大流行病期间贸易冲击对女性劳动参与的因果效应及其微观机制识别：理论、应用与政策含义

女性劳动者在经济上往往比男性群体更为脆弱，尤其在大流行病期间，贸易冲击加剧了劳动力市场波动，暴露了女性面临的经济机遇和挑战。识别大流行病期间贸易冲击影响女性劳动参与的微观机制，对于改善女性福利、实现长期的性别包容性增长意义深远。为此，本项目创新性地结合传统统计调查数据与高频实时监测数据，通过理论建模和实证分析，考察大流行病期间贸易冲击影响女性就业的因果效应，并探讨其中的微观机制与政策含义。本项目拟从四个方面展开研究：第一，应用机器学习的前沿方法获取高频监测数据，并结合传统统计数据，刻画贸易与女性就业的长短期典型特征；第二，扩展贸易引力模型，从理论上考察大流行病与贸易变动的关系；第三，借助家庭分工与职业搜寻的视角，对贸易冲击影响女性劳动参与的微观机制进行建模；第四，使用更高频的数据捕捉短期贸易冲击、使用贸易伙伴的特征构建外生的工具变量，从实证上评估贸易冲击对女性就业的长期与短期因果效应。

Female workers tend to be economically more vulnerable than male counterparts. This is especially the case during the pandemic when trade shocks have exacerbated labor market fluctuations, exposing the economic opportunities and challenges faced by women. Identifying of the micro mechanisms underlying the effect of trade shocks on female labor force participation during the pandemic is of great significance to improving women's welfare and achieving long-term gender inclusive growth. This project aims to evaluate the causal impact of trade shocks on female employment during the pandemic with an emphasis on the underlying micro mechanisms and policy implications. To this end, we innovatively combine traditional statistical survey data with high-frequency real-time monitoring data. This project intends to explore four main issues. First, we use high-frequency monitoring data and traditional statistical data to capture short-term and long-term characteristics of trade and female employment based on cutting-edge machine learning approach. Second, we extend the gravity model to theoretically investigate the relationship between factors of the pandemic and changes in trade flows. Third, we model the mechanisms through which trade shocks affect female labor force participation from the perspectives of household division of labor and job search. Finally, by using higher-frequency data and constructing exogenous instrumental variables based on characteristics of trade partners, we empirically assess the short-term and long-term impacts of trade shocks on women's employment.