Publications

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.

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.

We develop a quantitative macroeconomic theory of child mental health. The theory is grounded in child psychiatry, formalized in a life-cycle heterogeneous agent model of child development, and disciplined using micro data on mental health of children and parents. Intergenerational transmission of mental illness arises due to both biological factors and parental behavior. Parents experiencing mental illness have negative expectations and lose time due to rumination. As a result, they invest less in their child’s mental health. We use the model to evaluate policies designed to improve child mental health. We show that subsidizing mental health treatment for children generates sizable welfare gains.

How does wartime rebel governance shape post-conflict institutions? We study this in Nepal, where the Maoist People's War (1996–2006) dismantled a 240-year caste-based monarchy and ended with Maoists entering democratic politics. During the conflict, Maoists established sub-national “People’s Governments” that administered justice, collected taxes, and delivered local services. Using a spatial regression-discontinuity design, we show that exposure to People's Governments increased political knowledge and participation especially among historically marginalized indigenous groups (Janajatis). Exposure also reshaped party institutions and inter-party competition: candidate-selection committees in more exposed areas have 26 percent more Janajati members who, drawing on novel implicit-attitude data, exhibit less pro-upper caste bias. Non-Maoist parties' Janajati nomination rates nearly double in fully exposed areas, consistent with competition for newly mobilized voters. Nearly two decades on, local governments in exposed areas score 0.2–0.3 standard deviations higher on state capacity indices and receive 13% more in conditional federal grants. These findings show that when rebel groups enter competitive democratic politics, wartime governance institutions can — through citizen mobilization, party gatekeeping, and cross-party competition — enable a more inclusive and capable post-war state.

AI/ML methods are increasingly used in economics to generate binary variables (or labels) via classification algorithms. When these generated variables are included as covariates in regressions, even small misclassification errors can induce large biases in OLS estimators and invalidate standard inference. We study whether the bootstrap can correct this bias and deliver valid inference. We first show that a seemingly natural fixed-label bootstrap, which generates data using estimated labels but relies on a corrupted version in estimation, is generally invalid unless a strong independence condition between the latent true labels and other covariates holds. We then propose a coupled-label bootstrap that jointly resamples the true and imputed labels, and show it is valid without this condition. Two finite-sample adjustments further improve coverage: a variance correction for uncertainty in estimated misclassification rates and a Hessian rotation for near-singular designs. We illustrate the methods in simulations and apply them to investigate the relationship between wages and remote work status.

We compare how well agents aggregate information in two repeated social learning environments. In the first setting agents have access to a public data set. In the second they have access to the same data, and also to the past actions of others. Despite the fact that actions contain no additional payoff-relevant information, and despite potential herd behavior, free riding and information overload issues, observing and imitating the actions of others leads agents to take the optimal action more often in the second setting. We also investigate the effect of group size, as well as a setting in which agents observe private data and others’ actions.

Signaling is wasteful. But how wasteful? We study the fraction of surplus dissipated in a separating equilibrium. For isoelastic environments, this waste ratio has a simple formula: β/(β + σ), where β is the benefit elasticity (reward to higher perception) and σ is the elasticity of higher types’ relative cost advantage. The ratio is constant across types and is independent of other parameters, including convexity of cost in the signal. We show that the directional effects of β and σ on waste extend to non-isoelastic environments.

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.

In GMM estimation it is well known that if the number of moment conditions grows with the sample size, GMM asymptotics differ from the standard case with moment size fixed as the sample size tends to infinity. The present work explores infinite dimensional GMM estimation under various conditions on the moment conditions and the weight matrix. Our approach employs a partial sum process formed by the moment conditions to represent high dimensional moments and an invariance principle to capture the infinite dimensional asymptotics as the moment size grows. Next, the GMM weight matrix is assumed to converge to one of two kernels at the limit: a continuous kernel or the Dirac delta function. Combining these different conditions enables development of a large sample theory for most efficient GMM estimation. The effects of permuting the moment conditions on GMM efficiency are also explored. The resulting theory is applied to weak instrumental variable estimation and the Angrist and Krueger (1991) data are re-analyzed in an empirical application of the new methods.

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.

As AI systems shift from directing users to content toward consuming it directly, publishers need a new revenue model: charging AI crawlers for content access. This model, called pay-per-crawl, must solve a problem of mechanism selection at scale: content is too heterogeneous for a fixed pricing framework. Different sub-types warrant not only different price levels but different pricing rules based on different unstructured features, and there are too many to enumerate or design by hand. We propose the LM Tree, an adaptive pricing agent that grows a segmentation tree over the content library, using LLMs to discover what distinguishes high-value from low-value items and apply those attributes at scale, from binary purchase feedback alone. We evaluate the LM Tree on real content from a major German technology publisher, using 8,939 articles and 80,451 buyer queries with willingness-to-pay calibrated from actual AI crawler traffic. The LM Tree achieves a 65% revenue gain over a single static price and a 47% gain over two-category pricing, outperforming even the publisher’s own 8-segment editorial taxonomy by 40%—recovering content distinctions the publisher’s own categories miss.

This paper studies how new varieties enter markets and become locally available. We provide causal evidence of demand externalities that operate in two steps. First, information about new varieties diffuses directly through real-world social ties among consumers. Second, early purchases generate an indirect spillover to firms: local retailers learn from 'pioneer' consumers which new varieties are most likely to succeed and adjust their product offerings accordingly. We study this process in the context of direct-to-consumer imports. Using customs records on individuals' purchases matched to population-wide social networks, international migrant links, and retailer catchment areas, we document economically meaningful demand externalities. Product-specific demand shocks abroad transmit through migrant networks and shift which varieties consumers purchase. Leveraging these shocks as a plausibly exogenous source of local demand variation, we show strong peer effects: prior purchases by close neighbors, coworkers, or friends increase an individual’s likelihood of purchasing the same variety, especially for premium and visible goods. We leverage this result to identify an indirect spillover from consumers to firms: retailers are more likely to add a variety when it becomes popular among consumers in their catchment area. Combining the instrument with linked consumer--retailer data and a self-conducted retailer survey, we show that this response reflects learning about latent demand for varieties not yet stocked locally. Together, social diffusion and retailer learning generate demand multipliers that reshape local product availability and expand access to global variety.

Here we provide our solutions to the First Proof questions. We also discuss the best responses from publicly available AI systems that we were able to obtain in our experiments prior to the release of the problems on February 5, 2026. We hope this discussion will help readers with the relevant domain expertise to assess such responses.

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.