Examining Multilevel Poverty-Causing Factors in Poor Villages: a Hierarchical Spatial Regression Model

Wang, Yanhui; Jiang, Yuewen; Yin, Duoduo; Liang, Chenxia; Duan, Fuzhou

doi:10.1007/s12061-021-09388-1

Cited by 15 publications

(11 citation statements)

References 38 publications

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“…The present study also examines the amount of variability in multidimensional child poverty using multilevel analysis for the effect of each level [ 48 , 50 ]. The variation in the prevalence of multidimensional child poverty has been presented with the help of VPC.…”

Section: Discussionmentioning

confidence: 99%

Prevalence and correlates of multidimensional child poverty in India during 2015–2021: A multilevel analysis

et al. 2022

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Despite increasing research and programs to eradicate poverty, poverty still exists and is a far greater concern for children than adults, leading child poverty to become a political, economic, and social issue worldwide and in India. The current study aims to find variations in the prevalence of child poverty and associated factors in India during 2015–21. In the current study, we used two consecutive rounds of the National Family Health Survey (NFHS-4, 2015–16 & NFHS-5, 2019–21) to estimate child poverty (aged 0–59 months) using the Alkire-Foster method. The multilevel logistic regression analyses were performed to find the important cofounder and cluster level variation in child poverty. The results show that about 38 percent of children were multidimensionally poor in 2015–16, which reduced to 27 percent in 2019–21. The decomposition analysis suggests that contribution of nutrition domain to child poverty increases over time, whereas the standard of living substantially declines from NFHS-4 to NFHS-5. The multilevel analysis results show that the age and sex of the child, age and years of schooling of the mother, children ever born, religion, caste, wealth quintile and central, northeast, north and west regions are significantly associated with child poverty over time. Further, the variance participation coefficient statistics show that about 12 percent of the variation in the prevalence of child poverty could be attributed to differences at the community level. The prevalence of child poverty significantly declines over time, and the community-level variation is higher than the district-level in both surveys. However, the community-level variation shows increases over time. The finding suggests a need to improve the nutritional status and standard of living of most deprived households by promoting a child-centric and dimension-specific approach with more focus on PSU-level intervension should adopt in order to lessen child poverty in India.

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Section: Discussionmentioning

confidence: 99%

Prevalence and correlates of multidimensional child poverty in India during 2015–2021: A multilevel analysis

et al. 2022

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“…Одновременно автором проводится тест на однородность дисперсии на уровнях МСА и штатов (Raudenbush еt al., 2011) и пространственной автокорреляции данных при помощи индексов Морана и множителей Лагранжа (Wang et al, 2021). Совокупные результаты указанных тестов на пространственную зависимость выражены через σ 2 , значимость бинарной иерархической модели -при помощи критерия χ 2 , а логарифм правдоподобияln l(θ).…”

Section: обсуждение результатовunclassified

Modeling the Spatial Development of the US Cross-Border Regions as Mesolevel Socio-Economic Systems

Минат

2022

Ekonomicheskaya nauka sovremennoy Rossii

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The cross-border nature of the spatial development of economically and socially significant regions identified in the geospace of the largest federal states of the modern world necessitates their study from the standpoint of the mesoeconomics of development as complex, open and dynamic systems in space-time. The object of this study is the cross-border regions of the United States with Mexico and Canada, which are distinguished by a characteristic set of specific properties of both system-structural and socio-economic nature. According to the systemic economic theory, the identified cross-border regions of the United States show the principle of duality of spatial development, and within the framework of evolutionary theory – a specific “socio-economic genotype” of the territory. Using elements of mesoeconomic modeling, the author identified and substantiated some trends in the spatial development of different groups of cross-border regions of the United States over a long time period of 1970–2020 and as a forecast until 2030. Based on a comparative analysis carried out on the results of modeling and testing, the author's hypothesis about a multidirectional change in the dynamics of indicators of socio-economic spatial development, which depends on the regional features of the spatiotemporal evolution of specific transboundary territories, is confirmed – as socio-economic systems of the mesolevel. The obtained results, revealing the spatial dependence of the data, indicate the growth of spatial relationships of both social and economic nature within the studied cross-border regions of the United States, which determine the spatiotemporal evolution of the latter in the geoeconomic and geocultural space of the United States and neighboring states. Based on the results of mesoeconomic modeling, the existence of an evolutionary-cyclical nature and duality of the studied mesosystems is empirically confirmed, having their own socio-economic genotype of territories, depending on the insurmountable specifics of spatial diversity and unevenness in the conditions of the market organization of American society. The main conclusion is that regional interest, which has dialectical unity and variability, manifested over time, causes different directions and levels of spatial development of specific regions of the United States as integral dynamic and open socio-economic systems of the mesoterritorial level, reflecting the gradual increase in economic regionalization. The approach used, taking into account the specifics of spatial development, is of interest for the study of similar subject issues in relation to large federal states that have a transboundary nature of genetically determined socio-economic interaction.

