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課程概述 |
課程概述:
This course will survey empirical methods in labor economics. We will focus on recent advances in these methods as well as their empirical applications. The topics will include randomized (field) experiments, matching method, instrumental variables, differences-in-differences method, synthetic controls method, regression discontinuity (kink) design, machine learning method, text mining, and GIS data. The applications in labor economics include labor market discrimination, life-cycle labor supply, social insurance, labor demand, and tax incidence. We will especially focus on the practical implementation of these methods and tips for data management by writing a term paper. After taking this course, students should be able to conduct empirical research independently.
課程簡介影片:
https://causaldatalab.wordpress.com/2019/01/07/topics-in-labor-economics-empirical-methods-and-applications-spring-2019/ |
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參考書目 |
Pierre Cahuc, Stephane Carcillo and Andre Zylberberg, Labor Economics, Second Edition
Angrist and Pischke (2016), Mastering Metrics: The Path from Cause to Effect
Angrist and Pischke (2009), Mostly Harmless Econometrics
Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani (2017), An Introduction to Statistical Learning with Applications in R |