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Machine Learning-Facilitated Policy Intensity Analysis: A Proposed Procedure and Its Application

Author

Listed:
  • Su Xie

    (Huazhong Agricultural University
    Digital Agriculture Research Institute, Huazhong Agricultural University)

  • Hang Xiong

    (Huazhong Agricultural University
    Digital Agriculture Research Institute, Huazhong Agricultural University)

  • Linmei Shang

    (University of Bonn)

  • Yong Bao

    (Purdue University)

Abstract

Policy intensity is a crucial determinant of policy effectiveness. Analysis of policy intensity can serve as a basis for policy impact evaluation and enable policymakers to make necessary adjustments. Previous studies relied on manual scoring and mainly addressed specialized policies with limited numbers of texts. However, when dealing with text-rich policies, the method inevitably introduced bias and was time-consuming. In this paper, we propose a procedure facilitated by machine learning to analyze the intensity of not only specified but also comprehensive policies with large amounts of texts. Our machine learning-based approach assigns scores to the policy measure dimension, then cross-multiplies with two other dimensions, policy title and document type, to calculate intensity. The efficacy of our approach was demonstrated through a case study of China’s environmental policies for livestock and poultry husbandry, which showed improved performance over traditional methods in terms of efficiency and objectivity.

Suggested Citation

  • Su Xie & Hang Xiong & Linmei Shang & Yong Bao, 2024. "Machine Learning-Facilitated Policy Intensity Analysis: A Proposed Procedure and Its Application," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 174(3), pages 881-904, September.
    • Handle: RePEc:spr:soinre:v:174:y:2024:i:3:d:10.1007_s11205-024-03416-6
    DOI: 10.1007/s11205-024-03416-6
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    References listed on IDEAS

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    1. Ruyin Long & Wenhua Cui & Qianwen Li, 2017. "The Evolution and Effect Evaluation of Photovoltaic Industry Policy in China," Sustainability, MDPI, vol. 9(12), pages 1-40, November.
    2. Shuzhong Ma & Jiwen Guo & Hongsheng Zhang, 2019. "Policy Analysis and Development Evaluation of Digital Trade: An International Comparison," China & World Economy, Institute of World Economics and Politics, Chinese Academy of Social Sciences, vol. 27(3), pages 49-75, May.
    3. Youzhu Li & Rui He & Jinsi Liu & Chongguang Li & Jason Xiong, 2021. "Quantitative Evaluation of China’s Pork Industry Policy: A PMC Index Model Approach," Agriculture, MDPI, vol. 11(2), pages 1-21, January.
    4. Libecap, Gary D., 1978. "Economic Variables and the Development of the Law: The Case of Western Mineral Rights," The Journal of Economic History, Cambridge University Press, vol. 38(2), pages 338-362, June.
    5. Kong, Yuan & Feng, Chao & Yang, Jun, 2020. "How does China manage its energy market? A perspective of policy evolution," Energy Policy, Elsevier, vol. 147(C).
    6. Begoña Elizalde-San Miguel & Vicente Díaz Gandasegui & Maria T. Sanz García, 2019. "Family Policy Index: A Tool for Policy Makers to Increase the Effectiveness of Family Policies," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 142(1), pages 387-409, February.
    7. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    8. Junseop Shim & Chisung Park & Mark Wilding, 2015. "Identifying policy frames through semantic network analysis: an examination of nuclear energy policy across six countries," Policy Sciences, Springer;Society of Policy Sciences, vol. 48(1), pages 51-83, March.
    9. Zhang, Guoxing & Deng, Nana & Mou, Haizhen & Zhang, Zhe George & Chen, Xiaofeng, 2019. "The impact of the policy and behavior of public participation on environmental governance performance: Empirical analysis based on provincial panel data in China," Energy Policy, Elsevier, vol. 129(C), pages 1347-1354.
    10. Jorge Garcés Ferrer & Francisco Ródenas Rigla & Carla Vidal Figueroa, 2016. "Application of Social Policy Index (SPI) Amended in Three OECD Countries: Finland, Spain and Mexico," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 127(2), pages 529-539, June.
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