Welfare, Inequality and Poverty Analysis with rtip: An Approach Based on Stochastic Dominance

Berihuete, Angel; Ramos, Carmen D.; Sordo, Miguel Á.

doi:10.32614/rj-2018-029

Cited by 5 publications

(3 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In contrast, after entering the period of relative poverty governance, low income may only be a symptom of relative poverty; the root causes of poverty for farmers are the lack of viability, subjective aspirations, and the culture of poverty in poor areas, etc., and using income alone to measure relative poverty is too one-sided [ [26] , [27] , [28] , [29] , [30] ]. Compared to absolute poverty, relative poverty criteria are more diverse and multidimensional, and poverty types will be more multidimensional and complex [ [31] , [32] , [33] ], requiring the measurement of multiple influences such as income, health, ability, and psychology [ 30 , 34 , 35 ]. Angulo focused on five dimensions to analyze poverty, including family education, conditions of children and youth, employment, health and access to public facilities, and housing conditions [ 36 ].…”

Section: Literature Reviewmentioning

confidence: 99%

What influence farmers’ relative poverty in China: A global analysis based on statistical and interpretable machine learning methods

Huang,

Liu,

et al. 2023

Heliyon

View full text Add to dashboard Cite

Section: Literature Reviewmentioning

confidence: 99%

What influence farmers’ relative poverty in China: A global analysis based on statistical and interpretable machine learning methods

Huang,

Liu,

et al. 2023

Heliyon

View full text Add to dashboard Cite

“…This package provides valuable tools that allow to perform poverty dominance analyses. Also, the rtip package in R, due to Berihuete et al (2018), allows to estimate the ordinates of the Generalized Lorenz curve and to test Generalized Lorenz dominance by means of the statistical testing procedure proposed by Xu (1997). Moberg and Solow (2015) also provide a code to test for stochastic dominance, which is available both for MATLAB and R.…”

Section: Statistical Inference and Empirical Applicationsmentioning

confidence: 99%

A Review of Stochastic Dominance Methods for Poverty Analysis

García‐Gómez

Pérez

Prieto‐Alaiz

2019

Journal of Economic Surveys

View full text Add to dashboard Cite

Stochastic dominance techniques have been mainly employed in poverty analyses to overcome what it is called the multiplicity of poverty indices problem. Moreover, in the multidimensional context, stochastic dominance techniques capture the possible relationships between the dimensions of poverty as they rely upon their joint distribution, unlike most multidimensional poverty indices, which are only based on marginal distributions. In this paper, we first review the general definition of unidimensional stochastic dominance and its relationship with poverty orderings. Then we focus on the conditions of multivariate stochastic dominance and their relationship with multidimensional poverty orderings, highlighting the additional difficulties that the multivariate setting involves. In both cases, we focus our discussion on first‐ and second‐order dominance, though some guidelines on higher order dominance are also mentioned. We also present an overview of some relevant empirical applications of these methods that can be found in the literature in both univariate and multivariate contexts.

show abstract

“…Friedman (1965) clearly defined relative poverty as the poverty exposed by the changing living standards. Amartya Sen (Atkinson et al 2002;Banerjee and Duflo 2014) held that poverty is capability deprivation and inspired multidimensional poverty indices based on income poverty, unveiling the era of multidimensional poverty measurement (Banzhaf et al 2014;Berihuete et al 2018;Chen et al 2018Chen et al , 2021Christiaensen and Todo 2014). Subsequently, income level was taken as the relative poverty standard, and relative poverty was measured by the expenditure method and the asset index method (Decancq and Lugo 2012).…”

Section: Introductionmentioning

confidence: 99%

Measurement and identification of relative poverty level of pastoral areas: an analysis based on spatial layout

Lin

Gao

Zhu

et al. 2022

Environ Sci Pollut Res

View full text Add to dashboard Cite

Pastoral areas are the key difficulty in China’s pursuit of common prosperity and a key region for China to build the northern ecological safety barrier and to realize the Two Centenary Goals. It is of great significance to scientifically evaluate the quality of rural life (QRL), measure the relative poverty level (RPL), and identify the relatively poor areas, making it possible to dock poverty elimination with rural revitalization. Based on the socio-economic data of 18 pastoral areas in Inner Mongolia, this paper draws on spatial layout theory to evaluate QRL and measures RPL by the natural breakpoint method and then identifies the relatively poor areas in Inner Mongolia. The results show that (1) the QRLs of pastoral areas in Inner Mongolia were unbalanced and highly polarized. The mean score of QRLs was 0.2598. Eleven (61.11%) of the counties/banners had a QRL smaller than the mean score. On the spatial layout of QRLs, the western areas were stronger than the central areas. High QRL counties/banners are mainly concentrated in the western region. In the central region, the QRLs were very fragmented, falling onto all five levels. (2) The pastoral areas in Inner Mongolia differed significantly in RPL. The mean score of RPL stood at 0.3788. Nine counties/banners (50%) had an RPL greater than the mean. Contrary to the spatial layout features of QRLs, the central pastoral areas in Inner Mongolia had stronger RPLs than the eastern ones. High RPL counties/banners are mostly clustered in the central region. The spatial layout of RPLs is relatively reasonable in the central region: the RPLs decreased gradually from Dorbod Banner. (3) Nearly 45% of the pastoral areas in central and western Inner Mongolia face serious relative poverty and a high risk of returning to poverty. Eight counties/banners (45%) were identified as high composite relative poverty areas. From spatial layout, the composite relatively poor counties/banners clustered clearly, mainly in the western region. Finally, this paper establishes a warning mechanism against large-scale returning to poverty, aiming to lower composite RPL. The research results provide empirical reference and implementation path for consolidating the results of poverty eradication and facilitating rural revitalization.

show abstract

Welfare, Inequality and Poverty Analysis with rtip: An Approach Based on Stochastic Dominance

Cited by 5 publications

References 21 publications

What influence farmers’ relative poverty in China: A global analysis based on statistical and interpretable machine learning methods

What influence farmers’ relative poverty in China: A global analysis based on statistical and interpretable machine learning methods

A Review of Stochastic Dominance Methods for Poverty Analysis

Measurement and identification of relative poverty level of pastoral areas: an analysis based on spatial layout

Contact Info

Product

Resources

About