Knowledge that Transforms

We develop and test a model of social duties. The model distinguishes unconditional duties to take or avoid certain actions from conditional duties that depend on the actions’ payoffs. It also distinguishes strict duties (must do) from liberal duties (ought to do). The disutility associated with duty violation depends on the duty’s strictness and is proportional to the externality the violation causes. The model rationalizes behavioral patterns that are incompatible with consequentialist preferences. Our tests comprise parameter estimation based on subjects’ behavior across different situations as well as measurement of duties through spectators’ appropriateness ratings.

How efficiently do markets reallocate capital in booms and busts? Using a novel dataset of offshore drilling contracts I examine the role of matching in shaping industry reallocation. Oil companies search and match with capital (rigs) in a decentralized market. I find oil and gas booms increase the option value of searching which leads agents to avoid bad matches, reducing mismatch through a sorting effect. I provide an identification strategy to disentangle unobserved demand changes from the sorting effect. Estimating a model, I find substantial benefits to the sorting effect and an intermediary but that demand smoothing policies are ineffective. ∗Department of Economics, Arizona State University. Email: [email protected]. Thank you to Robert Porter, Mar Reguant, and Gaston Illanes for their encouragement and advice. I would also like to thank Gaurab Aryal, Vivek Bhattacharya, Igal Hendel, Ken Hendricks, Bill Rogerson, Mark Satterthwaite, and Yuta Toyama for useful comments and suggestions. I thank seminar participants at ASU, Cornell, Duke Fuqua, IIOC, LSE (Management), Microsoft Research, Monash University, NYU, NYU (Stern), Penn State, UCL, and U Maryland AREC. This research was supported by a grant from the Center for the Study of Industrial Organization at Northwestern. I acknowledge IHS and Rigzone for providing data.

The US government is the dominant supplier of global safe assets and faces a downward sloping demand for its debt. In this paper, we ask if the US exercises its market power when issuing debt, and we study its macroeconomic consequences. We develop a model of the global economy in which US public debt generates a nonpecuniary value for its holders, analyze the equilibrium in which the US government is themonopoly provider of this safe asset, and contrast this casewith the one inwhich the US government acts as a price taker. We use variation in estimated demand elasticities for US debt during highand low-volatility regimes to empirically distinguish between these two models and find that the data reject the price-taking behavior in favor of the monopoly one. We then quantify the distortions due to market power and find that it generates a significant underprovision of safe assets, a sizable markup in the convenience yield, and large welfare benefits for the US to the detriment of the rest of the world. Finally, we study the implications of increasing competition in safe assets from other sovereigns and private institutions. ∗Aprevious draft of this paper circulated under the title “TheMacroeconomic Implications of USMarket Power in Safe Assets.” We thank Manuel Amador, Andy Atkeson, Anmol Bhandari, V. V. Chari, Chris Conlon, Marco Duarte, Simon Gilchrist, Oleg Itskhoki, Rohan Kekre, Arvind Krishnamurthy, Zhengyang Jiang, Ricardo Lagos, Hyunju Lee, Hanno Lustig, Matteo Maggiori, Lorenzo Magnolfi, Dmitry Mukhin, Chris Sullivan, and Venky Venkateswaran, as well as numerous seminar and conference participants, for helpful comments and suggestions. We also thank Duong Dang for superb research assistance.

The goal of this paper is to link attitude toward risk over personal consumption with attitude toward risk over social consumptions. Because many everyday choices involve risk, these attitudes enter virtually every realm of individual decision-making. We provide necessary and sufficient conditions for deducing preferences over risky social choices (which have consequences both for the Decision Maker and for others) from risky personal choices (which have consequences only for the Decision Maker) and riskless social choices, and we offer an experimental test of the theory. The experiments generate a rich dataset that enables completely non-parametric revealed preference tests of the theory at the level of the individual subject. Many subjects behave as predicted by the theory but a substantial fraction do not.

Algorithm designers increasingly optimize not only for accuracy, but also for the fairness of the algorithm across pre-defined groups. We study the tradeoff between fairness and accuracy for any given set of inputs to the algorithm. We propose and characterize a fairness-accuracy frontier, which consists of the optimal points across a broad range of preferences over fairness and accuracy. Our results identify a simple property of the inputs, group-balance, which qualitatively determines the shape of the frontier. We further study an information-design problem where the designer flexibly regulates the inputs (e.g., by coarsening an input or banning its use) but the algorithm is chosen by another agent. Whether it is optimal to ban an input generally depends on the designer's preferences. But when inputs are group-balanced, then excluding group identity is strictly suboptimal for all designers, and when the designer has access to group identity, then it is strictly suboptimal to exclude any informative input.

Many ideas succeed in small trials but weaken considerably at scale. Using early childhood investment as a case study, this paper develops a dynamic microfounded human capital model stylized in the Chicago tradition. The framework features optimizing agents, complementary skill formation, and a policymaker choosing scaling strategies. The model shows that naive extrapolation from pilots systematically overestimates societal impact by overlooking voltage drops: declining benefit-cost profiles due to unrepresentative samples and contexts. Optimal scaling requires option C thinking, a mechanism-based design approach that anticipates these failures through backward induction from real-world implementation constraints. Studies in this special issue enrich the model’s insights.