MA Zhenbang, CHEN Xingpeng, CHEN Huan. Multi-scale Spatial Patterns and Influencing Factors of Rural Poverty: A Case Study in the Liupan Mountain Region, Gansu Province, China[J]. Chinese Geographical Science, 2018, 28(2): 296-312. doi: 10.1007/s11769-018-0943-9
Citation: MA Zhenbang, CHEN Xingpeng, CHEN Huan. Multi-scale Spatial Patterns and Influencing Factors of Rural Poverty: A Case Study in the Liupan Mountain Region, Gansu Province, China[J]. Chinese Geographical Science, 2018, 28(2): 296-312. doi: 10.1007/s11769-018-0943-9

Multi-scale Spatial Patterns and Influencing Factors of Rural Poverty: A Case Study in the Liupan Mountain Region, Gansu Province, China

doi: 10.1007/s11769-018-0943-9
Funds:  Under the auspices of National Natural Science Foundation of China (No. 41401204, 41471462), Fundamental Research Funds for the Central Universities (No. lzujbky-2013-128)
More Information
  • Corresponding author: MA Zhenbang.E-mail:zbma@lzu.edu.cn
  • Received Date: 2017-05-10
  • Rev Recd Date: 2017-09-08
  • Publish Date: 2018-04-27
  • The important role of spatial scale in exploring the geography of poverty as well as its policy implications has been noticed but with limited knowledge. To improve such limited understanding, we mainly investigated the spatial patterns and influencing factors of rural poverty (indicated by poor population and poverty incidence) at three different administrative levels in the Liupan Mountain Region, one of the fourteen poorest regions in China. Our results show that from a global perspective, poor areas are clustered significantly at the county-, township-, and village-level, and more greatly at a lower level. Locally, there is spatial mismatch among poverty hotspots detected not only by the same indicator at different levels but also by different indicators at the same level. A scale effect can be found in the influencing factors of rural poverty. That is, the number of significant factors increases, but the degree of their association with poverty incidence decreases at a lower level. Such scale effect indicates that poverty incidence at lower levels may be affected by more complex factors, including not only the new local ones but also the already appeared non-local ones at higher levels. However, the natural conditions tend to play a scale-independent role to poverty incidence. In response to such scale-dependent patterns and factors, anti-poverty policies can be 1) a multilevel monitoring system to reduce incomplete or even misleading single-level information and understanding; 2) the village-based targeting strategy to increase the targeting efficiency and alleviate the mentioned spatial mismatch; 3) more flexible strategies responding to the local impoverishing factors, and 4) different task emphasises for multilevel policymakers to achieve the common goal of poverty reduction.
  • [1] Alkire S, Foster J, 2011. Counting and multidimensional poverty measurement. Journal of Public Economics, 95(7-8):476-487. doi: 10.1016/j.jpubeco.2010.11.006
    [2] Alwang J, Siegel P B, Jorgensen S L, 2001. Vulnerability:a view from different disciplines. Social Protection Discussion Paper Series No.0115. Washington, D.C.:The World Bank, 1-42.
    [3] Amara M, Ayadi M, 2013. The local geographies of welfare in Tunisia:does neighbourhood matter? International Journal of Social Welfare, 22(1):90-103. doi:10.1111/j.1468-2397.2011. 00863.x
    [4] Amarasinghe U, Samad M, Anputhas M, 2005. Spatial clustering of rural poverty and food insecurity in Sri Lanka. Food Policy, 30(5-6):493-509. doi: 10.1016/j.foodpol.2005.09.006
    [5] Annim S K, Mariwah S, Sebu J, 2012. Spatial inequality and household poverty in Ghana. Economic Systems, 36(4):487-505. doi: 10.1016/j.ecosys.2012.05.002
    [6] Anselin L, 1995. Local indicators of spatial association-LISA. Geographical Analysis, 27(2):93-115. doi: 10.1111/j.1538-4632.1995.tb00338.x
