Knowledge that Transforms

Abstract While men and women make joint decisions about fertility, women give birth and are more likely to learn about a significant cost of childbearing—maternal health risk. Within couples in Zambia, men have systematically lower awareness of maternal risk factors and higher desire for children than their wives. We develop a model in which information asymmetries between partners over maternal health risk can persist in equilibrium due to strategic incentives and can generate disagreement over fertility that cannot be resolved with transfers. To study the effect of communication barriers on fertility, we design an experiment that varies whether the husband or the wife receives information about maternal health risk. One year after the intervention, men told about such risk exhibit significant gains in knowledge, report lower demand for children, and communicate this information to their wives, who also update their beliefs. Pregnancy falls significantly, while transfers remain unchanged relative to the control group. Meanwhile, when women are told about risk, they update their beliefs, but fail to transmit the information to their husbands, who do not change their demand for children. While pregnancy also falls among these couples, the decline is accompanied by a significant reduction in transfers and support to the wife. When childbearing costs, particularly those borne by one party, cannot be easily communicated within the household, targeting information can help overcome asymmetries and improve household decision-making.

Abstract We derive asymptotically optimal statistical decision rules for discrete choice problems when payoffs depend on a partially-identified parameter θ and the decision maker can use a point-identified parameter μ to deduce restrictions on θ. Examples include treatment choice under partial identification and pricing with rich unobserved heterogeneity. Our notion of optimality combines a minimax approach to handle the ambiguity from partial identification of θ given μ with an average risk minimization approach for μ. We show how to implement optimal decision rules using the bootstrap and (quasi-)Bayesian methods in both parametric and semiparametric settings. We provide detailed applications to treatment choice and optimal pricing. Our asymptotic approach is well suited for realistic empirical settings in which the derivation of finite-sample optimal rules is intractable.

Abstract 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.

Abstract 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.

Abstract 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.

Abstract 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.

Abstract 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.

Abstract 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.

Abstract 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.

Abstract 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.