企业韧性与财务对冲与运营对冲的选择

Viral V. Acharya, Heitor Almeida, Yakov Amihud, and Ping Liu #33340

Abstract: We investigate how firms manage financial default risk (on debt) and operational default risk (on delivery obligations). Financially constrained firms reduce operational hedging through inventory and supply chain in favor of cash holdings. Our model predicts that firms’ markup increases with financial default risk as they cut operational hedging costs. Empirical analysis confirms this prediction and shows that the markup-credit risk relationship strengthens during adverse aggregate shocks, particularly for firms exposed to lending disruptions. Market power alone cannot explain this relationship, which reflects firms’ strategic adjustments in operational hedging practices.

摘要：我们研究了企业如何管理财务违约风险（债务违约）和运营违约风险（交付义务）。在财务受限的情况下，企业会减少通过库存和供应链进行的运营对冲，而倾向于持有现金。我们的模型预测，当财务违约风险增加时，企业会减少运营对冲成本，从而提高利润率。实证分析验证了这一预测，并表明在整体经济遭遇不利冲击，尤其是企业面临信贷中断时，利润率与信用风险之间的关系会更为显著。这种关系并不能单纯用市场力量来解释，而是反映了企业在运营对冲实践中的战略调整。

投资者之间有限的风险转移：宏观金融模型的新基准

Xavier Gabaix, Ralph S. J. Koijen, Federico Mainardi, Sangmin Simon Oh, and Motohiro Yogo #33336

Abstract: We define risk transfer as the percent change in the market risk exposure for a group of investors over a given period. We estimate risk transfer using novel data on U.S. investors' portfolio holdings, flows, and returns at the security level with comprehensive coverage across asset classes and broad coverage across the wealth distribution (including 400 billionaires). Our key finding is that risk transfer is small with a mean absolute value of 0.65% per quarter. Leading macro-finance models with heterogeneous investors predict risk transfer that exceeds our estimate by a factor greater than ten because investors react too much to the time-varying equity premium. Thus, the small risk transfer is a new moment to evaluate macro-finance models. We develop a model with inelastic demand, calibrated to the standard asset pricing moments on realized and expected stock returns, that explains the observed risk transfer. The model is adaptable to other macro-finance applications with heterogeneous households.

摘要：我们将风险转移定义为一组投资者在特定时期内市场风险敞口的百分比变化。通过创新的数据，我们基于美国投资者的投资组合持仓、资金流动和证券级别回报（涵盖所有资产类别并广泛覆盖财富分布，包括400位亿万富翁）估算了风险转移。我们的关键发现是，风险转移的规模较小，季度平均绝对值仅为0.65%。主流的异质性投资者宏观金融模型预测的风险转移值比我们的估算值高出十倍以上，因为这些模型中投资者对不断变化的股票风险溢价反应过于敏感。因此，小规模的风险转移为评估宏观金融模型提供了一个新的衡量标准。我们提出了一个需求缺乏弹性的模型，该模型以实现的和预期的股票回报的标准资产定价矩为标定，能够很好地解释观察到的风险转移。这一模型还可适应包含异质性家庭的其他宏观金融应用。

人们是否正在逃离实施堕胎禁令的州？

Daniel L. Dench, Kelly Lifchez, Jason M. Lindo, and Jancy Ling Liu #33328

Abstract: In this study, we investigate whether reproductive rights affect migration. We do so using a synthetic difference-in-differences design that leverages variation from the 2022 Dobbs decision, which allowed states to ban abortion, and population flows based on change-of-address data from the United States Postal Service. The results indicate that abortion bans cause significant increases in net migration outflows, with effect sizes growing throughout the year after the decision. The most recent data point indicates that total abortion bans come at the cost of more than 36,000 residents per quarter. The effects are more prominent for single-person households than for family households, which may reflect larger effects on younger adults. We also find suggestive evidence of impacts for states that were hostile towards abortion in ways other than having total bans.

摘要：在本研究中，我们探讨了生育权是否会影响人口迁移。我们采用一种合成双重差分设计，利用2022年Dobbs裁决（该裁决允许各州禁止堕胎）带来的变化，以及基于美国邮政服务的地址变更数据来分析人口流动。研究结果表明，堕胎禁令导致了净迁出量的显著增加，并且在裁决后的一年内，这一影响的规模持续扩大。最新数据显示，全面堕胎禁令每季度导致超过36,000名居民流失。单人家庭的迁移效应比家庭户更为显著，这可能反映了对年轻成人的更大影响。此外，我们还发现了一些间接证据，表明那些以其他方式对堕胎持敌视态度的州（而非完全禁止堕胎的州）也可能受到类似的影响。

大规模的社区精准定位

Sudarno Sumarto, Elan Satriawan, Benjamin A. Olken, Abhijit Banerjee, Achmad Tohari, Vivi Alatas, and Rema Hanna #33322

Abstract: Community-based targeting, in which communities allocate social assistance using local information about who is poor, in experimental settings leads to nuanced allocations that reflect local concepts of poverty. What happens when it is scaled up, by either by making the stakes high, or by replicating the process nationwide? We study this by examining community targeting in both a high-stakes experiment, in which villages determined who would receive the Indonesian conditional cash transfer program – worth almost USD 1,000 over 6 years – and in a nationwide scaleup, whereby Indonesia used community-based meetings to allocate COVID-transfers to over 8 million households. We find that both the experimental scale-up and the massive national scale-up had broadly similar performance to the original experimental study. We find strongly progressive targeting as measured by baseline household consumption, though – as in the pilot – not quite as strong as if they had used a fully up-to-date proxy means test. In both scale-ups, we also find that the villages gave additional weight to locally-valued characteristics beyond pure consumption, such as widowhood, recent illness, and food expenditure shares, again echoing the findings from pilots. The results suggest that community targeting can perform well at scale, as predicted by the experimental study.