    [7] Anselin L, 2002. Under the hood issues in the specification and interpretation of spatial regression models. Agricultural Economics, 27(3):247-267. doi: 10.1016/S0169-5150(02)00077-4
    [8] Anselin L, 2003. GeoDaTM 0.9 User's Guide. Urbana-Champaign:University of Illinois.
    [9] Anselin L, Syabri I, Kho Y, 2010. GeoDa:an introduction to spatial data analysis. In:Fischer M M, Getis A (eds). Handbook of Applied Spatial Analysis. Berlin, Heidelberg:Springer. doi: 10.1007/978-3-642-03647-7_5
    [10] Benson T, Chamberlin J, Rhinehart I, 2005. An investigation of the spatial determinants of the local prevalence of poverty in rural Malawi. Food Policy, 30(5-6):532-550. doi:10.1016/j. foodpol.2005.09.004
    [11] Bird K, Hulme D, Moore K et al., 2002. Chronic poverty and remote rural areas. CPRC Working Paper No.13. London:Chronic Poverty Research Centre, 27-36.
    [12] Bird K, Shepherd A, 2003. Livelihoods and chronic poverty in semi-arid zimbabwe. World Development, 31(3):591-610. doi: 10.1016/S0305-750X(02)00220-6
    [13] Bird K, Higgins K, Harris D, 2010. Spatial poverty traps:an overview. CPRC Working Paper 161. London:Chronic Poverty Research Centre, 5-10.
    [14] Bloom D E, Canning D, Sevilla J, 2003. Geography and poverty traps. Journal of Economic Growth, 8(4):355-378. doi: 10.1023/A:1026294316581
    [15] Burke W J, Jayne T S, 2010. Spatial disadvantages or spatial poverty traps:household evidence from rural Kenya. CPRC Working Paper167. London:Chronic Poverty Research Centre, 16-27.
    [16] Carter P M R, Barrett C B, 2006. The economics of poverty traps and persistent poverty:an asset-based approach. Journal of Development Studies, 42(2):178-199. doi:10.1080/00220380 500405261
    [17] Cattell V, 2001. Poor people, poor places, and poor health:the mediating role of social networks and social capital. Social Science & Medicine, 52(10):1501-1516. doi: 10.1016/S0277-9536(00)00259-8
    [18] Curtis K J, Voss P R, Long D D, 2012. Spatial variation in poverty-generating processes:child poverty in the United States. Social Science Research, 41(1):146-159. doi:10.1016/j. ssresearch.2011.07.007
    [19] Data Center for Resources and Environmental Sciences, 2016. China meteorological data. Beijing:Chinese Academy of Sciences (RESDC).
    [20] Dercon S, 2001. Assessing vulnerability to poverty. Oxford:Oxford University, 1-79.
    [21] Donohue C, Biggs E, 2015. Monitoring socio-environmental change for sustainable development:developing a Multidimensional Livelihoods Index (MLI). Applied Geography, 62:391-403. doi: 10.1016/j.apgeog.2015.05.006
    [22] Dungan J L, Perry J N, Dale M R T et al., 2002. A balanced view of scale in spatial statistical analysis. Ecography, 25(5):626-640. doi: 10.1034/j.1600-0587.2002.250510.x
    [23] Editorial Board of Gansu Development Yearbook, 2014a. Gansu Development Yearbook 2014. Beijing:China Statistics Press.(in Chinese)
    [24] Elbers C, Lanjouw J O, Lanjouw P, 2003. Micro-level estimation of poverty and inequality. Econometrica, 71(1):355-364. doi: 10.1111/1468-0262.00399
    [25] Elbers C, Fujii T, Lanjouw P et al., 2007. Poverty alleviation through geographic targeting:how much does disaggregation help? Journal of Development Economics, 83(1):198-213. doi: 10.1016/j.jdeveco.2006.02.001
    [26] Epprecht M, Müller D, Minot N, 2011. How remote are Vietnam's ethnic minorities? An analysis of spatial patterns of poverty and inequality. The Annals of Regional Science, 46(2):349-368. doi: 10.1007/s00168-009-0330-7
    [27] Erenstein O, Hellin J, Chandna P, 2010. Poverty mapping based on livelihood assets:a meso-level application in the IndoGangetic Plains, India. Applied Geography, 30(1):112-125.
    [28] doi: 10.1016/j.apgeog.2009.05.001
    [29] Farrow A, Larrea C, Hyman G et al., 2005. Exploring the spatial variation of food poverty in Ecuador. Food Policy, 30(5-6):510-531. doi: 10.1016/j.foodpol.2005.09.005
