Village-level multidimensional poverty measurement in China: Where and how

Wang, Yanhui; Chen, Yefeng; Yao, Chen; Zhao, Wenji; Hu, Zhuowei; Duan, Fuzhou

doi:10.1007/s11442-018-1555-0

Cited by 43 publications

(25 citation statements)

References 21 publications

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“…Poverty is an issue that has long plagued the world and is a major concern affecting the development of human societies [13]. Scholars have proposed a multidimensional poverty measurement method based on the multidimensionality, region-specificity and dynamic characteristics of poverty [14,15].…”

Section: Introductionmentioning

confidence: 99%

Impact of the COVID-19 Pandemic on Farm Households’ Vulnerability to Multidimensional Poverty in Rural China

et al. 2021

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The COVID−19 pandemic has significantly impacted the economy and livelihoods of people worldwide. To analyze the impact of the pandemic on material conditions, income levels, health conditions, industrial development and employment opportunities of farmers in China’s rural areas, especially poor areas and explore whether farmers can achieve stable poverty eradication during the COVID−19 pandemic, we interviewed 2662 farm households in poverty−stricken areas of China and used the multidimensional poverty measurement model, three−step feasible generalized least squares and propensity score matching to analyze data. We achieved the following results. First, the overall level of multidimensional poverty vulnerability index (MPVI) of the surveyed households was low and the MPVI of each dimension varied significantly. The MPVI of households in the treated group was higher than that of the control group. Second, COVID−19 increased farm households’ vulnerability to multidimensional poverty in poverty−stricken regions; MPVI increased by 27.9%. Third, COVID−19′s impact on various dimensions differed: the greatest impact was on the vulnerability to health deprivation, followed by industrial development, employment and income deprivation. However, the pandemic slightly reduced the vulnerability to material deprivation. Finally, we proposed various measures in response to the impact of the pandemic to assist farm households in poverty−stricken areas.

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Section: Introductionmentioning

confidence: 99%

Impact of the COVID-19 Pandemic on Farm Households’ Vulnerability to Multidimensional Poverty in Rural China

et al. 2021

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“…It extends across four provinces, that is, Hunan, Hubei, Chongqing, and Guizhou, covering 11 counties of Hubei, 37 counties of Hunan, 7 counties of Chongqing, and 16 counties of Guizhou, with a total of 11,303 villages involved in the national "Entire-Village Advancement" project. As a typical poverty-stricken area with the old revolutionary base and various minorities, the study area has been in widespread and deep poverty with inhomogeneous distribution, facing many integrated difficulties in rising out of deep poverty from the collection of "nature, ecological, economic, social, and cultural tradition" disadvantages (Wang & Chen, 2018). Therefore, it is necessary to study the special spatial distribution patterns and regional poverty factors of poverty-stricken villages, which are conductive for helping the poor get out of poverty by providing supplementary decision-making suggestions to narrow the regional development gap.…”

Section: Study Areamentioning

confidence: 99%

Detecting village‐level regional development differences: A GIS and HLM method

Wang

Liang

2018

Growth and Change

Self Cite

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To respond to the problems that the previous research mainly targeted the poverty at larger scale and ignored individual effect or contextual effect during exploring poverty contributing factors, we attempt to use spatial cluster analysis and multilevel linear regression model to target the poverty at village level from the perspective of spatial poverty, so as to identify where the poor villages are, and why they are so poor, thereby targeting poverty interventions. Specially, we adopt four types of spatial cluster indices to detect the spatial aggregation distribution of villages, and design HLM model to examine the poverty contributing factors from both village level and county level. The case study from Wuling contiguous destitute area show that: (1) The overall distribution shows a spatial pattern of large scattered but small concentration, scatters‐polar core‐axis‐clump coexisted. (2) Poverty contributing factors at village level from high to low are: per cultivated area, safe drinking water access ratio, terrain type, suffered frequency of natural disasters, road access ratio, and distance from the nearest town’s bazaar. The contribution degree of county‐level factors to the villages’ poverty from high to low are: second gross enrollment ratio, per capita GDP. (3) 45.1% of the difference among the villages’ poverty degrees comes from the development differences among poverty‐stricken villages themselves, and 54.9% from that among counties they belong to. Contributing factors at village level account for 61.4% of the variation of village‐level independent variables, and factors at county level contributed to 65.3% of the variation of county‐level independent variables.

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“…At the same time, the ecological environment in hilly areas is generally fragile, and it would be easy for the outbreak of various geological disasters to destroy natural resources. In addition, hilly areas are mostly underdeveloped and cannot enjoy equal rights in the urban-rural system, so, the development of these areas is constrained by the environment of policy and economy [18,19]. Rural areas are the most basic regional organism in China, and play a fundamental role in the social and economic development of the whole country.…”

Section: Introductionmentioning

confidence: 99%

Following Rural Functions to Classify Rural Sites: An Application in Jixi, Anhui Province, China

Ren

2021

Land

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Rural areas are a type of self-organized regional living environment, with multi-functional symbiosis between humans and land; their functional attributes are function superposition, function difference, and dominant function. The evolution of rural functions is a gradual process and follows the general law of the development of self-organizing systems, which evolutes from the state of general development, competition without rules, and, finally, to an order controlled by the dominant function. By constructing an indicator system and measurement model of rural function evaluation, this study took 11 towns in a hilly area of Jixi County as regional units to analyze the differentiation characteristics and rules of rural functions; the functions include agricultural production functions, nonagricultural production functions, life and leisure functions, and ecological functions. The results show the following: (1) The index of agricultural production functions, life and leisure functions, and ecological functions in Jixi County is higher, while the index of nonagricultural production functions is lower; (2) all towns have at least one function belongings to the “high state strong potential zone”, and some towns show a weak comprehensiveness; (3) the interaction between different functions should be considered when determining the dominant functions of the towns; (4) the formation mechanism of a dominant function has a high correlation with its main influencing factors; and (5) nine types of characteristic village are determined, according to the coupling of village characteristic resources and town dominant functions.

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Village-level multidimensional poverty measurement in China: Where and how

Cited by 43 publications

References 21 publications

Impact of the COVID-19 Pandemic on Farm Households’ Vulnerability to Multidimensional Poverty in Rural China

Impact of the COVID-19 Pandemic on Farm Households’ Vulnerability to Multidimensional Poverty in Rural China

Detecting village‐level regional development differences: A GIS and HLM method

Following Rural Functions to Classify Rural Sites: An Application in Jixi, Anhui Province, China

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