Skip to main content

Advertisement

SpringerLink
Log in

Identifying the Spatial–Temporal Patterns of Vulnerability to Re-Poverty and its Determinants in Rural China

  • Published:
Applied Spatial Analysis and Policy Aims and scope Submit manuscript

Abstract

China has achieved remarkable results and made great contributions in poverty reduction alleviation. However, with the continuous advancement of poverty alleviation, the emergence of re-poverty has become a tangible problem. In this study, an analysis framework for vulnerability to re-poverty (VRP) is established. Furthermore, the spatial–temporal patterns and obstacle factors of VRP in rural China from 2000 to 2017 are explored. The results show the overall spatial pattern of VRP in rural China in the past 18 years exemplifies spatial heterogeneity. Notably, the “Hu Huanyong Line” is the boundary, and are significant differences between the east and west China. Next, VRP shows a significant global spatial positive correlation, indicating a significant spatial agglomeration characteristic. Moreover, the local spatial autocorrelation analysis shows that VRP has a certain spatial dependence. Finally, the rate of urbanization and number of rural employees have become the key obstacles to VRP in rural China. Therefore, VRP in different regions are influenced by the proportion of gross output value of agriculture to gross regional output value, average annual precipitation, elevation, relief degree of land surface, number of welfare agencies, and the Normalized Difference Vegetation Index (NDVI). Based on these findings, some policy recommendations are proposed, including promoting new urbanization, providing rural employment, strengthening infrastructure, and improving the resilience of ecological environments.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • Acconcia, A., Carannante, M., Misuraca, M., & Scepi, G. (2020). Measuring vulnerability to poverty with latent transition analysis. Social Indicators Research, 151, 1–31

    Article  Google Scholar 

  • Adger, W. N. (2006). Vulnerability. Global Environmental Change, 16, 268–281

    Article  Google Scholar 

  • Alkire, S., & Santos, M. E. (2010). OPHI working paper 2010. Acute multidimensional poverty: A new index for developing countries. http://origin-hdr.undp.org/fr/devhumain/parlons-dh/2010-08/HDRP_2010_11.pdf. Accessed July 2010

  • Alkire, S., & Foster, J. (2011). Counting and multidimensional poverty measurement. Journal of Public Economics, 95(7–8), 476–487

    Article  Google Scholar 

  • Alkire, S., & Santos, M. E. (2014). Measuring Acute poverty in the developing world: Robustness and scope of the multidimensional poverty index. World Development, 59, 251–274

    Article  Google Scholar 

  • Amartya, S. (1983). Poverty and Famines: An Essay on Entitlement and Deprivation. Oxford University Press.

  • Azeem, M. M., Mugera, A. W., & Schilizzi, S. (2018). Vulnerability to multi-dimensional poverty: An empirical comparison of alternative measurement approaches. The Journal of Development Studies, 54, 1612–1636

    Article  Google Scholar 

  • Berg, M. V. D. (2010). Household income strategies and natural disasters: Dynamic livelihoods in rural Nicaragua. Ecological Economics, 69(3), 592–602

    Article  Google Scholar 

  • Bossert, W., Chakravarty, S. R., & D’Ambrosio, C. (2013). Multidimensional poverty and material deprivation with discrete data. Review of Income and Wealth, 59(1), 29–43

    Article  Google Scholar 

  • Bourguignon, F., & Chakravarty, S. (2003). The measurement of multidimensional poverty. Journal of Economic Inequality, 1(1), 25–49

    Article  Google Scholar 

  • Cao, M. T., Xu, D. D., Xie, F. T., Liu, E. L., & Liu, S. Q. (2016). The influence factors analysis of households’ poverty vulnerability in southwest ethnic areas of China based on the hierarchical linear model: A case study of Liangshan Yi autonomous prefecture. Applied Geography, 66, 144–152

    Article  Google Scholar 

  • Chen, G. J., & Liu, Q. (2006). Evaluation of sustainable land management and diagnosis of obstacle at county scale in Sichuan basin: Taking Lezhi county as example. Wuhan University Journal of Natural Sciences, 11, 1046–1051

    Article  Google Scholar 

  • China’s National Bureau of Statistics. (2019). Statistical communiqué of the People’s Republic of China on 2019 national economic and social development. http://www.stats.gov.cn/english/PressRelease/202002/t20200228_1728917.html. Accessed 28 Feb 2020

  • Cui, X. P. (2019). Research on the rural revitalization strategy under the background of the new normal of economy. EMSD, 2019, 5

    Google Scholar 

  • Cui, X. F., Yang, S., Zhang, G. H., Liang, B., & Li, F. (2020). An exploration of a synthetic construction land use quality evaluation based on economic-social-ecological coupling perspective: A case study in major Chinese cities. International Journal of Environmental Research and Public Health, 17(10)

  • Dercon, S. (2001). Assessing vulnerability. Oxford University.

