编者按：美国全国经济研究所（NBER）是美国最大的经济学研究组织，其发布的工作论文代表着经济学研究最新的成果，每周一发布新论文。本周（9.23-9.29）共发布 27 篇新工作论文，本期将会推送中间 9 篇论文的题目与摘要，供读者学习。
学术财经研究团队翻译。
证据的选择性披露：一项实验
Agata Farina, Guillaume Fréchette, Alessandro Ispano, Alessandro Lizzeri, and Jacopo Perego #32975
Abstract: We conduct an experimental analysis of selective disclosure in communication. In our model, an informed sender aims to influence a receiver by disclosing verifiable evidence that is selected from a larger pool of available evidence. Our experimental design leverages the rich comparative statics predictions of this model, enabling a systematic test of the relative importance of evidence selection versus evidence concealment in communication. Our findings confirm the key qualitative predictions of the theory, suggesting that selection, rather than concealment, is often the dominant distortion in communication. We also identify deviations from the theory: A minority of senders overcommunicate relative to predictions, while some receivers partially neglect the selective nature of the evidence they observe.
摘要：我们对交流中的选择性披露进行了实验分析。在我们的模型中，一位知情的发送者通过披露可验证的证据来影响接收者，而这些证据是从一大堆可用证据中挑选出来的。我们的实验设计利用了该模型丰富的比较静态预测，从而能够系统地测试证据选择与证据隐瞒在交流中的相对重要性。实验结果证实了理论的关键定性预测，表明在交流中，证据选择往往比证据隐瞒是更为主要的扭曲因素。我们还发现了一些偏离理论的现象：少数发送者的交流超过了预测的范围，而部分接收者则部分忽略了他们所观察到的证据的选择性本质。
大规模挖掘中国历史文献：一种基于机器学习的清朝国家能力研究方法
Wolfgang Keller, Carol H. Shiue, and Sen Yan #32982
Abstract: Primary historical sources are often by-passed for secondary sources due to high human costs of accessing and extracting primary information–especially in lower-resource settings. We propose a supervised machine-learning approach to the natural language processing of Chinese historical data. An application to identifying different forms of social unrest in the Veritable Records of the Qing Dynasty shows that approach cuts dramatically down the cost of using primary source data at the same time when it is free from human bias, reproducible, and flexible enough to address particular questions. External evidence on triggers of unrest also suggests that the computer-based approach is no less successful in identifying social unrest than human researchers are.
摘要：由于获取和提取原始信息的人力成本高，原始历史文献常常被次级文献所取代，尤其是在资源较少的环境中。我们提出了一种基于监督机器学习的中文历史数据自然语言处理方法。通过应用该方法识别《清实录》中不同形式的社会动荡，表明此方法能够显著降低使用原始文献数据的成本，同时避免人为偏见、具有可重复性，并且足够灵活以应对特定问题。外部证据表明，关于动荡触发因素的计算机方法在识别社会动荡方面并不逊色于人类研究者。
合成数据与社会科学研究：SIPP合成数据的准确性评估与实践考量
Jordan C. Stanley and Evan S. Totty #32979
Abstract: Synthetic microdata – data retaining the structure of original microdata while replacing original values with modeled values for the sake of privacy – presents an opportunity to increase access to useful microdata for data users while meeting the privacy and confidentiality requirements for data providers. Synthetic data could be sufficient for many purposes, but lingering accuracy concerns could be addressed with a validation system through which the data providers run the external researcher’s code on the internal data and share cleared output with the researcher. The U.S. Census Bureau has experience running such systems. In this chapter, we first describe the role of synthetic data within a tiered data access system and the importance of synthetic data accuracy in achieving a viable synthetic data product. Next, we review results from a recent set of empirical analyses we conducted to assess accuracy in the Survey of Income & Program Participation (SIPP) Synthetic Beta (SSB), a Census Bureau product that made linked survey-administrative data publicly available. Given this analysis and our experience working on the SSB project, we conclude with thoughts and questions regarding future implementations of synthetic data with validation.
摘要：合成微观数据——即在保留原始微观数据结构的同时，用模型生成的值替换原始数据值以保护隐私——为数据用户提供了获取有用微观数据的机会，同时满足了数据提供者的隐私和保密要求。合成数据在许多用途上可能已经足够，但关于准确性的持续担忧可以通过一种验证系统来解决，在该系统中，数据提供者使用内部数据运行外部研究者的代码，并将审核后的结果分享给研究者。美国人口普查局在运行此类系统方面有相关经验。在本章中，我们首先描述了合成数据在分层数据访问系统中的角色，以及合成数据准确性在实现可行的合成数据产品中的重要性。接下来，我们回顾了我们最近进行的实证分析结果，这些分析旨在评估《收入与项目参与调查》(SIPP) 合成Beta (SSB) 的准确性，这是美国人口普查局发布的一个将调查与行政数据链接并公开的产品。基于这项分析以及我们在SSB项目上的工作经验，我们最后提出了对未来实施带有验证机制的合成数据的一些思考和问题。
谁应该工作多少？
Timo Boppart, Per Krusell, and Jonna Olsson #32977
Abstract: A production efficiency perspective naturally leads to the prescription that more productive individuals should work more than less productive individuals. Yet, systematic differences in actual hours worked across high- and low-wage individuals are barely noticeable. We highlight that the insurance available to households is an important determinant behind this fact. Using a dynamic heterogeneous-agent model with insurance frictions, income effects calibrated to match aggregate hours across time and space, and financial frictions that deliver realistic wealth dispersion, we report stark effects of insurance: perfect insurance would raise aggregate labor productivity by 9.6 percent and decrease hours worked by 7.7 percent.
