diff --git "a/raw_rss_feeds/https___arxiv_org_rss_econ.xml" "b/raw_rss_feeds/https___arxiv_org_rss_econ.xml" --- "a/raw_rss_feeds/https___arxiv_org_rss_econ.xml" +++ "b/raw_rss_feeds/https___arxiv_org_rss_econ.xml" @@ -7,969 +7,336 @@ http://www.rssboard.org/rss-specification en-us - Wed, 21 Jan 2026 05:00:11 +0000 + Fri, 23 Jan 2026 05:00:03 +0000 rss-help@arxiv.org - Wed, 21 Jan 2026 00:00:00 -0500 + Fri, 23 Jan 2026 00:00:00 -0500 Saturday Sunday - On Analyzing the Conditions for Stability of Opportunistic Supply Chains Under Network Growth - https://arxiv.org/abs/2601.11566 - arXiv:2601.11566v1 Announce Type: new -Abstract: Even large firms such as Walmart, Apple, and Coca-Cola face persistent fluctuations in costs, demand, and raw material availability. These are not \textit{rare events} and cannot be evaluated using traditional disruption models focused on infrequent events. Instead, sustained volatility induces opportunistic behavior, as firms repeatedly reconfigure partners in absence of long-term contracts, often due to trust deficits. The resulting web of transient relationships forms opportunistic supply chains (OSCs). To capture OSC evolution, we develop an integrated mathematical framework combining a Geometric Brownian Motion (GBM) model to represent stochastic price volatility, a Bayesian learning model to describe adaptive belief updates regarding partner reliability, and a Latent Order Logistic (LOLOG) network model for endogenous changes in network structure. This framework is implemented in an agent-based simulation to examine how volatility, trust, and network structure jointly shape SC resilience. Our modeling approach identifies critical volatility threshold; a tipping point beyond which the network shifts from a stable, link-preserving regime to a fragmented regime marked by rapid relationship dissolution. We analytically establish monotonic effects of volatility on profitability, trust, and link activation; derive formal stability conditions and volatility-driven phase transitions, and show how these mechanisms shape node importance and procurement behavior. These theoretical mechanisms are illustrated through computational experiments reflecting industry behaviors in fast fashion, electronics, and perishables. Overall, our contribution is to develop an integrated GBM-Bayesian-LOLOG framework to analyze OSC stability and our model can be extended to other OSCs including humanitarian, pharmaceutical, and poultry networks. - oai:arXiv.org:2601.11566v1 - econ.GN - math.OC - q-fin.EC - Wed, 21 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Gurkirat Wadhwa, Priyank Sinha - - - Reevaluating Causal Estimation Methods with Data from a Product Release - https://arxiv.org/abs/2601.11845 - arXiv:2601.11845v1 Announce Type: new -Abstract: Recent developments in causal machine learning methods have made it easier to estimate flexible relationships between confounders, treatments and outcomes, making unconfoundedness assumptions in causal analysis more palatable. How successful are these approaches in recovering ground truth baselines? In this paper we analyze a new data sample including an experimental rollout of a new feature at a large technology company and a simultaneous sample of users who endogenously opted into the feature. We find that recovering ground truth causal effects is feasible -- but only with careful modeling choices. Our results build on the observational causal literature beginning with LaLonde (1986), offering best practices for more credible treatment effect estimation in modern, high-dimensional datasets. - oai:arXiv.org:2601.11845v1 - econ.EM - stat.ME - Wed, 21 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Justin Young, Muthoni Ngatia, Eleanor Wiske Dillon - - - Public Education Spending and Income Inequality - https://arxiv.org/abs/2601.11928 - arXiv:2601.11928v1 Announce Type: new -Abstract: This paper investigates the relationship between public education spending and income inequality across U.S. counties from 2010 to 2022 using quantile regression methods. The analysis shows that total per pupil education spending is consistently associated with a small increase in income inequality, with stronger effects in high inequality counties. In contrast, the composition of education spending plays a substantially more important role. Reallocating budgets toward instructional, support service, and other current expenditures significantly reduces income inequality, particularly at the upper quantiles of the Gini distribution. Capital outlays and interest payments exhibit weaker and mixed effects. Economic and demographic factors, especially poverty, median income, and educational attainment, remain dominant drivers of inequality. Overall, the results demonstrate that how education funds are allocated matters more than how much is spent, underscoring the importance of budget composition in using public education policy to promote equity. - oai:arXiv.org:2601.11928v1 - econ.EM - Wed, 21 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Ishmael Amartey - - - Nonlinear Dynamic Factor Analysis With a Transformer Network - https://arxiv.org/abs/2601.12039 - arXiv:2601.12039v1 Announce Type: new -Abstract: The paper develops a Transformer architecture for estimating dynamic factors from multivariate time series data under flexible identification assumptions. Performance on small datasets is improved substantially by using a conventional factor model as prior information via a regularization term in the training objective. The results are interpreted with Attention matrices that quantify the relative importance of variables and their lags for the factor estimate. Time variation in Attention patterns can help detect regime switches and evaluate narratives. Monte Carlo experiments suggest that the Transformer is more accurate than the linear factor model, when the data deviate from linear-Gaussian assumptions. An empirical application uses the Transformer to construct a coincident index of U.S. real economic activity. - oai:arXiv.org:2601.12039v1 - econ.EM - cs.LG - Wed, 21 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Oliver Snellman - - - Irreversible Failure Reverses the Value of Information - https://arxiv.org/abs/2601.12046 - arXiv:2601.12046v1 Announce Type: new -Abstract: We study dynamic games with hidden states and absorbing failure, where belief-driven actions can trigger irreversible collapse. In such environments, equilibria that sustain activity generically operate at the boundary of viability. We show that this geometry endogenously reverses the value of information: greater informational precision increases the probability of collapse on every finite horizon. We formalize this mechanism through a limit-viability criterion, and model opacity as a strategic choice of the information structure via Blackwell garbling. When failure is absorbing, survival values become locally concave in beliefs, implying that transparency destroys equilibrium viability while sufficient opacity restores it. In an extended game where agents choose the information structure ex ante, strictly positive opacity is necessary for equilibrium survival. The results identify irreversible failure--not coordination, misspecification, or ambiguity--as a primitive force generating an endogenous demand for opacity in dynamic games. - oai:arXiv.org:2601.12046v1 + Ecosystem Competition and Cross-Market Subsidization: A Dynamic Theory of Platform Pricing + https://arxiv.org/abs/2601.15303 + arXiv:2601.15303v1 Announce Type: new +Abstract: Platform giants in China have operated with persistently compressed margins in highly concentrated markets for much of the past decade, despite market shares exceeding 60\% in core segments. Standard theory predicts otherwise: either the weaker firm exits, or survivors raise prices to monopoly levels. We argue the puzzle dissolves once firms are viewed as ecosystem optimizers rather than single-market profit maximizers. We develop a dynamic game in which a firm's willingness to subsidize depends on the spillover value its users generate in adjacent markets -- what we call \textit{ecosystem complementarity}. When this complementarity is strong enough, perpetual below-cost pricing emerges as the unique stable equilibrium. The result is not predation in the classical sense; there is no recoupment phase. It is a permanent state of subsidized competition, rational for each firm individually but potentially inefficient in aggregate. We characterize the equilibrium, establish its dynamic stability, and show that welfare losses compound over time as capital flows into subsidy wars rather than innovation. The model's predictions are consistent with observed patterns in Chinese platform markets and suggest that effective antitrust intervention should target cross-market capital flows rather than prices. + oai:arXiv.org:2601.15303v1 econ.TH cs.GT - Wed, 21 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Nicholas H. Kirk - - - Measuring growth and convergence at the mesoscale - https://arxiv.org/abs/2601.12158 - arXiv:2601.12158v1 Announce Type: new -Abstract: Global inequality has shifted inward, with rising dispersion increasingly occurring within countries rather than between them. Using 8,790 newly harmonised Functional Urban Areas (FUAs), micro-founded labour-market regions encompassing 3.9 billion people and representing approximately 80% of global GDP, we show that national aggregates systematically, and increasingly, misrepresent the dynamics of growth, convergence, and structural change. Drawing on high-resolution global GDP data and country-level capability measures, we find that the middle-income trampoline that previously drove global convergence is flattening. This divergence in the lower-income regime does not reflect poverty traps: low-income FUAs exhibit positive expected growth, and the transition curve displays no stable low-income equilibrium. Instead, productive capabilities, proxied by the Economic Complexity Index, define distinct growth regimes. FUAs converge within capability strata but diverge across them, and capability upgrading follows a predictable J-curve marked by short-run disruption and medium-run acceleration. These findings suggest that national convergence policies may be systematically misaligned with the geographic scale at which capability accumulation operates. - oai:arXiv.org:2601.12158v1 - econ.GN - q-fin.EC - Wed, 21 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Isaak Mengesha, Debraj Roy - - - A Robust Similarity Estimator - https://arxiv.org/abs/2601.12198 - arXiv:2601.12198v1 Announce Type: new -Abstract: We construct and analyze an estimator of association between random variables based on their similarity in both direction and magnitude. Under special conditions, the proposed measure becomes a robust and consistent estimator of the linear correlation, for which an exact sampling distribution is available. This distribution is intrinsically insensitive to heavy tails and outliers, thereby facilitating robust inference for correlations. The measure can be naturally extended to higher dimensions, where it admits an interpretation as an indicator of joint similarity among multiple random variables. We investigate the empirical performance of the proposed measure with financial return data at both high and low frequencies. Specifically, we apply the new estimator to construct confidence intervals for correlations based on intraday returns and to develop a new specification for multivariate GARCH models. - oai:arXiv.org:2601.12198v1 - econ.EM - Wed, 21 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Ilya Archakov - - - The Economics of Digital Intelligence Capital: Endogenous Depreciation and the Structural Jevons Paradox - https://arxiv.org/abs/2601.12339 - arXiv:2601.12339v1 Announce Type: new -Abstract: This paper develops a micro-founded economic theory of the AI industry by modeling large language models as a distinct asset class-Digital Intelligence Capital-characterized by data-compute complementarities, increasing returns to scale, and relative (rather than absolute) valuation. We show that these features fundamentally reshape industry dynamics along three dimensions. First, because downstream demand depends on relative capability, innovation by one firm endogenously depreciates the economic value of rivals' existing capital, generating a persistent innovation pressure we term the Red Queen Effect. Second, falling inference prices induce downstream firms to adopt more compute-intensive agent architectures, rendering aggregate demand for compute super-elastic and producing a structural Jevons paradox. Third, learning from user feedback creates a data flywheel that can destabilize symmetric competition: when data accumulation outpaces data decay, the market bifurcates endogenously toward a winner-takes-all equilibrium. We further characterize conditions under which expanding upstream capabilities erode downstream application value (the Wrapper Trap). A calibrated agent-based model confirms these mechanisms and their quantitative implications. Together, the results provide a unified framework linking intelligence production upstream with agentic demand downstream, offering new insights into competition, scalability, and regulation in the AI economy. - oai:arXiv.org:2601.12339v1 - econ.GN - q-fin.EC - Wed, 21 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Yukun Zhang, Tianyang Zhang - - - How Well Do LLMs Predict Human Behavior? A Measure of their Pretrained Knowledge - https://arxiv.org/abs/2601.12343 - arXiv:2601.12343v1 Announce Type: new -Abstract: Large language models (LLMs) are increasingly used to predict human behavior. We propose a measure for evaluating how much knowledge a pretrained LLM brings to such a prediction: its equivalent sample size, defined as the amount of task-specific data needed to match the predictive accuracy of the LLM. We estimate this measure by comparing the prediction error of a fixed LLM in a given domain to that of flexible machine learning models trained on increasing samples of domain-specific data. We further provide a statistical inference procedure by developing a new asymptotic theory for cross-validated prediction error. Finally, we apply this method to the Panel Study of Income Dynamics. We find that LLMs encode considerable predictive information for some economic variables but much less for others, suggesting that their value as substitutes for domain-specific data differs markedly across settings. - oai:arXiv.org:2601.12343v1 - econ.EM - cs.AI - stat.ML - Wed, 21 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Wayne Gao, Sukjin Han, Annie Liang - - - Economic complexity and regional development in India: Insights from a state-industry bipartite network - https://arxiv.org/abs/2601.12356 - arXiv:2601.12356v1 Announce Type: new -Abstract: This study investigates the economic complexity of Indian states by constructing a state-industry bipartite network using firm-level data on registered companies and their paid-up capital. We compute the Economic Complexity Index and apply the fitness-complexity algorithm to quantify the diversity and sophistication of productive capabilities across the Indian states and two union territories. The results reveal substantial heterogeneity in regional capability structures, with states such as Maharashtra, Karnataka, and Delhi exhibiting consistently high complexity, while others remain concentrated in ubiquitous, low-value industries. The analysis also shows a strong positive relationship between complexity metrics and per-capita Gross State Domestic Product, underscoring the role of capability accumulation in shaping economic performance. Additionally, the number of active firms in India demonstrates a persistent exponential growth at an annual rate of 11.2%, reflecting ongoing formalization and industrial expansion. The ordered binary matrix displays the characteristic triangular structure observed in complexity studies, validating the applicability of complexity frameworks at the sub-national level. This work highlights the usefulness of firm-based data for assessing regional productive structures and emphasizes the importance of capability-oriented strategies for fostering balanced and sustainable development across Indian states. By demonstrating the usefulness of firm registry data in data constrained environments, this study advances the empirical application of economic complexity methods and provides a quantitative foundation for capability-oriented industrial and regional policy in India. - oai:arXiv.org:2601.12356v1 - econ.GN - physics.soc-ph - q-fin.EC - Wed, 21 Jan 2026 00:00:00 -0500 + cs.IT + math.IT + Fri, 23 Jan 2026 00:00:00 -0500 new http://creativecommons.org/licenses/by/4.0/ - Joel M Thomas, Abhijit Chakraborty - - - Generative AI as a Non-Convex Supply Shock: Market Bifurcation and Welfare Analysis - https://arxiv.org/abs/2601.12488 - arXiv:2601.12488v1 Announce Type: new -Abstract: The diffusion of Generative AI (GenAI) constitutes a supply shock of a fundamentally different nature: while marginal production costs approach zero, content generation creates congestion externalities through information pollution. We develop a three-layer general equilibrium framework to study how this non-convex technology reshapes market structure, transition dynamics, and social welfare. In a static vertical differentiation model, we show that the GenAI cost shock induces a kinked production frontier that bifurcates the market into exit, AI, and human segments, generating a ``middle-class hollow'' in the quality distribution. To analyze adjustment paths, we embed this structure in a mean-field evolutionary system and a calibrated agent-based model with bounded rationality. The transition to the AI-integrated equilibrium is non-monotonic: rather than smooth diffusion, the economy experiences a temporary ecological collapse driven by search frictions and delayed skill adaptation, followed by selective recovery. Survival depends on asymmetric skill reconfiguration, whereby humans retreat from technical execution toward semantic creativity. Finally, we show that the welfare impact of AI adoption is highly sensitive to pollution intensity: low congestion yields monotonic welfare gains, whereas high pollution produces an inverted-U relationship in which further AI expansion reduces total welfare. These results imply that laissez-faire adoption can be inefficient and that optimal governance must shift from input regulation toward output-side congestion management. - oai:arXiv.org:2601.12488v1 - econ.GN - cs.CY - q-fin.EC - Wed, 21 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Yukun Zhang, Tianyang Zhang + Liang Chen - Partial Identification under Stratified Randomization - https://arxiv.org/abs/2601.12566 - arXiv:2601.12566v1 Announce Type: new -Abstract: This paper develops a unified framework for partial identification and inference in stratified experiments with attrition, accommodating both equal and heterogeneous treatment shares across strata. For equal-share designs, we apply recent theory for finely stratified experiments to Lee bounds, yielding closed-form, design-consistent variance estimators and properly sized confidence intervals. Simulations show that the conventional formula can overstate uncertainty, while our approach delivers tighter intervals. When treatment shares differ across strata, we propose a new strategy, which combines inverse probability weighting and global trimming to construct valid bounds even when strata are small or unbalanced. We establish identification, introduce a moment estimator, and extend existing inference results to stratified designs with heterogeneous shares, covering a broad class of moment-based estimators which includes the one we formulate. We also generalize our results to designs in which strata are defined solely by observed labels. - oai:arXiv.org:2601.12566v1 - econ.EM - math.ST - stat.ME - stat.TH - Wed, 21 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Bruno Ferman, Davi Siqueira, Vitor Possebom - - - The Global Food Trade Network as a Complex Adaptive System: A Review of Structure, Evolution, and Resilience - https://arxiv.org/abs/2601.12710 - arXiv:2601.12710v1 Announce Type: new -Abstract: The global food system has metamorphosed from a loose aggregation of bilateral exchanges into a highly intricate, interdependent Global Food Trade Network (FTN). This comprehensive review synthesizes the extant literature to examine the FTN through the rigorous lens of complex network science, moving beyond traditional economic trade models to quantify the system's topological architecture. We delineate the network's historical transition from a unipolar, efficiency-driven system dominated by Western hegemony to a multipolar, regionalized structure characterized by high clustering and scale-free heterogeneity. Special emphasis is placed on the dual nature of connectivity, which functions simultaneously as a buffer against local production variances and a conduit for global contagion. By conceptualizing the FTN as a multiplex system-distinguishing between the robust topology of wheat, the brittle regionalism of rice, and the polarized "dumbbell" structure of soy-we elucidate the distinct structural vulnerabilities inherent in modern food security. Furthermore, we analyze the impact of recent high-magnitude shocks, specifically the COVID-19 pandemic and the Russia-Ukraine conflict, illustrating the critical trade-off between logistical efficiency and systemic resilience. The review concludes by assessing the future trajectory of the network under anthropogenic climate change, predicting a poleward migration of comparative advantage that necessitates a paradigm shift from isolationist protectionism to cooperative network redundancy. - oai:arXiv.org:2601.12710v1 - econ.TH - physics.class-ph - Wed, 21 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Zebiao Li, Xueying Wu, Chengyi Tu - - - Quasi-Concavity, Convexity of Optimal Actions, and the Local Single-Crossing Property - https://arxiv.org/abs/2601.12783 - arXiv:2601.12783v1 Announce Type: new -Abstract: This note presents two results. First, it shows that under mild conditions, a decision problem is quasi-concave if the set of optimal actions is convex under every belief. Second, it shows that if a decision problem is quasi-concave, then it satisfies the local single crossing property after relabeling the states. - oai:arXiv.org:2601.12783v1 + Pairwise Beats All-at-Once: Behavioral Gains from Sequential Choice Presentation + https://arxiv.org/abs/2601.15332 + arXiv:2601.15332v1 Announce Type: new +Abstract: This paper presents the Sequential Rationality Hypothesis, which argues that consumers are better able to make utility-maximizing decisions when products appear in sequential pairwise comparisons rather than in simultaneous multi-option displays. Although this involves higher cognitive costs than the all-at-once format, the current digital market, with its diverse products listed by review ratings, pricing, and paid products, often creates inconsistent choices. The present work shows that preparing the list sequentially supports more rational choice, as the consumer tries to minimize cognitive costs and may otherwise make an irrational decision. If the decision remains the same on both offers, then that is a consistent preference. The platform uses this approach by reducing cognitive costs while still providing the list in an all-at-once format rather than sequentially. To show how sequential exposure reduces cognitive overload and prevents context-dependent errors, we develop a bounded attention model and extend the monotonic attention rule of the random attention model to theorize the sequential rational hypothesis. Using a theoretical design with common consumer goods, we test these hypotheses. This theoretical model helps policymakers in digital market laws, behavioral economics, marketing, and digital platform design consider how choice architectures may improve consumer choices and encourage rational decision-making. + oai:arXiv.org:2601.15332v1 econ.TH - Wed, 21 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by-nc-nd/4.0/ - Kailin Chen - - - Liability Sharing and Staffing in AI-Assisted Online Medical Consultation - https://arxiv.org/abs/2601.12817 - arXiv:2601.12817v1 Announce Type: new -Abstract: Liability sharing and staffing jointly determine service quality in AI-assisted online medical consultation, yet their interaction is rarely examined in an integrated framework linking contracts to congestion via physician responses. This paper develops a Stackelberg queueing model where the platform selects a liability share and a staffing level while physicians choose between AI-assisted and independent diagnostic modes. Physician mode choice exhibits a threshold structure, with the critical liability share decreasing in loss severity and increasing in the effort cost of independent diagnosis. Optimal platform policy sets liability below this threshold to trade off risk transfer against compliance costs, revealing that liability sharing and staffing function as substitute safety mechanisms. Higher congestion or staffing costs tilt optimal policy toward AI-assisted operation, whereas elevated loss severity shifts the preferred regime toward independent diagnosis. The welfare gap between platform and social optima widens with loss severity, suggesting greater scope for incentive alignment in high-stakes settings. By endogenizing physician mode choice within a congested service system, this study clarifies how liability design propagates through queueing dynamics, offering guidance for calibrating contracts and capacity in AI-assisted medical consultation. - oai:arXiv.org:2601.12817v1 - econ.GN - q-fin.EC - Wed, 21 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Yang Xiao - - - Quantitative Methods in Finance - https://arxiv.org/abs/2601.12896 - arXiv:2601.12896v1 Announce Type: new -Abstract: These lecture notes provide a comprehensive introduction to Quantitative Methods in Finance (QMF), designed for graduate students in finance and economics with heterogeneous programming backgrounds. The material develops a unified toolkit combining probability theory, statistics, numerical methods, and empirical modeling, with a strong emphasis on implementation in Python. Core topics include random variables and distributions, moments and dependence, simulation and Monte Carlo methods, numerical optimization, root-finding, and time-series models commonly used in finance and macro-finance. Particular attention is paid to translating theoretical concepts into reproducible code, emphasizing vectorization, numerical stability, and interpretation of outputs. The notes progressively bridge theory and practice through worked examples and exercises covering asset pricing intuition, risk measurement, forecasting, and empirical analysis. By focusing on clarity, minimal prerequisites, and hands-on computation, these lecture notes aim to serve both as a pedagogical entry point for non-programmers and as a practical reference for applied researchers seeking transparent and replicable quantitative methods in finance. - oai:arXiv.org:2601.12896v1 - econ.EM - Wed, 21 Jan 2026 00:00:00 -0500 + math.CO + Fri, 23 Jan 2026 00:00:00 -0500 new http://creativecommons.org/licenses/by/4.0/ - Eric Vansteenberghe - - - Realised quantile-based estimation of the integrated variance - https://arxiv.org/abs/2601.13006 - arXiv:2601.13006v1 Announce Type: new -Abstract: In this paper, we propose a new jump robust quantile-based realised variance measure of ex-post return variation that can be computed using potentially noisy data. The estimator is consistent for the integrated variance and we present feasible central limit theorems which show that it converges at the best attainable rate and has excellent efficiency. Asymptotically, the quantile-based realised variance is immune to finite activity jumps and outliers in the price series, while in modified form the estimator is applicable with market microstructure noise and therefore operational on high-frequency data. Simulations show that it has superior robustness properties in finite sample, while an empirical application illustrates its use on equity data. - oai:arXiv.org:2601.13006v1 - econ.EM - Wed, 21 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - 10.1016/j.jeconom.2010.04.008 - Kim Christensen, Roel Oomen, Mark Podolskij - - - A machine learning approach to volatility forecasting - https://arxiv.org/abs/2601.13014 - arXiv:2601.13014v1 Announce Type: new -Abstract: We inspect how accurate machine learning (ML) is at forecasting realized variance of the Dow Jones Industrial Average index constituents. We compare several ML algorithms, including regularization, regression trees, and neural networks, to multiple Heterogeneous AutoRegressive (HAR) models. ML is implemented with minimal hyperparameter tuning. In spite of this, ML is competitive and beats the HAR lineage, even when the only predictors are the daily, weekly, and monthly lags of realized variance. The forecast gains are more pronounced at longer horizons. We attribute this to higher persistence in the ML models, which helps to approximate the long-memory of realized variance. ML also excels at locating incremental information about future volatility from additional predictors. Lastly, we propose a ML measure of variable importance based on accumulated local effects. This shows that while there is agreement about the most important predictors, there is disagreement on their ranking, helping to reconcile our results. - oai:arXiv.org:2601.13014v1 - econ.EM - Wed, 21 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - 10.1093/jjfinec/nbac032 - Kim Christensen, Mathias Siggaard, Bezirgen Veliyev + Dipankar Das - Spectral Dynamics and Regularization for High-Dimensional Copulas - https://arxiv.org/abs/2601.13281 - arXiv:2601.13281v1 Announce Type: new -Abstract: We introduce a novel model for time-varying, asymmetric, tail-dependent copulas in high dimensions that incorporates both spectral dynamics and regularization. The dynamics of the dependence matrix' eigenvalues are modeled in a score-driven way, while biases in the unconditional eigenvalue spectrum are resolved by non-linear shrinkage. The dynamic parameterization of the copula dependence matrix ensures that it satisfies the appropriate restrictions at all times and for any dimension. The model is parsimonious, computationally efficient, easily scalable to high dimensions, and performs well for both simulated and empirical data. In an empirical application to financial market dynamics using 100 stocks from 10 different countries and 10 different industry sectors, we find that our copula model captures both geographic and industry related co-movements and outperforms recent computationally more intensive clustering-based factor copula alternatives. Both the spectral dynamics and the regularization contribute to the new model's performance. During periods of market stress, we find that the spectral dynamics reveal strong increases in international stock market dependence, which causes reductions in diversification potential and increases in systemic risk. - oai:arXiv.org:2601.13281v1 - econ.EM - q-fin.RM - stat.AP - Wed, 21 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Koos B. Gubbels, Andre Lucas - - - The accumulation of knowledge with intra-industry knowledge spillovers: A competition game and the Nash equilibrium based on firm cost minimisation - https://arxiv.org/abs/2601.13282 - arXiv:2601.13282v1 Announce Type: new -Abstract: This paper examines a competition game whose key variables are the R&D efforts (e.g. R&D expenditures) and accumulated knowledge of firms located in a specific region. The most significant element of accumulated knowledge is knowledge spillovers. These are considered intra-industry as it is assumed that the firms operate within the same industry (i.e. similar types of firms) and competitors offer similar products. The present study identifies a Nash equilibrium based on firm cost minimisation. This is derived under the assumption that the firms under examination act rationally and are primarily concerned with achieving optimal outcomes - specifically, by minimising their total costs. - oai:arXiv.org:2601.13282v1 + Bundling and Price-Matching in Competitive Complementary Goods Markets + https://arxiv.org/abs/2601.15350 + arXiv:2601.15350v1 Announce Type: new +Abstract: We study mixed bundling and competitive price-matching guarantees (PMGs) in a duopoly selling complementary products to heterogeneous customers. One retailer offers mixed bundling while the rival sells only a bundle. We characterize unique pure-strategy Nash equilibria across subgames and compare them to a no-bundling benchmark. Mixed bundling strictly dominates whenever an equilibrium exists. Conditional on bundling, PMG adoption trades off strategic demand capture against margin losses on loyal customers and varies systematically with relative demand responsiveness to prices and complementarities. + oai:arXiv.org:2601.15350v1 econ.TH - Wed, 21 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by-nc-sa/4.0/ - Vasilios Kanellopoulos - - - AI Skills Improve Job Prospects: Causal Evidence from a Hiring