Knowledge that Transforms

This paper studies the problem of experiment design by an ambiguity-averse decision-maker who trades off subjective expected performance against robust performance guarantees. This framework accounts for real-world experimenters’ preference for randomization. It also clarifies the circumstances in which randomization is optimal: when the available sample size is large and robustness is an important concern. We apply our model to shed light on the practice of rerandomization, used to improve balance across treatment and control groups. We show that rerandomization creates a trade-off between subjective performance and robust performance guarantees. However, robust performance guarantees diminish very slowly with the number of rerandomizations. This suggests that moderate levels of rerandomization usefully expand the set of acceptable compromises between subjective performance and robustness. Targeting a fixed quantile of balance is safer than targeting an absolute balance objective.

We study the complexity of rules by paying experimental subjects to implement a series of algorithms and then eliciting their willingness-to-pay to avoid implementing them again in the future. The design allows us to examine hypotheses from the theoretical “automata” literature about the characteristics of rules that generate complexity costs. We find substantial aversion to complexity and a number of regularities in the characteristics of rules that make them complex and costly for subjects. Experience with a rule, the way a rule is represented, and the context in which a rule is implemented (mentally versus physically) also influence complexity.

Macroeconomic news announcements are elaborate and multidimensional. We consider a framework in which jumps in asset prices around announcements reflect both the response to observed surprises in headline numbers and to latent factors, reflecting other news in the release. Non-headline news, for which there are no expectations surveys, is unobservable to the econometrician but nonetheless elicits a market response. We estimate the model by the Kalman filter, which efficiently combines OLS and heteroskedasticity-based event study estimators in one step. With the inclusion of a single latent surprise factor, essentially all yield curve variance in event windows are explained by news.

Increasingly, algorithms are supplanting human decision-makers in pricing goods and services. To analyze the possible consequences, we study experimentally the behavior of algorithms powered by Artificial Intelligence (Q-learning) in a workhorse oligopoly model of repeated price competition. We find that the algorithms consistently learn to charge supracompetitive prices, without communicating with one another. The high prices are sustained by collusive strategies with a finite phase of punishment followed by a gradual return to cooperation. This finding is robust to asymmetries in cost or demand, changes in the number of players, and various forms of uncertainty.

We analyze selection into screening in the context of recommendations that breast cancer screening start at age 40. Combining medical claims with a clinical oncology model, we document that compliers with the recommendation are less likely to have cancer than younger women who select into screening or women who never screen. We show this selection is quantitatively important: shifting the recommendation from age 40 to 45 results in three times as many deaths if compliers were randomly selected than under the estimated patterns of selection. The results highlight the importance of considering characteristics of compliers when making and designing recommendations.

Fang and Gong (2017) develop a procedure to detect potential over-billing of Medicare by physicians. In their empirical analysis, they use aggregated claims data that can overstate the number of services performed due to features of Medicare billing. In this comment, I show how auditors can use detailed claims-level data to better target improper overbilling.

We study the impact of regulating product entry and quality information requirements on an oligopoly equilibrium and consumer welfare. Product testing can reduce consumer uncertainty, but also increase entry costs and delay entry. Using variation between EU and US medical device regulations, we document patterns consistent with valuable learning from more stringent US requirements. To derive welfare implications, we pair the data with a model of supply, demand, and testing regulation. US policy is indistinguishable from the policy that maximizes total surplus in our estimated model, while the European Union could benefit from more testing. “Post-market surveillance” could further increase surplus.

We present a new equilibrium search model where consumers initially search among discount opportunities, but are willing to pay more as a deadline approaches, eventually turning to full-price sellers. The model predicts equilibrium price dispersion and rationalizes discount and full-price sellers coexisting without relying on ex ante heterogeneity. We apply the model to online retail sales via auctions and posted prices, where failed attempts to purchase reveal consumers' reservation prices. We find robust evidence supporting the theory. We quantify dynamic search frictions arising from deadlines and show how, with deadline-constrained buyers, seemingly neutral platform fee increases can cause large market shifts.

We examine whether shared collective experiences help build a national identity, by looking at the impact of national football teams’ victories in sub-Saharan Africa. We find that individuals surveyed in the days after an important victory of their country’s national team are 37 percent less likely to identify primarily with their ethnic group, and 30 percent more likely to trust other ethnicities, than those interviewed just before. Crucially, national team achievements also reduce violence: countries that (barely) qualified to the Africa Cup of Nations experience less civil conflict (9 percent fewer episodes) in the following months than countries that (barely) did not.

This paper studies when religion can hamper diffusion of knowledge and economic development, and through which mechanism. I examine Catholicism in France during the Second Industrial Revolution (1870–1914). In this period, technology became skill-intensive, leading to the introduction of technical education in primary schools. I find that more religious locations had lower economic development after 1870. Schooling appears to be the key mechanism: more religious areas saw a slower adoption of the technical curriculum and a push for religious education. In turn, religious education was negatively associated with industrial development 10 to 15 years later, when schoolchildren entered the labor market.