The impact of government funding of poverty reduction programmes

Jung, Suhyun; Cho, Seong‐Hoon; Roberts, Roland K.

doi:10.1111/pirs.12089

Cited by 8 publications

(12 citation statements)

References 60 publications

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“…While PES do not target poverty alleviation exclusively, incentive payments are assumed similar to subsidy programs that target poverty alleviation. Numerous studies deal with the consequences of subsidy programs, including PES, that focus on various aspects of poverty, such as health care, housing, education, and public welfare (Besley & Kanbur ; Bétrisey, Mager, & Rist, ; Bulte, Lipper, Stringer, & Zilberman, ; Daw, Brown, Rosendo, & Pomeroy, ; Diswandi ; Fan, Gulati, & Thorat, ; Gauvin, Uchida, Rozelle, Xu, & Zhan, ; Jung, Cho, & Roberts, ; Mehmood & Sadiq ; Milder, Scherr, & Bracer, ; Pagiola, Arcenas, & Platais, ; Remler, Korenman, & Hyson, ; Rosen ; Samii, Lisiecki, Kulkarni, Paler, & Chavis, ; Santiago, Galster, & Tatian, ; Schultz ; Smeeding et al ). Among these studies, some find evidence that PES alleviate poverty (Bétrisey et al ; Bulte et al ; Daw et al ; Diswandi ; Gauvin et al ; Milder et al ; Samii et al ), while others show that PES are ineffective in mitigating poverty or even unintentionally traps the poor in a poverty trap (Barrett, Travis, & Dasgupta, ; Cao, Wang, Song, Chen, & Feng, ; Cao, Zhong, Yue, Zeng, & Zeng, ; Karsenty ; Kronenberg, & Hubacek ; Roe ).…”

Section: Methodsmentioning

confidence: 99%

“…We test the hypothesis by estimating the following spatial panel model similar to Jung et al ():

(P_{i}^{t + n ~ t}) = α goodbreakinfix+ \sum_{j = 1}^{N} w_{italicij} P_{ij - 1}^{t} δ + X_{i}^{t} β + μ_{i} goodbreakinfix+ λ^{t} goodbreakinfix+ ε_{i}^{t},

where j represents the j th county, X is a vector of explanatory variables including forest return, weighted‐average return of nonforested land uses, demographic and employment characteristics, and a time‐period dummy variable (i.e., 1 for observation in

t + n ~ t

= 1992–2001 and 0 for observation in

t + n ~ t

= 2001–2011),

α

is a constant parameter,

δ

is a parameter for the spatially lagged poverty rate,

β

is a parameter vector,

μ

and

λ

respectively denote unobserved spatial and time specific effects, and

ε

is an error term. The poverty rate is the percentage of individuals with incomes below the US Census Bureau poverty threshold based on the family size and the age of its member (U.S. Census Bureau ).…”

Section: Methodsmentioning

confidence: 99%

“…We test the hypothesis by estimating the following spatial panel model similar to Jung et al (2015):…”

Section: Poverty Alleviation Modelmentioning

confidence: 99%

“…The employment variables include percentages of total employment in different industries (i.e., agriculture, manufacture, public, finance, and leisure) with the information, professional, scientific, management, administrative, and waste management service industries (referred to as "other employment percentage") as the reference category. The demographic and employment variables are control variables that influence changes in the poverty rate (e.g., Aassve, Iacovou, & Mencarini, 2006;Engster 2012;Fujiura & Yamaki 2000;House 1989;Hoynes, Page, & Stevens, 2006;Jung et al 2015). The demographic and employment data are collected from the U.S. Bureau of Labor Statistics (1990) and the U.S. Census Bureau (2000Bureau ( , 2010.…”

Section: Poverty Alleviation Modelmentioning

confidence: 99%

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Spatial targeting of payments for ecosystem services to achieve conservation goals and promote social equity and economic impact

Soh

Cho

2019

Natural Resource Modeling

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The objective of this research was to identify optimal spatial targeting and distributions of a fixed PES (payment systems for ecosystem services) budget that achieves the three objectives of maximizing forest‐based carbon storage to improve cost efficiency, maximizing poverty alleviation to promote equity, and maximizing economic impact to encourage economic development. We used the Central and Southern Appalachian Region as a case study to develop a county‐level framework for identifying optimal county targets and optimal budget allocations for forest‐based carbon sequestration that addresses the three objectives. The results show that optimal budgets are more geographically widespread under the multiple‐objective priority scenarios than under the single‐objective of maximizing carbon cost efficiency, and the optimal spatial distributions of the four priority scenarios do not change appreciably across priority scenarios. The quantified relationships reveal that different priority weights among the priority scenarios yield both competitive trade‐offs and synergistic relationships between the objectives.

