ALT Studies on price-optimized EV charging consistently find that the common price signal synchronizes charging of the entire fleet of cars in the cheapest hours and creates new "rebound" load peaks in low-voltage grids. Almost all of these studies treat wholesale prices as exogenous, i.e., unaffected by the shifting EV load. This paper argues that this assumption overstates the problem: charging in low-price hours raises the wholesale price in those hours, which spreads charging across hours in equilibrium. I simulate price-optimized charging under real-time prices calibrated to German 2025 data. Even when adjusting for the increase in electricity demand, the coincident import peak of the local grid rises by 260%. After adding a model of equilibrium wholesale prices that takes the price-quantity feedback into account, the increase is reduced to a mere 5%. Allowing for vehicle-to-grid, the export peak rises by 115% and falls by 6%, respectively. The result holds across vehicle and charging

ALT This study develops a country-level measure of the economic-industrial capacity of European NATO Allies to mobilise, replenish, maintain, and sustain military capability under strategic stress. The measure comprises four components: macroeconomic capacity, sectoral resilience, defence-industrial capability, and logistics and sustainment. The analysis covers 28 European Allies and evaluates a balanced baseline against five geoeconomic stress scenarios. The results distinguish baseline capacity from stress vulnerability and provide an assessment framework for identifying the economic-industrial bottlenecks that can constrain sustained military readiness. Baseline scores vary materially across countries, with a sample mean of 0.610 and a range from 0.471 to 0.759. The calibrated geoeconomic shocks reduce effective capacity in every scenario, though the effects are highly heterogeneous. The combined-stress scenario has the largest average effect, lowering the sample mean to 0.491, a declin

ALT AI progress can feed back into AI research in two ways. Capability improvements allow AIs to perform a larger share of AI research tasks, while efficiency improvements raise AI research output per unit of compute on tasks already automated. In a task automation model of AI research, I show that a software-driven intelligence explosion - a self-sustaining acceleration in AI progress with fixed compute and human labor - requires both margins to improve sufficiently fast, and neither margin can substitute for the other. To see whether these conditions hold, I calibrate both margins with data on recent AI progress. I estimate that capability progress is likely fast enough to support an SIE, while efficiency progress is on the knife-edge. In Monte Carlo simulations, an SIE occurs in 49% of draws, indicating that an SIE is plausible but uncertain based on historical trends in AI progress. The single largest source of uncertainty is the rate of efficiency progress, whose uncertainty is respon

ALT Most analyses of AI and employment ask which jobs are exposed. This paper asks what happens to the pipeline that produces experts. It extends the model of Caosun and Aral (2026) from a single worker to a firm with two cohorts, juniors and seniors, and studies how expertise formation changes when codified work is automated. Because junior work is the most codified, demand for juniors falls first; the stock of seniors falls later but further, as the pipeline stops replacing the experts who retire. In the baseline calibration, junior demand bottoms out about a fifth below its 2022 level before recovering most of that ground, while the senior stock falls by a quarter and later halves. Because expertise forms through unassisted practice on work that AI cannot do, the time needed to develop an expert from within rises from about five years to nine. The costs fall on a different party from the one that decides on adoption: a manager with a three-year horizon sets AI use to maximise near-term

ALT Population aging and fertility decline are occurring simultaneously worldwide, straining labor supply and pension systems. China began a gradual delayed-retirement (GDR) reform in 2025, raising statutory retirement ages on different schedules for men, female cadres, and female workers across birth cohorts. To evaluate its effects on older individuals and within the family, we develop a model of older adults' retirement, intergenerational transfers, and adult children's fertility decisions, with parameters estimated via fuzzy regression discontinuity at the pre-2025 statutory retirement ages. Counterfactual simulations show that, for mature reform cohorts, the GDR reform increases older employment and monetary transfers to adult children but reduces grandparental childcare. Because the reduction in childcare outweighs the increase in monetary transfers, fertility and older adults' welfare decline, with particularly large losses among lower-income women. Fully flexible retirement, mandat

