RIDIR: A Big Data Approach to Understanding American Growth

RIDIR：了解美国增长的大数据方法

• 批准号: 1831524
• 负责人: Konstantinos Arkolakis
• 金额: $ 100.49万
• 依托单位: National Bureau of Economic Research Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1831524&HistoricalAwards=false
• 关键词: RIDIR; Big; Data; Approach; Understanding

This project uses a big data approach to find out what lies behind the tremendous growth in the American economy in the nineteenth and twentieth centuries. This award supports a team of researchers, Costas Arkolakis and Michael Peters at Yale University, and Sun Kyoung Lee at Columbia University, who employ artificial intelligence and machine learning technologies to link and then analyze massive amounts of historical US federal census, Department of Labor, and Bureau of Labor Statistics data. The transformation of the US economy during this time period was remarkable, from a rural economy at the beginning of the 19th century to an industrial nation by the end. More strikingly, after lagging behind the technological frontier for most of the nineteenth century, the United States entered the twenty-first century as the global technological leader and the richest nation in the world. Results from this project will allow Americans to understand how people lived and how business operated. It will reveal the past that led us to where we are now in terms of people, geography, prices and wages, wealth, revenue, output, capital, numbers and types of workers, urbanization, migration, and industrialization.In this project, multiple data sets, involving variables such as family status, age, ethnicity, occupation, literacy, and income, are linked. This enables researchers to build a robust picture of the factors that shaped the American economic geography and to illuminate major aspects of America's economic growth during the "Second Industrial Revolution" (1850-1940), a period characterized by an influx of people of European origin. One analysis constructs pairs of parent-child data and investigates, over time, how a child's chance of moving up relative to the child's parents has evolved. Another reveals how economic shocks and policies affected trends in intergenerational mobility. A third uses digitized plant-level Census of Manufactures data to explore the factors that contributed to manufacturing growth at the firm level and how this, in turn, affected America's aggregate economic growth. The data will be shared with other researchers in fields such as economics, history and sociology, enabling them to uncover the underlying forces of America's remarkable economic transition and growth.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.

该项目使用大数据方法来找出19世纪和20世纪美国经济巨大增长的背后原因。该奖项支持一组研究人员，耶鲁大学的科斯塔斯·阿科拉基斯和迈克尔·彼得斯，以及哥伦比亚大学的孙坤·李，他们利用人工智能和机器学习技术来链接并分析美国联邦人口普查、劳工部和劳工统计局的大量历史数据。这一时期美国经济的转变是令人瞩目的，从19世纪初的农村经济到19世纪末的工业国。更引人注目的是，在19世纪的大部分时间里，美国落后于技术前沿，进入21世纪，美国成为全球技术领导者和世界上最富有的国家。这个项目的结果将让美国人了解人们是如何生活的，商业是如何运作的。它将揭示带领我们走到今天的人、地理、价格和工资、财富、收入、产出、资本、工人数量和类型、城市化、移民和工业化的过去。在这个项目中，涉及家庭状况、年龄、种族、职业、识字率和收入等变量的多个数据集相互关联。这使研究人员能够对塑造美国经济地理的因素建立一幅清晰的图景，并阐明“第二次工业革命”(1850-1940)期间美国经济增长的主要方面，这一时期的特点是欧洲血统的人大量涌入。一项分析构建了成对的亲子数据，并调查了随着时间的推移，孩子相对于父母晋升的机会是如何演变的。另一份报告揭示了经济冲击和政策是如何影响代际流动趋势的。第三项研究使用数字化的工厂级制造业普查数据，探索在企业层面推动制造业增长的因素，以及这反过来如何影响美国的总体经济增长。这些数据将与经济学、历史学和社会学等领域的其他研究人员共享，使他们能够揭示美国非凡的经济转型和增长的潜在力量。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。