Papers

Remote work has fundamentally reshaped urban economic life, and the spatial organisation of activity across cities. However, access to flexible work is distributed unevenly across industries, income groups, and genders, creating disparities in health risks, social mixing, and economic opportunity. Understanding where workplace dependence (WPD) is concentrated is therefore important, yet its distribution across urban areas remains poorly understood. Here we pair fine-grained hourly population data with detailed company records in a large European city to examine how location, industry composition, and socio-economic characteristics shape physical workplace attendance. By comparing workplace activity during periods of low versus high COVID-19 restrictions, we identify the determinants of WPD. We find that while industry and firm productivity are key drivers, the relationship between WPD, income, and gender is highly contingent on distance from the city center. Near the centre, female-majority and income-diverse locations show the highest WPD, consistent with a residual, place-bound service workforce. These findings reveal a spatially contingent 'service trap' at the urban core, extending remote-work inequalities beyond individuals to the urban ecosystem as a whole.

Equality before the law is a human right, and access to high-quality legal aid for indigent defendants is essential to enforce it. In a context where all defendants have access to a lawyer, I study the impact of denying legal aid on court outcomes. I combine double machine learning and a new administrative dataset linking aid to court outcomes in New South Wales, Australia, to learn the assignment function, whose inputs are known. I find that applicants who fail the means test and hire private lawyers are 10 percentage points less likely to be incarcerated than if they passed and relied on legal aid. Given an average incarceration length of nearly four years, this gap is significant. However, I find evidence suggesting that they spend more time in jail if they are incarcerated. A government preference for broad access to aid over allocated time per case could explain this pattern. Keywords: Indigent Defense, Crime, Criminal Justice. JEL: I30, K14, H44.

The United States is a currency union where multiple risk-sharing mechanisms— migration, fiscal transfers, income diversification and credit markets—buffer consumption from local income fluctuations. We show that risk sharing not only directly smooths consumption but also indirectly stabilizes income by dampening the local multiplier. Combining causal estimates from regional military buildups with a multi-region quantitative model, we find that current levels of risk-sharing cut state-level consumption volatility by a factor of four. Crucially, the indirect stabilization of income accounts for nearly half of this effect, implying substantially larger benefits from integration than conventional measures suggest.

This paper explores how financial institutions pass interest rate risk through to product markets using the life insurance industry as a setting. We show theoretically that it is optimal for insurers to distort product issuance across maturities to offset duration gaps. We examine insurers exogenously exposed to interest rate risk through their variable annuity liabilities after the 2008 financial crisis. Consistent with our mechanism, exposed insurers developed negative duration gaps, increased markups on long-duration products, and rebalanced product issuance toward shorter-duration products to hedge. This response reduced long-duration life insurance coverage by 12.1% of GDP between 2005 and 2023.

On two-sided content platforms, symmetric two-sided isolation (assigning matched fractions of creators and viewers to isolated treatment and control submarkets) is widely used for creator-side and cold-start experiments because it removes cross-arm marketplace interference. Isolation, however, thins each viewer's candidate catalog, and intuition suggests the resulting engagement cost should fade as the platform grows: a small fraction of a vast catalog is still vast. We show that, in an order-statistics model of engagement, whether this intuition holds depends on the upper tail of match quality. Extreme-value theory yields tail-class loss laws with a sharp dichotomy: for light or bounded tails the loss vanishes as the candidate pool grows, whereas under heavy tails it converges to a size-independent constant, so expanding the candidate pool, even by orders of magnitude, does not asymptotically eliminate the cost. Evidence from two production experiments on a platform with millions of active creators is consistent with this picture: a pure A/A traffic sweep reveals a measurable, depth-graded engagement cost; a one-sided catalog ablation independently shows that per-viewer thinning contributes to the loss; and a tail index calibrated on the small exploration pool predicts an effect consistent with the one observed in the far larger full-catalog ablation. Isolation thus carries a price that experimenters should budget for, like any other cost. We give practitioners a preflight procedure that estimates it before launch, sizes traffic accordingly, and recommends a fallback design when the predicted cost exceeds a chosen tolerance.

