IDEAS home Printed from https://ideas.repec.org/a/spr/soinre/v174y2024i3d10.1007_s11205-024-03416-6.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11205-024-03416-6
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11205-024-03416-6?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. 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).
    3. 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).
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Qiaoqiao Zhan & Katsunori Furuya & Xiaolan Tang & Zhehui Li, 2024. "Policy Development in China’s Protected Scenic and Historic Areas," Land, MDPI, vol. 13(2), pages 1-24, February.
    3. Yingkai Yin & Hongxin Ma & Zhenni Wu & Aobo Yue, 2023. "How Does China Build Its Fintech Strategy? A Perspective of Policy Evolution," Sustainability, MDPI, vol. 15(13), pages 1-21, June.
    4. Ba, Zhichao & Ma, Yaxue & Cai, Jinyao & Li, Gang, 2023. "A citation-based research framework for exploring policy diffusion: Evidence from China's new energy policies," Technological Forecasting and Social Change, Elsevier, vol. 188(C).
    5. Tutz, Gerhard & Pößnecker, Wolfgang & Uhlmann, Lorenz, 2015. "Variable selection in general multinomial logit models," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 207-222.
    6. Viet Hoang Dinh & Didier Nibbering & Benjamin Wong, 2023. "Random Subspace Local Projections," CAMA Working Papers 2023-34, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    7. Ernesto Carrella & Richard M. Bailey & Jens Koed Madsen, 2018. "Indirect inference through prediction," Papers 1807.01579, arXiv.org.
    8. Rui Wang & Naihua Xiu & Kim-Chuan Toh, 2021. "Subspace quadratic regularization method for group sparse multinomial logistic regression," Computational Optimization and Applications, Springer, vol. 79(3), pages 531-559, July.
    9. Mkhadri, Abdallah & Ouhourane, Mohamed, 2013. "An extended variable inclusion and shrinkage algorithm for correlated variables," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 631-644.
    10. Masakazu Higuchi & Mitsuteru Nakamura & Shuji Shinohara & Yasuhiro Omiya & Takeshi Takano & Daisuke Mizuguchi & Noriaki Sonota & Hiroyuki Toda & Taku Saito & Mirai So & Eiji Takayama & Hiroo Terashi &, 2022. "Detection of Major Depressive Disorder Based on a Combination of Voice Features: An Exploratory Approach," IJERPH, MDPI, vol. 19(18), pages 1-13, September.
    11. Susan Athey & Guido W. Imbens & Stefan Wager, 2018. "Approximate residual balancing: debiased inference of average treatment effects in high dimensions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 80(4), pages 597-623, September.
    12. Vincent, Martin & Hansen, Niels Richard, 2014. "Sparse group lasso and high dimensional multinomial classification," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 771-786.
    13. Chen, Le-Yu & Lee, Sokbae, 2018. "Best subset binary prediction," Journal of Econometrics, Elsevier, vol. 206(1), pages 39-56.
    14. Perrot-Dockès Marie & Lévy-Leduc Céline & Chiquet Julien & Sansonnet Laure & Brégère Margaux & Étienne Marie-Pierre & Robin Stéphane & Genta-Jouve Grégory, 2018. "A variable selection approach in the multivariate linear model: an application to LC-MS metabolomics data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 17(5), pages 1-14, October.
    15. Fan, Jianqing & Jiang, Bai & Sun, Qiang, 2022. "Bayesian factor-adjusted sparse regression," Journal of Econometrics, Elsevier, vol. 230(1), pages 3-19.
    16. Chuliá, Helena & Garrón, Ignacio & Uribe, Jorge M., 2024. "Daily growth at risk: Financial or real drivers? The answer is not always the same," International Journal of Forecasting, Elsevier, vol. 40(2), pages 762-776.
    17. Jun Li & Serguei Netessine & Sergei Koulayev, 2018. "Price to Compete … with Many: How to Identify Price Competition in High-Dimensional Space," Management Science, INFORMS, vol. 64(9), pages 4118-4136, September.
    18. Sung Jae Jun & Sokbae Lee, 2024. "Causal Inference Under Outcome-Based Sampling with Monotonicity Assumptions," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(3), pages 998-1009, July.
    19. Rina Friedberg & Julie Tibshirani & Susan Athey & Stefan Wager, 2018. "Local Linear Forests," Papers 1807.11408, arXiv.org, revised Sep 2020.
    20. Juan Luo & Chong Xu & Boyu Yang & Xiaoyu Chen & Yinyin Wu, 2022. "Quantitative Analysis of China’s Carbon Emissions Trading Policies: Perspectives of Policy Content Validity and Carbon Emissions Reduction Effect," Energies, MDPI, vol. 15(14), pages 1-20, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:soinre:v:174:y:2024:i:3:d:10.1007_s11205-024-03416-6. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.