ALT We study a model in which artificial intelligence lowers the cost of writing faster than the cost of reading. Authors differ in reputation, and time-constrained readers, such as referees, choose between ignoring, verifying, and trusting papers. As writing costs fall, the band of verified papers shrinks: outsiders are no longer read, and established authors are no longer checked. In the “costless writing limit,” the share of verified papers vanishes, and a paper's impact depends on its author's reputation alone. Readers can escape the flood by asking AI to summarize the literature. These summaries aggregate papers reliably only if their errors are sufficiently independent. Once the share of papers written with the same AI exceeds a threshold, summaries inherit the AI's errors, and AI review filters out the original research that could correct them.

ALT We provide the first systematic evidence on whether financial markets price political pressure on the Federal Reserve as a signal of future monetary policy. Using approximately four decades of Reuters newswire articles from 1988 to 2025, classified by an open-weight large language model ensemble into dovish and hawkish pressure, we estimate the daily response of interest rate expectations across the term structure, the US dollar, inflation compensation, and market volatility. Dovish pressure lowers near-term rate expectations by roughly one basis point per pressure event—equivalent to a four-percent increase in the implied probability of a rate cut at the next FOMC meeting—with the effect decaying monotonically and vanishing beyond the three-month horizon. Hawkish pressure has no detectable effect. Furthermore, excluding the Trump presidencies does not weaken the estimate, ruling out the possibility that the result is confined to an idiosyncratic recent episode. The response is accompa

ALT We examine whether lower costs of running AI models lead to lower prices, and whether delays in observing competitors' prices and limits on price changes affect how much consumers benefit. We study a simulated market with two differentiated sellers, logit demand, and tabular Q learning. A factorial experiment varies the age of rival price information, how often sellers can revise prices, whether production costs fall, and differences in firm quality and costs. We evaluate policies trained on 60 paired market seeds in each of 16 conditions, for 960 training runs, and measure expected consumer surplus after learning ends. In the main setting with identical firms and falling costs, the estimated interaction between information delay and repricing frequency on consumer surplus is -0.00025 normalized units, with a 95% paired seed bootstrap interval of [-0.0402, 0.0393]. The interval includes zero, as do intervals for tests of whether the interaction changes with the cost regime or differenc

ALT This paper compares financial-market reactions to the releases of DeepSeek R1 in January 2025 and Kimi K3 in July 2026. Both AI models were presented as credible Chinese competitors near the capability frontier, but their releases conveyed different economic information. DeepSeek R1 combined open weights with unusually low reported training and inference costs, prompting investors to reassess expected demand for computing infrastructure and producing large stock-price decreases among semiconductor and data-centre-related firms. Kimi K3 instead entered at broadly frontier prices and generated a market reaction concentrated more heavily among rival Chinese AI laboratories. Drawing on descriptive event-window evidence, model capability and cost comparisons, subsequent industry developments, and the historical diffusion of open-source software, the paper argues that open-weight models redistribute rents across the AI value chain rather than eliminating them. Incumbents remain protected by

ALT 35.2% of task time in world employment is at least partly delegable to AI agents in 2026, and only 2.5% can be handed over in full. Existing measures score exposure to generative AI, mostly for the United States. We build the Agent Delegability Index (ADI): two language models rate all 18,796 O*NET 30.2 task statements on a frozen, preregistered rubric of delegation levels and barriers; tasks are mapped to 433 ISCO-08 unit groups and weighted by ILOSTAT employment in 146 countries. Four findings. First, delegable employment rises with income, from 22.5% in low-income to 48.7% in high-income countries, a slope of 7.23 percentage points per log point of GDP per capita. This delegation divide is compositional: the physical barrier dominates the jobs poorer countries employ most. Second, at the occupation level ADI tracks exposure closely (r = 0.93 with Eloundou et al. and 0.89 with Felten et al.), above the preregistered bound of 0.80; ADI adds delegation levels and barriers on top of exp