In this paper, I study to what extent countries differ in their preferences for quality and their technologies for improving quality. The paper also quantifies the contribution of those differences to the differences in gains from trade across countries. I adopt ANTONIADES, which allows endogenous quality choice of firms. This paper extends that framework into multi-country and multi-sector setting. The new feature is that bilateral trade liberalization yields spillover effects on the competitiveness of another country. This paper structurally estimates the model under multi-country and multi-sector framework. It is found that richer countries generally have stronger valuation for quality. The quantification demonstrates that variations in the strength of quality preferences and in the technology of improving quality across countries add to their heterogeneities in market competitiveness. Subsequently, I simulate a 5% increase in the trade barriers. If the quality channel is shut down, countries with stronger preferences for quality have larger degrees of underestimations in their losses from the trade barrier. Finally, gains from a universal rise in quality preference are unequal among countries, with larger economies generally gaining more than smaller economies.

Does generative artificial intelligence (AI) widen or narrow productivity gaps across workers? We study this in a randomized online experiment with 1,174 adults aged 25-45 who completed a workplace-style problem-solving task with or without a generative AI assistant, followed by an unassisted module. AI improves performance for all participants, but gains are larger among those with less education. Without AI, higher-education participants outperform lower-education participants by 0.548 standard deviations; with AI, the gap falls to 0.139, closing about three-quarters of the initial difference. Chat logs show that lower-education participants obtain substantial assistance, while higher-education participants use AI more effectively. Gains are not purely due to delegation: treated participants do not perform worse once AI is removed, and lower-education participants retain part of their improvement, although a sizable gap re-emerges. Intensive AI use raises assisted performance regardless of participants' own effort, but follow-up performance improves only when intensive use is combined with sustained effort. Generative AI narrows effective productivity differences in task execution, while human-capital differences continue to shape unassisted performance and tool use.

A good representation of the population of firms and households is essential for large-scale economic models. While there exist good methods to create synthetic populations of households, creating synthetic populations of firms and, crucially, their supply chain links, is typically much harder. Here, we introduce a flexible method to create synthetic supply networks that match both the known properties of firm-level supply networks and the properties of aggregated input-output tables used in macroeconomic models. Our method is fast, and because it uses only publicly available data, it is fully reproducible and can be easily extended.

We argue that speculators preying on the closing rebalances of leveraged exchange-traded funds (LETFs) contributed to the Korean market's extreme volatility in 2026. An LETF's mandated daily rebalance is sized by the day's return, which generates an upward-sloping demand at market close. In response, rational speculators pre-position, enlarge the fund's order, and liquidate into the demand they have induced. Consistent with this mechanism, Korean stocks tracked by LETFs reverse about 75% of their first-day response to pre-open U.S. news by the next close and oscillate for several days thereafter, a pattern absent in every control group. Our quantification implies that self-reinforcing rebalance raised SK Hynix's annualized volatility from 100.0% to 136.7% over nine weeks and cost its products' predominantly retail holders 17.6% of their initial investment. Dispersing the rebalance across the trading day may backfire, whereas a flexible leverage multiple could help.

This paper examines the relationship between borderlands and political violence in Africa. Using spatiotemporal data on conflict events from 1997 to 2024 alongside the innovative OECD Spatial Conflict Dynamics indicator (SCDi), it suggests that borderlands see more conflict than non-borderlands. Political violence tends to decrease very rapidly with the distance to borders in Africa. The share of violent events recorded in borderlands varies over time and mirrors the cycles of violence that affect specific regions. It was very high in the early 2000s due to the wars in the Gulf of Guinea and Great Lakes region, declined for less than a decade, and increased substantially in the 2010s. Finally, the paper shows that when conflicts diffuse across borders, they tend to occupy a greater spatial extent, rather than relocate from one side of the border to the other.

