Economics

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New submissions (showing 4 of 4 entries )

[1] arXiv:2509.20465 [cn-pdf, pdf, html, other]

Title: Integrated analysis of informality, minimum wage, and monopsony power: A synthesis of meta-analyses with unified theoretical underpinnings Show Chinese title

Title: 非正式性、最低工资和买方垄断力量的综合分析：元分析的综合与统一的理论基础

Ricardo Alonzo Fernandez Salguero

Subjects: Theoretical Economics (econ.TH)

This document offers a synthesis of recent economic literature on three interconnected areas of labor markets: informality, the effects of the minimum wage, and monopsony power. Through the consolidation and meta-analysis of findings from multiple existing systematic reviews and meta-analyses, their causes, consequences, and associated public policies are examined. It is concluded that conventional views on these topics often overestimate the magnitude of effects. Policies to reduce informality based solely on lowering formalization costs are largely ineffective, while increased enforcement at the extensive margin shows more promising results. The effects of the minimum wage on employment, measured through the own-wage elasticity (OWE), are consistently modest, suggesting that job losses are limited compared to wage gains. To reconcile and microfound these findings, an integrated theoretical model of firm optimization is introduced, simultaneously incorporating firm heterogeneity, monopsony power, and endogenous formality decisions, rigorously demonstrating how the interaction of these forces can explain the observed empirical regularities. A cross-cutting and significant finding is the omnipresence of publication bias across all these study areas, which tends to inflate the magnitude of the effects reported in the published literature. Corrected estimates of the effects generally approach zero. Meta-regression is established as an indispensable tool for identifying heterogeneity and the true underlying effects in the empirical evidence, compelling a recalibration of both theory and economic policy recommendations towards a more nuanced and integrated approach. Show Chinese abstract

本文综合了近期关于劳动力市场三个相互关联领域的经济文献：非正规性、最低工资的影响以及买方垄断力量。 通过整合和元分析多个现有系统综述和元分析的研究成果，探讨了其原因、后果及相关的公共政策。 结论认为，对这些主题的传统观点往往高估了影响的程度。 仅通过降低正规化成本来减少非正规性的政策效果不大，而在广度边际上的加强执行则显示出更有希望的结果。 通过自身工资弹性（OWE）衡量的最低工资对就业的影响一直较为温和，表明与工资增长相比，就业损失是有限的。 为了调和并微观基础化这些发现，引入了一个整合的企业优化理论模型，同时结合企业异质性、买方垄断力量和内生的正规性决策，严格证明了这些力量的相互作用如何可以解释观察到的经验规律。 一个贯穿所有研究领域的显著发现是，出版偏差在所有这些研究领域中普遍存在，这往往会夸大发表文献中报告的影响程度。 校正后的效应估计通常接近于零。 元回归被确立为识别实证证据中的异质性和真正潜在效应的不可或缺的工具，迫使理论和经济政策建议重新调整，采取更加细致和综合的方法。

[2] arXiv:2509.20634 [cn-pdf, pdf, html, other]

Title: Recidivism and Peer Influence with LLM Text Embeddings in Low Security Correctional Facilities Show Chinese title

Title: 低安全矫正设施中的再犯与同伴影响使用大语言模型文本嵌入

Shanjukta Nath, Jiwon Hong, Jae Ho Chang, Keith Warren, Subhadeep Paul

Subjects: Econometrics (econ.EM) ; Artificial Intelligence (cs.AI) ; General Economics (econ.GN) ; Methodology (stat.ME)

We find AI embeddings obtained using a pre-trained transformer-based Large Language Model (LLM) of 80,000-120,000 written affirmations and correction exchanges among residents in low-security correctional facilities to be highly predictive of recidivism. The prediction accuracy is 30\% higher with embedding vectors than with only pre-entry covariates. However, since the text embedding vectors are high-dimensional, we perform Zero-Shot classification of these texts to a low-dimensional vector of user-defined classes to aid interpretation while retaining the predictive power. To shed light on the social dynamics inside the correctional facilities, we estimate peer effects in these LLM-generated numerical representations of language with a multivariate peer effect model, adjusting for network endogeneity. We develop new methodology and theory for peer effect estimation that accommodate sparse networks, multivariate latent variables, and correlated multivariate outcomes. With these new methods, we find significant peer effects in language usage for interaction and feedback. Show Chinese abstract