ALT This paper studies whether fiscal consolidation is associated with deeper multidimensional deprivation among older women than among older men, and through which channel. Using six waves of SHARE across eleven European countries, it builds a multidimensional deprivation index spanning functional health, economic insecurity, and social exclusion, and relates it to the cyclically adjusted primary balance (CAPB) at the country-wave level in a two-way fixed-effects model. Consolidation raises deprivation on average with a null average gender gap, which is later found to conceal regional heterogeneity where the gender gap is positive and significant in Southern and Eastern Europe, and absent in Continental/Nordic Europe. This regional pattern holds under individual clustering but does not survive clustering at the country level. Further, the economic insecurity channel the literature would predict runs in the opposite direction, with men's measured insecurity rising more steeply than women's

ALT Rooftop photovoltaics (PV) can support rapid urban decarbonization, but their contribution is constrained by the imbalance between PV generation and demand, both shaped by local climate. Growing urban electric-vehicle (EV) deployment creates distributed storage near rooftop PV, shifting PV electricity across time. Here we simulate rooftop PV integrated with EVs as batteries across 5,000 grid cells globally, using hourly meteorology and climate-induced demand. We find that PV-EV systems can supply up to 84% of annual building and mobility electricity demand. Self-sufficiency is highest where daytime demand aligns with generation and seasonal variability remains limited. Using PV capacity factor alone to screen PV-EV potential undervalues 21.9% of the world's 1,000 most populous cities because generation-side metrics overlook the temporal coupling of PV generation, electricity demand and EV storage. Substantial PV-EV potential is concentrated in many low-latitude cities, offering a pathw

ALT Research on why people contribute to open source software (OSS) and related open collaboration platforms spans more than two decades, but its findings conflict, and studies often treat the work people contribute and their intention to keep contributing as interchangeable. Drawing on self-determination theory and motivation crowding theory, we meta-analyze 41 studies. The main analysis pools 178 correlations of eight antecedents with contribution effort and continuance intention from 29 independent samples (N = 9,632). Seven antecedents relate positively to effort, all eight relate positively to intention, and each relates at least as strongly to intention as to effort. Contrary to a common assumption, intrinsic motivation is not more strongly related to effort than extrinsic motivation, but its gap between intention and effort is larger. Reputation relates to effort mainly on platforms with a reputation system, and we find no sign that monetary rewards crowd out enjoyment. The findings

ALT Public procurement laws trade rules against discretion with value thresholds: below them, buyers use faster and less competitive procedures. Colombia offers an unusually clean setting to measure how much buyers manipulate estimated values to stay below the line and what it costs. Thresholds are entity-specific (five budget bands set by law) and move every January with the minimum wage, while the round numbers that usually confound bunching estimates stay fixed. Using the universe of Colombian procurement records (SECOP I and II, 2011-2026; 8,569,218 SECOP II processes) and each entity-year's own threshold, we find sharp bunching of estimated values just below both legal cutoffs: 49,631 excess processes below the small-purchase threshold (86% of the mass observed in the last five percent below it) and 3,157 below the tender threshold. The spike tracks the threshold, not the round numbers: it moves with the minimum wage, appears only at each entity's own band, and is an order of magnitud

ALT Changes in occupational task content may have different economic implications depending on whether the tasks gained or lost are occupation-specific or already widespread across the economy. Existing measures of occupational task change largely abstract from this distinction, treating occupations as self-contained task bundles rather than accounting for how tasks are distributed across occupations. We develop a framework to measure a distinct dimension of occupational change, one that goes beyond task expertise or task content: task structure. Using this new framework that tracks tasks across occupations and over time, we separate changes in task quantity from changes in occupational uniqueness, defined as the distinctiveness of an occupation's task bundle relative to other occupations. Using a harmonized dataset of 196563 task statements from the Dictionary of Occupational Titles and O*NET spanning 1977-2018, we measure task diffusion, occupational uniqueness, and distinct dimensions o