Experiment - https://arxiv.org/abs/2601.13286 - arXiv:2601.13286v1 Announce Type: new -Abstract: The growing adoption of artificial intelligence (AI) technologies has heightened interest in the labour market value of AI-related skills, yet causal evidence on their role in hiring decisions remains scarce. This study examines whether AI skills serve as a positive hiring signal and whether they can offset conventional disadvantages such as older age or lower formal education. We conduct an experimental survey with 1,700 recruiters from the United Kingdom and the United States. Using a paired conjoint design, recruiters evaluated hypothetical candidates represented by synthetically designed resumes. Across three occupations - graphic designer, office assistant, and software engineer - AI skills significantly increase interview invitation probabilities by approximately 8 to 15 percentage points. AI skills also partially or fully offset disadvantages related to age and lower education, with effects strongest for office assistants, where formal AI certification plays an additional compensatory role. Effects are weaker for graphic designers, consistent with more skeptical recruiter attitudes toward AI in creative work. Finally, recruiters' own background and AI usage significantly moderate these effects. Overall, the findings demonstrate that AI skills function as a powerful hiring signal and can mitigate traditional labour market disadvantages, with implications for workers' skill acquisition strategies and firms' recruitment practices. - oai:arXiv.org:2601.13286v1 econ.GN - cs.AI q-fin.EC - Wed, 21 Jan 2026 00:00:00 -0500 + Fri, 23 Jan 2026 00:00:00 -0500 new http://creativecommons.org/licenses/by/4.0/ - Fabian Stephany, Ole Teutloff, Angelo Leone + Esmat Sangari, Rajni Kant Bansal - Human-AI Collaboration in Radiology: The Case of Pulmonary Embolism - https://arxiv.org/abs/2601.13379 - arXiv:2601.13379v1 Announce Type: new -Abstract: We study how radiologists use AI to diagnose pulmonary embolism (PE), tracking over 100,000 scans interpreted by nearly 400 radiologists during the staggered rollout of a real-world FDA-approved diagnostic platform in a hospital system. When AI flags PE, radiologists agree 84% of the time; when AI predicts no PE, they agree 97%. Disagreement evolves substantially: radiologists initially reject AI-positive PEs in 30% of cases, dropping to 12% by year two. Despite a 16% increase in scan volume, diagnostic speed remains stable while per-radiologist monthly volumes nearly double, with no change in patient mortality -- suggesting AI improves workflow without compromising outcomes. We document significant heterogeneity in AI collaboration: some radiologists reject AI-flagged PEs half the time while others accept nearly always; female radiologists are 6 percentage points less likely to override AI than male radiologists. Moderate AI engagement is associated with the highest agreement, whereas both low and high engagement show more disagreement. Follow-up imaging reveals that when radiologists override AI to diagnose PE, 54% of subsequent scans show both agreeing on no PE within 30 days. - oai:arXiv.org:2601.13379v1 + Vibe Coding Kills Open Source + https://arxiv.org/abs/2601.15494 + arXiv:2601.15494v1 Announce Type: new +Abstract: Generative AI is changing how software is produced and used. In vibe coding, an AI agent builds software by selecting and assembling open-source software (OSS), often without users directly reading documentation, reporting bugs, or otherwise engaging with maintainers. We study the equilibrium effects of vibe coding on the OSS ecosystem. We develop a model with endogenous entry and heterogeneous project quality in which OSS is a scalable input into producing more software. Users choose whether to use OSS directly or through vibe coding. Vibe coding raises productivity by lowering the cost of using and building on existing code, but it also weakens the user engagement through which many maintainers earn returns. When OSS is monetized only through direct user engagement, greater adoption of vibe coding lowers entry and sharing, reduces the availability and quality of OSS, and reduces welfare despite higher productivity. Sustaining OSS at its current scale under widespread vibe coding requires major changes in how maintainers are paid. + oai:arXiv.org:2601.15494v1 econ.GN q-fin.EC - Wed, 21 Jan 2026 00:00:00 -0500 + Fri, 23 Jan 2026 00:00:00 -0500 new http://creativecommons.org/licenses/by/4.0/ - Paul Goldsmith-Pinkham, Chenhao Tan, Alexander K. Zentefis + Mikl\'os Koren, G\'abor B\'ek\'es, Julian Hinz, Aaron Lohmann - Accelerator and Brake: Dynamic Persuasion with Dead Ends - https://arxiv.org/abs/2601.13686 - arXiv:2601.13686v1 Announce Type: new -Abstract: We study optimal dynamic persuasion in a bandit experimentation model where a principal, unlike in standard settings, has a single-peaked preference over the agent's stopping time. This non-monotonic preference arises because maximizing the agent's effort is not always in the principal's best interest, as it may lead to a dead end. The principal privately observes the agent's payoff upon success and uses the information as the instrument of incentives. We show that the optimal dynamic information policy involves at most two one-shot disclosures: an accelerator before the principal's optimal stopping time, persuading the agent to be optimistic, and a brake after the principal's optimal stopping time, persuading the agent to be pessimistic. A key insight of our analysis is that the optimal disclosure pattern -- whether gradual or one-shot -- depends on how the principal resolves a trade-off between the mean of stopping times and its riskiness. We identify the Arrow-Pratt coefficient of absolute risk aversion as a sufficient statistic for determining the optimal disclosure structure. - oai:arXiv.org:2601.13686v1 + Stabilizing Welfare-Maximizing Decisions via Endogenous Transfers + https://arxiv.org/abs/2601.15563 + arXiv:2601.15563v1 Announce Type: new +Abstract: Many multiagent systems rely on collective decision-making among self-interested agents, which raises deep questions about coalition formation and stability. We study social choice with endogenous, outcome-contingent transfers, where agents voluntarily form contracts that redistribute utility depending on the collective decision, allowing fully strategic, incentive-aligned coalition formation. We show that under consensus rules, individually rational strong Nash equilibria (IR-SNE) always exist, implementing welfare-maximizing outcomes with feasible transfers, and provide a simple, efficient algorithm to construct them. For more general anonymous, monotonic, and resolute rules, we identify necessary conditions for profitable deviations, sharply limiting destabilizing coalitions. By bridging cooperative and noncooperative perspectives, our approach shows that transferable utility can achieve core-like stability, restoring efficiency and budget balance even where classical impossibility results apply. Overall, this framework offers a practical and robust way to coordinate large-scale strategic multiagent systems. + oai:arXiv.org:2601.15563v1 econ.TH - Wed, 21 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by-nc-nd/4.0/ - Zhuo Chen, Yun Liu - - - Liabilities for the social cost of carbon - https://arxiv.org/abs/2601.13834 - arXiv:2601.13834v1 Announce Type: new -Abstract: We estimate the national social cost of carbon using a recent meta-analysis of the total impact of climate change and a standard integrated assessment model. The average social cost of carbon closely follows per capita income, the national social cost of carbon the size of the population. The national social cost of carbon measures self-harm. Net liability is defined as the harm done by a country's emissions on other countries minus the harm done to a country by other countries' emissions. Net liability is positive in middle-income, carbon-intensive countries. Poor and rich countries would be compensated because their current emissions are relatively low, poor countries additionally because they are vulnerable. - oai:arXiv.org:2601.13834v1 - econ.GN - q-fin.EC - Wed, 21 Jan 2026 00:00:00 -0500 + cs.GT + Fri, 23 Jan 2026 00:00:00 -0500 new http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Matthew K. Agrawala, Richard S. J. Tol - - - How Disruptive is Financial Technology? - https://arxiv.org/abs/2601.14071 - arXiv:2601.14071v1 Announce Type: new -Abstract: We study whether Fintech disrupts the banking sector by intensifying competition for scarce deposits funds and raising deposit rates. Using difference-in-difference estimation around the exogenous removal of marketplace platform investing restrictions by US states, we show the cost of deposits increase by approximately 11.5% within small financial institutions. However, these price changes are effective in preventing a drain of liquidity. Size and geographical diversification through branch networks can mitigate the effects of Fintech competition by sourcing deposits from less competitive markets. The findings highlight the unintended consequences of the growing Fintech sector on banks and offer policy insights for regulators and managers into the ongoing development and impact of technology on the banking sector. - oai:arXiv.org:2601.14071v1 - econ.GN - q-fin.EC - Wed, 21 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Douglas Cumming, Hisham Farag, Santosh Koirala, Danny McGowan + Joshua Kavner - Hot Days, Unsafe Schools? The Impact of Heat on School Shootings - https://arxiv.org/abs/2601.14094 - arXiv:2601.14094v1 Announce Type: new -Abstract: Using data on school shooting incidents in U.S. K--12 schools from 1981 to 2022, we estimate the causal effects of high temperatures on school shootings and assess the implications of climate change. We find that days with maximum temperatures exceeding 90$^\circ$F lead to a 80\% increase in school shootings relative to days below 70$^\circ$F. Consistent with theories linking heat exposure to aggression, high temperatures increase homicidal and threat-related shootings but have no effect on accidental or suicidal shootings. Heat-induced shootings occur disproportionately during periods of greater student mobility and reduced supervision, including before and after school hours and lunch periods. Higher temperatures increase shootings involving both student and non-student perpetrators. We project that climate change will increase homicidal and threat-related school shootings in the U.S. by 8\% under SSP2--4.5 (moderate emissions) and by 14\% under SSP5--8.5 (high emissions) by 2091--2100, corresponding to approximately 23 and 39 additional shootings per decade, respectively. The present discounted value of the resulting social costs is \$343 million and \$592 million (2025 dollars), respectively. - oai:arXiv.org:2601.14094v1 - econ.GN - q-fin.EC - Wed, 21 Jan 2026 00:00:00 -0500 + Screening for Choice Sets + https://arxiv.org/abs/2601.15580 + arXiv:2601.15580v1 Announce Type: new +Abstract: We study a screening problem in which an agent privately observes a set of feasible technologies and can strategically disclose only a subset to the principal. The principal then takes an action whose payoff consequences for both players are publicly known. Under the assumption that the possible technology sets are ordered by set inclusion, we show that the optimal mechanism promises the agent a utility that is weakly increasing as the reported set expands, and the choice of the principal maximizes her own utility subject to this promised utility constraint. Moreover, the optimal promised utility either coincides with the agent's utility under the complete information benchmark or remains locally constant, with the number of constant segments bounded by the number of downward-sloping segments of the complete information benchmark. + oai:arXiv.org:2601.15580v1 + econ.TH + cs.GT + Fri, 23 Jan 2026 00:00:00 -0500 new http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Seunghyun Lee, Goeun Lee - - - Foreign influencer operations: How TikTok shapes American perceptions of China - https://arxiv.org/abs/2601.14118 - arXiv:2601.14118v1 Announce Type: new -Abstract: How do authoritarian regimes strengthen global support for nondemocratic political systems? Roughly half of the users of the social media platform TikTok report getting news from social media influencers. Against this backdrop, authoritarian regimes have increasingly outsourced content creation to these influencers. To gain understanding of the extent of this phenomenon and the persuasive capabilities of these influencers, we collect comprehensive data on pro-China influencers on TikTok. We show that pro-China influencers have more engagement than state media. We then create a realistic clone of the TikTok app, and conduct a randomized experiment in which over 8,500 Americans are recruited to use this app and view a random sample of actual TikTok content. We show that pro-China foreign influencers are strikingly effective at increasing favorability toward China, while traditional Chinese state media causes backlash. The findings highlight the importance of influencers in shaping global public opinion. - oai:arXiv.org:2601.14118v1 - econ.GN - q-fin.EC - Wed, 21 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Trevor Incerti, Jonathan Elkobi, Daniel Mattingly + Tan Gan, Yingkai Li - Trade relationships during and after a crisis - https://arxiv.org/abs/2601.14150 - arXiv:2601.14150v1 Announce Type: new -Abstract: I study how firms adjust to temporary disruptions in international trade relationships organized through relational contracts. I exploit an extreme, plausibly exogenous weather shock during the 2010-11 La Ni\~na season that restricted Colombian flower exporters' access to cargo terminals. Using transaction-level data from the Colombian-U.S. flower trade, I show that importers with less-exposed supplier portfolios are less likely to terminate disrupted relationships, instead tolerating shipment delays. In contrast, firms facing greater exposure experience higher partner turnover and are more likely to exit the market, with exit accounting for a substantial share of relationship separations. These findings demonstrate that idiosyncratic shocks to buyer-seller relationships can propagate into persistent changes in firms' trading portfolios. - oai:arXiv.org:2601.14150v1 - econ.GN - q-fin.EC - Wed, 21 Jan 2026 00:00:00 -0500 + Three's a crowd: Identification challenges in the triple difference model with spillover effects + https://arxiv.org/abs/2601.15764 + arXiv:2601.15764v1 Announce Type: new +Abstract: The paper studies identification in triple-difference designs when spillover effects contaminate one or more control groups. We show that, under conventional identifying assumptions, the triple-difference model fails to identify both the treatment effect and the spillover effect under such interference. To overcome this limitation, we propose an alternative specification, the double-triple-difference model, and explicitly formalize identifying assumptions and spillover structures required for consistent identification of both effects. We derive formal identification results and assess the performance of the proposed model through Monte Carlo simulations. An empirical application evaluating a Special Economic Zone in Italy is provided. + oai:arXiv.org:2601.15764v1 + econ.EM + Fri, 23 Jan 2026 00:00:00 -0500 new - http://creativecommons.org/licenses/by-nc-nd/4.0/ - Alejandra Martinez - - - Settling the Score: Portioning with Cardinal Preferences - https://arxiv.org/abs/2307.15586 - arXiv:2307.15586v5 Announce Type: cross -Abstract: We study a portioning setting in which a public resource such as time or money is to be divided among a given set of candidates, and each agent proposes a division of the resource. We consider two families of aggregation rules for this setting -- those based on coordinate-wise aggregation and those that optimize some notion of welfare -- as well as the recently proposed independent markets rule. We provide a detailed analysis of these rules from an axiomatic perspective, both for classic axioms, such as strategyproofness and Pareto optimality, and for novel axioms, some of which aim to capture proportionality in this setting. Our results indicate that a simple rule that computes the average of the proposals satisfies many of our axioms and fares better than all other considered rules in terms of fairness properties. We complement these results by presenting two characterizations of the average rule. - oai:arXiv.org:2307.15586v5 - cs.GT - econ.TH - Wed, 21 Jan 2026 00:00:00 -0500 - cross - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - 10.1016/j.artint.2026.104487 - Edith Elkind, Matthias Greger, Patrick Lederer, Warut Suksompong, Nicholas Teh - - - Latent Variable Phillips Curve - https://arxiv.org/abs/2601.11601 - arXiv:2601.11601v1 Announce Type: cross -Abstract: This paper re-examines the empirical Phillips curve (PC) model and its usefulness in the context of medium-term inflation forecasting. A latent variable Phillips curve hypothesis is formulated and tested using 3,968 randomly generated factor combinations. Evidence from US core PCE inflation between Q1 1983 and Q1 2025 suggests that latent variable PC models reliably outperform traditional PC models six to eight quarters ahead and stand a greater chance of outperforming a univariate benchmark. Incorporating an MA(1) residual process improves the accuracy of empirical PC models across the board, although the gains relative to univariate models remain small. The findings presented in this paper have two important implications: First, they corroborate a new conceptual view on the Phillips curve theory; second, they offer a novel path towards improving the competitiveness of Phillips curve forecasts in future empirical work. - oai:arXiv.org:2601.11601v1 - q-fin.ST - econ.GN - q-fin.EC - Wed, 21 Jan 2026 00:00:00 -0500 - cross http://creativecommons.org/licenses/by/4.0/ - Daniil Bargman, Francesca Medda, Akash Sedai Sharma + Silvia De Nicol\`o, Beatrice Biondi, Mario Mazzocchi - The Dynamic and Endogenous Behavior of Re-Offense Risk: An Agent-Based Simulation Study of Treatment Allocation in Incarceration Diversion Programs - https://arxiv.org/abs/2601.12441 - arXiv:2601.12441v1 Announce Type: cross -Abstract: Incarceration-diversion treatment programs aim to improve societal reintegration and reduce recidivism, but limited capacity forces policymakers to make prioritization decisions that often rely on risk assessment tools. While predictive, these tools typically treat risk as a static, individual attribute, which overlooks how risk evolves over time and how treatment decisions shape outcomes through social interactions. In this paper, we develop a new framework that models reoffending risk as a human-system interaction, linking individual behavior with system-level dynamics and endogenous community feedback. Using an agent-based simulation calibrated to U.S. probation data, we evaluate treatment allocation policies under different capacity constraints and incarceration settings. Our results show that no single prioritization policy dominates. Instead, policy effectiveness depends on temporal windows and system parameters: prioritizing low-risk individuals performs better when long-term trajectories matter, while prioritizing high-risk individuals becomes more effective in the short term or when incarceration leads to shorter monitoring periods. These findings highlight the need to evaluate risk-based decision systems as sociotechnical systems with long-term accountability, rather than as isolated predictive tools. - oai:arXiv.org:2601.12441v1 - cs.CY - econ.GN - q-fin.EC - Wed, 21 Jan 2026 00:00:00 -0500 - cross - http://creativecommons.org/licenses/by-nc-nd/4.0/ - Chuwen Zhang, Pengyi Shi, Amy Ward - - - The Cost of EFX: Generalized-Mean Welfare and Complexity Dichotomies with Few Surplus Items - https://arxiv.org/abs/2601.12849 - arXiv:2601.12849v1 Announce Type: cross -Abstract: Envy-freeness up to any good (EFX) is a central fairness notion for allocating indivisible goods, yet its existence is unresolved in general. In the setting with few surplus items, where the number of goods exceeds the number of agents by a small constant (at most three), EFX allocations are guaranteed to exist, shifting the focus from existence to efficiency and computation. We study how EFX interacts with generalized-mean ($p$-mean) welfare, which subsumes commonly-studied utilitarian ($p=1$), Nash ($p=0$), and egalitarian ($p \rightarrow -\infty$) objectives. We establish sharp complexity dichotomies at $p=0$: for any fixed $p \in (0,1]$, both deciding whether EFX can attain the global $p$-mean optimum and computing an EFX allocation maximizing $p$-mean welfare are NP-hard, even with at most three surplus goods; in contrast, for any fixed $p \leq 0$, we give polynomial-time algorithms that optimize $p$-mean welfare within the space of EFX allocations and efficiently certify when EFX attains the global optimum. We further quantify the welfare loss of enforcing EFX via the price of fairness framework, showing that for $p > 0$, the loss can grow linearly with the number of agents, whereas for $p \leq 0$, it is bounded by a constant depending on the surplus (and for Nash welfare it vanishes asymptotically). Finally we show that requiring Pareto-optimality alongside EFX is NP-hard (and becomes $\Sigma_2^P$-complete for a stronger variant of EFX). Overall, our results delineate when EFX is computationally costly versus structurally aligned with welfare maximization in the setting with few surplus items. - oai:arXiv.org:2601.12849v1 + Do people expect different behavior from large language models acting on their behalf? Evidence from norm elicitations in two canonical economic games + https://arxiv.org/abs/2601.15312 + arXiv:2601.15312v1 Announce Type: cross +Abstract: While delegating tasks to large language models (LLMs) can save people time, there is growing evidence that offloading tasks to such models produces social costs. We use behavior in two canonical economic games to study whether people have different expectations when decisions are made by LLMs acting on their behalf instead of themselves. More specifically, we study the social appropriateness of a spectrum of possible behaviors: when LLMs divide resources on our behalf (Dictator Game and Ultimatum Game) and when they monitor the fairness of splits of resources (Ultimatum Game). We use the Krupka-Weber norm elicitation task to detect shifts in social appropriateness ratings. Results of two pre-registered and incentivized experimental studies using representative samples from the UK and US (N = 2,658) show three key findings. First, people find that offers from machines - when no acceptance is necessary - are judged to be less appropriate than when they come from humans, although there is no shift in the modal response. Second - when acceptance is necessary - it is more appropriate for a person to reject offers from machines than from humans. Third, receiving a rejection of an offer from a machine is no less socially appropriate than receiving the same rejection from a human. Overall, these results suggest that people apply different norms for machines deciding on how to split resources but are not opposed to machines enforcing the norms. The findings are consistent with offers made by machines now being viewed as having both a cognitive and emotional component. + oai:arXiv.org:2601.15312v1 cs.GT cs.AI - cs.MA - econ.TH - Wed, 21 Jan 2026 00:00:00 -0500 - cross - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Eugene Lim, Tzeh Yuan Neoh, Nicholas Teh - - - Modelling viable supply networks with cooperative adaptive financing - https://arxiv.org/abs/2601.13210 - arXiv:2601.13210v1 Announce Type: cross -Abstract: We propose a financial liquidity policy sharing method for firm-to-firm supply networks, introducing a scalable autonomous control function for viable complex adaptive supply networks. Cooperation and competition in supply chains is reconciled through overlapping collaborative sets, making firms interdependent and enabling distributed risk governance. How cooperative range - visibility - affects viability is studied using dynamic complex adaptive systems modelling. We find that viability needs cooperation; visibility and viability grow together in scale-free supply networks; and distributed control, where firms only have limited partner information, outperforms centralised control. This suggests that policy toward network viability should implement distributed supply chain financial governance, supporting interfirm collaboration, to enable autonomous control. - oai:arXiv.org:2601.13210v1 - physics.soc-ph - cs.SI - cs.SY - econ.TH - eess.SY - nlin.AO - Wed, 21 Jan 2026 00:00:00 -0500 - cross - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Yaniv Proselkov, Liming Xu, Alexandra Brintrup - - - Conservation priorities to prevent the next pandemic - https://arxiv.org/abs/2601.13349 - arXiv:2601.13349v1 Announce Type: cross -Abstract: Diseases originating from wildlife pose a significant threat to global health, causing human and economic losses each year. The transmission of disease from animals to humans occurs at the interface between humans, livestock, and wildlife reservoirs, influenced by abiotic factors and ecological mechanisms. Although evidence suggests that intact ecosystems can reduce transmission, disease prevention has largely been neglected in conservation efforts and remains underfunded compared to mitigation. A major constraint is the lack of reliable, spatially explicit information to guide efforts effectively. Given the increasing rate of new disease emergence, accelerated by climate change and biodiversity loss, identifying priority areas for mitigating the risk of disease transmission is more crucial than ever. We present new high-resolution (1 km) maps of priority areas for targeted ecological countermeasures aimed at reducing the likelihood of zoonotic spillover, along with a methodology adaptable to local contexts. Our study compiles data on well-documented risk factors, protection status, forest restoration potential, and opportunity cost of the land to map areas with high potential for cost-effective interventions. We identify low-cost priority areas across 50 countries, including 277,000 km2 where environmental restoration could mitigate the risk of zoonotic spillover and 198,000 km2 where preventing deforestation could do the same, 95% of which are not currently under protection. The resulting layers, covering tropical regions globally, are freely available alongside an interactive no-code platform that allows users to adjust parameters and identify priority areas at multiple scales. Ecological countermeasures can be a cost-effective strategy for reducing the emergence of new pathogens; however, our study highlights the extent to which current conservation efforts fall short of this goal. - oai:arXiv.org:2601.13349v1 - q-bio.PE - econ.GN - q-fin.EC - Wed, 21 Jan 2026 00:00:00 -0500 - cross - http://creativecommons.org/licenses/by-sa/4.0/ - Leonardo Viotti, Luis Diego Herrera, Garo Batmanian, Franck Berthe, Rachael Kramp - - - A uniformity principle for spatial matching - https://arxiv.org/abs/2601.13426 - arXiv:2601.13426v1 Announce Type: cross -Abstract: Platforms matching spatially distributed supply to demand face a fundamental design choice: given a fixed total budget of service range, how should it be allocated across supply nodes ex ante, i.e. before supply and demand locations are realized, to maximize fulfilled demand? We model this problem using bipartite random geometric graphs where $n$ supply and $m$ demand nodes are uniformly distributed on $[0,1]^k$ ($k \ge 1$), and edges form when demand falls within a supply node's service region, the volume of which is determined by its service range. Since each supply node serves at most one demand, platform performance is determined by the expected size of a maximum matching. We establish a uniformity principle: whenever one service range allocation is more uniform than the other, the more uniform allocation yields a larger expected matching. This principle emerges from diminishing marginal returns to range expanding service range, and limited interference between supply nodes due to bounded ranges naturally fragmenting the graph. For $k=1$, we further characterize the expected matching size through a Markov chain embedding and derive closed-form expressions for special cases. Our results provide theoretical guidance for optimizing service range allocation and designing incentive structures in ride-hailing, on-demand labor markets, and drone delivery networks. - oai:arXiv.org:2601.13426v1 - math.PR - cs.DS + cs.CL + cs.CY + cs.HC econ.GN - math.OC q-fin.EC - Wed, 21 Jan 2026 00:00:00 -0500 + Fri, 23 Jan 2026 00:00:00 -0500 cross - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Taha Ameen, Flore Sentenac, Sophie H. Yu + http://creativecommons.org/licenses/by/4.0/ + Pawe{\l} Niszczota, Elia Antoniou - Bridging the Gap Between Estimated and True Regret Towards Reliable Regret Estimation in Deep Learning based Mechanism Design - https://arxiv.org/abs/2601.13489 - arXiv:2601.13489v1 Announce Type: cross -Abstract: Recent advances, such as RegretNet, ALGnet, RegretFormer and CITransNet, use deep learning to approximate optimal multi item auctions by relaxing incentive compatibility (IC) and measuring its violation via ex post regret. However, the true accuracy of these regret estimates remains unclear. Computing exact regret is computationally intractable, and current models rely on gradient based optimizers whose outcomes depend heavily on hyperparameter choices. Through extensive experiments, we reveal that existing methods systematically underestimate actual regret (In some models, the true regret is several hundred times larger than the reported regret), leading to overstated claims of IC and revenue. To address this issue, we derive a lower bound on regret and introduce an efficient item wise regret approximation. Building on this, we propose a guided refinement procedure that substantially improves regret estimation accuracy while reducing computational cost. Our method provides a more reliable foundation for evaluating incentive compatibility in deep learning based auction mechanisms and highlights the need to reassess prior performance claims in this area. - oai:arXiv.org:2601.13489v1 - cs.GT + Robust X-Learner: Breaking the Curse of Imbalance and Heavy Tails via Robust Cross-Imputation + https://arxiv.org/abs/2601.15360 + arXiv:2601.15360v1 Announce Type: cross +Abstract: Estimating Heterogeneous Treatment Effects (HTE) in industrial applications such as AdTech and healthcare presents a dual challenge: extreme class imbalance and heavy-tailed outcome distributions. While the X-Learner framework effectively addresses imbalance through cross-imputation, we demonstrate that it is fundamentally vulnerable to "Outlier Smearing" when reliant on Mean Squared Error (MSE) minimization. In this failure mode, the bias from a few extreme observations ("whales") in the minority group is propagated to the entire majority group during the imputation step, corrupting the estimated treatment effect structure. To resolve this, we propose the Robust X-Learner (RX-Learner). This framework integrates a redescending {\gamma}-divergence objective -- structurally equivalent to the Welsch loss under Gaussian assumptions -- into the gradient boosting machinery. We further stabilize the non-convex optimization using a Proxy Hessian strategy grounded in Majorization-Minimization (MM) principles. Empirical evaluation on a semi-synthetic Criteo Uplift dataset demonstrates that the RX-Learner reduces the Precision in Estimation of Heterogeneous Effect (PEHE) metric by 98.6% compared to the standard X-Learner, effectively decoupling the stable "Core" population from the volatile "Periphery". + oai:arXiv.org:2601.15360v1 + stat.ML cs.LG - econ.GN - q-fin.EC - Wed, 21 Jan 2026 00:00:00 -0500 + econ.EM + stat.ME + Fri, 23 Jan 2026 00:00:00 -0500 cross http://creativecommons.org/licenses/by/4.0/ - Shuyuan You, Zhiqiang Zhuang, Kewen Wang, Zhe Wang + Eichi Uehara - The Collapse of Multilayer Predation and the Emergence of a Monolithic Leviathan - https://arxiv.org/abs/2601.13544 - arXiv:2601.13544v1 Announce Type: cross -Abstract: This paper constructs a multilayer recursive game model to demonstrate that in a rule vacuum environment, hierarchical predatory structures inevitably collapse into a monolithic political strongman system due to the conflict between exponentially growing rent dissipation and the rigidity of bottom-level survival constraints. We propose that the rise of a monolithic political strongman is essentially an "algorithmic entropy reduction" achieved through forceful means by the system to counteract the "informational entropy increase" generated by multilayer agency. However, the order gained at the expense of social complexity results in the stagnation of social evolutionary functions. - oai:arXiv.org:2601.13544v1 + Can Rising Consumption Deepen Inequality? + https://arxiv.org/abs/2601.15537 + arXiv:2601.15537v1 Announce Type: cross +Abstract: The impact of rising consumption