我们发现使用基于预训练变换器的大语言模型（LLM）获得的AI嵌入，针对低安全矫正设施中居民之间的80,000至120,000条肯定语和纠正交流，对再犯具有高度预测性。 与仅使用进入前协变量相比，使用嵌入向量的预测准确率提高了30%。 然而，由于文本嵌入向量是高维的，我们对这些文本进行零样本分类，将其转换为用户定义类别的低维向量，以帮助解释同时保留预测能力。 为了揭示矫正设施内部的社会动态，我们利用多变量同伴效应模型估计这些LLM生成的语言数值表示中的同伴效应，并调整网络内生性。 我们开发了新的方法论和理论用于同伴效应估计，以适应稀疏网络、多变量潜在变量和相关多变量结果。 通过这些新方法，我们发现了在互动和反馈中的语言使用显著同伴效应。

[3] arXiv:2509.20790 [cn-pdf, pdf, html, other]

Title: Börgers's Open Question Resolved Show Chinese title

Title: 博格斯的开放问题已解决

Siyang Xiong

Subjects: Theoretical Economics (econ.TH)

Focusing on stochastic finite-action mechanisms, we study implementation in undominated strategies and iteratively undominated strategies. We establish both possibility and impossibility results that resolve the open question in B\"orgers (1995). Contrary to the conventional understanding that positive results on Nash implementation need separability, quasilinearity, or infinite action sets, we provide -- to our knowledge -- the first positive result beyond those demanding assumptions. Show Chinese abstract

专注于随机有限动作机制，我们研究在非占优策略和迭代非占优策略下的实施问题。 我们建立了可能性和不可能性结果，解决了Börgers (1995)中的开放问题。 与传统观点相反，即纳什实施的积极结果需要可分离性、准线性或无限动作集，我们提供——据我们所知——第一个超越这些假设的积极结果。

[4] arXiv:2509.21096 [cn-pdf, pdf, html, other]

Title: Overidentification testing with weak instruments and heteroskedasticity Show Chinese title

Title: 弱工具变量和异方差性下的过度识别检验

Stuart Lane, Frank Windmeijer

Comments: 45 pages

Subjects: Econometrics (econ.EM) ; Methodology (stat.ME)

Exogeneity is key for IV estimators, which can assessed via overidentification (OID) tests. We discuss the Kleibergen-Paap (KP) rank test as a heteroskedasticity-robust OID test and compare to the typical J-test. We derive the heteroskedastic weak-instrument limiting distributions for J and KP as special cases of the robust score test estimated via 2SLS and LIML respectively. Monte Carlo simulations show that KP usually performs better than J, which is prone to severe size distortions. Test size depends on model parameters not consistently estimable with weak instruments, so a conservative approach is recommended. This generalises recommendations to use LIML-based OID tests under homoskedasticity. We then revisit the classic problem of estimating the elasticity of intertemporal substitution (EIS) in lifecycle consumption models. Lagged macroeconomic indicators should provide naturally valid but frequently weak instruments. The literature provides a wide range of estimates for this parameter, and J frequently rejects the null of valid instruments. J often rejects the null whereas KP does not; we suggest that J over-rejects, sometimes severely. We argue that KP-test should be used over the J-test. We also argue that instrument invalidity/misspecification is unlikely the cause of the range of EIS estimates in the literature. Show Chinese abstract