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“…Если иерархический анализ требует обоснования целесообразности выделения групп, то пространственный анализ определяет необходимость предварительного определения наличия пространственной автокорреляции данных. Она преимущественно оценивается с помощью индекса Морана, хотя в целом для этого могут применяться и другие индексы: индекс Геттиса -Орда, индекс Джири (Wang et al, 2021). Значение индекса Морана (I) сравнивается с ожидаемым значением E(I) = -1 / (n -1).…”

Section: пэ № 4 2021unclassified

“…Принимая это во внимание, во второй половине прошлого века начали появляться работы, в которых осуществлялась попытка разделения влияния различных уровней иерархии управления . В последующем, опираясь на различные вариации показателей в разрезе административных уровней, обосновывалась необходимость перехода к использованию многоуровневых моделей Wang et al, 2021;Webber et al, 2019). Фактически в рамках таких исследований косвенно учитывались пространственные взаимодействия, т. к. вложенность одной территории в другую (поселений в районы, районов в регионы, регионов в страны) географически определена.…”

Section: Introductionunclassified

Economic Activity of Territories: Comparative Analysis of the Spatial Effects Assessing Methods

Timiryanova¹,

Zimin²,

Yusupov³

2021

Spatial Economics

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The article discusses a hierarchical and spatial approach to assessing the spatial dependence of data. The advantages and disadvantages of each approach and the potential for their combination are determined on the basis of a literature review.The results of the OLS, SAR, SEM, HLM, HSAR models are compared. Despite an interesting set of data (2285 municipalities in the context of 85 constituent entities of the Russian Federation), the emphasis in the work is not on identifying the relationship between the dependent variable and factors, but on comparing spatial effects that can be identified within each of the models under consideration. The calculations showed a significant influence on the dependent variable of the following factors: the share of the average number of employees in the resident population, the volume of investments in fixed assets per capita and the share of the urban population. This result was shown by all the constructed models. In the context of models, the identified spatial effects have their own characteristics. The inclusion of spatial matrices is possible at the upper (for example, the subject of the Russian Federation), lower (for example, the municipal level), or both levels simultaneously. In hierarchical models, spatial relationships are additionally taken into account by grouping the objects of observation on a territorial basis. Calculations have shown that the spatial lag is not significant in all models. Spatial error is significant at the municipal level in the SEM model and at the regional level in the HLM and HSAR models. Additionally, hierarchical models showed a significant influence of the region on the municipalities variation. In general, the results of modeling and evaluating modelsquality are ambiguous. Despite this, the potential for expanding spatial econometrics on the basis of a combination of spatial and hierarchical (multilevel) modeling approaches is noted, and the need to select a model for each case is substantiated, taking into account the significance of spatial and hierarchical effects

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Examining Multilevel Poverty-Causing Factors in Poor Villages: a Hierarchical Spatial Regression Model

Cited by 15 publications

References 38 publications

Prevalence and correlates of multidimensional child poverty in India during 2015–2021: A multilevel analysis

Prevalence and correlates of multidimensional child poverty in India during 2015–2021: A multilevel analysis

Modeling the Spatial Development of the US Cross-Border Regions as Mesolevel Socio-Economic Systems

Economic Activity of Territories: Comparative Analysis of the Spatial Effects Assessing Methods

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