on wealth inequality remains an open question. Here we revisit and extend the Social Architecture of Capitalism agent-based model proposed by Ian Wright, which reproduces stylized facts of wealth and income distributions. In a previous study, we demonstrated that the macroscopic behavior of the model is predominantly governed by a single dimensionless parameter, the ratio between average wealth per capita and mean salary, denoted by R. The shape of the wealth distribution, the emergence of a two-class structure, and the level of inequality -- summarized by the Gini index -- were found to depend mainly on R, with inequality increasing as R increases. In the present work, we examine the robustness of this result by relaxing some simplifying assumptions of the model. We first allow transactions such as purchases, salary payments, and revenue collections to occur with different frequencies, reflecting the heterogeneous temporal dynamics of real economies. We then impose limits on the maximum fractions of wealth that agents can spend or collect at each step, constraining the amplitude of individual transactions. We find that the dependence of the inequality on R remains qualitatively robust, although the detailed distribution patterns are affected by relative frequencies and transaction limits. Finally, we analyze a further variant of the model with adaptive wages emerging endogenously from the dynamics, showing that self-organized labor-market feedback can either stabilize or amplify inequality depending on macroeconomic conditions. + oai:arXiv.org:2601.15537v1 physics.soc-ph - econ.EM - stat.AP - Wed, 21 Jan 2026 00:00:00 -0500 - cross - http://creativecommons.org/licenses/by-sa/4.0/ - Li Tuobang - - - On the Anchoring Effect of Monetary Policy on the Labor Share of Income and the Rationality of Its Setting Mechanism - https://arxiv.org/abs/2601.13675 - arXiv:2601.13675v1 Announce Type: cross -Abstract: Modern macroeconomic monetary theory suggests that the labor share of income has effectively become a core macroe-conomic parameter anchored by top policymakers through Open Market Operations (OMO). However, the setting of this parameter remains a subject of intense economic debate. This paper provides a detailed summary of these controversies, analyzes the scope of influence exerted by market agents other than the top policymakers on the labor share, and explores the rationality of its setting mechanism. - oai:arXiv.org:2601.13675v1 - stat.AP - econ.EM - Wed, 21 Jan 2026 00:00:00 -0500 - cross - http://creativecommons.org/licenses/by-sa/4.0/ - Li Tuobang - - - BallotRank: A Condorcet Completion Method for Graphs - https://arxiv.org/abs/2601.14015 - arXiv:2601.14015v1 Announce Type: cross -Abstract: We introduce BallotRank, a ranked preference aggregation method derived from a modified PageRank algorithm. It is a Condorcet-consistent method without damping, and empirical examination of nearly 2,000 ranked choice elections and over 20,000 internet polls confirms that BallotRank always identifies the Condorcet winner at conventional values of the damping parameter. We also prove that the method satisfies many of the same social choice criteria as other well-known Condorcet completion methods, but it has the advantage of being a natural social welfare function that provides a full ranking of the candidates. - oai:arXiv.org:2601.14015v1 - cs.GT econ.GN q-fin.EC - Wed, 21 Jan 2026 00:00:00 -0500 - cross - http://creativecommons.org/licenses/by/4.0/ - Ismar Volic, Jason Douglas Todd - - - Collective intelligence in science: direct elicitation of diverse information from experts with unknown information structure - https://arxiv.org/abs/2601.14047 - arXiv:2601.14047v1 Announce Type: cross -Abstract: Suppose we need a deep collective analysis of an open scientific problem: there is a complex scientific hypothesis and a large online group of mutually unrelated experts with relevant private information of a diverse and unpredictable nature. This information may be results of experts' individual experiments, original reasoning of some of them, results of AI systems they use, etc. We propose a simple mechanism based on a self-resolving play-money prediction market entangled with a chat. We show that such a system can easily be brought to an equilibrium where participants directly share their private information on the hypothesis through the chat and trade as if the market were resolved in accordance with the truth of the hypothesis. This approach will lead to efficient aggregation of relevant information in a completely interpretable form even if the ground truth cannot be established and experts initially know nothing about each other and cannot perform complex Bayesian calculations. Finally, by rewarding the experts with some real assets proportionally to the play money they end up with, we can get an innovative way to fund large-scale collaborative studies of any type. - oai:arXiv.org:2601.14047v1 - cs.GT - cs.AI - cs.MA - cs.SI - econ.TH - Wed, 21 Jan 2026 00:00:00 -0500 + Fri, 23 Jan 2026 00:00:00 -0500 cross http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Alexey V. Osipov, Nikolay N. Osipov - - - A simple model of interbank trading with tiered remuneration - https://arxiv.org/abs/2006.10946 - arXiv:2006.10946v2 Announce Type: replace -Abstract: Many countries have adopted negative interest rate policies with tiering remuneration, which allows for exemption from negative rates. This practice has led to higher interbank trading volumes, with market rates ranging between zero and the negative remuneration rates. This study proposes a basic model of an interbank market with tiering remuneration that can be tested with actual market data because of its simplicity and can indicate the level of the market rate created by the different exemption levels. By generalizing the model, we found that a tiering system is also suitable for maintaining a higher trading activity, regardless of the level of the remuneration rate. - oai:arXiv.org:2006.10946v2 - econ.GN - q-fin.EC - Wed, 21 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Toshifumi Nakamura + Jhordan Silveira de Borba, Celia Anteneodo, Sebastian Gon\c{c}alves - Market-Based Asset Price Probability - https://arxiv.org/abs/2205.07256 - arXiv:2205.07256v5 Announce Type: replace -Abstract: The random values and volumes of consecutive trades made at the exchange with shares of security determine its mean, variance, and higher statistical moments. The volume weighted average price (VWAP) is the simplest example of such a dependence. We derive the dependence of the market-based variance and 3rd statistical moment of prices on the means, variances, covariances, and 3rd moments of the values and volumes of market trades. The usual frequency-based assessments of statistical moments of prices are the limited case of market-based statistical moments if we assume that all volumes of consecutive trades with security are constant during the averaging interval. To forecast market-based variance of price, one should predict the first two statistical moments and the correlation of values and volumes of consecutive trades at the same horizon. We explain how that limits the number of predicted statistical moments of prices by the first two and the accuracy of the forecasts of the price probability by the Gaussian distribution. This limitation also reduces the reliability of Value-at-Risk by Gaussian approximation. The accounting for the randomness of trade volumes and the use of VWAP results in zero price-volume correlations. To study the price-volume empirical statistical dependence, one should calculate correlations of prices and squares of trade volumes or correlations of squares of prices and volumes. To improve the accuracy and reliability of large macroeconomic and market models like those developed by BlackRock's Aladdin, JP Morgan, and the U.S. Fed., the developers should explicitly account for the impact of random trade volumes and use market-based statistical moments of asset prices. - oai:arXiv.org:2205.07256v5 - econ.GN - q-fin.EC - q-fin.GN - q-fin.PR - Wed, 21 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Victor Olkhov - - - Policy Learning under Endogeneity Using Instrumental Variables - https://arxiv.org/abs/2206.09883 - arXiv:2206.09883v4 Announce Type: replace -Abstract: I propose a framework for learning individualized policy rules in observational data settings characterized by endogenous treatment selection and the availability of an instrumental variable. I introduce encouragement rules that manipulate the instrument. By incorporating the marginal treatment effect (MTE) as a policy invariant parameter, I establish the identification of the social welfare criterion for the optimal encouragement rule. Focusing on binary encouragement rules, I propose to estimate the optimal encouragement rule via the Empirical Welfare Maximization (EWM) method and derive the welfare loss convergence rate. I apply my method to advise on the optimal tuition subsidy assignment in Indonesia. - oai:arXiv.org:2206.09883v4 + Estimation and Inference for Synthetic Control Methods with Spillover Effects + https://arxiv.org/abs/1902.07343 + arXiv:1902.07343v3 Announce Type: replace +Abstract: Estimation and inference procedures for synthetic control methods often do not allow for the existence of spillover effects, which are plausible in many applications. In this paper, we consider estimation and inference for synthetic control methods, allowing for spillover effects. We propose estimators for both direct treatment effects and spillover effects and show that they are asymptotically unbiased. In addition, we propose an inferential procedure and show that it is asymptotically unbiased. Our estimation and inference procedure applies to cases with multiple treated units and/or multiple post-treatment periods, and to ones where the underlying factor model is either stationary or cointegrated. We discuss the bias from misspecified spillover structures and propose a test for correct specification. We apply our method to a classic empirical example that investigates the effect of California's tobacco control program as in Abadie et al. (2010) and find evidence of spillovers. We contrast our method with the pure-donor approach through a sensitivity analysis. + oai:arXiv.org:1902.07343v3 econ.EM - Wed, 21 Jan 2026 00:00:00 -0500 + Fri, 23 Jan 2026 00:00:00 -0500 replace http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Yan Liu + Jianfei Cao, Connor Dowd - Effective and Scalable Programs to Facilitate Labor Market Transitions for Women in Technology - https://arxiv.org/abs/2211.09968 - arXiv:2211.09968v5 Announce Type: replace -Abstract: We evaluate two interventions facilitating technology-sector transitions for women in Poland: Mentoring, focused on expanding professional networks, and Challenges, focused on building credible skill signals. Randomizing oversubscribed admissions, we find both programs substantially increase technology employment at twelve months - by 15 percentage points for Mentoring and 11 p.p. for Challenges. The distinct mechanisms through which the programs operate translate to heterogeneous treatment effects across geography, career stage, and baseline credentials. These differential effects create scope for improved allocation: algorithmic targeting across programs outperforms random assignment by 86% and experts' selection into Mentoring by 11%. - oai:arXiv.org:2211.09968v5 - econ.GN - q-fin.EC - Wed, 21 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Susan Athey, Emil Palikot - - - Identification in Multiple Treatment Models under Discrete Variation - https://arxiv.org/abs/2307.06174 - arXiv:2307.06174v2 Announce Type: replace -Abstract: We develop a marginal treatment effect based method to learn about causal effects in multiple treatment models with discrete instruments. We allow selection into treatment to be governed by a general class of threshold crossing models that permit multidimensional unobserved heterogeneity. An inherent complication is that the primitives characterizing the selection model are not generally point-identified. Allowing these primitives to be point-identified up to a finite-dimensional parameter, we show how a two-step computational program can be used to obtain sharp bounds for a number of treatment effect parameters when the marginal treatment response functions are allowed to satisfy only nonparametric shape restrictions or are additionally parameterized. We demonstrate the benefits of our method by revisiting Kline and Walters' (2016) empirical analysis of the Head Start program. Our approach relaxes their point-identifying assumptions on the selection model and marginal treatment response functions, allowing us to assess the robustness of their conclusions. - oai:arXiv.org:2307.06174v2 - econ.EM - Wed, 21 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Vishal Kamat, Samuel Norris, Matthew Pecenco - - - Interpreting Event-Studies from Recent Difference-in-Differences Methods - https://arxiv.org/abs/2401.12309 - arXiv:2401.12309v2 Announce Type: replace -Abstract: This note discusses the interpretation of event-study plots produced by recent difference-in-differences methods. I show that even when specialized to the case of non-staggered treatment timing, the default plots produced by software for several of the most popular recent methods do not match those of traditional two-way fixed effects (TWFE) event-studies. The plots produced by the new methods may show a kink or jump at the time of treatment even when the TWFE event-study shows a straight line. This difference stems from the fact that the new methods construct the pre-treatment coefficients asymmetrically from the post-treatment coefficients. As a result, visual heuristics for evaluating violations of parallel trends using TWFE event-study plots should not be immediately applied to those from these methods. I conclude with practical recommendations for constructing and interpreting event-study plots when using these methods. - oai:arXiv.org:2401.12309v2 + What Impulse Response Do Instrumental Variables Identify? + https://arxiv.org/abs/2208.11828 + arXiv:2208.11828v4 Announce Type: replace +Abstract: The local projection-instrumental variable (LP-IV) literature has been largely silent on cases in which impulse responses are set-identified, arising when the shock of interest is composite and instruments are correlated with multiple components. We demonstrate that LP-IV estimands constructed using one instrument at a time identify affine combinations of impulse responses to structural shock components with instrument-specific and potentially negative weights, challenging standard causal interpretation. The two-stage least squares compounds the identification problem. However, we show that individual LP-IV estimands characterize the identified set when sign restrictions on the correlations between instruments and structural shock components are imposed. Under weak stationarity, these identified sets are sharp and cannot be further narrowed in key cases. Two empirical examples--decomposing the U.S. government spending multiplier and disentangling pure monetary shocks from central bank information shocks--illustrate the usefulness of our approach. + oai:arXiv.org:2208.11828v4 econ.EM - stat.ME - Wed, 21 Jan 2026 00:00:00 -0500 + Fri, 23 Jan 2026 00:00:00 -0500 replace http://creativecommons.org/licenses/by/4.0/ - Jonathan Roth + Bonsoo Koo, Seojeong Lee, Myung Hwan Seo, Masaya Takano - Database for the meta-analysis of the social cost of carbon (v2026.1) - https://arxiv.org/abs/2402.09125 - arXiv:2402.09125v4 Announce Type: replace -Abstract: A new version of the database for the meta-analysis of estimates of the social cost of carbon is presented. New records were added, and new fields on gender and stochasticity. - oai:arXiv.org:2402.09125v4 - econ.GN - q-fin.EC - Wed, 21 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Richard S. J. Tol - - - To be or not to be: Roughness or long memory in volatility? - https://arxiv.org/abs/2403.12653 - arXiv:2403.12653v2 Announce Type: replace -Abstract: We develop a framework for composite likelihood estimation of parametric continuous-time stationary Gaussian processes. We derive the asymptotic theory of the associated maximum composite likelihood estimator. We implement our approach on a pair of models that have been proposed to describe the random log-spot variance of financial asset returns. A simulation study shows that it delivers good performance in these settings and improves upon a method-of-moments estimation. In an empirical investigation, we inspect the dynamic of an intraday