摘要：从生产效率的角度来看，更高生产力的个体应该比生产力较低的个体工作更多。然而，实际中高薪和低薪个体的工作时间差异几乎不明显。我们强调，家庭可获得的保险是造成这一现象的重要因素。通过使用一个带有保险摩擦、收入效应和金融摩擦的动态异质代理模型，其中收入效应经过校准以匹配不同时间和空间的总工时，金融摩擦则带来了现实的财富分配差异，我们发现保险的影响非常显著：如果有完美保险，整体劳动生产率将提高9.6%，而总工时将减少7.7%。
人工智能时代的经济政策挑战
Anton Korinek #32980
Abstract: This paper examines the profound challenges that transformative advances in AI towards Artificial General Intelligence (AGI) will pose for economists and economic policymakers. I examine how the Age of AI will revolutionize the basic structure of our economies by diminishing the role of labor, leading to unprecedented productivity gains but raising concerns about job disruption, income distribution, and the value of education and human capital. I explore what roles may remain for labor post-AGI, and which production factors will grow in importance. The paper then identifies eight key challenges for economic policy in the Age of AI: (1) inequality and income distribution, (2) education and skill development, (3) social and political stability, (4) macroeconomic policy, (5) antitrust and market regulation, (6) intellectual property, (7) environmental implications, and (8) global AI governance. It concludes by emphasizing how economists can contribute to a better understanding of these challenges.
摘要：本文探讨了人工智能（AI）向通用人工智能（AGI）发展的变革性进步将为经济学家和经济政策制定者带来的深刻挑战。我分析了AI时代如何通过削弱劳动力的作用，彻底改变我们经济的基本结构，带来前所未有的生产力提升，同时引发关于就业中断、收入分配、教育价值和人力资本的担忧。我还探讨了在AGI时代劳动力可能保留的角色，以及哪些生产要素将变得更加重要。接着，文章确定了AI时代经济政策的八大关键挑战：(1) 不平等和收入分配，(2) 教育和技能发展，(3) 社会和政治稳定，(4) 宏观经济政策，(5) 反垄断和市场监管，(6) 知识产权，(7) 环境影响，以及 (8) 全球AI治理。文章最后强调了经济学家如何为更好理解这些挑战作出贡献。
黑人赔偿与儿童福祉：框架与政策考量
Rohan Kekre and Moritz Lenel #32976
Abstract: We study the source of exchange rate fluctuations using a general equilibrium model accommodating shocks in goods and financial markets. These shocks differ in their induced comovements between exchange rates, interest rates, and quantities. A calibration matching data from the U.S. and G10 currency countries implies that persistent shocks to relative demand, reflected in persistent interest rate differentials, account for 75% of the variance in the dollar/G10 exchange rate. Shocks to currency intermediation are important, however, in generating deviations from uncovered interest parity at high frequencies and explaining the dollar appreciation in crises.
摘要：我们使用一个通用均衡模型研究了汇率波动的来源，该模型能够考虑商品市场和金融市场的冲击。这些冲击在汇率、利率和数量之间产生的联动效应各不相同。通过对美国和G10货币国家的数据进行校准，结果表明，相对需求的持续冲击（反映为持久的利率差异）解释了美元/G10汇率波动方差的75%。然而，货币中介的冲击在高频下导致未覆盖利率平价偏离，并解释了危机期间美元升值的现象，这也是一个重要因素。
异质代理下的财政与货币政策
Adrien Auclert, Matthew Rognlie, and Ludwig Straub #32991
Abstract: In the past decade, a new paradigm for fiscal and monetary policy analysis has emerged, combining the canonical macro model of income and wealth inequality with the New Keynesian model. These Heterogeneous-Agent New Keynesian (“HANK”) models feature new transmission channels and allow for the joint study of aggregate and distributional effects. We review key developments in this literature through the lens of a unified “canonical HANK model”. Monetary and balanced-budget fiscal policy have similar aggregate effects as in the standard new Keynesian model, while deficit-financed fiscal policy is much more expansionary. We discuss the split between direct and indirect effects of policy, and also the implications of cyclical income risk, maturity structure, nominal assets, behavioral frictions, and many other extensions to the model. Throughout, we highlight the benefits of using sequence-space methods to solve and analyze this class of models.
