IDEAS home Printed from https://ideas.repec.org/a/spr/soinre/v130y2017i2d10.1007_s11205-015-1190-4.html
   My bibliography  Save this article

A PPI-MVM Model for Identifying Poverty-Stricken Villages: A Case Study from Qianjiang District in Chongqing, China

Author

Listed:
  • Yanhui Wang

    (Capital Normal University
    Louisiana State University
    Capital Normal University)

  • Leyi Qian

    (Capital Normal University
    Capital Normal University)

Abstract

To support China’s national poverty alleviation strategies, it is urgent to develop a scientific method for identifying the poverty-stricken villages and the contributing factors. Based on the anti-poverty plan of “Entire-Village Advancement” of China and the human-environment interaction perspective, the paper proposes a participatory poverty identification model that utilizes geographic information system to quantify and integrate various contributing factors for poverty at the village level. First, a set of poverty identification factors are determined from the human-environment interaction perspective. Secondly, the game theory is used to combine the participatory subjective weight method and the objective entropy method to weight the factors, and a participatory poverty identification with minimum variance model is developed to identify the poverty-stricken villages and their contributing factors. Finally, the model is applied to Qianjiang District in Chongqing, and the case study demonstrates the effectiveness of the model. The model not only identifies the poverty-stricken villages systematically but also helps guide policies for effective poverty interventions.

Suggested Citation

  • Yanhui Wang & Leyi Qian, 2017. "A PPI-MVM Model for Identifying Poverty-Stricken Villages: A Case Study from Qianjiang District in Chongqing, China," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 130(2), pages 497-522, January.
    • Handle: RePEc:spr:soinre:v:130:y:2017:i:2:d:10.1007_s11205-015-1190-4
    DOI: 10.1007/s11205-015-1190-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11205-015-1190-4
    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/s11205-015-1190-4?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. Luc J. Christiaensen & Kalanidhi Subbarao, 2005. "Towards an Understanding of Household Vulnerability in Rural Kenya," Journal of African Economies, Centre for the Study of African Economies, vol. 14(4), pages 520-558, December.
    2. Dariush Hayati & Ezatollah Karami & Bill Slee, 2006. "Combining Qualitative and Quantitative Methods in the Measurement of Rural Poverty: The Case of Iran," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 75(3), pages 361-394, February.
    3. Hentschel, Jesko, et al, 2000. "Combining Census and Survey Data to Trace the Spatial Dimensions of Poverty: A Case Study of Ecuador," The World Bank Economic Review, World Bank, vol. 14(1), pages 147-165, January.
    4. Bejoy Thomas & Roldan Muradian & Gerard De Groot & Arie De Ruijter, 2009. "Multidimensional Poverty and Identification of Poor Households: A Case from Kerala, India," Journal of Human Development and Capabilities, Taylor & Francis Journals, vol. 10(2), pages 237-257.
    5. Islam, Asadul & Maitra, Pushkar, 2012. "Health shocks and consumption smoothing in rural households: Does microcredit have a role to play?," Journal of Development Economics, Elsevier, vol. 97(2), pages 232-243.
    6. Yuan Zhang & Guanghua Wan, 2006. "An Empirical Analysis of Household Vulnerability in Rural China," Journal of the Asia Pacific Economy, Taylor & Francis Journals, vol. 11(2), pages 196-212.
    7. Minot, Nicholas & Baulch, Bob & Epperecht, Michael, 2006. "Poverty and inequality in Vietnam: spatial patterns and geographic determinants," Research reports 148, International Food Policy Research Institute (IFPRI).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yanhui Wang & Wenping Qi, 2021. "Multidimensional spatiotemporal evolution detection on China’s rural poverty alleviation," Journal of Geographical Systems, Springer, vol. 23(1), pages 63-96, January.
    2. Yanhui Wang & Jianchen Zhang, 2018. "Integrating BP and MGWR-SL Model to Estimate Village-Level Poor Population: An Experimental Study from Qianjiang, China," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 138(2), pages 639-663, July.
    3. Zhixi Xu & Zhongliang Cai & Shufan Wu & Xinran Huang & Ji Liu & Junying Sun & Shiliang Su & Min Weng, 2019. "Identifying the Geographic Indicators of Poverty Using Geographically Weighted Regression: A Case Study from Qiandongnan Miao and Dong Autonomous Prefecture, Guizhou, China," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 142(3), pages 947-970, April.
    4. Yanhui Wang & Shoujie Jia & Wenping Qi & Chong Huang, 2022. "Examining Poverty Reduction of Poverty-Stricken Farmer Households under Different Development Goals: A Multiobjective Spatio-Temporal Evolution Analysis Method," IJERPH, MDPI, vol. 19(19), pages 1-21, October.

