Knowledge that Transforms

We study identification and inference in first-price auctions with risk-averse bidders and selective entry, building on a flexible framework we call the Affiliated Signal with Risk Aversion (AS-RA) model. Assuming exogenous variation in either the number of potential bidders (N) or a continuous instrument (z) shifting opportunity costs of entry, we provide a sharp characterization of the nonparametric restrictions implied by equilibrium bidding. This characterization implies that risk neutrality is nonparametrically testable. In addition, with sufficient variation in both N and z, the AS-RA model primitives are nonparametrically identified (up to a bounded constant) on their equilibrium domains. Finally, we explore new methods for inference in set-identified auction models based on Chen et al. (2018, Econometrica, vol. 86, 1965–2018), as well as novel and fast computational strategies using Mathematical Programming with Equilibrium Constraints. Simulation studies reveal the good finite-sample performance of our inference methods, which can readily be adapted to other set-identified flexible equilibrium models with parameter-dependent support.

Not all barrels of oil are created equal: their extraction varies in both private cost and carbon intensity. Leveraging a comprehensive micro-dataset on world oil fields, alongside detailed estimates of carbon intensities and private extraction costs, this study quantifies the additional emissions and costs from having extracted the “wrong” deposits. We do so by comparing historical deposit-level supplies to counterfactuals that factor in pollution costs, while keeping annual global consumption unchanged. Between 1992 and 2018, carbon misallocation amounted to at least 11.00 gigatons of CO2-equivalent (GtCO2eq), incurring an environmental cost evaluated at $2.2 trillion (US$ 2018). This translates into a significant supply-side ecological debt for major producers of high-carbon oil. Looking forward, we estimate the gains from making deposit-level extraction socially optimal at about 9.30 GtCO2eq, valued at $1.9 trillion, along a future aggregate demand pathway coherent with the objective of net-zero emissions in 2050, and document unequal reserve stranding across oil nations.

We propose a simple and general model of experimentation in which reaching untried levels of a stock variable may, after a stochastic delay, lead to a catastrophe. Hence, at any point in time a catastrophe might well be under way, due to past experiments. We show how to measure this legacy of the past from prior beliefs and the chronicle of stock levels. We characterize the optimal policy as a function of the legacy and show that it leads to a new protocol for planning that applies to a general class of problems, encompassing the study of pandemics or climate change. Several original policy predictions follow, e.g. experimentation can stop but resume later.

How do regional productivity shocks or transportation infrastructure improvements affect aggregate welfare? In a general class of spatial equilibrium models, we provide a formula for aggregate welfare changes, decomposed into terms associated with (i) technology [Fogel (1964), Railroads and American Economic Growth (Baltimore: Johns Hopkins Press), Hulten (1978) “Growth Accounting with Intermediate Inputs”, The Review of Economic Studies, 45, 511–518], (ii) spatial dispersion of marginal utility, (iii) fiscal externalities, (iv) technological externalities, and (v) redistribution. We further use this decomposition to derive a general formula for optimal spatial transfers and show that, whenever optimal transfers are in place, the technology term alone captures the aggregate welfare effects of technological shocks. We apply our framework to study welfare gains from improving the US highway network. We find that changes in the spatial dispersion of marginal utility are as important as technological externalities in accounting for the deviations from the Fogel-Hulten benchmark to assess welfare gains.

Our study analyses 6 million civil judgments in China from 2014 to 2018, documenting gender disparities that disfavour female litigants. We investigate the impact of an open justice reform that mandated courts to broadcast legal proceedings live on a centralized online platform. By exploiting variations in its implementation across courts and over time and employing both difference-in-differences and Bartik IV approaches, we find that gender disparities in chances of winning decrease as broadcast intensity increases. Analysis of the textual content of judicial decisions provides further evidence that these changes in judicial outcomes stem from altered judge behaviours (i.e. attention and effort) under enhanced judicial transparency. Our results demonstrate how information technology shapes judges’ conduct, underscoring its broader potential to improve accountability in public institutions.

Normal-form two-player games are categorized by players into K analogy classes so as to minimize the prediction error about the behaviour of the opponent. This results in Clustered Analogy-Based Expectation Equilibria in which strategies are analogy-based expectation equilibria given the analogy partitions and analogy partitions minimize the prediction errors given the strategies. We distinguish between environments with self-repelling analogy partitions in which some mixing over partitions is required and environments with self-attractive partitions in which several analogy partitions can arise, thereby suggesting new channels of belief heterogeneity and equilibrium multiplicity. Various economic applications are discussed.

We study attention utility, the hedonic pleasure or pain derived purely from paying attention to information, which differs from the news utility that arises from gaining new information. The main, field, study examines brokerage account login data to show that investors pay disproportionate attention to already-known positive information on their stocks. Through its effect on logins, this selective attention affects their trading activity. Three experimental studies then show that (1) investors are more likely to engage in a paid task that will involve attention to a prior investment if that investment has gained value; (2) paying attention to a winning stock is more motivating than a doubling of monetary incentives; and that (3) attention has value independent of information acquisition.

For standard statistical problems, we provide new evidence documenting (1) multimodality and (2) instability in probability estimates, including from irrelevant changes in problem description. The evidence motivates a model in which, when solving a problem, people represent each hypothesis by attending to its salient features while neglecting other, potentially more relevant, ones. Only the statistics associated with salient features are used. The model unifies biases in judgments about i.i.d. draws, such as the Gambler's Fallacy and insensitivity to sample size, with biases in inference such as under- and overreaction and insensitivity to the weight of evidence. The model makes predictions for how changes in the salience of specific features jointly shapes known biases and measured attention to features, but also create entirely new biases. We test and confirm these predictions experimentally. Salience-driven attention to features emerges as a unifying framework for biases conventionally explained using a variety of stable heuristics or distortions of Bayes' rule.

This article develops the uniform asymptotic theory for local projection (LP) regression when the true lag order of the model is unknown and potentially infinite. The theory allows for varying degrees of persistence in the data, growing response horizons, and general conditionally heteroskedastic martingale-difference shocks. Based on the theory, we make two main contributions. First, we show that LPs can achieve semiparametric efficiency at a given horizon under classical assumptions on the data, provided that the controlled lag order diverges. Thus, the commonly perceived efficiency loss of LPs can become asymptotically negligible with many controls. Second, we propose LP-based inference procedures for (level and cumulated) impulse responses that possess robustness properties not shared by existing methods. Inference methods using two distinct standard errors are considered. The uniform validity for the first method depends on a zero fourth-order cumulant condition on shocks, while that of the second holds more generally for conditionally heteroskedastic martingale-difference shocks. We propose a bootstrap procedure that improves finite-sample performance and extend the standard error construction to structural responses.

We show that the financial sector’s asset supply elasticities are sufficient statistics summarizing its macroeconomic effects for a large class of financial frictions. These elasticities are crucial for a wide range of policy questions, ranging from the size of fiscal multipliers to the relative effectiveness of asset purchases targeting the financial sector versus tax cuts targeting households. Workhorse macroeconomic models imply different values of these elasticities, generating output responses to policies that differ by orders of magnitude. We construct empirical measures of these elasticities and evaluate their policy implications in a quantitative model with household heterogeneity and illiquidity.