measure of the spot log-realized variance computed with high-frequency data from the cryptocurrency market. The evidence supports a mechanism, where the short- and long-term correlation structure of stochastic volatility are decoupled in order to capture its properties at different time scales. This is further backed by an analysis of the associated spot log-trading volume. - oai:arXiv.org:2403.12653v2 - econ.EM - q-fin.MF - Wed, 21 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Mikkel Bennedsen, Kim Christensen, Peter Christensen - - - Potential weights and implicit causal designs in linear regression - https://arxiv.org/abs/2407.21119 - arXiv:2407.21119v4 Announce Type: replace -Abstract: When we interpret linear regression as estimating causal effects justified by quasi-experimental treatment variation, what do we mean? This paper formalizes a minimal criterion for quasi-experimental interpretation and characterizes its necessary implications. A minimal requirement is that the regression always estimates some contrast of potential outcomes under the true treatment assignment process. This requirement implies linear restrictions on the true distribution of treatment. If the regression were to be interpreted quasi-experimentally, these restrictions imply candidates for the true distribution of treatment, which we call implicit designs. Regression estimators are numerically equivalent to augmented inverse propensity weighting (AIPW) estimators using an implicit design. Implicit designs serve as a framework that unifies and extends existing theoretical results on causal interpretation of regression across starkly distinct settings (including multiple treatment, panel, and instrumental variables). They lead to new theoretical insights for widely used but less understood specifications. - oai:arXiv.org:2407.21119v4 - econ.EM - stat.ME - Wed, 21 Jan 2026 00:00:00 -0500 + Ironing Without Concavification + https://arxiv.org/abs/2402.11881 + arXiv:2402.11881v2 Announce Type: replace +Abstract: I propose a new approach to solving standard screening problems when the monotonicity constraint binds. A simple geometric argument shows that when virtual values are quasi-concave, the optimal allocation can be found by appropriately truncating the solution to the relaxed problem. I provide an algorithm for finding this optimal truncation when virtual values are concave. + oai:arXiv.org:2402.11881v2 + econ.TH + Fri, 23 Jan 2026 00:00:00 -0500 replace http://creativecommons.org/licenses/by/4.0/ - Jiafeng Chen + Filip Tokarski - The Turing Valley: How AI Capabilities Shape Labor Income - https://arxiv.org/abs/2408.16443 - arXiv:2408.16443v2 Announce Type: replace -Abstract: Current AI systems are better than humans in some knowledge dimensions but weaker in others. Guided by the long-standing vision of machine intelligence inspired by the Turing Test, AI developers increasingly seek to eliminate this "jagged" nature by pursuing Artificial General Intelligence (AGI) that surpasses human knowledge across domains. This pursuit has sparked an important debate, with leading economists arguing that AGI risks eroding the value of human capital. We contribute to this debate by showing how AI capabilities in different dimensions shape labor income in a multidimensional knowledge economy. AI improvements in dimensions where it is stronger than humans always increase labor income, but the effects of AI progress in dimensions where it is weaker than humans depend on the nature of human-AI communication. When communication allows the integration of partial solutions, improvements in AI's weak dimensions reduce the marginal product of labor, and labor income is maximized by a deliberately jagged form of AI. In contrast, when communication is limited to sharing full solutions, improvements in AI's weak dimensions can raise the marginal product of labor, and labor income can be maximized when AI achieves high performance across all dimensions. These results point to the importance of empirically assessing the additivity properties of human-AI communication for understanding the labor-market consequences of progress toward AGI. - oai:arXiv.org:2408.16443v2 + The Software Complexity of Nations + https://arxiv.org/abs/2407.13880 + arXiv:2407.13880v2 Announce Type: replace +Abstract: Despite the growing importance of the digital sector, research on economic complexity and its implications continues to rely mostly on administrative records, e.g. data on exports, patents, and employment, that have blind spots when it comes to the digital economy. In this paper we use data on the geography of programming languages used in open-source software to extend economic complexity ideas to the digital economy. We estimate a country's software economic complexity index (ECIsoftware) and show that it complements the ability of measures of complexity based on trade, patents, and research to account for international differences in GDP per capita, income inequality, and emissions. We also show that open-source software follows the principle of relatedness, meaning that a country's entries and exits in programming languages are partly explained by its current pattern of specialization. Together, these findings help extend economic complexity ideas and their policy implications to the digital economy. + oai:arXiv.org:2407.13880v2 econ.GN + cs.SI + physics.soc-ph q-fin.EC - Wed, 21 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Enrique Ide, Eduard Talam\`as - - - Revisiting the Identification of the Conduct Parameter in Homogeneous Goods Markets - https://arxiv.org/abs/2410.16998 - arXiv:2410.16998v3 Announce Type: replace -Abstract: We revisit the identification of the conduct parameter in homogeneous goods markets. Lau (1982) argues that the conduct parameter is not identified if and only if the inverse demand function is separable, except for a specific separable function. This result has been regarded as an extension of the result in Bresnahan (1982) to more general settings. However, we show that Lau's claim is incorrect and provide a new characterization of the non-identification. Our characterization shows that a demand function with demand rotation instruments is the necessary and sufficient condition for the identification of the conduct parameter. Therefore, our result properly generalizes the role of demand rotation instruments in identifying the conduct parameter, as highlighted by Bresnahan (1982), to more general settings. - oai:arXiv.org:2410.16998v3 - econ.EM - Wed, 21 Jan 2026 00:00:00 -0500 + Fri, 23 Jan 2026 00:00:00 -0500 replace http://creativecommons.org/licenses/by/4.0/ - Yuri Matsumura, Suguru Otani - - - Uncertain and Asymmetric Forecasts - https://arxiv.org/abs/2411.05938 - arXiv:2411.05938v2 Announce Type: replace -Abstract: This paper develops distribution-based measures that extract policy-relevant information from subjective probability distributions beyond point forecasts. We introduce two complementary indicators that operationalize the second and third moments of beliefs. First, a Normalized Uncertainty measure applies a variance-stabilizing transformation that removes mechanical level effects around policy-relevant anchors. Empirically, uncertainty behaves as a state variable: it amplifies perceived de-anchoring following monetary-policy shocks and weakens and delays pass-through to credit conditions, particularly across loan maturities. Second, an Asymmetry Coherence indicator combines the median and skewness of subjective distributions to identify coherent directional tail risks. Directional asymmetry is largely orthogonal to uncertainty and is primarily reflected in monetary-policy responses rather than real activity. Overall, the results show that properly measured uncertainty governs state-dependent transmission, while distributional asymmetries convey distinct information about macroeconomic risks. - oai:arXiv.org:2411.05938v2 - econ.GN - q-fin.EC - Wed, 21 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Eric Vansteenberghe + 10.1016/j.respol.2026.105422 + S\'andor Juh\'asz, Johannes Wachs, Jermain Kaminski, C\'esar A. Hidalgo - Sectorial Exclusion Criteria in the Marxist Analysis of the Average Rate of Profit: The United States Case (1960-2020) - https://arxiv.org/abs/2501.06270 - arXiv:2501.06270v2 Announce Type: replace -Abstract: The long term estimation of the Marxist average rate of profit does not adhere to a theoretically grounded standard regarding which economic activities should or should not be included for such purposes, which is relevant because methodological non uniformity can be a significant source of overestimation or underestimation, generating a less accurate reflection of the capital accumulation dynamics. This research aims to provide a standard Marxist decision criterion regarding the inclusion and exclusion of economic activities for the calculation of the Marxist average profit rate for the case of United States economic sectors from 1960 to 2020, based on the Marxist definition of productive labor, its location in the circuit of capital, and its relationship with the production of surplus value. Using wavelet transformed Daubechies filters with increased symmetry, empirical mode decomposition, Hodrick Prescott filter embedded in unobserved components model, and a wide variety of unit root tests the internal theoretical consistency of the presented criteria is evaluated. Also, the objective consistency of the theory is evaluated by a dynamic factor autoregressive model, Principal Component Analysis via Singular Value Decomposition, and regularized Horseshoe regression. The results are consistent both theoretically and econometrically with the logic of Classical Marxist political economy. - oai:arXiv.org:2501.06270v2 - econ.GN + The Subtlety of Optimal Paternalism in a Population with Bounded Rationality + https://arxiv.org/abs/2410.13658 + arXiv:2410.13658v3 Announce Type: replace +Abstract: We study the subtlety of optimal paternalism when a utilitarian planner has the power to design a discrete choice set for a heterogeneous population with bounded rationality. We first consider the planning problem in abstraction. We show that the policy that most effectively constrains or influences choices depends multiplicatively on the preferences of the population and the choice probabilities conditional on preferences that measure the suboptimality of behavior. We then study two settings in which the planner may mandate an action or decentralize decision making. One setting supposes that individuals measure utility with additive random error and maximize mismeasured rather than actual utility. Then optimal planning requires knowledge of the distribution of measurement errors. The other setting studies binary treatment choice when the planner can mandate a treatment conditional on publicly observed personal covariates or can enable individuals to choose their own treatments conditional on private information. Here we focus on situations where bounded rationality takes the form of deviations between subjective and objective probabilities of uncertain outcomes. To illustrate, we consider clinical decision making in medicine. In toto, our cautionary analysis shows that determination of optimal policy requires the planner to possess extensive knowledge that is rarely available. We warn that research in behavioral public economics should avoid overoptimistic claims regarding the nature of optimal paternalistic policies. We argue that credible study of utilitarian planning should consider not only the population but also the planner to be boundedly rational. + oai:arXiv.org:2410.13658v3 econ.EM econ.TH - q-fin.EC - Wed, 21 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Jose Mauricio Gomez Julian - - - Kotlarski's lemma for dyadic models - https://arxiv.org/abs/2502.02734 - arXiv:2502.02734v2 Announce Type: replace -Abstract: We show how to identify the distributions of the latent components in the two-way dyadic model for bipartite networks $y_{i,\ell}= \alpha_i+\eta_{\ell}+\varepsilon_{i,\ell}$. This is achieved by a repeated application of the extension of the classical lemma of Kotlarski (1967) in Evdokimov and White (2012). We provide two separate sets of assumptions under which all the latent distributions are identified. Both rely on some of the latent components being identically distributed. - oai:arXiv.org:2502.02734v2 - econ.EM - Wed, 21 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Grigory Franguridi, Hyungsik Roger Moon - - - Trade and pollution: Evidence from India - https://arxiv.org/abs/2502.09289 - arXiv:2502.09289v3 Announce Type: replace -Abstract: What happens to pollution when developing countries open their borders to trade? Theoretical predictions are ambiguous, and empirical evidence remains limited. We study the effects of the 1991 Indian trade liberalization reform on water pollution. The reform abruptly and unexpectedly lowered import tariffs, increasing exposure to trade. Larger tariff reductions are associated with relative increases in water pollution. The estimated effects imply a 0.11 standard deviation increase in water pollution for the median district exposed to the tariff reform. - oai:arXiv.org:2502.09289v3 - econ.GN - q-fin.EC - Wed, 21 Jan 2026 00:00:00 -0500 + Fri, 23 Jan 2026 00:00:00 -0500 replace http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Malin Niemi, Nicklas Nordfors, Anna Tompsett + Charles F. Manski, Eytan Sheshinski - Policy Learning with Confidence - https://arxiv.org/abs/2502.10653 - arXiv:2502.10653v3 Announce Type: replace -Abstract: This paper introduces a rule for policy selection in the presence of estimation uncertainty, explicitly accounting for estimation risk. The rule belongs to the class of risk-aware rules on the efficient decision frontier, characterized as policies offering maximal estimated welfare for a given level of estimation risk. Among this class, the proposed rule is chosen to provide a reporting guarantee, ensuring that the welfare delivered exceeds a threshold with a pre-specified confidence level. We apply this approach to the allocation of a limited budget among social programs using estimates of their marginal value of public funds and associated standard errors. - oai:arXiv.org:2502.10653v3 + Life Sequence Transformer: Generative Modelling of Socio-Economic Trajectories from Administrative Data + https://arxiv.org/abs/2506.01874 + arXiv:2506.01874v2 Announce Type: replace +Abstract: Generative modelling with Transformer architectures can simulate complex sequential structures across various applications. We extend this line of work to the social sciences by introducing a Transformer-based generative model tailored to longitudinal socio-economic data. Our contributions are: (i) we design a novel encoding method that represents socio-economic life histories as sequences, including overlapping events across life domains; and (ii) we adapt generative modelling techniques to simulate plausible alternative life trajectories conditioned on past histories. Using large-scale data from the Italian social security administration (INPS), we show that the model can be trained at scale, reproduces realistic labour market patterns consistent with known causal relationships, and generates coherent hypothetical life paths. This work demonstrates the feasibility of generative modelling for socio-economic trajectories and opens new opportunities for policy-oriented research, with counterfactual generation as a particularly promising application. + oai:arXiv.org:2506.01874v2 econ.EM stat.ME - Wed, 21 Jan 2026 00:00:00 -0500 + Fri, 23 Jan 2026 00:00:00 -0500 replace http://creativecommons.org/licenses/by/4.0/ - Victor Chernozhukov, Sokbae Lee, Adam M. Rosen, Liyang Sun + Alberto Cabezas, Carlotta Montorsi - Do Determinants of EV Purchase Intent vary across the Spectrum? Evidence from Bayesian Analysis of US Survey Data - https://arxiv.org/abs/2504.09854 - arXiv:2504.09854v3 Announce Type: replace -Abstract: While electric vehicle (EV) adoption has been widely studied, most research focuses on the average effects of predictors on purchase intent, overlooking variation across the distribution of EV purchase intent. This paper makes a threefold contribution by analyzing four unique explanatory variables, leveraging large-scale US survey data from 2021 to 2023, and employing Bayesian ordinal probit and Bayesian ordinal quantile modeling to evaluate the effects of these variables-while controlling for other commonly used covariates-on EV purchase intent, both on average and across its full distribution. By modeling purchase intent as an ordered outcome-from "not at all likely" to "very likely"-we