Working Paper
The Innovation Cost of Short Political Horizons: Evidence from Local Leaders’ Promotion in China
Slides for presentation at EEA 2022
Abstract: This paper examines how politicians’ time horizons affect the choices between policies that yield short-versus long-term growth. I digitize the career histories of Chinese city leaders, link them to economic policies and innovation outcomes, and exploit political connections formed through previous work ties to generate variation in leaders’ promotion expectations. I find that when leaders are connected, they can rationally expect an earlier promotion. Such expectations lead them to pursue a fast-over-slow strategy for growth: higher spending on infrastructure, lower spending on science and technology, and lower effort in promoting innovation. As a result, the local economy has higher short-term growth but lower future patenting and long-term growth.
Does Chinese Research Hinge on US Coauthors ? Evidence from the China Initiative (with Philippe Aghion, Céline Antonin, Luc Paluskiewicz, Raphäel Wargon, David Strömberg and Karolina Westin)
Slides for presentation at NBER SI 2023
Abstract: Launched in November 2018 by the Trump administration, the China Initiative was meant to “protect US intellectual property and technologies against Chinese Economic Espionage”. In practice, it made administrative procedures more complicated and funding less accessible for collaborative projects between Chinese and US researchers. In this paper we use information from the Scopus database to analyze how the China Initiative shock affected the volume, quality and direction of Chinese research. We find a negative effect of the Initiative on the average quality of both the publications and the co-authors of Chinese researchers with prior US collaborations. Moreover, this negative effect has been stronger for Chinese researchers with higher research productivity and/or who worked on US-dominated fields and/or topics prior to the shock. Finally, we find that Chinese researchers with prior US collaborations reallocated away from US coauthors and basic research after the shock.
Work in Progress
The Political Economy of Industrial Policy in China (with Ruixue Jia, David Strömberg and Yang Yang)
Abstract: We examine how the interplay between government and market affects the efficacy in promoting innovation in China. We collect a raw sample of 1.5 million policy documents of all kinds, construct the universe of industrial policy documents for Chinese governments at all levels, and propose novel methods to identify and structuralize policy documents using natural language processing. Currently we are focusing on industrial policies for the Strategic Emerging Industries, a label created by the Chinese government since 2009 for targeting innovation. We link policies to politicians’ career concerns, firms’ entry, and innovation outcomes to examine the drivers of the policy-making and implementation as well as their impact on firm dynamics. With this project we aim to not only lay the infrastructure for future studies on industrial policies but also expand our existing knowledge on how the interplay between industrial policy and market competition affects innovation.
Response to People’s Demand Without People’s Voice: Protests, Information and Political Accountability in China
Abstract: I study the rise of protests in China since the early 2000s and explore how leaders at different levels respond. I build novel data to link protest events to leaders’ policy agendas and career outcomes by analyzing news coverage and leaders’ CV profiles. I exploit the underlying social connections across Chinese cities and the dynamics of protests in connected cities to implement a shift-share IV strategy. My preliminary findings show that upper-level leaders respond to protest events by lowering the importance of economic performance in promoting local leaders, and local-level leaders respond by higher redistributive spending.
Social Connections and the Spatial Spread of COVID-19 in China
Abstract: I study how well the spread of COVID-19 across Chinese cities can be predicted by social connections and travel flows across cities. I analyze a panel of 300 cities for the period from January 23rd to March 23rd, 2020. I construct a measure, SocialMediaConnection, of social connections using aggregated social media communications across cities from Weibo, one of China’s largest social media platforms. I find that SocialMediaConnection outperforms travel flows in predicting the arrival time of COVID-19: cities with higher SocialMediaConnection to Wuhan, the initial outbreak center, have their first COVID-19 cases earlier. I also find that both social media and travel connections have dual effects on local transmission, because they correlate with interpersonal contacts, but also capture communication about infection risks. The second effect is particularly pronounced for SocialMediaConnection. Consistent with this, I find that social distancing increases in cities with strong SocialMediaConnection to cities with high infection rates.