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    <title>econ updates on arXiv.org</title>
    <link>http://rss.arxiv.org/rss/econ</link>
    <description>econ updates on the arXiv.org e-print archive.</description>
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      <title>Endogenous Inequality Aversion: Decision criteria for triage and other ethical tradeoffs</title>
      <link>https://arxiv.org/abs/2601.22250</link>
      <description>arXiv:2601.22250v1 Announce Type: new 
Abstract: Medical ``Crisis Standards of Care'' call for a utilitarian allocation of scarce resources in emergencies, while favoring the worst-off under normal conditions. Inspired by such triage rules, we introduce social welfare functions whose distributive tradeoffs depend on the prevailing level of aggregate welfare. These functions are inherently self-referential: they take the welfare level as an input, even though that level is itself determined by the function. In our formulation, inequality aversion varies with welfare and is therefore self-referential. We provide an axiomatic foundation for a family of social welfare functions that move from Rawlsian to utilitarian criteria as overall welfare falls, thereby formalizing triage guidelines. We also derive the converse case, in which the social objective shifts from Rawlsianism toward utilitarianism as welfare increases.</description>
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      <category>econ.TH</category>
      <pubDate>Mon, 02 Feb 2026 00:00:00 -0500</pubDate>
      <arxiv:announce_type>new</arxiv:announce_type>
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      <dc:creator>Federico Echenique, Teddy Mekonnen, M. Bumin Yenmez</dc:creator>
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      <title>Model Selection in Panel Data Models: A Generalization of the Vuong Test</title>
      <link>https://arxiv.org/abs/2601.22354</link>
      <description>arXiv:2601.22354v1 Announce Type: new 
Abstract: This paper generalizes the classical Vuong (1989) test to panel data models by employing modified profile likelihoods and the Kullback-Leibler information criterion. Unlike the standard likelihood function, the profile likelihood lacks certain regular properties, making modification necessary. We adopt a generalized panel data framework that incorporates group fixed effects for time and individual pairs, rather than traditional individual fixed effects. Applications of our approach include linear models with non-nested specifications of individual-time effects.</description>
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      <category>econ.EM</category>
      <pubDate>Mon, 02 Feb 2026 00:00:00 -0500</pubDate>
      <arxiv:announce_type>new</arxiv:announce_type>
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      <dc:creator>Jinyong Hahn, Zhipeng Liao, Konrad Menzel, Quang Vuong</dc:creator>
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    <item>
      <title>Screening with Advertisements</title>
      <link>https://arxiv.org/abs/2601.22404</link>
      <description>arXiv:2601.22404v1 Announce Type: new 
Abstract: We investigate a seller's revenue-maximizing mechanism in a setting where a desirable good is sold together with an undesirable bad (e.g., advertisements) that generates third-party revenue. The buyer's private information is two-dimensional: valuation for the good and willingness to pay to avoid the bad. Following the duality framework of Daskalakis, Deckelbaum, and Tzamos (2017), whose results extend to our setting, we formulate the seller's problem using a transformed measure $\mu$ that depends on the third-party payment $k$. We provide a near-characterization for optimality of three pricing mechanisms commonly used in practice -- the Good-Only, Ad-Tiered, and Single-Bundle Posted Price -- and introduce a new class of tractable, interpretable two-dimensional orthant conditions on $\mu$ for sufficiency. Economically, $k$ yields a clean comparative static: low $k$ excludes the bad, intermediate $k$ separates ad-tolerant and ad-averse buyers, and high $k$ bundles ads for all types.</description>
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      <category>econ.TH</category>
      <pubDate>Mon, 02 Feb 2026 00:00:00 -0500</pubDate>
      <arxiv:announce_type>new</arxiv:announce_type>
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      <dc:creator>Kolagani Paramahamsa</dc:creator>
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    <item>
      <title>Using SVM to Estimate and Predict Binary Choice Models</title>
      <link>https://arxiv.org/abs/2601.22659</link>
      <description>arXiv:2601.22659v1 Announce Type: new 
Abstract: The support vector machine (SVM) has an asymptotic behavior that parallels that of the quasi-maximum likelihood estimator (QMLE) for binary outcomes generated by a binary choice model (BCM), although it is not a QMLE. We show that, under the linear conditional mean condition for covariates given the systematic component used in the QMLE slope consistency literature, the slope of the separating hyperplane given by the SVM consistently estimates the BCM slope parameter, as long as the class weight is used as required when binary outcomes are severely imbalanced. The SVM slope estimator is asymptotically equivalent to that of logistic regression in this sense. The finite-sample performance of the two estimators can be quite distinct depending on the distributions of covariates and errors, but neither dominates the other. The intercept parameter of the BCM can be consistently estimated once a consistent estimator of its slope parameter is obtained.</description>