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Section: Methodsmentioning

confidence: 99%

“…We test the hypothesis by estimating the following spatial panel model similar to Jung et al ():

(P_{i}^{t + n ~ t}) = α goodbreakinfix+ \sum_{j = 1}^{N} w_{italicij} P_{ij - 1}^{t} δ + X_{i}^{t} β + μ_{i} goodbreakinfix+ λ^{t} goodbreakinfix+ ε_{i}^{t},

t + n ~ t

= 1992–2001 and 0 for observation in

t + n ~ t

= 2001–2011),

α

is a constant parameter,

δ

is a parameter for the spatially lagged poverty rate,

β

is a parameter vector,

μ

and

λ

respectively denote unobserved spatial and time specific effects, and

ε

Section: Methodsmentioning

confidence: 99%

“…We test the hypothesis by estimating the following spatial panel model similar to Jung et al (2015):…”

Section: Poverty Alleviation Modelmentioning

confidence: 99%

Section: Poverty Alleviation Modelmentioning

confidence: 99%

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Spatial targeting of payments for ecosystem services to achieve conservation goals and promote social equity and economic impact

Soh

Cho

2019

Natural Resource Modeling

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“…更易陷入贫困。新扶贫形势下，农户脱贫与其自身生计密切相关，刘永茂等 [110] 提出了用于分析政府和社区支持活动对农户生计多样性弹性的作用与影响的通用弹性测度和分析框架，并可用于判断贫困农户的发展能力和生计多样性可持续性。Hua 等 [111] 从减贫视角分析和评价了生计资产在可持续生计 [112] 。小额信贷不仅可以直接影响经济增长，而且还可以通过提高资本积累和就业率间接地的影响经济增长。小额贷款以社会创新的方式通过资产转让来扶贫 [114] 。贫困农民的教育水平和他们的经验是影响贷款需求的重要因素 [115,116] 。贫困人口有提高收入的诉求，收入不平等会导致贫困 [117] 。对于贫困人口来说，技能和资本都可以限制生产力的选择，很多发展中国家关于提供培训或放松流动管制的项目越来越多。减贫战略必须考虑经济环境变化 [118] ，现在学者也越来越关注产业扶贫 [119][120][121] 、生态扶贫 [122][123][124] 等。也有文献为技能培训 [125] 、小额贷款 [126] 以及资助小企业发展 [127] 对提高贫困人口收入所产生的影响提供了实证检验证据。同时，提高教育水平有助于脱贫 [128][129][130] [133,134] 。同时，也有不少国际组织对发展中国家提供扶贫援助，例如联合国国际农业发展基金 (IF-AD) [135][136][137][138] 、世界银行 (WB) [139,140] 等。扶贫项目绩效及能力建设会对扶贫项目的可持续性产生重大影响 [141] 。现有扶贫模式相关研究主要关注如下方面：小额贷款 [115,126] 、综合农业发展 [135] 、新农合制度改革 [134] 、技能培训 [125,128] 、产业扶贫 [119,121] 、旅游扶贫 [120] 、家庭信贷 [112] 、资产转让 [114] 、提高教育水平 [129] 、资助小企业发展 [127] 、老年教育 [130] 、森林可持续发展战略…”

Section: 直接关系视角unclassified

生态环境和灾害对贫困影响的研究综述

程欣¹,

Xin²,

帅传敏³

et al. 2018

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Contiguous Extremely Poverty-Stricken Areas have become the main battlefield in China' s poverty alleviation. Poverty is usually interwoven with eco-environmental and disaster factors, which is the most difficult work in China' s poverty alleviation. Based on the literature, we analyzed the relationship between the eco-environment and poverty, and disaster and poverty. We explored poverty reduction demands of the poor and poverty alleviation modes. Finally, we proposed a systematic theoretical framework for poverty alleviation that could reconcile environmental protection and disaster mitigation with poverty eradication. We found that the relationship between eco-environments and poverty is mutual and complicated, and the key influential factors contain environmental degradation factors, resource factors and multidimensional poverty factors. Related research on the relationship between disasters and poverty mainly focuses on three perspectives: vulnerability, direct relationship and household livelihoods. The poverty reduction demands of the poor present a diversification trend, but previous studies seldom consider the environment and disaster factors. Therefore, in future research on the relationship between eco-environments and poverty should pay more attention to sustainable development issues. The indirect impacts of disasters on poverty are worthy of attention. Issues such as the vulnerability and post-disaster restoration of the poor could not be ignored. Attention should be paid to the development of innovative poverty alleviation modes, and actively promoting the organic combination of renewable energy and targeted poverty alleviation. Related research should comprehensively consider key factors associated with ecological environments, disasters and poverty as well as their interrelationships in an attempt to put forward poverty reduction strategies systematically.

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