Labor and Public Economics

Divorce reshapes family life, yet little is known about one of its most consequential features: the allocation of child custody. We study the impact of joint versus sole custody on both parents and children using rich administrative data from Sweden linked to over 25 years of newly-collected court custody rulings. To address selection concerns, we exploit random assignment of custody disputes to judges who differ sharply in their propensity to grant joint custody. For fathers, joint custody substantially raises earnings and improves mental health, consistent with sustained paternal involvement enhancing labor market attachment and psychological well-being. In contrast, there are no measurable labor market or mental health effects for mothers. Turning to children, joint custody increases standardized test scores and school quality without affecting mental health outcomes. Joint custody increases fathers’ chances of remarriage, keeps separated parents in closer geographic proximity, and has no effect on intimate partner violence allegations against either partner. These findings inform longstanding debates over the role of child custody in shaping post-divorce family life.

Using administrative panel data on Norwegian investors’ portfolios, we document strong but slow portfolio allocation responses to a persistent wealth-tax-induced shock to the equity premium. Short-run responses resemble the modest sensitivity documented using surveys. The longer-run responses are much larger and can be rationalized by moderate risk aversion. We document that equity premium shocks affect stock market entry but not exits, suggesting that entry costs dominate participation costs. Our finding of slow responses supports the asset-pricing literature that uses adjustment frictions to explain important asset-pricing puzzles, and has implications for optimal capital taxation when tax rates differ across assets.

Roughly one-third of U.S. households rent their homes, yet measuring who owns rental property is difficult: ownership is frequently obscured by LLCs, partnerships, and other intermediary entities that separate legal from economic control. We develop a method that traces ownership through administrative records—combining deeds and property assessments with the Census Bureau’s Business Register, IRS Schedule K-1 filings, and SEC filings on REITs—to identify ultimate owners and construct property portfolios across the full landlord size distribution. Applying the method to 11 large CBSAs, we find that individual landlords own a large majority of rental units, though their share varies meaningfully across markets. We also show that the widely used mailing-address aggregation approach both under- and over-states portfolio size in systematic ways. The method is designed to scale to national coverage and to support measurement of landlord identity, portfolio composition, and ownership concentration in U.S. rental markets. We also discuss the method’s current limitations and outline directions for refinement and validation.

We document and explain the gap between measures of AI exposure and measures of AI adoption in the workplace. This leads us to propose a new AI adoption index based on comparative advantage. Using the representative German DiWaBe employee survey linked to worker and establishment information, we compare worker-reported AI use to prominent exposure measures and find that the relationship is weak. Motivated by this gap, we develop a framework in which adoption depends not only on technical feasibility—AI’s absolute advantage measured by exposure—but on profitability—AI’s comparative (dis)advantage relative to a specific worker—balancing AI productivity against AI user costs and worker productivity against wages. We operationalize this framework at the task level by (i) estimating worker productivity relative to pay, (ii) mapping exposure indices into AI productivity, and (iii) inferring task-specific AI user costs from revealed-preference adoption. The resulting occupation-level index accounts for 60% of cross-occupation variation in observed AI adoption, compared to 14% for an exposure-only model. The two approaches diverge substantially for approximately 30% of workers, highlighting that comparative advantage—not exposure alone—is crucial for assessing AI’s labor-market impact.

This paper examines the theoretical and empirical consequences of rank-based reward systems in schools in which students’ performance and effort are evaluated relative to their peers. In such environments, classmates act simultaneously as competitors—due to rank-determined rewards—and as educators through peer learning and assistance. Using nationally representative panel survey data from U.S. high schools, combined with administrative information on the location assignments of new refugee student cohorts, we exploit variation in school competition policies and class ability compositions to identify empirically their dual effects on student effort and peer learning. We develop a theoretical tournament model with heterogeneous students who adjust their effort in response to the effort of similar peers and in which students learn from peers. The model predicts that when rewards depend on relative standing, adding higher-ability students to a cohort will reduce both incumbent academic effort and peer assistance, particularly in schools emphasizing rank-based awards, while adding lower-ability students has the opposite effects. Empirical tests of the model confirm these predictions. In schools with strong rank-based reward policies, the addition of stronger peers reduces high-performing incumbent students’ homework time and eliminates the positive spillovers from peer learning observed in less competitive settings. The adverse effects are concentrated among high-ability incumbents, while lower-ability students—who are less likely to win competitive awards—are largely unaffected. The results indicate that performance-based competition undermines cooperative peer learning and reduces student effort and overall academic performance, especially in institutions with high-ability students that explicitly emphasize relative ranking in determining academic recognition.

Solar Radiation Modification (SRM) has been proposed as a potential tool to limit increases in global or regional temperatures caused by anthropogenic greenhouse gas emissions. While previous research has extensively examined the climate system’s response to various SRM strategies, as well as their aggregate economic consequences, the regional distribution of economic impacts has received less attention. In this study, we use NorESM2–DIAM—an Earth System Model coupled to a high-resolution integrated assessment model—to assess the economic impacts, measured in GDP per capita, in an idealised SRM scenario where incoming solar radiation is reduced by 1%. Our results suggest that, relative to a baseline without SRM, most countries experience economic gains under SRM, with only a few countries facing negative impacts. Low-income countries tend to see the largest benefits, reducing global economic inequality relative to the baseline. However, reduced damages and lower inequality are accompanied by higher emissions under SRM, potentially leading to additional adverse effects not captured here. These findings highlight potential trade-offs between economic benefits, reduced inequality, and increased emissions relevant for SRM governance.