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      <category>econ.EM</category>
      <pubDate>Mon, 02 Feb 2026 00:00:00 -0500</pubDate>
      <arxiv:announce_type>new</arxiv:announce_type>
      <dc:rights>http://creativecommons.org/licenses/by/4.0/</dc:rights>
      <dc:creator>Yoosoon Chang, Joon Y. Park, Guo Yan</dc:creator>
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    <item>
      <title>A Real-Options-Aware Multi-Criteria Framework for Ex-Ante Real Estate Redevelopment Use Selection</title>
      <link>https://arxiv.org/abs/2601.22166</link>
      <description>arXiv:2601.22166v1 Announce Type: cross 
Abstract: A growing share of the existing real estate stock exhibits persistent underperformance that can no longer be explained by cyclical market phases or inadequate maintenance alone. In many cases, technically recoverable assets located in non-marginal contexts fail to generate economic value consistent with the capital immobilized. This condition reflects a structural misalignment between intended use and effective demand rather than episodic market weakness, and calls for a decision framework capable of integrating value, risk, complexity, and irreversibility in strategic use selection. This study proposes a decision-analytic framework for the ex-ante selection of intended use in real estate redevelopment processes. The framework integrates real-options logic on irreversibility and managerial flexibility with a multi-criteria decision-analysis structure, enabling comparative evaluation of expected economic value, market and operational risk, technical and managerial complexity, and time-to-income. By treating redevelopment primarily as a problem of strategic option selection rather than design or financial optimization, the framework operationalizes option value preservation through disciplined ex-ante screening. Illustrative cases demonstrate how this integration of real options reasoning and MCDA reduces over-complexification and misalignment across different asset types and urban contexts.</description>
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      <category>q-fin.GN</category>
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      <pubDate>Mon, 02 Feb 2026 00:00:00 -0500</pubDate>
      <arxiv:announce_type>cross</arxiv:announce_type>
      <dc:rights>http://creativecommons.org/licenses/by/4.0/</dc:rights>
      <dc:creator>Roberto Garrone</dc:creator>
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    <item>
      <title>The Widening Profitability Gap between Renewable and Fossil Power Firms in Europe</title>
      <link>https://arxiv.org/abs/2601.22167</link>
      <description>arXiv:2601.22167v1 Announce Type: cross 
Abstract: Mobilising private capital is a critical bottleneck of the energy transition, yet recent crisis-driven windfall profits for fossil power firms suggest that market signals may still favour carbon-intensive assets. Here we analyse a panel of 900 European power firms (2001-2023) to resolve whether these profits reflect a durable profitability advantage or a crisis-driven anomaly. Using machine-learning clustering and Bayesian model averaging, we identify a structural divergence: wind and solar portfolios exhibit rising profitability, with return on assets among wind-dominated firms increasing by over 6% between 2014 and 2023. Conversely, higher fossil portfolio shares are increasingly associated with lower profitability, with marginal effects reaching -4% by 2023, while renewable-dominated firms match or outperform their fossil-heavy counterparts across most European regions. These findings suggest that the record profits of fossil incumbents were distinct outliers, masking an ongoing decline in the profitability of carbon-intensive business models.</description>
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      <pubDate>Mon, 02 Feb 2026 00:00:00 -0500</pubDate>
      <arxiv:announce_type>cross</arxiv:announce_type>
      <dc:rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0/</dc:rights>
      <dc:creator>Robin Fischer, Anton Pichler</dc:creator>
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    <item>
      <title>Persuasive Privacy</title>
      <link>https://arxiv.org/abs/2601.22945</link>
      <description>arXiv:2601.22945v1 Announce Type: cross 
Abstract: We propose a novel framework for measuring privacy from a Bayesian game-theoretic perspective. This framework enables the creation of new, purpose-driven privacy definitions that are rigorously justified, while also allowing for the assessment of existing privacy guarantees through game theory. We show that pure and probabilistic differential privacy are special cases of our framework, and provide new interpretations of the post-processing inequality in this setting. Further, we demonstrate that privacy guarantees can be established for deterministic algorithms, which are overlooked by current privacy standards.</description>