reveal how covariate effects differ across levels of interest. This is the first application of ordinal quantile modeling in the EV adoption literature, uncovering heterogeneity in how potential buyers respond to key factors. For instance, confidence in development of charging infrastructure and belief in environmental benefits are linked not only to higher interest among likely adopters but also to reduced resistance among more skeptical respondents. Notably, we identify a gap between the prevalence and influence of key predictors: although few respondents report strong infrastructure confidence or frequent EV information exposure, both factors are strongly associated with increased intent across the spectrum. These findings suggest clear opportunities for targeted communication and outreach, alongside infrastructure investment, to support widespread EV adoption. - oai:arXiv.org:2504.09854v3 - econ.GN - q-fin.EC - stat.AP - Wed, 21 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Nafisa Lohawala, Mohammad Arshad Rahman - - - Probabilistic Forecasting of Climate Policy Uncertainty: The Role of Macro-financial Variables and Google Search Data - https://arxiv.org/abs/2507.12276 - arXiv:2507.12276v3 Announce Type: replace -Abstract: Accurately forecasting Climate Policy Uncertainty (CPU) is essential for designing climate strategies that balance economic growth with environmental objectives. Elevated CPU levels can delay regulatory implementation, hinder investment in green technologies, and amplify public resistance to policy reforms, particularly during periods of economic stress. Despite the growing literature documenting the economic relevance of CPU, forecasting its evolution and understanding the role of macro-financial drivers in shaping its fluctuations have not been explored. This study addresses this gap by presenting the first effort to forecast CPU and identify its key drivers. We employ various statistical tools to identify macro-financial exogenous drivers, alongside Google search data to capture early public attention to climate policy. Local projection impulse response analysis quantifies the dynamic effects of these variables, revealing that household financial vulnerability, housing market activity, business confidence, credit conditions, and financial market sentiment exert the most substantial impacts. These predictors are incorporated into a Bayesian Structural Time Series (BSTS) framework to produce probabilistic forecasts for both US and Global CPU indices. Extensive experiments and statistical validation demonstrate that BSTS with time-invariant regression coefficients achieves superior forecasting performance. We demonstrate that this performance stems from its variable selection mechanism, which identifies exogenous predictors that are empirically significant and theoretically grounded, as confirmed by the feature importance analysis. From a policy perspective, the findings underscore the importance of adaptive climate policies that remain effective across shifting economic conditions while supporting long-term environmental and growth objectives. - oai:arXiv.org:2507.12276v3 - econ.EM - stat.AP - Wed, 21 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Donia Besher, Anirban Sengupta, Tanujit Chakraborty - - - A Theory of Saving under Risk Preference Dynamics - https://arxiv.org/abs/2511.03142 - arXiv:2511.03142v3 Announce Type: replace -Abstract: Empirical evidence shows that wealthy households have substantially higher saving rates and markedly lower marginal propensity to consume (MPC) than other groups. Existing theory cannot account for this pattern unless under restrictive assumptions on returns, discounting, and preferences. This paper develops a general theory of optimal savings with preference shocks, allowing risk aversion to vary across states and over time, and shows that incorporating such heterogeneity in risk attitudes fundamentally reshapes the asymptotic dynamics of consumption and saving. In particular, zero asymptotic MPCs (100% asymptotic saving rates) arise under markedly weaker conditions than in existing theory. Strikingly, such outcomes occur whenever there is a positive probability that agents become less risk averse in the future. Therefore, the vanishing MPC emerges as a generic feature rather than a knife-edge result of the optimal savings model, offering a more theoretically robust and empirically consistent account of the saving behavior of wealthy households. - oai:arXiv.org:2511.03142v3 + Misperception and informativeness in statistical discrimination + https://arxiv.org/abs/2508.20053 + arXiv:2508.20053v2 Announce Type: replace +Abstract: We study the interplay of information and prior (mis)perceptions in a Phelps-Aigner-Cain-type model of statistical discrimination in the labor market. We decompose the effect on average pay of an increase in how informative observables are about workers' skills into a non-negative instrumental component, reflecting increased surplus due to better matching of workers with tasks, and a perception-correcting component capturing how extra information diminishes the importance of prior misperceptions about the distribution of skills in the worker population. We sign the perception-correcting term: it is non-negative (non-positive) if the population was ex-ante under-perceived (over-perceived). We then consider the implications for pay gaps between equally-skilled populations that differ in information, perceptions, or both, and identify conditions under which improving information narrows pay gaps. + oai:arXiv.org:2508.20053v2 econ.TH - math.OC - Wed, 21 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Qingyin Ma, Xinxi Song, Alexis Akira Toda - - - A Unified Metric Architecture for AI Infrastructure: A Cross-Layer Taxonomy Integrating Performance, Efficiency, and Cost - https://arxiv.org/abs/2511.21772 - arXiv:2511.21772v4 Announce Type: replace -Abstract: The growth of large-scale AI systems is increasingly constrained by infrastructure limits: power availability, thermal and water constraints, interconnect scaling, memory pressure, data-pipeline throughput, and rapidly escalating lifecycle cost. Across hyperscale clusters, these constraints interact, yet the main metrics remain fragmented. Existing metrics, ranging from facility measures (PUE) and rack power density to network metrics (all-reduce latency), data-pipeline measures, and financial metrics (TCO series), each capture only their own domain and provide no integrated view of how physical, computational, and economic constraints interact. This fragmentation obscures the structural relationships among energy, computation, and cost, preventing a coherent optimization across sector and how bottlenecks emerge, propagate, and jointly determine the efficiency frontier of AI infrastructure. - This paper develops an integrated framework that unifies these disparate metrics through a three-domain semantic classification and a six-layer architectural decomposition, producing a 6x3 taxonomy that maps how various sectors propagate across the AI infrastructure stack. The taxonomy is grounded in a systematic review and meta-analysis of all metrics with economic and financial relevance, identifying the most widely used measures, their research intensity, and their cross-domain interdependencies. Building on this evidence base, the Metric Propagation Graph (MPG) formalizes cross-layer dependencies, enabling systemwide interpretation, composite-metric construction, and multi-objective optimization of energy, carbon, and cost. - The framework offers a coherent foundation for benchmarking, cluster design, capacity planning, and lifecycle economic analysis by linking physical operations, computational efficiency, and cost outcomes within a unified analytic structure. - oai:arXiv.org:2511.21772v4 - econ.GN - q-fin.EC - Wed, 21 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Qi He - - - Making Event Study Plots Honest: A Functional Data Approach to Causal Inference - https://arxiv.org/abs/2512.06804 - arXiv:2512.06804v2 Announce Type: replace -Abstract: Event study plots are the centerpiece of Difference-in-Differences (DiD) analysis, but current plotting methods cannot provide honest causal inference when the parallel trends and/or no-anticipation assumption fails. We introduce a novel functional data approach to DiD that directly enables honest causal inference via event study plots. Our DiD estimator converges to a Gaussian process in the Banach space of continuous functions, enabling powerful simultaneous confidence bands. This theoretical contribution allows us to turn an event study plot into a rigorous honest causal inference tool through equivalence and relevance testing: Honest reference bands can be validated using equivalence testing in the pre-treatment period, and honest causal effects can be tested using relevance testing in the post-treatment period. We demonstrate the performance of our method in simulations and two case studies. - oai:arXiv.org:2512.06804v2 - econ.EM - Wed, 21 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Chencheng Fang, Dominik Liebl - - - From Many Models, One: Macroeconomic Forecasting with Reservoir Ensembles - https://arxiv.org/abs/2512.13642 - arXiv:2512.13642v2 Announce Type: replace -Abstract: Model combination is a powerful approach for achieving superior performance compared to selecting a single model. We study both theoretically and empirically the effectiveness of ensembles of Multi-Frequency Echo State Networks (MFESNs), which have been shown to achieve state-of-the-art macroeconomic time series forecasting results (Ballarin et al., 2024a). The Hedge and Follow-the-Leader schemes are discussed, and their online learning guarantees are extended to settings with dependent data. In empirical applications, the proposed Ensemble Echo State Networks demonstrate significantly improved predictive performance relative to individual MFESN models. - oai:arXiv.org:2512.13642v2 - econ.EM - stat.ML - Wed, 21 Jan 2026 00:00:00 -0500 + Fri, 23 Jan 2026 00:00:00 -0500 replace http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Giovanni Ballarin, Lyudmila Grigoryeva, Yui Ching Li + Matteo Escud\'e, Paula Onuchic, Ludvig Sinander, Quitz\'e Valenzuela-Stookey - Decision Rules in Choice Under Risk - https://arxiv.org/abs/2601.02964 - arXiv:2601.02964v2 Announce Type: replace -Abstract: We study choice among lotteries in which the decision maker chooses from a small library of decision rules. At each menu, the applied rule must make the realized choice a strict improvement under a dominance benchmark on perceived lotteries. We characterize the maximal Herfindahl-Hirschman concentration of rule shares over all locally admissible assignments, and diagnostics that distinguish rules that unify behavior across many menus from rules that mainly act as substitutes. We provide a MIQP formulation, a scalable heuristic, and a finite-sample permutation test of excess concentration relative to a menu-independent random-choice benchmark. Applied to the CPC18 dataset (N=686 subjects, each making 500-700 repeated binary lottery choices), the mean MRCI is 0.545, and 64.1% of subjects reject random choice at the 1% level. Concentration gains are primarily driven by modal-payoff focusing, salience-thinking, and regret-based comparisons. - oai:arXiv.org:2601.02964v2 - econ.GN - q-fin.EC - Wed, 21 Jan 2026 00:00:00 -0500 + The comparative statics of dominance + https://arxiv.org/abs/2512.15341 + arXiv:2512.15341v2 Announce Type: replace +Abstract: In finite problems comprising objects, situations, and an object- and situation-contingent payoff function, we study the comparative statics of the set of undominated objects, meaning those for which there exists no mixture over objects that is superior whatever the situation. We consider both weak and strict dominance (corresponding to different degrees of 'strictness' in the definition of superiority). Our main theorem characterises those payoff transformations which robustly expand the not-weakly-dominated and not-strictly-dominated sets: the necessary and sufficient condition is that payoffs be transformed separately across situations, in either a monotone-concave or a constant manner. We apply our results to Pareto frontiers and games. + oai:arXiv.org:2512.15341v2 + econ.TH + Fri, 23 Jan 2026 00:00:00 -0500 replace http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Avner Seror + Gregorio Curello, Ludvig Sinander, Mark Whitmeyer Two-Step Regularized HARX to Measure Volatility Spillovers in Multi-Dimensional Systems https://arxiv.org/abs/2601.03146 - arXiv:2601.03146v3 Announce Type: replace + arXiv:2601.03146v4 Announce Type: replace Abstract: We identify volatility spillovers across commodities, equities, and treasuries using a hybrid HAR-ElasticNet framework on daily realized volatility for six futures markets over 2002--2025. Our two step procedure estimates own-volatility dynamics via OLS to preserve persistence, then applies ElasticNet regularization to cross-market spillovers. The sparse network structure that emerges shows equity markets (ES, NQ) act as the primary volatility transmitters, while crude oil (CL) ends up being the largest receiver of cross-market shocks. Agricultural commodities stay isolated from the larger network. A simple univariate HAR model achieves equally performing point forecasts as our model, but our approach reveals network structure that univariate models cannot. Joint Impulse Response Functions trace how shocks propagate through the network. Our contribution is to demonstrate that hybrid estimation methods can identify meaningful spillover pathways while preserving forecast performance. - oai:arXiv.org:2601.03146v3 + oai:arXiv.org:2601.03146v4 econ.GN q-fin.EC - Wed, 21 Jan 2026 00:00:00 -0500 + Fri, 23 Jan 2026 00:00:00 -0500 replace http://arxiv.org/licenses/nonexclusive-distrib/1.0/ Mindy L. Mallory - The Promise of Time-Series Foundation Models for Agricultural Forecasting: Evidence from Commodity Prices - https://arxiv.org/abs/2601.06371 - arXiv:2601.06371v2 Announce Type: replace -Abstract: Forecasting agricultural markets remains challenging due to nonlinear dynamics, structural breaks, and sparse data. A long-standing belief holds that simple time-series methods outperform more advanced alternatives. This paper provides the first systematic evidence that this belief no longer holds with modern time-series foundation models (TSFMs). Using USDA ERS monthly commodity price data from 1997-2025, we evaluate 17 forecasting approaches across four model classes, including traditional time-series, machine learning, deep learning, and five state-of-the-art TSFMs (Chronos, Chronos-2, TimesFM 2.5, Time-MoE, Moirai-2), and construct annual marketing year price predictions to compare with USDA's futures-based season-average price (SAP) forecasts. We show that zero-shot foundation models consistently outperform traditional time-series methods, machine learning, and deep learning architectures trained from scratch in both monthly and annual forecasting. Furthermore, foundation models remarkably outperform USDA's futures-based forecasts on three of four major commodities despite USDA's information advantage from forward-looking futures markets. Time-MoE delivers the largest accuracy gains, achieving 54.9% improvement on wheat and 18.5% improvement on corn relative to USDA ERS benchmarks on recent data (2017-2024 excluding COVID). These results point to a paradigm shift in agricultural forecasting. - oai:arXiv.org:2601.06371v2 - econ.EM - stat.AP - Wed, 21 Jan 2026 00:00:00 -0500 + An $\Omega(\log(N)/N)$ Lookahead is Sufficient to Bound Costs in the Overloaded Loss Network + https://arxiv.org/abs/2601.14538 + arXiv:2601.14538v2 Announce Type: replace +Abstract: I study the simplest model of revenue management with reusable resources: admission control of two customer classes into a loss queue. This model's long-run average collected reward has two natural upper bounds: the deterministic relaxation and the full-information offline problem. With these bounds, we can decompose the costs faced by the online decision maker into (i) the \emph{cost of variability}, given by the difference between the deterministic value and the offline value, and (ii) the \emph{cost of uncertainty}, given by the difference between the offline value and the online value. \cite{Xie2025} established that the sum of these two costs is $\Theta(\log N)$, as the number of servers, $N$, goes to infinity. I show that we can entirely attribute this $\Theta(\log N)$ rate to the cost of uncertainty, as the cost of variability remains $O(1)$ as $N \rightarrow \infty$. In other