Projecting unemployment durations: A factor-flows simulation approach with application to the COVID-19 recession
中文摘要
我们提出一种基于模拟的三步因子-流量方法,用以预测失业的持续时间分布。第一步:估计个体在就业、临时裁员、永久裁员、主动离职、新进入以及退出劳动力等状态之间的转换风险率,每一风险率均取决于一个总体成分以及个体的劳动力历史。第二步:利用因子模型将总体成分与总体失业率相联系。第三步:将个体层面的持续时间依赖、因子结构与失业率的辅助预测相结合,模拟出个体劳动力历史的面板。将我们的方法应用于针对新冠疫情衰退的11月Blue Chip(蓝筹)预测,我们推算出2020年4月被裁员的75万名工人在八个月后仍处于失业状态。此后长期失业总量继续上升,最终达到420万人失业超过26周、140万人失业超过46周。在更为悲观的复苏情景下,长期失业的上升幅度更大,但由于劳动力市场更替程度较高,其水平仍低于大衰退时期。
Abstract
We propose a three-step factor-flows simulation-based approach to forecast the duration distribution of unemployment. Step 1: estimate individual transition hazards across employment, temporary layoff, permanent layoff, quitter, entrant, and out of the labor force, with each hazard depending on an aggregate component as well as an individual’s labor force history. Step 2: relate the aggregate components to the overall unemployment rate using a factor model. Step 3: combine the individual duration dependence, factor structure, and an auxiliary forecast of the unemployment rate to simulate a panel of individual labor force histories. Applying our approach to the November Blue Chip forecast of the COVID-19 recession, we project that 750,000 workers laid off in April 2020 remain unemployed eight months later. Total long-term unemployment rises thereafter and eventually reaches 4.2 million individuals unemployed for more than 26 weeks and 1.4 million individuals unemployed for more than 46 weeks. Long-term unemployment rises even more in a more pessimistic recovery scenario, but remains below the level in the Great Recession due to a high amount of labor market churn.