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      <pubDate>Mon, 02 Feb 2026 00:00:00 -0500</pubDate>
      <arxiv:announce_type>cross</arxiv:announce_type>
      <dc:rights>http://creativecommons.org/licenses/by/4.0/</dc:rights>
      <dc:creator>Joshua J Bon, James Bailie, Judith Rousseau, Christian P Robert</dc:creator>
    </item>
    <item>
      <title>Realized Stochastic Volatility Model with Skew-t Distributions for Improved Volatility and Quantile Forecasting</title>
      <link>https://arxiv.org/abs/2401.13179</link>
      <description>arXiv:2401.13179v4 Announce Type: replace 
Abstract: Accurate forecasting of volatility and return quantiles is essential for evaluating financial tail risks such as value-at-risk and expected shortfall. This study proposes an extension of the traditional stochastic volatility model, termed the realized stochastic volatility model, that incorporates realized volatility as an efficient proxy for latent volatility. To better capture the stylized features of financial return distributions, particularly skewness and heavy tails, we introduce three variants of skewed t-distributions, two of which incorporate skew-normal components to flexibly model asymmetry. The models are estimated using a Bayesian Markov chain Monte Carlo approach and applied to daily returns and realized volatilities from major U.S. and Japanese stock indices. Empirical results demonstrate that incorporating both realized volatility and flexible return distributions substantially improves the accuracy of volatility and tail risk forecasts.</description>
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      <category>econ.EM</category>
      <pubDate>Mon, 02 Feb 2026 00:00:00 -0500</pubDate>
      <arxiv:announce_type>replace</arxiv:announce_type>
      <dc:rights>http://creativecommons.org/licenses/by/4.0/</dc:rights>
      <dc:creator>Makoto Takahashi, Yuta Yamauchi, Toshiaki Watanabe, Yasuhiro Omori</dc:creator>
    </item>
    <item>
      <title>Divide and Diverge</title>
      <link>https://arxiv.org/abs/2405.20564</link>
      <description>arXiv:2405.20564v5 Announce Type: replace 
Abstract: Political polarization can be beneficial to competing political parties. I study how electoral competition itself generates incentives to polarize voters, even when parties are ex ante identical and motivated purely by political power, interpreted as office rents or influence. I develop a probabilistic voting model with aggregate popularity shocks in which parties have decreasing marginal utility from political power. Equilibrium policy convergence fails. Platform differentiation provides insurance against electoral volatility by securing loyal voter bases and stabilizing political power. In a unidimensional policy space, parties' equilibrium payoffs rise as voters on opposite sides of the median become more extreme, including when polarization is driven by changes in the opponent's supporters. In a multidimensional setting, parties benefit from ideological coherence, the alignment of disagreements across issues. The results have implications for polarizing political communication, party identity, and electoral institutions.</description>
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      <pubDate>Mon, 02 Feb 2026 00:00:00 -0500</pubDate>
      <arxiv:announce_type>replace</arxiv:announce_type>
      <dc:rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0/</dc:rights>
      <dc:creator>Giampaolo Bonomi</dc:creator>
    </item>
    <item>
      <title>Identity and Cooperation in Multicultural Societies: An Experimental Investigation</title>
      <link>https://arxiv.org/abs/2507.02511</link>
      <description>arXiv:2507.02511v2 Announce Type: replace 
Abstract: Immigration has shaped many nations, posing the challenge of integrating immigrants into society. While economists often focus on immigrants' economic outcomes compared to natives (such as education, labor market success, and health) social interactions between immigrants and natives are equally crucial. These interactions, from everyday exchanges to teamwork, often lack enforceable contracts and require cooperation to avoid conflicts and achieve efficient outcomes. However, socioeconomic, ethnic, and cultural differences can hinder cooperation. Thus, evaluating integration should also consider its impact on fostering cooperation across diverse groups. This paper studies how priming different identity dimensions affects cooperation between immigrant and native youth. Immigrant identity includes both ethnic ties to their country of origin and connections to the host country. We test whether cooperation improves by making salient a specific identity: Common identity (shared society), Multicultural identity (ethnic group within society), or Neutral identity. In a lab in the field experiment with over 390 adolescents, participants were randomly assigned to one of these priming conditions and played a Public Good Game. Results show that immigrants are 13 percent more cooperative than natives at baseline. Natives increase cooperation by about 3 percentage points when their multicultural identity is primed, closing the initial gap with immigrant peers.</description>