words, I show that anticipating future fluctuations is sufficient to bound operating costs -- smoothing out these fluctuations is unnecessary. In fact, I show that an $\Omega(\log(N)/N)$ lookahead window is sufficient to bound operating costs. + oai:arXiv.org:2601.14538v2 + econ.TH + math.PR + Fri, 23 Jan 2026 00:00:00 -0500 replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Le Wang, Boyuan Zhang + http://creativecommons.org/licenses/by/4.0/ + Robert L. Bray - The Connection Between Monetary Policy and Housing Prices: Public Perception and Expert Communication - https://arxiv.org/abs/2601.08957 - arXiv:2601.08957v2 Announce Type: replace -Abstract: We study how the general public perceives the link between monetary policy and housing markets. Using a large-scale, cross-country survey experiment in Austria, Germany, Italy, Sweden, and the United Kingdom, we examine households' understanding of monetary policy, their beliefs about its impact on house prices, and how these beliefs respond to expert information. We find that while most respondents grasp the basic mechanisms of conventional monetary policy and recognize the connection between interest rates and house prices, literacy regarding unconventional monetary policy is very low. Beliefs about the monetary policy-housing nexus are malleable and respond to information, particularly when it is provided by academic economists rather than central bankers. Monetary policy literacy is strongly related to education, gender, age, and experience in housing and mortgage markets. Our results highlight the central role of housing in how households interpret monetary policy and point to the importance of credible and inclusive communication strategies for effective policy transmission. - oai:arXiv.org:2601.08957v2 + Implementing Optimal Taxation: A Constrained Optimization Framework for Tax Reform + https://arxiv.org/abs/2508.03708 + arXiv:2508.03708v3 Announce Type: replace-cross +Abstract: While optimal taxation theory provides clear prescriptions for tax design, translating these insights into actual tax codes remains difficult. Existing work largely offers theoretical characterizations of optimal systems, while practical implementation methods are scarce. Bridging this gap involves designing tax rules that meet theoretical goals, while accommodating administrative, distributional, and other practical constraints that arise in real-world reform. We develop a method casting tax reform as a constrained optimization problem by parametrizing the entire income tax code as a set of piecewise linear functions mapping tax-relevant inputs into liabilities and marginal rates. This allows users to impose constraints on marginal rate schedules, limits on income swings, and objectives like revenue neutrality, efficiency, simplicity, or distributional fairness that reflect both theoretical and practical considerations. The framework is computationally tractable for complex tax codes and flexible enough to accommodate diverse constraints, welfare objectives and behavioral responses. Whereas existing tools are typically used for ex-post `what-if' analysis of specific reforms, our framework explicitly incorporates real-world reform constraints and jointly optimizes across the full tax code. We illustrate the framework in several simulated settings, including a detailed reconstruction of the Dutch income tax system. For the Dutch case, we generate a family of reforms that smooth existing spikes in marginal tax rates to any desired cap, reduce the number of rules, and impose hard caps on income losses households can experience from the reform. We also introduce \texttt{TaxSolver}, an open-source package, allowing policymakers and researchers to implement and extend the framework. + oai:arXiv.org:2508.03708v3 + q-fin.GN + cs.SY econ.GN + eess.SY q-fin.EC - Wed, 21 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by-nc-sa/4.0/ - Philipp Poyntner, Sofie R. Waltl - - - Optimal Conditional Inference in Adaptive Experiments - https://arxiv.org/abs/2309.12162 - arXiv:2309.12162v2 Announce Type: replace-cross -Abstract: We study batched bandit experiments and consider the problem of inference conditional on the realized stopping time, assignment probabilities, and target parameter, where all of these may be chosen adaptively using information up to the last batch of the experiment. Absent further restrictions on the experiment, we show that inference using only the results of the last batch is optimal. When the adaptive aspects of the experiment are known to be location-invariant, in the sense that they are unchanged when we shift all batch-arm means by a constant, we show that there is additional information in the data, captured by one additional linear function of the batch-arm means. In the more restrictive case where the stopping time, assignment probabilities, and target parameter are known to depend on the data only through a collection of polyhedral events, we derive computationally tractable and optimal conditional inference procedures. - oai:arXiv.org:2309.12162v2 - stat.ME - cs.LG - econ.EM - math.ST - stat.TH - Wed, 21 Jan 2026 00:00:00 -0500 - replace-cross - http://creativecommons.org/licenses/by/4.0/ - Jiafeng Chen, Isaiah Andrews - - - Assessing Utility of Differential Privacy for RCTs - https://arxiv.org/abs/2309.14581 - arXiv:2309.14581v2 Announce Type: replace-cross -Abstract: Randomized controlled trials (RCTs) have become powerful tools for assessing the impact of interventions and policies in many contexts. They are considered the gold standard for causal inference in the biomedical fields and many social sciences. Researchers have published an increasing number of studies that rely on RCTs for at least part of their inference. These studies typically include the response data that has been collected, de-identified, and sometimes protected through traditional disclosure limitation methods. In this paper, we empirically assess the impact of privacy-preserving synthetic data generation methodologies on published RCT analyses by leveraging available replication packages (research compendia) in economics and policy analysis. We implement three privacy-preserving algorithms, that use as a base one of the basic differentially private (DP) algorithms, the perturbed histogram, to support the quality of statistical inference. We highlight challenges with the straight use of this algorithm and the stability-based histogram in our setting and described the adjustments needed. We provide simulation studies and demonstrate that we can replicate the analysis in a published economics article on privacy-protected data under various parameterizations. We find that relatively straightforward (at a high-level) privacy-preserving methods influenced by DP techniques allow for inference-valid protection of published data. The results have applicability to researchers wishing to share RCT data, especially in the context of low- and middle-income countries, with strong privacy protection. - oai:arXiv.org:2309.14581v2 - stat.AP - cs.CR - econ.EM - Wed, 21 Jan 2026 00:00:00 -0500 + Fri, 23 Jan 2026 00:00:00 -0500 replace-cross http://creativecommons.org/licenses/by/4.0/ - Kaitlyn R. Webb, Soumya Mukherjee, Aratrika Mustafi, Aleksandra Slavkovi\'c, Lars Vilhuber + Mark Verhagen, Menno Schellekens, Michael Garstka - Semiparametric Off-Policy Inference for Optimal Policy Values under Possible Non-Uniqueness - https://arxiv.org/abs/2505.13809 - arXiv:2505.13809v4 Announce Type: replace-cross -Abstract: Off-policy evaluation (OPE) constructs confidence intervals for the value of a target policy using data generated under a different behavior policy. Most existing inference methods focus on fixed target policies and may fail when the target policy is estimated as optimal, particularly when the optimal policy is non-unique or nearly deterministic. - We study inference for the value of optimal policies in Markov decision processes. We characterize the existence of the efficient influence function and show that non-regularity arises under policy non-uniqueness. Motivated by this analysis, we propose a novel \textit{N}onparametric \textit{S}equenti\textit{A}l \textit{V}alue \textit{E}valuation (NSAVE) method, which achieves semiparametric efficiency and retains the double robustness property when the optimal policy is unique, and remains stable in degenerate regimes beyond the scope of existing asymptotic theory. We further develop a smoothing-based approach for valid inference under non-unique optimal policies, and a post-selection procedure with uniform coverage for data-selected optimal policies. - Simulation studies support the theoretical results. An application to the OhioT1DM mobile health dataset provides patient-specific confidence intervals for optimal policy values and their improvement over observed treatment policies. - oai:arXiv.org:2505.13809v4 - math.ST - econ.EM - stat.ML - stat.TH - Wed, 21 Jan 2026 00:00:00 -0500 + Bipartiteness in Progressive Second-Price Multi-Auction Networks with Perfect Substitute + https://arxiv.org/abs/2511.19225 + arXiv:2511.19225v2 Announce Type: replace-cross +Abstract: We consider a bipartite network of buyers and sellers, where the sellers run locally independent Progressive Second-Price (PSP) auctions, and buyers may participate in multiple auctions, forming a multi-auction market with perfect substitute. The paper develops a projection-based influence framework for decentralized PSP auctions. We formalize primary and expanded influence sets using projections on the active bid index set and show how partial orders on bid prices govern allocation, market shifts, and the emergence of saturated one-hop shells. Our results highlight the robustness of PSP auctions in decentralized environments by introducing saturated components and a structured framework for phase transitions in multi-auction dynamics. This structure ensures deterministic coverage of the strategy space, enabling stable and truthful embedding in the larger game. We further model intra-round dynamics using an index to capture coordinated asynchronous seller updates coupled through buyers' joint constraints. Together, these constructions explain how local interactions propagate across auctions and gives premise for coherent equilibria--without requiring global information or centralized control. + oai:arXiv.org:2511.19225v2 + cs.GT + cs.DS + econ.TH + Fri, 23 Jan 2026 00:00:00 -0500 replace-cross http://creativecommons.org/licenses/by/4.0/ - Haoyu Wei + Jordana Blazek, Frederick C. Harris Jr - Bias correction for Chatterjee's graph-based correlation coefficient - https://arxiv.org/abs/2508.09040 - arXiv:2508.09040v2 Announce Type: replace-cross -Abstract: Azadkia and Chatterjee (2021) recently introduced a simple nearest neighbor (NN) graph-based correlation coefficient that consistently detects both independence and functional dependence. Specifically, it approximates a measure of dependence that equals 0 if and only if the variables are independent, and 1 if and only if they are functionally dependent. However, this NN estimator includes a bias term that may vanish at a rate slower than root-$n$, preventing root-$n$ consistency in general. In this article, we (i) analyze this bias term closely and show that it could become asymptotically negligible when the dimension is smaller than four; and (ii) propose a bias-correction procedure for more general settings. In both regimes, we obtain estimators (either the original or the bias-corrected version) that are root-$n$ consistent and asymptotically normal. - oai:arXiv.org:2508.09040v2 - stat.ME + A Blessing in Disguise: How DeFi Hacks Trigger Unintended Liquidity Injections into US Money Markets + https://arxiv.org/abs/2601.08263 + arXiv:2601.08263v2 Announce Type: replace-cross +Abstract: Do vulnerabilities in Decentralized Finance (DeFi) destabilize traditional short-term funding markets? While the prevailing "Contagion Hypothesis" posits that the liquidation of stablecoin reserves triggers fire-sale spirals that transmit distress to traditional markets , we document a robust "Flight-to-Quality" effect to the contrary. In the wake of major DeFi exploits, spreads on 3-month AA-rated commercial paper (CP) exhibit a paradoxical narrowing. We identify a "liquidity recycling" mechanism driving this outcome: capital fleeing DeFi protocols is re-intermediated into the traditional financial system via Prime Money Market Funds (MMFs) , where strict regulatory constraints (e.g., SEC Rule 2a-7) compel these funds to purchase high-quality paper. Our estimates indicate that this institutional demand shock quantitatively overwhelms the supply shock driven by stablecoin issuer redemptions. Rather than acting as vectors of financial contagion , these crypto native shocks serve as an inadvertent "safety valve" in segmented markets , providing transient liquidity support and effectively subsidizing borrowing costs for high-grade issuers in the real economy. + oai:arXiv.org:2601.08263v2 + q-fin.GN econ.EM - math.ST - stat.TH - Wed, 21 Jan 2026 00:00:00 -0500 + Fri, 23 Jan 2026 00:00:00 -0500 replace-cross - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Mona Azadkia, Leihao Chen, Fang Han + http://creativecommons.org/licenses/by-nc-nd/4.0/ + Tingyi Lin - In Defense of the Pre-Test: Valid Inference when Testing Violations of Parallel Trends for Difference-in-Differences - https://arxiv.org/abs/2510.26470 - arXiv:2510.26470v2 Announce Type: replace-cross -Abstract: The difference-in-differences (DID) research design is a key identification strategy which allows researchers to estimate causal effects under the parallel trends assumption. While the parallel trends assumption is counterfactual and cannot be tested directly, researchers often examine pre-treatment periods to check whether the time trends are parallel before treatment is administered. Recently, researchers have been cautioned against using preliminary tests which aim to detect violations of parallel trends in the pre-treatment period. In this paper, we argue that preliminary testing can -- and should -- play an important role within the DID research design. We propose a new and more substantively appropriate conditional extrapolation assumption, which requires an analyst to conduct a preliminary test to determine whether the severity of pre treatment parallel trend violations falls below an acceptable level before extrapolation to the post-treatment period is justified. This stands in contrast to prior work which can be interpreted as either setting the acceptable level to be exactly zero (in which case preliminary tests lack power) or assuming that extrapolation is always justified (in which case preliminary tests are not required). Under mild assumptions on how close the actual violation is to the acceptable level, we provide a consistent preliminary test as well confidence intervals which are valid when conditioned on the result of the test. The conditional coverage of these intervals overcomes a common critique made against the use of preliminary testing within the DID research design. To illustrate the performance of the proposed methods, we use synthetic data as well as data on recentralization of public services in Vietnam and right-to-carry laws in Virginia. - oai:arXiv.org:2510.26470v2 - stat.ME + The Maintenance and Necessity of Universal Rules: Scale, Hierarchy, the Cost of Justice, and Civilizational Development + https://arxiv.org/abs/2601.14325 + arXiv:2601.14325v2 Announce Type: replace-cross +Abstract: Building upon previous research, this paper further explores the topological foundations for maintaining universal rules within ultra-large-scale societies. It finds that in small-scale societies, absolute egalitarianism and the rule of law can be compatible through peer monitoring within a fully connected network. However, in ultra-large-scale societies, to maintain high-dimensional rules capable of protecting innovation and property rights, a complex hierarchical structure including "high-fragility" nodes must be constructed. Through quantitative analysis of power structures, this paper proves that a flattened, two-tier structure inevitably leads to the degradation of the rule of law. Only a social topology with sufficient hierarchical depth can escape the deathly trap of the Leviathan while expanding in scale, thereby sustaining the dynamic evolution of civilization. + oai:arXiv.org:2601.14325v2 + physics.soc-ph econ.EM - Wed, 21 Jan 2026 00:00:00 -0500 + Fri, 23 Jan 2026 00:00:00 -0500 replace-cross - http://creativecommons.org/licenses/by/4.0/ - Jonas M. Mikhaeil, Christopher Harshaw + http://creativecommons.org/licenses/by-sa/4.0/ + Li Tuobang