Key Determinants of Think Tank Attention in China

智库议题注意力分配的驱动因素研究

Authors

  • Kong Yuan School of Public Policy and Management, Tsinghua University 收稿
  • Zhu Xufeng School of Public Policy and Management, Tsinghua University

Keywords:

Think tank issue attention, Policy knowledge market, Supply–demand framework, LDA topic modeling, China’s policy research institutions

Abstract

What factors determine the attention allocation of think tanks? The policy knowledge market in China is currently experiencing rapid development. Think tanks are witnessing substantial growth in terms of institutional size and research output, accompanied by a noticeable surge in knowledge demands from both the government and the public. The widespread adoption of social media has also further bridged the information gap between the supply and demand sides. In this evolving market landscape, the allocation of attention to policy issues has become a pivotal decision-making process for think tanks. Efficiently distributing limited resources across a spectrum of policy issues has a profound impact on the sustainability of think tanks. This article introduces a supply-demand framework to elucidate how think tanks allocate attention to policy issues. In a market environment, think tanks determine the policy issues they focus on, not solely based on their supply-side characteristics but, more importantly, by being responsive to dynamic shifts in external policy knowledge demands. The government and the public, as core actors within the policy system, directly influence think tanks' allocation of attention through their demand signals. We have constructed a novel database to measure think tanks' issue attention in China from 2016 to 2020 by utilizing social media data and text classification algorithms. Leveraging the vast amount of objective data from think tanks on social media platforms in China, we have conducted data collection, cleansing, and analysis over a five-year period. This has resulted in a comprehensive dataset comprising over 230,000 posts published by 204 Chinese think tanks between 2016 and 2020, with a total character count exceeding 250 million. To identify the policy issues that think tanks pay attention to, we have employed the unsupervised topic model, Latent Dirichlet Allocation (LDA), effectively capturing the changes in attention across 25 policy topics within the Chinese think tank community from 2016 to 2020. Subsequently, we have developed an innovative empirical framework that integrates multiple data sources to test the theoretical framework and research hypotheses outlined above. This study collects and combines diverse data sources, including State Council executive meetings and Baidu Index. This research reveals the multifaceted factors influencing think tanks' issue attention, stemming from both supply and demand dynamics. The rapid emergence and growth of the policy knowledge market not only drive think tanks towards professionalization but also underscore the importance of their ability to sense external environmental changes and optimize resource allocation across various policy issues. Think tanks in China are increasingly recognizing the importance of being agile in responding to evolving policy knowledge demands to maintain their relevance and effectiveness. The theoretical contribution of this study primarily lies in several aspects. Concerning think tank research, this paper systematically discusses the driving factors in the policy knowledge production process of think tanks. In the context of issue attention research, this paper expands the scope of issue attention. Last but not least, this study's findings provide valuable insights for policymakers, think tanks, and researchers seeking to understand the evolving dynamics of the policy knowledge market in China.

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Published

2025-05-29

Issue

Section

Research Article ○ Abstract Only

How to Cite

Yuan, K., & Xufeng, Z. (2025). Key Determinants of Think Tank Attention in China: 智库议题注意力分配的驱动因素研究. Studies in Science of Science, 43(5), 334-347. https://casscience.cn/siss/article/view/83

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