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      <pubDate>Mon, 02 Feb 2026 00:00:00 -0500</pubDate>
      <arxiv:announce_type>replace</arxiv:announce_type>
      <dc:rights>http://creativecommons.org/licenses/by-nc-nd/4.0/</dc:rights>
      <dc:creator>Natalia Montinari, Matteo Ploner, Veronica Rattini</dc:creator>
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    <item>
      <title>Direct Bias-Correction Term Estimation for Average Treatment Effect Estimation</title>
      <link>https://arxiv.org/abs/2509.22122</link>
      <description>arXiv:2509.22122v2 Announce Type: replace 
Abstract: This study considers the estimation of the direct bias-correction term for estimating the average treatment effect (ATE). Let $\{(X_i, D_i, Y_i)\}_{i=1}^{n}$ be the observations, where $X_i$ denotes $K$-dimensional covariates, $D_i \in \{0, 1\}$ denotes a binary treatment assignment indicator, and $Y_i$ denotes an outcome. In ATE estimation, $h_0(D_i, X_i) = \frac{1[D_i = 1]}{e_0(X_i)} - \frac{1[D_i = 0]}{1 - e_0(X_i)}$ is called the bias-correction term, where $e_0(X_i)$ is the propensity score. The bias-correction term is also referred to as the Riesz representer or clever covariates, depending on the literature, and plays an important role in construction of efficient ATE estimators. In this study, we propose estimating $h_0$ by directly minimizing the Bregman divergence between its model and $h_0$, which includes squared error and Kullback--Leibler divergence as special cases. Our proposed method is inspired by direct density ratio estimation methods and generalizes existing bias-correction term estimation methods, such as covariate balancing weights, Riesz regression, and nearest neighbor matching. Importantly, under specific choices of bias-correction term models and Bregman divergence, we can automatically ensure the covariate balancing property. Thus, our study provides a practical modeling and estimation approach through a generalization of existing methods.</description>
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      <category>econ.EM</category>
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      <category>stat.ML</category>
      <category>stat.TH</category>
      <pubDate>Mon, 02 Feb 2026 00:00:00 -0500</pubDate>
      <arxiv:announce_type>replace</arxiv:announce_type>
      <dc:rights>http://creativecommons.org/licenses/by-nc-nd/4.0/</dc:rights>
      <dc:creator>Masahiro Kato</dc:creator>
    </item>
    <item>
      <title>ScoreMatchingRiesz: Score Matching for Debiased Machine Learning and Policy Path Estimation</title>
      <link>https://arxiv.org/abs/2512.20523</link>
      <description>arXiv:2512.20523v2 Announce Type: replace 
Abstract: We propose ScoreMatchingRiesz, a family of Riesz representer estimators based on score matching. The Riesz representer is a key nuisance component in debiased machine learning, enabling $\sqrt{n}$-consistent and asymptotically efficient estimation of causal and structural targets via Neyman-orthogonal scores. We formulate Riesz representer estimation as a score estimation problem. This perspective stabilizes representer estimation by allowing us to leverage denoising score matching and telescoping density ratio estimation. We also introduce the policy path, a parameter that captures how policy effects evolve under continuous treatments. We show that the policy path can be estimated via score matching by smoothly connecting average marginal effect (AME) and average policy effect (APE) estimation, which improves the interpretability of policy effects.</description>
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      <category>econ.EM</category>
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      <pubDate>Mon, 02 Feb 2026 00:00:00 -0500</pubDate>
      <arxiv:announce_type>replace</arxiv:announce_type>
      <dc:rights>http://creativecommons.org/licenses/by-nc-nd/4.0/</dc:rights>
      <dc:creator>Masahiro Kato</dc:creator>
    </item>
    <item>
      <title>DeepGreen: Effective LLM-Driven Greenwashing Monitoring System Designed for Empirical Testing -- Evidence from China</title>
      <link>https://arxiv.org/abs/2504.07733</link>
      <description>arXiv:2504.07733v2 Announce Type: replace-cross 
