• About Me

    I'm a job market candidate from the Department of Economics at The Chinese University of Hong Kong. My research interests are macro development, trade and spatial economics. I combine micro-level big data with machine learning technologies, reduced-form analysis tools, and quantitative models to explore various topics related to China's economic development.

    I am currently on the job market (2022-2023), and I will be available for interviews. This is my most updated curriculum vitae (Dropbox link or Baidu link).

    Contact

    Email: zhuzhitao@link.cuhk.edu.hk

    Personal Website: https://www.zhitaozhu.net or https://www.zhitaozhu.cn

  • Research

    Job Market Paper

    On (Un)Congested Roads: A Quantitative Analysis of Infrastructure Investment Efficiency Using Truck GPS Data (with Simon Alder and Zheng (Michael) Song) [Link (Dropbox link or Baidu link)]

    This paper aims to quantify the gain from investments in a transportation network where the elasticity of driving time to traffic (``the congestion elasticity'') may differ across roads. We first use high-frequency GPS data from half a million Chinese trucks to uncover the congestion elasticity heterogeneity in China's city-to-city road links. We find that one-third of the links are uncongested and no more than 40% are associated with a large congestion elasticity comparable to the recent estimates for the developed economies. In contrast, using similar real-time traffic data for inter-region highways in England, we find that almost all the roads are associated with a large congestion elasticity. We next incorporate the congestion elasticity heterogeneity into a general equilibrium trade model with optimal route choices developed by Allen and Arkolakis (2019) and structurally estimate the model. To calculate the returns to investment in each link, we infer the benefit from the estimated model and calculate the construction cost and the opportunity cost of land directly from the data. We find the returns to be highly unequal in China and the heterogeneity in the congestion elasticity can account for more than half of the dispersion. Numerical simulations show that the dispersion is a robust indicator of misallocation, and optimized investments with a reasonable budget generate sizable welfare gains. Moreover, the optimal investment allocation turns out to be orthogonal to the actual allocation in the most developed provinces. Our findings suggest a severe misallocation of road investments in China.

    Publication

    Chasing or Cheating? Theory and Evidence on China’s GDP Manipulation (with Shuo Chen, and Xue Qiao), 2021, Journal of Economic Behavior and Organization. [Link]

    Gone with the Wind? Emissions of Neighboring Coal-Fired Power Plants and Local Public Health in China (with Shuo Chen, Yiran Li, and Guang Shi), 2021, China Economic Review. [Link]

    Running out of Steam? A Political Incentive Perspective of FDI Inflows in China (with Shuo Chen, Xiaowei Rose Luo, and Danqing Wang), 2020, Journal of International Business Studies. [Link]

    Working Paper

    Club-based Promotions in Organizations: Evidence and Theory (with Shuo Chen, and Xinyu Fan), 2022, submitted. [Link]

    Work in Progress

    Save Lives from Roads: Traffic Accidents and the Welfare Effects of Road Safety Improvement

    A Dynamic Spatial Analysis of China's Economic Reforms

    Persistent Hometown Favoritism

    Ming Garrisons (weisuo), Folktales, and Long-term Culture Formation

  • Teaching

    Teaching Assistant at CUHK

    • Analysis of China's Economy by Prof. Zheng Song (Fall 2022, 2021)
    • China and Global Economy by Prof. Dan Lyu (Spring 2022, 2021, 2020)
    • Global and Regional Economic Integration by Prof. Gene H. Chang (Spring 2019)
    • China and Global Economy by Prof. Gene H. Chang (Spring 2018)
    • Development Economics by Prof. Ying Bai (Fall 2022, 2020, 2019, 2018, 2017)

    Teaching Assistant at Fudan University

  • Contact

    Room 908, Esther Lee Building, Department of Economics, The Chinese University of Hong Kong