Knowledge that Transforms

We study contracts between naive present‐biased consumers and risk‐neutral firms. We show that the welfare loss from present bias vanishes as the contracting horizon grows. This is true both when bargaining power is on the consumers' and on the firms' side, when consumers cannot commit to long‐term contracts, and when firms do not know the consumers' naiveté. However, the welfare loss from present bias does not vanish when firms do not know the consumers' present bias or when they cannot offer exclusive contracts.

We study the joint evolution of the sectoral composition and the investment rate of developing economies. Using panel data for several countries in different stages of development, we document three novel facts: (a) the share of industry and the investment rate are strongly correlated and follow a hump‐shaped profile with development, (b) investment goods contain more domestic value added from industry and less from services than consumption goods do, and (c) the evolution of the sectoral composition of investment and consumption goods differs from the one of GDP. We build a multi‐sector growth model to fit these patterns and provide two important results. First, the hump‐shaped evolution of investment demand explains half of the hump in industry with development. Second, asymmetric sectoral productivity growth helps explain the decline in the relative price of investment goods along the development path, which in turn increases capital accumulation and promotes growth.

Most jobs require teamwork. Are some people good team players? In this paper, we design and test a new method for identifying individual contributions to team production. We randomly assign people to multiple teams and predict team performance based on previously assessed individual skills. Some people consistently cause their team to exceed its predicted performance. We call these individuals “team players.” Team players score significantly higher on a well‐established measure of social intelligence, but do not differ across a variety of other dimensions, including IQ, personality, education, and gender. Social skills —defined as a single latent factor that combines social intelligence scores with the team player effect—improve team performance about as much as IQ. We find suggestive evidence that team players increase effort among teammates.

We present a new multi‐sector growth model that features nonhomothetic, constant elasticity of substitution preferences, and accommodates long‐run demand and supply drivers of structural change for an arbitrary number of sectors. The model is consistent with the decline in agriculture, the hump‐shaped evolution of manufacturing, and the rise of services over time. We estimate the demand system derived from the model using household‐level data from the United States and India, as well as historical aggregate‐level panel data for 39 countries during the postwar period. The estimated model parsimoniously accounts for the broad patterns of sectoral reallocation observed among rich, miracle, and developing economies. Our estimates support the presence of strong nonhomotheticity across time, income levels, and countries. We find that income effects account for the bulk of the within‐country evolution of sectoral reallocation.

A profit‐maximizing seller has a single unit of a good to sell. The bidders have a pure common value that is drawn from a distribution that is commonly known. The seller does not know the bidders' beliefs about the value and thinks that beliefs are designed adversarially by Nature to minimize profit. We construct a strong maxmin solution to this joint mechanism design and information design problem, consisting of a mechanism, an information structure, and an equilibrium, such that neither the seller nor Nature can move profit in their respective preferred directions, even if the deviator can select the new equilibrium. The mechanism and information structure solve a family of maxmin mechanism design and minmax information design problems, regardless of how an equilibrium is selected. The maxmin mechanism takes the form of a proportional auction : each bidder submits a one‐dimensional bid, the aggregate allocation and aggregate payment depend on the aggregate bid, and individual allocations and payments are proportional to bids. We report a number of additional properties of the maxmin mechanisms, including what happens as the number of bidders grows large and robustness with respect to the prior over the value.

This paper studies a penalized statistical decision rule for the treatment assignment problem. Consider the setting of a utilitarian policy maker who must use sample data to allocate a binary treatment to members of a population, based on their observable characteristics. We model this problem as a statistical decision problem where the policy maker must choose a subset of the covariate space to assign to treatment, out of a class of potential subsets. We focus on settings in which the policy maker may want to select amongst a collection of constrained subset classes: examples include choosing the number of covariates over which to perform best‐subset selection, and model selection when approximating a complicated class via a sieve. We adapt and extend results from statistical learning to develop the Penalized Welfare Maximization (PWM) rule. We establish an oracle inequality for the regret of the PWM rule which shows that it is able to perform model selection over the collection of available classes. We then use this oracle inequality to derive relevant bounds on maximum regret for PWM. An important consequence of our results is that we are able to formalize model‐selection using a “holdout” procedure, where the policy maker would first estimate various policies using half of the data, and then select the policy which performs the best when evaluated on the other half of the data.

Kitamura and Stoye (2018) recently proposed a nonparametric statistical test for random utility models of consumer behavior. The test is formulated in terms of linear inequality constraints and a quadratic objective function. While the nonparametric test is conceptually appealing, its practical implementation is computationally challenging. In this paper, we develop a column generation approach to operationalize the test. These novel computational tools generate considerable computational gains in practice, which substantially increases the empirical usefulness of Kitamura and Stoye's statistical test.

We introduce random evolving lotteries to study preference for non‐instrumental information. Each period, the agent enjoys a flow payoff from holding a lottery that will resolve at the terminal date. We provide a representation theorem for non‐separable risk consumption preferences and use it to characterize agents' attitude to non‐instrumental information. To address applications, we characterize peak‐trough utilities that aggregate trajectories of flow utilities linearly but, in addition, put weight on the best (peak) and worst (trough) lotteries along each path. We show that the model is consistent with recent experimental evidence on attitudes to information, including a preference for gradual arrival of good news and the ostrich effect, that is, decision makers' tendency to prefer information after good news to information after bad news.

We study the consequences of measurement error in the dependent variable of random‐coefficients models, focusing on the particular case of quantile regression. The popular quantile regression estimator of Koenker and Bassett (1978) is biased if there is an additive error term. Approaching this problem as an errors‐in‐variables problem where the dependent variable suffers from classical measurement error, we present a sieve maximum likelihood approach that is robust to left‐hand‐side measurement error. After providing sufficient conditions for identification, we demonstrate that when the number of knots in the quantile grid is chosen to grow at an adequate speed, the sieve‐maximum‐likelihood estimator is consistent and asymptotically normal, permitting inference via bootstrapping. Monte Carlo evidence verifies our method outperforms quantile regression in mean bias and MSE. Finally, we illustrate our estimator with an application to the returns to education highlighting changes over time in the returns to education that have previously been masked by measurement‐error bias.