Welcome to my website!
My name is Shiyang Xiao. I am a Ph.D. candidate in the Maxwell School of Citizenship and Public Affairs at Syracuse University, advised by Professor Yilin Hou. I am on the academic job market for 2023-24.
My research interests span the fields of public administration, policy process, governance and bureaucracy, intergovernmental relations, and public budgeting and finance. Methodologically, I have a strong interest and experience in applying big data and computational text analysis methods to social science research. My work has been published in peer-reviewed journals, including Journal of Public Administration Research and Theory, Public Budgeting & Finance, Socius: Sociological Research for a Dynamic World, and the online column at China Public Administration Review.
I constructed the “Chinese Industrial Policy Attention Dataset” (CIPAD) which holds over 2500 Chinese industrial policies from 2001 to 2019, along with their allocation of attention across 155 manufacturing industry categories. This dataset allows for quick identification of the most frequently mentioned industry categories in each policy and comparison of similarities between policies.
If you have any question, feel free to email me at sxiao11@syr.edu.
- Public Administration
- Policy Process
- Governance and Bureaucracy
- Intergovernmental Relations
- Public Budgeting and Finance
- Computational Social Science
Ph.D. in Social Science, 2024 (Expected)
Syracuse University
M.A. in Public Policy and Management, 2017
Tsinghua University
B.A. in Philosophy and Economics, 2015
Peking University
Peer-Reviewed Publications
[1] Chen, C., Xiao, S., & Zhao, B. (2023). Machine Learning Meets the Journal of Public Budgeting & Finance: Topics and Trends Over 40 Years. Public Budgeting & Finance, 43(4): 3-23.
[2] Xiao, S., & Zhu, X. (2022). Bureaucratic Control and Strategic Compliance: How Do Subnational Governments Implement Central Guidelines in China? Journal of Public Administration Research and Theory, 32(2), 342-359.
[3] Ma, Y., & Xiao, S. (2021). Math and Science Identity Change and Paths into and out of STEM: Gender and Racial Disparities. Socius, 7.
[4] Xiao, S. (2020). Multi-Centre Collaborative Governance Network For Epidemic Prevention and Control. China Public Administration Review "Frontline Observation on the Fight against Covid-19" Online Column.
Book Chapter
[1] Zhang, F., & Xiao, S. (2016). The U.S. Government Fiscal and Debt Crisis: Lessons for China. Beijing: Peking University Press.
Data
Chinese Industrial Policy Attention Dataset (CIPAD)
"Chinese Industrial Policy Attention Dataset" (CIPAD) is an original dataset which contains 612 central-level and 1907 provincial-level Chinese industrial policies from 2001 to 2019, along with their allocation of attention across 155 finely segmented manufacturing industry categories.
A novel design of the CIPAD is that, by using computational text analysis techniques, full text of each industrial policy is transformed into a distribution-of-attention vector. Each vector describes the attention allocation of an industrial policy to the 155 industry categories in manufacturing sector.
By transforming each policy full text into a distribution-of-attention vector, the CIPAD allows researchers to accomplish the following tasks: 1) Quickly identify the most frequently mentioned industry categories in each policy; 2) compare the similarities of targeted industry categories between policies; 3) track changes in government’s attention to different industry categories over time; 4) compare similarities of policy attention between different governments. Below I will demonstrate how the CIPAD helps researchers to achieve the above tasks.
If you want to learn more about the CIPAD, please check the Dataset Introduction here.
Teaching
Teaching Assistant
[1] Summer 2019, Public Policy and Good Governance Program, Teaching Assistant
Maxwell School of Citizenship and Public Affairs, Syracuse University
[2] Spring 2017, Development Strategy and Planning, Teaching Assistant
School of Public Policy & Management, Tsinghua University
Guest Lectures
[1] 2021, “Big Data Methods in Social Science Research”
Hamilton College
[2] 2021, “2021, Two-Day Lecture on “Application of Big Data Methods in Social Science Research: Take Computational Text Analysis as an Example”
School of Public Policy & Management, Tsinghua University