This paper studies how automation changes the stationary distribution of income, consumption, and wealth in an incomplete-market economy. An automating sector trades productivity gains and labor-cost savings against adoption costs. Households differ by wealth and by a two-state skill process, saving in a capital/equity claim, while facing uninsurable skill risk. Competitive factor prices and aggregate capital clear jointly with household Hamilton–Jacobi–Bellman equations and the stationary Kolmogorov forward equation. Automation affects consumption through labor-income incidence, precautionary saving, skill mobility, ownership of automation rents, and the stationary capital stock. In an adverse-incidence scenario with high exposure and concentrated ownership, decentralized automation lowers stationary consumption and capital relative to the no-automation allocation, whereas with stronger productivity and complementarity, lower obsolescence, and broader ownership, automation raises output, consumption, and capital. Reversing the assumed skill-mobility response also raises consumption, output, and capital substantially at a fixed automation intensity. A proxy diagnostic combining U.S. evidence on AI adoption, investment, labor-income pass-through, equity ownership, and MPC frames the current economy near the boundary between the two scenarios. The model provides a quantitative framework for separating automation’s aggregate gains from its distributional incidence. [page::0]
We present a null-validated topological analysis of Bitcoin and S&P 500 returns, using the L1 norm of persistence landscapes from sliding-window delay embeddings as a dynamic geometry descriptor. The topological signal co-moves with stochastic volatility during stress periods, but the strength and temporal pattern of this association vary across markets. Surrogate-based null models (shuffle and phase-randomized) reject explanations based on marginal distributions or linear second-order dependence alone, indicating sensitivity to nonlinear and phase-dependent temporal organization beyond traditional volatility metrics. The results show that persistence landscape norms provide complementary information about market dynamics across regimes and markets [page::11][page::16][page::22]
This paper develops a joint optimization framework for allocating a fixed monthly budget between human headcount and generative-AI token capacity under cognitive frictions and stochastic spending. Using a Cobb–Douglas production core augmented with an exponential friction term, the model yields a closed-form optimal headcount (N*) and a transcendental condition for per-capita token intensity (T*), with a bordered-Hessian condition ensuring a local maximum. Comparative statics show that higher per-engineer token volatility raises T* and lowers N*, implying a substitution at the margin even though inputs are production complements. Skewness (γ1) acts as a pure deadweight tax on the budget, shrinking available resources without altering substitution dynamics. The direction of substitution depends on whether dispersion is invariant to mean or scales with it, offering an empirically testable regime change. These insights are framed within a chance-constrained stochastic budgeting approach and central-limit/Cornish–Fisher corrections [page::1].
This paper revisits the exchange rate disconnect puzzle using generative AI to forecast currency returns from economic fundamentals. Employing ChatGPT and DeepSeek, a comprehensive dataset of 544 indicators spanning 1996–2024 is analyzed to extract AI-derived fundamentals, showing significant cross-sectional predictability across major currencies. A simple AI-driven AIFX strategy—going long currencies with strong fundamentals and short those with weak fundamentals—achieves an annualized Sharpe ratio above 0.7, with alpha that remains significant after controlling for standard FX factors. Multiple robustness checks (GPT-4o and DeepSeek-V3, weighted signals, and time-series specs) support the central results. Four look-ahead bias tests indicate the results arise from AI reasoning rather than memorization, and the Taylor-rule framework linking fundamentals to monetary policy emerges as a key mechanism behind the predictability. [page::1]
Axient introduces a physically backed margin layer for binary event markets that separates leverage maturity from claim maturity and extinguishes debt before finality, leaving only a fully funded residual claim [page::0]. It distinguishes four execution objects—quoted, matched, settled proceeds, and final redemption—to avoid circular hard-flat rules and to manage settlement risk explicitly [page::1]. The canonical mechanism selects at hard-flat time u the smallest admissible sale that conservatively covers the maximum future debt over the settlement horizon plus a buffer, using a lower envelope of settled proceeds and a realized audit minimum after settlement to validate debt coverage [page::1][page::14]. The framework proves robust ex-ante debt-clearing, debt-free finality invariants, and a boundary showing that L>1 cannot guarantee zero shortfall without external collateral or guarantees outside the registered operating set [page::16][page::17][page::29]. It also derives an exact book-dependent leverage envelope, introduces aggregate liquidity discipline to prevent top-of-book depth reuse, and separates operating vs. failure sets with reserve-bucket allocations, all supported by a deterministic verifier and architecture that maps to a reference venue ecosystem [page::8][page::22][page::34]. The contribution is a mechanism-design result with a boundary rather than production safety claims, reserving empirical calibration for a separate study [page::0][page::14].
This paper re-examines the central estimate of COVID-19 learning loss with a comprehensive set of bias-correction methods to address publication bias, p-hacking, and mechanical dependence in standardized effect sizes [page::0]. Using Betthäuser et al.'s dataset, RoBMA and MAIVE yield an effect size around -0.12 SD, equivalent to about 30% of a school year, and robust to multiple bias corrections [page::0]. Despite some signals of selective reporting, the results remain economically meaningful and statistically robust, implying the learning deficit is not explained away by publication bias [page::0].
We revisit the optimal long-run inflation rate using 777 estimates from 116 primary studies published between 1989 and 2026, the largest sample on the topic to date. To our knowledge, this is among the first economics meta-analyses in which primary-data extraction is done from start to finish through a documented and auditable large-language-model pipeline, calibrated against a hand-coded training set and released for replication. The literature points to an optimum of about 0.6 percentage points per year, well below the two-percent targets used by most advanced-economy central banks. The gap should not be automatically read as a verdict against the two-percent norm. Measurement error in published price indices could close, widen, or even reverse the gap, and the structural literature itself cannot pin down the sign of the required correction. Bayesian model averaging over the full set of structural moderators shows that cross-study variation is driven by real modelling choices rather than by selective reporting. The main drivers are the choice of monetary benchmark (Friedman rule vs. laissez-faire), the transactions-frictions technology, the assumed shock structure, and the class of nominal-rigidity contract. The non-parametric caliper test finds no upward bunching at the two-percent target. The paper contributes a reproducible LLM-assisted extraction pipeline for structurally calibrated literature and a quantitative decomposition of where the optimal-inflation literature disagrees. [page::1]
Global coal trade is surprisingly resilient to disruptions at major maritime chokepoints. Using a source-sink cost-optimisation model over a global maritime network, the authors show feasible chokepoints rarely sever supply; rerouting and substitution among exporters and importers moderate costs and exporters’ revenues. In most chokepoint scenarios, import costs rise by as little as about 0.5 $/t, with costs in some Atlantic markets falling modestly. The notable exception is a severe restriction around the Taiwan Strait and South & East China Seas, which could lift China’s thermal coal costs by roughly 4.5 $/t and metallurgical coal costs by about 2.5 $/t, while reducing other regions’ costs by 5–10 $/t. Overall, maritime chokepoints create regional market segmentation but do not impose globally uniform cost effects or revenue outcomes. [page::0][page::1][page::10][page::11]
ATLAS v1.0 introduces Google’s Activity, Task, Landscape, and Adoption Study, mapping 15M de-identified Gemini interactions to 800 occupations, 4000 tasks, 300 household activities, across 150 countries and 140 languages. Key early findings show broad but shallow work adoption (≈68% of occupations, ≈21% task saturation) and extensive non-work use (≈98% of waking hours), with notable wage/education correlations, regional and linguistic diffusion patterns, and significant home productivity implications that may escape standard GDP measures [page::0][page::1][page::2][page::14][page::46].