摘要：在过去十年中，财政与货币政策分析领域出现了一个新的范式，将收入和财富不平等的经典宏观模型与新凯恩斯主义模型结合在一起。这些“异质代理新凯恩斯主义”（HANK）模型引入了新的传导渠道，能够同时研究总量效应和分配效应。我们通过一个统一的“经典HANK模型”回顾了该领域的关键发展。货币政策和平衡预算的财政政策对总体经济的影响与标准新凯恩斯主义模型类似，而赤字融资的财政政策则具有更强的扩张性。我们讨论了政策的直接和间接效应的分离问题，以及周期性收入风险、债务期限结构、名义资产、行为摩擦等对模型的扩展和影响。我们还特别强调了使用“序列空间方法”来求解和分析这一类模型的优势。
行为衰减
Benjamin Enke, Thomas Graeber, Ryan Oprea, and Jeffrey Yang #32973
Abstract: We report a large-scale examination of behavioral attenuation: due to information-processing constraints, the elasticity of people’s decisions with respect to economic fundamentals is generally too small. We implement more than 30 experiments, 20 of which were crowd-sourced from leading experts. These experiments cover a broad range of economic decisions, from choice and valuation to belief formation, from strategic games to generic optimization problems, involving investment, savings, effort supply, product demand, taxes, environmental externalities, fairness, cooperation, beauty contests, information disclosure, search, policy evaluation, memory, forecasting and inference. In 93% of our experiments, the elasticity of decisions to fundamentals decreases in participants’ cognitive uncertainty, our measure of the severity of information-processing constraints. Moreover, in decision problems with objective solutions, we observe elasticities that are universally smaller than is optimal. Many widely-studied decision anomalies represent special cases of behavioral attenuation. We discuss both its limits and why it often gives rise to the classic phenomenon of diminishing sensitivity.
摘要：我们报告了一项大规模的“行为衰减”研究：由于信息处理能力的限制，人们的决策对经济基本面的弹性通常过小。我们进行了超过30个实验，其中20个实验由顶尖专家进行众包。这些实验涵盖了广泛的经济决策领域，包括选择与估值、信念形成、策略游戏、通用优化问题，涉及投资、储蓄、努力供给、产品需求、税收、环境外部性、公平性、合作、美丽竞赛、信息披露、搜索、政策评估、记忆、预测和推理。在我们93%的实验中，参与者的认知不确定性——我们衡量信息处理限制严重性的指标——增加时，决策对基本面的弹性减小。此外，在具有客观解决方案的决策问题中，我们观察到的弹性普遍小于最优水平。许多广泛研究的决策异常都是“行为衰减”的特例。我们还讨论了行为衰减的局限性及其为何常常引发经典的“敏感度递减”现象。
用于改进个人所得税数据访问的安全查询系统
Amy O'Hara, Stephanie Straus, Ron Borzekowski, Paul Arnsberger, and Barry Johnson #32969
Abstract: In recent years, important and headline-grabbing findings have emerged from research using individual income tax data for statistical purposes. Demand for these microdata, accessible under the tax administration authority of the Internal Revenue Code and through the IRS Statistics of Income (SOI) Division’s Joint Statistical Research Program, continues to grow. This paper describes a new approach to address demand from government agencies and nonprofit institutions for such statistics. The project explores the feasibility of a privacy preserving secure query system (SQS) linking end-users of the data, a data intermediary, and SOI. In the early stages of development, end-users may be state or local government agencies or nonprofit institutions (e.g., non-degree programs at community colleges); the intermediary is Georgetown University; and all processing will be done within and by SOI staff. The objective is for an SQS client, such as a state department of social services, to prepare and submit a dataset with personal identifiers for SOI to match to individual income tax records, in order to produce tables of predefined output statistics. This efficient and automated process should allow greater production of evidence at much lower cost and burden for clients and SOI.
摘要：近年来，利用个人所得税数据进行统计研究的成果引起了广泛关注，产生了许多重要的研究发现。根据《国内税收法典》的税务管理权限，以及通过IRS收入统计局（SOI）联合统计研究项目访问这些微观数据的需求持续增长。本文描述了一种新的方法，以应对政府机构和非营利组织对此类统计数据的需求。该项目探讨了一种隐私保护的安全查询系统（SQS）的可行性，该系统将数据终端用户、数据中介和SOI连接起来。在开发的早期阶段，终端用户可能是州或地方政府机构或非营利机构（例如，社区学院的非学位项目）；中介方为乔治城大学；所有数据处理将由SOI工作人员在内部完成。其目标是让SQS客户端（如某州社会服务部门）准备并提交包含个人身份标识符的数据集，由SOI匹配到个人所得税记录，以生成预定义的统计结果表。这一高效、自动化的过程应能以更低的成本和负担为客户和SOI提供更多的证据产出。
往期精选：
