Knowledge that Transforms

The ongoing revolution in deep learning is reshaping research across many fields, including economics. Its effects are especially clear in solving dynamic economic models. These models often lack closed-form solutions, so economists have long relied on numerical methods such as value function iteration, perturbation, and projection techniques. Unfortunately, these approaches suffer from the curse of dimensionality, which makes global solutions computationally infeasible as the number of state variables increases. Deep learning offers a different approach: flexible tools that solve dynamic economic models by minimizing residuals in equilibrium conditions and that can handle high-dimensional problems. This development promises to broaden the scope of quantitative economics. I illustrate the approach using the neoclassical growth model.

We consider why fertility has fallen in recent decades in almost all high-income countries. We begin by documenting declining total fertility and rising childlessness across cohorts, highlighting the need to focus on cohort versus period-specific fertility rates. With this motivation, we propose a conceptual model of fertility determination that augments the standard Becker model with an explicit role for social norms and cohort-specific contextual factors, including broad social and economic influences and an expanded set of consumption and lifestyle options. We posit that these forces have led to “shifting priorities,” reducing the centrality of parenthood. We then review existing empirical evidence and conclude that the decline in fertility likely reflects a complex mix of changing norms around work, parenting, gender roles, and leisure consistent with our cohort-based conceptual framework. We conclude with suggestions for future research and a brief discussion of policy implications.

Something is amiss in empirical economics. Despite the advances of the credibility revolution, published estimates tend to be inflated and overconfident. We argue that this stems from a weakness in the dominant econometric framework: treating the researcher like a calculator that mechanically implements the econometric method. We use several examples to show how properties of estimators change dramatically with humans-in-the-loop. Under plausible assumptions on researcher behavior, low-power estimators such as instrumental variables exhibit high degrees of bias, even with a first-stage F-statistic of 200. Threshold testing on the first-stage F-statistic can reduce bias, contrary to Angrist and Kolesár (2024). And standard errors understate uncertainty, since they ignore variation due to researchers' subjective choices. Ignoring the role of humans "in the research loop" can lead to highly biased and unreliable findings. Modifying econometric practices to address the human factor is a critical frontier of the credibility revolution.

In Unforgiving Places Jens Ludwig makes a strong claim for the behavioral economics of crime, scoring it a knockout when pitted against conventional wisdom. Focusing on gun violence, he proposes a unifying explanation that draws on disciplines beyond economics, especially sociology, criminology, and psychology. Although the intended audience is mainly policymakers and the general public, scholars will benefit from reading Ludwig's thought-provoking book. In this essay, I evaluate the book's evidence and elaborate on the research needed to resolve unsettled hypotheses. I focus on several key issues, including linking self-control theory to cognitive processes, informal neighborhood social control, legitimacy in community policing, toxic environmental effects on cognition, the limits of administrative data, age-specific pathways of gun carrying, and the impact of social change. In the spirit of consilience that Unforgiving Places calls for, I argue that further engagement of the book's framework with these issues yields a promising research agenda.

We review the flourishing literature on the automobile industry since the seminal work of Berry, Levinsohn, and Pakes (1995), or briefly BLP. Their work provides a structural equilibrium framework that forms a basis for conducting policy counterfactuals in several key areas of interest: competition policy and antitrust, trade policy, and taxation and environmental policy. The demand side of the “BLP framework” is micro-founded and allows for rich consumer heterogeneity to generate flexible substitution patterns between products. The supply side specifies marginal costs and accounts for imperfect competition. Our analysis focuses on two main questions. First, how has the framework been tailored to specific situations and how convincingly has it been evaluated to generate trust in the empirical findings and policy conclusions? Second, what has been learned about policy issues relevant in the automotive industry using the BLP equilibrium framework?

Refugees are international migrants escaping persecution and crises whose economic success in their countries of destination is threatened by lack of access to labor and credit markets, limited information on employment opportunities, and loss of human capital and assets. In this paper we review the economic literature that analyzes and evaluates interventions designed to address these hurdles and facilitate refugees’ economic integration. We also draw important general lessons from a wide variety of these interventions and identify areas for further research.

Intermediary asset pricing posits that financial institutions play a central role in financial markets, and that their decisions shape asset prices beyond simply reflecting the preferences of the average household. This perspective helps make sense of key empirical patterns: the excess volatility of asset prices, differences in price movements across asset classes, the cross-section of expected returns within asset classes, and specific arbitrage opportunities and price dislocations. We also review the implications of intermediary asset pricing for macroeconomic dynamics, international economics, and policy. In this approach, a primary channel of financial regulation and monetary policy is through alleviating constraints or removing risk from intermediary balance sheets during periods of stress. We highlight both existing progress and gaps for future research.

We examine whether managerial cognitive heuristics spill over from personal to corporate decisions. We identify “anchoring CEOs” who anchor on the 52-week high in personal stock trading and show that this behavior extends to corporate financial decisions. These CEOs are more likely to issue seasoned equity offerings near the 52-week high and place greater weight on the target’s 52-week high in acquisition pricing, with the latter associated with negative abnormal returns. The effect is stronger under competitive pressure and uncertainty but weaker with stronger governance and CEO experience. Our findings highlight cross-domain persistence in managerial heuristics and the role of governance in mitigating behavioral distortions.