  • Dercon, S. (2002). Income risk, coping strategies, and safety nets. World Bank Research Obsever, 17, 141–166

    Article  Google Scholar 

  • Ding, J. J., Jin, N. B., Wang, Z., & Liu, C. (2021). Heterogeneity and evolution characteristics of poverty reduction effect of prefecture-level cities’ urbanization in Hunan, Chongqing, Sichuan, Yunnan Guizhou and Guangxi. Scientia Geographica Sinica, 41(3), 522–533

    Google Scholar 

  • Dong, J. Y. (2018). 40 years of poverty alleviation reform in China: history, experience and prospect. Review of Economic Research, 56, 11-17+29

    Google Scholar 

  • Ellis, F., & Bahiigwa, G. (2003). Livelihoods and rural poverty reduction in Uganda. World Development, 31(6), 997–1013

    Article  Google Scholar 

  • Fan, H. S. (2018). Research on the construction of early warning mechanism for return to poverty. Studies on Socialism with Chinese Characteristics, 01, 57–63

    Google Scholar 

  • Fan, L. M., & Xie, E. (2014). Do public transfers reduce vulnerability? Economic Research Journal, 8, 67–78

  • Gallardo, M. (2020). Measuring vulnerability to multidimensional poverty. Social Indicators Research, 148(280), 67–103

    Article  Google Scholar 

  • Goodchild, M. F., & Shiren, Y. (1992). A hierarchical spatial data structure for global geographic information systems. Ncgia National Center for Geographic Information & Analysis, 54, 31–44

    Google Scholar 

  • Guo, Y. Z., Zhou, Y., & Liu, Y. S. (2019). Targeted poverty alleviation and rural revitalization in poverty-stricken areas: Internal logic and mechanism. Bulletin of Chinese Academy of Sciences, 31(3), 269–278

    Google Scholar 

  • Guo, Q., Liao, H. P., Wang, Z. Y., Liu, Y. L., & Li, T. (2021). Measurement of rural poverty alleviation sustainability and return-to-poverty risk identification in Qinling-Bashan Mountains: A case study of Chengkou County Chongqing Municipality. Progress in Geography, 40(2), 232–244

    Article  Google Scholar 

  • Hu, H. Y. (1990). The distribution, regionalization and prospect of China’s population. Acta Geographica Sinica, 139–145

  • Lee, S. W., & Zhee, W. S. (2001). Independent and linked migration: Individual return of employment opportunity and household return of poverty status to African American interstate migration. The Annals of Regional Science, 35(4), 605–635

    Article  Google Scholar 

  • Li, L. R., & Chen, Y. (2019). Measurement and analysis of multi-dimensional poverty vulnerability of rural households in China. Statistics & Decision, 35(11), 76–81

    Google Scholar 

  • Li, X., Qi, W. P., Tan, C., Wang, Y. H., & Wang, B. X. (2019a). Measuring multi-dimensional poverty vulnerability of rural household from the perspective of capability: An empirical analysis based on CHNS data. Chinese High Technology Letters, 29(11), 1136–1147

    Google Scholar 

  • Li, Y. J., Qi, X. H., Lin, Y., & Wu, Y. (2019b). Characteristics and influencing factors of vulnerability to re-poverty in Liupan mountainous area within the background of poverty withdrawal. Journal of Subtropical Resources and Environment, 14(4), 63–70

    Google Scholar 

  • Li, X. Y. (2017). Will there be an era without “poverty” after 2020? http://www.sohu.com/a/128816336_481285. Accessed 14 Mar 2017

  • Liu, L. Q. (2003). Characteristic analysis of re-poverty population and countermeasure in Shaanxi province’s. Population Journal, 23(4), 20–24

    Google Scholar 

  • Liu, Y. H., & Xu, Y. (2016). A geographic identification of multidimensional poverty in rural China under the framework of sustainable livelihoods analysis. Applied Geography, 73, 62–76