外生性对于工具变量估计器至关重要，可以通过过度识别（OID）检验来评估。 我们讨论了Kleibergen-Paap（KP）秩检验作为一种异方差稳健的OID检验，并将其与典型的J检验进行比较。 我们推导了J检验和KP检验在弱工具变量下的异方差极限分布，作为通过两阶段最小二乘法（2SLS）和有限信息最大似然法（LIML）分别估计的稳健得分检验的特殊情况。 蒙特卡洛模拟显示，KP通常比J表现更好，而J容易出现严重的尺寸扭曲。 检验尺寸取决于无法用弱工具变量一致估计的模型参数，因此建议采用保守方法。 这推广了在同方差情况下使用基于LIML的OID检验的建议。 然后我们重新审视了生命周期消费模型中估计跨期替代弹性（EIS）的经典问题。 滞后的宏观经济指标应提供自然有效但经常较弱的工具变量。 文献中对该参数提供了广泛范围的估计，而J检验经常拒绝有效工具变量的原假设。 J检验经常拒绝原假设而KP检验不拒绝；我们建议J检验过度拒绝，有时甚至非常严重。 我们认为应优先使用KP检验而不是J检验。 我们还认为，工具变量无效/误设不太可能是文献中EIS估计范围广泛的原因。

Cross submissions (showing 1 of 1 entries )

[5] arXiv:2509.21172 (cross-list from cs.LG) [cn-pdf, pdf, html, other]

Title: Inverse Reinforcement Learning Using Just Classification and a Few Regressions Show Chinese title

Title: 使用分类和少量回归的逆强化学习

Lars van der Laan, Nathan Kallus, Aurélien Bibaut

Subjects: Machine Learning (cs.LG) ; Econometrics (econ.EM) ; Optimization and Control (math.OC) ; Machine Learning (stat.ML)

Inverse reinforcement learning (IRL) aims to explain observed behavior by uncovering an underlying reward. In the maximum-entropy or Gumbel-shocks-to-reward frameworks, this amounts to fitting a reward function and a soft value function that together satisfy the soft Bellman consistency condition and maximize the likelihood of observed actions. While this perspective has had enormous impact in imitation learning for robotics and understanding dynamic choices in economics, practical learning algorithms often involve delicate inner-loop optimization, repeated dynamic programming, or adversarial training, all of which complicate the use of modern, highly expressive function approximators like neural nets and boosting. We revisit softmax IRL and show that the population maximum-likelihood solution is characterized by a linear fixed-point equation involving the behavior policy. This observation reduces IRL to two off-the-shelf supervised learning problems: probabilistic classification to estimate the behavior policy, and iterative regression to solve the fixed point. The resulting method is simple and modular across function approximation classes and algorithms. We provide a precise characterization of the optimal solution, a generic oracle-based algorithm, finite-sample error bounds, and empirical results showing competitive or superior performance to MaxEnt IRL. Show Chinese abstract

逆强化学习（IRL）旨在通过揭示潜在奖励来解释观察到的行为。 在最大熵或奖励的Gumbel扰动框架中，这相当于拟合一个奖励函数和一个软值函数，它们共同满足软贝尔曼一致性条件并最大化观察到动作的可能性。 虽然这种观点在机器人模仿学习和经济学中理解动态选择方面产生了巨大影响，但实际的学习算法通常涉及精细的内循环优化、重复的动态规划或对抗训练，所有这些都会使现代高度表达的函数逼近器（如神经网络和提升方法）的使用变得复杂。 我们重新审视softmax IRL，并表明总体最大似然解由涉及行为策略的线性固定点方程表征。 这一观察将IRL简化为两个现成的监督学习问题：概率分类用于估计行为策略，迭代回归用于求解固定点。 该方法在函数逼近类和算法上简单且模块化。 我们提供了最优解的精确表征，一种通用的基于oracle的算法，有限样本误差界限，并提供了实证结果，显示其性能与MaxEnt IRL相比具有竞争力或更优。

Replacement submissions (showing 6 of 6 entries )

[6] arXiv:2301.02728 (replaced) [cn-pdf, pdf, other]

Title: Diffusion in dynamic networks with time-varying inputs to allocate responsibility Show Chinese title

Title: 动态网络中的扩散与随时间变化的输入以分配责任

Rosa van den Ende, Dylan Laplace Mermoud

Subjects: Theoretical Economics (econ.TH)

Responsibility in complex networks extends beyond direct actions: players should also bear responsibility for the indirect effects within their supply chains or network. We introduce a novel framework to allocate responsibility for indirect environmental, social, and economic impacts across a dynamic network. Unlike static approaches, our framework accounts for the evolving structure of supply chains, financial systems, and other interconnected systems, where relationships change over time. We use the time-dependent Laplacian matrix to capture how responsibility propagates through the network, revealing a diffusion process that aligns with key axioms of fairness: linearity, efficiency, symmetry, and the independent player property. We show that approximating the responsibility measure preserves these properties, supporting the use of our framework as a rigorous method to allocate responsibility in real-world networks. Show Chinese abstract

