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（10.7-10.13）共发布 26 篇新工作论文，本期将会推送中间 8 篇论文的题目与摘要，供读者学习。
学术财经研究团队翻译。
美国选举人口预测的陷阱
Richard Calvo, Vincent Pons, and Jesse M. Shapiro #33016
Abstract: Many observers have forecast large partisan shifts in the US electorate based on demographic trends. Such forecasts are appealing because demographic trends are often predictable even over long horizons. We backtest demographic forecasts using data on US elections since 1952. We envision a forecaster who fits a model using data from a given election and uses that model, in tandem with a projection of demographic trends, to predict future elections. Even a forecaster with perfect knowledge of future demographic trends would have performed poorly over this period—worse even than one who simply guesses that each election will have a 50-50 partisan split. Enriching the set of demographics available does not change this conclusion. We discuss both mechanical and economic reasons for this finding, and show suggestive evidence that parties adjust their platforms in accordance with changes in the electorate.
摘要：许多观察者基于人口趋势预测了美国选民中会出现大的党派转变。这样的预测颇具吸引力，因为人口趋势即使在较长时间内也往往是可预测的。我们通过使用自1952年以来美国选举的数据对这些人口预测进行了回测。我们设想了一个预测者，该预测者使用某次选举的数据来拟合一个模型，并结合人口趋势的预测来预测未来选举。即便这个预测者对未来人口趋势有着完美的了解，预测效果在这一时期仍然表现不佳，甚至还不如简单猜测每次选举为50-50的党派分布。扩展可用的人口统计数据集也不会改变这一结论。我们讨论了这一发现背后的机械和经济原因，并提供了初步证据，表明政党会根据选民构成的变化调整其政策纲领。
在线商业模式、数字广告与用户福利
Daron Acemoglu, Daniel Huttenlocher, Asuman Ozdaglar, and James Siderius #33017
Abstract: We present a model where social media platforms offer plans that intermix entertaining content with digital advertising (“ads”). Users derive utility from entertainment and learn about their valuation for a product from ads. While some users are fully rational, others naïvely perceive digital ads as more informative than they actually are. We characterize the profit-maximizing business model of the platform and show that welfare is lower when the platform monetizes through advertising instead of subscription both for naïfs (because they are targeted by intense digital advertising, which makes them over-optimistic about product quality and over-purchase the product) and for sophisticates (because the inflated demand from naïfs increases the firm’s price). This negative welfare effect is intensified when the platform can offer mixed business models that separate the naïve and sophisticated users into different plans. Our results are robust to firm-level and platform-level competition, because digital ads soften competition between both firms and platforms. We also show how digital ad taxes can improve welfare.
摘要：我们提出了一个模型，其中社交媒体平台提供将娱乐内容与数字广告（“广告”）混合的计划。用户从娱乐中获得效用，并通过广告了解他们对产品的评价。一些用户是完全理性的，另一些用户则天真地认为数字广告比实际更具信息性。我们刻画了平台的利润最大化商业模式，并展示了当平台通过广告而非订阅模式进行货币化时，用户福利下降的情况。对于天真的用户（因为他们受到密集的数字广告影响，过于乐观地认为产品质量很好，导致过度购买产品）以及成熟的用户（因为天真的用户推高了需求，导致企业提高了价格）来说，广告货币化都会降低福利。当平台可以提供混合商业模式，将天真用户和成熟用户分开到不同的计划中时，这种负面福利效应会加剧。我们的结果在企业层面和平台层面的竞争中都是稳健的，因为数字广告减弱了企业与平台之间的竞争。我们还展示了如何通过对数字广告征税来改善用户福利。
使用大型语言模型进行理论化
Matteo Tranchero, Cecil-Francis Brenninkmeijer, Arul Murugan, and Abhishek Nagaraj #33033
Abstract: Large Language Models (LLMs) are proving to be a powerful toolkit for management and organizational research. While early work has largely focused on the value of these tools for data processing and replicating survey-based research, the potential of LLMs for theory building is yet to be recognized. We argue that LLMs can accelerate the pace at which researchers can develop, validate, and extend strategic management theory. We propose a novel framework called Generative AI-Based Experimentation (GABE) that enables researchers to conduct exploratory in silico experiments that can mirror the complexities of real-world organizational settings, featuring multiple agents and strategic interdependencies. This approach is unique because it allows researchers to unpack the mechanisms behind results by directly modifying agents’ roles, preferences, and capabilities, and asking them to reveal the explanations behind decisions. We apply this framework to a novel theory studying strategic exploration under uncertainty. We show how our framework can not only replicate the results from experiments with human subjects at a much lower cost, but can also be used to extend theory by clarifying boundary conditions and uncovering mechanisms. We conclude that LLMs possess tremendous potential to complement existing methods for theorizing in strategy and, more broadly, the social sciences.
