Knowledge that Transforms

We formalize a research design to uncover the mechanisms underlying long-term reductions in energy consumption caused by a widely implemented nudge. We consider two channels: technology adoption and habit formation. Using data from 38 natural field experiments, we isolate the role of technology adoption by comparing treatment and control homes after the initial resident moves, which discontinues the treatment for a home. We find that fully half of energy reductions persist in the home after treatment ends and show this persistence is consonant with a technology adoption channel. The role of technology in creating persistent behaviour change has important implications for designing behavioural interventions and evaluating their long-term social impacts.

Studies have shown that the standard law of belief updating—Bayes’ rule—is descriptively invalid in various settings. In this paper, I introduce and analyse a generalization of Bayes’ rule—Coarse Bayesian updating—accommodating much of the empirical evidence. I characterize the model axiomatically, show how it generates several well-known biases, and derive its main implications in static and dynamic settings. Each axiom expresses a property of Bayes’ rule but, conditional on the others, stops just short of making the agent fully Bayesian. The model employs standard primitives, making it suitable for applications; I demonstrate this by applying it to a standard setting of decision under risk, leading to a close relationship with the Blackwell information ordering and comparative measures of cognitive sophistication and bias.

Allocative wages—the labor costs considered when deciding to form or dissolve a long-term employment relationship—are more sensitive to cyclical conditions for more educated workers. Specifically, college-educated workers’ allocative wages are highly pro-cyclical, while high school dropouts’ wages exhibit only moderate cyclicality. Further, as education increases, an increasing share of the sensitivity of allocative wages is driven by the persistent scarring effects of the cyclical position at the time of hiring on the wages associated with higher levels of tenure, amounting to more than a third of the overall sensitivity for the college educated. The greater job stability of the more educated—and therefore the exposure to scarring—contributes to these differences. In addition, more significant scarring at each horizon of tenure amplifies the effect. In service of documenting these facts, I develop new methods for inferring the sensitivity of labor costs to shocks when agents are forward-looking and wages may be intertemporally smoothed.

Over the latest monetary policy tightening cycle, capital has been flowing from low-inflation countries to high-inflation countries. This pattern of capital flows is consistent with the predictions of an open-economy model with nominal rigidities where cost-push shocks generate an inflationary episode and capital flows freely across countries. Yet, by raising demand for domestic non-tradable goods and services, capital inflows cause unwelcome upward pressure on firms’ costs in countries most severely hit by these shocks. We find that a reverse pattern of capital flows would have improved the output-inflation trade-off globally, hence requiring a less aggressive monetary tightening in the most severely hit countries and delivering overall welfare gains.

I examine the impact of patent term on R&D and innovation in the presence of policy anticipation, common in real-world settings. Using a difference-in-difference design, I exploit quasi-experimental variation in US patent term across technological fields due to the ratification of TRIPs agreements in 1995. Despite a general increase in average patent term, in most fields innovators faced a considerable probability of patent term reduction for future innovations. Three key findings emerge: (1) R&D and innovation accelerate more in fields with a higher probability of patent term reduction, i.e. a shorter average patent term extension, before implementation. (2) This heightened activity persists for at least 5 years postimplementation, driven by indirect effects where the news-related acceleration fosters further innovation through technological externalities linked to cumulative knowledge creation. (3) Conversely, the direct effect of a shorter extension in patent term would stimulate relatively less innovation, absent the indirect effects of anticipation.

We exploit a panel of city-level data with rich demographic information to estimate the distributional effects of Department of Defense spending and its effects on a range of social outcomes. The income and employment generated by defence spending accrue predominantly to households without a bachelor's degree. These households as well as Black and Hispanic households tend to disproportionately benefit from this spending. Defence spending also promotes a range of beneficial social outcomes that are often targeted by government programs, including reductions in poverty, divorce rates, disability rates, and mortality rates, as well as increases in homeownership rates, health insurance rates, and occupational prestige. We compare the effects of defence spending with the effects of general demand shocks and explore reasons for the differential effects of the shocks.

Auction models are convenient abstractions of informal price-formation processes that arise in markets for assets or services. These processes involve frictions like bidder recruitment costs for sellers, participation costs for bidders, and limitations on sellers’ commitment abilities. This paper develops an auction model that captures such frictions. We derive novel insights, notably that outcomes are often inefficient, that markets sometimes unravel, and that the observability of competition may have a large effect.

Why are job polarization and income inequality higher in large U.S. cities? I offer a new explanation: when house prices grow faster in large cities, middle-income households increasingly cannot afford to own a house there. They move to smaller cities and the middle of the income distribution in large cities hollows out, making them more polarized and unequal. I document that (1) cities with higher price growth experienced larger polarization and increase in inequality since 1980 and (2) middle-income households migrate more often to cheaper locations for housing-related reasons than low- or high-income households. Using a spatial equilibrium model with tenure choice and skill heterogeneity, I find that excess growth of prices relative to incomes and rents in large cities accounts for nearly all of the gap in polarization and almost one-half of the gap in inequality growth between large and small cities from 1980 to 2019.

Regulatory concerns over a tension between expanding financial access and limiting default have led to significant restrictions on contract terms in a number of countries, despite limited evidence on their effectiveness. We use a large nation-wide RCT to examine new borrower responses to changes in interest rates and minimum payments for a credit card that accounted for 15% of all first-time formal loans in Mexico. Default rates were 19% over the 26 month experiment and a 30 pp decrease in interest rates decreased default by 2.5 pp with no effects on the newest borrowers. Doubling minimum payments increased default by 0.8 pp during the experiment but reduced it by 1 pp afterwards, possibly by reducing debt. Matching the experimental sample to their formal employment histories we find that the effect of job separation—more common among new borrowers—on default is seven times larger than the effect of the 30 pp interest rate change. We provide a simple framework for interpreting the experimental results, and rationalize the smaller contract term effects by their limited effects on cash flow rather than by differences in per-peso impacts.

Human decision-makers frequently override the recommendations generated by predictive algorithms, but it is unclear whether these discretionary overrides add valuable private information or reintroduce human biases and mistakes. We develop new quasi-experimental tools to measure the impact of human discretion over an algorithm on the accuracy of decisions, even when the outcome of interest is only selectively observed, in the context of bail decisions. We find that 90% of the judges in our setting underperform the algorithm when they make a discretionary override, with most making override decisions that are no better than random. Yet the remaining 10% of judges outperform the algorithm in terms of both accuracy and fairness when they make a discretionary override. We provide suggestive evidence on the behaviour underlying these differences in judge performance, showing that the high-performing judges are more likely to use relevant private information and are less likely to overreact to highly salient events compared to the low-performing judges.