We document employment preferences of workers at the margin of informality using open-ended questions and discrete choice experiments in Brazil’s largest favela complex. Stated preferences emphasize pay and tangible job benefits rather than meaning or purpose, while primary complaints center on poor management, customers, and inflexible schedules. Workers exhibit high valuations for all formal sector amenities on average—unemployment insurance, parental leave, and termination notice—as well as for learning opportunities, but lower for non-formal sector amenities such as shorter commutes. Valuations vary systematically by employment sector in ways consistent with sorting: formal workers value formal amenities most, the self-employed value them least or not at all, and the informally employed exhibit mixed valuations. These patterns are also consistent with learning and endowment effects, for which we find suggestive evidence.

This paper examines the impact of early childcare on academic achievement for children in grade 5 and grade 9, based on a 2003 policy expansion that created quasi-random variation in slot availability for children aged 1–2. Starting childcare one year earlier increases math scores by 9.7% of a standard deviation (SD) in grade 9. Children whose mothers do not hold a high school diploma benefit by a significant 28% of a SD at grade 9, reducing the math achievement gap from children of higher-educated mothers by about one third. We also present evidence of strong improvements for children of immigrants.

We develop a new approach to estimating earnings, job, and employment dynamics using subjective expectations data from the NY Fed Survey of Consumer Expectations. These data provide beliefs about future earnings offers and acceptance probabilities, offering direct information on counterfactual outcomes and enabling identification under weaker assumptions. Our framework avoids biases from selection and unobserved heterogeneity that affect models using realized outcomes. First-step fixed-effects regressions identify risk, persistence, and transition effects; second-step GMM recovers the covariance structure of unobserved heterogeneities such as ability, mobility, and match quality. We find lower risk and persistence of the individual productivity component than in prior work, but greater heterogeneity in ability and match quality. Simulations show that reduced-form estimates overstate persistence and volatility on individual-level productivity due to job transitions and sorting. After accounting for heterogeneity, volatility declines and becomes flat across the earnings distribution. These results underscore the value of expectations data.

Since the late 1980s, extreme poverty has declined sharply, life expectancy and schooling have increased, and electoral democracy has expanded. However poverty reduction has slowed in recent years, particularly following the COVID-19 pandemic, amid intensifying conflict, fragility, climate risks, democratic backsliding, and the erosion of global trends—including trade integration and geopolitical stability—that once supported growth. These dynamics raise three interrelated questions: what barriers impede further progress; where will future growth in lower-income countries come from; and how can growth be broadly shared. Taking stock of 15 chapters forthcoming in Volume 6 of the Handbook of Development Economics, we discuss how external conditions, state capacity and policy choices shape development; analyze the shifting growth drivers, including trade, technology and the rise of services; discuss persistent inequality and distributional tensions; and conjecture that investing in institutions and people pays off.

Doctors often treat similar patients differently, which affects health outcomes and medical spending. We assess the recent literature on doctor decision-making through the lens of a model that incorporates diagnostic and procedural skills, beliefs, incentives, and differences in patient pools. Decision-making is affected by beliefs, training, experience, peer effects, financial incentives, and time constraints. Interventions to improve decision-making include providing information, guidelines, and technologies like electronic medical records and algorithmic decision tools. Economists have made progress in understanding doctor decision-making, but applications of that knowledge to improving health care are still limited.

We develop a methodology for modeling household income processes when subjective probabilistic assessments of future income are available. This allows us to flexibly estimate conditional cdf s directly using elicited individual subjective probabilities, and to obtain empirical measurements of subjective risk and subjective persistence. We then use two longitudinal surveys collected in rural India and rural Colombia to explore the nature of perceived income dynamics in those contexts. Our results suggest linear income processes are rejected in favor of more flexible versions in both cases; subjective income distributions feature heteroskedasticity, conditional skewness and nonlinear persistence.

We evaluate the effects of mandatory disclosures of firm-specific gender wage gaps in Brazil—the first developing country to enact a large-scale pay transparency law. The mandate took effect in 2023, automatically releasing to the public government-curated reports showing firm-specific gender wage gaps, separately by major occupational groups, for formal sector firms with 100 employees or more. Regression discontinuity estimates on outcomes one year after the release show that the mandated disclosures had no effects on the gender wage ratio, average wages for men or women, or the number of occupations within firms. We document that gender wage gaps persist in the formal sector as of year-end 2024, with women earning 10% less than men on average after conditioning on a flexible vector of observables, including hours worked, work experience, years of education, and occupation codes. Gender wage gaps increase with firm size for all occupational groups except production workers, which are male-dominated and have stronger union presence. Two major employer associations and a political party have challenged the legality of the policy at the Supreme Court, arguing that the mandated disclosures violate data protection rules, expose commercially sensitive wage data constituting trade secrets, conflict with existing labor legislation that permits legitimate wage differentials, and impose penalties without due process. We hypothesize two non-mutually-exclusive channels for the null effects: information frictions—workers may not receive, understand, or find actionable the technical reports—and labor market frictions—workers may be unable to act on information given limited outside options and high transition costs in Brazil’s frictional labor market.