CAREER: Macroeconomic Policies: from Optimal Government Transfers to Regulating New Technologies

职业：宏观经济政策：从最优政府转移支付到监管新技术

• 批准号: 2236412
• 负责人: Martin Beraja
• 金额: $ 48.92万
• 依托单位: National Bureau of Economic Research Inc
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2028-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2236412&HistoricalAwards=false
• 关键词: CAREER; Macroeconomic; Policies; Optimal; Government

Government policy plays a key role in modern economies. In the short-term, governments help stabilize business cycles. Fiscal transfers, such as stimulus checks, have recently become an important tool in alleviating US recessions. Two projects funded by this award quantify how large stimulus transfers should be and to what extent they stabilize regional business cycles in a fiscal union. Over longer periods of time, governments are responsible for regulating new technologies and managing their consequences. New challenges brought to the fore by digital and automation technologies motivate the remaining three projects funded by this award. The projects investigate the misuse of artifical intelligence to support surveillance states, the optimal regulation of digital industries where data can lead to concentration, and how governments should manage episodes of labor reallocation where new technologies displace workers. By informing policy, these projects will benefit disadvantaged populations in the US who are disproportionately impacted by recessions and automation, foster US national security interests and democratic stability, and help ensure the US remains a leader in the digital industries of the future. The educational component of this award will disseminate the research findings to policymakers and journalists, as well to graduate students through a tutorial ran by the National Bureau of Economic Research.The projects funded by the award advance our understanding of core issues in macroeconomics, but also connect to broader questions in political economy, industrial organization, and labor economics. The first project recognizes that households' marginal propensity to consume out of a stimulus transfer varies with its size. A key determinant of such size-dependence is the durability of goods. The project develops a state-of-the art model of durables demand, calibrates it to match key moments in US micro-data, and uses it to quantify the optimal size of stimulus transfers. The second project applies a semi-structural methodology for policy counterfactuals to state-level US data to construct a US economy without fiscal integration. The third project collects global data on facial recognition AI trade. It documents new facts about US and Chinese exports of this surveillance technology to autocracies and democracies. The fourth project builds a model of the life-cycle of oligopolistic industries, such as digital industries where data is a key input. The equilibrium features an initial firm entry phase, followed by a shakeout and later industry concentration. The model is calibrated to match US data on digital industries and is used to study optimal industry regulation. The last project begins from the observation that worker displacement is a common feature of many episodes of labor reallocation, such as those induced by automation or the transition to clean technologies. Displaced workers face reallocation and borrowing frictions in such episodes. The project develops a heterogeneous agents model which incorporates these frictions. It uses it to study second best policies that slow down technological adoption or help worker reallocation.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

政府政策在现代经济中起着关键作用。在短期内，政府有助于稳定业务周期。财政转移（例如刺激检查）最近已成为减轻美国衰退的重要工具。该奖项资助的两个项目量化了应有多大刺激转移以及在财政联盟中稳定区域业务周期的程度。在更长的时间内，政府负责规范新技术并管理其后果。数字和自动化技术带来了新的挑战，激发了该奖项资助的其余三个项目。这些项目调查了人工智能以支持监视状态的滥用，对数据可以导致集中注意力的数字行业的最佳监管以及政府应如何管理新技术使工人取代工人的劳动力重新分配事件。通过告知政策，这些项目将使美国受益的人口受益，这些人口受到衰退和自动化的影响不成比例的，促进美国国家安全利益和民主稳定，并帮助确保美国仍然是未来数字工业的领导者。该奖项的教育组成部分将通过国家经济研究局的教程向研究生传播研究结果，并向研究生传播研究结果。该奖项资助的项目提高了我们对宏观经济学中核心问题的理解，但也与政治经济，工业组织和实验室中更广泛的问题联系在一起。第一个项目认识到，家庭从刺激转移中消耗的边际倾向随其大小而异。这种大小依赖性的关键决定因素是商品的耐用性。该项目开发了耐用需求的最先进模型，对其进行校准以匹配美国微型数据的关键时刻，并使用它来量化刺激转移的最佳尺寸。第二个项目将政策反事实的半结构方法应用于州级的美国数据，以在没有财政整合的情况下构建美国经济。第三个项目收集有关面部识别AI贸易的全球数据。它记录了有关我们和中国向专制和民主国家出口的新事实。第四个项目建立了寡头行业的生命周期模型，例如数据是关键输入的数字行业。平衡具有初始的公司进入阶段，随后是摇摆不定和后来的行业集中度。该模型经过校准以匹配美国关于数字行业的数据，并用于研究最佳行业法规。最后一个项目始于观察到，工人位移是劳动重新分配的许多情节的共同特征，例如自动化引起的劳动力或清洁技术的过渡。流离失所的工人在此类情节中面临重新分配和借用摩擦。该项目开发了一种混合这些摩擦的异质代理模型。它利用它来研究降低技术采用或帮助工人重新分配的第二最佳政策。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估来获得支持。