责任在复杂网络中不仅限于直接行为：参与者还应对其供应链或网络中的间接影响负责。 我们引入了一个新的框架，以在动态网络中分配间接环境、社会和经济影响的责任。 与静态方法不同，我们的框架考虑了供应链、金融系统和其他相互关联系统的演变结构，其中关系随时间变化。 我们使用时变拉普拉斯矩阵来捕捉责任如何在网络中传播，揭示了一个与公平的关键公理相一致的扩散过程：线性、效率、对称性和独立参与者属性。 我们表明，近似责任度量保留了这些性质，支持将我们的框架作为一种严格的方法，在现实世界的网络中分配责任。

[7] arXiv:2503.11375 (replaced) [cn-pdf, pdf, html, other]

Title: Difference-in-Differences Meets Synthetic Control: Doubly Robust Identification and Estimation Show Chinese title

Title: 差异法与合成控制方法的结合：双重稳健识别与估计

Yixiao Sun, Haitian Xie, Yuhang Zhang

Subjects: Econometrics (econ.EM)

Difference-in-Differences (DiD) and Synthetic Control (SC) are widely used methods for causal inference in panel data, each with distinct strengths and limitations. We propose a novel method for short-panel causal inference that integrates the advantages of both approaches. Our method delivers a doubly robust identification strategy for the average treatment effect on the treated (ATT) under either of two non-nested assumptions: parallel trends or a group-level SC condition. Building on this identification result, we develop a unified semiparametric framework for estimating the ATT. Notably, the identification-robust moment function satisfies Neyman orthogonality under the parallel trends assumption but not under the SC assumption, leading to different asymptotic variances across the two identification strategies. To ensure valid inference, we propose a multiplier bootstrap method that consistently approximates the asymptotic distribution under either assumption. Furthermore, we extend our methodology to accommodate repeated cross-sectional data and staggered treatment designs. As an empirical application, we evaluate the impact of the 2003 minimum wage increase in Alaska on family income. Finally, in simulation studies based on empirically calibrated data-generating processes, we demonstrate that the proposed estimation and inference methods perform well in finite samples under either identification assumption. Show Chinese abstract

双重差分（DiD）和合成控制（SC）是面板数据中用于因果推断的常用方法，各有其独特的优点和局限性。 我们提出了一种新的短期面板因果推断方法，结合了两种方法的优势。 我们的方法在两种非嵌套假设之一下，即平行趋势或群体层面的SC条件，为处理组的平均处理效应（ATT）提供了双重稳健的识别策略。 基于这一识别结果，我们开发了一个统一的半参数框架来估计ATT。 值得注意的是，在平行趋势假设下，识别稳健的矩函数满足Neyman正交性，但在SC假设下不满足，导致两种识别策略下的渐近方差不同。 为了确保有效的推断，我们提出了一种乘子Bootstrap方法，该方法在任一假设下都能一致地逼近渐近分布。 此外，我们将方法扩展以适应重复横截面数据和交错处理设计。 作为实证应用，我们评估了2003年阿拉斯加最低工资提高对家庭收入的影响。 最后，在基于经验校准的数据生成过程的模拟研究中，我们证明了所提出的估计和推断方法在任一识别假设下在有限样本中表现良好。

[8] arXiv:2508.13635 (replaced) [cn-pdf, pdf, html, other]

Title: Interpreting the Interpreter: Can We Model post-ECB Conferences Volatility with LLM Agents? Show Chinese title

Title: 解释解释器：我们能否用LLM代理对ECB会议后的波动性进行建模？

Umberto Collodel

Subjects: General Economics (econ.GN)