    [30] Francis P, James R, 2003. Balancing rural poverty reduction and citizen participation:the contradictions of Uganda's decentralization program. World Development, 31(2):325-337. doi: 10.1016/S0305-750X(02)00190-0
    [31] Gansu Rural Yearbook Editorial Board, 2014b. Gansu Rural Yearbook 2014. Beijing:China Statistics Press. (in Chinese)
    [32] Geospatial Data Cloud, 2016. GDEMDEM 30M. Beijing:Computer Network Information Center, Chinese Academy of Sciences.
    [33] Grant U, Hulme D, Moore K et al., 2004. The chronic poverty report 2004-05. Manchester:Chronic Poverty Research Cen-tre, 30-51.
    [34] Hentschel J, Lanjouw J O, Lanjouw P 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, 14(1):147-165. doi: 10.1093/wber/14.1.147
    [35] Holt J B, 2007. The topography of poverty in the United States:a spatial analysis using county-level data from the Community Health Status Indicators project. Preventing Chronic Disease, 4(4):A111.
    [36] Imran M, Stein A, Zurita-Milla R, 2014. Investigating rural poverty and marginality in Burkina Faso using remote sensingbased products. International Journal of Applied Earth Observation and Geoinformation, 26:322-334. doi: 10.1016/j.jag.2013.08.012
    [37] Jalan J, Ravallion M, 1997. Spatial poverty traps? Washington, D.C.:The World Bank, 4-10.
    [38] Jalan J, Ravallion M, 2002. Geographic poverty traps? A micro model of consumption growth in rural China. Journal of Applied Econometrics, 17(4):329-346. doi: 10.1002/jae.645
    [39] Kam S P, Hossain M, Bose M L et al., 2005. Spatial patterns of rural poverty and their relationship with welfare-influencing factors in Bangladesh. Food Policy, 30(5-6):551-567. doi: 10.1016/j.foodpol.2005.10.001
    [40] Kim R, Mohanty S K, Subramanian S V, 2016. Multilevel geographies of poverty in India. World Development, 87:349-359. doi: 10.1016/j.worlddev.2016.07.001
    [41] Legendre P, Fortin M J, 1989. Spatial pattern and ecological analysis. Vegetatio, 80(2):107-138. doi: 10.1007/bf00048036
    [42] Li Yurui, Cao Zhi, Zheng Xiaoyu et al., 2016. Regional and sustainable approach for Target-Poverty Alleviation and development of China. Bulletin of Chinese Academy of Sciences, 31(3):279-288. (in Chinese)
    [43] Liu Yansui, Zhou Yang, Liu Jilai, 2016. Regional differentiation characteristics of rural poverty and targeted poverty alleviation strategy in China. Bulletin of Chinese Academy of Sciences, 31(3):269-278. (in Chinese)
    [44] Liu Yansui, Li Jintao, 2017. Geographic detection and optimizing decision of the differentiation mechanism of rural poverty in China. Acta Geographica Sinica, 72(1):161-173. (in Chinese)
    [45] 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. doi: 10.1016/j.apgeog.2016.06.004
    [46] Luo Qing, Fan Xinsheng, Gao Genghe et al., 2016. Spatial distribution of poverty village and influencing factors in Qinba Mountains. Economic Geography, 36(4):126-132. (in Chinese)
    [47] Minot N, 2000. Generating disaggregated poverty maps:an application to Vietnam. World Development, 28(2):319-331. doi: 10.1016/s0305-750x(99)00126-6
    [48] Minot N, Baulch B, 2005. Spatial patterns of poverty in Vietnam and their implications for policy. Food Policy, 30(5-6):461-475. doi: 10.1016/j.foodpol.2005.09.002
    [49] Minot N, Baulch B, Epprecht M, 2006. Poverty and Inequality in Vietnam:Spatial Patterns and Geographic Determinants. Washington, D.C.:International Food Policy Research Institute (IFPRI).
    [50] Mitchell A, 2005. The ESRI Guide to GIS Analysis, Volume 2:Spatial Measurements & Statistics. Redlands:ESRI.
    [51] Moran P A P, 1948. The interpretation of statistical maps. Journal of the Royal Statistical Society, 10(2):243-251.
    [52] National Bureau of Statistics of China, 2014. China County Statistical Yearbook 2014. Beijing:China Statistics Press. (in Chinese)