ALT In 2024 the Korea Baseball Organization became the first top-tier league to replace the home-plate umpires ball-strike judgment with an automated system. Using over a million pitches from 2021 to 2026, we separate the change into the removal of human discretion and the imposition of a new zone geometry. The machine eliminated count-dependent calls, the value of catcher framing and a small advantage for star batters, rents worth only a few runs per player-season. Because the machines zone scales with height and treats every matchup alike, it redistributed strikes toward tall batters and across matchups, but few pitches are exposed to these changes, and their effects on plate-appearance outcomes are precisely estimated and close to zero. Pitchers re-optimized quickly by throwing higher, with left-handers catching up to an existing trend, whereas we find no evidence that batters adjusted. The results suggest that the incidence of replacing human judgment with a rule depends on its geometr

ALT As AI voice agents get deployed in high-volume contact centers, firms can route low-intent leads to AI and allocate high-intent leads to human workers. Drawing on sequential search theory, we posit that AI-induced task allocation reshapes work, enriching opportunities facing human workers but also raising their reservation value for continuing with the lead in hand, making them less willing to persist. We study the effects of an AI voice-agent deployment at a Chinese online health-insurance brokerage using more than a half million customer outreach records surrounding the rollout. Adopting a regression-discontinuity-in-time design, we trace how worker behavior changes across the workday. Results show that the enrichment of human-facing opportunities concentrates early in the workday, raising the probability that a call is a high-intent lead by about 23 percentage points on the first three calls and fading to zero later. However, customer acquisition by human workers declines precisely

ALT In previous studies, using agent-based simulations of a model of a capitalist economy proposed by Ian Wright, we showed that inequality in both wealth and income distributions is largely governed by a single key parameter: the ratio between average wealth per capita and the mean salary, denoted by R. This parameter captures the relative importance of accumulated assets compared with labor income. In the model, larger values of R are systematically associated with higher levels of inequality, as measured by the Gini coefficient. In the present work, we investigate whether this relationship is also observed in real-world economies. Using empirical data from the World Inequality Database (WID) and the Organization for Economic Co-operation and Development (OECD), we construct empirical estimates of R and compare them with standard measures of income and wealth inequality. We find a positive relation between R and inequality across several empirical settings, with the strongest relationshi

ALT Artificial intelligence (AI) makes it easier than ever to produce research outputs. The pressing question is how researchers should organize their work with this technology. The question is not new: software engineering and security research have long studied how teams of people and machines keep control of a shared body of work with many contributors. We distil principles from the practices these literatures report and transpose them to AI-assisted research. The principles form TRACE: Track records, Review against sources, Accept independently, Control transitions, and Evaluate corrections. Together, the five principles give researchers a common basis for remaining in control while integrating AI into their workflows. By bounding units of work and attending to how each is shaped, they help researchers pace AI-assisted work to human attention rather than to the speed at which AI produces content. We also introduce TRACE-OS, an open source operating system for AI-assisted research proje

ALT Input-output analysis with linear programming is widely used in the planning of national economies. However, linear programming attains its optimum at a vertex of the feasible set, and a small deviation of the optimal plan in some direction violates a constraint. So far, little attention has been paid to finding sets of production plans which guarantee both the supply-demand balance of an economy and a given level of its gross domestic product (GDP), and the sphere packing approach cannot be applied directly, since the balance equations define a set with an empty interior. In this paper, we introduce the set of (ε,δ)-solutions, in which GDP may be smaller than its optimal value by at most ε and supply may differ from demand by at most δ, and we find a sphere with the maximum radius inscribed into this set by solving a linear programming problem. We prove that the optimal radius is positive for positive tolerances, that it is non-decreasing, concave and piecewise linear with respect to