This paper develops a novel method to simulate financial market reactions to European Central Bank (ECB) press conferences using Large Language Models (LLMs). We create a behavioral, agent-based simulation of 30 synthetic traders, each with distinct risk preferences, cognitive biases, and interpretive styles. These agents interpret ECB communication and forecast Euro interest rate swap levels at 3-month, 2-year, and 10-year maturities, with cross-sectional variation serving as a measure of market uncertainty. Using a comprehensive dataset of 283 ECB press conferences (1998-2025), we evaluate three prompting strategies: naive zero-shot, few-shot (enriched with historical context), and an advanced iterative 'LLM-as-a-Judge' framework. We find that even the naive approach achieves substantial correlations (roughly 0.5) between synthetic disagreement and actual post-announcement OIS volatility, particularly for medium- and long-term maturities. The 'LLM-as-a-Judge' framework further enhances performance upon initial application, reaching correlations of almost 0.6 and identifying key drivers of volatility. Our results demonstrate that LLM-based agents can effectively capture the heterogeneous interpretive processes through which monetary policy signals propagate to financial markets, moving beyond traditional reduced-form event studies. This provides central banks with a practical tool to ex-ante evaluate communication strategies and anticipate market reactions, while offering researchers a micro-founded framework for understanding expectation formation in response to central bank communication. Show Chinese abstract

本文开发了一种新方法，利用大型语言模型（LLMs）模拟金融市场对欧洲央行（ECB）新闻发布会的反应。 我们创建了一个行为型、基于代理的30个合成交易者模拟，每个交易者具有不同的风险偏好、认知偏差和解释风格。 这些代理解读ECB的沟通内容，并预测3个月、2年和10年期限的欧元利率互换水平，横截面差异作为市场不确定性的衡量指标。 使用涵盖283场ECB新闻发布会（1998-2025）的全面数据集，我们评估了三种提示策略：简单的零样本、少量样本（结合历史背景）以及一种先进的迭代“LLM-as-a-Judge”框架。 我们发现，即使简单的零样本方法也能在合成分歧与实际公告后OIS波动率之间实现显著相关性（约为0.5），特别是在中长期期限上。 “LLM-as-a-Judge”框架在初次应用时进一步提升了性能，相关性接近0.6，并识别出波动性的关键驱动因素。 我们的结果表明，基于LLM的代理可以有效捕捉货币政策信号传播到金融市场的异质解释过程，超越传统的简约事件研究。 这为中央银行提供了一种实用工具，用于事先评估沟通策略并预测市场反应，同时为研究人员提供了一个微观基础框架，以理解对央行沟通的预期形成过程。

[9] arXiv:2508.20069 (replaced) [cn-pdf, pdf, html, other]

Title: There must be an error here! Experimental evidence on coding errors' biases Show Chinese title

Title: 这里必须有一个错误！ 关于编码错误偏差的实验证据

Bruno Ferman, Lucas Finamor

Subjects: General Economics (econ.GN)

Quantitative research relies heavily on coding, and coding errors are relatively common even in published research. In this paper, we examine whether individuals are more or less likely to check their code depending on the results they obtain. We test this hypothesis in a randomized experiment embedded in the recruitment process for research positions at a large international economic organization. In a coding task designed to assess candidates' programming abilities, we randomize whether participants obtain an expected or unexpected result if they commit a simple coding error. We find that individuals are almost 20% more likely to detect coding errors when they lead to unexpected results. This asymmetry in error detection depending on the results they generate suggests that coding errors may lead to biased findings in scientific research. Show Chinese abstract

定量研究高度依赖编码，即使在已发表的研究中，编码错误也相对常见。 在本文中，我们考察了个体根据所获得的结果，更有可能或不太可能检查其代码。 我们在一家大型国际经济组织的研究职位招聘过程中进行了一项随机实验来测试这一假设。 在一项旨在评估候选人编程能力的编码任务中，我们随机安排参与者如果犯了一个简单的编码错误，是否会得到预期或意外的结果。 我们发现，当编码错误导致意外结果时，个体检测编码错误的可能性几乎高出20%。 根据他们生成的结果，错误检测的这种不对称性表明，编码错误可能导致科学研究中的偏差结果。

[10] arXiv:2509.15247 (replaced) [cn-pdf, pdf, html, other]

Title: Demand and consumer surplus in the payday-loan market: Evidence from British Columbia Show Chinese title