摘要：大型语言模型（LLMs）正逐渐成为管理与组织研究的强大工具。尽管早期的研究主要集中于这些工具在数据处理和复制基于调查研究中的价值，LLMs在理论构建中的潜力尚未被充分认可。我们认为，LLMs可以加速研究人员开发、验证和扩展战略管理理论的进程。我们提出了一个名为“基于生成式AI的实验”（GABE）的新框架，允许研究人员进行探索性计算机模拟实验，这些实验能够反映真实世界组织环境的复杂性，涉及多个代理人和战略相互依赖性。该方法的独特之处在于，它允许研究人员通过直接修改代理人的角色、偏好和能力，揭示结果背后的机制，并让代理人解释决策的原因。我们将这一框架应用于一个关于不确定性下战略探索的新理论研究。我们展示了这一框架不仅可以以更低的成本复制基于人类受试者的实验结果，还可以通过明确边界条件和揭示机制来扩展理论。我们总结道，LLMs在补充现有的战略理论化方法以及更广泛的社会科学研究中具有巨大潜力。
家庭影响的传递
Sadegh S.M. Eshaghnia, James J. Heckman, Rasmus Landersø, and Rafeh Qureshi #33023
Abstract: This paper studies intergenerational mobility—the transmission of family influence. We develop and estimate measures of lifetime resources motivated by economic theory that account for differences in life-cycle trajectories, and uncertainty about future income. We identify the effects of parents’ resources on child outcomes through policy shocks at different childhood ages that affect family investments. Parents’ expected lifetime resources are stronger predictors of child outcomes than the income measures traditionally used in the literature on social mobility. Moreover, while effects estimated through exogenous variation in parents’ expected lifetime resources are smaller in magnitude than their correlational counterparts, they are still sizable and largest in early childhood. The paper illustrates how integrating key insights from different literatures when studying intergenerational mobility allows for a better understanding of the importance of factors such as the family’s role, changes in individual life cycles across generations, and the expectations and trajectories individuals face across their lifetimes.
摘要：本文研究了代际流动性——即家庭影响的传递。我们基于经济理论开发并估算了终身资源的衡量方法，这些方法考虑了生命周期轨迹的差异以及对未来收入的不确定性。通过不同儿童年龄段的政策冲击来识别父母资源对孩子结果的影响，这些冲击会影响家庭的投资。父母预期的终身资源比传统社会流动性文献中使用的收入指标更能预测孩子的结果。此外，尽管通过父母预期终身资源的外生变化估算的效果在数值上小于相关分析中的结果，但它们仍然具有显著影响，且在儿童早期最为突出。本文展示了在研究代际流动性时整合不同领域的关键见解，能够更好地理解家庭角色、个体跨代生命周期变化以及个体在其一生中面临的预期和发展轨迹等因素的重要性。
年长且关系广泛与年轻且富有创造力：网络与新科学思想的传播
Wei Cheng and Bruce A. Weinberg #33030
Abstract: The adoption of new ideas is critical for realizing their full potential and for advancing the knowledge frontier but it involves analyzing innovators, potential adopters, and the networks that connect them. This paper applies natural language processing, network analysis, and a novel fixed effects strategy to study how the aging of the biomedical research workforce affects idea adoption. We show that the relationship between adoption and innovator career age varies with network distance. Specifically, at short distances, young innovators’ ideas are adopted the most, while at greater network distances, mid-career innovators’ ideas have the highest adoption. The main reason for this contrast is that young innovators are close to young potential adopters who are more open to new ideas, but mid-career innovators are more central in networks. Overall adoption is hump-shaped in the career age of innovators. Simulations show that the aging of innovators and of potential adopters have comparable effects on the adoption of important new ideas.
