Yanhui Wu
Dr. Yanhui WU
Economics
Management and Strategy
Associate Professor

3917 8508

KK 931

Academic & Professional Qualification

Ph.D. in Economics, London School of Economics

MSc. in Economics, London School of Economics

B.A. in Economics, Sun Yat-sen (Zhongshan) University

Biography

Dr. Wu is Associate Professor of Economics at the University of Hong Kong and Research Fellow of the Centre for Economic Policy Research (CEPR). Prior to HKU, he was Assistant Professor of Finance and Business Economics at the Marshall School of Business of the University of Southern California. His research concentrates on two areas: media economics and organizational economics. In media economics, he studies the economy of mass communication, particularly the underexplored subject of the media in China. In organizational economics, his research focuses on the organization of knowledge-intensive activities, particularly in the digital economy. Recently, he has worked with data scientists to apply cutting-edge big data methods to various areas in the social sciences. His work has been published at top economics and management journals, including the American Economic Review, Review of Economics and Statistics, Economic Journal, Journal of Economic Perspectives, Management Science, and Organization Science.

Teaching
  • Economics of Organization and Strategy (MEcon)
  • Introduction to Causal Inference and Machine Learning (undergraduate)
Research Interest
  • Media Economics, Organizational Economics, Development Economics, Chinese Economy, Big Data and Computational Social Science
Selected Publications
  • Intentional Control of Type I Error Over Unconscious Data Distortion: A Neyman-Pearson Approach to Text Classification (with Lucy Xia, Richard Zhao and Xin Tong), Journal of the American Statistical Association, 116(533): 68-81, March, (2021).
  • Media Bias in China (with Bei Qin and David Strӧmberg), American Economic Review, 108(9): 2442-76, September, (2018).
  • Incentive Contracts and the Allocation of Talent, Economic Journal, 127(607): 2744-2783, December, (2017).
  • Authority, Incentives and Performance: Evidence from a Chinese Newspaper, Review of Economics and Statistics, 99(1): 16-31, March, (2017).
  • Why Does China Allow Freer Social Media? Protests versus Surveillance and Propaganda (with Bei Qin and David Strӧmberg), Journal of Economic Perspectives, 31(1): 117-40 (2017).
  • Organizational Structure and Product Choice in Knowledge Intensive Firms, Management Science, 61(8): 1830-1848 (2015).
  • Knowledge, Communication and Organizational Capabilities (with Luis Garicano), Organization Science, 23(5):1382-1397 (2012).
Awards and Honours
  • European Research Council Advanced Grant, Co-PI, 2018-2022
  • USC Marshall Outlier Research Fund, PI, 2016-2018
  • USC Greif Entrepreneurship Research Award, 2014
  • Vincent Cheng Scholarship (LSE), 2007-2010
  • Chevening Scholarship (British Council), 2003-2004
Recent Publications
How should Hong Kong explore GBA’s unique I&T opportunities?

正當全國經濟實力雄厚的城市紛紛招賢納士、擴張高等院校、興辦科技園之際,筆者不禁想起自1999年起,在深圳舉辦的中國國際高新技術成果交易會,那本地、海外的科研人才蜂擁而至,濟濟一堂的盛況。深圳至今發展成首屈一指的科技大城,香港在科技產業的發展瞠乎其後。面臨新一波創新及科技熱潮,如何後來居上,甚至脫穎而出,無疑是本港未來經濟發展的一個迫切問題。

How should Hong Kong explore GBA’s unique I&T opportunities?

正當全國經濟實力雄厚的城市紛紛招賢納士、擴張高等院校、興辦科技園之際,筆者不禁想起自1999年起,在深圳舉辦的中國國際高新技術成果交易會,那本地、海外的科研人才蜂擁而至,濟濟一堂的盛況。深圳至今發展成首屈一指的科技大城,香港在科技產業的發展瞠乎其後。面臨新一波創新及科技熱潮,如何後來居上,甚至脫穎而出,無疑是本港未來經濟發展的一個迫切問題。

Speaking to Prominent Social Problems in the Changing World – Dr. Yanhui WU

Born and raised in Guangdong, Dr. Yanhui Wu always has a special attachment to Hong Kong. Joining us in January 2020 as an Associate Professor in Economics and Management and Strategy, Dr. Wu sees it as a great opportunity to contribute his academic intellect for our betterment and progression.

Domestic integration in economy, global search for talent

筆者三個月前在本欄撰文,倡議「化西入中」的港式教育,吸引中國內地學生來港求學,從而促進香港和內地的人才流動,助推粵港澳大灣區經濟融合。業界一些朋友看了此文,頗感興趣,致電商討。一位朋友特別前來香港大學,跟筆者探討教育和人才問題。寒暄幾句後,他便單刀直入:「你提出的觀點,無非是把香港當作內地人才加工的基地,然後走內銷的道路。這對你們搞教育的當然是一盤好生意,但對香港又有什麼『着數』呢?」此問乍聽突兀,細想卻值得思辨一番。

Domestic integration in economy, global search for talent

筆者三個月前在本欄撰文,倡議「化西入中」的港式教育,吸引中國內地學生來港求學,從而促進香港和內地的人才流動,助推粵港澳大灣區經濟融合。業界一些朋友看了此文,頗感興趣,致電商討。一位朋友特別前來香港大學,跟筆者探討教育和人才問題。寒暄幾句後,他便單刀直入:「你提出的觀點,無非是把香港當作內地人才加工的基地,然後走內銷的道路。這對你們搞教育的當然是一盤好生意,但對香港又有什麼『着數』呢?」此問乍聽突兀,細想卻值得思辨一番。

Hong Kong’s role in Greater Bay Area’s talent development

去年夏天筆者剛從洛杉磯移居到香港這座闊別多年的城市,就接到所屬經管學院通知,因學生報名人數突然幾近翻倍,需多教一門應用計量經濟學的碩士課程。2019冠狀病毒病疫情下,香港特區的大學還能增產擴容,實屬難得。

Hong Kong’s role in Greater Bay Area’s talent development

去年夏天筆者剛從洛杉磯移居到香港這座闊別多年的城市,就接到所屬經管學院通知,因學生報名人數突然幾近翻倍,需多教一門應用計量經濟學的碩士課程。2019冠狀病毒病疫情下,香港特區的大學還能增產擴容,實屬難得。

Intentional Control of Type I Error Over Unconscious Data Distortion: A Neyman–Pearson Approach to Text Classification

This article addresses the challenges in classifying textual data obtained from open online platforms, which are vulnerable to distortion. Most existing classification methods minimize the overall classification error and may yield an undesirably large Type I error (relevant textual messages are classified as irrelevant), particularly when available data exhibit an asymmetry between relevant and irrelevant information. Data distortion exacerbates this situation and often leads to fallacious prediction. To deal with inestimable data distortion, we propose the use of the Neyman–Pearson (NP) classification paradigm, which minimizes Type II error under a user-specified Type I error constraint. Theoretically, we show that the NP oracle is unaffected by data distortion when the class conditional distributions remain the same. Empirically, we study a case of classifying posts about worker strikes obtained from a leading Chinese microblogging platform, which are frequently prone to extensive, unpredictable and inestimable censorship. We demonstrate that, even though the training and test data are susceptible to different distortion and therefore potentially follow different distributions, our proposed NP methods control the Type I error on test data at the targeted level. The methods and implementation pipeline proposed in our case study are applicable to many other problems involving data distortion. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.