Comparing Income Poverty with Multidimensional Well-being Based on the "Conversion Efficiency"

Xie, Yingbai; Xie, En

doi:10.1007/s11205-020-02561-y

Cited by 4 publications

(6 citation statements)

References 15 publications

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“…Third, the variables related to public services ( internet access and landline telephone service ) have a positive impact on decreasing poverty. These findings are consistent with studies carried out around the world (Berger, 2019; Gautam et al., 2021; Liu et al., 2017; Olarinde et al., 2020; Xie & Xie, 2021). For education, evidence suggests that, in addition to being a social right, it allows households to make more informed decisions about the investment of their assets, which in turn enables them to obtain a higher income (Berger, 2019; Gautam et al., 2021; Liu et al., 2017; Olarinde et al., 2020).…”

Section: Resultssupporting

confidence: 91%

Determinants of poverty among Indigenous people in Mexico's Guerrero Mountain Region

Mora‐Rivera

Fierros‐González

García‐Mora

2023

Development Policy Review

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MotivationThe eradication of poverty is one of Mexico's greatest challenges. This challenge is even greater for Indigenous communities, where seven out of ten people were living in poverty in 2018. Despite the economic, social, and cultural impacts on Indigenous people, little literature has addressed the determinants of Indigenous poverty in Mexico, while studies on the Guerrero Mountain Region (GMR) are practically non‐existent.PurposeThis study aims to identify the socioeconomic and contextual determinants of income and multidimensional poverty in the GMR, one of the poorest regions in Mexico and Latin America, primarily inhabited by Indigenous people.Methods and approachWe use data on 989 individuals in the GMR, collected during the 2018 Socioeconomic Conditions Module of Mexico's National Household Income and Expenditure Survey. To examine the main determinants of individual poverty, we use Bayesian logistic regression (BLR), which allows us to use the data to update information about the parameters and evaluate their distributional properties.The method simplifies multi‐causal elements by classifying them into categories of well‐being that incorporate more than economic factors.FindingsThe income‐poor population is also multidimensionally poor. Education helps to reduce poverty: households that spend more on schooling are less likely to be poor. Households with many members, those with high dependency ratios, and those with members living with disability all tend to be poor. Households with access to landlines and the internet are less likely to be poor.Policy implicationsMore diagnosis of poverty is needed: it should consider overlapping vulnerabilities (institutional, socioeconomic, environmental, and sociocultural) in this region for each Indigenous group. Public policies need to be monitored for performance.

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Section: Resultssupporting

confidence: 91%

Determinants of poverty among Indigenous people in Mexico's Guerrero Mountain Region

Mora‐Rivera

Fierros‐González

García‐Mora

2023

Development Policy Review

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“…The Cobb‐Douglas and Translog forms are most commonly used. According to the likelihood ratio tests, we adopt a more flexible Translog form, as follows:

\mathrm{ln}{y}_{i}={\beta }_{0}+\sum _{j}{\beta }_{j}{\rm{ln}}{x}_{j}+\frac{1}{2}\sum _{j}\sum _{k}{\beta }_{{jk}}{\rm{ln}}{x}_{j}{\rm{ln}}{x}_{k}+{v}_{i}-{u}_{i},

where

{y}_{i}

denotes the total production and operating income of household

i

, this method of treating total income as the result of resource transformation is consistent with Xie and Xie (2021);

x

represents input factors in the production and operation of household

i

, including labor, capital, and land, of which labor is the amount of labor input time in various production and operation activities measured in hours; capital refers to the capital input in agricultural production and nonagricultural production, including vehicles, machinery, and factory buildings, and land represents the planting area.…”

Section: Methodsmentioning

confidence: 99%

Impact of rural households' digital ability on their production efficiency in China

Kojima

et al. 2023

Agribusiness

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Rural digitalization has become a key part of modernization in China. However, this rural digitalization follows a top‐down approach, from government guidance to private construction. Rural households' digital abilities have not received sufficient attention. This study explores rural households' digital abilities and their impact on production efficiency. One thousand eight hundred and seventy households are grouped into digital households (who have access to both digital and traditional channels) and traditional households (who only access traditional channels) based on their access to information channels for comparative analysis. The two‐parameter item response theory is applied to measure digital ability; stochastic frontier models are constructed to test the impact of digital ability on production efficiency; and a stochastic meta‐frontier approach is used to compare the efficiency between groups. The results indicate that: (1) digital households have stronger digital abilities than traditional households. (2) Digital ability is positively correlated with production efficiency. This effect is most evident among traditional households, followed by social households (digital households who use more social channels than nonsocial channels), and finally, nonsocial households (digital households who use more nonsocial channels). (3) Human capital (education, health, and skills) of laborers, off‐farm work, and village internet infrastructure all promote efficiency significantly. Research findings can help policymakers formulate targeted interventions to improve rural households' livelihoods.

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“…Different measurement approaches have been used to study the influencing factors of poverty reduction efficiency. Models evaluating poverty reduction efficiency parameters include the stochastic frontier approach [15], regression analysis [16], the TOPSIS method [17], and intuitionistic fuzzy analysis [18]. The influencing factors of poverty reduction efficiency were analyzed based on natural resource endowment [19], socio-economic development [20], and the implementation of poverty reduction policies.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Previous studies have generally measured efficiency using the stochastic frontier approach (SFA) and data envelopment analysis (DEA) methods. The DEA method has the following advantages: First, the DEA method does not need to set a specific function form and can deal with multiple comparable indicators, thereby avoiding the structural deviation caused by the incorrect setting of the production function in other calculations (e.g., SFA) [15]. Second, the calculated efficiency value can be divided into the technical progress index and the technical efficiency index.…”

Section: Data Envelopment Analysis Modelmentioning

confidence: 99%

How Do Ecosystem Services Affect Poverty Reduction Efficiency? A Panel Data Analysis of State Poverty Counties in China

Cao

Ouyang

Jun

2022

IJERPH

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Scientific evaluation of the interaction between poverty reduction efficiency (PRE) and ecosystem services (ES) in state poverty counties is essential in promoting the rural revitalization strategy and the construction of an ecological civilization. Using the DEA model, the InVEST model, and fixed-effect panel data, this study was analyzed using the panel data of 832 poverty counties in China for 2010–2019 to evaluate the relationship between poverty reduction efficiency and ecosystem services. The main results are as follows: (1) The overall poverty reduction efficiency showed an upward trend, while ES exhibited a declining trend with spatial heterogeneity. The poverty reduction efficiency of state poverty counties in the western region increased rapidly. (2) The impact of different types of ecosystem services on poverty reduction efficiency varied considerably. Habitat quality was significantly negatively impacted, while food production and carbon storage showed significant positive effects. There was a significant positive relationship between ecosystem services and poverty reduction efficiency in all regions, with the eastern region having the strongest correlation. (3) The panel regression analysis showed a significant positive impact. The environmental parameters were the primary factors affecting poverty reduction efficiency, while economic and social factors were the driving and external factors. The rural revitalization strategy should strive towards the win-win effect of ecological protection and economic development.

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Comparing Income Poverty with Multidimensional Well-being Based on the "Conversion Efficiency"

Cited by 4 publications

References 15 publications

Determinants of poverty among Indigenous people in Mexico's Guerrero Mountain Region

Determinants of poverty among Indigenous people in Mexico's Guerrero Mountain Region

Impact of rural households' digital ability on their production efficiency in China

How Do Ecosystem Services Affect Poverty Reduction Efficiency? A Panel Data Analysis of State Poverty Counties in China

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