大规模就业冲击后的部门数字化强度和国内生产总值增长:一个简单的推断练习
Giovanni Gallipoli1, Christos A Makridis2
1University of British Columbia, CEPR and HCEO.
概括
一个新的算法使用最小的数据预测经济产出动态. 数字密集型行业在COVID-19大流行后显示出较温和的就业和GDP影响,有助于经济复苏预测.
科学领域:
- 经济学 经济学 经济学
- 计量经济学 计量经济学
- 计算社会科学 计算社会科学
背景情况:
- COVID-19 疫情造成了前所未有的经济破坏.
- 了解部门产出动态和复苏对于经济政策至关重要.
- 欧昆定律描述了失业率与GDP之间的关系,但需要根据现代经济结构进行更新.
研究的目的:
- 为预测输出动态引入一种新的依赖状态的算法.
- 通过将行业异质性纳入,将Okun定律泛化.
- 分析数字技术采用对COVID-19后经济复苏的影响.
主要方法:
- 开发了一个依赖状态的算法,需要最小的数据.
- 杆行业异质性措施来概括经济产出预测.
- 将算法应用于加拿大的就业和国内生产总值 (GDP) 数据.
主要成果:
- 数字密集型行业经历了疫情后较轻的就业冲击.
- 在数字密集型行业,GDP反应的波动性较小,控制了就业变化.
- 预测显示,总产量将在八个季度内恢复,但会有部门差异.
结论:
- 数字技术的采用可以在经济冲击期间调解就业和生产力损失.
- 特定行业的数字强度会影响性和恢复模式.
- 该算法为预测异构行业的经济动态提供了有价值的见解.
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