Knowledge that Transforms

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 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.

We estimate valence measures of candidates running in U.S. House elections from data on vote shares. Our identification and estimation strategy builds on ideas developed for estimating production functions, allowing us to control for possible endogeneity of campaign spending and sample selection of candidates due to endogenous entry. We find that incumbents have substantially higher valence measures than challengers running against them, resulting in about 3.5 percentage‐point differences in the vote share, on average. Eliminating differences in the valence of challengers and incumbents results in an increase in the winning probability of a challenger from 6.5% to 12.1%. Our measure of candidate valence can be used to study various substantive questions of political economy. We illustrate its usefulness by studying the source of incumbency advantage in U.S. House elections.

Many argue that minimum wages can prevent efficiency losses from monopsony power. We assess this argument in a general equilibrium model of oligopsonistic labor markets with heterogeneous workers and firms. We decompose welfare gains into an efficiency component that captures reductions in monopsony power and a redistributive component that captures the way minimum wages shift resources across people. The minimum wage that maximizes the efficiency component of welfare lies below $8.00 and yields gains worth less than 0.2% of lifetime consumption. When we add back in Utilitarian redistributive motives, the optimal minimum wage is $11 and redistribution accounts for 102.5% of the resulting welfare gains, implying offsetting efficiency losses of −2.5%. The reason a minimum wage struggles to deliver efficiency gains is that with realistic firm productivity dispersion, a minimum wage that eliminates monopsony power at one firm causes severe rationing at another. These results hold under an EITC and progressive labor income taxes calibrated to the U.S. economy.

We study the implications of optimal insurance provision for long‐run welfare and inequality in economies with persistent private information. A principal insures an agent whose private type follows an ergodic, finite‐state Markov chain. The optimal contract always induces immiseration : the agent's consumption and utility decrease without bound. Under positive serial correlation, it also backloads high‐powered incentives : the sensitivity of the agent's utility with respect to his reports increases without bound. These results extend—and help elucidate the limits of—the hallmark immiseration results for economies with i.i.d. private information. Numerically, we find that persistence yields faster immiseration, higher inequality, and novel short‐run distortions. Our analysis uses recursive methods for contracting with persistent types and allows for binding global incentive constraints.

There has been a call for caution regarding the standard procedure for Bayesian inference in set‐identified structural vector autoregressions on the grounds that the common practice of using a uniform prior over the set of orthogonal matrices induces a non‐uniform prior for individual impulse responses or other quantities of interest. This paper challenges this call by formally showing that when the focus is on joint inference, the uniform prior over the set of orthogonal matrices is not only sufficient but also necessary for inference based on a uniform joint prior distribution over the identified set for the vector of impulse responses. In addition, we show how to conduct inference based on a uniform joint prior distribution for the vector of impulse responses.