摘要：社区定位（Community-based targeting）是一种利用社区对贫困状况的本地信息来分配社会援助的方式。在实验环境中，这种方法能够根据当地对贫困的理解进行细致的分配。那么，当这种方法被大规模推广，例如在高利益环境中实施，或在全国范围内复制时，会发生什么呢？我们通过研究两种情境下的社区定位来探讨这个问题：一种是高利益实验环境下的社区定位，村庄决定哪些人能够获得印尼的有条件现金转移计划（6年内总额接近1,000美元）；另一种是在全国范围内的推广，印尼通过社区会议为超过800万户家庭分配COVID-19援助金。研究发现，无论是实验性的扩大规模还是全国范围的大规模推广，其表现与原始实验研究的结果大体相似。根据基线家庭消费数据衡量，社区定位表现出强烈的进步性分配（即向更贫困家庭倾斜），尽管——与试点研究类似——这种分配的精准度不如完全更新的代理经济状况测试（proxy means test）。在两种推广情境中，我们还发现，村庄在分配时会额外考虑当地重视的特征，例如丧偶、近期患病以及食品支出的比例等，这与试点研究的发现一致。研究结果表明，社区定位在大规模推广中能够表现良好，这验证了实验研究的预测。

选民偏好联动性：从美国总统选举预测市场中获得的超越民调的洞见

Mikhail Chernov, Vadim Elenev, and Dongho Song #33339

Abstract: We propose a novel time-series econometric framework to forecast U.S. Presidential election outcomes in real time by combining polling data, economic fundamentals, and political prediction market prices. Our model estimates the joint dynamics of voter preferences across states. Applying our approach to the 2024 Presidential Election, we find a two-factor structure driving the vast majority of the variation in voter preferences. We identify electorally similar state clusters without relying on historical data or demographic models of voter behavior. Our simulations quantify the correlations between state-level election outcomes. Failing to take the correlations into account can bias the forecasted win probability for a given candidate by more than 10 percentage points. We find Pennsylvania to be the most pivotal state in the 2024 election. Our results provide insights for election observers, candidates, and traders.

摘要：我们提出了一种新颖的时间序列计量经济学框架，通过结合民调数据、经济基本面和政治预测市场价格，实时预测美国总统选举的结果。我们的模型估计了各州选民偏好的联合动态。将这一方法应用于2024年总统选举，我们发现驱动选民偏好大部分变化的是一个两因子结构。我们在不依赖历史数据或选民行为的人口统计模型的情况下，识别出了在选举上相似的州群。我们的模拟量化了各州选举结果之间的相关性。如果忽视这些相关性，可能会导致某一候选人预测胜选概率的偏差超过10个百分点。我们发现宾夕法尼亚州是2024年选举中最关键的州。我们的研究结果为选举观察者、候选人和交易者提供了重要的洞见。

呼唤所有家长：利用行为洞察提升发展中国家幼儿发展的成果

Juanita Bloomfield, Ana I. Balsa, Alejandro Cid, and Philip Oreopoulos #33338

Abstract: Early childhood in developing countries faces a greater prevalence of risk factors and limited resources, underscoring the need for effective, scalable support models. We develop and experimentally evaluate a multi-component approach to enhance family well-being over-the-phone. The program combines scalable outreaches including calls by a teleoperator, messages, a chatbot, and an AI tool. Targeted at highly vulnerable families with children aged 0 to 3 in Uruguay, the intervention promotes positive parenting practices at home, fosters language development, and provides personalized assistance to help families access government benefits. The intervention was implemented with 1,360 families eligible for support from the government agency Uruguay Crece Contigo over an eight-month period. We find that the program increases weekly frequency of parental engagement in stimulating activities and reduces parental stress. Treated families gain enhanced access to social benefits, including cash transfers and employment support programs.

摘要：发展中国家的幼儿阶段面临更多的风险因素和有限的资源，这凸显了对有效且可扩展的支持模式的需求。我们设计并实验性地评估了一种通过电话提升家庭福祉的多组件方法。该项目结合了多种可扩展的沟通方式，包括电话运营员拨打的电话、短信、聊天机器人以及人工智能工具。该干预措施针对乌拉圭最脆弱的有0至3岁儿童的家庭，旨在促进家庭中积极的育儿实践、推动语言发展，并提供个性化帮助以协助家庭获取政府福利。干预措施在8个月的时间内覆盖了1,360个符合政府机构“乌拉圭伴你成长”（Uruguay Crece Contigo）支持条件的家庭。研究发现，该项目提高了家长每周参与刺激性活动的频率，降低了家长的压力。参与干预的家庭更容易获得社会福利，包括现金转移支付和就业支持项目。

往期精选：

      重磅：学术财经•学术笔记全汇总

重磅：学术财经全球价值链专题学术笔记大汇总（附下载链接失效学术笔记推文）

重磅| 基金申请内部学习和交流

【学术笔记·第37篇】风险和全球供应链：我们知道什么和我们需要知道什么

【学术笔记·第36篇】各有所长：全球供应链风险敞口（Exposure）测度

【学术笔记·第35篇】日本生产者价格指数理论与实证：最终需求-中间需求加总系统

【学术笔记·第34篇】北美自由贸易的贸易和福利影响估计

【学术笔记 第33篇】 英属印度时期铁路：交通基础设施的影响评估（AER）

【学术笔记 第32篇】 技术、地理和贸易(经典EK模型）

【学术笔记 第31篇】2018年贸易战对美国物价和福利的影响

【学术笔记 第30篇】全球价值链和国家部门层面实际有效汇率