摘要：新思想的采用对于实现其全部潜力和推动知识前沿至关重要，但这涉及对创新者、潜在采用者以及将他们连接起来的网络的分析。本文运用了自然语言处理、网络分析以及一种新的固定效应策略，研究了生物医学研究人员的年龄对思想采纳的影响。我们发现，创新者的职业年龄与思想采纳之间的关系因网络距离而异。具体来说，在短距离内，年轻创新者的想法被采用得最多，而在更大的网络距离上，中年职业阶段的创新者的想法被采用得最多。这一对比的主要原因在于，年轻创新者与那些更开放接受新思想的年轻潜在采用者关系更近，而中年创新者在网络中则处于更中心的位置。总体而言，思想采纳呈现出与创新者职业年龄的倒U型关系。模拟结果显示，创新者和潜在采用者的年龄增长对重要新思想的采纳具有相似的影响。
清洁能源的经济影响
Costas Arkolakis and Conor Walsh #33028
Abstract: In this paper we assess the economic impacts of moving to a renewable-dominated grid in the US. We use projections of capital costs to develop price bounds on future wholesale power prices at the local geographic level. We then use a class of spatial general equilibrium models to estimate the effect on wages and output of prices falling below these bounds in the medium term. Power prices fall anywhere between 20% and 80%, depending on local solar resources, leading to an aggregate real wage gain of 2-3%. Over the longer term, we show how moving to clean power represents a qualitative change in the aggregate growth process, alleviating the “resource drag” that has slowed recent productivity growth in the US.
摘要：在本文中，我们评估了美国向以可再生能源为主的电网转型的经济影响。我们使用资本成本的预测，来估算未来在当地地理层面上的批发电价的价格区间。接着，我们运用一种空间一般均衡模型的类别，估算中期内电价下降至该区间下限时对工资和产出的影响。根据当地的太阳能资源，电价的降幅在20%到80%之间不等，导致整体实际工资增长2%到3%。从长期来看，我们展示了向清洁能源转型如何在整体增长过程中带来质的变化，减轻了最近拖累美国生产率增长的“资源拖累”问题。
与人工智能合作的受益者ABC法则：能力、信念与校准
Andrew Caplin, David J. Deming, Shangwen Li, Daniel J. Martin, Philip Marx, Ben Weidmann, and Kadachi Jiada Ye #33021
Abstract: We use a controlled experiment to show that ability and belief calibration jointly determine the benefits of working with Artificial Intelligence (AI). AI improves performance more for people with low baseline ability. However, holding ability constant, AI assistance is more valuable for people who are calibrated, meaning they have accurate beliefs about their own ability. People who know they have low ability gain the most from working with AI. In a counterfactual analysis, we show that eliminating miscalibration would cause AI to reduce performance inequality nearly twice as much as it already does.
摘要：我们通过一项控制实验表明，能力和信念校准共同决定了与人工智能（AI）合作的收益。对于低基础能力的人来说，AI能更显著地提高他们的表现。然而，在能力相同的情况下，AI的帮助对那些校准良好的人更有价值，也就是说，这些人对自己的能力有准确的认知。那些知道自己能力较低的人，从与AI合作中获益最多。在一个反事实分析中，我们展示了消除信念失调（即校准不准）将使AI减少表现不平等的效果几乎翻倍。
代际转移对财富分配的影响：国际比较
Charles Yuji Horioka #33015
Abstract: In this paper, I analyze detailed data on intergenerational transfers in 4 countries (China, India, Japan, and the United States) from the “Japan Household Panel Survey on Consumer Preferences and Satisfaction (JHPS-CPS)” which has been conducted by the Institute of Social and Economic Research of Osaka University in these 4 countries since 2003, in order to shed light on the impact of intergenerational transfers on household wealth disparities and on possible reasons for the substantial differences in household wealth disparities among the 4 countries. Almost all of the evidence I present suggests that intergenerational transfers have a disequalizing impact on household wealth disparities and promote the transmission of household wealth disparities from generation to generation in all 4 countries although the magnitude of these effects varies considerably from country to country. Moreover, the evidence I present sheds considerable light on possible reasons for the substantial differences in household wealth disparities among the 4 countries.
摘要：在本文中，我分析了来自四个国家（中国、印度、日本和美国）关于代际转移的详细数据，这些数据来源于由大阪大学社会经济研究所自2003年以来在这四个国家开展的“日本家庭消费者偏好与满意度面板调查 (JHPS-CPS)”。研究旨在揭示代际转移对家庭财富差距的影响，以及导致四国之间家庭财富差距显著差异的可能原因。几乎所有证据表明，代际转移对家庭财富差距具有不平等化的影响，并促进了财富差距在代际间的传递，虽然这种影响的程度在各国之间有显著差异。此外，我提供的证据对解释四国之间家庭财富差距显著差异的可能原因提供了重要的见解。
往期精选：