    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. Arouri, Mohamed & Nguyen, Cuong & Youssef, Adel Ben, 2015. "Natural Disasters, Household Welfare, and Resilience: Evidence from Rural Vietnam," World Development, Elsevier, vol. 70(C), pages 59-77.
    2. Baiyegunhi, L.J.S. & Fraser, Gavin C.G., 2010. "Determinants of Household Poverty Dynamics in Rural Regions of the Eastern Cape Province, South Africa," 2010 AAAE Third Conference/AEASA 48th Conference, September 19-23, 2010, Cape Town, South Africa 97078, African Association of Agricultural Economists (AAAE).
    3. Azeem, Muhammad Masood & Mugera, Amin W. & Schilizzi, Steven, 2016. "Poverty and vulnerability in the Punjab, Pakistan: A multilevel analysis," Journal of Asian Economics, Elsevier, vol. 44(C), pages 57-72.
    4. Nguyen Viet, Cuong, 2005. "Targeting the Poor in Vietnam using a Small Area Estimation Method," MPRA Paper 25761, University Library of Munich, Germany.
    5. Lei He & Shuyi Zhou, 2024. "Measuring household vulnerability to medical expenditure shock: method and its empirical application," International Journal of Health Economics and Management, Springer, vol. 24(3), pages 465-480, September.
    6. Ward, Patrick S., 2016. "Transient Poverty, Poverty Dynamics, and Vulnerability to Poverty: An Empirical Analysis Using a Balanced Panel from Rural China," World Development, Elsevier, vol. 78(C), pages 541-553.
    7. Nicholas Minot & Bob Baulch, 2005. "Poverty Mapping with Aggregate Census Data: What is the Loss in Precision?," Review of Development Economics, Wiley Blackwell, vol. 9(1), pages 5-24, February.
    8. Jesse Naidoo, 2009. "Finite-Sample Bias and Inconsistency in the Estimation of Poverty Maps," SALDRU Working Papers 36, Southern Africa Labour and Development Research Unit, University of Cape Town.
    9. Tomoki Fujii, 2016. "Concepts and measurement of vulnerability to poverty and other issues: a review of literature," Chapters, in: Jacques Silber & Guanghua Wan (ed.), The Asian ‘Poverty Miracle’, chapter 3, pages 53-83, Edward Elgar Publishing.
    10. Gaurav, Sarthak, 2015. "Are Rainfed Agricultural Households Insured? Evidence from Five Villages in Vidarbha, India," World Development, Elsevier, vol. 66(C), pages 719-736.
    11. Muhammad Masood Azeem & Amin W. Mugera & Steven Schilizzi & Kadambot H. M. Siddique, 2017. "An Assessment of Vulnerability to Poverty in Punjab, Pakistan: Subjective Choices of Poverty Indicators," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 134(1), pages 117-152, October.
    12. Yanhui Wang & Heying Li, 2017. "Modeling Comprehensive Dispersion of the Administrative Villages and Its Association with Economic Poverty: A Case Study from Poverty-Stricken Mountainous County, China," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 133(1), pages 67-91, August.
    13. You, Jing, 2013. "The role of microcredit in older children’s nutrition: Quasi-experimental evidence from rural China," Food Policy, Elsevier, vol. 43(C), pages 167-179.
    14. Yanhui Wang & Jianchen Zhang, 2018. "Integrating BP and MGWR-SL Model to Estimate Village-Level Poor Population: An Experimental Study from Qianjiang, China," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 138(2), pages 639-663, July.
    15. Benson, Todd & Chamberlin, Jordan & Rhinehart, Ingrid, 2005. "Why the poor in rural Malawi are where they are: An Analysis of the Spatial Determinants of the Local Prevalence of Poverty," FCND discussion papers 198, International Food Policy Research Institute (IFPRI).
    16. Esso-Hanam Atake, 2018. "Health shocks in Sub-Saharan Africa: are the poor and uninsured households more vulnerable?," Health Economics Review, Springer, vol. 8(1), pages 1-13, December.
    17. Lisa Daniels & Nicholas Minot, 2021. "Do remote areas benefit from economic growth? Evidence from Uganda," Journal of International Development, John Wiley & Sons, Ltd., vol. 33(3), pages 545-568, April.
    18. Yuan Zhang & Guanghua Wan, 2008. "Can We Predict Vulnerability to Poverty?," WIDER Working Paper Series RP2008-82, World Institute for Development Economic Research (UNU-WIDER).
    19. Romano, Donato & Carraro, Alessandro, 2015. "Price Shocks, Vulnerability and Food and Nutrition Security among Rural and Urban Households in Tanzania," 2015 Fourth Congress, June 11-12, 2015, Ancona, Italy 207281, Italian Association of Agricultural and Applied Economics (AIEAA).
    20. Shijiang Chen & Mingyue Liang & Wen Yang, 2022. "Does Digital Financial Inclusion Reduce China’s Rural Household Vulnerability to Poverty: An Empirical Analysis From the Perspective of Household Entrepreneurship," SAGE Open, , vol. 12(2), pages 21582440221, June.

    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:130:y:2017:i:2:d:10.1007_s11205-015-1190-4. 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.