Knowledge that Transforms

In this note, I discuss avenues for future research stemming from Besley's [this issue] theoretical approach on the interconnections between civicness, institutions, and state‐fiscal capacity. First, I lay down some ideas on how one could extend the framework to model fragility traps that characterize many low‐income countries and study issues related to nation‐building, conflict, and heterogeneity across space and ethnic lines in the provision of public goods. Second, I discuss the relevance of the approach for the analysis of authoritarian populism that is spreading in developed countries and emerging markets.

We propose a model of preferences in which the effect of randomization on ambiguity depends on how the unknown probability law is determined. We adopt the framework of Anscombe and Aumann (1963) and relax the axioms. In the resulting representation of the individual's preference, the individual has a collection of sets of priors <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" display="inline"> <a:mi mathvariant="script">M</a:mi> </a:math>. She believes that before she moves, nature has chosen an unknown scenario (a set of priors) from <d:math xmlns:d="http://www.w3.org/1998/Math/MathML" display="inline"> <d:mi mathvariant="script">M</d:mi> </d:math>, and from that scenario, nature will choose a prior after she moves. The representation illustrates how randomization may partially eliminate the effect of ambiguity.

This paper develops a multi‐stage general‐equilibrium model of global value chains (GVCs) and studies the specialization of countries within GVCs in a world with barriers to international trade. With costly trade, the optimal location of production of a given stage in a GVC is not only a function of the marginal cost at which that stage can be produced in a given country, but is also shaped by the proximity of that location to the precedent and the subsequent desired locations of production. We show that, other things equal, it is optimal to locate relatively downstream stages of production in relatively central locations. We also develop and estimate a tractable, quantifiable version of our model that illustrates how changes in trade costs affect the extent to which various countries participate in domestic, regional, or global value chains, and traces the real income consequences of these changes.

The presence of a westward‐moving frontier of settlement shaped early U.S. history. In 1893, the historian Frederick Jackson Turner famously argued that the American frontier fostered individualism. We investigate the “frontier thesis” and identify its long‐run implications for culture and politics. We track the frontier throughout the 1790–1890 period and construct a novel, county‐level measure of total frontier experience (TFE). Historically, frontier locations had distinctive demographics and greater individualism. Long after the closing of the frontier, counties with greater TFE exhibit more pervasive individualism and opposition to redistribution. This pattern cuts across known divides in the United States, including urban–rural and north–south. We provide evidence on the roots of frontier culture, identifying both selective migration and a causal effect of frontier exposure on individualism. Overall, our findings shed new light on the frontier's persistent legacy of rugged individualism.

Asymptotic bootstrap validity is usually understood as consistency of the distribution of a bootstrap statistic, conditional on the data, for the unconditional limit distribution of a statistic of interest. From this perspective, randomness of the limit bootstrap measure is regarded as a failure of the bootstrap. We show that such limiting randomness does not necessarily invalidate bootstrap inference if validity is understood as control over the frequency of correct inferences in large samples. We first establish sufficient conditions for asymptotic bootstrap validity in cases where the unconditional limit distribution of a statistic can be obtained by averaging a (random) limiting bootstrap distribution. Further, we provide results ensuring the asymptotic validity of the bootstrap as a tool for conditional inference, the leading case being that where a bootstrap distribution estimates consistently a conditional (and thus, random) limit distribution of a statistic. We apply our framework to several inference problems in econometrics, including linear models with possibly nonstationary regressors, CUSUM statistics, conditional Kolmogorov–Smirnov specification tests and tests for constancy of parameters in dynamic econometric models.

I study a mechanism design problem in which a designer allocates a single good to one of several agents, and the mechanism is followed by an aftermarket —a post‐mechanism game played between the agent who acquired the good and third‐party market participants. The designer has preferences over final outcomes, but she cannot design the aftermarket. However, she can influence its information structure by publicly disclosing information elicited from the agents by the mechanism. I introduce a class of allocation and disclosure rules, called cutoff rules , that disclose information about the buyer's type only by revealing information about the realization of a random threshold (cutoff) that she had to outbid to win the object. When there is a single agent in the mechanism, I show that the optimal cutoff mechanism offers full privacy to the agent. In contrast, when there are multiple agents, the optimal cutoff mechanism may disclose information about the winner's type; I provide sufficient conditions for optimality of simple designs. I also characterize aftermarkets for which restricting attention to cutoff mechanisms is without loss of generality in a subclass of all feasible mechanisms satisfying additional conditions.

Liver exchange has been practiced in small numbers, mainly to overcome blood‐type incompatibility between patients and their living donors. A donor can donate either his smaller left lobe or the larger right lobe, although the former option is safer. Despite its elevated risk, right‐lobe transplantation is often utilized due to size‐compatibility requirement with the patient. We model liver exchange as a market‐design problem, focusing on logistically simpler two‐way exchanges, and introduce an individually rational, Pareto‐efficient, and incentive‐compatible mechanism. Construction of this mechanism requires novel technical tools regarding bilateral exchanges under partial‐order‐induced preferences. Through simulations we show that not only can liver exchange increase the number of transplants by more than 30%, it can also increase the share of the safer left‐lobe transplants.

Theories of bounded rationality often assume a rich dataset of choices from many overlapping menus, limiting their practical applicability. In contrast, we study the problem of identifying the distribution of cognitive characteristics in a population of agents from a minimal dataset that consists of aggregate choice shares from a single menu, and includes no observable covariates of any kind. With homogeneous preferences, we find that “consideration capacity” and “consideration probability” distributions can both be recovered effectively if the menu is sufficiently large. This remains true generically when tastes are heterogeneous with a known distribution. When the taste distribution is unknown, we show that joint choice share data from three “occasions” are generically sufficient for full identification of the cognitive distribution, and also provide substantial information about tastes.

We study games of incomplete information as both the information structure and the extensive form vary. An analyst may know the payoff‐relevant data but not the players' private information, nor the extensive form that governs their play. Alternatively, a designer may be able to build a mechanism from these ingredients. We characterize all outcomes that can arise in an equilibrium of some extensive form with some information structure. We show how to specialize our main concept to capture the additional restrictions implied by extensive‐form refinements.

We consider statistical inference in games. Each player obtains a small random sample of other players' actions, uses statistical inference to estimate their actions, and chooses an optimal action based on the estimate. In a sampling equilibrium with statistical inference (SESI), the sample is drawn from the distribution of players' actions based on this process. We characterize the set of SESIs in large two‐action games, and compare their predictions to those of Nash equilibrium, and for different sample sizes and statistical inference procedures. We then study applications to competitive markets, markets with network effects, monopoly pricing, and search and matching markets.