Knowledge that Transforms

We examine the split‐sample robust inference (SSRI) methodology introduced by Chernozhukov, Demirer, Duflo, and Fernandez‐Val for quantifying uncertainty in heterogeneous treatment effect estimates produced by machine learning (ML) models. Although SSRI properly accounts for the additional variability due to sample splitting, its computational cost becomes prohibitive with complex ML models. We propose an alternative approach based on randomization inference (RI) that preserves the broad applicability of SSRI while eliminating the need for repeated sample splitting. Leveraging cross‐fitting and design‐based inference, the RI procedure yields valid confidence intervals with substantially reduced computational burden. Simulation studies demonstrate that the RI method preserves the statistical efficiency of SSRI while scaling to much larger applications and more complex settings.

We study the joint design of dynamic incentives and performance feedback for an environment with a coarse (all‐or‐nothing) measure of performance, and show that hiding information from the agent can be an optimal way to motivate effort. Using a novel approach to incentive compatibility, we derive a two‐phase solution that begins with a “silent phase” where the agent is given no feedback and is asked to work non‐stop, and ends with a “full‐transparency phase” where the agent stops working as soon as a performance threshold is met. Hiding information leads to greater effort, but an ignorant agent is also more expensive to motivate. The two‐phase solution—where the agent's ignorance is fully frontloaded—stems from a “backward compounding effect” that raises the cost of hiding information as time passes.

Government debt can be rolled over forever without primary surpluses in some stochastic economies, including some economies that are dynamically efficient. In an overlapping‐generations model with constant growth rate, g , of labor‐augmenting productivity, and with shocks to the durability of capital, we show that along a balanced growth path, the maximum sustainable ratio of bonds to capital is attained when the risk‐free interest rate, r f , equals g . Furthermore, this maximal ratio maximizes utility per capita along a balanced growth path and ensures that the economy is dynamically efficient.

We develop and estimate a model of consumer search with spatial learning. Consumers make inferences from previously searched objects to unsearched objects that are nearby in attribute space, generating path dependence in search sequences. The estimated model rationalizes patterns in data on online consumer search paths: search tends to converge to the chosen product in attribute space, and consumers take larger steps away from rarely purchased products. Eliminating spatial learning reduces consumer welfare by 12%: cross‐product inferences allow consumers to locate better products in a shorter time. Spatial learning has important implications for product recommendations on retail platforms. We show that consumer welfare can be reduced by unrepresentative product recommendations and that consumer‐optimal product recommendations depend on both consumer learning and competition between platforms.

We study random assignment of indivisible objects among a set of agents, when each agent is to receive one object and has strict preferences over the objects. Random Serial Dictatorship (RSD) satisfies equal treatment of equals, ex post efficiency, and strategy‐proofness. Answering a longstanding open question, we show that RSD is not characterized by those properties—there are other mechanisms satisfying equal treatment of equals, ex post efficiency, and strategy‐proofness which are not welfare‐equivalent to RSD. On the other hand, we show that RSD is not Pareto dominated by any mechanism that is (i) strategy‐proof and (ii) boundedly invariant. Moreover, the same holds for all mechanisms that are ex post efficient, strategy‐proof, and boundedly invariant: no such mechanism is dominated by any other mechanism that is strategy‐proof and boundedly invariant.

Renewable energy and battery storage are seen as complementary technologies that can together facilitate reductions in carbon emissions. We develop and estimate a framework to calculate the equilibrium effects of large‐scale battery storage. Using data from California, we find that the first storage unit breaks even by 2024 without subsidies when the renewable energy share reaches 50%. Equilibrium effects are important: the first 5000 MWh of storage capacity would reduce wholesale electricity prices by 5.6%, but an increase from 25,000 to 50,000 MWh would only reduce these prices by 2.6%. Large‐scale batteries will reduce revenues to both dispatchable generators and renewable energy sources. The equilibrium effects lead battery adoption to be virtually non‐existent until 2030, without a storage mandate or subsidy. A 30% capital cost subsidy—such as the one in the U.S. Inflation Reduction Act—achieves 5000 MWh of battery capacity by 2024, similar to the level required under California's storage mandate.

We consider general Bayesian persuasion problems where the receiver’s utility is single-peaked in a one-dimensional action. We show that a signal that pools at most two states in each realization is always optimal and that such “pairwise” signals are the only solutions under a nonsingularity condition on utilities. Our core results provide conditions under which the induced receiver action is single-dipped or single-peaked on each set of nested signal realizations. We also provide conditions for the optimality of either full disclosure or negative assortative disclosure, where all signal realizations are nested. Methodologically, our results rely on novel duality and complementary slackness theorems. Our analysis extends to a general problem of assigning one-dimensional inputs to productive units, which we call “optimal productive transport.” This problem covers additional applications including matching with peer effects (assigning workers to firms, students to schools, or residents to neighborhoods), robust option pricing (assigning future asset prices to price distributions), and partisan gerrymandering (assigning voters to districts).

We create a novel reign‐level data set for European monarchs, covering all major European states between the 10th and 18th centuries. We first document a strong positive relationship between rulers' cognitive ability and state performance. To address endogeneity issues, we exploit the facts that (i) rulers were appointed according to hereditary succession, independent of their ability, and (ii) the widespread inbreeding among the ruling dynasties of Europe led over centuries to quasirandom variation in ruler ability. We code the degree of blood relationship between the parents of rulers, which also reflects “hidden” layers of inbreeding from previous generations. The coefficient of inbreeding is a strong predictor of ruler ability, and the corresponding instrumental variable results imply that ruler ability had a sizeable effect on the performance of states and their borders. This supports the view that “leaders made history,” shaping the European map until its consolidation into nation states. We also show that rulers mattered only where their power was largely unconstrained. In reigns where parliaments checked the power of monarchs, ruler ability no longer affected their state's performance.

I propose a method for selecting seeds to maximize contagion. First, fit a random graph model using a coarse categorization of individuals. Next, compute a seed multiplier for each category—this is the average number of new infections a seed generates. Finally, seed the category with the highest multiplier. Relative to the most common methods, my approach requires far less granular data, and it consumes less computing power—the problem scales with the number of categories, not the number of individuals. I validate the methodology through simulations using real network data.

This paper investigates how children affect women in science, using biographies in the American Men of Science (MoS 1956), linked with publications. First, we show that mothers have a unique life cycle pattern of productivity: While other scientists peak in their mid‐30s, mothers become less productive at that age and reach peak productivity in their early‐40s. Next, we estimate event studies of marriage, comparing mothers and fathers with other married scientists. Event study estimates show that the productivity of mothers declines until children reach school age, while fathers experience no change. These differences have important implications for tenure and participation: Just 27% of mothers achieve tenure, compared with 48% of fathers and 46% of other women. When women carried the full burden of childcare, the time costs of raising the baby boom led to a great loss of female scientists.