IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0301679.html
   My bibliography  Save this article

Spatial and temporal differences and convergence analysis of multidimensional relative poverty in ethnic areas

Author

Listed:
  • Jing Cheng
  • Xiaobin Yu

Abstract

Reducing multidimensional relative poverty is one of the important issues in the current global poverty governance field. This article takes 12 ethnic regions in China as the research object and constructs a multidimensional relative poverty measurement system. The calculated multidimensional relative poverty index is decomposed according to provinces, cities, dimensions, and indicators. Then, the Dagum Gini coefficient and convergence analysis are used to analyze spatiotemporal heterogeneity and convergence characteristics. The results show that the multi-dimensional relative poverty situation of various provinces in ethnic minority areas has improved from 2012 to 2021, among which Tibet province is the most serious and Shaanxi is the best. According to the analysis of convergence, it was observed that there is no σ-convergence of multidimensional relative poverty in ethnic areas in general, and there is absolute β-convergence in general and in the southwest and northwest regions, and there is no absolute β-convergence in the northeast region. Based on this, policy recommendations for reducing multidimensional relative poverty are proposed at the end of the article. Compared with previous studies, this article focuses on ethnic regions that are easily overlooked. Starting from the dimensions of economy, social development, and ecological environment, the poverty measurement system has been enriched.

Suggested Citation

  • Jing Cheng & Xiaobin Yu, 2024. "Spatial and temporal differences and convergence analysis of multidimensional relative poverty in ethnic areas," PLOS ONE, Public Library of Science, vol. 19(4), pages 1-20, April.
    • Handle: RePEc:plo:pone00:0301679
    DOI: 10.1371/journal.pone.0301679
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0301679
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0301679&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0301679?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
    ---><---

