The Faculty of Social Sciences (FSS) is holding a series of Computational Social Sciences Workshop. We have the pleasure to invite Prof. Han ZHANG to deliver the fifth seminar on “Robots and protest: does increased protest among Chinese workers result in more automation?”. Details of the Workshop are as follows:

Date: 20 April 2023 (Thursday)
Time: 12:30 – 14:00
Venue: E21B-G016
Language: English

Please register by 19/04/2023 (Wednesday) 1pm:

https://forms.gle/yHaC5rkVyLRwy8bL6

Speaker: Prof. ZHANG obtained his Ph.D. in Sociology at Princeton University in 2020, under the supervision of Matthew Salganik, and B.S. in Computer Science and B.A. in History, 2013, from Peking University. His research spans computational social science, social movements, social networks, and quantitative methods. Recent substantive projects include Protests and their causes and consequences; Global surveillance and its social impact. His recent methodological projects include Image as data, Robust use of machine learning algorithms in social science research, and Graph neural network and survey data.

Abstract: The rising level of automation has increasingly attracted scholars’ attention. On the other hand, there are many studies of the consequences of social movements, but relatively fewer studies focus on their economic consequences, and even fewer studies have examined their consequences on automation. This article bridges the gap between the two literatures by hypothesizing that a rising number of labor protests will lead to a higher level of automation. We argue that political economy factors influence the adoption of more automation. Protests anticipate higher wages and labor costs; contest for social power with employers and the state, and, in extreme cases, pose a public relations challenge to employers, which will likely push employers to replace human workers with robots. We empirically test the relationship by using two protest event datasets in China, the China Labor Bulletin and Collective Action from Social Media and robot data from the International Federation of Robotics. Statistical analysis shows that provinces and industries that have more protests also tend to concentrate more robots, and the results are robust to most specifications and placebo tests. The findings have implications for both understanding the mechanism explaining rising automation and the consequences of social protests.

The Faculty of Social Sciences (FSS) is holding a series of Computational Social Sciences Workshop. We have the pleasure to invite Prof. Han ZHANG to deliver the fifth seminar on “Robots and protest: does increased protest among Chinese workers result in more automation?”. Details of the Workshop are as follows:

Date: 20 April 2023 (Thursday)
Time: 12:30 – 14:00
Venue: E21B-G016
Language: English

Please register by 19/04/2023 (Wednesday) 1pm:

https://forms.gle/yHaC5rkVyLRwy8bL6

Speaker: Prof. ZHANG obtained his Ph.D. in Sociology at Princeton University in 2020, under the supervision of Matthew Salganik, and B.S. in Computer Science and B.A. in History, 2013, from Peking University. His research spans computational social science, social movements, social networks, and quantitative methods. Recent substantive projects include Protests and their causes and consequences; Global surveillance and its social impact. His recent methodological projects include Image as data, Robust use of machine learning algorithms in social science research, and Graph neural network and survey data.

Abstract: The rising level of automation has increasingly attracted scholars’ attention. On the other hand, there are many studies of the consequences of social movements, but relatively fewer studies focus on their economic consequences, and even fewer studies have examined their consequences on automation. This article bridges the gap between the two literatures by hypothesizing that a rising number of labor protests will lead to a higher level of automation. We argue that political economy factors influence the adoption of more automation. Protests anticipate higher wages and labor costs; contest for social power with employers and the state, and, in extreme cases, pose a public relations challenge to employers, which will likely push employers to replace human workers with robots. We empirically test the relationship by using two protest event datasets in China, the China Labor Bulletin and Collective Action from Social Media and robot data from the International Federation of Robotics. Statistical analysis shows that provinces and industries that have more protests also tend to concentrate more robots, and the results are robust to most specifications and placebo tests. The findings have implications for both understanding the mechanism explaining rising automation and the consequences of social protests.

The Faculty of Social Sciences (FSS) is holding a series of Computational Social Sciences Workshop. We have the pleasure to invite Prof. Haipeng SHEN to deliver the fourth seminar on “Testing and Support Recovery of Correlation Structures for Matrix-Valued Observations with an Application to Stock Market Data”. Details of the Workshop are as follows:

Date: 28 Mar 2023 (Tues)
Time: 12:30 – 14:00
Venue: E21B-G016
Language: English

Please register by 27/03/2023 (Monday) 1pm:

https://forms.gle/HdGUmzfpniqdQTor6

Speaker: Prof. Haipeng SHEN obtained his PhD in Statistics from University of Pennsylvania in 2003. His research focuses on Data-driven decision making in the face of uncertainty: big data, business analytics, healthcare analytics and service engineering. He joined HKU in 2015 as a Professor of Innovation and Information Management. Before joining HKU, he was a Professor of Statistics and Operations Research, University of North Carolina at Chapel Hill, USA.

Abstract: Estimation of the covariance matrix of asset returns is crucial to portfolio construction. As suggested by economic theories, the correlation structure among assets differs between emerging markets and developed countries. It is therefore imperative to make rigorous statistical inference on correlation matrix equality between the two groups of countries. However, if the traditional vector-valued approach is undertaken, such inference is either infeasible due to limited number of countries comparing to the relatively abundant assets, or invalid due to the violations of temporal independence assumption. This highlights the necessity of treating the observations as matrix-valued rather than vector-valued. With matrix-valued observations, our problem of interest can be formulated as statistical inference on covariance structures under sub-Gaussian distributions, i.e., testing non-correlation and correlation equality, as well as the corresponding support estimations. We develop procedures that are asymptotically optimal under some regularity conditions. Simulation results demonstrate the computational and statistical advantages of our procedures over certain existing state-of-the-art methods for both normal and non-normal distributions. Application of our procedures to stock market data reveals interesting patterns and validates several economic propositions via rigorous statistical testing.