https://news.mcmedia.cam/feed/Economics MCMedia News RSS Feed - Economics specific feed https://news.mcmedia.cam/rss-info.html MCMedia RSS Generator v1.0 Sun, 17 May 2026 02:30:33 +0000 https://arxiv.org/abs/2605.14575 Fri, 15 May 2026 00:00:00 -0400 arXiv:2605.14575v1 Announce Type: new Abstract: The aim of this study is to empirically investigate the existence of a sectoral asset price channel of monetary policy in the region of the six republics of former Yugoslavia. The study constructs sectoral indices for the entire region, building on the idea that one regional stock exchange may provide more efficiency for the listed companies in the region, while monetary policy relevance for it may be sector-specific. We employ panel vector autoregressive model to observe impulse responses of sectoral indices to innovations in monetary policy, while then disentangle the long- from the short-run relationships per index through a Pooled Mean Group estimation. Overall, we document presence of the asset price channel in the finance and telecom sectors, likely driven by the established multinational corporate networks fostering sub-market regionalization. Yet, this is not the case for the manufacturing and electricity sectors, which may imply that local stock markets are yet too fragmented and space for a more efficient regional stock market, either in the true sense of the word or, more realistically, though enhanced regional cooperation of the stock exchanges certainly exists. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2605.13866 Fri, 15 May 2026 00:00:00 -0400 arXiv:2605.13866v1 Announce Type: cross Abstract: Humans increasingly delegate decisions to language models, yet whether these systems reproduce or reshape human patterns of discrimination remains unclear. Here we run a large-scale study to analyse whether language models use demographic information in hiring decisions. We show, across 27 models and 177 occupations, that language models give female and Black candidates hiring advantages relative to otherwise-comparable male and white candidates, while giving disabled candidates disadvantages. The differences are meaningful in magnitude: the role of race, gender, and disability status is comparable to six months to one year of additional education. Post-training alignment is the primary driver: relative to matched pre-trained models, alignment amplifies advantages for female and Black candidates by 325% and 330%, and disadvantages for disabled candidates by 171%. Compared with previous human correspondence studies, language models reverse the direction of racial discrimination, attenuate the disability penalty, and amplify the female advantage by 190%. Alignment changes how models use qualification signals: alignment increases returns to skills and work experience overall, but relatively more so for female and Black candidates. Meanwhile, the absence of qualification signals harms marginalised groups more, particularly for disabled candidates, differences that may explain the asymmetry of alignment effects across groups we observe. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2605.10060 Fri, 15 May 2026 00:00:00 -0400 arXiv:2605.10060v2 Announce Type: replace Abstract: I study a decentralized marriage market with search frictions, costly pre-marital skill investments, and non-transferable utility. Despite a symmetric environment, the market can exhibit asymmetric equilibria, with one gender investing more in skills than the other; in some environments, the asymmetric equilibrium is unique. A microfounded model of household utility maximization shows that this transition from a unique symmetric equilibrium to a unique asymmetric equilibrium can be driven by rising labor-market wages for high-skilled workers: as the skill premium rises, one gender ends up fully investing while the other invests substantially less. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2410.02091 Fri, 15 May 2026 00:00:00 -0400 arXiv:2410.02091v3 Announce Type: replace-cross Abstract: Generative artificial intelligence (AI) facilitates content production and enhances ideation capabilities, which can significantly influence developer productivity and participation in software development. To explore its impact on collaborative open-source software (OSS) development, we investigate the role of GitHub Copilot, a generative AI pair programmer, in OSS development where multiple distributed developers voluntarily collaborate. Using GitHub's proprietary Copilot usage data, combined with public OSS project data obtained from GitHub, we find that Copilot use increases project-level code contributions by 5.9%. This gain is driven by a 3.4% rise in developer coding participation and a 2.1% increase in individual productivity. However, Copilot use also leads to an increase in coordination time by 8% due to more code discussions. This reveals an important tradeoff: While AI expands who can contribute and how much they contribute, it slows coordination in collective development efforts. Despite this tension, the combined effect of these two competing forces remains positive, indicating a net gain in overall project-level timely merge of code contributions from using AI pair programmers. Interestingly, we also find the effects differ across developer roles. Peripheral developers show relatively smaller increases in project-level code contributions and experience larger increases in coordination time than core developers. In summary, our study underscores the dual role of AI pair programmers in affecting project-level code contributions and coordination time in OSS development. Our findings on the differential effects between core and peripheral developers also provide important implications for the structure of OSS communities in the long run. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2603.16659 Fri, 15 May 2026 00:00:00 -0400 arXiv:2603.16659v3 Announce Type: replace-cross Abstract: Reinforcement-learned reasoning has powered recent AI leaps on verifiable tasks, including mathematics, code, and structure prediction. The harder bottleneck is evaluative judgment in low-verifiability domains, where no oracle anchors reward and the core question is which untested ideas deserve attention. We test whether institutional traces, the record of what fields published, where, and at which tier, can serve as a training signal for AI evaluators. Across eight social science disciplines (psychology, economics, communication, sociology, political science, management, business and finance, public administration), we built held-out four-tier research-pitch benchmarks and supervised-fine-tuned (SFT) LLMs on field-specific publication outcomes. The fine-tuned models cleared the 25 percent chance baseline and exceeded frontier-model performance by wide margins, with best single-model accuracy ranging from 55.0 percent in public administration to 85.5 percent in psychology. In management, evaluated against 48 expert gatekeepers, 174 junior researchers, and 11 frontier reasoning models, the best single fine-tuned model (Qwen3-4B) reached 59.2 percent, 17.6 percentage points above expert majority vote (41.6 percent, non-tied) and 28.1 percentage points above the frontier mean (31.1 percent). The fine-tuned models also showed calibrated confidence: confidence rose when predictions were correct and fell when wrong, mirroring how a skilled reviewer can say "I'm sure" versus "I'm guessing." Selective triage on this signal reached very high accuracy on the highest-confidence subsets in every field. Institutional traces, we conclude, encode a scalable training signal for the low-verifiability judgment on which science depends. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2405.04764 Fri, 15 May 2026 00:00:00 -0400 arXiv:2405.04764v3 Announce Type: replace Abstract: We study a dynamic model in which a principal monitors agents based on historical data of infractions. This data informs when and at what intensity to monitor; the monitoring decision, in turn, selects the collected data, shaping the principal's future learning. We analyze this feedback loop using a bandit model in which the underlying monitoring environment evolves according to a hidden Markov process. Because data collection is endogenous, how the principal uses this information is critical: surprisingly, a myopic approach renders historical data completely valueless. By endogenizing the agent's incentives, we demonstrate that the principal's purely informational motive to explore serves as an endogenous commitment device. This inherent drive to gather data compels persistent vigilance, strictly lowering the equilibrium infraction rate and restoring the power of deterrence. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2503.02740 Fri, 15 May 2026 00:00:00 -0400 arXiv:2503.02740v2 Announce Type: replace Abstract: We consider voting rules in settings where voters' identities are difficult to verify. Voters can manipulate the process by casting multiple votes under different identities or abstaining from voting. Immunities to such manipulations are called \emph{false-name-proofness} and \emph{participation}, respectively. For the universal domain of (strict) preferences, these properties together imply \emph{anonymity} and are incompatible with \emph{neutrality}. For the domain of preferences defined over all subsets of a given set of objects, both \emph{false-name-proofness} and \emph{participation} cannot be met by rules that are also \emph{onto}, \emph{object neutral}, and \emph{tops-only}. However, when preferences over subsets of objects are restricted to be separable, all these properties can be satisfied. Furthermore, the domain of separable preferences is maximal for these properties. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2509.19019 Fri, 15 May 2026 00:00:00 -0400 arXiv:2509.19019v2 Announce Type: replace Abstract: A well-known feature of overlapping generations economies is that the First Welfare Theorem fails and equilibrium may be inefficient. The Cass (1972) criterion furnishes a necessary and sufficient condition for efficiency, but it does not address the existence of efficient equilibria, and Cass, Okuno, and Zilcha (1979) provide nonexistence examples. A closely related question (known as the Hahn (1965) problem) deals with the existence of monetary equilibria. In this paper, I provide sufficient conditions for the existence of optimal monetary equilibria on consumption-loan, non-stationary overlapping generations economies without durable, dividend-paying assets, cash-in-advance constraints, wealth-transfer mechanisms, or transaction costs. Essentially, the economy must be prone to savings. Furthermore, I develop an algorithm to find these optimal monetary equilibria as the limit of nested compact sets. These compact sets are the result of a backward calculation through equilibrium equations departing from the set of optimal monetary equilibria of well-behaved tail economies. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2512.22051 Fri, 15 May 2026 00:00:00 -0400 arXiv:2512.22051v2 Announce Type: replace Abstract: Consider a social-choice function (SCF) is chosen to decide votes in a formal system, including votes to replace the voting method itself. Agents vote according to their ex-ante belief over what decisions are considered, and whether they prefer them to be decided by the incumbent SCF or the suggested replacement. The existing SCF then aggregates the agents' votes and arrives at a decision of whether it should itself be replaced. An SCF is self-maintaining if it can not be replaced in such fashion by any other SCF. Our focus is on the implications of self-maintenance for centralization. For this purpose, unlike [Barbera and Jackson, 2004], we do not generally restrict attention to anonymous SCFs. We also do not restrict attention to neutral SCFs, unlike [Koray, 2000]. We present results considering optimistic, pessimistic and i.i.d. approaches with respect to agent beliefs, different tie-breaking rules, and different SCF domains. To highlight two of the results, (i) for the i.i.d. unbiased case with arbitrary tie-breaking and general Boolean functions, we prove an Arrow-Style Theorem for Dynamics: We show that only a dictatorship is self-maintaining, and any other SCF has a path of changes that arrives at a dictatorship. (ii) With a pessimistic approach, tie-breaking that prefers the status quo, and WMGs, we provide a tight characterization of the self-maintaining rules, which are exactly all games with minimal winning coalitions of size at most 2. We then consider two extensions, (i) forward-looking voters, (ii) Where the voter utility depends on wisdom of the crowd effects. In both cases, less centralized SCFs become self-maintaining. All in all we provide a basic framework and body of results for centralization dynamics and stability, applicable for institution design, especially in formal De-Jure systems, such as Blockchain Decentralized Autonomous Organizations (DAOs). Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2604.10638 Fri, 15 May 2026 00:00:00 -0400 arXiv:2604.10638v2 Announce Type: replace Abstract: On platforms where time-to-contract is itself payoff-relevant--Aalsmeer's flower auctions, ride-hailing dispatch, on-demand-labor matching--the textbook revenue equivalence between Dutch and first-price formats holds the trading outcome fixed. Once participation is endogenous and both sides bear waiting costs, the trading format directly shapes who enters, market thickness, volume, and platform revenue. The platform's ranking of the descending clock against immediate and batched posted-price benchmarks is decided by two estimable primitives on each side of the market: an earnings gap and a timing gap. A bidirectional four-case classification identifies when the descending clock dominates at every level of waiting costs, only above a floor, only below a ceiling, or not at all; the last case is unconditional -- when the descending clock charges no more per trade and contracts no faster than the posted-price benchmark, it cannot win. No format admits a universal ranking. The local verdict propagates through endogenous entry, and cross-side complementarity amplifies shared local advantages into joint dominance. A conditional revenue theorem converts entry and volume gains into a platform-revenue ranking. In calibrated parameterizations the revenue-ranking switching boundary lies near $p_0/\bar v\approx 1$, inside the empirical range for ride-hailing platforms. A measurement protocol provides explicit nonparametric estimators for the six reduced-form objects and a test statistic for the dominance condition, and a Lean~4 formalization audits the algebraic and order-theoretic content. In markets where goods or services cannot wait, the speed of the trading mechanism is a primitive of market design. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2605.13362 Fri, 15 May 2026 00:00:00 -0400 arXiv:2605.13362v2 Announce Type: replace-cross Abstract: Computational social choice and algorithmic decision theory offer rich aggregation theory but no comprehensive process for egalitarian self-governance: aggregation, deliberation, amendment, and consensus are each considered in isolation, with key metric-space aggregators being NP-hard. Here, we propose constitutional governance in metric spaces, integrating these stages into a coherent polynomial-time protocol for constitutional governance. The constitution assigns, per amendable component including itself, a metric space, aggregation rule, and supermajority threshold. Amendments proceed by members voting with their ideal elements, followed by members submitting public proposals carrying supermajority public support under the revealed votes. Public proposals can be sourced from deliberation among members, vote aggregation, or AI mediation. The constitutional rule adopts a supported proposal with positive maximal score, if there is one, else retains the status quo. With Constitutional Consensus, a community can run the constitutional governance protocol on members' personal computing devices (e.g., smartphones), achieving digital sovereignty. We focus on the utility of the generalised median, prove that at majority threshold no misreport weakly dominates sincere voting, and study the compromise gap between best peak and unconstrained optimum. We instantiate the framework to seven canonical settings -- electing officers, setting rates, allocating budgets, ranking priorities, selecting boards, drafting bylaws, and amending the constitution. By unifying metric-space aggregation, reality-aware social choice, supermajority amendment, constitutional consensus, deliberative coalition formation, and AI mediation, this work delivers a comprehensive solution to the constitutional governance of digital communities and organisations. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2605.12551 Thu, 14 May 2026 00:00:00 -0400 arXiv:2605.12551v1 Announce Type: new Abstract: The film industry is characterized by significant financial uncertainty, where large production investments do not always guarantee commercial success. This study analyzes the relationship between release season, production budget, and movie financial performance using the Full TMDB Movies Dataset 2024. A data mining framework incorporating association rule mining, clustering, machine learning, and SHAP analysis was applied to identify key drivers of revenue and profitability. The results show that release season has limited predictive influence on revenue and return on investment (ROI). In contrast, production budget, popularity, and audience ratings are significantly more influential. Association rule mining revealed that high-budget films with poor ratings are strongly associated with negative ROI outcomes. Random Forest regression achieved substantially stronger predictive performance than Decision Tree regression, with an $R^2$ value of 0.652. SHAP analysis further confirmed that budget and popularity are the dominant predictors of box office revenue, while timing-related variables contribute minimally. These findings suggest that financial success in the film industry is driven more by production investment and market attention than by seasonal release strategies, providing practical insights for budgeting, release planning, and financial risk management. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2605.13320 Thu, 14 May 2026 00:00:00 -0400 arXiv:2605.13320v1 Announce Type: cross Abstract: We conduct the first rigorous study of electricity price volatility for the full panel of electricity prices across three European generation zones. By interpreting the observed day-ahead prices as local averages of a latent price process governed by a stochastic partial differential equation, we develop estimators of the weekly integrated variance. The inherently infinite dimensional setting introduce several complications that are not relevant in the conventional finite dimensional semimartingale setting, and we spend considerable effort in dealing with these. In particular, we must account for both mean-reversion in prices and semigroup-smoothing in the estimated variance. We provide a detailed decomposition and interpretation of the empirical estimates across three vastly different European generation zones, namely Germany, Norway, and Spain. Our findings indicate that each zone has very different drivers of volatility, and that the impact of generation variables differs considerably. We document that leverage effects appear to be present at first sight, but disappear once we condition on suitable state variables, thereby showing that electricity price volatility does not generally exhibit asymmetric responses to price shocks. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2311.02299 Thu, 14 May 2026 00:00:00 -0400 arXiv:2311.02299v5 Announce Type: replace Abstract: We show, using three empirical applications, that linear regression estimates predicated on the assumption of sparsity are fragile in two ways. First, we document that different choices of the regressor matrix which do not impact ordinary least squares (OLS) estimates, such as the choice of baseline category with categorical controls, can move sparsity-based estimates by two standard errors or more. Second, we develop two tests of the sparsity assumption by comparing sparsity-based estimators with OLS. The tests tend to reject the sparsity assumption in all three applications. Unless the number of regressors is comparable to or exceeds the sample size, OLS yields more robust inference at little efficiency cost. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2506.20035 Thu, 14 May 2026 00:00:00 -0400 arXiv:2506.20035v3 Announce Type: replace Abstract: We show that some forms of p-hacking cannot be detected by examining the histogram of t-statistics or their p-values. Even when p-hacking is detectable, standard tests may lack power. We propose a novel test that detects every form of selective reporting that is detectable from the distribution of reported t-statistics. Our test statistic is the distance between the smoothed empirical t-curve and the set of possible honest distributions. This projection test is sharp and can only be evaded by selective reporting that also evades all other valid tests of restrictions on the t-curve. We also show how to avoid spurious rejections caused by some benign distortions in the t-curve. Applying the test to the Brodeur et al. (2020) meta-dataset, we find that the t-curves for RCTs and IVs are more distorted than could arise by chance, (de)rounding, or the Student-t approximation. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2604.05327 Thu, 14 May 2026 00:00:00 -0400 arXiv:2604.05327v2 Announce Type: replace Abstract: This article introduces a framework for evaluating statistical decisions under both prior ambiguity and likelihood misspecification. We begin with an ambiguity set - a frequentist model that pairs a possibly misspecified likelihood with every possible prior - and uniformly expand it by a Kullback-Leibler radius to accommodate likelihood misspecification. We show that optimal decisions under this framework are equivalent to minimax decisions with an exponentially tilted loss function. Misspecification manifests as an exponential tilting of the loss, while ambiguity corresponds to a search for the least favorable prior. This separation between ambiguity and misspecification enables local asymptotic analysis under global misspecification, achieved by localizing the priors alone. Remarkably, for both estimation and treatment assignment, we show that optimal decisions coincide with those under correct specification, regardless of the degree of misspecification. These results extend to semi-parametric models. As a practical consequence, our findings imply that practitioners should prefer maximum likelihood over the simulated method of moments, and efficient GMM estimators - such as two-step GMM - over diagonally weighted alternatives. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2605.13679 Thu, 14 May 2026 00:00:00 -0400 arXiv:2605.13679v1 Announce Type: new Abstract: There has long been an apparent consensus in the literature on intra-household allocation and fertility that greater paternal involvement in childcare relaxes maternal time constraints, enabling mothers to increase their labor supply or leisure. Recent evidence, particularly from South Korea, challenges this view: increases in fathers' childcare time have coincided with a further increase in mothers' time dedicated to child-rearing. This paper develops an Overlapping Generations (OLG) growth model to address such a puzzle. The central mechanism and our main innovation hinge on the functional form of the childcare technology. When maternal and paternal time are substitutes, the conventional result holds. However, when they are complements, greater paternal involvement necessarily raises maternal childcare time, depressing fertility and redirecting household resources toward child quality. We further argue that the elasticity of substitution should not be interpreted as a pure preference parameter, as it also reflects the social and institutional norms, the skills each parent brings to child-rearing and their intergenerational transmission. The model is extended to study the effectiveness of pro-natalist subsidies, suggesting that such policies may generate an unintended anti-fertility bias. Numerical simulations calibrated loosely to South Korean data confirm that the model is consistent with the observed quantity-quality trade-off and the persistence of low fertility despite active pro-natalist policy. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2605.12802 Thu, 14 May 2026 00:00:00 -0400 arXiv:2605.12802v1 Announce Type: cross Abstract: This paper studies when strategic understanding acquired in one mechanism can be transferred to another. We introduce a framework in which agents' knowledge is represented as a set of payoff comparisons they can make, and use it to formalize what it means to understand that a strategy profile is an equilibrium. We first apply this framework to mechanisms that are strategically equivalent-that is, share the same game form up to relabeling of actions-and show that agents' understanding of equilibrium transfers across such mechanisms once the relevant action correspondences are explained to them. We then define strategic analogy, a weaker notion that allows not only actions but also types to be remapped, and show that understanding of equilibrium transfers across strategically analogous mechanisms once agents recognize how actions and types correspond. Applications include single-item auctions, scoring auctions, and nonlinear pricing with capacity constraints. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2602.21971 Thu, 14 May 2026 00:00:00 -0400 arXiv:2602.21971v2 Announce Type: replace Abstract: Given the challenge of achieving societal welfare in an environmentally sustainable way, the Index of Sustainable Economic Welfare (ISEW) has emerged as an alternative indicator of progress in response to critiques of Gross Domestic Product (GDP). The ISEW compares the benefits of economic activity with its social and environmental costs. So far, most studies empirically analyse the ISEW for past developments, while no studies have simulated the ISEW using a dynamic macroeconomic model. We address this important gap by incorporating the ISEW into COMPASS, an ecological macroeconomic model that features the Doughnut of biophysical boundaries and social thresholds. First, we analyse how the ISEW is affected by three social and environmental policies: a carbon tax, income redistribution, and working-time reduction. We find that the ISEW grows in all scenarios. The strongest improvement over business-as-usual arises when all policies are combined, while the individual policies mostly affect the ISEW positively. Only in the case of working-time reduction, the ISEW decreases. Our study underscores the benefit of dynamically modelling the ISEW for anticipating the net effect of multiple impulses and their interconnections on the indicator. Second, we explore how the ISEW compares to GDP and the Doughnut when evaluating social and environmental policies. Our results suggest that the ISEW is better than GDP at capturing their effects, but it omits the full environmental costs of growth. We argue that the Doughnut, with its comprehensive picture of biophysical boundaries and social thresholds, provides better guidance for policymakers striving for sustainable wellbeing. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2605.13039 Thu, 14 May 2026 00:00:00 -0400 arXiv:2605.13039v1 Announce Type: new Abstract: A principal decides whether to approve an agent based on a noisy signal (e.g., test scores) generated by the agent. High-quality agents can produce high signals on average at lower cost, but the realizations are subject to noise that depends on the screening technology's precision. We uncover a paradoxical "pitfall of precision": when precision is already high, further improvements reduce screening accuracy and lower the principal's welfare. This occurs because greater precision incentivizes strategic signaling from more low-quality agents, outweighing the direct benefit from improved precision. The pitfall of precision also has implications for statistical discrimination: groups with noisier technologies face lower approval rates yet may be favored ex ante -- a reversal of discrimination. We also examine how commitment power helps mitigate the pitfall. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2605.13222 Thu, 14 May 2026 00:00:00 -0400 arXiv:2605.13222v1 Announce Type: new Abstract: Strategic crisis analysis needs representations that combine qualitative expert judgement, explicit interdependence, and auditable update rules without requiring fully specified payoffs or probabilities. Scenario Bundle Analysis (SBA), developed by Amos Perlmutter and Reinhard Selten, provides such a starting point, but the original formulation leaves several database, topology, and update interfaces implicit. This paper presents a formal refinement and extension of the original SBA framework, introducing a two-layer architecture that separates a static scenario database from a dynamic scenario tree system. The extended framework incorporates a richer attitude vocabulary: beliefs, desires, intentions, fears, and coalitional commitments, with expectations treated as doxastic attitudes. It also adds a domain/modifier layer for contextual framing, a topology on admissible scenario spaces\index{Scenario space}, typed assessment-state updates, and multi-criteria evaluation. Mathematical definitions are stated with sufficient precision to support computational implementation. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2510.04698 Thu, 14 May 2026 00:00:00 -0400 arXiv:2510.04698v3 Announce Type: replace-cross Abstract: Humans systematically misrepresent probability in a stereotyped inverse-S pattern. It has been documented for decades, but its origin remains unexplained. We propose a Bayesian encoding-decoding account in which probabilities are represented by noisy internal signals and decoded by Bayes-risk minimization. For bounded probability stimuli, we show that distortion decomposes into boundary regression, likelihood repulsion, and prior attraction, yielding a key prediction: the classic inverse-S-shaped weighting pattern implies a U-shaped allocation of encoding precision with greater sensitivity near 0 and 1. Across judgment of relative frequency, lottery pricing, and risky choice, this U-shape is recovered from data without imposing any functional form on the encoding, and our framework outperforms deterministic weighting functions, bounded log-odds models, uniform-encoding Bayesian accounts, and matched efficient-coding models on held-out data. In a new dot probability estimation experiment with bimodal stimulus statistics, the recovered prior tracks the new distribution while the recovered encoding remains U-shaped. Together, these results identify the inverse-S-shaped probability weighting function as the joint product of a stable U-shaped encoding and a flexible prior, integrated by optimal Bayesian decoding. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2302.14234 Thu, 14 May 2026 00:00:00 -0400 arXiv:2302.14234v5 Announce Type: replace-cross Abstract: We develop a versatile methodology for multidimensional mechanism design that incorporates side information about agents to generate high welfare and high revenue simultaneously. Side information sources include advice from domain experts, predictions from machine learning models, and even the mechanism designer's gut instinct. We design a tunable mechanism that integrates side information with an improved VCG-like mechanism based on weakest types, which are agent types that generate the least welfare. We show that our mechanism, when its side information is of high quality, generates welfare and revenue competitive with the prior-free total social surplus, and its performance decays gracefully as the side information quality decreases. We consider a number of side information formats including distribution-free predictions, predictions that express uncertainty, agent types constrained to low-dimensional subspaces of the ambient type space, and the traditional setting with known priors over agent types. In each setting we design mechanisms based on weakest types and prove performance guarantees. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2603.20609 Thu, 14 May 2026 00:00:00 -0400 arXiv:2603.20609v2 Announce Type: replace Abstract: Data regulations increasingly enable consumers to switch among market segments, making segmentation an endogenous outcome of strategic interaction. We study a model in which consumers choose segments before a monopolist sets segment-specific prices, and define a segmentation as strategy-proof when no consumer with positive measure can profitably deviate. Our main result provides a complete welfare characterization: in every strategy-proof segmentation, producer surplus is pinned at the uniform monopoly profit, consumer surplus ranges from the uniform monopoly level to the buyer-optimal level, and every consumer is weakly better off. We construct strategy-proof segmentations attaining every feasible outcome in this range. A finite-consumer model microfounds our solution concept, with equilibrium outcomes converging to our characterization as the population grows large. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2405.07860 Thu, 14 May 2026 00:00:00 -0400 arXiv:2405.07860v4 Announce Type: replace Abstract: We give an order-explicit large deviation bound for the difference between a high-dimensional $U$-statistic and its H\'{a}jek projection. In particular, we show that any $U$-statistic of order $b$ on $n$ observations, with a $d$-dimensional kernel whose coordinates have $\psi_1$-Orlicz norm at most $\phi$, has a maximum deviation from its H\'{a}jek projection of order $O_p(\phi b n^{-1}\log^2(dn))$. The proof relies on the development of novel order-explicit moment inequalities for higher-order Hoeffding components. We show that this rate is unimprovable, up to the polynomial factor on the logarithmic term. As corollaries, we obtain new Bernstein-type concentration and Gaussian approximation results for high-dimensional $U$-statistics. We apply these results to establish the consistency of a set of resampling-based simultaneous confidence intervals built around a class of nonparametric regression estimators constructed with subsampled kernels. This class encompasses several forms of random forest regression, including Generalized Random Forests. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2509.06697 Wed, 13 May 2026 00:00:00 -0400 arXiv:2509.06697v2 Announce Type: replace Abstract: Accurate forecasting of exchange rates remains a persistent challenge, particularly for emerging economies such as Brazil, Russia, India, and China (BRIC). These series exhibit long memory and nonlinearity that conventional time series models struggle to capture. Exchange rate dynamics are further influenced by several key drivers, including global economic policy uncertainty, US equity market volatility, US monetary policy uncertainty, oil price growth rates, and short-term interest rates. These empirical complexities underscore the need for a flexible framework that can jointly accommodate long memory, nonlinearity, and the influence of external drivers. We propose a Neural AutoRegressive Fractionally Integrated Moving Average (NARFIMA) model that combines the long memory structure of ARFIMA with the nonlinear learning capability of neural networks while incorporating exogenous variables. We establish asymptotic stationarity of NARFIMA and quantify forecast uncertainty using conformal prediction intervals. Empirical results show that NARFIMA consistently outperforms benchmark methods in forecasting BRIC exchange rates. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2510.14285 Wed, 13 May 2026 00:00:00 -0400 arXiv:2510.14285v2 Announce Type: replace Abstract: Volatility estimation is a central problem in financial econometrics, but becomes particularly challenging when jump activity is high, a phenomenon observed empirically in highly traded financial securities. In this paper, we revisit the problem of spot volatility estimation for an It\^o semimartingale with jumps of unbounded variation. We construct truncated kernel-based estimators and debiased variants that extend rate-optimal spot volatility estimation to a wider range of jump activity indices, from the previously available bound $Y 20/11$. Compared with earlier work, our approach achieves smaller asymptotic variances through the use of more general kernels and an optimal choice for the bandwidth convergence rate, and also has broader applicability under more flexible model assumptions. A comprehensive simulation study confirms that our procedures outperform competing methods in finite samples. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2512.06946 Wed, 13 May 2026 00:00:00 -0400 arXiv:2512.06946v2 Announce Type: replace Abstract: This article develops a significance test for the Difference-in-Differences (DiD) estimator based on dual-margin randomization, in which both the treatment and time indicators are independently permuted to generate an empirical null distribution of the DiD estimator. We situate the proposal explicitly within the landscape of existing inference methods for the DiD estimator, including OLS-based $t$-tests, heteroskedasticity-robust standard errors, cluster-robust variance estimators (CRVE), and the recently proposed jackknife standard errors of Hansen (2025). We show that CRVE-based procedures can be severely anti-conservative in small samples, motivating a nonparametric alternative. We formally characterise the permutation space induced by dual randomization, showing that it expands by a factor of $\binom{n}{n_T}$ relative to single-margin permutation tests, and provide an information-theoretic justification for balanced Bernoulli reshuffling. A controlled simulation study, augmented with robustness experiments under non-Gaussian and heteroskedastic errors, demonstrates that the doubly randomised test maintains accurate empirical size at all sample sizes considered, while HC0 and CRVE1 $t$-tests are substantially anti-conservative at small $n$. Crucially, this parametric inflation is driven by the leverage structure of the regressor matrix rather than by the error variance: heteroskedasticity-robust standard errors do not directly address the leverage-driven finite-sample distortion documented here, whereas randomization-based inference is insulated from both error-distributional and variance-structural departures by construction. Power costs relative to the Hansen jackknife test are real but bounded, and become negligible as $n$ grows. The proposed procedure is implemented in the sigDD R package and validated on four empirical datasets from the applied economics literature. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2506.23619 Wed, 13 May 2026 00:00:00 -0400 arXiv:2506.23619v2 Announce Type: replace-cross Abstract: This paper investigates the impact of posterior drift on out-of-sample forecasting accuracy in overparametrized machine learning models. We document the loss in performance when the loadings of the data generating process change between the training and testing samples. This matters crucially in settings in which regime changes are likely to occur, for instance, in financial markets. Applied to equity premium forecasting, our results underline the sensitivity of a market timing strategy to sub-periods and to the bandwidth parameters that control the complexity of the model. For the average investor, we find that focusing on holding periods of 15 years can generate very heterogeneous returns, especially for small bandwidths. Large bandwidths yield much more consistent outcomes, but are far less appealing from a risk-adjusted return standpoint. All in all, our findings tend to recommend cautiousness when resorting to large linear models for stock market predictions. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2409.17035 Wed, 13 May 2026 00:00:00 -0400 arXiv:2409.17035v4 Announce Type: replace Abstract: Recent empirical evidence shows that investments in ICT disproportionately improve the performance of larger firms versus smaller ones. However, ICT may not be all alike, as they differ in their impact on firms' organisational structure. We investigate the effect of the use of cloud services on the long run size growth rate of French firms. We find that cloud services positively impact firms' growth rates, with smaller firms experiencing more significant benefits compared to larger firms. Our findings suggest cloud technologies help reduce barriers to digitalisation, which affect especially smaller firms. By lowering these barriers, cloud adoption enhances scalability and unlocks untapped growth potential. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2410.07906 Wed, 13 May 2026 00:00:00 -0400 arXiv:2410.07906v2 Announce Type: replace Abstract: Structural change consists of industrial diversification towards more productive, knowledge intensive activities. However, changes in the productive structure bear inherent links with job creation and income distribution. In this paper, we investigate the consequences of structural change, defined in terms of labour shifts towards more complex industries, on employment growth, wage inequality, and functional distribution of income. The analysis is conducted for European countries using data on disaggregated industrial employment shares over the period 2010-2018. First, we identify patterns of industrial specialisation by validating a country-industry industrial employment matrix using a bipartite weighted configuration model (BiWCM). Secondly, we introduce a country-level measure of labour-weighted Fitness, which can be decomposed in such a way as to isolate a component that identifies the movement of labour towards more complex industries, which we define as structural change. Thirdly, we link structural change to i) employment growth, ii) wage inequality, and iii) labour share of the economy. The results indicate that our structural change measure is associated negatively with employment growth. However, it is also associated with lower income inequality. As countries move to more complex industries, they drop the least complex ones, so the (low-paid) jobs in the least complex sectors disappear. Finally, structural change predicts a higher labour ratio of the economy; however, this is likely to be due to the increase in salaries rather than by job creation. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2508.12471 Wed, 13 May 2026 00:00:00 -0400 arXiv:2508.12471v4 Announce Type: replace Abstract: Do high-return fields of study provide greater protection in labor market during crises? I construct pre-pandemic premia for major technical fields in India and examine whether workers in higher field-premium fields experience resilient labor market outcomes during COVID-19. Using a difference-in-difference with continuous treatment design, I find that field-premium advantages did not emerge immediately at the onset of the pandemic but through gradual adjustment during later phases. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2601.02964 Wed, 13 May 2026 00:00:00 -0400 arXiv:2601.02964v3 Announce Type: replace Abstract: Behavioral theories rest on parsimony: a small number of mechanisms organizing many decisions. We define a Maximum Rule Concentration Index that measures how parsimoniously a dataset of risky choices can be organized through a library of simple, parameter-free decision rules drawn from canonical behavioral theories: salience, regret, disappointment, modal-payoff focusing, extreme-outcome screening, and limited attention. Applied to three lottery-choice datasets, the data exhibit detectable parsimony: for a majority of subjects, observed concentration exceeds what standard utility models generate on the same menus. The concentration organizes around salience thinking, modal-payoff focusing, and regret. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2605.09642 Wed, 13 May 2026 00:00:00 -0400 arXiv:2605.09642v2 Announce Type: replace Abstract: In traditional rural societies, where social ties are embedded in physical space, the diffusion of emerging technologies may be amplified through socio-spatial contagion (SSC). Such processes may play a key role in accelerating residential PV adoption in off-grid regions. Yet empirical evidence on SSC in PV adoption remains largely limited to affluent, grid-connected settings, while off-grid regions often lack systematic installation records. To address these gaps, we use a deep learning segmentation model to extract PV installations from a decade-long series of remote sensing imagery across 507 off-grid settlement clusters (hereafter, communities). This enables data-driven spatio-temporal point pattern inference of SSC in data-scarce contexts. SSC is quantified through the range and intensity of clustering of new installations around prior adopters, and the dynamics of these dimensions are linked to adoption outcomes. We found that SSC is nearly ubiquitous, often spanning most of the community's spatial extent, while exhibiting substantial heterogeneity in intensity. Although SSC intensifies over time, its effects remain temporally concentrated, peaking within 1 to 2 years of nearby installations and weakening thereafter. SSC intensity is positively associated with adoption rates in both cross-sectional and temporal analyses. However, the relationship between SSC range and adoption changes over time - in early diffusion phases, adoption growth is associated with range expansion, whereas in later phases it is associated with range contraction. This shift reflects a transition from clustering to consolidation of installations. These findings highlight the potential of seeding interventions to accelerate PV diffusion in off-grid regions. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2605.11350 Wed, 13 May 2026 00:00:00 -0400 arXiv:2605.11350v1 Announce Type: cross Abstract: Generative Artificial Intelligence (AI) tools are rapidly adopted in the workplace and in education, yet the empirical evidence on AI's impact remains mixed. We propose a model of human-AI interaction to better understand and analyze several mechanisms by which AI affects productivity. In our setup, human agents with varying skill levels exert utility-maximizing effort to produce certain task outcomes with AI assistance. We find that incorporating either endogeneity in skill development or in AI unreliability can induce a productivity paradox: increased levels of AI assistance may degrade productivity, leading to potentially significant shortfalls. Moreover, we examine the long-term distributional effect of AI on skill, and demonstrate that skill polarization can emerge in steady state when accounting for heterogeneity in AI literacy -- the agent's capability to identify and adapt to inaccurate AI outputs. Our results elucidate several mechanisms that may explain the emergence of human-AI productivity paradoxes and skill polarization, and identify simple measures that characterize when they arise. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2605.11736 Wed, 13 May 2026 00:00:00 -0400 arXiv:2605.11736v1 Announce Type: cross Abstract: In approval-based budget division, the task is to allocate a divisible resource to the candidates based on the voters' approval preferences over the candidates. For this setting, Brandl et al. [2021] have shown that no distribution rule can be strategyproof, efficient, and fair at the same time. In this paper, we aim to circumvent this impossibility theorem by focusing on approximate strategyproofness. To this end, we analyze the incentive ratio of distribution rules, which quantifies the maximum multiplicative utility gain of a voter by manipulating. While it turns out that several classical rules have a large incentive ratio, we prove that the Nash product rule ($\mathsf{NASH}$) has an incentive ratio of $2$, thereby demonstrating that we can bypass the impossibility of Brandl et al. by relaxing strategyproofness. Moreover, we show that an incentive ratio of $2$ is optimal subject to some of the fairness and efficiency properties of $\mathsf{NASH}$, and that the positive result for the Nash product rule even holds when voters may report arbitrary concave utility functions. Finally, we complement our results with an experimental analysis. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2605.12094 Wed, 13 May 2026 00:00:00 -0400 arXiv:2605.12094v1 Announce Type: cross Abstract: We study Bayesian persuasion when the receiver evaluates actions by reward-side Conditional Value-at-Risk (CVaR) rather than expected utility. CVaR preferences break the standard action-based direct-recommendation reduction: merging signals that recommend the same action can change the receiver's tail-risk ranking and destroy incentive compatibility. We show that this failure does not imply intractability in the explicit finite-state model. Each CVaR action value is max-affine in the posterior, and refining recommendations by the active affine piece yields an active-facet revelation principle and an exact polynomial-size linear program. We further identify a representation boundary: listed polyhedral risks remain tractable by the same LP, whereas succinctly represented facet families make exact persuasion NP-hard. Finally, we give a finite-precision approximation scheme for risk preferences determined by finitely many stable posterior statistics. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2407.21198 Wed, 13 May 2026 00:00:00 -0400 arXiv:2407.21198v3 Announce Type: replace Abstract: We compute the lattice operations for the (pairwise) stable set in many-to-many matching markets when only path-independence on agents' choice functions is imposed. To do this, we first show that the sets of firm-quasi-stable and worker-quasi-stable many-to-many matchings form lattices. Then, we construct Tarski operators on these lattices whose fixed points coincide with the set of stable matchings, and show that iterating these operators from suitable quasi-stable matchings yields the lattice operations in the stable set. These operators resemble lay-off and vacancy chain dynamics, respectively. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2504.01829 Wed, 13 May 2026 00:00:00 -0400 arXiv:2504.01829v4 Announce Type: replace Abstract: How does one test empirically the hypothesis that a decision maker (DM) is being influenced by information via Bayesian persuasion? In this paper, I consider a DM whose state-dependent preferences are known to an analyst, who sees the conditional distribution of choices given the state. I provide necessary and sufficient conditions for the dataset to be consistent with the DM being Bayesian persuaded by an unobserved sender who generates a distribution of signals to ex-ante optimize the sender's expected payoff. I thereby provide a tool for empirical work on information design. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2605.08812 Tue, 12 May 2026 00:00:00 -0400 arXiv:2605.08812v1 Announce Type: new Abstract: In educational settings, AI can be used as a learning aid, but can also be used to avoid schoolwork, thereby passing classes while learning little. Many existing studies on the impact of AI on education focus on AI use in controlled settings or with specialized tools. In this paper, the dropoff in ChatGPT activity during non-school summer months in 2023 and 2024 is used to identify areas with heavy educational AI use and thus estimate the educational impact of AI as it is actually used. I find no meaningful impact of AI usage on high school test score averages in either direction. These results imply that, to the extent that high school students use AI to avoid learning, it either does not matter much for their test performance or is cancelled out by positive uses of AI in the aggregate. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2605.09182 Tue, 12 May 2026 00:00:00 -0400 arXiv:2605.09182v1 Announce Type: new Abstract: Daniel Kahneman and Amos Tversky argued for challenging inside views (informed by contextual specifics) with outside views (based on historical "base rates" for certain event types). A reasonable inside view of the prospects for the global economy in this century is that growth will converge to 2.5%/year or less: population growth is expected to slow or halt by 2100; and as more countries approach the technological frontier, economic growth should slow as well. To test that view, this paper models gross world product (GWP) observed since 10,000 BCE or earlier, in order to estimate a base distribution for changes in the growth rate as a function of the GWP level. For econometric rigor, it casts a GWP series as a sample path in a stochastic diffusion whose specification is novel yet rooted in neoclassical growth theory. After estimation, most observations fall between the 40th and 60th percentiles of predicted distributions. The fit implies that GWP explosion is all but inevitable, in a median year of 2047. The friction between inside and outside views highlights two insights. First, accelerating growth is more easily explained by theory than is constant growth. Second, the world system may be less stable than traditional growth theory and the growth record of the last two centuries suggest. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2605.10291 Tue, 12 May 2026 00:00:00 -0400 arXiv:2605.10291v1 Announce Type: new Abstract: Recent advances in generative artificial intelligence (AI) are reshaping who enters entrepreneurship, but not who reaches the top of the quality distribution. Using data on over 160,000 product launches on Product Hunt, we find that entrepreneurial entry increased sharply following the public release of ChatGPT-3.5, driven disproportionately by solo entrepreneurs. This shift toward solo entry is particularly pronounced in categories that historically favored team-based ventures. However, much of this growth reflects low-commitment, experimental entry and does not translate into greater representation among the highest-quality outcomes. Team-based ventures are increasingly dominant in the top tiers of platform rankings. These findings suggest that generative AI lowers barriers to solo entrepreneurship while reinforcing team-based advantages. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2605.10447 Tue, 12 May 2026 00:00:00 -0400 arXiv:2605.10447v1 Announce Type: cross Abstract: Agent-based models (ABMs) are increasingly used in macroeconomics, but their analysis still often relies on ad hoc Monte Carlo campaigns with heterogeneous statistical effort across parameter settings. We show how statistical model checking (SMC), implemented through MultiVeStA, can provide a principled analysis layer for a realistic macroeconomic ABM without rewriting the simulator in a dedicated formalism. Our case study is the heuristic-switching Keynes+Schumpeter(K+S) model, analysed hrough a transient sensitivity campaign over one-parameter sweeps, two macro observables (unemployment and GDP growth), and one auxiliary micro-level probe (market share) on the post-warmup phase of a 600-step horizon. The analysis is driven by reusable temporal queries, observable-specific precision targets, and confidence-based stopping rules that automatically determine the simulation effort required by each configuration. Results show a clear contrast across parameter families: macro-financial and structural sweeps produce the strongest transient effects, whereas several heuristic-rule sweeps remain much weaker under the same precision policy. More broadly, the paper shows that SMC can support reproducible and informative quantitative analysis of substantively rich economic ABMs, while making uncertainty estimates and simulation cost explicit parts of the reported results. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2605.10486 Tue, 12 May 2026 00:00:00 -0400 arXiv:2605.10486v1 Announce Type: cross Abstract: The introduction of leverage on prediction-market event contracts raises three structurally distinct questions that have not been addressed jointly: how leverage changes manipulation incentives, how it interacts with informed-trading rents, and how regulatory frameworks should respond. This paper develops a theoretical framework for the first two and a synthesis of the existing regulatory landscape for the third. The principal analytical move is a two-axis manipulation taxonomy distinguishing market-price manipulation from real-world outcome manipulation, where the manipulator affects the underlying event itself. Continuous-underlying derivative markets generally do not make outcome manipulation a venue-level payoff channel; event-linked markets do. Within this taxonomy, leverage plays asymmetric roles: it scales market-price manipulation linearly but shifts the cost-benefit threshold for outcome manipulation, and it scales informed-trading rents in three ways (direct multiplication, Sharpe-ratio preservation, detection-cost amortization). Section 7 connects Paper 1's pre-emption and halt-protocol findings (CC-007b, CC-008) to three manipulation channels: pre-emption introduced by the dynamic-margin engine, halt-arbitrage introduced by the resolution-zone halt protocol, and strategic bad-debt-shifting that no engine in Paper 1's framework family addresses. The framework's manipulation-resistance contribution is a re-allocation of attack surface, not a net reduction. The regulatory synthesis covers principal jurisdictions (US, EU, UK, Singapore, offshore) and identifies three regulatory-arbitrage pathways. The paper concludes with 14 recommendations for venue operators, regulatory bodies, and the research community, separated into framework-independent and framework-conditional categories. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2406.05408 Tue, 12 May 2026 00:00:00 -0400 arXiv:2406.05408v3 Announce Type: replace Abstract: We study \emph{Human Projection} (HP): people's tendency to evaluate AI using the same frameworks they use for humans -- treating features such as task difficulty and the reasonableness of mistakes as diagnostic of overall ability. We formalize HP and its consequences for equilibrium adoption, testing its predictions experimentally. First, people project human difficulty onto AI, overestimating performance on human-easy tasks, underestimating it on human-hard ones, and over-updating after easy failures and hard successes -- leading to systematic misspecification when AI performance is jagged rather than human-ordered. Second, HP interprets observed performance through a single ability index, inducing all-or-nothing adoption even when AI outperforms humans on only some tasks; experimentally stripping AI of human-like cues weakens cross-task generalization and reduces over-adoption. Finally, a field experiment with a parenting-advice chatbot shows that less humanly reasonable mistakes cause larger drops in trust and future engagement. Anthropomorphic AI design can amplify HP, misaligning beliefs and distorting adoption. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2505.22873 Tue, 12 May 2026 00:00:00 -0400 arXiv:2505.22873v2 Announce Type: replace Abstract: We present a novel framework for high-resolution forecasting of residential heating demand and non-heating electricity demand using probabilistic deep learning models. Because our models are trained on electricity consumption from a predominantly gas-heated region, the learned electricity demand patterns primarily reflect non-heating end uses such as lighting, appliances, and cooling. We focus specifically on providing hourly building-level electricity and heating demand forecasts for the residential sector. Leveraging multimodal building-level information -- including data on building footprint areas, heights, nearby building density, nearby building size, land use patterns, and high-resolution weather data -- and probabilistic modeling, our methods provide granular insights into demand heterogeneity. Validation at the building level underscores a step change improvement in performance relative to NREL's ResStock model, which has emerged as a research community standard for residential heating and electricity demand characterization. In building-level heating and electricity estimation backtests, our probabilistic models respectively achieve RMSE scores 18.8% and 27.6% lower than those based on ResStock, with probabilistic forecast quality measured via WIS improving by 59% for both applications. By offering an open-source, scalable, high-resolution platform for demand estimation and forecasting, this research advances the tools available for policymakers and grid planners, contributing to the broader effort to decarbonize the U.S. building stock and meeting climate objectives. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2602.01022 Tue, 12 May 2026 00:00:00 -0400 arXiv:2602.01022v3 Announce Type: replace Abstract: Behavioral parameters such as loss aversion, herding, and extrapolation are central to asset pricing models but remain difficult to measure reliably. We develop a framework that treats large language models (LLMs) as calibrated measurement instruments for behavioral parameters. Using four models and 24{,}000 agent--scenario pairs, we document systematic rationality bias in baseline LLM behavior, including attenuated loss aversion, weak herding, and near-zero disposition effects relative to human benchmarks. Profile-based calibration induces large, stable, and theoretically coherent shifts in several parameters, with calibrated loss aversion, herding, extrapolation, and anchoring reaching or exceeding benchmark magnitudes. To assess external validity, we embed calibrated parameters in an agent-based asset pricing model, where calibrated extrapolation generates short-horizon momentum and long-horizon reversal patterns consistent with empirical evidence. Our results establish measurement ranges, calibration functions, and explicit boundaries for eight canonical behavioral biases. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2602.11992 Tue, 12 May 2026 00:00:00 -0400 arXiv:2602.11992v2 Announce Type: replace Abstract: We conduct a pre-registered randomized controlled trial to test for income targeting in labor supply decisions among sellers of a Swedish street paper. Unlike most workers, these sellers choose their own hours and face severe liquidity constraints and volatile incomes. Treated individuals received a 25 percent bonus per copy sold for the duration of an issue, simulating an increase in earnings potential. Consistent with standard labor supply theory, they sold more papers and, by our measures, worked longer hours and took fewer days off. These findings contrast with studies on intertemporal labor supply that find small substitution effects. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2604.09871 Tue, 12 May 2026 00:00:00 -0400 arXiv:2604.09871v2 Announce Type: replace Abstract: This paper studies how the organization of production shapes democratic accountability. I propose a model in which learning economies make specialization productively efficient: most workers perform one-domain tasks, while a small set of integrators with cross-domain knowledge keep the system coherent. When policy consequences run across domains, integrators understand them better than specialists. Electoral competition then tilts government policies toward integrators' interests, while low aggregate system knowledge weakens governance and reduces the fraction of public resources converted into citizen-valued services. Labor markets leave these civic margins unpriced, failing to internalize the political returns to system knowledge. Broadening specialists can therefore raise welfare relative to the market allocation. The model speaks to debates on liberal arts education and the effects of AI. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2605.09029 Tue, 12 May 2026 00:00:00 -0400 arXiv:2605.09029v1 Announce Type: new Abstract: Communication is secret if a message is independent of the state; however, the receiver's subsequent action may still reveal that she has acted on hidden information. This paper studies when secret communication can also provide plausible deniability: under single-crossing preferences, every action induced by the sender's message must be rationalizable using the receiver's baseline information alone. We characterize joint information structures that satisfy both secrecy and plausible deniability. We show that plausible deniability restricts communication exactly when the baseline message is directional -- meaning its likelihood is monotone in the state. Combining this restriction with secrecy, we show that, for directional messages, frontier communication reveals at most whether the state lies above or below a cutoff. Finally, we identify conditions under which a greatest feasible communication structure exists and can be constructed explicitly in a simple way. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2605.09083 Tue, 12 May 2026 00:00:00 -0400 arXiv:2605.09083v1 Announce Type: new Abstract: Many economic interventions are designed as marginal changes in incentives. Yet in environments shaped by coordination, institutional persistence, and path dependence, such reforms often leave behavior largely unchanged. This paper studies interventions in games when equilibrium selection displays status-quo inertia: if the pre-intervention equilibrium remains a Nash equilibrium after policy, it continues to be selected. In that environment, price-based interventions and simple option expansion may fail even when they improve welfare in a partial-equilibrium sense. By contrast, interventions that modify the feasible action space, especially deletion and replacement interventions, can be substantially more effective because they remove the strategic basis for persistence. We develop a simple framework, derive general results, provide complete proofs, and illustrate the economics with examples from climate transition, platform regulation, financial reform, and industrial modernization. The analysis highlights a basic policy lesson: when inefficient equilibria are institutionally entrenched, the central problem is often not how to price the existing game more finely, but how to change the game itself. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2605.09136 Tue, 12 May 2026 00:00:00 -0400 arXiv:2605.09136v1 Announce Type: new Abstract: Noise traders can be dispensed with entirely. Partial revelation of information through prices arises under any non-exponential expected utility preference, including CRRA, without noise traders, random endowments, supply shocks, hedging motives, or behavioral biases. The model contains zero exogenous noise. The mechanism is a mismatch between the space in which market clearing aggregates signals and the Bayesian sufficient statistic. CARA demand is linear in log-odds, so prices aggregate in log-odds space and reveal the statistic exactly. Every other preference aggregates differently; the resulting Jensen gap makes revelation partial. I prove that CARA is the unique fully revealing preference class, characterize the rational expectations equilibrium via a contour integration fixed point, and verify that partial revelation survives learning from prices. The Grossman-Stiglitz paradox is resolved: information acquisition has positive value within the rational class. Numerical solution of the rational expectations fixed point at K = 3 confirms partial revelation, positive trade volume, and positive value of information across the full range of CRRA risk aversion, vanishing only in the CARA limit. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2605.10495 Tue, 12 May 2026 00:00:00 -0400 arXiv:2605.10495v1 Announce Type: cross Abstract: This paper develops a quantitative framework to assess the robustness of Bayes-optimal decisions in finite decision problems under model uncertainty. We introduce two complementary stability notions for acts: the robustness radius, measuring the largest perturbation of a reference prior under which an act remains Bayes-optimal, and the contamination need, quantifying the minimal perturbation required for an act to become Bayes-optimal under some nearby prior. Both concepts are characterized via linear programming formulations and computed efficiently using bisection methods exploiting monotonicity properties. Building on these stability measures, we propose a cost-adjusted stability criterion that integrates robustness considerations with act-specific selection costs, yielding a parametric family of decision rules indexed by a regularization parameter. We analyze how optimal act selection evolves along this parameter and derive selection paths that reveal structural transitions between stability-driven and cost-driven regimes. The framework is applied to a portfolio choice problem under uncertainty between different economic regimes. Concretely, using data on historical ETF returns, we compute robustness and contamination profiles for six portfolio strategies and analyze their behavior under heterogeneous belief specifications. The results illustrate that robustness-based selection refines classical expected utility by accounting for prior misspecification. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2605.10505 Tue, 12 May 2026 00:00:00 -0400 arXiv:2605.10505v1 Announce Type: cross Abstract: We propose a game-theoretic framework for adaptive multi-agent intelligent systems. Unlike classical game theory, which often treats strategies as primitive objects chosen by perfectly rational agents, the proposed framework provides a mathematical foundation for studying equilibrium in NeuroAI and can be viewed as an extension of game theory under relaxed assumptions, including partial observability, bounded computation, and uncertainty. At its core, Multilevel Interactive Equilibrium (MIE) generalizes the classical Nash equilibrium to intelligent systems with internal computation. Rather than being defined solely at the level of observable behavior, equilibrium emerges when neural learning dynamics, cognitive representations, and behavioral strategies mutually stabilize between interacting agents. This framework applies uniformly to interactions between two biological brains, two artificial agents, or hybrid human-AI systems. We discuss applications of multilevel game theory to human-autonomous vehicle driving, human-machine interaction, human-large language model (LLM) interaction, and computational psychiatry. We also outline experimental strategies and computational methods for estimating MIE and discuss challenges and prospects for future research. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2407.02948 Tue, 12 May 2026 00:00:00 -0400 arXiv:2407.02948v3 Announce Type: replace Abstract: Patients often avoid medical tests because testing produces not only useful information but also painful beliefs. This paper studies optimal communication between a doctor and an information-avoidant patient who first decides whether to take a test and, after an unfavorable result, whether to accept treatment. The doctor can disclose information about how severe non-treatment would be if the patient is sick. The main tension is between warning and reassurance. A warning can make treatment compelling after diagnosis, but reassurance can make testing acceptable by preserving hope about the untreated prospect. I characterize the optimal policy. When the warning that supports treatment is compatible with testing, the doctor uses warning-in-advance. When such warning would deter testing, the doctor constructs an information greenhouse: a committed post-test information environment that reassures the patient about the untreated prospect. With voluntary consultation, reassurance must sometimes be moved before the test as precautionary comfort. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2505.03232 Tue, 12 May 2026 00:00:00 -0400 arXiv:2505.03232v3 Announce Type: replace Abstract: This paper studies preference aggregation under uncertainty in the multi-profile framework and characterizes a new class of aggregation rules that address classical concerns about Harsanyi's (1955) utilitarian rules. Our aggregation rules, which we call relative fair aggregation rules, are grounded in three key ideas: utilitarianism, egalitarianism, and the 0--1 normalization of individual utilities. These rules are parameterized by a set of weight vectors over individuals and evaluate each ambiguous alternative by taking the minimum weighted sum of 0--1 normalized utility levels over the weight set. For the characterization, we propose two novel axioms -- weak preference for mixing and restricted certainty independence -- developed by using a new method of objectively randomizing outcomes within the Savagean setting. Additional results clarify how these axioms capture the utilitarian and egalitarian attitudes of the rules. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2505.14639 Tue, 12 May 2026 00:00:00 -0400 arXiv:2505.14639v5 Announce Type: replace Abstract: This paper analyzes a cheap-talk model with multiple senders and one receiver. Each sender observes a noisy signal about an unknown state and sends a message; the receiver observes the message tally and chooses a policy. This setting shares certain features with voting models (e.g., Feddersen and Pesendorfer, 1997, 1998). The existing literature (e.g., Levit and Malenko, 2011; Battaglini, 2017) focuses on scenarios in which the receiver and the senders agree on the preferred policy in each state. In contrast, we explore environments in which the receiver and the senders disagree over the preferred policy in some states. We establish an equilibrium no-conflict result: in any non-babbling equilibrium, the senders and the receiver agree on the preferred policy at every realized message tally. We show that information aggregation fails, and the receiver cannot fully learn the state even as the number of senders grows large. We also identify a discontinuity in information transmission relative to the implications of the existing literature. Finally, introducing a mediator can improve information transmission and restore efficiency. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2511.12456 Mon, 11 May 2026 00:00:00 -0400 arXiv:2511.12456v4 Announce Type: replace-cross Abstract: Existing auction mechanisms are vulnerable to bidder collusion, which substantially degrades revenue and non-colluder welfare. To design truthful mechanisms resilient to collusion, we introduce a novel approach that leverages a machine learning classifier to predict (even imprecisely) which bidders are colluding. We first establish a Bulow-Klemperer-type result for multi-unit auctions with single-minded bidders, demonstrating that collusion significantly harms existing mechanisms only when the colluding coalition is large. Consequently, we focus our design on settings with many colluders. Building on the welfare-optimal Vickrey-Clarke-Groves (VCG) mechanism, we propose two novel truthful mechanisms: VCG-Posted Price (V-PoP) and Conditional-Posted Price (C-PoP). V-PoP applies VCG to non-colluding bidders and posted prices to colluding ones, and ensuring truthfulness is non-trivial because we must dynamically split the quantity of items between these groups based on the values of the non-colluder bids. C-PoP further advances this by computing a posted price conditioned on non-colluder bids, and ensuring truthfulness is non-obvious because the posted price is chosen using the values of the non-colluder bids. Because real-world classifiers make errors, we provide theoretical lower bounds on the auction price of V-PoP and C-PoP under misclassification, which theory shows acts as a proxy for welfare and revenue. Crucially, our bounds yield actionable insights for classifier design, revealing that false negatives (misclassifying colluders as non-colluders) are preferable to false positives (misclassifying non-colluders as colluders). Numerical experiments demonstrate that our mechanisms achieve high welfare and revenue against collusion, even when utilizing simple, low-cost classifiers. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2602.20281 Mon, 11 May 2026 00:00:00 -0400 arXiv:2602.20281v3 Announce Type: replace Abstract: We study incentive design when multiple principals simultaneously design mechanisms for their respective teams in environments with strategic spillovers. In this environment, each principal's set of incentive-compatible mechanisms--those that satisfy their own agents' incentive compatibility constraints--depends on the mechanisms offered by the other teams. Following a classic example by Myerson (1982), such games may lack equilibrium due to discontinuities in the correspondence of incentive-compatible mechanisms. We establish general conditions for equilibrium existence by introducing a novel approach that involves tracking both the outcome distributions along the truthful-obedient path and the sets of outcome distributions achievable through unilateral deviations, thereby providing a foundation for analyzing a wide range of multi-principal mechanism design with team production and agency problems. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2304.05515 Mon, 11 May 2026 00:00:00 -0400 arXiv:2304.05515v3 Announce Type: replace Abstract: Cursed Equilibrium of Eyster and Rabin (2005) has been a leading theory for explaining winner's-curse-type behavior in static Bayesian games, but it faces conceptual limitations when applied to dynamic games. Two recent extensions, Cursed Sequential Equilibrium (CSE) by Fong, Lin and Palfrey (2025) and Sequential Cursed Equilibrium (SCE) by Cohen and Li (2026), address these limitations in fundamentally different ways. Complementing these two papers, this paper provides a systematic comparison of CSE and SCE, clarifying their conceptual foundations and technical implications, including their notions of cursedness, belief updating, and treatment of public histories. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2605.07671 Mon, 11 May 2026 00:00:00 -0400 arXiv:2605.07671v1 Announce Type: cross Abstract: Eliciting truthful reports from autonomous agents is a core problem in scalable AI oversight: a principal scores the agent's report using a strictly proper scoring rule, but the agent also benefits from the report through a non-accuracy channel (approval for autonomous action, allocation share, downstream control). The same structure appears in classical mechanism-design settings such as marketplace operation. Our main result is an endogeneity: the principal's optimal oversight necessarily uses a non-affine approval function to screen types, yet any non-affine approval makes truthful reporting suboptimal under the combined objective whenever deviation is undetectable. The principal cannot avoid the perturbation that undermines calibration. This impossibility holds for all strictly proper scoring rules, with a closed-form perturbation formula. A constructive escape exists: a step-function approval threshold achieves first-best screening for every strictly proper scoring rule, because the agent's binary inflate-or-not choice creates a type-space threshold regardless of the generator's curvature. Under the Brier score specifically, the type-independent inflation cost yields a welfare equivalence between second-best and first-best; we prove this equivalence is unique to Brier (the welfare gap under smooth $C^1$ oversight is bounded below by $\Omega(\text{Var}(1/G'') (\gamma/\beta)^2)$ for every non-Brier rule). Two instances develop the framework: AI agent oversight (the lead motivating setting) and marketplace operation (a parallel mechanism-design domain). The message for AI alignment is direct: smooth scoring-based oversight cannot elicit truthful reports from a strategic agent; sharp thresholds are the calibration-preserving design. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2605.06987 Mon, 11 May 2026 00:00:00 -0400 arXiv:2605.06987v1 Announce Type: cross Abstract: Aligning large language models (LLMs) to human preferences typically relies on aggregating pooled feedback into a single reward model. However, this standard approach assumes that all labelers share the same underlying preferences, ignoring the fact that real-world labelers are highly heterogeneous and usually anonymous. Consequently, relying solely on binary choice data fundamentally distorts the learned policy, making the true population-average preference unidentifiable. To overcome this critical limitation, we demonstrate that augmenting preference datasets with a simple, secondary signal -- the user's response time -- can restore the identifiability of the population's average preference. By modeling each decision as a Drift-Diffusion Model (DDM), we introduce a novel, consistent estimator of heterogeneous preferences that successfully corrects the distortions of standard choice-only labels. We prove that our estimator asymptotically converges to the true average preference even in extreme cases where each anonymous labeler contributes only a single choice. Empirically, across both synthetic and real-world datasets, our method consistently outperforms standard baselines that otherwise fail and plateau at a bias floor. Because response times are essentially free to record and require zero user tracking or identification, our results bring promises and open up new opportunities for future data-collection pipelines to improve the social benefit without requiring user-level identifiers or repeated elicitations. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2605.06721 Mon, 11 May 2026 00:00:00 -0400 arXiv:2605.06721v1 Announce Type: cross Abstract: This note proposes a simple polynomial-time method for constructing an ex ante stable school-choice lottery satisfying equal treatment of equals. The method applies the ETE reassignment to a constrained efficient stable matching and yields a lottery that is not ordinally dominated by any other ex ante stable lottery. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2605.07528 Mon, 11 May 2026 00:00:00 -0400 arXiv:2605.07528v1 Announce Type: new Abstract: We propose an aggregate notion of non-transferable utility (NTU) stability for decentralized matching markets with fixed prices, where market clearing is achieved through one-sided money burning, which can be interpreted as waiting. Agents are grouped into observable types and are indifferent among individuals within type; equilibrium is defined at the type level and delivers equal indirect utility within each type. We introduce money burning into two types of NTU models: In a deterministic model, we relate our notion to classical Gale--Shapley stability and show how money burning decentralizes stable outcomes under aggregation. We then introduce separable random utility, obtaining an NTU counterpart to Choo and Siow (2006). We prove the existence and uniqueness of equilibrium and provide a stationary queueing interpretation. Finally, we develop a generalized deferred acceptance algorithm based on alternating constrained discrete-choice problems and prove its convergence to the unique equilibrium. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2605.07469 Mon, 11 May 2026 00:00:00 -0400 arXiv:2605.07469v1 Announce Type: new Abstract: We study which outcomes are implementable by disclosing coarse statistics of a data-generating process rather than its full distribution. Players observe data whose joint distribution is only partially known: they know the expectations of finitely many random variables and form beliefs by maximum-entropy inference. We obtain two characterizations. When message spaces are unrestricted, implementable outcomes coincide with jointly coherent outcomes, expanding the set of correlated equilibria. With canonical mechanisms, implementability reduces to a single cross-entropy condition: the target outcome must lie on the cross-entropy level set of some correlated equilibrium that passes through that equilibrium itself. Examples and several classes of games illustrate the reach of the framework. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2601.18991 Mon, 11 May 2026 00:00:00 -0400 arXiv:2601.18991v2 Announce Type: replace-cross Abstract: USDC and USDT are the dominant stablecoins pegged to \$1 with a total market capitalization of over \$300B and rising. Stablecoins make dollar value globally accessible with secure transfer and settlement. Yet in practice, these stablecoins experience periods of stress and de-pegging from their \$1 target, posing significant systemic risks. The behavior of market participants during these stress events and the collective actions that either restore or break the peg are not well understood. This paper addresses the question: who restores the peg?. We develop a dynamic, agent-based mean-field game framework for fiat-collateralized stablecoins, in which a large population of arbitrageurs and retail traders strategically interact across primary and secondary markets during a de-peg episode. The key advantage of this equilibrium formulation is that it endogenously maps market frictions into a market-clearing price path and implied net order flows, allowing us to attribute peg-reverting pressure by channel and to stress-test when a given infrastructure becomes insufficient for recovery. Using three historical de-peg events, we show that the calibrated equilibrium reproduces observed recovery half-lives and yields an order flow decomposition in which system-wide stress is predominantly stabilized by primary-market arbitrage. Finally, a quantitative sensitivity analysis identifies a non-linear breakdown threshold, beyond which a de-peg becomes markedly slower to reverse. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2604.25826 Mon, 11 May 2026 00:00:00 -0400 arXiv:2604.25826v2 Announce Type: replace Abstract: We show that the leading bubble test suffers severe size distortion when fundamentals incorporate general-purpose technology adoption. Embedding a hump-shaped technology shock in the Campbell-Shiller present-value model, we prove that the fundamental price becomes locally explosive during adoption, contaminating the test's limit distribution with a non-centrality parameter proportional to the shock's peak. We propose a fundamental-versus-speculative decomposition that projects prices onto observable technology proxies and applies the test to the residual. Empirically, the decomposition eliminates evidence of speculation in the 2020-2025 AI rally while confirming a speculative peak confined to December 1999-March 2000 in the dot-com episode. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2511.06545 Mon, 11 May 2026 00:00:00 -0400 arXiv:2511.06545v2 Announce Type: replace Abstract: As generative artificial intelligence (GenAI) automates coding tasks and expands access to technical resources, this paper examines how GenAI-enabled coding automation, colloquially known as "vibecoding," affects digital entrepreneurial entry and venture performance. We exploit ex-ante variation in ventures' exposure to vibecoding based on the product characteristics of their initial launches and estimate difference-in-differences models around the diffusion of GenAI coding tools. Vibecoding increases first-time launches and shortens time to launch, but economically viable entry rises only where vibecoding augments, rather than fully automates, product development. In these partially exposed product segments, viable entry increases by 11%, driven entirely by ventures founded by individuals with STEM education or work experience, especially those whose most recent employment was outside middle management. Among ventures launched before GenAI became widely accessible, performance gains similarly concentrate among partially exposed ventures with engineering-intensive initial teams. Together, these results suggest that GenAI-enabled coding automation does not eliminate the value of technical expertise. Instead, vibecoding creates the greatest value when it complements internal engineering capabilities, allowing ventures to delegate lower-level coding tasks to GenAI while shifting human effort toward higher-level problem solving and dynamic adaptation. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2508.00208 Mon, 11 May 2026 00:00:00 -0400 arXiv:2508.00208v3 Announce Type: replace Abstract: The rapid growth of digital shopping channels has prompted many traditional retailers to invest in e-commerce websites and mobile apps. While prior literature shows that multichannel customers are more valuable, it overlooks how the motive for adopting a new channel shapes post-adoption behavior. Using transaction-level data from a major Brazilian pet supplies retailer, we study offline-only consumers who adopt online shopping via four distinct pathways: organic adoption, the COVID-19 pandemic, Black Friday promotions, and a loyalty program. We examine how these pathways affect post-adoption spend, profitability, and channel usage using consumer-level panel data and difference-in-differences estimates. We find that all adopters increase spending relative to offline-only consumers, but their post-adoption behaviors differ systematically by adoption motive. Promotion-driven adopters engage in forward buying and exhibit lower subsequent profitability, whereas COVID-19 adopters display stronger offline persistence consistent with consumer inertia and habit theory. Our findings have important managerial implications: firms should design promotions that discourage stockpiling, reinforce habits among customers pushed online by external shocks, and explicitly account for heterogeneity in channel adoption motives when forecasting customer lifetime value and assessing the breakeven and ROI of promotions designed to induce the adoption of new channels. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2212.07384 Mon, 11 May 2026 00:00:00 -0400 arXiv:2212.07384v5 Announce Type: replace Abstract: We propose a methodology to estimate the market value of pharmaceutical drugs. Our approach combines the event study method with a discounted cash flow model that infers drug values from stock market responses to drug development announcements. We estimate the average value of a drug developed by small firms (those below the 95th percentile of market capitalization) to be \$2.16 billion. At the preclinical stage, the risk-adjusted and present discounted average net value of drugs is \$50 million. Leveraging these estimates, we also determine the expected drug development cost at the start of the discovery stage to be \$38 million. We estimate values and costs for several therapeutic areas (e.g., neoplasm, infections) and explore applying these estimates to design policies that support drug development through drug buyouts and targeted preclinical interventions. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2110.13814 Mon, 11 May 2026 00:00:00 -0400 arXiv:2110.13814v3 Announce Type: replace Abstract: We study actual bidding behavior when a new auction format gets introduced into the marketplace. More specifically, we investigate this question using a novel dataset on internet display advertising auctions that exploits a staggered adoption by different publishers (sellers) of first-price auctions (FPAs), instead of the traditional second-price auctions (SPAs). We analyze the auction format change using difference-in-differences regressions and a synthetic difference-in-differences estimator, which better handles pre-trends. The results show that revenue per sold impression (price) jumps considerably for treated publishers relative to control publishers, with increases ranging from 25% to 75% of the pre-treatment price level of the treated group. Moreover, for later auction format changes, the increase in price levels under FPAs relative to those under SPAs tends to dissipate over time, reminiscent of the revenue equivalence theorem, although the extent of this reversion depends on the specification. We view these results as suggestive of initially insufficient bid shading following the format change, as opposed to an immediate transition to a new Bayesian Nash equilibrium, with prices tending to decline in several specifications in a manner consistent with gradual adjustment in bidding behavior as bidders learn to shade their bids. Our work constitutes one of the first field studies on bidders'responses to auction format changes, providing an important complement to theoretical model predictions. As such, it provides valuable information to auction designers when considering the implementation of different formats. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2605.07996 Mon, 11 May 2026 00:00:00 -0400 arXiv:2605.07996v1 Announce Type: cross Abstract: Nash equilibrium serves as a fundamental mathematical tool in economics and game theory. However, it classically assumes knowledge of player utilities, whereas economics generally regards preferences as more fundamental. To leverage equilibrium analysis in strategic scenarios, one must first elicit numerical utilities consistent with player preferences, a delicate and time-consuming process. In this work, we forgo precise utilities and generalize the Nash equilibrium to a setting where we only assume a player is capable of providing an ordinal ranking of their actions within the context of other players' joint actions. The key technical challenge is to rethink the definition of a best-response. While the classical definition identifies actions maximizing expected payoff, we naturally look towards social choice theory for how to aggregate preferences to identify the most preferred actions. We define this generalized notion of a context-ordinal Nash equilibrium, establish its existence under mild conditions on aggregation methods, introduce notions of regularization, approximation, and regret, explore complexity for simple settings, and develop learning rules for computing such equilibria. In doing so, we provide a generalization of Nash equilibrium and demonstrate its direct applicability to elicited preferences in human experiments. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2603.00041 Mon, 11 May 2026 00:00:00 -0400 arXiv:2603.00041v2 Announce Type: replace-cross Abstract: Causal machine learning (ML) recovers graphical structures that inform us about potential cause-and-effect relationships. Most progress has focused on cross-sectional data with no explicit time order, whereas recovering causal structures from time series data remains the subject of ongoing research in causal ML. In addition to traditional causal ML, this study assesses econometric methods that some argue can recover causal structures from time series data. The use of these methods can be explained by the significant attention the field of econometrics has given to causality, and specifically to time series, over the years. This presents the possibility of comparing the causal discovery performance between econometric and traditional causal ML algorithms. We seek to understand if there are lessons to be incorporated into causal ML from econometrics, and provide code to translate the results of these econometric methods to the most widely used Bayesian Network R library, bnlearn. We investigate the benefits and challenges that these algorithms present in supporting policy decision-making, using the real-world case of COVID-19 in the UK as an example. Four econometric methods are evaluated in terms of graphical structure, model dimensionality, and their ability to recover causal effects, and these results are compared with those of eleven causal ML algorithms. Amongst our main results, we see that econometric methods provide clear rules for temporal structures, whereas causal-ML algorithms offer broader discovery by exploring a larger space of graph structures that tends to lead to denser graphs that capture more identifiable causal relationships. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2512.23694 Mon, 11 May 2026 00:00:00 -0400 arXiv:2512.23694v2 Announce Type: replace-cross Abstract: Reliable long-horizon value prediction is difficult in offline reinforcement learning because fitted value methods combine bootstrapping, function approximation, and distribution shift, while standard guarantees often require Bellman completeness or realizability. We introduce Bellman calibration, a weak reliability criterion requiring that states assigned similar predicted values have average Bellman targets that agree with those predictions. This criterion yields a scalar calibration error for diagnosing systematic numerical miscalibration, which we estimate from off-policy data using doubly robust Bellman target estimates. We then propose Iterated Bellman Calibration, a model-agnostic post-hoc procedure that recalibrates any learned value predictor by fitting a one-dimensional map of its original prediction, with histogram and isotonic variants. We prove finite-sample guarantees showing that Bellman calibration error is controlled at one-dimensional nonparametric rates without Bellman completeness or value-function realizability. Our value-error bounds separate statistical estimation, finite-iteration, and approximation errors, clarifying when calibration improves value prediction and when its gains are limited by the information in the original predictor or insufficient coverage. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2509.21172 Mon, 11 May 2026 00:00:00 -0400 arXiv:2509.21172v2 Announce Type: replace-cross Abstract: Inverse reinforcement learning (IRL) aims to infer rewards from observed behavior, but rewards are not identified from the policy alone: many reward--value pairs can rationalize the same actions. Meaningful reward recovery therefore requires a normalization, yet existing normalized IRL methods often rely on anchor-action restrictions or specialized neural architectures. We study reward recovery in the maximum-entropy, or Gumbel-shock, model under a broad class of statewise affine normalizations, with anchor-action constraints as a special case. This yields Generalized Policy-to-$Q$-to-Reward (GenPQR), a modular procedure that estimates the behavior policy, evaluates its soft $Q$-function through the Bellman equation, and recovers the normalized reward. Both stages can be implemented with off-the-shelf classification and regression methods. We prove modular finite-sample guarantees under general function approximation, with separate policy-estimation and $Q$-estimation errors. As a concrete instantiation, we study GenPQR with fitted $Q$-evaluation, reducing IRL to policy estimation followed by regression. Experiments show that GenPQR matches or improves reward recovery relative to DeepPQR while remaining simpler and more modular. Compared with DeepPQR, our theory goes beyond anchor actions, accommodates large and continuous action spaces, makes coverage requirements explicit, and is not tied to a specific neural-network architecture or training procedure. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2506.06776 Mon, 11 May 2026 00:00:00 -0400 arXiv:2506.06776v3 Announce Type: replace Abstract: This paper studies the problem of testing whether a system of linear equality and inequality constraints admits a solution when the coefficients of that system may have to be estimated. We show that a wide range of inferential questions in partially identified models can be formulated as hypotheses of this form. Our approach exploits an alternative characterization of the hypothesis based on whether the value of a certain linear program is equal to zero. Building on this characterization, we develop bootstrap-based testing procedures and establish their uniform validity over large classes of data-generating processes. Simulation results demonstrate good finite-sample performance, even for moderate sample sizes. We illustrate the usefulness of the approach in two empirical applications. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2605.07065 Mon, 11 May 2026 00:00:00 -0400 arXiv:2605.07065v1 Announce Type: cross Abstract: Individual treatment effects are not point-identified from data. The Probability of Necessity and Sufficiency (PNS) circumvents this limitation by characterizing individual-level causality through intersection bounds derived from combined experimental and observational data. In finite samples, however, standard plug-in estimators systematically fail: they violate structural probability constraints and suffer from extremum bias induced by max-min operators, yielding spuriously narrow intervals. We propose a neural framework for finite-sample PNS estimation that resolves both pathologies. We introduce an anchored neural architecture that guarantees structural constraint satisfaction by construction. To correct extremum bias, we employ precision-corrected intersection-bound inference, leveraging Epistemic Neural Networks for scalable, high-dimensional uncertainty quantification. Empirical evaluations confirm that this approach maintains nominal coverage and exact constraint validity in high-dimensional regimes where standard estimators systematically undercover. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2605.06686 Mon, 11 May 2026 00:00:00 -0400 arXiv:2605.06686v1 Announce Type: cross Abstract: Previous research has investigated the potential of refugee matching for boosting refugee outcomes, first considered by Bansak et al. (2018). This paper demonstrates the stability of counterfactual impact evaluation results in the context of refugee matching in the United States using a range of off-policy evaluation methods. In order to estimate counterfactual impact and test the robustness of our results, we employ several evaluation methods, including inverse probability weighting (IPW) and multiple variants of augmented inverse probability weighting (AIPW). We also consider various modifications, including alternative modeling architectures and different assignment procedures. The impact estimates remain consistent in magnitude in all scenarios as well as statistically significant in most cases. Furthermore, the estimates are also consistent with the results originally presented in Bansak et al. (2018). Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2605.05456 Fri, 08 May 2026 00:00:00 -0400 arXiv:2605.05456v1 Announce Type: new Abstract: Local projections (LP) and vector autoregressions (VAR) are the two standard tools for impulse response analysis, but they often display a finite-sample trade-off: LP is typically less biased but more volatile, while VAR is more precise but can be biased under misspecification. We propose an easy-to-implement estimator-averaging approach that combines LP and VAR at each horizon by minimizing the mean squared error of the impulse response itself, rather than in-sample fit. We derive closed-form oracle weights for this finite-sample risk problem, develop feasible AR-sieve-bootstrap procedures, and compare them against an Rsquare-based model-averaging benchmark. For a benchmark class of short-memory linear data generating processes in which LP and VAR are both consistent, we establish the consistency and limiting distribution of the feasible averaged estimator. Monte Carlo results show meaningful risk reductions relative to LP and VAR alone. In an empirical application revisiting Bauer and Swanson (2023), estimator averaging delivers stable and economically intuitive responses for yields, activity, prices, and credit spreads. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2605.06482 Fri, 08 May 2026 00:00:00 -0400 arXiv:2605.06482v1 Announce Type: new Abstract: Municipal 311 call centers and complaint intake systems face a structural mismatch between incoming volume and classification capacity. The staff and heuristics available to triage, route, and prioritize complaints cannot scale with demand. This bottleneck produces differential service quality that follows income and racial lines (\cite{liu2024sla}). We develop an equity-centered reinforcement learning (RL) framework that augments call classification capacity across six New York City Department of Buildings (DOB) operational domains: boiler safety, crane and derrick oversight, heat and hot water complaints, housing complaint triage, scaffold safety, and Natural Area District (SNAD) protection. Rather than replacing human classifiers, our agents act as intelligent intake routers: learning to assign incoming complaints to action categories: escalate, batch, defer, inspect now. The proposed technique is designed to maximize throughput, minimize misclassification cost, and actively narrow historical equity gaps in service delivery. We formalize each domain as a Markov Decision Process (MDP) in which equitable classification coverage is a first-class reward objective. Post-hoc SHAP attribution reveals that complaint recurrence and neighborhood-level statistics are stronger predictors of actionable violations than raw complaint volume. This finding has direct implications for complaint routing given the demographic correlates of those features. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2605.06491 Fri, 08 May 2026 00:00:00 -0400 arXiv:2605.06491v1 Announce Type: new Abstract: We develop a general estimation and inference procedure for the common parameters in linear panel data regression models with nonparametric two-way specification of unobserved heterogeneity. The procedure takes as input any first-step estimators of the nonparametric regression function and the fixed effects and relies on two key ingredients: First, we develop moment conditions for the common parameters that are Neyman orthogonal with respect to the nonparametric regression function. Second, we employ a novel adjustment of the nonparametric regression estimator so the estimated fixed effects do not generate incidental parameter biases. Together, these ensure that the resulting estimator of the common parameters is root-NT -- asymptotically normally distributed under weak conditions on the estimators of fixed effects and regression function. Next, we propose a novel two-step estimator of the nonparametric regression function and the fixed effects and verify that this particular estimator satisfies the conditions of our general theory. A numerical study shows that the proposed estimators perform well in finite samples. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2605.05609 Fri, 08 May 2026 00:00:00 -0400 arXiv:2605.05609v1 Announce Type: cross Abstract: We study contextual dynamic pricing with linear valuations and bounded-support agnostic noise, whose induced demand curve may be non-Lipschitz with arbitrary jumps and atoms. Such discontinuities break the cross-context interpolation arguments used by smooth-demand pricing algorithms, while the best previous method achieved only $\tilde O(T^{3/4})$ regret. We propose Conservative-Markdown Redirect-UCB Pricing, a polynomial-time algorithm that combines randomized parameter estimation, conservative residual-grid probing, and confidence-based one-step redirection. Our algorithm achieves $\tilde O(T^{2/3})$ optimal regret, matching the known lower bounds of Kleinberg and Leighton (2003) up to logarithmic factors and improving over the previous upper bound of Xu and Wang (2022). Under stochastic well-conditioned contexts, this closes the long-existing open regret gap in linear-valuation contextual pricing under agnostic non-Lipschitz noise distribution. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2408.12577 Fri, 08 May 2026 00:00:00 -0400 arXiv:2408.12577v3 Announce Type: replace Abstract: As an IT-enabled multi-passenger mobility service, microtransit can improve accessibility, reduce congestion, and promote sustainability. However, realizing its business potential requires a deeper understanding of traveler preferences, highlighting the need for more effective tools for demand forecasting and revenue management, especially when actual usage data are limited. We propose an innovative modeling approach that integrates travel behavioral insights into microtransit policymaking. The approach operates by (1) leveraging citywide synthetic data to achieve greater spatiotemporal granularity, (2) estimating a nonparametric nested model for joint travel mode and ride-pass subscription choices, and (3) employing a simulation-based method to calculate revenue and traveler benefits under various policy scenarios. We demonstrate the applicability of our approach through a case study in Arlington, TX, one of the largest deployments of microtransit (Via) in the U.S. Using the simulation-based workflow, we evaluate alternative policy scenarios, including ride-pass discounts, event-based subsidies, and place-based subsidies, to assess their impacts on microtransit ridership, system revenue, and traveler welfare. The results indicate that reducing the weekly pass price from $25 to $18.9 and the monthly pass price from $80 to $71.5 would increase total revenue by approximately $127 per day. A 100% trip fare discount could reduce 61 car trips to AT&T Stadium during a game event while generating an additional 82 microtransit trips per day to Medical City Arlington. However, achieving these mode shifts would require subsidies of approximately $533 per event and $483 per day, respectively. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2410.20885 Fri, 08 May 2026 00:00:00 -0400 arXiv:2410.20885v2 Announce Type: replace Abstract: We propose a new estimator for the Generalised Dynamic Factor Model (GDFM) that simplifies estimation by avoiding frequency-domain methods. Our key theoretical insight shows that under reasonable conditions the dynamic common component can be represented in terms of a finite number of lags of contemporaneously pervasive factors. In this case the dynamic factor decomposition of the GDFM reduces to the OLS regression of observed variables on estimated factors and their lags, with factors obtained via static principal components. The approach naturally accommodates weak (non-pervasive) factors within the dynamic common space addressing an important limitation of existing methods. We establish consistency and asymptotic normality for both the dynamic and weak common components. An application to a large European macroeconomic dataset demonstrates strong empirical performance and uncovers a sizeable weak common component - particularly in sentiment indicators and several other variables - revealing dynamics that standard methods overlook. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2506.18188 Fri, 08 May 2026 00:00:00 -0400 arXiv:2506.18188v2 Announce Type: replace Abstract: A key challenge for targeted antipoverty programs in developing countries is that policymakers must rely on estimated rather than observed income, which leads to substantial targeting errors. The policy problem is not only to predict income, but to decide how noisy income estimates should be translated into feasible transfers. I formulate this as a statistical decision problem in which a policymaker chooses transfers to minimize a poverty-targeting loss subject to a fixed budget and a no-taxation constraint. I show that the standard plug-in rule, which treats estimated incomes as true, is inadmissible. I develop a nonparametric empirical Bayes targeting rule that assigns transfers using posterior distributions of true poverty gaps. Although the budget and no-taxation constraints make the targeting rule nonsmooth, Bayes regret is governed by the accuracy of the posterior functionals that determine the oracle allocation. In simulations using household survey data from nine African countries, the empirical Bayes rule reaches substantially more poor households and systematically improves poverty reduction relative to plug-in OLS and machine-learning benchmarks. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2511.03236 Fri, 08 May 2026 00:00:00 -0400 arXiv:2511.03236v2 Announce Type: replace Abstract: This article introduces a leave-one-out regression adjustment (LOORA) for estimating average treatment effects in randomized controlled trials. In finite samples, LOORA removes the bias of conventional regression adjustment and yields exact variance formulas for regression-adjusted Horvitz-Thompson and difference-in-means estimators. Ridge regularization curbs the influence of high-leverage observations, improving stability and precision in small samples. In large samples, LOORA matches the variance of the regression-adjusted estimator in Lin (2013) while remaining exactly unbiased. Two within-subject experimental applications, each providing a realistic joint distribution of potential outcomes as ground truth, show that LOORA removes substantial bias and achieves confidence interval coverage close to the nominal level. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2605.04961 Fri, 08 May 2026 00:00:00 -0400 arXiv:2605.04961v2 Announce Type: replace Abstract: This paper develops efficient GMM estimation when the moment conditions are misspecified. We observe that the influence function of the standard GMM estimator under misspecification depends on both the original moment conditions and their Jacobian, motivating a new class of estimators based on augmented moment conditions with recentering. The standard GMM estimator is a special case within this class, and generally suboptimal. By optimally weighting the augmented system, we obtain a misspecification-efficient (ME) estimator with the smallest asymptotic variance for the same GMM pseudo-true value. In linear models, the asymptotic variance of ME estimator reduces to the textbook efficient-GMM variance formula $(G'W^{*}G)^{-1}$, where $W^{*}$ is the inverse of the variance of residualized moments after projection on the Jacobian $G$. We consider a feasible double-recentered bootstrap estimator, which can be considered as a misspecification-robust and efficient version of Hall and Horowitz (1996) recentered bootstrap GMM estimator, and also consider a split-sample ME estimator. Finally, we establish uniform local asymptotic minimax bounds over a class of weighting matrices. We illustrate the proposed methods in simulation and empirical examples. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2605.05898 Fri, 08 May 2026 00:00:00 -0400 arXiv:2605.05898v1 Announce Type: new Abstract: Migration is reshaping demographic landscapes across Europe, raising urgent questions about adapting to rapid population changes. This study examines the canton of Fribourg, Switzerland, which experienced a 30% population increase over the past 15 years, driven by international and internal migration. As local governments face mounting pressures from demographic shifts in housing, education, and social services, understanding the causal effects of migration is essential for evidence-based policymaking. We study how migration reshapes local demographic, educational, and housing outcomes across 112 Fribourg municipalities (2010-2021). Using the intertemporal difference-in-differences estimator of De Chaisemartin and D'Haultfoeuille (2024), which accommodates staggered timing and cumulative, non-binary treatment, we identify the effect of a one-percentage-point increase in cumulative migration balance (relative to baseline population). Migration exposure generates modest but persistent adjustments across demographic, educational, and housing dimensions. Both migration types reduce the share of elderly residents, and international inflows are associated with higher birth counts. Internal migration increases resident students and alters compulsory and secondary-school cohorts, while international migration slightly reduces the tertiary-education share. Housing adjustments are gradual and concentrated in household composition and selected dwelling types, with international migration increasing mid-sized households and internal migration reducing mixed-use dwellings. Though yearly effects are small, their persistence yields meaningful cumulative changes. Overall, migration acts as a counterweight to population aging and generates incremental adjustments in service demand, underscoring the need to incorporate migration exposure into cantonal and municipal planning. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2605.06411 Fri, 08 May 2026 00:00:00 -0400 arXiv:2605.06411v1 Announce Type: cross Abstract: Global food security depends on tightly coupled international supply chains including natural gas, mineral fertilizers, and staple crops. Earlier research has examined potential consequences of disruptions in each of these domains separately but not from a systemic perspective. Here we integrate bilateral trade in natural gas, nitrogen, phosphorus and potassium fertilizers, and eleven staple crops accounting for approximately 70% of plant-based calories into a cascading-impact model spanning 208 countries, 20 geopolitical blocs, and the period 1992-2023. Under complete trade isolation, up to 22% of global caloric consumption would be lost, with a peak in the most recent evaluated years. Structural vulnerabilities vary greatly. Regions largely lacking some parts of the supply chain face near-total crop supply collapse, while few countries can cover the whole nexus through domestic resource endowments and production capacities. Temporal trends highlight a substantial increase in vulnerability globally, most prominently in the EU with a near two-fold increase since the 1990s. Market power is most concentrated and most volatile in the upstream gas and mineral-fertilizer layers, from which shocks propagate downstream. Food stocks provide only limited resilience with half of humanity living in countries disposing of stock lasting less than three months. Our results identify the upstream supply chains as the structural bottlenecks of the global agrifood system and propose leverage points to enhance resilience. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2408.00885 Fri, 08 May 2026 00:00:00 -0400 arXiv:2408.00885v3 Announce Type: replace Abstract: First-nature geography shapes the location of prosperity. I provide evidence by investigating the effects when it suddenly changes. In 1825 a storm breached the Agger Isthmus. This connected Denmark's west Limfjord Region to the North Sea. I demonstrate that trade followed. Prosperity relocated with it: population rose 27.0 percent within a generation - an elasticity of 1.6 relative to market access - with occupational shifts toward fishing and manufacturing. Fertility, not migration, drove the expansion. A mirror experiment, the waterway's closure circa 1086-1208, caused symmetric declines in medieval coin and building finds. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2503.15443 Fri, 08 May 2026 00:00:00 -0400 arXiv:2503.15443v5 Announce Type: replace Abstract: Elites disproportionately influence policymaking, yet little is known about their fairness and efficiency preferences -- key determinants of support for redistributive policies. We investigate these preferences in an incentivized lab experiment with future elites: Ivy League MBA students. We find that MBA students implement substantially more unequal earnings distributions than the average American, regardless of whether inequality stems from luck or merit. Their redistributive choices are also far more responsive to efficiency costs than the near-zero response found in representative U.S. samples. These patterns partly reflect distinct fairness ideals: a large share of MBA students falls outside standard classifications, instead displaying "weak meritocratic" tendencies that tolerate inequality even when it stems from luck. These findings identify a channel through which elite preferences may sustain U.S. inequality. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2410.01871 Fri, 08 May 2026 00:00:00 -0400 arXiv:2410.01871v3 Announce Type: replace-cross Abstract: In an era of "moving fast and breaking things", regulators have moved slowly to pick up the safety, bias, and legal debris left in the wake of broken Artificial Intelligence (AI) deployment. While there is much-warranted discussion about how to address the safety, bias, and legal woes of state-of-the-art AI models, rigorous and realistic mathematical frameworks to regulate AI are lacking. Our paper addresses this challenge, proposing an auction-based regulatory mechanism that provably incentivizes agents (i) to deploy compliant models and (ii) to participate in the regulation process. We formulate AI regulation as an all-pay auction where enterprises submit models for approval. The regulator enforces compliance thresholds and further rewards models exhibiting higher compliance than their peers. We derive Nash Equilibria demonstrating that rational agents will submit models exceeding the prescribed compliance threshold. Empirical results show that our regulatory auction boosts compliance rates by 20% and participation rates by 15% compared to baseline regulatory mechanisms, outperforming simpler frameworks that merely impose minimum compliance standards. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2605.02680 Fri, 08 May 2026 00:00:00 -0400 arXiv:2605.02680v2 Announce Type: replace Abstract: We document the rise of negative earnings between 1980 and 2019: a secular increase in the percent of firms reporting losses, both among public firms and in the broader universe of US corporations, and a secular increase in the persistence of losses year-to-year among public firms. This rise has occurred alongside a spreading of the sales and earnings distribution and a recomposition of firm spending away from production costs and traditional investment and towards sales general and administrative expenses. We rationalize these phenomena with a model of heterogenous firms engaging in supply and demand shifting investment. Our model includes a scale elasticity of demand determining the relationship between the intensive margin of demand (demand per customer) and the extensive margin of demand (number of customers). We are able to quantitatively match the rise in reported losses and qualitatively match (1) the increased persistence of losses, (2) the spreading of the sales and earning distribution and (3) the recomposition of firm spending with this parameter as the single driver of changes across steady state equilibria. The rise in the scale elasticity associated with the increase in reported losses has non-trivial aggregate implications: in our model it lowers GDP by -9.1% by reallocating labor away from goods and capital production and reallocating demand away from productive firms. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2604.20050 Fri, 08 May 2026 00:00:00 -0400 arXiv:2604.20050v2 Announce Type: replace Abstract: Can Large Language Models (AI agents) aggregate dispersed private information through trading and reason about the knowledge of others by observing price movements? We conduct a controlled experiment where AI agents trade in a prediction market after receiving private signals, measuring information aggregation by the log error of the last price. We find that although the median market is effective at aggregating information in the easy information structures, increasing the complexity has a significant and negative impact, suggesting that AI agents may suffer from similar limitations as humans when reasoning about others. Consistent with our theoretical predictions, information aggregation remains unaffected by allowing cheap talk communication, changing the duration of the market or initial price, and strategic prompting, thus demonstrating that prediction markets are robust. We establish that "smarter" AI agents perform better at aggregation and they are more profitable. Surprisingly, giving them feedback about past performance has no impact on aggregation. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2603.03136 Fri, 08 May 2026 00:00:00 -0400 arXiv:2603.03136v2 Announce Type: replace Abstract: Using on-chain Polygon data, we analyze Polymarket's 2024 U.S. Presidential Election market and develop a transaction-level accounting framework with two components: a volume decomposition that separates exchange-equivalent turnover from share minting and burning, and trader-level disagreement measures. Naive aggregation reports $958M of October Trump-market volume, compared with $391M under our decomposition. Market quality improved as arbitrage-deviation half-lives fell from hours to under a minute and Kyle's {\lambda} dropped from 0.53 to 0.01. During October's large-account episode, capital flowed into both sides simultaneously, consistent with heterogeneous-beliefs trading rather than one-sided manipulation. The framework generalizes to other tokenized prediction markets. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2601.19886 Fri, 08 May 2026 00:00:00 -0400 arXiv:2601.19886v2 Announce Type: replace Abstract: The race for artificial intelligence (AI) dominance often prioritizes scale over efficiency. Hyper-scaling is the common industry approach: larger models, more data, and as many computational resources as possible. Using more resources is a simpler path to improved AI performance. Thus, efficiency has been de-emphasized. Consequently, the need for costly computational resources has marginalized academics and smaller companies. Simultaneously, increased energy expenditure, due to growing AI use, has led to mounting environmental costs. In response to accessibility and sustainability concerns, we argue for research into, and implementation of, market-based methods that incentivize AI efficiency. We believe that incentivizing efficient operations and approaches will reduce emissions while opening new opportunities for academics and smaller companies. As a call to action, we propose a cap-and-trade system for AI. Our system provably reduces computations for AI deployment, thereby lowering emissions and monetizing efficiency to the benefit of academics and smaller companies. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2601.22112 Thu, 07 May 2026 00:00:00 -0400 arXiv:2601.22112v3 Announce Type: replace Abstract: I study symmetric competitions in which each player chooses an arbitrary distribution over a one-dimensional performance index, subject to a convex cost. I establish existence of a symmetric equilibrium, document various properties it must possess, and provide a characterization via the first-order approach. Manifold applications--to R\&D competition, oligopolistic competition with product design, and rank-order contests--follow. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2504.15997 Thu, 07 May 2026 00:00:00 -0400 arXiv:2504.15997v3 Announce Type: replace Abstract: We develop an efficient method for solving non-convex constrained optimization problems that are pervasive in economics. The optimal solution to these problems often involves randomization. We employ a Lagrangian framework and prove that the value of the saddle point characterizing the optimal random solution equals the value of the deterministic dual problem. Our algorithm solves this dual via subgradient descent and recovers the optimal random solution directly from deterministic optima computed along the iterations. For many non-convex economic problems, our method is orders of magnitude faster than linear programming, making previously intractable lottery problems feasible. As an application, we solve for optimal Mirrleesian income taxation with multi-dimensional types. We show that heterogeneity in productivity and Frisch elasticity can make randomization welfare-improving over the optimal deterministic schedule. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2504.02077 Thu, 07 May 2026 00:00:00 -0400 arXiv:2504.02077v2 Announce Type: replace Abstract: In financial applications, latency advantages -- the ability to make decisions later than others, even without the ability to see what others have done -- can provide individual participants with an edge by allowing them to gather additional relevant information. For example, a trader who is able to act even milliseconds after another trader may receive information about changing prices on other exchanges that lets them make a profit at the expense of the latter. To better understand the economics of latency advantages, we consider a common-value auction with a reserve price in which some bidders may have more information about the value of the item than others, e.g., by bidding later. We provide a characterization of the equilibrium strategies, and study the welfare and auctioneer revenue implications of the last-mover advantage. We show that the auction does not degenerate completely and that the seller is still able to capture some value. We study comparative statics of the equilibrium under different assumptions about the nature of the latency advantage. Under the assumptions of the Black-Scholes model, we derive formulas for the last mover's expected profit, as well as for the sensitivity of that profit to their timing advantage. We apply our results to the design of blockchain protocols that aim to run auctions for financial assets on-chain, where incentives to increase timing advantages can put pressure on the decentralization of the system. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2512.24520 Thu, 07 May 2026 00:00:00 -0400 arXiv:2512.24520v2 Announce Type: replace Abstract: How should nations price carbon? This paper examines how the treatment of global inequality, captured by regional welfare weights, affects optimal carbon prices. I develop theory to identify the conditions under which accounting for differences in marginal utilities of consumption across countries leads to more stringent global climate policy in the absence of international transfers. I further establish a connection between the optimal uniform carbon prices implied by different welfare weights and heterogeneous regional preferences over climate policy stringency. In calibrated simulations, I find that accounting for global inequality reduces optimal global emissions relative to an inequality-insensitive benchmark. This holds both when carbon prices are regionally differentiated, with emissions 21% lower, and when they are constrained to be globally uniform, with the uniform carbon price 15% higher. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2605.04707 Thu, 07 May 2026 00:00:00 -0400 arXiv:2605.04707v1 Announce Type: cross Abstract: The impact of lithium isotopic enrichment on the global deployment of nuclear fusion energy is analysed. Lithium - the 6Li isotope in particular - is essentially one of two elemental fuels required by fusion reactors for tritium breeding. Whilst variable consumption of lithium is low enough to present negligible cost, it is instead the large stored inventory volume (50-100 tonnes) and its required enrichment that compound to significantly drive capital costs. These costs are driven by the inefficiency of the tritium breeding process, making this challenge fundamental to almost all fusion power plant concepts. Financing would further compound these effects, making lithium fusion fuels more akin to an upfront capital expenditure than operational expenditure. Other potential barriers to fusion deployment created by lithium are also discussed: enrichment technologies of today are shown to be too expensive, not scalable, and environmentally risky, and highly enriched 6Li is a controlled substance. Mitigating actions include: developing alternative enrichment technologies that are affordable, scalable, and do not rely on mercury; incorporating lithium enrichment as an explicit cost driver in reactor design processes, producing more compact reactors with smaller lithium inventories; establishing distinct enrichment levels to enable supply chain monitoring for misuse; and the most radical solution: breeding blankets that use natural, unenriched lithium. These actions may impact tritium breeding capabilities, which calls for an urgent re-assessment of the tritium breeding paradigm. Whatever solution is sought, lithium supply is a mission-critical issue that needs urgently addressing. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2605.04522 Thu, 07 May 2026 00:00:00 -0400 arXiv:2605.04522v1 Announce Type: cross Abstract: We propose DAO-enabled decentralized physical AI (DePAI), a democratic architecture for coordinating humans and autonomous machines in the operation and governance of physical-digital systems. We (1) synthesize foundations in blockchains, decentralized autonomous organizations (DAOs), and cryptoeconomics; (2) connect DAO design with digital-democracy research on deliberation and voting, showing how each can advance the other; (3) position DAO-governed decentralized physical infrastructure networks (DePIN) within a vertically integrated stack that links energy and sensing to connectivity, storage/compute, models, and robots; (4) show how these elements specify workflows that couple machine execution with human oversight, enabling enhanced self-organization of techno-socio-economic systems, which we call DePAI; and (5) analyze risks, including security, centralization, incentive failure, legal exposure, and the crowding-out of intrinsic motivation, and argue for value-sensitive design and continuously adaptive governance. DePAI offers a path to scalable, resilient self-organization that integrates physical infrastructure, AI, and community ownership under transparent rules, on-chain incentives, and permissionless participation, aiming to preserve human autonomy. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2605.04479 Thu, 07 May 2026 00:00:00 -0400 arXiv:2605.04479v1 Announce Type: cross Abstract: This research establishes ESG as a state dependent insurance mechanism against equity crashes by addressing the decoupling of unconditional alpha from tail risk resilience. By validating market stress regimes as distinct economic states through a drawdown-based truncation rule, the study demonstrates that high ESG ratings materially reduce the incidence of discrete crash events during systemic drawdowns. To address the selection bias and high-dimensional confounding inherent in traditional linear frameworks, we implement Double Machine Learning as a structural deconfounding layer. Unlike simple predictive modeling, the Double Machine Learning framework utilizes machine learning to handle complex nuisance parameters, allowing us to isolate the asymmetric treatment effects of ESG across different market states. Distributional analysis reveals the underlying mechanism as ESG specifically attenuates the severity of realized tail losses at the most adverse quantiles instead of shifting the entire return distribution. Confirmed by structural estimates, this protection functions as priced insurance that incurs performance drags during stable periods while providing critical resilience when tail risks are most acute. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2605.04207 Thu, 07 May 2026 00:00:00 -0400 arXiv:2605.04207v1 Announce Type: cross Abstract: We study contextual dynamic pricing under a semiparametric demand model in which the purchase probability is $1-F(p-m(\mathbf{x}))$, where $m(\mathbf{x})$ captures mean utility as a function of product features and buyer covariates, and $F$ is an unknown market-noise distribution. Existing methods either incur suboptimal regret or rely on restrictive structural assumptions. We propose a stagewise greedy pricing algorithm that iteratively refines the estimate of $F$ via local polynomial regression while pricing greedily with current estimates. By exploiting feature diversity, the algorithm reuses endogenous samples collected during exploitation for nonparametric estimation, avoiding costly global random exploration used in prior work. We establish a general regret bound that applies to any estimator $\hat m$ of the utility function, and derive explicit rates for linear, nonparametric additive, and sparse linear classes of $m$. For the linear class, our regret scales as $T^{\max\{1/2,\,3/(2\beta+1)\}}$, where $\beta$ is the smoothness of $F$ and $T$ is the time horizon. This improves the best known rates for semiparametric contextual pricing and achieves the parametric $\sqrt{T}$ rate when $\beta \ge 5/2$. We further prove a matching lower bound, showing the optimality of our rate, and present numerical experiments that corroborate the theory and demonstrate the practical advantages of iterative refinement. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2604.26811 Thu, 07 May 2026 00:00:00 -0400 arXiv:2604.26811v2 Announce Type: replace-cross Abstract: Investor sentiment reflects the collective attitude of investors towards the asset, whether positive, negative or neutral. Market information, such as news and relevant social media posts, plays a significant role in shaping investor sentiment, which influences investment decisions accordingly. The sentiment for one single company may spill over to other relevant companies which are in the same industry. The information spillover network pattern between news and social media may also differ, as they are two different media sources. In this study, we introduce a network-based transfer entropy method to measure and compare the information transmission of news and social media sentiment across the technology companies. We examine whether and to what extent sentiment information from one company can transfer to other companies, and how different the spillover effect is for news and social media. The result signifies a stronger intensity of news information flow among the tech companies after COVID-19. We also highlight the companies which act as information hubs in the sentiment network. Furthermore, we identify the companies which lead the strongest information flow chain. Overall, this study provides a novel perspective in modelling sentiment spillover under two different media sources, and we find that news and social media show a different information transmission pattern during the studied period. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2605.04124 Thu, 07 May 2026 00:00:00 -0400 arXiv:2605.04124v1 Announce Type: cross Abstract: Modern heterogeneity-robust difference-in-differences estimators derive their asymptotic properties under iid, cluster, or fixed-design frameworks that abstract from complex survey sampling, yet practitioners routinely apply them to nationally representative surveys with stratified cluster designs. We show that, under standard regularity conditions, the influence functions of each smooth IF-based or regression-based modern DiD estimator satisfy Binder's (1983) smoothness conditions, so the standard stratified-cluster variance formula applied to their values produces design-consistent standard errors. A Monte Carlo study with 66,000 replications shows where the design effect comes from. HC1 standard errors that treat observations as iid produce coverage as low as 34% under a baseline survey design and below 11% under informative sampling. Combining the survey-weighted point estimate with PSU-level clustering - the practitioner's cluster=psu heuristic - recovers near-nominal coverage across all scenarios. Adding strata and finite-population corrections yields incremental precision but is not required for valid coverage. Survey-weighted doubly robust estimation produces well-calibrated inference when parallel trends hold only conditionally. An NHANES illustration of the ACA dependent coverage provision shows that point estimates and standard errors change substantively - enough to reverse significance conclusions - when the survey design is accounted for. We provide diff-diff (https://github.com/igerber/diff-diff), an open-source Python package implementing design-based variance for fifteen modern DiD estimators. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2605.05056 Thu, 07 May 2026 00:00:00 -0400 arXiv:2605.05056v1 Announce Type: new Abstract: This paper develops a difference-in-differences (DiD) estimation method that selects the optimal length of pre-trends by minimizing the mean squared error (MSE). Conventional DiD regression models, such as the two-way fixed effects model or the event study model, may suffer from accuracy and validity concerns. If the sample size is small, the estimator may have a larger variance. Also, pre-tests often lack power to detect violations of the parallel trends assumption as Roth (2022) highlights. By focusing on the bias and variance tradeoff, the proposed method derives the MSE-optimal estimator from the optimal length of pre-trends. Simulation results and an empirical application demonstrate the practical applicability of the proposed method. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2605.05037 Thu, 07 May 2026 00:00:00 -0400 arXiv:2605.05037v1 Announce Type: new Abstract: We study the estimation of average effects in nonlinear panel data models with fixed effects when the time dimension $T$ is only moderately large. Our approach, called approximate operator inversion (AOI), offers a new perspective on bias correction. Instead of first estimating unit-specific fixed effects and then correcting the resulting plug-in bias, AOI approximately inverts the likelihood-induced mapping from the fixed-effect distribution to the outcome distribution. AOI can be interpreted as the limit of an infinitely iterated bias correction scheme, and this limit is available in closed form. We show that the bias of the AOI estimator has a rate double robustness property and converges to zero at an exponential rate in $T$ under regularity conditions. Our asymptotic theory requires $T \to \infty$, but the exponential convergence rate of the bias means that finite-sample performance is very good even for moderately large $T$. We establish asymptotic normality and provide feasible inference. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2605.04771 Thu, 07 May 2026 00:00:00 -0400 arXiv:2605.04771v1 Announce Type: new Abstract: This paper develops a method to use singles' data in a non-parametric revealed preference setting of collective household choice. We use it to test the controversial assumption of preference stability between singles and couples, without data on intra-household allocation or marital transitions. We show that, under the preference-stability hypothesis, consumption choices from an endogenously matched population admit a conditional random-utility representation over counterfactual pairings of couples and singles. Preference stability is testable as a feasibility restriction on the observed marginal choice distributions. We reject the hypothesis using consumption data from the Dutch LISS, the Russian RLMS, and the Spanish ECPF panels. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2604.24489 Wed, 06 May 2026 00:00:00 -0400 arXiv:2604.24489v3 Announce Type: replace Abstract: Heinsohn and Steiger's "Eigentum, Zins und Geld" (1996) proposes the property premium as the foundational determinant of interest, replacing time preference. This paper examines whether the replacement succeeds. It does not. The two arguments against time preference, the savings-inelasticity claim after Hahn and the portfolio-shift claim after Keynes, both fail on standard microeconomic grounds. With time preference intact, the property premium sits within the standard decomposition of the interest rate. In ordinary collateralized credit it coincides with the risk premium. Only when the lender is a money-issuing bank with a real redemption obligation does a third term enter the decomposition that standard asset-pricing theory does not articulate. That third term is Heinsohn and Steiger's genuine contribution. The paper discusses its apparent disappearance or disguised operation after 2008, and the circularity of a property anchor measured in money. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2605.03307 Wed, 06 May 2026 00:00:00 -0400 arXiv:2605.03307v1 Announce Type: cross Abstract: We develop a mechanism for unsecured lending among pseudonymous users that does not rely on collateral, legal identity, or centralized underwriting. New borrowers enter only through sponsors who delegate part of their own credit capacity, so onboarding a new account reallocates existing borrowing power rather than minting new capacity. Default losses flow back along the sponsor path, while repayment creates earned credit that expands future borrowing capacity. We prove that delegation conserves aggregate credit capacity, that revocation and default remain local to a unique sponsor path, and that a simple cap on earned-credit growth makes repay-then-default weakly unprofitable. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2206.09918 Wed, 06 May 2026 00:00:00 -0400 arXiv:2206.09918v4 Announce Type: replace Abstract: We study a class of finite-action disclosure games in which the sender's preferences are state-independent and the receiver's optimal action depends only on the expected state. While receiver-preferred equilibria in these games involve full revelation, other equilibria are less well understood. We show that any equilibrium payoff can be obtained with a disclosure strategy corresponding to a partition with a laminar structure that allows pooling of nonadjacent states. In a sender-preferred equilibrium, such a structure balances inducing more sender-favorable actions with deterring deviations. Leveraging this insight, we identify conditions under which the sender does not benefit from commitment power. We then apply these results to study selling with quality disclosure and influencing voters. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2408.16898 Wed, 06 May 2026 00:00:00 -0400 arXiv:2408.16898v4 Announce Type: replace Abstract: We propose a refinement of the maxmin approach to robustness. A mechanism's payoff guarantee over an ambiguity set is \emph{robust} if the guarantee is approximately satisfied at priors near the ambiguity set (in the weak topology). We show that many maxmin-optimal mechanisms in the literature give payoff guarantees that are not robust. Such mechanisms are often tailored to degenerate worst-case priors, making them simple but fragile. Conversely, some commonly used ambiguity sets satisfy a structural property which ensures that every associated payoff guarantee is robust. We show how any ambiguity set can be slightly enriched to satisfy this property. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2511.04996 Wed, 06 May 2026 00:00:00 -0400 arXiv:2511.04996v2 Announce Type: replace Abstract: We study efficient, linear, and symmetric (ELS) values, a central family of allocation rules for cooperative games with transferable-utility (TU-games) that includes the Shapley value, the CIS value, and the ENSC value. We first show that every ELS value can be written as the Shapley value of a suitably transformed TU-game. We then introduce three types of invariance axioms for fixed player populations. The first type consists of composition axioms, and the second type is active-player consistency. Each of these two types yields a characterization of a subclass of the ELS values that contains the family of least-square values. Finally, the third type is nullified-game consistency: we define three such axioms, and each axiom yields a characterization of one of the Shapley, CIS, and ENSC values. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2605.03204 Wed, 06 May 2026 00:00:00 -0400 arXiv:2605.03204v1 Announce Type: cross Abstract: Peer effect estimation requires precise network measurement, yet most empirical networks are noisy, rendering standard estimators inconsistent. To address measurement error in networks, we propose a method to estimate peer effects in networks whose expected adjacency matrix is low-rank. Our key result shows that peer effects over a true unobserved network are asymptotically equivalent to peer effects over the expected adjacency matrix. This result reduces peer effect estimation in noisy networks to low-rank matrix estimation targeting the expected adjacency matrix. We develop our theory for weighted networks observed with additive noise, but simulations suggest approach can be applied more generally when there is a low-rank estimation method suited to a particular noise structure. We demonstrate via simulations that our approach applies to egocentric samples, aggregated relational data, and networks with missing edges, each requiring a different low-rank estimation method. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2605.03997 Wed, 06 May 2026 00:00:00 -0400 arXiv:2605.03997v1 Announce Type: cross Abstract: Skill scores, which measure the relative improvement of a forecasting method over a benchmark via consistent scoring functions and proper scoring rules, are a standard tool in forecast evaluation, yet their sampling uncertainty is rarely rigorously quantified. With modern forecasting applications being increasingly multivariate and involving evaluations across multiple horizons, variables, spatial locations, and forecasting methods, standard tools like the pairwise Diebold-Mariano forecast accuracy test or pointwise confidence intervals fail to account for the multiple comparison problem, leading to inflated Type I error rates and invalid joint inference. To address the lack of a coherent, statistically rigorous framework for quantifying uncertainty across these multi-dimensional evaluation problems, we introduce simultaneous confidence bands for expected scores and skill scores. Our framework provides a versatile tool for joint inference that is applicable to any forecast type from mean and quantile to full distributional forecasts. We develop a bootstrap implementation and show that our bands are valid under multivariate extensions of the classical Diebold-Mariano assumptions. We demonstrate the practical utility of the approach in two case studies by quantifying the benefits of time-varying parameter models for macroeconomic forecasting, and by comparing data-driven and physics-based models in probabilistic weather forecasting. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2507.00555 Wed, 06 May 2026 00:00:00 -0400 arXiv:2507.00555v2 Announce Type: replace Abstract: Structural estimation in economics often makes use of models formulated in terms of moment conditions. While these moment conditions are generally well-motivated, it is often unknown whether the moment restrictions hold exactly. We consider a framework where researchers model their belief about the potential degree of misspecification via a prior distribution and adopt a quasi-Bayesian approach for performing inference on structural parameters. We provide quasi-posterior concentration results, verify that quasi-posteriors can be used to obtain approximately optimal Bayesian decision rules under the maintained prior structure over misspecification, and provide a form of frequentist coverage results. We illustrate the approach through empirical examples where we obtain informative inference for structural objects allowing for substantial relaxations of the requirement that moment conditions hold exactly. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2511.01680 Wed, 06 May 2026 00:00:00 -0400 arXiv:2511.01680v3 Announce Type: replace Abstract: Social scientists are increasingly turning to unstructured datasets to unlock new empirical insights, e.g., estimating descriptive statistics of or causal effects on quantitative measures derived from text, audio, or video data. In many such settings, unsupervised analysis is of primary interest, in that the researcher does not want to (or cannot) manually pre-specify all important aspects of the unstructured data to measure; they are interested in "discovery." This paper proposes a general and flexible framework for pursuing such discovery from unstructured data in a statistically principled way. The framework leverages recent methods from the literature on AI interpretability to map unstructured data points to high-dimensional, sparse, and interpretable "concept embeddings"; computes statistics from these concept embeddings for testing interpretable, concept-by-concept hypotheses; performs selective inference on these hypotheses using algorithms validated by new results in high-dimensional central limit theory, producing a selected set ("discoveries"); and both generates and evaluates human-interpretable natural language descriptions of these discoveries. The proposed framework has few researcher degrees of freedom, is robust to data snooping and other post-selection inference concerns, and facilitates fast and inexpensive sensitivity analysis and replication. Applications to recent descriptive and causal analyses of unstructured data in empirical economics are explored. Open source code is provided for researchers to implement the framework in their own projects. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2604.07181 Wed, 06 May 2026 00:00:00 -0400 arXiv:2604.07181v2 Announce Type: replace Abstract: Empirical research shows that individuals' responses to treatments vary along latent characteristics, such as innate ability or motivation. Therefore, a policymaker seeking to maximize welfare may consider designing policies based on observed characteristics and estimated latent traits. I characterize how the estimates' precision affects the worst-case performance of policies deriving rate-sharp regret bounds for assignment rules that include or exclude them, highlighting new trade-offs with the policy space complexity. I then study how a policymaker can solve such trade-offs by designing tailored data collections and derive a sufficient condition for a collection plan to be minimax optimal. In an empirical application in development economics, I show that including a proxy for entrepreneurs' business skills in targeting cash transfers increases welfare by 5%, and halves the probability of generating welfare losses. Moreover, I estimate the optimal allocation of resources between improving the precision of the proxy via repeated measurements, and increasing sample size. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2605.03881 Wed, 06 May 2026 00:00:00 -0400 arXiv:2605.03881v1 Announce Type: new Abstract: This paper develops a formal critique of scalar fiscal aggregation in the IS LM BP/Mundell Fleming framework. It shows that when fiscal policy is composed of heterogeneous instruments current purchases, public investment and transfers to different households the aggregate variable G is sufficient for output analysis only under a restrictive gradient condition: all instruments must have identical marginal effects on output. The paper proves this condition, derives composition weighted multipliers, identifies aggregation bias and extends the open economy IS LM BP model to incorporate fiscal composition, public capital, debt dynamics and risk-premium effects. A reproducible computational exercise with symbolic checks, derivative tests, accounting identities, adversarial counterexamples, sensitivity sweeps, Monte Carlo simulations and stress tests confirms the internal consistency of the argument. The contribution is methodological: IS LM BP remains useful as a compact equilibrium framework, but fiscal policy analysis requires vector-valued instruments and state-contingent multipliers rather than a single homogeneous spending variable. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2605.03966 Wed, 06 May 2026 00:00:00 -0400 arXiv:2605.03966v1 Announce Type: new Abstract: This paper addresses the structure and dynamics of an open market economy and its relations with the real interest rate. In this respect, the paper is situated within a broad conventional literature. However, it departs from the standard approach to the interest rate by treating it as a control variable. Even so, the analysis concludes that the two main determinants of the interest rate are the future utility discount rate and expectations regarding future multifactor productivity (labor efficiency). Furthermore, increases in such expectations lead to increases in both the interest rate and wages. These results are consistent with to those obtained with the Cass, Koopmans, Ramsey model. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2605.03980 Wed, 06 May 2026 00:00:00 -0400 arXiv:2605.03980v1 Announce Type: new Abstract: Venture capital outcomes are dominated by a small number of extreme successes, making it difficult to distinguish investor skill from favorable realizations in a highly skewed return distribution. We study this question by comparing empirical VC portfolios to a constrained random benchmark that preserves key portfolio characteristics, including timing, geography, sector composition, and portfolio size, while randomizing individual company selection. Across funding stages, empirical portfolio distributions appear remarkably close to their random benchmarks. We find no evidence that portfolio construction increases the probability of high-multiple outcomes: the right tail remains statistically indistinguishable from random allocation. Deviations in the lower part of the distribution are small and sensitive to the interpretation of zero outcomes, suggesting at most weak evidence of downside improvement. We further introduce a rank-based benchmark distribution to evaluate outperformance at each position in the cross-section. This analysis shows that even the best-performing portfolios do not exceed the outcomes expected for their rank under random sampling. Our results suggest that VC portfolio outcomes are largely consistent with constrained random allocation, highlighting the difficulty of identifying aggregate skill in heavy-tailed investment environments. A similar conclusion holds for the performance of financial analysts in predicting future earnings. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2605.03210 Wed, 06 May 2026 00:00:00 -0400 arXiv:2605.03210v1 Announce Type: cross Abstract: We argue that AI-saturated markets are likely to create Veblen-good premiums, which we term human-provenance premiums, for verified human presence, and hence AI governance should treat human-provenance verification as labor infrastructure. Generative and agentic AI systems lower the cost of many standardized cognitive, creative, and coordination tasks, weakening the scarcity premiums that have supported much middle-tier knowledge work. We argue that this pressure may produce an asymmetric barbell-shaped structure of value capture in advanced economies: high-volume synthetic production controlled by owners of AI infrastructure at one pole, and scarce, high-status human labor valued for verified human presence at the other. We advance three claims. First, AI compresses the value of standardized middle-tier labor by making good-enough synthetic substitutes scalable at low marginal cost, hollowing out the middle of the skill distribution currently categorized by knowledge work. Second, this compression reallocates demand for human labor toward work valued for its visible human character. We term this performative humanity and distinguish three forms of labor: relational presence, aesthetic provenance, and accountability. Third, as these premiums depend on credible verification, AI governance should treat human-provenance systems as labor infrastructure rather than as luxury authenticity labels. To evaluate hybrid human-AI work, we propose constitutive human presence as the relevant standard: human labor retains premium value when human judgment, attention, accountability, authorship, or relational participation is not incidental to the output but constitutive of what is being purchased. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2504.20429 Wed, 06 May 2026 00:00:00 -0400 arXiv:2504.20429v2 Announce Type: replace Abstract: This paper develops a method for estimating housing production functions when builders choose capital after observing local conditions that are unobserved by the econometrician. Because observed capital variation reflects both technological substitution and endogenous responses to these conditions, existing estimators can transmit unobserved heterogeneity into the estimated production function. The method treats the unobserved local conditions that affect capital choice as a scalar Markov state and combines the capital share equation with Markov moments implemented using repeated cross-sectional construction data. Monte Carlo simulations and an application to newly constructed housing in Tokyo's 23 special wards show that accounting for this heterogeneity changes estimated capital-land elasticities and yields elasticities close to constant returns to scale. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2511.11846 Wed, 06 May 2026 00:00:00 -0400 arXiv:2511.11846v2 Announce Type: replace Abstract: I introduce a novel approach to structural modelling and estimation of continuous demand systems, utilising consideration sets to analyse differentiated products markets with very large choice sets and purchases over multiple goods, multiple units, and across product categories. I apply it to study intra-store competition in the Portuguese supermarket industry between 2020 and 2023, during which the country faced the COVID pandemic. Anonymised transaction-level point-of-sale data is sufficient to estimate price elasticities across almost 30 000 goods and more than 500 product categories. Results suggest mark-ups remained stable throughout the sample period, with a short-lived, slight increase post-pandemic observed only in the highest-mark-up-percentile goods. The implied mark-ups match observed price volatility, profit margin surveys, as well as reports on shifting consumer tastes during the sample period. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2410.13978 Tue, 05 May 2026 00:00:00 -0400 arXiv:2410.13978v4 Announce Type: replace Abstract: I study a principal-agent model in which a principal hires an agent to collect information about an unknown continuous state. The agent acquires a signal whose distribution is centered around the state, controlling the signal's precision at a cost. The principal observes neither the precision nor the signal, but rather, using transfers that can depend on the state, incentivizes the agent to choose high precision and report the signal truthfully. I identify a sufficient and necessary condition on the agent's information structure which ensures that there exists an optimal transfer with a simple cutoff structure: the agent receives a fixed prize when his prediction is close enough to the state and receives nothing otherwise. This condition is mild and applies to all signal distributions commonly used in the literature. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2604.09340 Tue, 05 May 2026 00:00:00 -0400 arXiv:2604.09340v2 Announce Type: replace Abstract: Economic institutions often influence market outcomes not by directly controlling sellers' menus, but by shaping the market composition sellers face. We study the welfare effects of this upstream choice in a monopoly screening model. An upstream actor chooses the distribution of buyer valuations, after which a monopolist screens optimally. We characterize the consumer surplus-profit frontier across market compositions: as the weight on consumer surplus varies, the payoff pair induced by the optimal market composition traces the Pareto frontier. If profit receives at least as much weight as consumer surplus, the optimal market composition collapses to the top type. Otherwise, it exhibits no exclusion, no interior bunching, and a positive mass at the highest valuation. Under a mild curvature condition, the optimal market composition is unique. Greater weight on consumer surplus makes the market less top-heavy: the differentiated interior expands and the premium top segment shrinks. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2605.01594 Tue, 05 May 2026 00:00:00 -0400 arXiv:2605.01594v1 Announce Type: new Abstract: This study proposes a framework for estimating demand in differentiated product markets with high dimensional product characteristics, building upon the seminal Berry, Levinsohn, and Pakes (1995) model, using market level data. We allow for a very large set of potential product characteristics, where the number of characteristics may exceed the number of market observations. Our contributions are twofold. First, we establish a general estimation theory for BLP models featuring high-dimensional nuisance parameters. We propose a Neyman orthogonal estimator specifically adapted to this framework, utilizing machine learning techniques, such as Lasso, to construct nuisance parameter estimators that are plugged into the Neyman orthogonal estimator. This approach offers a significant advantage: it achieves $\sqrt{T}$-asymptotic normality for parameters of interest--such as the price coefficient and price heterogeneity--even when nuisance parameters are estimated at slower rates due to their high dimensionality. Second, we apply this theory to a specialized BLP model under approximate sparsity, developing an estimation strategy for the high-dimensional nuisance parameters. The approximate sparsity condition posits that nuisance parameters can be controlled, up to a small approximation error, by a small and unknown subset of variables. In an economic context, this implies that while products have a vast array of characteristics, consumers focus on only a small subset of these due to bounded rationality. This condition makes the recovery of parameters of interest feasible by enabling nuisance parameter estimators to converge at the required rates. The practical performance of the method is evaluated through comprehensive Monte Carlo simulations, which demonstrate its efficacy in finite samples. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2605.01665 Tue, 05 May 2026 00:00:00 -0400 arXiv:2605.01665v1 Announce Type: new Abstract: The convolution of a Gaussian and a Cauchy distribution, known as the Voigt distribution, is widely used in spectroscopy and provides a natural framework for modeling heavy-tailed measurement noise. We derive analytical expressions for its density, score, Hessian, and conditional moments using the scaled complementary error function, enabling stable maximum likelihood estimation without numerical convolution, finite-difference derivatives, or pseudo-Voigt approximations. The conditional expectation of the latent Gaussian component is governed by a redescending location score, so extreme observations are automatically discounted rather than propagated. This structure motivates the Gauss-Cauchy Convolution (GCC) filter for state-space models with Gaussian latent dynamics and heavy-tailed measurement errors. In an application to log realized volatility for the Technology Select Sector SPDR Fund, the GCC filter separates persistent latent variation from transient measurement noise and improves on Gaussian, Student-$t$, Huber, and related robust alternatives. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2605.02311 Tue, 05 May 2026 00:00:00 -0400 arXiv:2605.02311v1 Announce Type: new Abstract: This paper studies a simple dynamic linear panel regression model with interactive fixed effects in which the variable of interest is measured with error. To estimate the dynamic coefficient, we consider the least-squares minimum distance (LS-MD) estimation method. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2605.02414 Tue, 05 May 2026 00:00:00 -0400 arXiv:2605.02414v1 Announce Type: new Abstract: This paper studies sample-size design for finite-population test-and-roll experiments, where a decision-maker first conducts an experiment on $m$ units and then assigns the remaining $N-m$ units to the treatment that performs better in the experiment. We consider welfare-aware sample-size choice, which involves an exploration-exploitation tradeoff: larger experiments improve the rollout decision but impose welfare losses on experimental units assigned to the inferior treatment. We show that the standard absolute minimax regret criterion can lead to implausibly small experiments by over-penalizing exploration in its worst-case objective. To address this limitation, we propose the Worst-case Marginal Benefit (WMB) rule, which compares the worst-case marginal benefit of adding one more matched pair to the experiment with the corresponding marginal exploration cost. We establish a simple rule-of-thirds benchmark. For Bernoulli outcomes, after excluding pathological cases, the WMB criterion yields the optimal sample size of $m \approx N/3$ through a Gaussian approximation. For Gaussian outcomes with a known common variance, the same benchmark arises exactly. These results provide a prior-free and practically implementable guide for welfare-based sample-size design. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2605.01311 Tue, 05 May 2026 00:00:00 -0400 arXiv:2605.01311v1 Announce Type: cross Abstract: Offline evaluation of language models from usage logs is biased when model choice is confounded: the same user-side factors that influence which model is used can also influence how its output is judged, so raw comparisons of logged scores mix self-selected populations rather than estimating a common quantity of interest. A small randomized experiment can break this bias by overriding model choice, but in practice such experiments are scarce and costly. We study a three-source design that combines a large confounded observational log (OBS) for scale, a small randomized experiment (EXP) for unconfounded scoring, and an offline simulator (SIM) that replays candidate models on cached contexts. Our main result is an identification theorem showing that the randomized experiment and the simulator are together enough to recover causal model values; the observational log enters only afterward, to reduce estimation error rather than to make the causal comparison valid. Six estimator families are evaluated in a controlled semi-synthetic validation and in two real-task cached benchmarks for summarization and coding. No family dominates every regime; relative performance depends on the amount of unbiased EXP supervision and on how closely the target reward aligns with OBS-derived structure. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2410.23852 Tue, 05 May 2026 00:00:00 -0400 arXiv:2410.23852v3 Announce Type: replace Abstract: We develop a unified estimation and inference framework for dyadic network formation with individual fixed effects, covering both transferable-utility (TU) and nontransferable-utility (NTU) links under general link functions. Under NTU, bilateral consent makes the fixed effects non-additive and the log-likelihood non-concave in the high-dimensional fixed effects, so differencing and profile-likelihood methods fail. We combine a joint method-of-moments initial estimator, a Le Cam one-step refinement, and a split-network jackknife bagging step that removes the incidental parameter bias without inflating variance. The resulting homophily estimator is asymptotically normal, unbiased, and attains the Cram\'er--Rao lower bound without requiring the log-likelihood to be concave in the fixed effects; we extend the theory to average partial effects and establish robustness to link-function misspecification. Simulations under both TU and NTU designs confirm these predictions. Applied to Thai village networks (TU), kinship and wealth differences both increase linking; in the Nyakatoke risk-sharing network (NTU), wealth differences have no significant effect, mirroring the two regimes' distinct logics. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2411.01799 Tue, 05 May 2026 00:00:00 -0400 arXiv:2411.01799v4 Announce Type: replace Abstract: We propose a novel method for estimating nonseparable selection models. We show that, for a given selection function, the potential outcome distributions are nonparametrically identified from the selected outcome distributions and can be recovered using a simple iterative algorithm based on a contraction mapping. This result enables a full-information approach to estimating selection models without imposing parametric or separability assumptions on the outcome equation. We propose a two-step estimation strategy for the potential outcome distributions and the parameters of the selection function and establish the consistency and asymptotic normality of the resulting estimators. Monte Carlo simulations demonstrate that our approach performs well in finite samples. The method is applicable to a wide range of empirical settings, including consumer demand models with only transaction prices, auctions with incomplete bid data, and Roy models with data on accepted wages. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2501.07514 Tue, 05 May 2026 00:00:00 -0400 arXiv:2501.07514v4 Announce Type: replace Abstract: Sequential search models provide a powerful framework for studying consumer search using rich data that records the sequence of consumer actions taken during the search process. In existing empirical applications, their implementation often builds on optimal policies, in which later decisions depend on outcomes from earlier actions that are often fully observed by researchers. Therefore, implementation is largely restricted by computation burden and limited model flexibility. This paper establishes a theoretical equivalence showing that, under common and mild assumptions of Independence and Invariance, a sequential search process is optimal if and only if a corresponding ranking over all feasible actions throughout the process holds, thereby introducing a ranking representation of optimal sequential search. This representation enables a novel, simple, and unified empirical strategy for implementing sequential search models. For the classic \cite{weitzman1979optimal} model, the proposed approach reduces simulation requirements while improving accuracy, computational efficiency, and ease of implementation. We further show that the same strategy extends to a broad class of sequential search settings, including partially observed action sequences and multi-stage information acquisition, such as discovery. Overall, the results enhance both the tractability and the empirical applicability of sequential search models. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2512.18678 Tue, 05 May 2026 00:00:00 -0400 arXiv:2512.18678v2 Announce Type: replace Abstract: Inference for fixed effects estimators is often unreliable due to Nickell- and incidental parameter biases. While these issues are well understood for classical two-dimensional panels, little is known about three-dimensional panel structures (e.g., sender x receiver x time). We develop inferential theory for a broad class of linear and nonlinear fixed effects M-estimators in this setting, covering bipartite, directed, and undirected network panel data, multiple specifications of additively separable unobserved effects, and both strictly exogenous and predetermined regressors. Our analysis reveals fundamentally different asymptotic properties compared to two-dimensional panels. In particular, we find a sharp dichotomy across specifications: (i) when unobserved effects vary along a single panel dimension, the estimator is asymptotically unbiased; (ii) when they vary along two panel dimensions, the estimator suffers from a severe inference problem characterized by a degenerate asymptotic distribution. We resolve the latter by deriving explicit bias formulas and proposing analytically debiased estimators with nondegenerate, correctly centered asymptotic distributions. An empirical application studies dynamic network formation in a directed panel of bilateral trade relationships. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2604.09821 Tue, 05 May 2026 00:00:00 -0400 arXiv:2604.09821v2 Announce Type: replace Abstract: On a 93-actor quarterly panel mixing macro indicators, institutional data, and firm-level investment ratios, global factor augmentation degrades prediction for actor subgroups whose dynamics are misrepresented by the shared basis. A two-stage architecture -- global pooled AR(1) for shared persistence, block-specific local models for residual dynamics -- improves full-panel out-of-sample $R^2$ from 0.630 to 0.677 ($\Delta = +0.047$, CI $[+0.036, +0.058]$, 10/10 windows, placebo $p \leq 0.001$). A held-out decade test (block partition frozen on 2005--2014 data, evaluated on unseen 2015--2024 windows) confirms the gain ($\Delta = +0.050$, 10/10), and a stratified placebo that fixes the macro/firm data-type split and permutes only firm-sector assignments corroborates ($z = 7.25$, $p \leq 0.001$). Cross-regime replication on a 109-actor UK/EU heterogeneous panel ($\Delta = +0.017$, 8/8 windows) and a combined US + UK/EU panel of 202 actors ($\Delta = +0.030$, placebo $z = 9.68$ -- exceeding the original US-only $z = 7.82$) confirms the architecture transfers across regimes. A 146-firm CapEx/Assets robustness check refines the scope condition: the gain depends on cross-sectional dispersion in autoregressive structure, which data-type heterogeneity reliably produces but which is also present in firm-only panels under suitable ratio choices. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2511.08303 Tue, 05 May 2026 00:00:00 -0400 arXiv:2511.08303v2 Announce Type: replace-cross Abstract: This study investigates treatment effect estimation in the semi-supervised setting, also can be interpreted as prediction-powered inference. In our setting, we can use not only the standard triple of covariates, treatment indicator, and outcome, but also unlabeled auxiliary covariates. For this problem, we develop efficiency bounds and efficient estimators whose asymptotic variance aligns with the efficiency bound. In the analysis, we introduce two different data-generating processes: the one-sample setting and the two-sample setting. The one-sample setting considers the case where we can observe treatment indicators and outcomes for a part of the dataset, which is also called the censoring setting. In contrast, the two-sample setting considers two independent datasets with labeled and unlabeled data, which is also called the case-control setting or the stratified setting. In both settings, we find that by incorporating auxiliary covariates, we can lower the efficiency bound and obtain an estimator with an asymptotic variance smaller than that without such auxiliary covariates. We frame our framework as prediction-powered causal inference. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2604.14486 Tue, 05 May 2026 00:00:00 -0400 arXiv:2604.14486v2 Announce Type: replace-cross Abstract: Tweedie's formula is central to measurement-error analysis and empirical Bayes. Under Gaussian noise, the formula identifies the posterior mean directly from the observed-data density, bypassing nonparametric deconvolution. Beyond a few classical examples, however, no general theory explains when analogous identities hold, how they are structured, or how to derive them for non-Gaussian noise and for posterior functionals other than the mean. This paper develops such a framework for additive-noise models. I characterize when conditional expectations of an unobserved latent variable, given the observed signal, admit direct expressions in terms of the observed density -- identities I call Tweedie representations -- and show that they are governed by a linear map, the Tweedie functional. Under general conditions, I prove that this functional exists, is unique, and is continuous. I also provide a constructive method for deriving it by extending the inverse Fourier transform of an explicit tempered distribution. This recasts the search for Tweedie-type formulas as a problem in the calculus of tempered distributions. The framework recovers the classical Gaussian formula and yields new representations for posterior means under non-Gaussian noise. I apply the method to construct unbiased representations of nonlinear functionals of latent variables and to derive Tweedie formulas for the product-Laplace mechanism used in differential privacy. Finally, I show that the approach extends beyond the standard additive model. In the heteroskedastic Gaussian sequence model, where the noise covariance is itself random, a change of variables restores the required additive-noise structure conditionally, yielding Tweedie representations without additional restrictions on the joint law of the latent parameter and noise covariance. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2605.01715 Tue, 05 May 2026 00:00:00 -0400 arXiv:2605.01715v1 Announce Type: new Abstract: We study the design of strategy-proof and efficient mechanisms satisfying participation constraints in the job-matching problem. Each firm can hire multiple workers and each worker can be employed at only one firm. While firm utilities over subsets of workers are common knowledge, worker disutilities for working at each firm are private information. The VCG mechanism is the unique mechanism that is strategy-proof, efficient, and individually rational for workers; however, it may not be individual rational for firms. We show that the VCG mechanism is individually rational for firms if and only if firm utilities satisfy a condition called weak substitutes. We then strengthen participation constraints of firms to {\sl strong individual rationality}, which requires that each firm has no incentive to fire some of the workers assigned to it. The VCG mechanism is strongly individual rational if and only if firm utilities satisfy submodularity. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2605.01763 Tue, 05 May 2026 00:00:00 -0400 arXiv:2605.01763v1 Announce Type: new Abstract: This paper develops a unified framework for evaluating health outcomes that jointly incorporates equity and productivity. Extending beyond traditional QALYs, PALYs, and the more recent PQALYs, we introduce a broader class of evaluation functions that integrate equity- and productivity-sensitive conditions. By imposing several normative criteria, including independence from measurement scales and Pigou-Dalton transfer principles, we obtain tractable power-form representations. In balancing equity and efficiency, the framework provides a coherent foundation for assessing interventions in contexts where both health and productive capacity are at stake. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2605.01850 Tue, 05 May 2026 00:00:00 -0400 arXiv:2605.01850v1 Announce Type: new Abstract: Economic choices are often stochastic: the same person may make a different choice when facing the same alternatives repeatedly. Standard models assume that the degree of randomness reflects the size of utility differences, but choice inconsistencies could also reflect difficulty comparing alternatives. Recent studies estimate such comparison difficulty (or "complexity") by fitting functional forms to aggregate choice data under a representative agent assumption. However, aggregate data could violate standard models of random choice simply because of heterogeneity in preferences, even in the absence of variation in comparison difficulty. This paper develops a revealed preference framework, collective rationalizability, that tests for variation in comparison difficulty from aggregate data while explicitly accounting for heterogeneity. The framework characterizes whether violations of standard models can be explained by comparison difficulty alone, heterogeneity alone, or require both. I provide a statistical test with finite-sample inference and apply the method to two existing experiments. In both cases, heterogeneity alone explains observed failures of stochastic transitivity well, demonstrating that comparison difficulty can be not only theoretically but also empirically confused with heterogeneity in aggregate data. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2605.02756 Tue, 05 May 2026 00:00:00 -0400 arXiv:2605.02756v1 Announce Type: new Abstract: We study the emergence of conformity preferences in an environment in which agents choose effort under heterogeneous, possibly misspecified returns, and social interactions do not directly affect material payoffs. Some agents choose effort by trading off performance and conformity to expected peer behavior. We characterize subjective best responses. For any given beliefs, an optimal and unique level of peer pressure exists and is evolutionarily stable within groups of agents sharing the same misspecification. Such a level is zero for correctly specified agents and may be positive for misspecified ones. When the efficient level of peer pressure is interior, misspecified agents choose effort equal to their true return, resulting in an equilibrium behavior that is both self-confirming and Nash, allowing the persistence of misspecifications. Peer pressure need not generate long-run allocative distortions, but it creates a perceived value of social information. In equilibrium, this value depends only on misspecification, generating scope for informational rents. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2605.02776 Tue, 05 May 2026 00:00:00 -0400 arXiv:2605.02776v1 Announce Type: new Abstract: This paper studies how the possibility of strategic misreporting shapes endogenous communication networks. Agents observe noisy private signals about a common state, form costly communication links, exchange private messages with their neighbors, and then choose actions. Payoffs reward both accuracy and coordination with linked agents. A link is valuable because it gives access to information, but it is useful only if the induced local information structure makes truthful transmission incentive compatible. We show that clique components support truthful communication: within a clique, all members observe the same profile of local signals, choose the same posterior action, and therefore have no incentive to distort reports. With heterogeneous signal precisions and convex linking costs, the core selects assortative information clubs ordered by signal precision. These stable truthful networks need not be socially efficient. Because the informational value of precision is decreasing, concentrating high-precision agents in the same club may be privately stable but socially dominated by more mixed partitions. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2605.02817 Tue, 05 May 2026 00:00:00 -0400 arXiv:2605.02817v1 Announce Type: new Abstract: We show that a large effective number of commodities can be a source of equilibrium stability and uniqueness: expanding substitution opportunities strengthens aggregate substitution effects. We study finite dated-commodity exchange economies obtained by truncating a countably infinite-horizon environment with discounted, additively separable utilities. In this setting, the effective number of commodities is the discounted count of dated commodities, so greater patience makes distant commodities more relevant. With an appropriate normalization, equilibrium substitution effects accumulate at the rate of the effective number of commodities. When a preference diversification condition holds, equilibrium income effects grow at a lower rate. The condition is satisfied, for example, when agents have sparse or localized taste differences across commodities, or when their taste profiles become sufficiently heterogeneous as the commodity space expands. Hence, whenever the effective number of commodities is sufficiently large, every equilibrium is locally t\^atonnement stable, which in turn implies equilibrium uniqueness. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2605.01082 Tue, 05 May 2026 00:00:00 -0400 arXiv:2605.01082v1 Announce Type: cross Abstract: We study networked binary classification on a directed acyclic graph (DAG) where each agent observes only a subset of the feature columns of a shared dataset. Agents act sequentially along the DAG: each receives prediction columns from its parents (if any), augments its local features with these columns, fits a logistic predictor by minimizing binary cross-entropy (BCE), and forwards its prediction column to its outgoing neighbors. We ask whether this sequential distributed training procedure achieves information aggregation, meaning that some agent attains small excess loss compared to the best logistic predictor trained with access to all feature columns. This question was studied for linear regression under squared loss by Kearns, Roth, and Ryu (SODA 2026). Extending their guarantees to classification is nontrivial because their analysis relies on quadratic structure that does not directly transfer to BCE with a logistic link. We analyze the resulting sequential logit-passing protocol and prove: (i) an excess loss upper bound of $O(M/\sqrt{D})$ on depth-$D$ paths under the condition that every $M$ contiguous subsequence of $M$ agents collectively observe all features, and (ii) a close lower bound showing instances with excess loss of at least $\Omega(k/D)$ where $k$ is the dimension of the feature space. Together, these results identify network depth as a fundamental bottleneck for information aggregation in networked logistic regression. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2605.01521 Tue, 05 May 2026 00:00:00 -0400 arXiv:2605.01521v1 Announce Type: cross Abstract: We revisit games in partition function form, i.e. cooperative games where the payoff of a coalition depends on the partition of the entire set of players. We assume that each coalition computes its worth having probabilistic beliefs over the coalitional behavior of the outsiders, i.e., it assigns various probability distributions over the set of partitions that the outsiders can form. These beliefs are not necessarily consistent with respect to the actual choices of the outsiders. We apply this framework to symmetric partition function form games characterized by either positive or negative externalities and we derive conditions on coalitional beliefs that guarantee the non-emptiness of the core of the induced games. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2605.02681 Tue, 05 May 2026 00:00:00 -0400 arXiv:2605.02681v1 Announce Type: cross Abstract: We present two new classes of causal models of decision-making agents. Our approach is motivated by the needs of modeling the economics of computing systems. These systems are composed of subsystems and can exhibit endogenous limits on cognitive resources and value discounting. Structural Causal Decision Models (SCDMs) expand on Structural Causal Influence Models. Like SCIMs, they explicitly represent the causal relationships between model variables and the payoffs of agent decisions. Additionally, agent decisions can be constrained by their causal antecedents, and SCDMs can have open root variables for which no probability distribution or structural equation is given. We show that SCDMs have a well-defined and computationally useful property of composability. Building on SCDMs, we then define a Structural Causal Decision Process (SCDP) as a recurring SCDM with a discount variable. SCDPs benefit from the useful composition properties of SCDMs. Moreover, SCDPs are strictly more expressive than POMDPs because they do not assume rational belief formation. Indeed, an SCDP can endogenously model the memory-formation process, and is thus useful for modeling resource rational agents in dynamic settings. SCDPs are also capable of modeling variable discounting, a tool used widely in social scientific modeling. We pose that SCDPs are a useful framework for policy simulation for the digital economy, mechanism design for information systems, and digital twin modeling of cyberinfrastructure. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2411.03241 Tue, 05 May 2026 00:00:00 -0400 arXiv:2411.03241v3 Announce Type: replace Abstract: We study how coordinated disinformation campaigns affect elections. We develop a constrained information design model in which a sender deploys uninformative messages that mimic voters' exogenous informative signals. Voters initially opposed to the sender's preferred outcome receive favourable messages, while those in favour are targeted with unfavourable messages to dilute adverse information. The sender's ability to manipulate political outcomes increases with greater precision of voters' independent signals, but decreases with polarisation. When messaging is costly, the sender may stop targeting marginally opposing voters while moderating message extremism among supporters. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2412.09321 Tue, 05 May 2026 00:00:00 -0400 arXiv:2412.09321v5 Announce Type: replace Abstract: We introduce Coarse Q-learning (CQL), a reinforcement-learning model for bandit problems with stochastically varying menus. Alternatives are exogenously partitioned into similarity classes, and feedback from sampled alternatives is pooled within classes into class-level valuations. Choices follow multinomial logit over class valuations, and valuations update toward realized payoffs as in Q-learning. Using stochastic approximation, we derive the mean-field dynamics and characterize the steady states as smooth analogues of Valuation Equilibria. The model yields novel long-run phenomena in the high payoff-sensitivity limit: depending on the environment, CQL may exhibit multiple stable strict equilibria, a unique globally stable mixed equilibrium with indifference across classes, or no stable equilibrium at all, with valuations and choice probabilities converging instead to a stable limit cycle. These outcomes are driven by coarse aggregation and do not arise in the standard alternative-level benchmark. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2508.12073 Tue, 05 May 2026 00:00:00 -0400 arXiv:2508.12073v3 Announce Type: replace Abstract: This paper analytically demonstrates that, in a Two-Agent New Keynesian model with Rotemberg-type price and wage rigidities, monetary transmission can be amplified when two mechanisms are sufficiently strong: the heterogeneity-induced IS-slope effect and the price-stickiness channel. We also show when amplification weakens or disappears, most notably under pure wage stickiness, where the price channel shuts down and the heterogeneity-driven term vanishes. The framework features household heterogeneity between savers and hand-to-mouth households and is derived from microeconomic foundations while avoiding restrictive assumptions on relative wages or labor supply across types that are common in prior analytical work. The closed-form solution makes transparent how price stickiness, wage stickiness, and the share of hand-to-mouth households jointly shape amplification. We further derive a modified aggregate welfare loss function that quantifies how heterogeneity, operating through distributional effects from firm profits, changes the relative importance of stabilizing inflation. Overall, the tractable yet micro-founded analytical framework clarifies the interaction between household heterogeneity and nominal rigidities and identifies sufficient conditions under which monetary policy gains or loses traction. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2512.23274 Tue, 05 May 2026 00:00:00 -0400 arXiv:2512.23274v3 Announce Type: replace Abstract: I study multidimensional sequential screening. A monopolist contracts with a buyer who privately observes information about the distribution of their eventual valuations for multiple goods. After initial private information is reported and the contract is signed, the buyer learns and reports realized valuations. In these settings, the monopolist frontloads surplus extraction: Any information rents given to the buyer to elicit their true valuations can be extracted in expectation before those valuations are drawn, transforming the multidimensional screening problem by distorting buyer information rents compared to static screening. If the buyer's distributions over valuations are commonly FOSD ordered, regular for each good, and satisfy invariant dependencies (valuations can be dependent across goods, but how valuations are coupled cannot vary), the optimal mechanism coincides with independently offering the optimal sequential screening mechanism for each good. This rationalizes membership payments followed by separate sales schemes commonly used in practice. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2603.10867 Tue, 05 May 2026 00:00:00 -0400 arXiv:2603.10867v2 Announce Type: replace Abstract: A designer relies on an experimenter to provide information to a decision maker, but the experimenter has incentives to persuade rather than merely transmit information. Anticipating this motive, the designer can restrict the set of admissible experiments, but cannot prevent the experimenter from garbling any admissible experiment. We model this situation as delegation over experiments. The optimal delegation set is obtained by comparing maximally informative experiments among those the experimenter has no incentive to garble. When the experimenter's preferences are $S$-shaped, we characterize these experiments as double censorship. Relative to the full-delegation benchmark, double censorship features an intermediate pooling region, inducing a smaller pooling region for the highest states. We show that the designer strictly benefits from imposing a nontrivial delegation set that constrains persuasion while retaining information provision. Applying our results to recommender systems, we show that privacy constraints can arise endogenously to protect consumers against persuasion. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2603.22956 Mon, 04 May 2026 00:00:00 -0400 arXiv:2603.22956v2 Announce Type: replace Abstract: This paper explores a mechanism for mitigating sovereign risk in emerging markets without risks mutualization. The mechanism involves pooling diversified portfolios of sovereign bonds and issuing them in tranches, with the senior tranche offering low-risk payoffs protected by the subordination of the junior tranches. We argue that this mechanism is feasible for emerging markets. The senior bonds issued by the securitization vehicle attain the properties of a safe asset. The risk level of the junior bonds depends on the structure of the underlying sovereign bonds portfolio. Nevertheless, the properties of the synthetic bonds are, arguably, acceptable for the practical application of the proposed mechanism in promoting the development of financial markets in emerging markets and for practical tasks such as intergovernmental aid or lending. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2502.08597 Mon, 04 May 2026 00:00:00 -0400 arXiv:2502.08597v3 Announce Type: replace-cross Abstract: We analyze the performance of heterogeneous learning agents in asset markets with stochastic payoffs. Our main focus is on comparing Bayesian learners and no-regret learners who compete in markets and identifying the conditions under which each approach is more effective. We formally relate the notions of survival and market dominance studied in economics and the framework of regret minimization, thereby bridging these theories. A central finding is that regret plays a key role in market selection, but low regret alone does not guarantee survival: surprisingly, an agent may achieve even logarithmic regret and yet be driven out of the market when competing against a Bayesian learner with a finite prior that assigns positive probability to the correct model. At the same time, we show that Bayesian learning is highly fragile, while no-regret learning requires less knowledge of the environment and is therefore more robust. Motivated by this contrast, we propose two simple hybrid strategies that incorporate Bayesian updates while improving robustness and adaptability to distribution shifts, taking a step toward a best-of-both-worlds learning approach. More broadly, our work contributes to the understanding of dynamics of heterogeneous learning agents and their impact on markets. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2604.20664 Mon, 04 May 2026 00:00:00 -0400 arXiv:2604.20664v2 Announce Type: replace Abstract: We propose a model of causal persuasion, in which a sender selectively discloses a set of variables together with their true joint distribution and proposes a subjective causal model that binds them. A receiver is persuaded by this model only if the data conclusively identifies the causal link of interest. We characterize when such persuasion succeeds or fails, and how easily it can be achieved. We further show that if the receiver holds a pre-existing subjective model, debunking it is similar to persuading a receiver without one. To establish a true causal link, the sender often needs to disclose only one or two well-chosen variables. But to dispel a perceived link -- to persuade the receiver there is no causal relationship -- every common cause must be disclosed. Our results highlight a fundamental asymmetry in causal persuasion: Establishing causality is often much easier than ruling it out. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2601.14094 Mon, 04 May 2026 00:00:00 -0400 arXiv:2601.14094v3 Announce Type: replace Abstract: Using data on shootings in U.S.\ K--12 schools from 1981 to 2022, we estimate the effect of temperature on school shootings and assess climate-change impacts. We find that days with maximum temperatures above 90$^{\circ}$F increase school shooting incidence by approximately 90\% relative to days with maximum temperatures below 70$^{\circ}$F. The response is concentrated in interpersonal incidents and in non-class periods, such as before school, dismissal, after school, and lunch: shootings during these periods more than triple on days with maximum temperatures above 90$^{\circ}$F, while shootings during class time show no detectable temperature response. The estimated effects are positive for both indoor and outdoor shootings and are larger for shootings involving fatalities or injuries than for shootings involving only minor or no injuries. Applying the estimated dose-response to future warming, we estimate that interpersonal school shootings increase by 6\% by mid-century (2051--2060) under moderate emissions (SSP2--4.5) and 8\% under high emissions (SSP5--8.5), or about 12 and 16 additional incidents per decade. The present discounted value of mid-century social costs is \$599 million under SSP2--4.5 and \$799 million under SSP5--8.5, driven primarily by lost lifetime earnings among exposed students. The results suggest that climate damages in schools may include rare but high-cost safety events, not only heat stress and learning losses. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2404.17227 Mon, 04 May 2026 00:00:00 -0400 arXiv:2404.17227v3 Announce Type: replace Abstract: Trust mechanisms diverge between centralized and decentralized exchanges, representing distinct sociotechnical governance paradigms. However, quantifying trust dynamics and their redistribution between these architectures remains empirically challenging, limiting understanding of how institutional shocks affect market behavior. The FTX collapse offers a natural experiment to bridge this gap. Through an interdisciplinary approach combining causal inference and computational text analysis, we find significant price declines and capital reallocation from centralized to decentralized exchanges following the event. While sentiment metrics showed no sharp discontinuities, topic modeling and network analysis of Discord communities reveal that seasonal holiday discourse obscured underlying trust concerns in centralized exchange forums. These findings underscore the fragility of institutional trust architectures and demonstrate how mixed methods can illuminate behavioral patterns during systemic crises, offering insights for exchange risk management and regulatory assessment. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2605.00131 Mon, 04 May 2026 00:00:00 -0400 arXiv:2605.00131v1 Announce Type: cross Abstract: This paper studies a variant of the Set Covering Routing Problem (SCRP) motivated by post-disaster humanitarian logistics. We consider a hybrid distribution concept in which the majority of transportation is performed by helicopters, while ground transport is limited to the last mile, addressing severe accessibility constraints in disaster-affected regions. The resulting problem integrates landing site location, routing, and covering decisions, incorporating features of the Multi-Vehicle Covering Tour Problem (m-CTP) and the Vehicle Routing with Demand Allocation Problem (VRDAP) in a facility-capacitated, multi-depot setting. Due to the computational complexity of the problem, we develop an Adaptive Variable Neighborhood Search (AVNS) that combines established routing operators with novel mechanisms for covering decisions. The performance of the proposed approach is evaluated on benchmark instances for the related m-CTP and VRDAP problems, demonstrating competitive solution quality compared to problem-specific state-of-the-art approaches. Furthermore, we apply our AVNS to a real-world case study based on the 2024 flash floods in Afghanistan. The results highlight the practical relevance of the proposed framework and provide managerial insights into effective distribution strategies for disaster response operations. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2605.00108 Mon, 04 May 2026 00:00:00 -0400 arXiv:2605.00108v1 Announce Type: cross Abstract: Urban planners need up-to-date, global, and consistent street network models and indicators to measure resilience and performance, model accessibility, and target local quality-of-life interventions. This article presents up-to-date street network models and indicators for every urban area in the world. It uses 2025 urban area boundaries from the Global Human Settlement Layer, allowing users to join these data to hundreds of other urban attributes. Its workflow ingests 180 million OpenStreetMap nodes and 360 million OpenStreetMap edges across 10,351 urban areas in 189 countries. The code, models, and indicators are publicly available for reuse. These resources unlock worldwide urban street network science beyond samples as well as local analyses in under-resourced regions where models and indicators are otherwise less-accessible. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2605.00340 Mon, 04 May 2026 00:00:00 -0400 arXiv:2605.00340v1 Announce Type: new Abstract: The medium of exchange of the traditional economy is mainly the fiat currency of each country or region, and when cross-border transactions occur, they need to be settled according to the exchange rate. In the AI world, however, the medium of exchange tends to be a globally recognized currency. Especially when AI acts as an agent for cross-border capital pool and cross cyclical asset allocation, it needs a sound money that can resist the depreciation of fiat currency and store long-term value. Therefore, we propose a globally consensus and universally accepted monetary rule framework for the AI era. The devaluation of money runs through almost the whole process of history, from the weight reduction and purity decrease of metallic coin to the unanchored over-issuance of paper currency. Whether it is the periodic compulsory recoinage in medieval Europe or Gesell's stamp scrip, both are essentially mechanisms for taxing money holdings. Unlike Gesell's stamp scrip, Redeemable Self-Decaying/Devaluing Money (RSDM) is a tokenized commodity money. Its essential innovation is to fill the hole in the storage fee of metal coins through the self-devaluing of metal weight recorded on the deposit certificate (warehouse receipt) of metal coins. In a sense, RSDM is an innovative version of Jiaozi (a deposit receipt for base metal coin that emerged in Sichuan, China, about a thousand years ago). In this paper, we propose five forms of online and offline issuance of RSDM, providing a prototype for creating a globally recognized modern honest money. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2604.18778 Mon, 04 May 2026 00:00:00 -0400 arXiv:2604.18778v2 Announce Type: replace Abstract: We propose a clustered local projection (clustered LP) method to estimate impulse response functions in a class of time-varying models where parameter variation is linked to a low-dimensional matrix of observables. We show that the clustered LP recovers the conditional average response when the driving variables are exogenous and a weighted average of the conditional marginal effects when they are endogenous. We propose an iterative estimation method that first classifies the data using k-means, estimates impulse response functions via GMM, and evaluates differences across clustered LP estimates. Our Monte Carlo simulations illustrate the ability of clustered LP to approximate the conditional average response function. We employ our technique to examine how uncertainty influences the transmission of a contractionary monetary policy shock to the 5- and 10-year U.S. nominal Treasury yields. Our estimation results suggest macroeconomic and monetary policy uncertainty operate through complementary but distinct channels: the former primarily amplifies the risk compensation embedded in the term premium, while the latter governs the speed and persistence with which markets revise their expectations about the future rate path following a monetary policy shock. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2511.02816 Mon, 04 May 2026 00:00:00 -0400 arXiv:2511.02816v2 Announce Type: replace Abstract: In this paper, we examine identification in dynamic panel logit models with state dependence, a first-order Markov feedback process, and individual unobserved heterogeneity by introducing sufficient statistics for the feedback process and the unobserved heterogeneity. If a sequentially exogenous discrete covariate follows a first-order Markov process, identification via conditional likelihood is infeasible regardless of the time period. We also establish the failure of point identification beyond the conditional likelihood framework, which necessitates additional restrictions for identification. We present two assumptions for identification via conditional likelihood, imposed on the feedback process and the initial condition, respectively. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2501.15307 Mon, 04 May 2026 00:00:00 -0400 arXiv:2501.15307v2 Announce Type: replace Abstract: This paper introduces a direct differentiation-based framework that unifies the derivation of influence functions across parametric, nonparametric, and semiparametric models. We show that the Riesz representer of the functional derivative is obtained by orthogonally projecting the identification function onto the subspace of mean-zero functions. Consequently, the influence function emerges as a linear transformation of this centered moment function. The approach extends seamlessly to infinite-dimensional parameters, revealing a common algebraic form for influence functions across both finite- and infinite-dimensional parameters. Applied to semiparametric multi-step plug-in estimation, our method automatically yields locally robust moment functions and provides an explicit closed-form expression for the adjustment term. Finally, we leverage this framework to revisit the joint versus plug-in estimation debate, establishing verifiable sufficient conditions for their semiparametric efficiency equivalence even when nuisance parameters are over-identified. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2605.00459 Mon, 04 May 2026 00:00:00 -0400 arXiv:2605.00459v1 Announce Type: cross Abstract: We carry the deadline-resolved Information Leakage Score (ILS-dl) framework of Nechepurenko (2026a, 2026b) from a single-case proof of concept to a population-scale evaluation across 12,708 Polymarket markets, October 2020 to April 2026. We frame the paper as a scope-discovery study: scaling reveals that the framework's effective domain is materially narrower than initial framing suggested, and the principal obstacle is not score computation but resolution semantics. We report four findings. First, only 88 of 12,708 candidate markets (0.7%) yield computable ILS-dl values; only 1 of 32 markets in the ForesightFlow Insider Cases (FFIC) inventory is in scope, and 14 of 32 FFIC markets are flagged unclassifiable due to genuine resolution-criterion ambiguity. Second, only 12 of the 88 computed markets (13.6%) satisfy anchor-sensitivity, and an independent-second-pass T_event validation reaches 57.8% exact-date agreement, below the 90% ex-ante criterion. Third, raw ILS-dl medians are negative across all six (sub-bucket by period) cells, but a hazard-decay baseline correction we introduce yields a heterogeneous result: regulatory_formal post-2024 shifts to near-zero (-0.21 to -0.02), while regulatory_announcement post-2024 retains a 95% bootstrap CI entirely below zero. Fourth, the constant-hazard exponential of Nechepurenko (2026b) is rejected in favor of Weibull on the pooled post-2024 cell, but a per-subcategory check confirms the preference reflects category mixture rather than within-cell duration dependence. The implication is that detection of informed flow requires methodological refinement on the resolution-typology and score-baseline axes, not only on the score-computation axis where prior work concentrated. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2605.00614 Mon, 04 May 2026 00:00:00 -0400 arXiv:2605.00614v1 Announce Type: new Abstract: In this paper we study the least squares (LS) estimator in a linear panel regression model with unknown number of factors appearing as interactive fixed effects. Assuming that the number of factors used in estimation is larger than the true number of factors in the data, we establish the limiting distribution of the LS estimator for the regression coefficients as the number of time periods and the number of cross-sectional units jointly go to infinity. The main result of the paper is that under certain assumptions the limiting distribution of the LS estimator is independent of the number of factors used in the estimation, as long as this number is not underestimated. The important practical implication of this result is that for inference on the regression coefficients one does not necessarily need to estimate the number of interactive fixed effects consistently. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2605.00612 Mon, 04 May 2026 00:00:00 -0400 arXiv:2605.00612v1 Announce Type: new Abstract: We analyze linear panel regression models with interactive fixed effects and predetermined regressors, for example lagged-dependent variables. The first-order asymptotic theory of the least squares (LS) estimator of the regression coefficients is worked out in the limit where both the cross-sectional dimension and the number of time periods become large. We find two sources of asymptotic bias of the LS estimator: bias due to correlation or heteroscedasticity of the idiosyncratic error term, and bias due to predetermined (as opposed to strictly exogenous) regressors. We provide a bias-corrected LS estimator. We also present bias-corrected versions of the three classical test statistics (Wald, LR, and LM test) and show their asymptotic distribution is a chi-squared distribution. Monte Carlo simulations show the bias correction of the LS estimator and of the test statistics also work well for finite sample sizes. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2604.27187 Fri, 01 May 2026 00:00:00 -0400 arXiv:2604.27187v1 Announce Type: new Abstract: This paper studies the interactive fixed effects (IFE) estimator in a panel-data setting with heterogeneous treatment effects. We show that, if the treatment-effect heterogeneity admits a linear factor structure, the IFE estimator could fail to recover the average treatment effect on the treated units. The problem arises because the interactive fixed effects absorb the heterogeneity in the treatment effect, creating a \textit{bad-control} problem. With time-invariant factors or unit-invariant loadings in the treatment effect heterogeneity, identification may further break down due to multicollinearity. These problems are not present in alternative estimation methods that exclude treated units in post-treatment periods from the factor estimation. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2604.27215 Fri, 01 May 2026 00:00:00 -0400 arXiv:2604.27215v1 Announce Type: new Abstract: We prove the validity of using subsampling method for inference under a two-way clustered panel in which the time effects are serially correlated. Subsamples should be drawn without replacement from randomly partitioned individual index set and consecutive blocks of time effects. We present two subsampling inference methods: estimating the quantiles directly and constructing the confidence interval by first estimating the asymptotic variance. The quantile method is very adaptive, allowing for non-Gaussian limit which invalidates all existing methods in two-way clustering with serial correlation. Although the variance method only works under Gaussian limit, it comes with a data-driven bandwidth selection algorithm and a bias-correction under suitable estimators. Monte Carlo simulations demonstrate our methods exhibiting the desired coverage level in the finite sample except when the serial correlation is extremely strong. This paper is the first one that allows for inference on non-Gaussian asymptotics under two-way clustering with serial correlation. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2207.11890 Fri, 01 May 2026 00:00:00 -0400 arXiv:2207.11890v3 Announce Type: replace Abstract: The difference-in-differences (DID) design is one of the most popular methods used in empirical economics research. However, there is almost no work examining what the DID method identifies in the presence of a misclassified treatment variable. This paper studies the identification of treatment effects in DID designs when the treatment is misclassified. Misclassification arises in various ways, including when the timing of a policy intervention is ambiguous or when researchers need to infer treatment from auxiliary data. We show that the DID estimand is biased and recovers a weighted average of the average treatment effects on the treated (ATT) in two subpopulations -- the correctly classified and misclassified groups. In some cases, the DID estimand may yield the wrong sign and is otherwise attenuated. We provide bounds on the ATT when the researcher has access to information on the extent of misclassification in the data. We demonstrate our theoretical results using simulations and provide two empirical applications to guide researchers in performing sensitivity analysis using our proposed methods. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2509.16115 Fri, 01 May 2026 00:00:00 -0400 arXiv:2509.16115v3 Announce Type: replace Abstract: We introduce KRED (Korea Research Economic Database), a FRED-MD-compatible monthly macroeconomic database for Korea designed for data-rich policy analysis and cross-country comparison. KRED contains 125 monthly series from ECOS, KOSIS, and administrative labor-market sources, with coverage back to 1960. Using a balanced panel of 104 series over 2009:06--2025:12, principal-components analysis extracts four factors that explain about 30% of total variation. These factors correspond to financial conditions, real activity, housing and real-estate credit, and labor-market and price pressures, and their diffusion indices summarize major Korean macroeconomic episodes. We then use KRED in two empirical applications. First, factor-augmented VARs show that U.S. monetary tightening transmits strongly to Korea and that factor augmentation yields a more coherent inflation response than a low-dimensional VAR. Second, a grouped U.S.--Korea tensor autoregression shows that cross-country dependence is concentrated in financially oriented blocks, with stronger transmission from the U.S. financial block to Korea than in the reverse direction, while spillovers in real activity and housing are much weaker. KRED thus provides a transparent public database for Korean macroeconomic research and a useful building block for comparative work on macro-financial dependence in Asia. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2510.12911 Fri, 01 May 2026 00:00:00 -0400 arXiv:2510.12911v2 Announce Type: replace Abstract: Betas from spot regressions are central to asset pricing and risk management, as measures of systematic risk. This paper develops a new estimation and inference framework for spot regressions by leveraging high-frequency candlesticks, extending conventional (open-to-close) returns with intra-period high/low prices. Specifically, I construct candlestick-based estimators of regression parameters, including spot beta, by minimizing a quadratic risk under a fixed-k asymptotic framework. I then develop a feasible hypothesis testing procedure for spot betas with correct asymptotic size. Simulation results show that the proposed estimator reduces estimation risk relative to return-based estimators, especially in small samples, and the test achieves notably higher power. I apply the framework to assess the market neutrality of Bitcoin using 1-minute data on IBIT and SPY, finding deviations from neutrality, particularly in high-volatility periods. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2604.25977 Fri, 01 May 2026 00:00:00 -0400 arXiv:2604.25977v2 Announce Type: replace Abstract: Organizations routinely make strategic budget allocations under operational constraints, but often lack a principled way to assess whether realized allocations were close to the best feasible choices in hindsight. We present a retrospective auditing framework based on hindsight regret, defined as the opportunity cost of the realized allocation relative to a constraint-faithful benchmark under the same budget and stability guardrails. The framework estimates regime-specific spend--response functions from historical logs, computes feasible hindsight allocations via constrained optimization, and propagates uncertainty through Monte Carlo evaluation to produce regret distributions, expected lift, and probability-of-improvement summaries. This separates allocation inefficiency from uncertainty in the estimated response surfaces. Experiments on real marketing allocation logs show that the framework yields interpretable post-hoc diagnostics and reveals a practical trade-off between allocation flexibility and detectability: moderate feasible reallocations often capture most measurable gain, while larger shifts move into weak-support regions with higher uncertainty. The result is a practical method for auditing historical budget decisions when online experimentation is costly or infeasible. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2509.20194 Fri, 01 May 2026 00:00:00 -0400 arXiv:2509.20194v2 Announce Type: replace-cross Abstract: We introduce a new method for estimating the mean of an outcome variable within groups when researchers only observe the average of the outcome and group indicators across a set of aggregation units, such as geographical areas. Existing methods for this problem, also known as ecological inference, implicitly make strong assumptions about the aggregation process. We first formalize weaker conditions for identification which hold conditionally on covariates. To efficiently control for many covariates, we propose a debiased machine learning estimator that is based on nuisance functions restricted to a partially linear form. Our estimator admits a semiparametric sensitivity analysis which allows researchers to evaluate the impact of violations of the key identifying assumption. We also propose a nonparametric test for the identifying assumption itself. Finally, we derive asymptotically valid confidence intervals for local, unit-level estimates under additional assumptions. Simulations and validation on real-world data where ground truth is available demonstrate the advantages of our approach over existing methods. Open-source software is available which implements the proposed methods. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2604.28052 Fri, 01 May 2026 00:00:00 -0400 arXiv:2604.28052v1 Announce Type: new Abstract: Despite many decades of research, economically grounded models that analyse energy consumption and energy-efficiency adoption within a unified framework remain underdeveloped. This article addresses this gap by proposing a model of consumption, investment, and energy-efficiency adoption under uncertainty. It develops new definitions of the rebound and backfire effects, and integrates their welfare implications into a model of optimal subsidy design. Macro-level technology diffusion and energy consumption across heterogeneous agents are also formalised. Explicit results for core objects are derived, including the adoption threshold and post-adoption strategies, and these are shown to depend on agent wealth, introducing a novel channel through which financial conditions influence technology-adoption decisions. An approximation scheme is proposed to estimate welfare implications explicitly. Adoption of energy efficiency is shown to be welfare improving in the main. A detailed case study of a representative German single-family home illustrates the theoretical results. Numerical analysis indicates that the subsidy policy effectively steers aggregate energy consumption. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2511.09877 Fri, 01 May 2026 00:00:00 -0400 arXiv:2511.09877v2 Announce Type: replace Abstract: Digital platforms increasingly face a common challenge in the age of artificial intelligence (AI): how to elicit richer and more useful user-generated content (UGC) without fully automating content production. We study this question in the context of online reviews by examining Yelp's introduction of an AI-enabled topic nudging tool in 2023, which provides real-time prompts to guide reviewers in addressing key dimensions of the dining experience as they write. Using more than 1.5 million Yelp reviews and a differences-in-differences design, we find that AI-enabled topic nudges significantly reshape review generation. The nudges expand topical coverage, especially for underrepresented aspects such as service and ambiance, and lead to longer reviews, but they also reduce overall review volume. In addition, reviews become more textually complex and less readable, and receive fewer helpfulness votes on average. Further analysis shows that the decline in perceived helpfulness is mitigated when review content remains concentrated on a dominant dimension, highlighting the importance of informational focus. We also find heterogeneous effects: less experienced users expand topical coverage and review length more strongly, whereas experienced users exhibit greater complexity and larger declines in perceived helpfulness. Our findings extend research on AI and UGC by highlighting a distinct mode of AI deployment-guiding human contributions rather than generating content on users' behalf-and by revealing its benefits and unintended consequences for platform design. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2602.15699 Fri, 01 May 2026 00:00:00 -0400 arXiv:2602.15699v3 Announce Type: replace Abstract: This paper develops a geometric diagnostic framework for classical inequality decomposability. Representing the simplest nontrivial setting of three-person income distributions as points on the two-dimensional income-share simplex, we translate population-share-weighted and income-share-weighted decomposability into concrete geometric restrictions on within- and between-group residuals, making it possible to localise and characterise violations across measures. Applied to the Mean Log Deviation, the Gini coefficient, the coefficient of variation, and the Theil index, the analysis shows that decomposability is not a binary property as measures fail in qualitatively distinct ways, and the between-group residual is consistently the primary locus of failure. Negative between-group residuals render the decomposition uninterpretable and arise for the coefficient of variation and the Theil index under population-share weighting, and for the Mean Log Deviation under income-share weighting. Stylised numerical examples quantify the resulting misinterpretation scenarios for applied researchers. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2503.24324 Fri, 01 May 2026 00:00:00 -0400 arXiv:2503.24324v2 Announce Type: replace-cross Abstract: Agricultural price volatility, driven by market dynamics and meteorological factors such as temperature and precipitation, poses challenges for sustainable finance, planning, and policy. This study analyzes the impact of climate on crop price volatility for soybean in Madhya Pradesh (India) and Illinois (US), rice in Assam (India), wheat in North Dakota (US), cotton in Gujarat (India), and corn in Iowa (US). Using CMIP6 climate projections from the Copernicus Climate Change Service, we examine historical climate patterns and evaluate two future scenarios: SSP2-4.5 (moderate) and SSP5-8.5 (severe). We estimate conditional price volatility using the Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) model, and forecast this volatility with a Seasonal Autoregressive Integrated Moving Average with Exogenous Regressors (SARIMAX) model that incorporates meteorological variables. Finally, we apply the Black-Scholes framework to evaluate the cost of put-option-based insurance, which provides protection to farmers against adverse price drops linked to climate change. Our results highlight the role of meteorological data in improving agricultural risk modelling, enabling better design of insurance mechanisms, price stabilization tools, and sustainable policy interventions under climate uncertainty. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2604.28186 Fri, 01 May 2026 00:00:00 -0400 arXiv:2604.28186v1 Announce Type: cross Abstract: Most familiar equilibrium concepts, such as Nash and correlated equilibrium, guarantee only that no single player can improve their utility by deviating unilaterally. They offer no guarantees against profitable coordinated deviations by coalitions. Although the literature proposes solution concepts that provide stability against multilateral deviations (\emph{e.g.}, strong Nash and coalition-proof equilibrium), these generally fail to exist. In this paper, we study an alternative solution concept that minimizes coalitional deviation incentives, rather than requiring them to vanish, and is therefore guaranteed to exist. Specifically, we focus on minimizing the average gain of a deviating coalition, and extend the framework to weighted-average and maximum-within-coalition gains. In contrast, the minimum-gain analogue is shown to be computationally intractable. For the average-gain and maximum-gain objectives, we prove a lower bound on the complexity of computing such an equilibrium and present an algorithm that matches this bound. Finally, we use our framework to solve the \emph{Exploitability Welfare Frontier} (EWF), the maximum attainable social welfare subject to a given exploitability (the maximum gain over all unilateral deviations). Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2008.04401 Fri, 01 May 2026 00:00:00 -0400 arXiv:2008.04401v3 Announce Type: replace Abstract: This paper explores a new class of incomplete preferences -- termed ``connected preferences'' -- in which maximal domains of comparability are topologically connected. We provide necessary and sufficient conditions for continuous preferences to be connected. We also characterize their maximal domains of comparability. Our results extend classical findings in decision theory by linking topological properties of the choice space with the structure of preferences, offering a novel perspective on incompleteness in economic models. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2510.12653 Fri, 01 May 2026 00:00:00 -0400 arXiv:2510.12653v3 Announce Type: replace Abstract: I study how organisations choose selection procedures in a competitive environment. Two firms compete to hire candidates of unknown productivity from a common pool. Firms simultaneously post a selection procedure which consists of a test and an acceptance probability for each test outcome. After observing the firms' selection procedures, each candidate can apply to one of them. Firms can vary both the accuracy and difficulty of their test. The firms face two key considerations when choosing their selection procedure: the statistical properties of their test and the selection into the procedure by the candidates. I show that there is a unique symmetric equilibrium where the test is maximally accurate but minimally difficult. Intuitively, competition leads to maximal but misguided learning: firms end up having precise knowledge that is not payoff-relevant. In contrast, when firms face capacity constraints or have the possibility of making a wage offer, they use more difficult tests in equilibrium. I also consider asymmetric equilibria where one firm is more selective than another. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2604.26902 Fri, 01 May 2026 00:00:00 -0400 arXiv:2604.26902v2 Announce Type: replace Abstract: We study stability notions for networked many-to-many matching markets with individually insignificant agents in distributional form. Outcomes are formulated as joint distributions over characteristics of agents and contract choices. Characteristics can lie in an arbitrary Polish space. We provide a mechanical method for transferring existence results for finite matching models to large matching models for many stability notions. In particular, we show that tree-stable and pairwise-stable outcomes exist. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2602.06927 Fri, 01 May 2026 00:00:00 -0400 arXiv:2602.06927v3 Announce Type: replace-cross Abstract: Consider a community of scientists whose labs are each capable of conducting a different set of experiments. The scientists want to work together to confirm a new hypothesis, but to ensure blindness, their labs generally prohibit the scientists from communicating with each other. Further, each scientist can only make so many retractions to their lab before having to cease inquiry and suspend judgement forever. How might the scientists coordinate whether to affirm or suspend judgement on this hypothesis in light of their private experiments so that their labs are guaranteed to converge to the same conclusion and that this conclusion will not be a false positive? Call this problem 'inductive coordinated attack.' In this paper, we develop a logic for solving inductive coordinated attack by determining when and how a hypothesis can become what we call 'common inductive knowledge.' We begin by precisifying Lewis' account of common knowledge in Convention which describes the generation of higher-order expectations between agents as hinging upon agents' inductive standards and a shared witness. Our language has a rather rich syntax in order to capture equally rich notions central to Lewis' account; for instance, we speak of an agent 'having inductive reason to believe' a proposition and one proposition 'indicating' to an agent that another proposition holds. This syntax affords a novel topological semantics which, following Kelly 1996's approach in The Logic of Reliable Inquiry, takes as primitives agents' information bases. In particular, we endow each agent with a 'switching tolerance' meant to represent their personal inductive standards for learning. After establishing soundness of our proof system with respect to this semantics, we conclude by showing how our logic can be used to solve inductive coordinated attack. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2604.18767 Fri, 01 May 2026 00:00:00 -0400 arXiv:2604.18767v2 Announce Type: replace-cross Abstract: Recent disruptions at major maritime chokepoints have exposed the structural fragility of liner shipping networks. Existing indicators measure connectivity, but none quantify its structural vulnerability from a supply-side perspective. We propose the Maritime Connectivity Vulnerability Index (MCVI), capturing three dimensions mapped to distinct UNCTAD sources: low overall connectivity (LSCI), weak bilateral integration (LSBCI), and port infrastructure concentration (PLSCI). The index covers 185 economies over 2006-2025 using pooled fractional rank normalization and equal-weight aggregation from publicly available data. SIDS exhibit a mean vulnerability 0.234 points above non-SIDS economies, with the gap widening from 0.232 to 0.249 over two decades. A modest global decline of 4.2% is observed. Port concentration dominates for nearly 40% of economies (72 of 185), establishing infrastructure diversification as a distinct policy priority. Rankings are highly stable across alternative weighting schemes, normalization methods (Spearman rho = 0.97-0.999), and PCA-derived weights; Monte Carlo simulation (1,000 replications) confirms rho > 0.95 in every realization. External validation shows strong negative correlation with the World Bank Logistics Performance Index (rho = -0.61 across seven waves) and positive correlation with ad valorem maritime freight rates (rho = +0.32). Panel regressions reveal a vulnerability paradox whereby small trade-dependent economies are simultaneously the most trade-open and the most vulnerable. Pre-crisis MCVI predicts trade losses during the COVID-19 supply shock (rho = -0.25, p < 0.005), while the contrasting positive correlation during the 2008-2009 demand shock (rho = +0.23, p = 0.01) validates the supply-side specificity of the index. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2602.08144 Thu, 30 Apr 2026 00:00:00 -0400 arXiv:2602.08144v4 Announce Type: replace Abstract: We study competition between firms that contract with consumers before the consumers fully learn their product preferences. In a Hotelling duopoly, firms screen consumers by offering menus of option contracts. We characterize the unique equilibrium. Consumers select contracts from both firms. Each consumer is endogenously locked into the firm from which he chooses an option with a lower strike price. Lock-in yields inefficient consumption. Yet earlier contracting stiffens competition because less informed consumers are more homogeneous. Sufficiently early contracting raises consumer surplus relative to spot pricing -- reversing the ranking under monopoly. Exclusive contracting further increases consumer surplus by intensifying competition. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2604.25954 Thu, 30 Apr 2026 00:00:00 -0400 arXiv:2604.25954v1 Announce Type: cross Abstract: This paper examines the computational complexity of the \emph{Core Identification Problem} (CIP) in one-sided matching markets governed by the Top Trading Cycles (TTC) algorithm. The central contribution is a formal complexity separation: this paper proves that identifying which agents receive a core allocation is strictly easier than computing the full TTC allocation. Specifically, we show that CIP can be solved in $\bigO{Ln}$ time, where $L$ is the maximum number of preferences reported per agent, by computing the leading eigenvector of a preference-derived Markov transition matrix via randomized SVD\@. For sparse preference profiles ($L = \bigO{1}$, as in the NYC school choice where $L = 12$), this yields an algorithm $\bigO{n}$. This result strictly improves on the $\bigO{n \log n}$ complexity of the full TTC allocation (\cite{SabanSethuraman2013}) and matches the $\Omg{n}$ information-theoretic lower bound, establishing asymptotic optimality. The method inherits all properties of TTC: Pareto efficiency, individual rationality, and strategy-proofness, and is robust to preference noise for sufficiently large~$n$. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2604.26761 Thu, 30 Apr 2026 00:00:00 -0400 arXiv:2604.26761v1 Announce Type: new Abstract: We provide a theoretical framework to understand how widely used measures of choice difficulty relate. In a binary-option Bayesian expected-utility framework, we show that three measures of difficulty, (i) understanding (ex-ante value), (ii) choice randomness, and (iii) confidence that the chosen option is ex post correct, are, in general, unrelated, and that this result extends to other potential measures like attenuation. We provide intuitive sufficient conditions which align the orders, using both restrictions on Blackwell experiments that capture well known classes (such as logit) and restrictions on payoffs and demonstrate that in psychophysical tasks that pay only for correctness, confidence coincides with understanding. We show willingness-to-accept to switch, when measured in utils, is equivalent to understanding. Our results suggest caution in interpreting measures of choice difficulty as well as the degree of portability between economics and psychophysics experiments Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2604.26563 Thu, 30 Apr 2026 00:00:00 -0400 arXiv:2604.26563v1 Announce Type: new Abstract: This paper characterizes the single-peaked domain on a tree via the strategy-proofness of extreme rules defined on that tree. For any tree, these rules are unanimous and anonymous on any preference domain. In particular, we show that they are strategy-proof only on the single-peaked domain associated with that tree. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2604.18044 Thu, 30 Apr 2026 00:00:00 -0400 arXiv:2604.18044v2 Announce Type: replace-cross Abstract: This paper proposes a belief-based framework for social norms in environments where individuals choose a single action. Relaxing the assumption that the appropriateness standard is common knowledge, the framework allows individuals to be uncertain about this standard and to hold heterogeneous assessments and beliefs about others' assessments. Within the framework, perceived injunctive social norms, personal values, and empirical expectations, while distinct, are systematically connected through a common informational structure. The framework further clarifies how disclosed information shapes perceived norms: its effect depends on what is disclosed, whether it is publicly or privately revealed, and how the disclosed statistic encodes underlying private cues. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2604.18849 Thu, 30 Apr 2026 00:00:00 -0400 arXiv:2604.18849v3 Announce Type: replace Abstract: This study examines who adopts generative AI and whether early adoption has begun to reshape the task content of jobs across 35 European countries. Adoption ranges from under 3% to 25%. Occupational exposure strongly predicts uptake, but AI does not diffuse passively along exposure lines. At the worker level, skills, abstract task content, and employee organisational influence steepen the exposure-adoption gradient; at the country level, so do digitalisation and workplace training. A gender gap persists, concentrated in the most exposed occupations. A shift-share design finds no detectable effect of adoption on worker-reported task restructuring, consistent with an initial integration phase. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2602.21843 Thu, 30 Apr 2026 00:00:00 -0400 arXiv:2602.21843v2 Announce Type: replace Abstract: Artificial intelligence (AI) is advancing exponentially and is likely to have profound impacts on human wellbeing, social equity, and environmental sustainability. Here we argue that the "alignment problem" in AI research is also an economic alignment problem, as developing advanced AI within a growth-oriented economic system is likely to increase social, environmental, and existential risks. We show that post-growth research offers concepts and policies that could address the economic alignment problem and substantially reduce AI risks, such as by replacing optimisation with satisficing, using the Doughnut of social and planetary boundaries to guide development, and curbing systemic rebound with resource caps. We propose governance and business reforms that treat AI as a commons and prioritise tool-like autonomy-enhancing systems over agentic AI. Finally, we argue that the development of artificial general intelligence (AGI) requires new economic theories and models, for which post-growth scholarship provides a strong foundation. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2411.11186 Thu, 30 Apr 2026 00:00:00 -0400 arXiv:2411.11186v3 Announce Type: replace Abstract: Political messages increasingly bundle economic policy arguments with moral social policy stances. Using survey experiments with roughly 6,500 U.S. adults, I show that such bundling sharply weakens economic persuasion among respondents who disagree with the social stance: support falls by 13-20 percentage points relative to when the same economic message is sent alone, sometimes moving below pre-message levels. Bundling an aligned social stance does not increase persuasion. The main results are not driven by party cues, generalize across policy pairs, and are largely one-directional from social to economic issues, consistent with the predictions of a model of identity-based distancing. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2604.26634 Thu, 30 Apr 2026 00:00:00 -0400 arXiv:2604.26634v1 Announce Type: cross Abstract: Norway's electricity market is heavily dominated by hydropower, but the 2021--2022 energy crisis and stronger integration with Continental Europe have fundamentally altered price formation, reducing the reliability of forecasting models calibrated on historical data. Despite the critical need for updated models, a unified benchmark evaluating feature contributions across all structurally diverse Norwegian bidding zones remains lacking. Here we present a comprehensive evaluation of electricity price forecasting across all five Norwegian Nord Pool bidding zones. We constructed a multimodal hourly dataset spanning 2019--2025 and evaluated eight forecasting model families including LightGBM, ARX, and advanced deep learning architectures using a strictly causal test set. We implemented robust rolling-origin backtesting, leave-one-group-out feature ablation, and conditional regime analysis to dissect model performance and feature utility. Our results show that LightGBM achieves the best performance in every zone with MAE ranging from 1.64 to 5.74~EUR/MWh, while the ridge ARX model remains a highly competitive linear benchmark in northern zones. Feature ablation reveals that models relying solely on lagged prices and calendar variables achieve high accuracy and often match or exceed full multimodal integration. However, conditional regime analysis demonstrates that external features like reservoir levels and gas prices remain crucial to stratify forecast errors, which consistently increase under stressed market regimes. This highlights the practical value of model interpretability and regime awareness for decision makers facing structural changes in market dynamics. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2604.26220 Thu, 30 Apr 2026 00:00:00 -0400 arXiv:2604.26220v1 Announce Type: cross Abstract: Consumers are increasingly delegating purchase decisions to AI agents, providing natural-language descriptions of their preferences and identity. We argue that these representations constitute an information channel, role coherence, through which sellers can infer willingness to pay without explicit disclosure by the buyer agent, leading to preference leakage. In an experiment where a language-model buyer agent shops on behalf of a verbal consumer profile, we show that seller-side inference from dialogue alone recovers willingness to pay nearly one-for-one. Comparing this setting to a numeric-budget condition with confidentiality instructions cleanly isolates role coherence as distinct from instruction-following failure. Because this leakage arises from delegation itself, it cannot be mitigated at the prompt level. Instead, we propose architectural interventions that trade off personalization against preference privacy. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2604.26546 Thu, 30 Apr 2026 00:00:00 -0400 arXiv:2604.26546v1 Announce Type: new Abstract: We address the joint detection-and-attribution problem in cross-border financial contagion through a two-stage framework. The first stage applies wavelet-quantile transfer entropy across time-scales and lower, median, and upper-tail quantiles. The second stage attributes each significant link to one of five channels comprising of i) Trade, ii) Financial, iii) Geopolitical, iv) Behavioural, and v) Monetary Policy, via instrumental-variables two-stage least squares with channel-specific external instruments, LASSO-based instrument selection (Belloni, Chernozhukov and Hansen, 2014), local projections at one-, five-, and twenty-two-day horizons (Jorda, 2005), heteroskedasticity-based identification (Rigobon, 2003) for episodes in which over-identification is rejected, and Cinelli-Hazlett (2020) sensitivity bounds. The framework is applied to 18 G20 equity markets across eight crisis sub-periods spanning January 2006 to March 2026. Network density varies meaningfully across sub-periods (range 14% to 32%). Dominant-channel identification is robust across methods in the Pre-Crisis baseline and the European Sovereign Debt Crisis, both dominated by financial frictions; for the remaining six episodes identification is method-sensitive, and we report the share posterior alongside an explicit identification-status classification. Trade is empirically prominent across all post-2007 episodes, ranging from 9% during Pre-Crisis to 28% during the Global Financial Crisis. The behavioural channel is bounded above by 22% across all eight episodes under the de-confounded composite. The framework provides a methodologically disciplined account of cross-border contagion mechanisms and offers identification-status disclosure not systematically present in the existing literature. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2604.26457 Thu, 30 Apr 2026 00:00:00 -0400 arXiv:2604.26457v1 Announce Type: new Abstract: We identify a causal effect of top inventor inflows on the patent productivity of local inventors by combining the idea-generating process described by Marshall (1890) with the Bartik (1991) instruments involving the state taxes and commuting zone characteristics of the United States. We find that local productivity gains go beyond organizational boundaries and co-inventor relationships, which implies the partially nonexcludable good nature of knowledge in a spatial economy and pertains to the mysteries of the trade in the air. Our counterfactual experiment suggests that the spatial distribution of inventive activity is substantially distorted by the presence of state tax differences. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2512.17005 Thu, 30 Apr 2026 00:00:00 -0400 arXiv:2512.17005v2 Announce Type: replace Abstract: We take a new perspective on identification in structural dynamic models: rather than imposing restrictions alone, we optimize an objective. While definitive structural identification ultimately requires exogenous economic insight, a weighted correlation-maximizing objective yields an Order- and Scale-Invariant Scheme (OASIS) that selects the orthogonal rotation most aligned with designated target variables. In traditional SVARs, these targets are the reduced-form innovations, making OASIS a natural reference rotation. We show that recursive Cholesky identification is a constrained version of the same objective and that OASIS is systematically closer to perfect correlation, closing roughly twice as much of the gap as recursive orderings, both theoretically and empirically. The same framework also provides a principled estimation strategy for Proxy VARs (IV-SVARs), where the weighted criterion is essential for resolving overdetermination in multi-proxy systems while symmetrically accommodating proxy leakage. Revisiting 22 published SVARs, we find that reduced-form innovations are typically only weakly correlated, helping explain the historical robustness of recursive schemes. Applying OASIS to seminal proxy applications, however, reveals economically important leakage across shocks and shows that accounting for such leakage can materially alter substantive conclusions. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2511.03424 Thu, 30 Apr 2026 00:00:00 -0400 arXiv:2511.03424v2 Announce Type: replace Abstract: The standard fuzzy regression discontinuity (FRD) estimator is a ratio of differences of local polynomial estimators. I show that this estimator does not possess any finite integer moments, regardless of local polynomial degree, kernel function, or bandwidth. The estimator is heavy-tailed in small samples or when the treatment probability discontinuity at the cutoff is small. I present a generalized class of FRD estimators which preserves all finite moments from the data, indexed by a single tuning parameter, and nesting both standard FRD and sharp (SRD) estimators. Simple deterministic values of the tuning parameter lead to substantial improvements in median bias, median absolute deviation, and root mean squared error. Confidence intervals typically give reliable small-sample coverage in simulations. Estimator stability and performance are demonstrated using data on class size effects on educational attainment. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2412.09430 Thu, 30 Apr 2026 00:00:00 -0400 arXiv:2412.09430v3 Announce Type: replace Abstract: This paper generalizes several results on linear pooling from squared error loss to all kernel scores. The latter are a rich family of scoring rules that covers point and distribution forecasts for univariate and multivariate, discrete and continuous settings. Its members include the Continuous Ranked Probability Score for univariate distribution forecasting and the Energy Score for multivariate distribution forecasting. Our results indicate that forecast disagreement (measured as the average pairwise divergence of all component distributions) has important implications for the linear pool's performance. The results are useful for understanding and designing linear pools in general combination settings. In particular, they motivate using the linear pool (as opposed to other combination formulas) and yield a novel condition under which equal combination weights are optimal under a given kernel scoring rule. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2604.26169 Thu, 30 Apr 2026 00:00:00 -0400 arXiv:2604.26169v1 Announce Type: cross Abstract: Treatment allocation under budget constraints is a central challenge in digital advertising: advertisers must decide which users to show ads to while spending a limited budget wisely. The standard approach follows a two-stage offline pipeline - first collect historical data to estimate heterogeneous treatment effects (HTE), then solve a constrained optimization to allocate the budget. This works well with abundant data, but fails in cold-start settings such as new campaigns, new markets, or new customer segments where little historical data exists. We propose Budget-Constrained Causal Bandits (BCCB), an online framework that learns which users respond to ads while simultaneously spending the budget, making treatment decisions one user at a time. BCCB unifies three components into a single sequential process: learning individual-level ad effectiveness, exploring users whose response is uncertain, and pacing the budget over time. We evaluated on the Criteo Uplift dataset, a large-scale advertising dataset from a real randomized controlled trial. Our key finding is a data-efficiency crossover: offline methods require approximately 10,000 historical observations to produce reliable results, while BCCB operates effectively from the very first user. Furthermore, BCCB exhibits 3-5x lower performance variance between runs, making it more practical for real campaign planning. Among purely online methods, BCCB consistently outperforms standard Thompson Sampling, budgeted Thompson Sampling, and greedy HTE estimation across all budget levels tested. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2604.26826 Thu, 30 Apr 2026 00:00:00 -0400 arXiv:2604.26826v1 Announce Type: new Abstract: The maximum likelihood estimator in nonlinear panel data models with interactive fixed effects is biased. Several bias correction methods, such as analytical and jackknife approaches, have been proposed to enable valid inference. This paper shows that the parametric bootstrap also enables valid inference in such models. In particular, we show that the parametric bootstrap replicates the asymptotic distribution of the maximum likelihood estimator. Therefore, it yields asymptotically unbiased estimates and confidence sets with asymptotically correct coverage. We also propose a transformation-based bootstrap confidence interval that delivers improved finite-sample performance. Simulation results support the theoretical findings. Finally, we apply the proposed method to examine technological and product market spillover effects on firms' innovation behavior. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2604.26205 Thu, 30 Apr 2026 00:00:00 -0400 arXiv:2604.26205v1 Announce Type: new Abstract: Estimating dynamic discrete choice models with unobserved heterogeneity is computationally costly because it requires repeatedly solving fixed-point equations for all unobserved types. We develop the EM-NPL(q) framework that combines the Expectation-Maximization (EM) algorithm with an inner fixed-point solver truncated to q iterations. For the workhorse class of linear-in-parameters models, we establish a truncation-invariance result: for any q$\geq$1, EM-NPL(q) is numerically identical to the EM-NPL estimator that solves the inner fixed-point problem to convergence. Therefore, the choice of q affects computation but not statistical properties. We also establish consistency, asymptotic normality of our estimator, and local convergence of the EM-NPL(q) algorithm. In Monte Carlo simulations, EM-NPL(q) reduces runtime by at least 20% and can be 3--5 times faster. In an application to cola demand, we show that ignoring unobserved heterogeneity understates long-run own-price elasticities by up to 60%, short-run elasticities by up to 85%, and compensating variation from a soda tax by up to 90%. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2604.26088 Thu, 30 Apr 2026 00:00:00 -0400 arXiv:2604.26088v1 Announce Type: new Abstract: This paper studies sensitivity analysis of Stochastic Frontier Models. We elaborate relaxations of the baseline assumptions in the Stochastic Frontier Models and characterize the identified set under this relaxations. Furthermore, we derive the breakdown frontier for a relevant parameter of interest, the average inefficiency of a production unit. We show an application of the procedures on a well known dataset, and make the code available for the interested practitioner. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2604.25767 Wed, 29 Apr 2026 00:00:00 -0400 arXiv:2604.25767v1 Announce Type: new Abstract: Between 1949 and 1980, every U.S. state mandated public schools to provide educational services for disabled students. This is one of the largest education reforms in U.S. history, but little is known about its impacts. Given scarce data in this period, I compile survey and administrative datasets and set up a difference-in-difference design using variation in the mandates' timing. I show that the mandates increased both services for disabled students and preschool enrollments. In adulthood, disabled individuals below school age at a mandate's implementation became about 20% less likely to have no education, attained up to 0.23 more years of education, and were more likely to have worked. Although this policy could have taken away resources from non-disabled students, in fact, education and employment also increased for non-disabled individuals. These effects align with evidence that the mandates increased spending per student by up to 15%. Families were also impacted: the mandates increased employment among mothers of disabled children and the probability that disabled individuals became household heads. Over the long term, the mandates paid for themselves by generating government revenues in excess of their cost. These results provide new evidence on the large, broad impacts of expanding access to education for disabled students. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2509.09865 Wed, 29 Apr 2026 00:00:00 -0400 arXiv:2509.09865v3 Announce Type: replace Abstract: We characterize the family of utility functions satisfying linear fractional relative risk aversion (LFRRA) in terms of the Gauss hypergeometric functions. We apply this family, which nests various utility functions used in different strands of literature, to monopolistic competition and obtain the profit-maximizing price by generalizing the Lambert W function. We let firm-level data decide whether the RRA in each sector or in the aggregate economy is increasing, decreasing, or constant, which in turn determines whether markups are decreasing, increasing, or constant with respect to marginal costs. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2507.00067 Wed, 29 Apr 2026 00:00:00 -0400 arXiv:2507.00067v4 Announce Type: replace-cross Abstract: Many modern areas have not learned their lessons and often hope for the wisdom of later generations, resulting in them only possessing modern technology and difficult to iterate ancient civilizations. At present, there is no way to tell how we should learn from history and promote the gradual upgrading of civilization. Therefore, we must tell the history of civilization's progress and the means of governance, learn from experience to improve the comprehensive strength and survival ability of civilization, and achieve an optimal solution for the tempering brought by conflicts and the reduction of internal conflicts. Firstly, we must follow the footsteps of history and explore the reasons for the long-term stability of each country in conflict, including providing economic benefits to the people and means of suppressing them; then, use mathematical methods to demonstrate how we can achieve the optimal solution at the current stage. After analysis, we can conclude that the civilization transformed from human plowing to horse plowing can easily suppress the resistance of the people and provide them with the ability to resist; The selection of rulers should consider multiple institutional aspects, such as exams, elections, and drawing lots; Economic development follows a lognormal distribution and can be adjusted by population mean and standard deviation. Using a lognormal distribution with the maximum value to divide equity can adjust the wealth gap. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2604.25784 Wed, 29 Apr 2026 00:00:00 -0400 arXiv:2604.25784v1 Announce Type: new Abstract: Sequential equilibrium is one of the most fundamental refinements of Nash equilibrium for games in extensive form. However, it is not defined for extensive-form games in which a player can choose among a continuum of actions. We define a class of infinite extensive form games in which information behaves continuously as a function of past actions and define a natural notion of sequential equilibrium for this class. Sequential equilibria exist in this class and refine Nash equilibria. In standard finite extensive-form games, our definition selects the same strategy profiles as the traditional notion of sequential equilibrium. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2102.02666 Wed, 29 Apr 2026 00:00:00 -0400 arXiv:2102.02666v3 Announce Type: replace Abstract: We propose a new simple procedure called Population-Mean-Based Aggregation (PMBA) that enables a principal to "aggregate" information about an unknown state of the world from agents without understanding the information structure among them. PMBA only requires agents to communicate their beliefs about the state, and some agents to communicate their expectations of the population average belief. In a large population, for any finite number of possible states, and under weak assumptions on the information structure, allowing individual agents' beliefs to be misspecified, we show that PMBA infers the true state (in probability or almost surely under the stated conditions). We show how PMBA can be reinterpreted as a linear regression procedure, and how it can be used to aggregate information from a finite number of agents, allowing us to reuse existing results on inference in linear models. We conduct a novel experiment to show that the real-world performance of our procedure exceeds that of existing methods. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2406.13749 Wed, 29 Apr 2026 00:00:00 -0400 arXiv:2406.13749v2 Announce Type: replace Abstract: This paper studies how communication across experts prior to aggregation by a decision-maker affects the efficiency of forecast combination. When experts exchange information before reporting their forecasts, their signals become correlated through the communication network, altering aggregation efficiency even when forecasts are unbiased. The analysis introduces a statistic that characterizes how network structure shapes aggregation efficiency and shows that degree heterogeneity plays a central role. Among connected networks, regular networks attain the minimal level of aggregation distortion, while star networks generate the largest distortions within sparse connected structures. Random network benchmarks show that aggregation efficiency approaches the regular-network benchmark when expected degree either vanishes or becomes large as network size increases, whereas networks with constant expected degree generate intermediate distortions. These results provide a theoretical foundation for understanding how communication across experts affects forecast combination and establish a connection between the forecast combination literature and models of social learning in networks. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2501.07235 Wed, 29 Apr 2026 00:00:00 -0400 arXiv:2501.07235v2 Announce Type: replace Abstract: We model a market for data where an incumbent and a challenger compete for data from a producer. The incumbent has access to an exclusive data producer, and it uses this exclusive access, together with economies of scope in the aggregation of the data, as a strategy against the potential entry by the challenger. We assess the incumbent incentives to either deter or accommodate the entry of the challenger. We show that the incumbent will accommodate when the exclusive access is costly and when the economies of scope are low, and it will blockade or deter otherwise. The results would justify an access regulation that incentivizes the entry of the challenger, e.g., by increasing production costs for the exclusive data. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2506.16901 Wed, 29 Apr 2026 00:00:00 -0400 arXiv:2506.16901v2 Announce Type: replace Abstract: Without the assumption of complete, shared awareness, it is necessary to consider communication between agents who may entertain different representations of the world. A syntactic (language-based) approach provides powerful tools to address this problem. In this paper, we define translation operators between two languages which provide a `best approximation' for the meaning of propositions in the target language subject to its expressive power. We show that, in general, the translation operators preserve some, but not all, logical operations. We derive necessary and sufficient conditions for the existence of a joint state space and a joint language, in which the subjective state spaces of each agent, and their individual languages, may be embedded. This approach allows us to compare languages with respect to their expressiveness and thus, with respect to the properties of the associated state space. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2510.26387 Wed, 29 Apr 2026 00:00:00 -0400 arXiv:2510.26387v2 Announce Type: replace Abstract: We study welfare analysis for policy changes when supply and demand behavior are only partially known. We augment the robust approach pioneered by Kang and Vasserman (2025) by incorporating the supply side. We posit intervals of feasible pass-through and conduct (market-power) parameters, then apply them to two equilibrium snapshots to characterize the extremal supply-side terms entering welfare. We show that the supply-side bounds are attained by inverse pass-through functions that take only the two endpoint values of the specified interval, separated by a single price cutoff. Combining these supply-side extrema with demand-side shape restrictions, we produce simple bounds for consumer surplus, producer surplus, total surplus, and deadweight loss. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2603.11453 Wed, 29 Apr 2026 00:00:00 -0400 arXiv:2603.11453v2 Announce Type: replace Abstract: A forward-looking agent observes signals of a state that follows a Gaussian AR(1) process. He balances the cost of having imprecise beliefs with the cost of acquiring more precise signals. I characterize his optimal information acquisition policy, and analyze how his steady-state beliefs and costs depend on persistence (the AR(1) parameter) and patience (the agent's discount factor). Higher persistence has a non-monotone effect on belief precision and raises overall costs. Higher patience makes beliefs more precise and lowers overall costs. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2512.16745 Wed, 29 Apr 2026 00:00:00 -0400 arXiv:2512.16745v3 Announce Type: replace-cross Abstract: We propose using a discounted version of a convex combination of the log-likelihood with the corresponding expected log-likelihood such that when they are maximized they yield a filter, predictor and smoother for time series. This paper then focuses on working out the implications of this in the case of the canonical exponential family. The results are simple exact filters, predictors and smoothers with linear recursions. A theory for these models is developed and the models are illustrated on simulated and real data. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2512.12781 Wed, 29 Apr 2026 00:00:00 -0400 arXiv:2512.12781v2 Announce Type: replace Abstract: Using only retrospective data, we study the problem of predicting treatment effects for the same treatment/policy implemented in a different location or time period. We propose a distributionally robust estimator that minimizes the worst-case mean squared error for the prediction of treatment effect over a class of distributions defined by a Wasserstein neighborhood around the source distribution. Because the joint distribution of potential outcomes is unidentified, the problem is inherently one of partial identification. We characterize the sharp upper and lower bounds of the minimax optimizer by exploiting the Fr\'echet class of distributions consistent with the marginal distributions of potential outcomes. The resulting predictor preserves the sign of the average treatment effect under the source distribution but is shrunk toward zero, with the degree of shrinkage depending on the extent of treatment effect heterogeneity. We establish consistency and asymptotic normality of the bound estimators, develop a two-step inference procedure, and discuss the choice of the robustness parameter. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2604.25507 Wed, 29 Apr 2026 00:00:00 -0400 arXiv:2604.25507v1 Announce Type: new Abstract: We develop a nonparametric approach to identify and estimate consumer preferences and unobserved heterogeneity under nonlinear price schedules. Leveraging variation across multiple price schedules, we show that both the utility function and the distribution of preference types can be nonparametrically identified. The quantile function of unobserved types becomes solution of a functional equation, and we derive conditions ensuring identification. We propose an iterative approach for estimation, in which the regularization bias decays exponentially in the number of iterations while the variance grows only polynomially, yielding a near-parametric convergence rate. We propose a valid bootstrap procedure for finite-sample inference and extend the framework to accommodate potential endogeneity of prices and additional observed heterogeneity. Monte Carlo simulations and an empirical application to data from a European mail carrier demonstrate how we can recover the utility functions and preference distributions in finite samples. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2604.23023 Tue, 28 Apr 2026 00:00:00 -0400 arXiv:2604.23023v1 Announce Type: new Abstract: We develop a continuous-time penalized regression framework for the estimation of time-varying coefficients and variable selection when both the response and covariates are It\^o semimartingales with jumps. The coefficient paths are approximated by spline basis expansions and estimated via least squares from truncated high-frequency increments. In a finite-dimensional setting, we establish consistency and derive a feasible asymptotic distribution for the integrated coefficient estimator under infill asymptotics. We then extend the framework to high-dimensional settings in which the number of candidate covariates diverges, and show that a group-wise penalized estimator with a truncated $\ell_1$-penalty attains the oracle property, which delivers both consistent model selection and coefficient estimation. An empirical application to a large panel of more than two hundred high-frequency factors documents sparse factor structure across a large cross-section of stocks and industry portfolios. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2604.23176 Tue, 28 Apr 2026 00:00:00 -0400 arXiv:2604.23176v1 Announce Type: new Abstract: We study optimal estimation when the likelihood may be misspecified. Building on tools from the theory of decision-making under uncertainty, we analyze a class of axiomatically grounded optimality criteria which nests several existing misspecification-robust objectives. Within this class, we introduce the constrained multiplier criterion, which allows for flexible misspecification attitudes. We prove a local asymptotic minimax theorem for this criterion, extending a classical efficiency bound to a limit experiment which incorporates moment-constrained misspecification concerns. We characterize asymptotically optimal estimators as Bayes decision rules under a flat prior and an exponentially tilted likelihood that incorporates the moment constraints, and show that feasible plug-in analogs are asymptotically optimal. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2604.23770 Tue, 28 Apr 2026 00:00:00 -0400 arXiv:2604.23770v1 Announce Type: new Abstract: AI/ML methods are increasingly used in economics to generate binary variables (or labels) via classification algorithms. When these generated variables are included as covariates in regressions, even small misclassification errors can induce large biases in OLS estimators and invalidate standard inference. We study whether the bootstrap can correct this bias and deliver valid inference. We first show that a seemingly natural fixed-label bootstrap, which generates data using estimated labels but relies on a corrupted version in estimation, is generally invalid unless a strong independence condition between the latent true labels and other covariates holds. We then propose a coupled-label bootstrap that jointly resamples the true and imputed labels, and show it is valid without this condition. Two finite-sample adjustments further improve coverage: a variance correction for uncertainty in estimated misclassification rates and a Hessian rotation for near-singular designs. We illustrate the methods in simulations and apply them to investigate the relationship between wages and remote work status. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2604.24049 Tue, 28 Apr 2026 00:00:00 -0400 arXiv:2604.24049v1 Announce Type: new Abstract: Causal mediation analysis is a powerful tool for disentangling the total effect of a treatment into its direct effect on the outcome and its indirect effect mediated through an intermediate variable. However, in observational studies, confounding between treatment and potential outcomes typically renders the total and natural effects non-identifiable. In this work, we advance mediation analysis within the difference-in-differences framework. Under a mediator-adjusted parallel trends assumption and additional conditions, we demonstrate that natural indirect, direct, and total effects are identifiable in the treated group. We further derive efficient influence functions for these estimands, enabling the construction of multiply robust and nonparametrically efficient estimators. We establish the asymptotic properties of these estimators. Applying our methodology to data from the Job Corps Study, we find that job training significantly increases both short-term and long-term earnings, after controlling for the indirect effect through the proportion of weeks employed. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2604.24150 Tue, 28 Apr 2026 00:00:00 -0400 arXiv:2604.24150v1 Announce Type: new Abstract: This paper proposes linear estimation methods for dynamic fixed effects logit models only with time effects (i.e., those only with time dummies and only with time trends). The linear estimators point-identify transformations of parameters of interest for the models if five or more time periods are provided and then point-identify the parameters of interest. What it boils down to is that root-N consistent estimations are attainable for these models. Monte Carlo results corroborate this conclusion. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2604.24705 Tue, 28 Apr 2026 00:00:00 -0400 arXiv:2604.24705v1 Announce Type: new Abstract: Energy forecasting research faces a persistent comparability gap that makes it difficult to measure consistent progress over time. Reported accuracy gains are often not directly comparable because models are evaluated under study-specific datasets, time periods, information sets, and scoring setups, while widely used benchmarks and competition datasets are typically tied to fixed historical windows. This paper introduces the Energy-Arena, a dynamic benchmarking platform for operational energy time series forecasting that provides a continuously updated reference point as energy systems evolve. The platform operates as an open, API-based submission system and standardizes challenge definitions and submission deadlines aligned with operational constraints. Performance is reported on rolling evaluation windows via persistent leaderboards. By moving from retrospective backtesting to forward-looking benchmarking, the Energy-Arena enforces standardized ex-ante submission and ex-post evaluation, thereby improving transparency by preventing information leakage and retroactive tuning. The platform is publicly available at Energy-Arena.org. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2604.22933 Tue, 28 Apr 2026 00:00:00 -0400 arXiv:2604.22933v1 Announce Type: cross Abstract: We demonstrate that machine learning methods provide a powerful framework for modelling conditional asymmetric risk. Using a large cross-section of US stocks and a comprehensive set of firm characteristics, we show that allowing for nonlinearities significantly increases the out-of-sample performance across a wide range of asymmetric beta measures and forecasting horizons. Trading frictions, followed by characteristics related to intangibles, momentum and growth, emerge as the most important drivers of future risk dynamics. Reconstructing CAPM beta from forecasts of asymmetric beta components indicates that a more granular decomposition of systematic risk yields a more accurate representation of market beta. We also find that incorporating conditional beta forecasts into discounted cash flow models that account for the term structure of betas enhances equity valuation accuracy. Finally, we show that the statistical outperformance of conditional betas translates into economically significant benefits for market-neutral portfolio investors. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2604.23469 Tue, 28 Apr 2026 00:00:00 -0400 arXiv:2604.23469v1 Announce Type: cross Abstract: In this paper, a Mixed Data Sampling (MIDAS) model is studied when both low and high frequency variables are contaminated with measurement error. It is shown that the profile likelihood estimator becomes inconsistent in the presence of measurement error. Using the corrected score approach along with profile likelihood approach, a consistent estimator for parameters of MIDAS Measurement Error model is proposed. Small and large sample properties of the estimator are examined by performing a monte carlo simulation study and considering the effect of sample size, number of lags and profiling parameter. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2604.24652 Tue, 28 Apr 2026 00:00:00 -0400 arXiv:2604.24652v1 Announce Type: cross Abstract: Multi-armed bandits are widely used for sequential experimentation in clinical trials, recommendation systems, and online platforms. While regret minimization and valid inference from adaptively collected data have each been studied extensively, a basic question remains: when does adaptivity \emph{improve estimation precision} relative to uniform designs, and how should inference be balanced against the online cost of experimentation? We first study arm-level mean estimation under mean-squared-error (MSE) objectives. We characterize when an adaptive Neyman allocation, which allocates samples according to arm variance, yields strict MSE improvements over uniform sampling. When there is variance heterogeneity across arms, these improvements arise at modest sample sizes, clarifying that adaptivity can be preferable for inference not only asymptotically, but also in many practical finite-sample settings. We then study a joint inference-regret objective that accounts for the cost of assigning units to inferior arms during experimentation. We propose the Static-Allocation Rate Policy (SARP) and Neyman-Adaptive Rate Policy (NARP), which interpolates between inference- and regret-oriented policies by adjusting exploration to the local structure of the instance. We show that SARP and NARP converge to the complete-information benchmark at the optimal rate as the sampling budget grows. Our proposed policies are practically attractive as it linearly interpolates between any standard regret-minimizing algorithm and inference-targeting adaptive policies. Yet we show it still enjoys the oracle-based asymptotic optimal rate. Simulations support the theory by demonstrating improved precision over uniform allocation while controlling performance loss across a range of instances. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2301.07855 Tue, 28 Apr 2026 00:00:00 -0400 arXiv:2301.07855v4 Announce Type: replace Abstract: This paper studies inequality in digital participation across socioeconomic and demographic groups using the 2020 Canadian Internet Use Survey (CIUS). We combine survey-weighted logistic Lasso, an exact Shapley decomposition of age--education gaps, a sequential logit, and a bifactor item response theory (IRT) measure of digital literacy to identify who is excluded, why gaps persist, and where along the adoption path they arise. Education is the only determinant that remains significant at every rung of the digital ladder. Income inequality is most pronounced for virtual-wallet adoption; for online banking, employment and education together account for nearly half of the pro-rich concentration, indicating a broad socioeconomic gradient rather than a purely income-based divide. Persons with disabilities face the largest penalty at the digital-payments stage rather than at online banking, pointing to accessibility gaps in retail payment interfaces. Conditioning on digital literacy eliminates the education gradient at internet entry and reduces it by 61\% at the online banking rung, but a substantial residual persists, pointing to behavioral and institutional frictions beyond measurable competence. The youngest cohort records the lowest information-seeking score despite high digital engagement, and security deficits are concentrated among landed immigrants and visible minorities. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2311.09972 Tue, 28 Apr 2026 00:00:00 -0400 arXiv:2311.09972v4 Announce Type: replace Abstract: This paper studies inference in first-price and second-price sealed-bid auctions with many bidders, using an asymptotic framework where the number of bidders increases while the number of auctions remains fixed. Our approach enables asymptotically exact inference on key features, such as the winner's expected utility, the seller's expected revenue, and the tail of the valuation distribution, using only transaction price data. Our simulations demonstrate the accuracy of the methods in finite samples. We apply our methods to Hong Kong vehicle license auctions, focusing on high-priced, single-letter plates. Other relevant applications include online and art auctions. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2403.00347 Tue, 28 Apr 2026 00:00:00 -0400 arXiv:2403.00347v4 Announce Type: replace Abstract: The control function approach allows the researcher to identify various causal effects of interest. While powerful, it requires a strong invertibility assumption in the selection process, which limits its applicability. This paper expands the scope of the nonparametric control function approach by allowing the control function to be set-valued and derive sharp bounds on structural parameters. The proposed generalization accommodates a wide range of selection processes involving discrete endogenous variables, random coefficients, treatment selections with interference, and dynamic treatment selections. The framework also applies to partially observed or identified controls that are directly motivated from economic models. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2408.13047 Tue, 28 Apr 2026 00:00:00 -0400 arXiv:2408.13047v5 Announce Type: replace Abstract: Pooled panel analyses often mask heterogeneity in unit-specific treatment effects. This challenge, for example, crops up in studies of the impact of democracy on economic growth, where findings vary substantially due to differences in country composition. To address this challenge, this paper introduces the Temporal Difference-in-Differences (T-DiD) estimator that leverages temporal variation in the data to estimate unit-specific average treatment effects on the treated (ATT) with as few as two cross-sectional units. Under asymptotic parallel trends, limited anticipation, and temporal dependence conditions, the proposed DiD estimator is shown to be asymptotically normal. Provided at least two control units are available, the method is further complemented with an identification test that, unlike pre-trends tests, is more powerful and can detect violations of parallel trends in post-treatment periods. Empirical results using the DiD estimator suggest Benin's economy would have been 6.4% smaller on average over the 1993-2018 period had she not democratised. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2411.16978 Tue, 28 Apr 2026 00:00:00 -0400 arXiv:2411.16978v3 Announce Type: replace Abstract: We establish normal approximation in the Wasserstein metric for both non-degenerate and degenerate second-order U-statistics under cross-sectional dependence using Stein's method. For the non-degenerate case, our results extend recent studies on the asymptotic properties of sums of cross-sectionally dependent random variables. The degenerate case is more challenging due to the additional dependence induced by the nonlinearity of the U-statistic kernel. Through a specific implementation of Stein's method, we derive convergence rates under conditions on the mixing rate, the sparsity of the cross-sectional dependence structure, and the moments of the U-statistic kernel. Finally, we demonstrate the application of our theoretical results with a nonparametric specification test for data with cross-sectional dependence. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2604.12611 Tue, 28 Apr 2026 00:00:00 -0400 arXiv:2604.12611v4 Announce Type: replace Abstract: Empirical analyses of ordinal outcomes using repeated cross-sectional data rely on marginal distributions, leaving the joint distribution unobserved and the sources of distributional change unidentified. This paper develops a framework to measure and interpret such changes under limited information. The $L_1$ distance between cumulative distribution functions admits an optimal transport representation as the minimal reallocation of probability mass across ordered categories, which provides a foundation for the analysis. This yields both a scalar measure of discrepancy and a structured characterization of how distributional change must occur, which I term minimal-mobility configurations. To address missing data, I adopt a partial identification approach that delivers sharp bounds on the marginal distributions and, in turn, on both the discrepancy measure and its associated configurations. The resulting framework supports inference using standard resampling methods and provides a transparent basis for assessing sensitivity to nonresponse. An application to Arab Barometer data illustrates the approach. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2604.12263 Tue, 28 Apr 2026 00:00:00 -0400 arXiv:2604.12263v2 Announce Type: replace-cross Abstract: Policy-Relevant Treatment Effects (PRTEs) are generally not point-identified under standard Instrumental Variable (IV) assumptions when the instrument generates limited support in treatment propensity. We show that PRTE partial identification in the generalized Roy model can instead be formulated as a Constrained Conditional Optimal Transport (CCOT) problem over the joint conditional law of the potential outcome and the latent resistance. The resulting multidimensional CCOT problem reduces analytically to separable one-dimensional OT problems with product costs, yielding sharp closed-form bounds and avoiding direct solution of the original high-dimensional CCOT problem. We also develop estimation and inference procedures for these bounds: for discrete instruments, we use a Double Machine Learning (DML) approach based on Neyman-orthogonal scores that accommodates high-dimensional covariates while achieving the parametric $\sqrt{n}$ rate and asymptotic normality; for continuous instruments, we explicitly characterize the corresponding nonparametric convergence rates. The framework accommodates covariates, discrete and continuous instruments, and extensions to general treatment settings. In simulations and a bed-net subsidy application, the resulting bounds are substantially tighter than the moment-relaxation method. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2604.23058 Tue, 28 Apr 2026 00:00:00 -0400 arXiv:2604.23058v1 Announce Type: new Abstract: Firms are deploying more capable AI systems, but organizational controls often have not kept pace. These systems can generate greater productivity gains, but high-value uses require broader authority exposure -- data access, workflow integration, and delegated authority -- when governance controls have not yet decoupled capability from authority exposure. We develop an analytical model in which a firm jointly chooses AI deployment and cybersecurity investment under this governance-capability gap. The central result shows a deployment paradox: in high-loss environments, better AI can lead a firm to deploy less when capability is deployed through broader authority exposure under weak governance. Optimal deployment also falls below the no-risk benchmark, and this shortfall widens with breach-loss magnitude and with the authority exposure attached to more capable systems. Governance investment that reduces breach-loss magnitude shrinks the paradox region itself, while breach externalities expand the range of environments in which deployment is socially constrained. Governance maturity is therefore not merely a constraint on AI adoption. It is a condition that shapes whether capability improvements translate into productive deployment. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2604.23645 Tue, 28 Apr 2026 00:00:00 -0400 arXiv:2604.23645v1 Announce Type: new Abstract: Generative AI acts as a disruptive technological shock to evaluative organizations. In academic peer review, it enters both sides of the market: authors use AI to polish submissions, and reviewers use it to generate plausible reports without exerting evaluative effort. We develop a three-sided equilibrium model to analyze this dual adoption and derive a counterintuitive managerial implication for journal policy. We show that when AI capability crosses a critical threshold, reviewer effort collapses discontinuously. This transition creates a welfare misalignment: authors benefit from a weakened ``rat race,'' while editors suffer from degraded signal informativeness. Characterizing the editor's optimal constrained response, we identify a strict policy reversal. Before the AI transition, editors should tighten acceptance standards to curb rent-dissipating author polishing. After the transition, conventional intuition fails: editors must loosen acceptance standards while investing in AI detection, because further tightening only amplifies dissipative polishing without improving sorting. We prove analytically that this sign reversal is a structural consequence of the reviewer effort collapse under log-concave quality distributions. Ultimately, addressing AI in evaluative systems requires treating monitoring and loosened selectivity as complementary design instruments. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2604.24035 Tue, 28 Apr 2026 00:00:00 -0400 arXiv:2604.24035v1 Announce Type: new Abstract: Large monetary expansions do not necessarily generate consumer-price inflation, challenging scalar views of "money supply." Here we propose that monetary function is phase-dependent: newly issued base money can occupy distinct functional compartments with different coupling to prices. Starting from an accounting framework that separates reproduction, consumption, and reservation, we operationalize a measurable order parameter, phi=RB/MB, the reserve-share fraction of the monetary base. Using Japan's monthly record (1971-2026), we identify a compositional phase transition after 2013 from a cash-dominated to a reserve-dominated regime, quantitatively captured by a Landau-type order-parameter transition. Phase-conditional local projections using unexpected (residual) base-growth shocks show that, in Japan, unexpected base expansions are absorbed primarily as reserve balances-phi rises significantly-rather than entering the consumption-goods transaction sector; consequently, the core CPI inflation response is strongly attenuated and can even reverse sign. This demonstrates that increases in monetary supply do not necessarily cause inflation: the key is the "phase" in which incremental money accumulates (reservoir versus circulation). We further define function-specific efficiencies for reservation absorption and CPI transmission and provide an operational distinction between circulation-driven and reservation-dominant inflation regimes. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2604.24147 Tue, 28 Apr 2026 00:00:00 -0400 arXiv:2604.24147v1 Announce Type: new Abstract: Prediction markets are widely treated as forecasting devices that reveal collective expectations about uncertain futures. This article argues that under specifiable conditions they also function as coordination mechanisms: public probabilities that organize the behavior of voters, donors, journalists, traders, and institutions in ways that can be self-fulfilling or self-defeating. Most existing work asks whether prediction markets forecast accurately; this paper asks whether accurate forecasting is even the right criterion for a market that has become a public coordination device. Drawing on transaction-level evidence from the 2024 U.S. presidential election, we show that the social force of a market signal depends less on its size than on its persistence, the breadth of responding trader types, and cross-platform consensus. We introduce a Signal Credibility Index (SCI) -- combining the variance ratio VR(6), a two-sidedness diagnostic, and a trader-concentration adjustment -- as a microstructure-grounded criterion for predicting when price moves acquire behavioral traction. Applied to three major 2024 political shocks, the framework reveals that superficially similar events generated qualitatively distinct signal types with different implications for elite coordination. A cross-platform comparison establishes a systematic decoupling of social authority from epistemic robustness: the most visible market produced the least accurate forecasts. The framework carries direct implications for regulating prediction markets as democratic information infrastructure. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2604.24336 Tue, 28 Apr 2026 00:00:00 -0400 arXiv:2604.24336v1 Announce Type: new Abstract: Education is a major source of inequality in income and health. Polygenic indices for educational attainment (EA-PGI) capture both direct and indirect genetic influences on education, but their effects on income and health remain unclear. Using Finnish registry data on 51,056 graduates followed annually since graduation for up to 25 years, we report three findings. First, higher EA-PGI strongly predicts income growth, but only among higher educated people: tertiary-educated graduates at the 90th percentile earn EUR 45,392 (13.1 percent) higher discounted lifetime income than those at the 10th percentile. This effect is not mediated by overall health and is entirely absent for the secondary (high school)-educated workers, who do not benefit from higher EA-PGI levels. Second, EA-PGI does not predict income differences at labor market entry or the quality of the first employer, but rather higher job-to-job mobility toward higher-quality firms that drives the long-run income divergence. Third, controlling for parental EA-PGI in 12,871 parent-offspring trios reduces the discounted lifetime income gap by 71 percent, and the effect of paternal (but not maternal) EA-PGI on offspring income exceeds that of the offspring's own EA-PGI. These findings suggest that genetic factors associated with educational attainment predict income trajectories primarily through faster and more frequent changes to higher-paying employers. However, much of this association reflects indirect paternal genetic effects, consistent with enduring paternal patterns of intergenerational job and income transmission. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2604.24344 Tue, 28 Apr 2026 00:00:00 -0400 arXiv:2604.24344v1 Announce Type: new Abstract: This paper characterises optimal incentive schemes for ESG disclosure in a continuous-time principal-agent setting. We model a risk-averse principal (e.g., a platform or standard-setter) contracting with a team of heterogeneous agents whose disclosure signals are each correlated with a traded climate risk factor. The optimal contract balances incentive provision against the variance of aggregate payouts by leveraging three instruments: own-signal loading, cross-signal loadings across agents, and hedging tilts on the traded asset. We derive closed-form linear optimal controls in a tractable linear-quadratic-Gaussian framework. When the principal is nearly risk-neutral, the contract uses the traded asset purely to hedge the specific `enforcement risk' generated by high-powered incentives. As the principal's risk aversion increases, the optimal scheme converges to a `market-neutral' regime where aggregate asset exposure is eliminated and the cross-signal structure tightens to an `identity pooling' constraint. We characterise this limit analytically as a constrained quadratic program governed by an M-matrix. In the high-risk-aversion regime, heterogeneity creates genuinely new effects absent under symmetry: the cross-section of S-tilts must change sign (unless degenerate), and an agent's own-signal diagonal can turn negative when that row is too strongly exposed to the common traded factor relative to the rest of the group. The results provide a theoretical foundation for `mixed' compensation structures in Regenerative Finance (ReFi), rationalising the use of both stable payments and volatile governance tokens to optimise risk-sharing. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2604.23897 Tue, 28 Apr 2026 00:00:00 -0400 arXiv:2604.23897v1 Announce Type: cross Abstract: Markets are a promising way to coordinate AI agent activity for similar reasons to those used to justify markets more broadly. In order to effectively participate in markets, agents need to have informative signals of their own ability to successfully complete a task and the cost of doing so. We propose MarketBench, a benchmark for assessing whether AI agents have these capabilities. We use a 93-task subset of SWE-bench Lite, a software engineering benchmark, with six recently released LLMs as a demonstration. These LLMs are miscalibrated on both success probability and token usage, and auctions built from these self-reports diverge from a full-information allocation. A follow-up intervention where we add information about capabilities from prior experiments to the context improves calibration, but only modestly narrows the gap to a full-information benchmark. We also document the performance of a market-based scaffolding with these LLMs. Our results point to self-assessment as a key bottleneck for market-style coordination of AI agents. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2504.12413 Tue, 28 Apr 2026 00:00:00 -0400 arXiv:2504.12413v2 Announce Type: replace Abstract: This paper examines how Canadian firms balance the benefits of technology adoption against the rising risk of cyber security breaches. We merge data from the 2021 Canadian Survey of Digital Technology and Internet Use and the 2021 Canadian Survey of Cyber Security and Cybercrime to investigate the trade-off firms face when pursuing digitalization to enhance productivity and efficiency, balanced against the potential increase in cyber security risk. The analysis explores the extent of digital technology adoption, differences across industries, the subsequent associations with efficiency, and associated cyber security vulnerabilities. We build aggregate variables, such as the Business Digital Usage Score and a cyber security incidence variable to quantify each firm's digital engagement and cyber security risk. A survey-weight-adjusted Lasso estimator is employed, and a debiasing method for high-dimensional logit models is introduced to identify the predictors of technological efficiency and cyber risk. The analysis reveals a digital divide linked to firm size, industry, and workforce composition. While rapid expansion of tools such as cloud services or artificial intelligence can raise efficiency, it simultaneously heightens exposure to cyber threats, particularly among larger enterprises. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2508.09079 Tue, 28 Apr 2026 00:00:00 -0400 arXiv:2508.09079v2 Announce Type: replace Abstract: This paper develops a multilayer network approach for exploring the evolution of scientific disciplines, using the case of economics before and after the 2008 global financial crisis as a large-scale empirical testing ground. The units of analysis are journals, linked by social and intellectual relationships. The analysis covers all journals indexed in EconLit across three years (2006, 2012 and 2019). In the most recent year (2019), the dataset includes 909 journals, over 30,000 editorial board members, more than 260,000 authors, 134,000 articles, and nearly 2 million cited references. For each period, we model journals as connected in a four-layer multiplex network: the social relationships are based on shared editors (interlocking editorship) and shared authors (interlocking authorship), while the intellectual ones are based on shared references (bibliographic coupling) and textual similarity between articles. These four layers are integrated using Similarity Network Fusion to produce unified similarity networks from which journal communities are identified. Comparing the field across the three periods reveals a high degree of structural continuity. Although research topics changed after the crisis, the fundamental social and intellectual relationships among journals remained remarkably stable. A major result of the analysis is that editorial networks play the dominant role in shaping hierarchies and legitimize knowledge production within the discipline. Whether this finding holds in other scientific disciplines remains an open question for future research. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2509.12084 Tue, 28 Apr 2026 00:00:00 -0400 arXiv:2509.12084v4 Announce Type: replace Abstract: We show that since the mid-1990s, the trade-promoting effects of tariff liberalization have been increasingly offset by deteriorating geopolitical alignment, slowing trade globalization after 2007. To quantify this barrier, we use large language models to compile 833,485 geopolitical events across 193 countries, 1950--2024, and construct a bilateral geopolitical alignment score. Using local projections, we estimate that a one-standard-deviation permanent improvement in alignment raises bilateral trade by 22 percent in the long run. In an Armington framework, tariff reductions raised 2021 global trade by about 7.5 percent, while geopolitical deterioration reduced it by about 5.3 percent, with uneven welfare effects. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2512.21645 Tue, 28 Apr 2026 00:00:00 -0400 arXiv:2512.21645v2 Announce Type: replace Abstract: This paper investigates the structural transformation of the Democratic Republic of the Congo (DRC) tin market induced by the U.S. Dodd-Frank Act. Focusing on the breakdown of the pricing mechanism, we estimate the price elasticity of export demand from 2010 to October 2022 using a structural identification strategy that overcomes the lack of reliable unit value data. Our analysis reveals that the regulation effectively destroyed the price mechanism, with demand elasticity dropping to zero. This indicates the formation of a ``captive market'' driven by certification requirements rather than price competitiveness. Also, we find strong hysteresis; deregulation alone failed to restore market flexibility. The structural rigidity was finally broken not by policy suspension, but by the 2019 ``Huawei shock,'' an external demand surge that forced supply chain diversification. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2604.17697 Tue, 28 Apr 2026 00:00:00 -0400 arXiv:2604.17697v2 Announce Type: replace Abstract: We digitize and analyze the near-universe of National Socialist German Workers' Party (NSDAP) membership records and link them to newly digitized population and industrial censuses. Four findings emerge. First, as the party expanded, its membership came to resemble the broader population more closely in occupational, demographic, and religious terms. Second, SS members remained distinctly different: younger, more educated, and more fanatical, as proxied by membership portraits. Third, within communities, coworkers, and families, early membership generated hysteresis, with subsequent entrants drawn from the same groups. Finally, local increases in party membership are associated with subsequent deportations of Germany's Jews. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2510.24990 Tue, 28 Apr 2026 00:00:00 -0400 arXiv:2510.24990v2 Announce Type: replace-cross Abstract: Despite data's central role in AI production, it remains the least understood input. As AI labs exhaust public data and turn to proprietary sources, with deals reaching hundreds of millions of dollars, research across computer science, economics, law, and policy has fragmented. We establish data economics as a coherent field through three contributions. First, we characterize data's distinctive properties -- nonrivalry, context dependence, and emergent rivalry through contamination -- and trace historical precedents for market formation in commodities such as oil and grain. Second, we present systematic documentation of AI training data deals from 2020 to 2025, revealing persistent market fragmentation, five distinct pricing mechanisms (from per-unit licensing to commissioning), and that most deals exclude original creators from compensation. Third, we propose a formal hierarchy of exchangeable data units (token, record, dataset, corpus, stream) and argue for data's explicit representation in production functions. Building on these foundations, we outline four open research problems foundational to data economics: measuring context-dependent value, balancing governance with privacy, estimating data's contribution to production, and designing mechanisms for heterogeneous, compositional goods. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2604.12082 Tue, 28 Apr 2026 00:00:00 -0400 arXiv:2604.12082v2 Announce Type: replace-cross Abstract: Battery energy storage systems (BESS) participating in multi-market electricity trading require price forecasts to optimize dispatch decisions. A widely held assumption is that forecast accuracy, measured by standard metrics such as mean absolute error (MAE), drives trading performance. We challenge this assumption using a hierarchical three-layer optimization system trading simultaneously on frequency containment reserve (FCR), automatic frequency restoration reserve (aFRR), day-ahead, and continuous intraday (XBID) markets in Germany and Switzerland over 2020-2025, with real market data from Regelleistung.net and Swissgrid. We find that rank correlation (Kendall tau), rather than MAE, is the primary predictor of intraday dispatch value: forecasts above an empirical threshold of tau approximately 0.85-0.95 capture up to 97-100% of perfect-foresight revenue, while persistence forecasts with near-zero tau capture only 33%. This threshold is stable across market regimes and volatility levels, and reflects the ordinal structure of the dispatch problem. Furthermore, under reserve market constraints, FCR capacity revenue exceeds XBID by 6.5x per MW, making capacity allocation -- not forecast accuracy -- the primary driver of total revenue. In the Swiss market, hydrological surplus anomalies are significantly associated with balancing market revenue (p = 0.0005), a mechanism absent from existing German-focused literature. These findings reframe forecast evaluation for BESS operators: the relevant question is not what the MAE is, but whether the forecast achieves tau-sufficiency. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2604.23971 Tue, 28 Apr 2026 00:00:00 -0400 arXiv:2604.23971v1 Announce Type: new Abstract: This paper develops a decomposition methodology for common agency games in which each principal's payoff depends on her own outcome and the agent's type, but not on rivals' outcomes. The key step reduces each principal's best-response problem to a standard screening problem defined over the agent's indirect utility -- the upper envelope of her payoff over rivals' offerings. Individually best-responding mechanisms then assemble into a pure-menu perfect Bayesian equilibrium when a compatibility condition (utility-preserving recombination) ensures aligned tie-breaking across principals. Under a non-indifference condition, the decomposition recovers all equilibria except those sustained by menu items that no type of the agent actually selects but which nevertheless discipline the rival's screening problem. When principals' payoffs depend on the full allocation profile, the decomposition adapts only under substantive regularity conditions on the agent's off-path choice behavior, one of which coincides with Luce's choice axiom. I apply the methodology to two settings. In a quadratic-loss delegation model, equilibria feature one principal offering a finite menu of discrete ``regimes'' while the other receives piecewise full delegation within each regime. In a competitive bundling duopoly under intrinsic common agency, the decomposition yields equilibria exhibiting market splitting, in which firms specialize in complementary bundles, and asymmetric equilibria with a take-it-or-leave-it base contract paired with a nested or tree menu of upgrades. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2604.24546 Tue, 28 Apr 2026 00:00:00 -0400 arXiv:2604.24546v1 Announce Type: new Abstract: Regulatory and contractual constraints on individual exposures are standard in insurance and reinsurance markets, but a poorly designed constraint can distort the economic incentives of risk-averse agents. In the unconstrained problem, the classical comonotonic improvement theorem guarantees Pareto-optimal allocations that are nondecreasing in the aggregate loss. A constraint that is not stable under risk reduction can destroy this property. We show by example that Value-at-Risk caps lead to optimal allocations that are non-comonotonic in the aggregate loss. We identify componentwise convex-order solidity as a sufficient condition on the feasible set that restores the comonotonic improvement under constraints. If replacing any agent's allocation by a less risky one preserves feasibility, then every feasible allocation admits a feasible comonotonic improvement for all convex-order-consistent preferences. This criterion covers many constraints typical in risk management, but excludes Value-at-Risk caps and idiosyncratic deductibles. We illustrate the implications of our main result in a mean-variance risk-sharing application. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2604.24757 Tue, 28 Apr 2026 00:00:00 -0400 arXiv:2604.24757v1 Announce Type: new Abstract: Coordination is an important aspect of innovative contexts, where: the more innovative a course of action, the more uncertain its outcome. To study the interplay of coordination and informational ``complexity'', I embed a beauty-contest game into a complex environment. I identify a new conformity phenomenon. This effect may push towards the exploration of unknown alternatives or constitute a status-quo bias, depending on the network structure of players' interactions. In an application, I show that an organization with decentralized authority can implement profit maximization in a sufficiently complex environment. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2510.09859 Tue, 28 Apr 2026 00:00:00 -0400 arXiv:2510.09859v4 Announce Type: replace Abstract: A seller of a dynamic information service under an information-throughput constraint screens buyers who privately differ in urgency. We characterize the revenue-optimal mechanism: deploy a single preference-aligned belief process; screen buyers with a menu of stopping-time caps. The result rationalizes tokenized GenAI pricing, from consumer subscription tiers to B2B API service tiers. Extensions to heterogeneous valuations and endogenous reasoning quality preserve the qualitative conclusions. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2510.12420 Tue, 28 Apr 2026 00:00:00 -0400 arXiv:2510.12420v4 Announce Type: replace Abstract: As awareness of health and environmental issues grows, the demand for organic food is rising worldwide, yet consumers still struggle to distinguish genuine organic products from conventional ones. This information asymmetry creates incentives for some producers to mislabel conventional goods as organic in order to charge higher prices, threatening market integrity and trust. This paper develops a game-theoretic model of interactions among producers, consumers, and regulators in organic supply chains to study when fraud emerges and how it can be deterred. By analyzing extensive-form and repeated games with monitoring and penalties, we identify conditions under which honest labeling becomes a stable equilibrium and show how inspection frequency and reputation losses shape strategic behavior. Our results highlight the critical role of a neutral third party in overcoming information asymmetries and sustaining trust in organic markets. Government regulation and independent certification, combined with credible monitoring and meaningful penalties, discourage mislabeling, and support the sustainable growth of the organic food supply chain. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2603.20683 Tue, 28 Apr 2026 00:00:00 -0400 arXiv:2603.20683v3 Announce Type: replace Abstract: We study a contest in which $N$ players sequentially draw from a distribution as many times as they want at a fixed cost per draw, with no recall, and the highest accepted value wins a prize. In the unique symmetric equilibrium, the acceptance probability, expected search cost, and players' payoffs do not depend on the underlying distribution. Total search expenditure equals the prize (full rent dissipation). These distribution-free equilibrium properties extend to multiple prizes and to hierarchical competition among designers. The efficient prize that aligns competitive incentives with the social optimum is distribution-dependent: heavy-tailed distributions require much larger prizes. With finite number of draws, adding competitors can raise the quality threshold when search costs are low, reversing the discouragement of the unlimited-draw case. A planner choosing both the prize and the field size always prefers the minimum field ($N=2$) with unlimited draws, but heavy-tailed distributions and finitely many draws favor larger fields, as breadth of parallel exploration compensates for limited depth of individual search. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2603.18563 Tue, 28 Apr 2026 00:00:00 -0400 arXiv:2603.18563v2 Announce Type: replace-cross Abstract: As autonomous AI agents increasingly mediate online platform markets, a fundamental question emerges: do these markets generate stable strategic outcomes? In repeated strategic environments, the Nash equilibrium provides a natural benchmark for this stability. However, empirical evidence on off-the-shelf LLM agents is mixed, leaving it unclear whether independently deployed agents can converge to equilibrium behavior without explicit strategic post-training. In this paper, we provide an affirmative answer. Extending the Bayesian learning literature in theoretical economics, we prove that AI agents, acting as Bayesian posterior samplers rather than expected utility maximizers, are guaranteed to eventually become weakly close to a Nash equilibrium in infinitely repeated games. We further extend this analysis to settings in which stage payoffs are unknown ex ante, and agents observe only their privately realized stochastic payoffs, and obtain the same convergence guarantees. Finally, we empirically evaluate these theoretical implications across five repeated-game environments, ranging from the Prisoner's Dilemma to marketing promotion games. Taken together, our findings suggest that strategic stability in AI-mediated markets can emerge from the intrinsic reasoning and learning properties of modern AI agents, without the need for unrealistic universal fine-tuning. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2508.04371 Mon, 27 Apr 2026 00:00:00 -0400 arXiv:2508.04371v2 Announce Type: replace Abstract: This paper studies whether behavioral interventions designed to promote resource conservation in one domain generate spillovers in another. Using a natural field experiment involving over 2,000 residents, we identify the direct and spillover effects of real-time feedback and social comparisons on water and energy consumption. We implement three interventions: two targeting shower use and one targeting air-conditioning use. We find significant reductions in shower use from both water-saving interventions, but no direct effect of the energy-saving intervention on air-conditioning use. For spillovers, we estimate precise null effects of water-saving interventions on air-conditioning use, and of the energy-saving intervention on shower use. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2407.08750 Mon, 27 Apr 2026 00:00:00 -0400 arXiv:2407.08750v4 Announce Type: replace-cross Abstract: Large-scale streaming data are common in modern machine learning applications and have led to the development of online learning algorithms. Many fields, such as supply chain management, weather and meteorology, energy markets, and finance, have pivoted toward probabilistic forecasting. This results in the need not only for accurate learning of the expected value but also for learning the conditional heteroskedasticity and conditional moments. Against this backdrop, we present a methodology for online estimation of regularized, linear distributional models. The proposed algorithm combines recent developments in online estimation of LASSO models with the well-known GAMLSS framework. We provide a case study on day-ahead electricity price forecasting, in which we show the competitive performance of the incremental estimation combined with strongly reduced computational effort. Our algorithms are implemented in a computationally efficient Python package ondil. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2604.22532 Mon, 27 Apr 2026 00:00:00 -0400 arXiv:2604.22532v1 Announce Type: new Abstract: Empirical researchers routinely invoke the no-interference or \textit{individualistic treatment response} (ITR) assumption to identify causal effects in observational studies, despite concerns that interference across units may arise in many economic settings. This paper studies the causal content of standard ITR-based identification formulas when arbitrary interference is present. We show that, under restrictions on dependence between treatment assignments across units, conventional ITR-based identification formulas -- including those underlying selection-on-observables, instrumental variables, regression discontinuity designs, and difference-in-differences -- identify well-defined causal objects: types of \textit{average direct effects} (ADEs). These results do not require knowledge of the interference structure or specification of exposure mappings. We also propose a sensitivity analysis framework that quantifies the robustness of statistical inference to violations of treatment-assignment independence under arbitrary interference. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2604.22236 Mon, 27 Apr 2026 00:00:00 -0400 arXiv:2604.22236v1 Announce Type: cross Abstract: Human decision-makers often face choices about complex cases with many potentially relevant features, but limited bandwidth to inspect and integrate all available information. In such settings, we study algorithms that highlight a small subset of case-specific features for human consideration, rather than producing a single prediction or recommendation. We model highlighting as a constrained information policy that selects a small number of features to reveal. A central issue is how humans interpret the algorithm's choice of features: a sophisticated agent correctly conditions on the selection rule, while a naive agent updates only on revealed feature values and treats the selection event as exogenous. We show that optimizing highlighting for sophisticated agents can be computationally intractable, even in simple discrete and binary settings, whereas optimizing for naive agents is tractable as long as the maximal bandwidth is fixed. We also show that a highlighting policy that is optimal for sophisticated agents can perform arbitrarily poorly when deployed to naive agents, motivating robust, implementable alternatives. We illustrate our framework in a calibrated empirical exercise based on the American Housing Survey. Overall, our results establish the value of highlighting a context-specific set of features rather than a fixed one as a practically appealing and computationally feasible tool for achieving human-algorithm complementarity. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2011.00373 Mon, 27 Apr 2026 00:00:00 -0400 arXiv:2011.00373v3 Announce Type: replace Abstract: Many events and policies (treatments) occur at specific spatial locations, with researchers interested in their effects on nearby units. I approach the spatial treatment setting from an experimental perspective: What ideal experiment would we design to estimate the causal effects of spatial treatments? This perspective motivates a comparison between units near realized treatment locations and units near counterfactual (unrealized) candidate locations, which differs from current empirical practice. I derive design-based standard errors that are straightforward to compute. For observational data, I propose machine learning methods to find counterfactual candidate locations when observable characteristics, rather than potential outcomes, determine treatment probabilities. To accommodate methods for high-dimensional data in the theory, I extend a double machine learning result to the design-based framework with spatial correlations. I apply the proposed methods to study the causal effects of grocery stores on foot traffic to nearby businesses during COVID-19 shelter-in-place policies, finding a large positive effect at very short distances, with no effect at larger distances. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2412.20204 Mon, 27 Apr 2026 00:00:00 -0400 arXiv:2412.20204v3 Announce Type: replace Abstract: This paper considers filtering, parameter estimation, and testing for potentially dynamically misspecified state-space models. When dynamics are misspecified, filtered values of state variables often do not satisfy model restrictions, making them hard to interpret, and parameter estimates may fail to characterize the dynamics of filtered variables. To address this, a sequential optimal transportation approach is used to generate a model-consistent sample by mapping observations from a flexible reduced-form to the structural conditional distribution iteratively. Filtered series from the generated sample are model-consistent. Specializing to linear processes, a closed-form Optimal Transport Filtering algorithm is derived. Minimizing the discrepancy between generated and actual observations defines an Optimal Transport Estimator. Its large sample properties are derived. A specification test determines if the model can reproduce the sample path, or if the discrepancy is statistically significant. Empirical applications to DSGE models, affine term structure models, and trend-cycle decomposition illustrate the methodology and the results. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2505.01600 Mon, 27 Apr 2026 00:00:00 -0400 arXiv:2505.01600v3 Announce Type: replace Abstract: I study linear panel data models with predetermined regressors (such as lagged dependent variables) where coefficients are individual-specific, allowing for heterogeneity in the effects of the regressors on the dependent variable. I show that the model is not point-identified in a short panel context but rather partially identified, and I characterize the identified sets for the mean, variance, and CDF of the coefficient distribution. This characterization is general, accommodating discrete, continuous, and unbounded data, and it leads to computationally tractable estimation and inference procedures. I apply the method to study lifecycle earnings dynamics among U.S. households using the Panel Study of Income Dynamics (PSID) dataset. The results suggest the presence of unobserved heterogeneity in earnings persistence, implying that households face varying levels of earnings risk which, in turn, contribute to heterogeneity in their consumption and savings behaviors. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2603.28470 Mon, 27 Apr 2026 00:00:00 -0400 arXiv:2603.28470v2 Announce Type: replace Abstract: We propose a novel framework for conducting causal inference based on counterfactual densities. While the current paradigm of causal inference is mostly focused on estimating average treatment effects (ATEs), which restricts the analysis to the first moment of the outcome variable, our density-based approach is able to detect causal effects based on general distributional characteristics. Following the Oaxaca-Blinder decomposition approach, we consider two types of counterfactual density effects that together explain observed discrepancies between the densities of the treated and control group. First, the distribution effect is the counterfactual effect of changing the conditional density of the control group to that of the treatment group, while keeping the covariates fixed at the treatment group distribution. Second, the covariate effect represents the effect of a hypothetical change in the covariate distribution. Both effects have a causal interpretation under the classical unconfoundedness and overlap assumptions. Methodologically, our approach is based on analyzing the conditional densities as elements of a Bayes Hilbert space, which preserves the non-negativity and integration-to-one constraints. We specify a flexible functional additive regression model estimating the conditional densities. We apply our method to analyze the German East--West income gap, i.e., the observed differences in wages between East Germans and West Germans. While most of the existing studies focus on the average differences and neglect other distributional characteristics, our density-based approach is suited to detect all nuances of the counterfactual distributions, including differences in probability masses at zero. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2504.02518 Mon, 27 Apr 2026 00:00:00 -0400 arXiv:2504.02518v3 Announce Type: replace-cross Abstract: Probabilistic electricity price forecasting (PEPF) is vital for short-term electricity markets, yet the multivariate nature of day-ahead prices - spanning 24 consecutive hours - remains underexplored. At the same time, real-time decision-making requires methods that are both accurate and fast. We introduce an online algorithm for multivariate distributional regression models, allowing efficient modeling of the conditional means, variances, and dependence structures of electricity prices. The approach combines multivariate distributional regression with online coordinate descent and LASSO-type regularization (absolute shrinkage and selection operator), enabling scalable estimation in high-dimensional covariate spaces. Additionally, we propose a regularized estimation path over increasingly complex dependence structures, allowing for early stopping and avoiding overfitting. In a case study using historical data from the German day-ahead market, the proposed method yields interpretable and well-calibrated joint prediction intervals for the 24-dimensional price distribution and provides robust performance across a range of proper scoring rules. The results underscore the importance of modeling the dependence structure of electricity prices. Furthermore, we analyze the trade-off between predictive accuracy and computational costs for batch and online estimation and provide a high-performing open-source Python implementation in the ondil package. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2603.04328 Mon, 27 Apr 2026 00:00:00 -0400 arXiv:2603.04328v2 Announce Type: replace-cross Abstract: We study the deployment performance of machine learning based enforcement systems used in cryptocurrency anti money laundering (AML). Using forward looking and rolling evaluations on Bitcoin transaction data, we show that strong static classification metrics substantially overstate real world regulatory effectiveness. Temporal nonstationarity induces pronounced instability in cost sensitive enforcement thresholds, generating large and persistent excess regulatory losses relative to dynamically optimal benchmarks. The core failure arises from miscalibration of decision rules rather than from declining predictive accuracy per se. These findings underscore the fragility of fixed AML enforcement policies in evolving digital asset markets and motivate loss-based evaluation frameworks for regulatory oversight. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2601.20912 Mon, 27 Apr 2026 00:00:00 -0400 arXiv:2601.20912v2 Announce Type: replace Abstract: Text-based measurement in political research often treats classi6ication disagreement as random noise. We examine this assumption using con6idence-weighted human annotations of 5,000 social media messages by U.S. politicians. We 6ind that political communication is generally highly legible, with mean con6idence exceeding 0.99 across message type, partisan bias, and audience classi6ications. However, systematic variation concentrates in the constituency category, which exhibits a 1.79 percentage point penalty in audience classi6ication con6idence. Given the high baseline of agreement, this penalty represents a sharp relative increase in interpretive uncertainty. Within messages, intent remains clear while audience targeting becomes ambiguous. These patterns persist with politician 6ixed effects, suggesting that measurement error in political text is structured by strategic incentives rather than idiosyncratic coder error. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2205.11858 Mon, 27 Apr 2026 00:00:00 -0400 arXiv:2205.11858v2 Announce Type: replace Abstract: We consider the problem of designing prices for public transport where payment enforcing is done through random inspection of passengers' tickets as opposed to physically blocking their access. Passengers are fully strategic such that they may choose different routes or buy partial tickets in their optimizing decision. We derive expressions for the prices that make every passenger choose to buy the full ticket. Using travel and pricing data from the Washington DC metro, we show that a switch to a random inspection method for ticketing while keeping current prices could lead to more than 59% of revenue loss due to fare evasion, while adjusting prices to take incentives into consideration would reduce that loss to less than 20%, without any increase in prices. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2409.00832 Mon, 27 Apr 2026 00:00:00 -0400 arXiv:2409.00832v4 Announce Type: replace Abstract: In a satisficing equilibrium each agent $i$ plays one of her top $k_i$ actions in response to the actions of the other agents. Our concept unifies models of bounded rationality and yields predictions that differ from canonical solution concepts. We study its theoretical properties and show that it provides sharp predictions, exists in most games as well as in a broad new class of economic environments, admits standard epistemic and dynamic foundations, and is empirically falsifiable. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2508.14522 Mon, 27 Apr 2026 00:00:00 -0400 arXiv:2508.14522v3 Announce Type: replace Abstract: This paper studies general multi-unit probabilistic assignment problems involving indivisible objects, with a particular focus on achieving the fairness notion of equal treatment of equals (ETE) and satisfying various efficiency criteria. We extend the definition of ETE so that it accommodates a wide range of constraints and applications. We introduce the ETE reassignment procedure, which transforms any assignment into one that satisfies ETE, and examine whether the efficiency properties satisfied by the original assignment -- namely, ex-post efficiency, ordinal efficiency, and rank-minimizing efficiency -- are preserved under the ETE reassignment. We show that, while the ETE reassignment of an ex-post efficient assignment remains ex-post efficient, it may fail to preserve ordinal efficiency in general settings. However, since the ETE reassignment of a rank-minimizing assignment preserves rank-minimizing efficiency, there must exist an assignment satisfying both ETE and ordinal efficiency. Furthermore, we propose a computationally efficient method for constructing assignments that satisfy both ETE and ordinal efficiency under general upper bound constraints by combining the serial dictatorship rule with appropriately specified priority lists and the ETE reassignment procedure. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2604.10006 Mon, 27 Apr 2026 00:00:00 -0400 arXiv:2604.10006v2 Announce Type: replace Abstract: We study delegated Bayesian persuasion: a principal incentivizes an intermediary to design information via outcome-contingent transfers, while the intermediary privately chooses the experiment subject to convex costs. We characterize first-best implementability through a pair of alignment conditions on the principal's and intermediary's payoff indices. A local condition on the support of the target experiment is necessary; a global affine alignment condition is sufficient. We show that the gap between them is non-empty and provide a partial characterization of the intermediate region. When the first-best is unattainable, the principal's problem admits a virtual Bayesian persuasion representation: the second-best experiment maximizes the same concavified objective as the first-best, with the principal's payoff index distorted by a single scalar shadow price that summarizes the entire agency friction. Under entropy costs, moral hazard compresses posterior dispersion whenever the intermediary's utility differs across the actions it recommends. Explicit closed-form solutions for posteriors, mixing weights, and the optimal transfer schedule are derived for binary environments. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2411.11131 Mon, 27 Apr 2026 00:00:00 -0400 arXiv:2411.11131v2 Announce Type: replace-cross Abstract: We consider the problem of allocating heterogeneous and indivisible goods among strategic agents, with preferences over subsets of goods, when there is no medium of exchange. This model captures the well studied problem of fair allocation of indivisible goods. Serial-quota mechanisms are allocation mechanisms where there is a predefined order over agents, and each agent in her turn picks a predefined number of goods from the remaining goods. These mechanisms are clearly strategy-proof, non-bossy, and neutral. Are there other mechanisms with these properties? We show that for important classes of strict ordinal preferences (as lexicographic preferences, and as the class of all strict preferences), these are the only mechanisms with these properties. Importantly, unlike previous work, we can prove the claim even for mechanisms that are not Pareto-efficient. Moreover, we generalize these results to preferences that are cardinal, including any valuation class that contains additive valuations. We then derive strong negative implications of this result on truthful mechanisms for fair allocation of indivisible goods to agents with additive valuations. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2604.21258 Fri, 24 Apr 2026 00:00:00 -0400 arXiv:2604.21258v1 Announce Type: new Abstract: Survey data are widely used to study how income inequality, poverty, and welfare evolve over time. A common practice is to estimate the income distribution separately for each year, treating annual observations as independent cross-sections. For population subgroups with relatively small sample sizes, however, this approach can produce unstable parameter estimates, imprecise inference for inequality and poverty measures, and potentially misleading posterior probabilities of Lorenz and stochastic dominance. This paper develops flexible Bayesian models for time-varying income distributions that borrow strength across adjacent years by allowing the parameters of income distributions to evolve dynamically. We consider a random walk specification and an extended model with shrinkage priors. The proposed framework yields coherent inference for the full income distributions over time, as well as for associated inequality measures, poverty indices, and dominance probabilities. Simulation studies show that, relative to independent year-by-year models, the proposed approach produces substantially more precise and stable inference, while avoiding spurious variation in welfare comparisons. An application to the Aboriginal and residents of the Australian Capital Territory (ACT) population subgroups in the Household, Income and Labour Dynamics in Australia survey shows that the dynamic models deliver improved inference for income distributions and related welfare measures, and can change conclusions about distributional dominance over time. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2604.21548 Fri, 24 Apr 2026 00:00:00 -0400 arXiv:2604.21548v1 Announce Type: new Abstract: Treatment effect distributions are not identified without restrictions on the joint distribution of potential outcomes. Existing approaches either impose rank preservation -- a strong assumption -- or derive partial identification bounds that are often wide. We show that a single scalar parameter, rank stickiness, suffices for nonparametric point identification while permitting rank violations. The identified joint distribution -- the coupling that maximizes average rank correlation subject to a relative entropy constraint, which we call the Bregman-Sinkhorn copula -- is uniquely determined by the marginals and rank stickiness. Its conditional distribution is an exponential tilt of the marginal with a Bregman divergence as the exponent, yielding closed-form conditional moments and rank violation probabilities; the copula nests the comonotonic and Gaussian copulas as special cases. The empirical Bregman-Sinkhorn copula converges at the parametric $\sqrt{n}$-rate with a Gaussian process limit, despite the infinite-dimensional parameter space. We apply the framework to estimate the full treatment effect distribution, derive a variance estimator for the average treatment effect tighter than the Fr\'{e}chet--Hoeffding and Neyman bounds, and extend to observational studies under unconfoundedness. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2604.21672 Fri, 24 Apr 2026 00:00:00 -0400 arXiv:2604.21672v1 Announce Type: new Abstract: The emergence of agentic artificial intelligence (AI) represents a fundamental transformation in financial markets, characterized by autonomous systems capable of reasoning, planning, and adaptive decision-making with minimal human intervention. This comprehensive survey synthesizes recent advances in agentic AI across multiple dimensions of financial operations, including system architecture, market applications, regulatory frameworks, and systemic implications. We examine how agentic AI differs from traditional algorithmic trading and generative AI through its capacity for goal-oriented autonomy, continuous learning, and multi-agent coordination. Our analysis shows that while agentic AI offers substantial potential for enhanced market efficiency, liquidity provision, and risk management, it also introduces novel challenges related to market stability, regulatory compliance, interpretability, and systemic risk. Through a systematic review of foundational research, technical architectures, market applications, and governance frameworks, this survey provides scholars and practitioners with a structured understanding of how agentic AI is reshaping financial markets and identifies critical research directions for ensuring that these systems enhance both operational efficiency and market resilience. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2604.21260 Fri, 24 Apr 2026 00:00:00 -0400 arXiv:2604.21260v1 Announce Type: cross Abstract: We study semisupervised mean estimation with a small labeled sample, a large unlabeled sample, and a black-box prediction model whose output may be miscalibrated. A standard approach in this setting is augmented inverse-probability weighting (AIPW) [Robins et al., 1994], which protects against prediction-model misspecification but can be inefficient when the prediction score is poorly aligned with the outcome scale. We introduce Calibrated Prediction-Powered Inference, which post-hoc calibrates the prediction score on the labeled sample before using it for semisupervised estimation. This simple step requires no retraining and can improve the original score both as a predictor of the outcome and as a regression adjustment for semisupervised inference. We study both linear and isotonic calibration. For isotonic calibration, we establish first-order optimality guarantees: isotonic post-processing can improve predictive accuracy and estimator efficiency relative to the original score and simpler post-processing rules, while no further post-processing of the fitted isotonic score yields additional first-order gains. For linear calibration, we show first-order equivalence to PPI++. We also clarify the relationship among existing estimators, showing that the original PPI estimator is a special case of AIPW and can be inefficient when the prediction model is accurate, while PPI++ is AIPW with empirical efficiency maximization [Rubin et al., 2008]. In simulations and real-data experiments, our calibrated estimators often outperform PPI and are competitive with, or outperform, AIPW and PPI++. We provide an accompanying Python package, ppi_aipw, at https://larsvanderlaan.github.io/ppi-aipw/. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2406.19033 Fri, 24 Apr 2026 00:00:00 -0400 arXiv:2406.19033v3 Announce Type: replace Abstract: Building upon factor decomposition to overcome the curse of dimensionality inherent in multivariate volatility processes, we develop a factor model-based multivariate stochastic volatility (fMSV) framework. We propose a two-stage estimation procedure for the fMSV model: in the first stage, estimators of the factor model are obtained, and in the second stage, the MSV component is estimated using the estimated common factor variables. We derive the asymptotic properties of the estimators, taking into account the estimation of the factor variables. The prediction performances are illustrated by finite-sample simulation experiments and applications to portfolio allocation. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2308.15062 Fri, 24 Apr 2026 00:00:00 -0400 arXiv:2308.15062v4 Announce Type: replace-cross Abstract: Systematically biased forecasts are typically interpreted as evidence of forecasters' irrationality and/or asymmetric loss. In this paper we propose an alternative explanation: when forecasts inform policy decisions, and the resulting actions affect the realisation of the forecast target itself, forecasts may be optimally biased even under quadratic loss. The result arises in environments in which the forecaster is uncertain about the policymaker's reaction to the forecast, which is presumably the case in most applications. We motivate our theory by reviewing some stylised properties of Greenbook inflation forecasts. Our results point out that the presence of policy feedback poses a challenge to traditional tests of forecast rationality. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2604.16432 Fri, 24 Apr 2026 00:00:00 -0400 arXiv:2604.16432v2 Announce Type: replace-cross Abstract: AI in applications like screening job applicants had become widespread, and may contribute to unemployment especially among the young. Biases in the AIs may become baked into the job selection process, but even in their absence, reliance on a single AI is problematic. In this paper we derive a simple formula to estimate, or at least place an upper bound on, the precision of such approaches for data resembling realistic CVs: $P(q) \approx \frac{\rho n^b + q(1-\rho)}{1 + (n^b - 1)\rho}$ where $b \approx q^* + 0.8 (1 - \rho)$ and $q^*$ is $q$ clipped to $[0.07, 0.22]$ where $P(q)$ is the precision of the top $q$ quantile selected by a panel of $n$ AIs and $\rho$ is their average pairwise correlation. This equation provides a basis for considering how many AIs should be used in a Panel, depending on the importance of the decision. A quantitative discussion of the merits of using a diverse panel of AIs to support decision-making in such areas will move away from dangerous reliance on single AI systems and encourage a balanced assessment of the extent to which diversity needs to be built into the AI parts of the socioeconomic systems that are so important for our future. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2604.21103 Fri, 24 Apr 2026 00:00:00 -0400 arXiv:2604.21103v1 Announce Type: cross Abstract: Governments are increasingly interested in using AI to make administrative decisions cheaper, more scalable, and more consistent. But for probabilistic AI to be incorporated into public administration it must be embedded in a compliance layer that makes decisions reviewable, repeatable, and legally defensible. That layer can improve oversight by making departures from law easier to detect. But it can also create a stable approval boundary that political successors learn to navigate while preserving the appearance of lawful administration. We develop a formal model in which institutions choose the scale of automation, the degree of codification, and safeguards on iterative use. The model shows when these systems become vulnerable to strategic use from within government, why reforms that initially improve oversight can later increase that vulnerability, and why expansions in AI use may be difficult to unwind. Making AI usable can thus make procedures easier for future governments to learn and exploit. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2604.21334 Fri, 24 Apr 2026 00:00:00 -0400 arXiv:2604.21334v1 Announce Type: cross Abstract: Do large language models (LLMs) exhibit systematic ideological bias when reasoning about economic causal effects? As LLMs are increasingly used in policy analysis and economic reporting, where directionally correct causal judgments are essential, this question has direct practical stakes. We present a systematic evaluation by extending the EconCausal benchmark with ideology-contested cases - instances where intervention-oriented (pro-government) and market-oriented (pro-market) perspectives predict divergent causal signs. From 10,490 causal triplets (treatment-outcome pairs with empirically verified effect directions) derived from top-tier economics and finance journals, we identify 1,056 ideology-contested instances and evaluate 20 state-of-the-art LLMs on their ability to predict empirically supported causal directions. We find that ideology-contested items are consistently harder than non-contested ones, and that across 18 of 20 models, accuracy is systematically higher when the empirically verified causal sign aligns with intervention-oriented expectations than with market-oriented ones. Moreover, when models err, their incorrect predictions disproportionately lean intervention-oriented, and this directional skew is not eliminated by one-shot in-context prompting. These results highlight that LLMs are not only less accurate on ideologically contested economic questions, but systematically less reliable in one ideological direction than the other, underscoring the need for direction-aware evaluation in high-stakes economic and policy settings. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2604.21604 Fri, 24 Apr 2026 00:00:00 -0400 arXiv:2604.21604v1 Announce Type: cross Abstract: Contemporary cybersecurity governance assumes that professionals apply risk reasoning. Yet major organisational failures persist despite investment in tools, staffing, and credentials. This study investigates the structural source of that paradox. Cybersecurity speaks the language of risk, but its training architecture has shaped the profession to think in terms of threats. A sequential mixed-methods design integrated four analyses; NLP of the NIST NICE Framework v2.0.0 (2,111 TKS statements), SEM (n = 126 cybersecurity professionals), a control-group comparison (n = 133 general professionals), and thematic coding of seven leadership interviews. Four convergent findings emerged. First, "likelihood" and "probability" appear zero times across all TKS statements. Risk management content accounts for 4.5% of high-confidence semantic classifications, ranking 18th of 29 competency domains. NICE codifies threat-management activity while invoking risk mainly at the category level. Second, SEM showed that training exposure significantly predicts risk management competence directly and indirectly through conceptual salience, for a total effect of Beta = .629. However, the theoretically four-dimensional competence construct collapsed into a single factor, indicating epistemic compression. Third, cybersecurity professionals showed no measurable advantage over the general professional population in foundational risk reasoning; only 11.9% showed high differentiation. Fourth, all seven leaders expected Likelihood x Impact reasoning, yet five did not articulate the formula themselves. These findings support a structural conclusion: cybersecurity has taken professional form as a threat-management discipline that has borrowed risk vocabulary. Remediation requires redesign of professional formation, not marginal curriculum reform. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2412.00615 Fri, 24 Apr 2026 00:00:00 -0400 arXiv:2412.00615v4 Announce Type: replace Abstract: This paper examines the impact of racial discrimination in hiring on employment, wages, and wealth disparities between black and white workers. Using a labor search-and-matching model with racially prejudiced and non-prejudiced firms, we show that labor market frictions sustain discriminatory practices as an equilibrium outcome. These practices account for 57% of the racial unemployment gap, 48% of the average wage gap, and 16% of the median wealth gap. Discriminatory hiring also increases unemployment and wage volatility for black workers, increasing their labor market risks over the business cycle. Eliminating prejudiced firms reduces these disparities and improves the welfare of black workers as well as the overall economic welfare. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2503.07341 Fri, 24 Apr 2026 00:00:00 -0400 arXiv:2503.07341v2 Announce Type: replace Abstract: Recent advances in artificial intelligence (AI) have led to a wide range of predictions about its long-term impact on humanity. A central focus is the potential emergence of transformative AI (TAI), eventually capable of outperforming humans in all economically valuable tasks and fully automating labor. Discussed scenarios range from unprecedented economic growth and abundance ("post-scarcity" or "cornucopia") to human extinction after a misaligned TAI takes over ("AI doom"). However, the probabilities and implications of these scenarios remain highly uncertain. We contribute by organizing the various scenarios and evaluating their associated existential risks and economic outcomes in terms of aggregate welfare. Our results imply that even low-probability catastrophic outcomes justify substantial investments in AI safety and alignment research. This result highlights that current global efforts in AI safety and alignment research are insufficient relative to the scale and urgency of the risks posed by TAI. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2602.11442 Fri, 24 Apr 2026 00:00:00 -0400 arXiv:2602.11442v2 Announce Type: replace Abstract: Urban parks play a vital role in delivering various essential ecosystem services that significantly contribute to the well-being of urban populations. However, there is quite a limited understanding of how people value these ecosystem services differently. Here, we investigated the relationships among nine ecosystem service demands in urban parks across China using a large-scale survey with 20,075 responses and a point-allotment experiment. We found particularly high preferences for air purification and recreation services at the expense of other services among urban residents in China. These preferences were further reflected in three distinct demand bundles: air purification-dominated, recreation-dominated, and balanced demands. Each bundle delineated a typical group of people with different representative characteristics. Socio-economic and environmental factors, such as environmental interest and vegetation coverage, were found to significantly influence the trade-off intensity among service demands. These results underscore the necessity for tailored urban park designs that address diverse service demands with the aim of enhancing the quality of urban life in China and beyond sustainably. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2604.20652 Fri, 24 Apr 2026 00:00:00 -0400 arXiv:2604.20652v2 Announce Type: replace-cross Abstract: Large language models trained on human feedback may suppress fraud warnings when investors arrive already persuaded of a fraudulent opportunity. We tested this in a preregistered experiment across seven leading LLMs and twelve investment scenarios covering legitimate, high-risk, and objectively fraudulent opportunities, combining 3,360 AI advisory conversations with a 1,201-participant human benchmark. Contrary to predictions, motivated investor framing did not suppress AI fraud warnings; if anything, it marginally increased them. Endorsement reversal occurred in fewer than 3 in 1,000 observations. Human advisors endorsed fraudulent investments at baseline rates of 13-14%, versus 0% across all LLMs, and suppressed warnings under pressure at two to four times the AI rate. AI systems currently provide more consistent fraud warnings than lay humans in an identical advisory role. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2105.02457 Fri, 24 Apr 2026 00:00:00 -0400 arXiv:2105.02457v3 Announce Type: replace Abstract: We provide an original analysis of historical documents to describe the assignment procedures used to allocate entry-level civil service jobs in China from the tenth to the early twentieth century. The procedures tried to take different objectives into account through trial and error. By constructing a formal model that combines these procedures into a common framework, we compare their effectiveness in minimizing unfilled jobs and prioritizing high-level posts. We show that the problem was inherently complex such that changes made to improve the outcome could have the opposite effect. Based on a small modification of the last procedure used, we provide a new mechanism for producing maximum matchings under constraints in a transparent and public way. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2504.17061 Fri, 24 Apr 2026 00:00:00 -0400 arXiv:2504.17061v2 Announce Type: replace Abstract: In the context of aggregating von Neumann-Morgenstern utilities, we show that bounded violations of the Pareto conditions characterize aggregation rules that are approximately utilitarian. When a single utility function is intended to represent the preference judgments of a group of individuals and the Pareto principles are nearly satisfied, we prove that its distance from a weighted sum of individual cardinal utilities does not exceed half of the positive parameter that differentiates our weaker versions of the Pareto conditions from their conventional forms. This result suggests the stability of Harsanyi's (1955) aggregation theorem, in that small deviations from the Pareto principles lead to aggregation rules that remain close to utilitarian aggregation. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2602.19201 Thu, 23 Apr 2026 00:00:00 -0400 arXiv:2602.19201v2 Announce Type: replace Abstract: This paper develops an asymptotic and inferential theory for fixed-effects panel quantile regression (FEQR) that delivers inference robust to pervasive common shocks. Such shocks induce cross-sectional dependence that is central in many economic and financial panels but largely ignored in existing FEQR theory, which typically assumes cross-sectional independence and requires $T \gg N$. We show that the standard FEQR estimator remains asymptotically normal under the mild condition $(\log N)^2/T \to 0$, thereby accommodating empirically relevant regimes, including those with $T \ll N$. We further show that common shocks fundamentally alter the asymptotic covariance structure, rendering conventional covariance estimators inconsistent, and we propose a simple covariance estimator that remains consistent both in the presence and absence of common shocks. The proposed procedure therefore provides valid robust inference without requiring prior knowledge of the dependence structure, substantially expanding the applicability of FEQR methods in realistic panel data settings. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2601.17964 Thu, 23 Apr 2026 00:00:00 -0400 arXiv:2601.17964v2 Announce Type: replace Abstract: How do cost shocks pass through to prices in markets with price dispersion? We decompose the problem into two layers. In the competition layer, consumers' consideration sets determine equilibrium distributions of normalized margins. In the curvature layer, demand elasticity maps these margins into prices and pass-through rates. We prove the pricing game is strategically equivalent to a game over normalized margins, so equilibrium margin distributions are invariant to demand and costs. This separation yields closed-form pass-through formulas at each quantile of the price distribution, robust bounds across demand specifications, and sharp comparative statics linking market structure to incidence. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2406.07186 Thu, 23 Apr 2026 00:00:00 -0400 arXiv:2406.07186v4 Announce Type: replace Abstract: We study information aggregation in a dynamic trading model with partially informed traders. Ostrovsky [2012] showed that `separable' securities aggregate information in all equilibria, however, determining whether a security is separable requires knowing the exact information structure of agents. To remedy this problem, we allow traders to acquire signals with cost $\kappa$, in every period. We show that `$\kappa$ separable securities' characterize information aggregation and, as the cost decreases, almost all securities become $\kappa$ separable, irrespective of the traders' initial private information. Moreover, the switch to $\kappa$ separability happens not gradually but discontinuously, hence even a small decrease in costs can result in a security aggregating information. We provide a complete classification of securities in terms of how well they aggregate information, which surprisingly depends only on their payoff structure. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2604.20220 Thu, 23 Apr 2026 00:00:00 -0400 arXiv:2604.20220v1 Announce Type: new Abstract: This paper develops a highly general convex duality framework for the perturbed utility route choice (PURC) model. We show that the traveler's constrained, potentially non-smooth utility maximization problem admits a dual formulation: an unconstrained concave maximization problem with a differentiable objective. The unique optimal flow can be recovered link-by-link from any dual solution via the convex conjugates of link perturbation functions. These properties enable efficient gradient-based optimization for large-scale networks and fast computation for sensitivity analysis. Finally, the framework reveals a structural analogy between PURC and current flow in electrical circuits. Economics/econ.TH_(Theoretical_Economics) https://arxiv.org/abs/2510.17481 Thu, 23 Apr 2026 00:00:00 -0400 arXiv:2510.17481v2 Announce Type: replace Abstract: This paper proposes a model of tax compliance and fiscal capacity grounded in universalization reasoning. Citizens partially internalize the consequences of concealment by imagining a world in which everyone acted similarly, linking their compliance decisions to the perceived effectiveness of public spending. A selfish elite chooses between public goods and private rents, taking compliance as given. In equilibrium, citizens' moral internalization expands the feasible tax base and induces elites to allocate resources toward provision rather than appropriation. When the value of public spending is uncertain, morality enables credible reform: high-value elites can signal their type through provision, prompting citizens to increase compliance and raising fiscal capacity within the same period. The analysis thus identifies a moral channel through which states may escape low-capacity traps even under weak institutions. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2604.19969 Thu, 23 Apr 2026 00:00:00 -0400 arXiv:2604.19969v1 Announce Type: new Abstract: Standard intergenerational measures have been shown to understate the long-run persistence of socioeconomic advantages in developed countries. We study theoretically and empirically whether this pattern extends to less developed settings, using Indonesia as a case study. Using the Indonesian Family Life Survey (IFLS) and Census data, we study multigenerational correlations in education across three generations. Contrary to previous findings, we observe greater multigenerational mobility than parent-child correlations alone would suggest. We develop a theoretical framework to highlight two key factors influencing multigenerational dynamics in developing countries: (1) financial and credit constraints, and (2) cultural norms related to marital sorting. To confirm their relevance, we exploit regional variations in exposure to the 1997-98 Asian financial crisis and in marital customs. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2604.12783 Thu, 23 Apr 2026 00:00:00 -0400 arXiv:2604.12783v2 Announce Type: replace-cross Abstract: When variable selection methods are applied to bootstrapped and multiply imputed datasets, the set of selected variables typically varies across iterations. Aggregating results via the union rule can lead to overly dense models. We propose a sequential evidence aggregation procedure that models detection outcomes across perturbation iterations as Bernoulli trials and accumulates evidence for variable relevance through a likelihood-ratio process admitting an approximate Bayes-factor interpretation. The procedure provides both a variable inclusion criterion and a stopping rule that eliminates the need to fix the number of bootstrap-imputation iterations ex ante. A Monte Carlo study across 126 scenarios and an empirical illustration demonstrate the method's performance relative to existing aggregation approaches. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2602.21903 Thu, 23 Apr 2026 00:00:00 -0400 arXiv:2602.21903v2 Announce Type: replace Abstract: This paper develops a general method of inference for fixed effects models which is (i) automatic, (ii) computationally inexpensive, (iii) tuning parameter-free, and (iv) highly model agnostic. Specifically, we show how to combine a collection of subsample estimators into a jackknife $t$-statistic, from which hypothesis tests, confidence intervals, and $p$-values are readily obtained. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2604.19968 Thu, 23 Apr 2026 00:00:00 -0400 arXiv:2604.19968v1 Announce Type: cross Abstract: Traditional macroeconomic growth models rely on general equilibrium and continuous, frictionless institutional transitions, failing to account for the catastrophic structural collapses observed in empirical economic history. We propose the Stochastic Networked Governance (SNG) model, a discrete-time, agent-based framework that bridges econophysics, network science, and institutional economics. By defining jurisdictions through a binary institutional genome, the model formalizes institutional complementarity, endogenous growth, and the non-linear macroeconomic penalties of structural reform (the "J-Curve"). Using the CEPII Gravity Database and the IMF Systemic Banking Crises dataset, we move beyond theoretical topologies to execute an empirical historical simulation from 1970 to 2017 across the top 100 global economies. Through Monte Carlo ensembles, we demonstrate how scale-invariant exogenous shocks and spatial capital flight drive global phase transitions, exposing the mathematical mechanics of the 1989-1991 Soviet collapse, the Hub-Risk Paradigm, and the emergent resilience of spatially firewalled market networks. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2604.19833 Thu, 23 Apr 2026 00:00:00 -0400 arXiv:2604.19833v1 Announce Type: new Abstract: Financial firms have gone through three major technological waves: computerization in the 1980s and 1990s, the rise of indexing and passive investing in the 2000s and 2010s, and the AI and automation wave from roughly 2015 to the present. This project studies how much labor is required to manage capital across those waves by tracking a simple productivity measure: assets under management per employee. Using a small panel of representative firms, we compare changes in AUM per employee, revenue per employee, and operating expense intensity over time. The goal is not to identify causal effects, but to document stylized facts about how technology changes the scale of asset management work. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2604.19956 Thu, 23 Apr 2026 00:00:00 -0400 arXiv:2604.19956v1 Announce Type: new Abstract: Blockchain technology is widely expected to reduce transaction costs by automating contract enforcement and eliminating intermediaries; yet, the execution costs imposed by network congestion have received little attention in the operations management literature. We study on-chain peak shaving, the systematic scheduling of Ethereum transactions toward low-congestion windows to reduce gas fee exposure. We use transaction-level data from seven firms across seven industries (N = 62,142 transactions, January-March 2026). Gas fees vary significantly throughout the day: the peak-hour premium at 10 AM Eastern Time reaches USD 0.220 per transaction above the overnight baseline, driven primarily by speculative-arbitrage demand rather than operational activity. Firm-level scheduling responses are heterogeneous and not uniformly disciplined. Only three of seven firms transact disproportionately during off-peak hours; four transact counter-cyclically, concentrated in peak windows due to external deadlines or governance cycles. This heterogeneity is explained by two moderators: transaction deferrability and gas intensity. We formalize these into an On-Chain Scheduling Matrix that maps firms to four regimes: 1) full peak shaving, 2) selective peak shaving, 3) cost provisioning, and 4) accept-market-rate, with regime membership predicting both fee savings and residual cost floors (40-92 percent of actual expenditure). Theoretically, we extend Transaction Cost Economics to account for time-varying execution costs imposed by congestion externalities. In addition to extending Williamson's original cost taxonomy, we introduce a dual classification of gas fees as execution costs in timing but maladaptation costs in origin. The findings reposition on-chain gas-fee management alongside energy procurement and foreign exchange hedging as a domain requiring systematic operational planning. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2604.19798 Thu, 23 Apr 2026 00:00:00 -0400 arXiv:2604.19798v1 Announce Type: cross Abstract: Micro-scale street-level economic assessment is fundamental for precision spatial resource allocation. While Street View Imagery (SVI) advances urban sensing, existing approaches remain semantically superficial and overlook brand hierarchy heterogeneity and structural recession. To address this, we propose a visual-semantic and field-based spatiotemporal framework, operationalized via the Street Economic Vitality Index (SEVI). Our approach integrates physical and semantic streetscape parsing through instance segmentation of signboards, glass interfaces, and storefront closures. A dual-stage VLM-LLM pipeline standardizes signage into global hierarchies to quantify a spatially smoothed brand premium index. To overcome static SVI limitations, we introduce a temporal lag design using Location-Based Services (LBS) data to capture realized demand. Combined with a category-weighted Gaussian spillover model, we construct a three-dimensional diagnostic system covering Commercial Activity, Spatial Utilization, and Physical Environment. Experiments based on time-lagged geographically weighted regression across eight tidal periods in Nanjing reveal quasi-causal spatiotemporal heterogeneity. Street vibrancy arises from interactions between hierarchical brand clustering and mall-induced externalities. High-quality interfaces show peak attraction during midday and evening, while structural recession produces a lagged nighttime repulsion effect. The framework offers evidence-based support for precision spatial governance. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2409.08347 Thu, 23 Apr 2026 00:00:00 -0400 arXiv:2409.08347v5 Announce Type: replace Abstract: This paper develops a sensitivity analysis framework for the perturbed utility route choice (PURC) model and the accompanying stochastic traffic equilibrium model. We derive analytical sensitivity expressions for the Jacobian of the individual optimal PURC flow and equilibrium link flows with respect to link cost parameters under general assumptions. This allows us to determine the marginal change in link flows following a marginal change in link costs across the network. We show how to implement these results while exploiting the sparsity generated by the PURC model. Numerical examples illustrate the use of our method for estimating equilibrium link flows after link cost shifts, identifying critical design parameters, and quantifying uncertainty in performance predictions. Finally, we demonstrate the method in a large-scale example. The findings have implications for network design, pricing strategies, and policy analysis in transportation planning and economics, providing a bridge between theoretical models and real-world applications. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2510.02737 Thu, 23 Apr 2026 00:00:00 -0400 arXiv:2510.02737v2 Announce Type: replace Abstract: We introduce a model of dynamic matching with transferable utility, extending the static model of Shapley and Shubik (1971). Forward-looking agents have individual states that evolve with current matches. Each period, a matching market with market-clearing prices takes place. We prove the existence of an equilibrium with time-varying distributions of agent types and show it is the solution to a social planner's problem. We also prove that a stationary equilibrium exists. We introduce econometric shocks to account for unobserved heterogeneity in match formation. We propose two algorithms to compute a stationary equilibrium. We adapt both algorithms for estimation. We estimate a model of accumulation of job-specific human capital using data on Swedish engineers. Economics/econ.EM_(Econometrics) https://arxiv.org/abs/2604.19987 Thu, 23 Apr 2026 00:00:00 -0400 arXiv:2604.19987v1 Announce Type: new Abstract: Between 2005 and 2019, U.S. business applications rose 40 percent while conversion to employer firms fell by nearly half. We study whether boundary redrawing helps explain this pattern. Structured routine-cognitive work can be governed through deliverables and thinner buyer and supplier interfaces. When such work remains place-bound, outsourcing creates demand for domestic specialist suppliers. Across 722 commuting zones, a one percentage-point higher baseline routine employment share raises applications by 27.8 per 100,000 residents. Realized entry concentrates in micro-establishments, with no startup quality gains. Contract and industry evidence point to local supplier entry, not routine-manual displacement. Economics/econ.GN_(General_Economics) https://arxiv.org/abs/2604.02875 Thu, 23 Apr 2026 00:00:00 -0400 arXiv:2604.02875v2 Announce Type: replace Abstract: Understanding how corporate control concentrates in modern ownership systems is crucial in an economy increasingly shaped by cross-border mergers and acquisitions. Rather than expanding productive capacity, these operations reorganize ownership and control over existing firms through complex transnational structures involving financial intermediaries, holding companies, and investment vehicles. As a result, corporate control may become highly concentrated even when formal ownership appears fragmented. This paper examines how foreign direct investments-related capital centralization reshapes firm-level governance by tracing how control converges on individual companies through multi-layered ownership networks. Focusing on two strategically relevant Italian firms, we show that control is rarely exercised solely by ultimate owners, but instead arises from the interaction of a small set of financially interconnected intermediaries operating along transnational ownership chains. The results show how small equity stakes translate into substantial governance power, highlighting the role of financial intermediation and raising implications for strategic autonomy and economic sovereignty in key sectors. Economics/econ.GN_(General_Economics)