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Constructing HLM to examine multi-level poverty-contributing factors of farmer households: Why and how?

Author

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  • Yuewen Jiang
  • Chong Huang
  • Duoduo Yin
  • Chenxia Liang
  • Yanhui Wang

Abstract

Accurately identifying poverty-contributing factors of farmer households in an all-round way is the critical prerequisite and guarantee for taking targeted measures in poverty alleviation. From the combined perspectives of multi-level comprehensive detection and human-nature sustainable development, this study has designed a multi-level index system of household-level, village-level, and town-level, and constructed a nested three-level hierarchical linear model to examine the poverty-contributing factors of farmer households, and to reveal the significant ones and their multi-level interaction mechanism. The case test from Fugong County shows that: (1) Poverty-contributing factors are multi-level, showing both individual and background effects. 77.14% of the poverty is caused by household-level factors, 6.24% by village-level ones and 16.62% by town-level factors. (2) Significant poverty-contributing factors at different levels are different, identifying different contribution degrees to poverty gaps of farmer households. Five household-level factors show significant influence on poverty degree and account for 70.95% of the overall poverty gap among poor households, 11.70% for four village-level significant factors and 86.80% for two town-level ones, respectively. (3) Higher-level factors have different degrees of influence on the contribution difference of lower-level ones. The two town-level factors, terrain relief and town per capita annual income have explained 59.38% of the difference of village-level proportion of migrant workers’ contribution to poverty degree among towns and 89.89% of the difference of household-level per capita annual income's contribution to poverty degree among towns respectively. (4) Measures such as improving the type of access to roads, developing characteristic planting and breeding, and implementing relocation projects, can help poor households in the study area to reduce poverty. This study provides a new perspective for identifying farmers' poverty-contributing factors and technical reference and decision support for local departments to plan and implement targeted assistance and household-specific development policies.

Suggested Citation

  • Yuewen Jiang & Chong Huang & Duoduo Yin & Chenxia Liang & Yanhui Wang, 2020. "Constructing HLM to examine multi-level poverty-contributing factors of farmer households: Why and how?," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-17, January.
    • Handle: RePEc:plo:pone00:0228032
    DOI: 10.1371/journal.pone.0228032
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    1. Kim, Rockli & Mohanty, Sanjay K. & Subramanian, S.V., 2016. "Multilevel Geographies of Poverty in India," World Development, Elsevier, vol. 87(C), pages 349-359.
    2. Gustavo Canavire-Bacarreza & Marcos Robles, 2017. "Non-parametric analysis of poverty duration using repeated cross section: an application for Peru," Applied Economics, Taylor & Francis Journals, vol. 49(22), pages 2141-2152, May.
    3. Oihana Aristondo, 2018. "Poverty Decomposition in Incidence, Intensity and Inequality. A Review," Hacienda Pública Española / Review of Public Economics, IEF, vol. 225(2), pages 109-130, June.
    4. Sabina Alkire & Yingfeng Fang, 2019. "Dynamics of Multidimensional Poverty and Uni-dimensional Income Poverty: An Evidence of Stability Analysis from China," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 142(1), pages 25-64, February.
    5. Eun-Young Park & Su-Jung Nam, 2018. "Influential Factors of Poverty Dynamics among Korean Households that Include the Aged with Disability," Applied Research in Quality of Life, Springer;International Society for Quality-of-Life Studies, vol. 13(2), pages 317-331, June.
    6. Mohammad-Reza Peirovedin & Masood Mahdavi & Yousef-ali Ziyari, 2016. "An Analysis of Effective Factors on Spatial Distribution of Poverty in Rural Regions of Hamedan Province," International Journal of Geography and Geology, Conscientia Beam, vol. 5(5), pages 86-96.
    7. Gianni Betti & Francesca Gagliardi & Achille Lemmi & Vijay Verma, 2015. "Comparative measures of multidimensional deprivation in the European Union," Empirical Economics, Springer, vol. 49(3), pages 1071-1100, November.
    8. Yanhui Wang & Yefeng Chen, 2017. "Using VPI to Measure Poverty-Stricken Villages in China," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 133(3), pages 833-857, September.
    9. Wan-Lin Chiang & Tung-liang Chiang, 2018. "Risk Factors for Persistent Child Poverty during the First Five Years of Life in Taiwan Birth Cohort Study," Child Indicators Research, Springer;The International Society of Child Indicators (ISCI), vol. 11(3), pages 885-896, June.
    10. Michael Epprecht & Daniel Müller & Nicholas Minot, 2011. "How remote are Vietnam’s ethnic minorities? An analysis of spatial patterns of poverty and inequality," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 46(2), pages 349-368, April.
    11. Mohammad-Reza Peirovedin & Masood Mahdavi & Yousef-ali Ziyari, 2016. "An Analysis of Effective Factors on Spatial Distribution of Poverty in Rural Regions of Hamedan Province," International Journal of Geography and Geology, Conscientia Beam, vol. 5(5), pages 86-96.
    12. Harrison, Jane L. & Montgomery, Claire A. & Jeanty, P. Wilner, 2019. "A spatial, simultaneous model of social capital and poverty," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 78(C), pages 183-192.
    13. 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.
    14. Yanhui Wang & Chenxia Liang & Jiacun Li, 2019. "Detecting village‐level regional development differences: A GIS and HLM method," Growth and Change, Wiley Blackwell, vol. 50(1), pages 222-246, March.
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    1. Yuewen Jiang & Yanhui Wang & Wenping Qi & Benhe Cai & Chong Huang & Chenxia Liang, 2022. "Detecting Multilevel Poverty-Causing Factors of Farmer Households in Fugong County: A Hierarchical Spatial–Temporal Regressive Model," Agriculture, MDPI, vol. 12(11), pages 1-21, November.

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