We estimate the effect of air pollution from Bogot´a’s Bus Rapid Transit system on high school test scores. Using wind direction interacted with bus route intensity as an instrument, we find that schools more frequently downwind of BRT corridors score significantly lower in math and global exams. The instrumental variable estimates imply that an additional μg/m3 of PM2.5 reduces math scores by 0.10 standard deviations and global scores by 0.09 standard deviations, with no effect on language. NOx and PM10 show similar negative effects. Girls and students from higher-income households experience larger declines. A georeferenced household survey provides suggestive evidence that respiratory disease mediates the pollutionachievement relationship. These results reveal a human capital cost of diesel-powered transit and underscore the importance of cleaner fuel technologies in urban transportation policy.

This paper studies how within-city labor mobility restrictions shape the spatial allocation of economic activity. We examine the Shenzhen Wall, a boundary that increased commuting costs between Shenzhen’s Special Economic Zone (SEZ) and the rest of the city for nearly three decades. Using a regression discontinuity design, we document a sharp increase in firm entry and employment just outside the Wall, despite the policy advantages offered within the SEZ. This spatial gap persists even after the removal of the Wall. We show that this discontinuity is driven by differences in labor-market access. Commuting flows drop discontinuously across the boundary, and the effect on firm entry is substantially larger in labor-intensive industries. To quantify the aggregate effects, we embed bilateral commuting frictions into a quantitative spatial equilibrium model calibrated to the observed allocation of workers and price levels in 2024.

We examine whether corporate bonds react to geopolitical risk by analyzing how firms' bond return sensitivity to the geopolitical risk index influences subsequent performance. Our regressions show that higher geopolitical risk beta predicts lower future returns, reflecting investors' willingness to pay premiums for bonds that hedge rising geopolitical tensions. Difference-indifferences regressions indicate greater demand for high-beta bonds following intensified conflicts. The risk premium is larger for firms with elevated downside, international, supply-chain, and credit risks, particularly during volatile and uncertain economic conditions. Overall, we contribute to the literature by documenting the pricing effect of GPR on corporate bonds.

We study how land security shapes family migration and rural-origin children’s human capital amid stark urban-rural education gaps. Exploiting China’s staggered land reforms, we find that (i) strengthened land security reduces by 33% the likelihood migrants leave children behind; and (ii) each additional childhood year in urban areas raises university enrollment probability by 0.8 percentage point. A calibrated heterogeneous-agent model with incomplete markets, migration, and education implies that the reforms yielded a 2.1% consumption-equivalent welfare gain by reallocating talent and reducing precautionary distortions. General equilibrium adjustments in land markets are first-order for the reform's aggregate and distributional evaluations.

Agents arriving sequentially may adopt a new technology. Early adoption reveals information about its value, but individuals tend to wait and free-ride on information generated by others. Facing this challenge, we study how a designer — who controls the public release of past information — can improve social learning efficiency. The optimal design follows a threshold stopping rule: it keeps recommending adoption until the designer’s belief becomes more pessimistic than a time-varying threshold, which typically starts above the first-best threshold, later drops below it in some periods, and eventually returns to it. Meanwhile, public information flow is optimally restrained by over-recommending adoption, but not by under-recommending. We also examine special cases of conclusive-news learning. In good-news environments,

Recent decades have witnessed a rapid growth in proposed risk factors in the international currency market. While most currency factors are evaluated using conventional asset pricing tests that can be mechanically distorted, we identify the best asset pricing model using a survival-of-the-fittest approach. Our framework treats competing currency factor models as alternative specifications of the stochastic discount factor and compares them within a Bayesian framework. We find that a parsimonious three-factor model including the dollar (DOL), carry (CAR), and business-cycle or output-gap (GAP) factors dominates the model space. This model achieves the highest marginal likelihoods, delivers positive and precisely estimated risk premia, and spans the remaining factors.

We study allocation problems with reserve systems under minimum beneficiary-share guarantees—requirements that targeted matches constitute at least a specified percentage of total matches. While such mandates promote targeted matches, they inherently conflict with maximizing total matches. We characterize the complete non-domination frontier using minimal cycles, where each point represents an allocation that cannot increase targeted matches without sacrificing total matches. Our main results: (i) the frontier exhibits concave structure with monotonically decreasing slope, (ii) traversing from maximum targeted matches to maximum total matches reduces matches by at most half, (iii) the Repeated Hungarian Algorithm computes all frontier points in polynomial time, and (iv) mechanisms with beneficiary-share guarantees can respect category-dependent priority orderings but necessarily violate path-independence.

We develop a binocular directional forecasting framework that jointly leverages information from equity and option markets through a multimodal deep learning architecture. Realized price and trading dynamics, together with option-implied volatility information, are encoded as twodimensional images and processed using convolutional neural networks (CNNs), then integrated through a cross-attention mechanism with an adaptive gating network. This design enables bidirectional information flow across markets and state-dependent weighting of heterogeneous signals. Using U.S. equity and option daily data from 1996 to 2023, we show that the binocular model significantly outperforms stock-only benchmarks in out-of-sample directional prediction at both monthly and quarterly horizons. Trading strategies based on the fused forecasts achieve higher Sharpe ratios and lower turnover than traditional momentum and reversal strategies.

Many assignment systems require applicants to rank multi-attribute bundles (e.g., programs combining institution, major, and tuition). We study whether this reporting task is inherently difficult and how reporting interfaces affect accuracy and welfare. In laboratory experiments, we induce preferences over programs via utility over attributes, generating lexicographic, separable, or complementary preferences. We compare three reporting interfaces for the direct serial dictatorship mechanism: (i) a full ranking over programs; (ii) a lexicographic-nesting interface; and (iii) a weighted-attributes interface, the latter two eliciting rankings over attributes rather than programs. We also study the sequential serial dictatorship mechanism that is obviously strategyproof and simplifies reporting by asking for a single choice at each step. Finally, we run a baseline that elicits a full ranking over programs but rewards pure accuracy rather than allocation outcomes. Four main findings emerge.

We study how regional specialization patterns and welfare are affected by uncertainty and economies of scale in an open economy. We use a multi-sector spatial equilibrium model with sectoral economies of scale, aggregate uncertainty, and irreversible mobility decisions by heterogenous workers. We analytically characterize the interactions between specialization, economies of scale, and uncertainty. We find empirical support for the model predictions by focusing on the impact of aggregate changes in volatility of sectoral productivity on U.S. regional economies. We calibrate the model using detailed data on U.S. commuting zones and international trade, and extending hat-algebra methods to accommodate uncertainty. Quantitatively, we find that uncertainty shifts employment away from riskier sectors and locations, relative to a deterministic benchmark, lowering the U.S. aggregate gains from trade by at least a third.