    References listed on IDEAS

    as
    1. Wei Zou & Xiaopei Cheng & Zengzeng Fan & Wenxi Yin, 2023. "Multidimensional Relative Poverty in China: Identification and Decomposition," Sustainability, MDPI, vol. 15(6), pages 1-27, March.
    2. Wang, Yao & Lin, Boqiang, 2022. "Can energy poverty be alleviated by targeting the low income? Constructing a multidimensional energy poverty index in China," Applied Energy, Elsevier, vol. 321(C).
    3. Amit K. Chattopadhyay & T. Krishna Kumar & Iain Rice, 2020. "A social engineering model for poverty alleviation," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
    4. Vollmer, Frank & Alkire, Sabina, 2022. "Consolidating and improving the assets indicator in the global Multidimensional Poverty Index," World Development, Elsevier, vol. 158(C).
    5. Pasha, Atika, 2017. "Regional Perspectives on the Multidimensional Poverty Index," World Development, Elsevier, vol. 94(C), pages 268-285.
    6. Wang, Jinxian & Wang, Chen & Li, Sihao & Luo, Zhi, 2021. "Measurement of relative welfare poverty and its impact on happiness in China: Evidence from CGSS," China Economic Review, Elsevier, vol. 69(C).
    7. Spandagos, Constantine & Tovar Reaños, Miguel & Lynch, Muireann Á, 2023. "Energy poverty prediction and effective targeting for just transitions with machine learning," Papers WP762, Economic and Social Research Institute (ESRI).
    8. Spandagos, Constantine & Tovar Reaños, Miguel Angel & Lynch, Muireann Á., 2023. "Energy poverty prediction and effective targeting for just transitions with machine learning," Energy Economics, Elsevier, vol. 128(C).
    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. Yasmeen, Rizwana & Shah, Wasi Ul Hassan, 2025. "Impact of business cycles on energy poverty: Exploring the significance with sustainable development goals in newly industrialized economies," Applied Energy, Elsevier, vol. 378(PA).
    2. Li, Jiajia & Yang, Shiyu & Li, Jun & Li, Houjian, 2024. "Targeting SDG7: Identifying heterogeneous energy dilemmas for socially disadvantaged groups in India using machine learning," Energy Economics, Elsevier, vol. 138(C).
    3. Budría, Santiago & Bravo Chew, Leslie, 2025. "Enduring Inequalities: Analyzing Energy Poverty Inertia Across K-Means Clusters," IZA Discussion Papers 17809, Institute of Labor Economics (IZA).
    4. Xie, Li & Kong, Chun, 2024. "A fair grid connection cost-sharing model for electricity based on the random forest machine learning method," Utilities Policy, Elsevier, vol. 90(C).
    5. Zhang, Xiaojing & Khan, Khalid & Shao, Xuefeng & Oprean-Stan, Camelia & Zhang, Qian, 2024. "The rising role of artificial intelligence in renewable energy development in China," Energy Economics, Elsevier, vol. 132(C).
    6. Urszula Grzybowska & Agnieszka Wojewódzka-Wiewiórska & Gintarė Vaznonienė & Hanna Dudek, 2024. "Households Vulnerable to Energy Poverty in the Visegrad Group Countries: An Analysis of Socio-Economic Factors Using a Machine Learning Approach," Energies, MDPI, vol. 17(24), pages 1-23, December.
    7. Soto, Gonzalo H & Martinez-Cobas, Xavier, 2024. "Green energy policies and energy poverty in Europe: Assessing low carbon dependency and energy productivity," Energy Economics, Elsevier, vol. 136(C).
    8. Song, Malin & Pan, Heting & Shen, Zhiyang & Tamayo-Verleene, Kristine, 2024. "Assessing the influence of artificial intelligence on the energy efficiency for sustainable ecological products value," Energy Economics, Elsevier, vol. 131(C).
    9. Romel Ramón González-Díaz & Ángel Acevedo-Duque & Guido Salazar-Sepúlveda & Dante Castillo, 2021. "Contributions of Subjective Well-Being and Good Living to the Contemporary Development of the Notion of Sustainable Human Development," Sustainability, MDPI, vol. 13(6), pages 1-17, March.
    10. Dutta, Indranil & Nogales, Ricardo & Yalonetzky, Gaston, 2021. "Endogenous weights and multidimensional poverty: A cautionary tale," Journal of Development Economics, Elsevier, vol. 151(C).
    11. Gao, Yuan & Yu, Lu, 2024. "Understanding the impacts of ecological compensation policies on energy poverty: insights from forest communities in Zhejiang, China," Land Use Policy, Elsevier, vol. 142(C).
    12. Sam Jones, 2019. "Counting-based multidimensional poverty identification: From deprivation weights to bundles," WIDER Working Paper Series wp-2019-55, World Institute for Development Economic Research (UNU-WIDER).
    13. Yiyan Chen & Zhaoyun Tang, 2023. "A Study of Multidimensional and Persistent Poverty among Migrant Workers: Evidence from China’s CFPS 2014–2020," Sustainability, MDPI, vol. 15(10), pages 1-15, May.
    14. Hector Najera & Delfino Vargas & Fernando Cortés, 2025. "The scope of consensual deprivation indices for Mexico: reliability, criterion validity and invariance analyses," Quality & Quantity: International Journal of Methodology, Springer, vol. 59(1), pages 711-737, February.
    15. Sam Jones, 2022. "Extending multidimensional poverty identification: from additive weights to minimal bundles," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 20(2), pages 421-438, June.
    16. Ren, Zhiyuan & Zhu, Yuhan & Jin, Canyang & Xu, Aiting, 2023. "Social capital and energy poverty: Empirical evidence from China," Energy, Elsevier, vol. 267(C).
    17. Tamisai Chipunza & Lungile Ntsalaze, 2025. "Multi-dimensional poverty: a bibliometric analysis and content co-occurrence literature review," Palgrave Communications, Palgrave Macmillan, vol. 12(1), pages 1-12, December.
    18. Xia, Fangzhou & Huang, Jing & Zhang, Zhengfeng, 2024. "Government concerns, the benefit cliff, and land use: A comparative study of rural impoverished and marginalised impoverished groups," Land Use Policy, Elsevier, vol. 140(C).
    19. Yao Wang & Qiang Yang & Xuenan Wu & Ruichen Wang & Tilei Gao & Yuntong Liu, 2023. "A Study of Trends in Low-Energy Development Patterns in China: A Data-Driven Approach," Sustainability, MDPI, vol. 15(13), pages 1-18, June.
    20. Tang, Kai & Li, Zhenshan & He, Chun, 2023. "Spatial distribution pattern and influencing factors of relative poverty in rural China," Innovation and Green Development, Elsevier, vol. 2(1).

    More about this item

    Statistics

    Access and download statistics

    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:plo:pone00:0301679. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.