Ph.D. in Economics, London School of Economics
MSc. in Economics, London School of Economics
B.A. in Economics, Sun Yat-sen (Zhongshan) University
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.
- Economics of Organization and Strategy (MEcon)
- Introduction to Causal Inference and Machine Learning (undergraduate)
- Media Economics, Organizational Economics, Development Economics, Chinese Economy, Big Data and Computational Social Science
- 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).
- 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
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.
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.