Abstract: Motivated by the emerging adoption of Large Language Models (LLMs) in economics and management research, this paper investigates whether LLMs can reliably identify corporate greenwashing narratives and, more importantly, whether and how the greenwashing signals extracted from textual disclosures can be used to empirically identify causal effects. To this end, this paper proposes DeepGreen, a dual-stage LLM-Driven system for detecting potential corporate greenwashing in annual reports. Applied to 9369 A-share annual reports published between 2021 and 2023, DeepGreen attains high reliability in random-sample validation at both stages. Ablation experiment shows that Retrieval-Augmented Generation (RAG) reduces hallucinations, as compared to simply lengthening the input window. Empirical tests indicate that "greenwashing" captured by DeepGreen can effectively reveal a positive relationship between greenwashing and environmental penalties, and IV, PSM, Placebo test, which enhance the robustness and causal effects of the empirical evidence. Further study suggests that the presence and number of green investors can weaken the positive correlation between greenwashing and penalties. Heterogeneity analysis shows that the positive relationship between "greenwashing - penalty" is less significant in large-sized corporations and corporations that have accumulated green assets, indicating that these green assets may be exploited as a credibility shield for greenwashing. Our findings demonstrate that LLMs can standardize ESG oversight by early warning and direct regulators' scarce attention toward the subsets of corporations where monitoring is more warranted.</description>
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      <category>cs.CL</category>
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      <pubDate>Mon, 02 Feb 2026 00:00:00 -0500</pubDate>
      <arxiv:announce_type>replace-cross</arxiv:announce_type>
      <dc:rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0/</dc:rights>
      <dc:creator>Congluo Xu, Jiuyue Liu, Ziyang Li, Chengmengjia Lin</dc:creator>
    </item>
    <item>
      <title>Who Connects Global Aid? The Hidden Geometry of 10 Million Transactions</title>
      <link>https://arxiv.org/abs/2512.17243</link>
      <description>arXiv:2512.17243v2 Announce Type: replace-cross 
Abstract: The global aid system functions as a complex and evolving ecosystem; yet widespread understanding of its structure remains largely limited to aggregate volume flows. Here we map the network topology of global aid using a dataset of unprecedented scale: over 10 million transaction records connecting 2,456 publishing organisations across 230 countries between 1967 and 2025. We apply bipartite projection and dimensionality reduction to reveal the geometry of the system and unveil hidden patterns. This exposes distinct functional clusters that are otherwise sparsely connected. We find that while governments and multilateral agencies provide the primary resources, a small set of knowledge brokers provide the critical connectivity. Universities and research foundations specifically act as essential bridges between disparate islands of implementers and funders. We identify a core solar system of 25 central actors who drive this connectivity including unanticipated brokers like J-PAL and the Hewlett Foundation. These findings demonstrate that influence in the aid ecosystem flows through structural connectivity as much as financial volume. Our results provide a new framework for donors to identify strategic partners that accelerate coordination and evidence diffusion across the global network.</description>
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      <category>physics.soc-ph</category>
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      <pubDate>Mon, 02 Feb 2026 00:00:00 -0500</pubDate>
      <arxiv:announce_type>replace-cross</arxiv:announce_type>
      <dc:rights>http://creativecommons.org/licenses/by/4.0/</dc:rights>
      <dc:creator>Paul X. McCarthy, Xian Gong, Marian-Andrei Rizoiu, Paolo Boldi</dc:creator>
    </item>
    <item>
      <title>Multi-agent Adaptive Mechanism Design</title>
      <link>https://arxiv.org/abs/2512.21794</link>
      <description>arXiv:2512.21794v2 Announce Type: replace-cross 
Abstract: We study a sequential mechanism design problem in which a principal seeks to elicit truthful reports from multiple rational agents while starting with no prior knowledge of agents' beliefs. We introduce Distributionally Robust Adaptive Mechanism (DRAM), a general framework combining insights from both mechanism design and online learning to jointly address truthfulness and cost-optimality. Throughout the sequential game, the mechanism estimates agents' beliefs and iteratively updates a distributionally robust linear program with shrinking ambiguity sets to reduce payments while preserving truthfulness. Our mechanism guarantees truthful reporting with high probability while achieving $\tilde{O}(\sqrt{T})$ cumulative regret, and we establish a matching lower bound showing that no truthful adaptive mechanism can asymptotically do better. The framework generalizes to plug-in estimators, supporting structured priors and delayed feedback. To our knowledge, this is the first adaptive mechanism under general settings that maintains truthfulness and achieves optimal regret when incentive constraints are unknown and must be learned.</description>
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      <category>cs.GT</category>
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      <category>econ.TH</category>
      <pubDate>Mon, 02 Feb 2026 00:00:00 -0500</pubDate>
      <arxiv:announce_type>replace-cross</arxiv:announce_type>
      <dc:rights>http://creativecommons.org/licenses/by/4.0/</dc:rights>
      <dc:creator>Qiushi Han, David Simchi-Levi, Renfei Tan, Zishuo Zhao</dc:creator>
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