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

Autoři: Yuewen Jiang aff001;  Chong Huang aff002;  Duoduo Yin aff001;  Chenxia Liang aff001;  Yanhui Wang aff001
Působiště autorů: 3D Information Collection and Application Key Lab of Education Ministry, Capital Normal University, Beijing, China aff001;  State Key Lab of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China aff002
Vyšlo v časopise: PLoS ONE 15(1)
Kategorie: Research Article
doi: 10.1371/journal.pone.0228032


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.

Klíčová slova:

Agricultural workers – Economic development – Economic geography – Insurance – Roads – Socioeconomic aspects of health – Welfare (social security) – Labor mobility


1. Wang Y H, Chen Y F, Chi Y, Zhao W J, Hu Z W, Duan F Z. Village-level multidimensional poverty measurement in China: Where and how. Journal of Geographical Sciences. 2018, 28(10): 1444–1466.

2. Li Y R; Fan P C; Liu Y S. What makes better village development in traditional agricultural areas of China? Evidence from long-term observation of typical villages. HABITAT INTERNATIONAL. 2019, 83: 111–124.

3. Dunford M, Gao B Y, Li W B. Who, where and why? Characterizing China's rural population and residual rural poverty. Area Development and Policy. Forthcoming 2019.

4. Zhou Y, Guo Y Z, Liu Y S, Wu W X, Li Y R. Targeted poverty alleviation and land policy innovation: Some practice and policy implications from China. Land Use Policy. 2018, 74: 53–65.

5. Chen J, Yin S, Gebhardt H, Yang X J. Farmers’ livelihood adaptation to environmental change in an arid region: A case study of the Minqin Oasis, northwestern China. Ecological Indicators. 2018, 93: 411–423.

6. Khayyati M, Aazami M. Drought impact assessment on rural livelihood systems in Iran. Ecological Indicators. 2016, 69:850–858.

7. Harrison J L; Montgomery C A; Jeanty P W. A spatial, simultaneous model of social capital and poverty, Journal of Behavioral and Experimental Economics. 2019, 78: 183–192.

8. Ward P S. Transient poverty, poverty dynamics, and vulnerability to poverty: An empirical analysis using a balanced panel from rural China. World Development. 2016, 78: 541–553. doi: 10.1016/j.worlddev.2015.10.022 26855470

9. Carneiro D M, Bagolin I P, Tai S H T. Poverty determinants in Brazilian Metropolitan Areas from 1995 to 2009. Nova Economia. 2016, 26(1): 69–96.

10. Aristondo O. Poverty Decomposition in Incidence, Intensity and Inequality. A Review. Hacienda Publica Espanola-Review of Public Economics. 2018, (225): 109–130.

11. Chiang W L, Chiang T L. Risk Factors for Persistent Child Poverty during the First Five Years of Life in Taiwan Birth Cohort Study. Child Indicators Research. 2018, 11(3): 885–896.

12. Boemi S N, Papadopoulos A M. Monitoring energy poverty in Northern Greece: the energy poverty phenomenon. INTERNATIONAL JOURNAL OF SUSTAINABLE ENERGY. 2019, 38(1): 74–88.

13. Skare M, Prziklas Druzeta R, Skare D. Measuring poverty cycles in the US 1959–2013. Technological and Economic Development of Economy. 2018, 24(4): 1737–1754.

14. Harrison J L; Montgomery C A; Jeanty P W. A spatial, simultaneous model of social capital and poverty, Journal of Behavioral and Experimental Economics. 2019, 78: 183–192.

15. Epprecht M, Daniel Müller, Minot N. How remote are Vietnam’s ethnic minorities? An analysis of spatial patterns of poverty and inequality. The Annals of Regional Science. 2011, 46(2):349–368.

16. Betti G, Gagliardi F, Lemmi A, Verma V. Comparative measures of multidimensional deprivation in the European Union. Empirical Economics. 2015, 49(3): 1071–1100.

17. Ibrahim I, Baiquni M, Ritohardoyo S. Analysis of the factors affecting the poverty in rural areas around gold mine areas in West Sumbawa Regency. Journal of Degraded and Mining Lands Management. 2016, 3(3): 585–594.

18. Mahadevan R, Suardi S. Panel evidence on the impact of tourism growth on poverty, poverty gap and income inequality. CURRENT ISSUES IN TOURISM. 2019, 22(3): 253–264.

19. Odhiambo F O. Assessing the Predictors of Lived Poverty in Kenya: A Secondary Analysis of the Afrobarometer Survey 2016. JOURNAL OF ASIAN AND AFRICAN STUDIES. 2019, 54(3): 452–464.

20. Rockli K, Sanjay K M, Subramanian S V. Multilevel Geographies of Poverty in India. World Development. 2016, 87: 349–359.

21. Park E Y, Nam S J. Influential Factors of Poverty Dynamics among Korean Households that Include the Aged with Disability. Applied Research In Quality Of Life. 2018, 13(2): 317–331.

22. Ouyang Y S, Shimeles A, Thorbecke E. Revisiting cross-country poverty convergence in the developing world with a special focus on Sub-Saharan Africa. WORLD DEVELOPMENT. 2019, 117: 13–28.

23. Wang Y H, Liang C X, Li J C. Detecting village-level regional development differences: A GIS and HLM method. Growth and Change. 2019, 50: 222–246.

24. Huang X J, Huang X, He Y B. Assessment of livelihood vulnerability of land-lost farmers in urban fringes: A case study of Xi'an, China. Habitat International. 2017, 59:1–9.

25. Cao M T, Xu D D, Xie F T, Liu E L, Liu S Q. 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. 2016, 66: 144–152.

26. Ren Z P, Ge Y, Wang J F, Mao J Y, Zhang Q. Understanding the inconsistent relationships between socioeconomic factors and poverty incidence across contiguous poverty-stricken regions in China: Multilevel modelling. Spatial Statistics. 2017, 21: 406–420.

27. Kim R, Mohanty S K, Subramanian S V. Multilevel geographies of poverty in India. World Development. 2016, 87: 349–359.

28. Canavire-Bacarreza G, Robles M. Non-parametric analysis of poverty duration using repeated cross section: an application for Peru. Applied Economics. 2017, 49(22): 2141–2152.

29. Wang Y H, Chen Y F. Using VPI to Measure Poverty-Stricken Villages in China. Social Indicators Research. 2017, 133(3): 833–857.

30. Alkire S, Fang Y F. Dynamics of Multidimensional Poverty and Uni-dimensional Income Poverty: An Evidence of Stability Analysis from China. SOCIAL INDICATORS RESEARCH. 2019, 142(1): 25–64.

31. Michalek A, Madajova M S. Identifying regional poverty types in Slovakia. Geojournal. 2019, 84(1): 85–99.

32. Alam K. Poverty reduction through enabling factors. World Journal of Science Technology and Sustainable Development. 2017, 14(4):310–321.

33. Rank M R, Hirschl T A. The economic risk of childhood in America: Estimating the probability of poverty across the formative years. Journal of Marriage & Family. 1999, 61(4): 1058–1067.

34. Peirovedin M R, Mahdavi M, Ziyari Y. An analysis of effective factors on spatial distribution of poverty in rural regions of Hamedan Province. International Journal of Geography & Geology. 2016, 5(5): 86–96.

35. Gans H J. The positive functions of poverty. American Journal of Sociology. 1972, 78(2): 275–289.

36. Raudenbush S W, Bryk A S. A hierarchical linear model: A review. 2002, 59: 1–17.

Článek vyšel v časopise


2020 Číslo 1