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Abstract

This study examines how labor markets adjust to international trade shocks in recent decades, with a particular focus on the China trade shock that has fundamentally reshaped global manufacturing since the 1990s. To estimate the effect, the project will combine structural economic modeling with semi-parametric machine learning techniques to understand heterogeneous labor market responses across demographic groups and industries. By merging rigorous economic theory with flexible estimation methods, we aim to provide nuanced insights into the transition costs workers face when responding to trade disruption, addressing a gap in the current literature that often treats adjustment frictions as homogeneous across populations. After the estimation, we'll apply agent-based modelling framework on simulated counterfactual sample to experiment with different tariff intervention and agent features to further understand the dynamics and welfare implication.

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