This paper examines how political violence in West Africa is distributed between urban agglomerations and their surrounding areas using spatially disaggregated conflict and population data. Covering seventeen countries from 2012 to mid-2025, the analysis shows that political violence remains clearly tied to cities and their peripheries with the majority of violence occurring within and around urban agglomerations. Large cities record the highest number of incidents within their boundaries, while smaller towns, which are more frequently situated near borders, typically experience higher levels of violence outside their boundaries. However, urban-centric violence has also declined since 2012, indicating a progressive ruralization of conflict. These findings provide important regional context to the urbanization of conflict narrative by showing that West African insurgent movements use both urban and rural environments strategically. They also underscore the ongoing vulnerability of peripheral populations and the need for mobile and community-based security approaches outside major cities. Finally, they highlight the importance of strengthening border infrastructure, transport networks, and civic institutions as tools of both development and security.

We study how advertised products (e.g., Top Picks, Recommended, Featured) affect consumer choice on digital platforms and retail interfaces by extending the Luce (1959) (or multinomial logit) model. A consumer either focuses on the advertised items or considers the full menu, then chooses among the considered alternatives according to the Luce/logit rule. We characterize this model and show that its underlying primitives are uniquely identified from choice data. We also study a managerially important advertisement-design problem, in which a platform or retailer chooses the advertised subset to maximize expected profit, and we derive implementable design rules. We then introduce a richer framework in which advertising can influence both attention and preference. For this more general model, we provide a characterization and show how choice data can be used to separate the attention effect from the preference effect.

Digital State Capacity is the ability of governments to deploy ICT infrastructure and information systems to implement policy. This paper introduces a new measure of government ICT capacity based on an observable stock of deployable public-sector network infrastructure: public IPv4 address space held by government organisations. These address holdings are key inputs into digital administration because they support internet-facing systems, networked information exchange, and coordination across agencies and functions. The core panel covers approximately 150,000 country-entity records classified as government across more than 150 countries from 2019 to 2024 and can be disaggregated by administrative level and government function. In the 2019 to 2024 Admin-1 panel, government IP holdings are observed in 1,681 subnational regions across all years. We validate the measure at the crosscountry and subnational levels and apply it to government tasks related to corruption control and vaccination rollout. In illustrative country-year analysis, higher Digital State Capacity is associated with higher-quality governance and publicservice outcomes in the expected directions, including lower measured corruption and higher vaccination coverage. These associations are descriptive; they demonstrate the empirical relevance of the measure and are not causal estimates.

When in the lifecycle does a large gender wage gap emerge? South Korea has the largest aggregate gender pay gap (29%) in the OECD. Among recent college graduates the conditional gap is only 3.3% over 2008-2019, falling from 3.4% to 2.5%. Correcting for differential selection into employment with semiparametric and machine learning methods leaves it unchanged. Nonparametric and semiparametric bounds contain the point estimates. Selection-corrected quantile regression shows that the remaining entry-level gap is concentrated at the top of the distribution. In Korean panel data the corrected gap widens severalfold across the prime-age workforce, where the selection correction becomes first order. Korea's gender wage disparity is mostly generated after labor market entry.

We introduce a framework for preference-robust decision making when preferences over risk are modelled through generalised distortion risk measures. Unlike distributional robustness, our approach addresses ambiguity in the risk functional itself. We construct ambiguity sets on distortion (weight) functions using the Wasserstein distance and Bregman divergences, and derive closed-form expressions for the worst- and best-case distortion risk measures. We further extend the framework to rank-dependent utility, yielding preference-robust behavioural models. In particular, rank-dependent utility appears as a robustification of the expected utility model, yielding a novel way to address the Allais paradox.