    Article  Google Scholar 

  • Liu, X. P., Su, S. L., Wang, Y. J., Huang, Y., & Zhao, Y. (2014). The index system of spatial poverty of village level to monitor in concentrated contiguous areas with particular difficulties. Scientia Geographica Sinica, 34(4), 447–453

    Google Scholar 

  • Liu, Y. S., Zhou, Y., & Liu, J. L. (2016). Regional differentiation characteristics of rural poverty and targeted poverty alleviation strategy in China. Bulletin of Chinese Academy of Sciences, 31(3), 269–278

    Google Scholar 

  • Liu, Y. S., Liu, J. L., & Zhou, Y. (2017). Spatio-temporal patterns of rural poverty in China and targeted poverty alleviation strategies. Journal of Rural Studies, 52, 66–75

    Article  Google Scholar 

  • Lu, H. W., & Huang, C. W. (2016). SSAP. Annual report on targeted poverty alleviation in China https://www.ssap.com.cn/. Accessed Oct 2016

  • McCulloch, N., & Calandrino, M. (2003). Vulnerability and chronic poverty in rural Sichuan. World Development, 31(3)

  • Mina, C. D., & Imai, K. S. (2017). Estimation of vulnerability to poverty using a multilevel longitudinal model: Evidence from the Philippines. The Journal of Development Studies, 53(12), 2118–2144

    Article  Google Scholar 

  • Mon, D. L., Cheng, C. H., & Lin, J. C. (1994). Evaluating weapon system using fuzzy analytic hierarchy process based on entropy weight. Fuzzy Sets and Systems, 62(2), 127–134

  • Motala, S., Ngandu, S., & Mpungo, A. (2016). Gains for women from farmland redistribution in South Africa and sustainable pathways out of poverty–insights from recent evidence. Agenda, 30(4), 85–98

    Article  Google Scholar 

  • Muhammad, M. A., Amin, W. M., Steven, S., & Kadambot, H. M. S. (2017). An assessment of vulnerability to poverty in Punjab, Pakistan: Subjective choices of poverty indicators. Social Indicators Research, 134, 117–152

    Article  Google Scholar 

  • Mujumdar, N. A. (2001). World development report, 2000/2001: Attacking poverty. Indian Journal of Agricultural Economics, 56(1), 146

    Google Scholar 

  • Park, A., Wang, S. G., & Wu, G. B. (2002). Regional poverty targeting in China. Journal of Public Economics, 86, 123–153

    Article  Google Scholar 

  • Park, S., Howden, M., & Crimp, S. (2012). Information regional level policy development and actions for increased adaptive capacity in rural livelihoods. Environmental Science & Policy, 15(1), 23–37

    Article  Google Scholar 

  • Peng, W. S., Ye, L., Luo, Y. M., Kuang, T. T., Yang, C. J., & Xu, X. L. (2019). Differential mechanism detection of rural poverty in northwestern Tibetan Plateau, Sichuan Province. Chinese Journal of Agricultural Resources and Regional Planning, 40(06), 128–139

    Google Scholar 

  • Polsky, C., Neff, R., & Yarnal, B. (2007). Building comparable global change vulnerability assessments: The vulnerability scoping diagram. Global Environmental Change, 17, 472–485

    Article  Google Scholar 

  • Prowse, M. (2003). Towards a clearer understanding of “vulnerability” in relation to chronic poverty (CPRC working paper, Vol. 24). Manchester: Chronic Poverty Research Centre, University of Manchester.

  • Qi, W., Liu, S. H., Zhao, M. F., & Liu, Z. (2016). China’s different spatial patterns of population growth based on the “Hu Line”. Journal of Geographical Sciences, 26, 1611–1625

    Article  Google Scholar 

  • Roberts, G. M., & Yang, G. A. (2003). The international progress of sustainable development research: A comparison of vulnerability analysis and the sustainable livelihoods approach. Progress in Physical Geography, 22, 11–21

    Google Scholar 

  • Smit, B., & Wandel, J. (2006). Adaptation, adaptive capacity and vulnerability. Global Environmental Change, 16, 282–292

    Article  Google Scholar 

  • Sricharoen, T. (2011). A quantitative assessment on vulnerability to poverty and risk management of rural farm household in northeastern of Thailand. Economics and Finance, 2(4), 331–340

    Google Scholar 

  • Sun, H., Li, X. H., & Li, W. J. (2020). The Nexus between Credit Channels and Farm Household Vulnerability to Poverty: Evidence from Rural China. Sustainability, 12, 3019