    [53] Oden N L, 1984. Assessing the significance of a spatial correlogram. Geographical Analysis, 16(1):1-16. doi:10.1111/j. 1538-4632.1984.tb00796.x
    [54] Okwi P O, Ndeng'e G, Kristjanson P et al., 2007. Spatial determinants of poverty in rural Kenya. Proceedings of the National Academy of Sciences of the United States of America, 104(43):16769-16774. doi: 10.1073/pnas.0611107104
    [55] Olivia S, Gibson J, Rozelle S et al., 2011. Mapping poverty in rural China:how much does the environment matter? Environment and Development Economics, 16(2):129-153. doi: 10.1017/s1355770x10000513
    [56] Palmer-Jones R, Sen K, 2006. It is where you are that matters:the spatial determinants of rural poverty in India. Agricultural Economics, 34(3):229-242. doi:10.1111/j.1574-0864.2006. 00121.x
    [57] Park A, Wang S G, Wu G B, 2002. Regional poverty targeting in China. Journal of Public Economics, 86(1):123-153. doi:10. 1016/s0047-2727(01)00108-6
    [58] Park A, Wang S G, 2010. Community-based development and poverty alleviation:an evaluation of China's poor village investment program. Journal of Public Economics, 94(9-10):790-799. doi: 10.1016/j.jpubeco.2010.06.005
    [59] Partridge M D, Rickman D S, 2008. Place-based policy and rural poverty:insights from the urban spatial mismatch literature. Cambridge Journal of Regions, Economy and Society, 1(1):131-156. doi: 10.1093/cjres/rsm005
    [60] Pijanowski B C, Iverson L R, Drew C A et al., 2010. Addressing the interplay of poverty and the ecology of landscapes:a Grand Challenge Topic for landscape ecologists? Landscape Ecology, 25(1):5-16. doi: 10.1007/s10980-009-9415-z
    [61] Ravallion M, Wodon Q, 1999. Poor areas, or only poor people? Journal of Regional Science, 39(4):689-711. doi: 10.1111/0022-4146.00156
    [62] Rupasingha A, Goetzb S J, 2007. Social and political forces as determinants of poverty:a spatial analysis. The Journal of Socio-Economics, 36(4):650-671. doi:10.1016/j.socec.2006. 12.021
    [63] State Council of the People's Republic of China, 2011. The Outline for Development-oriented poverty reduction for China's rural areas (2011-2020). http://www.gov.cn/gongbao/content/2011/content_2020905.htm. 2016-11-26. (in Chinese)
    [64] Sunderlin W D, Dewi S, Puntodewo A et al., 2008. Why forests are important for global poverty alleviation:a spatial explanation. Ecology and Society, 13(2):24.
    [65] Voss P R, Long D D, Hammer R B et al., 2006. County child poverty rates in the US:a spatial regression approach. Population Research and Policy Review, 25(4):369-391. doi:10. 1007/s11113-006-9007-4
    [66] Ward J, Kaczan D, 2014. Challenging Hydrological Panaceas:water poverty governance accounting for spatial scale in the Niger River Basin. Journal of Hydrology, 519:2501-2514. doi: 10.1016/j.jhydrol.2014.05.068
    [67] World Bank, 2000. World Development Report 2000/2001:Attacking Poverty. Washington, D.C.:The World Bank.
    [68] World Bank, 2009. World development report 2009:reshaping economic geography. Washington, D.C.:The World Bank.
    [69] Wu J G, 2004. Effects of changing scale on landscape pattern analysis:scaling relations. Landscape Ecology, 19(2):125-138. doi:10.1023/B:LAND.0000021711.40074.ae Wu Lizong, 2012. Basic geographic data set of Gansu at the scale of 1?100 000. Beijing:National Science & Technology Infrastructure of China, National Earth System Science Data Sharing Infrastructure.
    [70] Xu Yueqing, Li Shuangcheng, Cai Yunlong, 2006. Spatial simulation using GIS and artificial neural network for regional poverty-A case study of Maotiaohe Watershed, Guizhou Province. Progress in Geography, 25(3):79-85. (in Chinese)
    [71] Zhang Yongli, Huang Zuhui, 2008. Characteristics and trends of the new generation migrants:survey and analysis on 10 villages in Gansu Province. Chinese Journal of Population Science, (2):80-87. (in Chinese)
  • 加载中
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Article Metrics