We revisit micro real rigidities as a source of monetary non-neutrality in a menu-cost model with variable markups, using firm-level evidence to pin down key primitives. We embed a non-CES demand system in a quantitative monetary model and use firm-dynamics evidence to identify demand curvature and firm-level productivity and demand processes. The calibrated model matches untargeted micro pricing moments, the markup distribution, and cost pass-through, while generating comparable non-neutrality. The key innovation is an empirically supported placement of idiosyncratic demand shocks that shifts residual demand, thereby moving desired markups and prices under non-CES demand. The broader implication is that this calibrated model provides a portable framework linking monetary economics with trade and IO evidence.

Hybrid platforms disadvantage third-party sellers through the platform fee and self-preferencing, and regulators have worried that constraining either instrument may intensify the other. We model a platform that chooses both instruments and find the opposite: single-instrument regulation is effective because the instruments are strategic complements, and regulating either instrument curbs the other. We also find that the two instruments achieve what monopolization achieves, higher prices and reduced consumer welfare, while passing every conventional antitrust test.

Many technology firms now build software products in the open, inviting outside developers to contribute alongside their employees on platforms like GitHub. Does this openness in product development pay off? Analyzing 977 U.S. high-tech firms from 2001 to 2025, this study finds that open-source adoption raised firms' gross margins by 4-5% on average. These gains flow largely through higher labor productivity, as firms integrate external contributors' diverse knowledge into internal workflows, broadening the organizational knowledge base without a commensurate rise in labor costs. However, the payoff emerges only when outside volunteers supply a meaningful share of the work (around 35% in this sample), and hinges on the firm's resource configuration. While there are multiple pathways to profitability, pairing open-source product development with sustained internal R&D is a core condition present in all high-profitability configurations. For managers, the lesson is to treat open source as a knowledge-integration strategy matched to the firm's resources, not merely a means of sourcing external effort at lower cost. For policymakers, the findings underscore the broad economic value of open-source ecosystems and the need for policies that foster skilled volunteer communities and public-private open-source infrastructure.

As artificial intelligence transforms labor markets, understanding what makes workers adaptable has become increasingly important. Existing approaches typically characterize human capital using occupations, educational credentials, or predefined skill taxonomies, providing limited insight into how the structure of workers' skill portfolios shapes resilience to technological change. We develop an agnostic network based framework that reconstructs the hierarchy and diversity of skills directly from observed patterns of skill co occurrence. Using longitudinal data on 2.4 million United States workers and 16,753 distinct skills from LinkedIn, we introduce three complementary measures of skill complexity: specialisation, capturing productive depth; diversity, capturing adaptive breadth; and the diversity frontier, measuring the highest attainable diversity conditional on a worker's level of specialisation. We show that these dimensions predict distinct career outcomes. Specialisation is most strongly associated with sorting into higher wage occupations, whereas diversity is associated with broader skill accumulation and occupational mobility. Workers closest to the diversity frontier are significantly more likely to acquire new skills, receive promotions, transition into occupations with lower exposure to automation than workers with comparable levels of specialisation but narrower skill portfolios. These findings distinguish productive from adaptive capital and demonstrate that workers' adaptive capacity depends not simply on possessing specialised expertise or broad capabilities, but on combining both. More broadly, our framework provides a data driven approach for measuring workforce resilience and identifying reskilling pathways, offering new tools for understanding human capital in rapidly changing labor markets.

We ask a representative sample to write prompts seeking spending and investing advice from LLMs, then simulate the lifetime effects of following the advice under realistic asset and labor market conditions. Applying this method to GPT-5.2, we find following the advice would move respondents toward life cycle theory: broader participation in diversified equity funds, age-declining equity shares, and larger savings buffers. Recommendations vary systematically by gender, prior AI experience, and financial literacy. For gender, two-thirds of recommended equity-share differences arise from men and women writing different prompts (demand), while one-third arise from gender labels attached to otherwise identical prompts (supply).