  • Tang, J., Qin, F. M., & Tang, S. (2020). Measurement and difference analysis of China’s high-quality economic development level. Statistics & Decision, 36(15), 5–8

    Google Scholar 

  • Turner, B. L., Kasperson, R. E., Matson, P. A., McCarthy, J. J., & Schiller, A. (2003). A frame work for vulnerability analysis in sustainability science. Proceedings of the National Academy of Sciences of the United States of America, 100, 8074–8079

    Article  Google Scholar 

  • United Nations. (2017). The Millennium Development Goals Report 2015. New York: United Nations. https://reliefweb.int/report/world/millennium-development-goals-report-2015-enar. Accessed 6 Jul 2015

  • Wagle, U. (2002). Rethinking poverty: Definition and measurement. International Social Science Journal, 54(171), 155–165

    Article  Google Scholar 

  • Wan, Z. (2016). New trend and solutions of China’s poverty. International Economic Review, 6, 47–62

    Google Scholar 

  • World Bank. (2017). The World Bank annual report 2017: End extreme poverty, boost shared prosperity. World Bank Group. http://documents.worldbank.org/curated/zh/777651507532530766/pdf/120298-WBAR-v1-PUBLIC-ARABIC.pdf. Accessed 1 Jan 2017

  • Wu, J. S., Lin, X., Wang, M. J., Peng, J., & Tu, Y. (2017). Assessing agricultural drought vulnerability by a VSD model: A case study in Yunnan province China. Sustainability, 9, 918

    Article  Google Scholar 

  • Wu, Y., Lin, Y., & Qi, X. H. (2019). Temporal/Spatial Evolution and Vulnerability of Return-to-Poverty After Successful Implementation of Alleviation Program in Fujian. Fujian Journal of Agricultural Sciences, 34(8), 985–993

    Google Scholar 

  • Yagoob, A. H., & Zuo, T. (2016). Patterns of economic growth and poverty in Sudan. Sustainable Development, 7(2), 2222–2855

    Google Scholar 

  • Yang, L. X. (2016). Attaching great importance to the problem of re-poverty in poverty alleviation. China Civil Affairs, 33(5), 18–20

    Google Scholar 

  • Zada, M., Shah, S. J., Cao, Y. K., Rauf, T., Khan, N., & Shah, S. A. A. (2019). Impact of small-to-medium size forest enterprises on rural livelihood: Evidence from Khyber-Pakhtunkhwa Pakistan. Sustainability, 11, 2989

    Article  Google Scholar 

  • Zhang, Y., & Wan, G. H. (2009). How precisely can we estimate vulnerability to poverty? Oxford Development Studies, 37(3), 277–287

    Article  Google Scholar 

  • Zheng, R. Q., & Cao, G. Q. (2016). Re-poverty population: Influencing factors, mechanism and risk control. Journal of Argo-Forestry Economics and Management, 15(6), 619–624

    Google Scholar 

  • Zhang, G. P., & Zhuang, T. H. (2011). Impacts of the natural disasters on poverty vulnerability of farmer households: based on empirical analysis of Yunnan Province in 2009. Journal of Sichuan Agricultural University, 29(1), 136–140

Download references

Funding

This work was supported by the National Social Science Foundation of China [18BJL126].

Author information

Authors and Affiliations

  1. School of Geographical Sciences, Fujian Normal University, Fuzhou, 350007, Fujian, China

    Ying Pan, Jing Chen, Xiaoyan Yan, Shilin Ye & Xinhua Qi

  2. State Key Laboratory of Subtropical Mountain Ecology, Fujian Normal University, Fuzhou, 350007, Fujian, China

    Ying Pan, Jing Chen, Xiaoyan Yan, Shilin Ye & Xinhua Qi

  3. School of Geography and Ocean Sciences, Nanjing University, Nanjing, 210023, Jiangsu, China

    Jinhuang Lin

  4. School of Economics, Zhejiang University of Finance & Economics, Hangzhou, 310018, Fujian, China

    Yecheng Xu

Authors
  1. Ying Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jing Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiaoyan Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jinhuang Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shilin Ye
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yecheng Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Xinhua Qi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jing Chen.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pan, Y., Chen, J., Yan, X. et al. Identifying the Spatial–Temporal Patterns of Vulnerability to Re-Poverty and its Determinants in Rural China . Appl. Spatial Analysis 15, 483–505 (2022). https://doi.org/10.1007/s12061-021-09407-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12061-021-09407-1

Keywords

Search

Navigation