Title: payday贷款市场中的需求和消费者剩余：来自不列颠哥伦比亚省的证据

Tim Zhang, Amity Quinn

Subjects: General Economics (econ.GN)

This study examines how interest rate caps affect the demand for payday loans, using aggregate data from British Columbia (2012--2019) during which the province's maximum fee was reduced from \$23 to \$17 and then to \$15 per \$100 borrowed. Estimating a linear demand function via OLS, we find that lowering interest rate caps significantly increases loan demand. We estimate that the \$8 decrease, from \$23 to \$15 per \$100, raised annual consumer surplus by roughly \$28.6 million (2012 CAD). A further reduction to \$14, starting in January 2025, would add another \$3.9 million per year. These results suggest that stricter interest rate caps can yield substantial consumer welfare gains. Show Chinese abstract

这项研究考察了利率上限如何影响即时贷款的需求，使用了不列颠哥伦比亚省（2012--2019年）的汇总数据，在此期间该省每借100美元的最高费用从23美元降至17美元，然后降至15美元。 通过OLS估计线性需求函数，我们发现降低利率上限显著增加了贷款需求。 我们估计，从每100美元23美元降至15美元的8美元下降，使年度消费者剩余增加了约2860万美元（2012年加元）。 从2025年1月开始进一步降至14美元，每年将增加390万美元。 这些结果表明，更严格的利率上限可以带来显著的消费者福利收益。

[11] arXiv:2505.08654 (replaced) [cn-pdf, pdf, html, other]

Title: An Efficient Multi-scale Leverage Effect Estimator under Dependent Microstructure Noise Show Chinese title

Title: 一种在依赖微结构噪声下的高效多尺度杠杆效应估计器

Ziyang Xiong, Zhao Chen, Christina Dan Wang

Subjects: Methodology (stat.ME) ; Econometrics (econ.EM) ; Statistical Finance (q-fin.ST)

Estimating the leverage effect from high-frequency data is vital but challenged by complex, dependent microstructure noise, often exhibiting non-Gaussian higher-order moments. This paper introduces a novel multi-scale framework for efficient and robust leverage effect estimation under such flexible noise structures. We develop two new estimators, the Subsampling-and-Averaging Leverage Effect (SALE) and the Multi-Scale Leverage Effect (MSLE), which adapt subsampling and multi-scale approaches holistically using a unique shifted window technique. This design simplifies the multi-scale estimation procedure and enhances noise robustness without requiring the pre-averaging approach. We establish central limit theorems and stable convergence, with MSLE achieving convergence rates of an optimal $n^{-1/4}$ and a near-optimal $n^{-1/9}$ for the noise-free and noisy settings, respectively. A cornerstone of our framework's efficiency is a specifically designed MSLE weighting strategy that leverages covariance structures across scales. This significantly reduces asymptotic variance and, critically, yields substantially smaller finite-sample errors than existing methods under both noise-free and realistic noisy settings. Extensive simulations and empirical analyses confirm the superior efficiency, robustness, and practical advantages of our approach. Show Chinese abstract

从高频数据中估计杠杆效应对于金融分析至关重要，但受到复杂且相关的微观结构噪声的挑战，这些噪声通常表现出非高斯的高阶矩。 本文介绍了一个新颖的多尺度框架，在这种灵活的噪声结构下实现高效且稳健的杠杆效应估计。 我们开发了两种新的估计量，即子抽样与平均杠杆效应（SALE）和多尺度杠杆效应（MSLE），它们通过一种独特的位移窗口技术，全面地结合了子抽样和多尺度方法。 这种设计简化了多尺度估计过程，并增强了对噪声的鲁棒性，而无需使用预平均方法。 我们建立了中心极限定理和稳定收敛性，其中MSLE在无噪声和有噪声情况下分别实现了最优的$n^{-1/4}$和近似最优的$n^{-1/9}$收敛速度。 我们框架效率的核心是一种专门设计的MSLE权重策略，该策略利用了不同尺度之间的协方差结构。 这显著降低了渐近方差，并且关键的是，在无噪声和现实噪声情况下，其有限样本误差明显小于现有方法。 广泛的模拟和实证分析证实了我们方法的优越效率、稳健性和实际优势。