Article views(823) PDF downloads(607) Cited by()

Proportional views
Related

Multi-scale Spatial Patterns and Influencing Factors of Rural Poverty: A Case Study in the Liupan Mountain Region, Gansu Province, China

doi: 10.1007/s11769-018-0943-9
Funds:  Under the auspices of National Natural Science Foundation of China (No. 41401204, 41471462), Fundamental Research Funds for the Central Universities (No. lzujbky-2013-128)
    Corresponding author: MA Zhenbang.E-mail:zbma@lzu.edu.cn

Abstract: The important role of spatial scale in exploring the geography of poverty as well as its policy implications has been noticed but with limited knowledge. To improve such limited understanding, we mainly investigated the spatial patterns and influencing factors of rural poverty (indicated by poor population and poverty incidence) at three different administrative levels in the Liupan Mountain Region, one of the fourteen poorest regions in China. Our results show that from a global perspective, poor areas are clustered significantly at the county-, township-, and village-level, and more greatly at a lower level. Locally, there is spatial mismatch among poverty hotspots detected not only by the same indicator at different levels but also by different indicators at the same level. A scale effect can be found in the influencing factors of rural poverty. That is, the number of significant factors increases, but the degree of their association with poverty incidence decreases at a lower level. Such scale effect indicates that poverty incidence at lower levels may be affected by more complex factors, including not only the new local ones but also the already appeared non-local ones at higher levels. However, the natural conditions tend to play a scale-independent role to poverty incidence. In response to such scale-dependent patterns and factors, anti-poverty policies can be 1) a multilevel monitoring system to reduce incomplete or even misleading single-level information and understanding; 2) the village-based targeting strategy to increase the targeting efficiency and alleviate the mentioned spatial mismatch; 3) more flexible strategies responding to the local impoverishing factors, and 4) different task emphasises for multilevel policymakers to achieve the common goal of poverty reduction.

MA Zhenbang, CHEN Xingpeng, CHEN Huan. Multi-scale Spatial Patterns and Influencing Factors of Rural Poverty: A Case Study in the Liupan Mountain Region, Gansu Province, China[J]. Chinese Geographical Science, 2018, 28(2): 296-312. doi: 10.1007/s11769-018-0943-9
Citation: MA Zhenbang, CHEN Xingpeng, CHEN Huan. Multi-scale Spatial Patterns and Influencing Factors of Rural Poverty: A Case Study in the Liupan Mountain Region, Gansu Province, China[J]. Chinese Geographical Science, 2018, 28(2): 296-312. doi: 10.1007/s11769-018-0943-9
Reference (71)

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return