Gains for women from farmland redistribution in South Africa and sustainable pathways out of poverty – insights from recent evidence

Motala, S.; Ngandu, Stewart; Mpungose, Aubrey

doi:10.1080/10130950.2017.1328806

Cited by 2 publications

(1 citation statement)

References 3 publications

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“…For instance, Zhang and Wan (2009) and Li & Chen (2019) used a panel data model from China's Rural Household Survey (CRHS) to estimate poverty vulnerability. Scholars constantly used the Sustainable Livelihoods Framework (SLF) to calculate the risk of vulnerability and sensitivity to poverty (Motala et al, 2016;Park et al, 2012;Zada et al, 2019). Other methods were also employed, including latent transition analysis (LTA) (Acconcia et al, 2020), which emphasized the internal processing abilities (Ellis & Bahiigwa, 2003) and the risk method, which was based on a framework emphasizing household production and living (Dercon, 2002).…”

Section: Literature Reviewmentioning

confidence: 99%

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

Pan

Chen

Yan

et al. 2021

Appl. Spatial Analysis

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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. KeywordsVulnerability to re-poverty (VRP) • Spatial-temporal patterns • Vulnerability scoping diagram • Rural • China * Jing Chen

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