowledge that Transforms

Two key features distinguish the general class of recursive preferences from the standard model of dynamic choice: (i) agents may care about the intertemporal distribution of risk, and (ii) their rates of time preference, rather than being fixed, may vary with the level of consumption. We investigate what these features imply in the context of a repeated strategic interaction. First, we show that opportunities for intertemporal trade may expand the set of feasible payoffs relative to that in a static interaction. Two distinct sources for such trade are identified: endogenous heterogeneity in the players' rates of time preference and a hedging motive pertaining to the intertemporal distribution of risk. The set of equilibrium payoffs may on the other hand shrink drastically as many efficient outcomes become unsustainable no matter the level of patience. This “antifolk” result occurs when the players prefer stage outcomes to be positively correlated rather than independent across time. Intuitively, such preferences make it inefficient to offset short‐term losses with future gains, while this is needed to ensure that security levels are met on path. We also establish a folk theorem: if security levels are met on path, such play can be sustained in a subgame perfect equilibrium provided that the players are sufficiently patient.

We propose a new simulation‐based estimation method, adversarial estimation, for structural models. The estimator is formulated as the solution to a minimax problem between a generator (which generates simulated observations using the structural model) and a discriminator (which classifies whether an observation is simulated). The discriminator maximizes the accuracy of its classification while the generator minimizes it. We show that, with a sufficiently rich discriminator, the adversarial estimator attains parametric efficiency under correct specification and the parametric rate under misspecification. We advocate the use of a neural network as a discriminator that can exploit adaptivity properties and attain fast rates of convergence.

In this paper, we formulate and estimate a flexible model of job mobility and wages with two‐sided heterogeneity. The analysis extends the finite mixture approach of Bonhomme, Lamadon, and Manresa (2019) and Abowd, McKinney, and Schmutte (2019) to develop a new Classification Expectation‐Maximization algorithm that ensures both worker and firm latent‐type identification using wage and mobility variations in the data. Workers receive job offers in worker‐type segmented labor markets. Offers are accepted according to a logit form that compares the value of the current job with that of the new job. In combination with flexibly estimated layoff and job finding rates, the analysis quantifies the four different sources of sorting: job preferences, segmentation, layoffs, and job finding. Job preferences are identified through job‐to‐job moves in a revealed preference argument. They are in the model structurally independent of the identified job wages, possibly as a reflection of the presence of amenities. We find evidence of a strong pecuniary motive in job preferences. While the correlation between preferences and current job wages is positive, the net present value of the future earnings stream given the current job correlates much more strongly with preferences for it. This is more so for short‐ than long‐tenure workers. In the analysis, we distinguish between type sorting and wage sorting. Type sorting is quantified by means of the mutual information index. Wage sorting is captured through correlation between identified wage types. While layoffs are less important than the other channels, we find all channels to contribute substantially to sorting. As workers age, job arrival processes are the key determinant of wage sorting, whereas the role of job preferences dictate type sorting. Over the life cycle, job preferences intensify, type sorting increases, and pecuniary considerations wane.

Since network data commonly consists of observations from a single large network, researchers often partition the network into clusters in order to apply cluster‐robust inference methods. Existing such methods require clusters to be asymptotically independent. Under mild conditions, we prove that, for this requirement to hold for network‐dependent data, it is necessary and sufficient that clusters have low conductance, the ratio of edge boundary size to volume. This yields a simple measure of cluster quality. We find in simulations that when clusters have low conductance, cluster‐robust methods control size better than HAC estimators. However, for important classes of networks lacking low‐conductance clusters, the former can exhibit substantial size distortion. To determine the number of low‐conductance clusters and construct them, we draw on results in spectral graph theory that connect conductance to the spectrum of the graph Laplacian. Based on these results, we propose to use the spectrum to determine the number of low‐conductance clusters and spectral clustering to construct them.

Identifying the strategies that are played is critical to understanding behavior in repeated games. This process is difficult because only choices (not strategies) are observable. Recently, a debate has emerged regarding whether subjects play mixed strategies in the indefinitely repeated prisoner's dilemma. We use an experimental approach to elicit mixed strategies from human subjects, thereby providing direct empirical evidence. We find that a majority of subjects use mixed strategies. However, the data also suggest subjects' strategies are becoming less mixed over time, and move toward three focal pure strategies: Tit For Tat, Grim Trigger, and Always Defect. We use the elicited strategies to provide an empirically‐relevant foundation for analyzing commonly used mixture model estimation procedures.

In this paper, I consider two normative questions: (1) how should policymakers approach tradeoffs that involve different age groups, and (2) at what rate should policymakers discount the consumption of future generations? I demonstrate that, under standard assumptions, these two questions are equivalent: caring more about the future means caring less about the elderly. Even small differences between the social discount rate and the market interest rate can have significant quantitative implications for the relative value placed on the consumption of different age groups.

We incorporate forward‐looking capital accumulation into a dynamic discrete choice model of migration. We characterize the steady‐state equilibrium; generalize existing dynamic exact‐hat algebra techniques to incorporate investment; and linearize the model to provide an analytical characterization of the economy's transition path using spectral analysis. We show that capital and labor dynamics interact to shape the economy's speed of adjustment toward steady state. We implement our quantitative analysis using data on capital stocks, populations, and bilateral trade and migration flows for U.S. states from 1965–2015. We show that this interaction between capital and labor dynamics plays a central role in explaining the observed decline in the rate of income convergence across U.S. states and the persistent and heterogeneous impact of local shocks.

How do political connections affect firm dynamics, innovation, and creative destruction? We extend a Schumpeterian growth model with political connections that help firms ease bureaucratic and regulatory burden. The model highlights how political connections influence an economy's business dynamism and innovation, and generates a number of implications guiding our empirical analysis. We construct a new large‐scale data set for the period 1993–2014, on the universe of firms, workers, and politicians, complemented with corporate financial statements, patent data, and election data, so as to define connected firms as those employing local politicians. We identify a leadership paradox: market leaders are much more likely to be politically connected, but much less likely to innovate. Political connections relate to a higher rate of survival, as well as growth in employment and revenues, but not in productivity—a result that we also confirm using the regression discontinuity design. At the aggregate level, gains from political connections do not offset losses stemming from lower reallocation and growth.

We study the investment incentives created by truthful mechanisms that allocate resources using approximation algorithms. Some approximation algorithms guarantee nearly 100% of the optimal welfare in the allocation problem but guarantee nothing when accounting for investment incentives. An algorithm's allocative and investment guarantees coincide if and only if its confirming negative externalities are sufficiently small. We introduce fast approximation algorithms for the knapsack problem that have no confirming negative externalities and guarantees close to 100% for both allocation and investment.

An agent observes the set of available projects and proposes some, but not necessarily all, of them. A principal chooses one or none from the proposed set. We solve for a mechanism that minimizes the principal's worst‐case regret. We compare the single‐project environment in which the agent can propose only one project with the multiproject environment in which he can propose many. In both environments, if the agent proposes one project, it is chosen for sure if the principal's payoff is sufficiently high; otherwise, the probability that it is chosen decreases in the agent's payoff. In the multiproject environment, the agent's payoff from proposing multiple projects equals his maximal payoff from proposing each project alone. The multiproject environment outperforms the single‐project one by providing better fallback options than rejection and by delivering this payoff to the agent more efficiently.