A Poverty Measurement Method Incorporating Spatial Correlation: A Case Study in Yangtze River Economic Belt, China

The UN 2030 Agenda sets poverty eradication as the primary goal of sustainable development. An accurate measurement of poverty is a critical input to the quality and efficiency of poverty alleviation in rural areas. However, poverty, as a geographical phenomenon, inevitably has a spatial correlation. Neglecting the spatial correlation between areas in poverty measurements will hamper efforts to improve the accuracy of poverty identification and to design policies in truly poor areas. To capture this spatial correlation, this paper proposes a new poverty measurement model based on a neural network, namely, the spatial vector deep neural network (SVDNN), which combines the spatial vector neural network model (SVNN) and the deep neural network (DNN). The SVNN was applied to measure spatial correlation, while the DNN used the SVNN output vector and explanatory variables dataset to measure the multidimensional poverty index (MPI). To determine the optimal spatial correlation structure of SVDNN, this paper compares the model performance of the spatial distance matrix, spatial adjacent matrix and spatial weighted adjacent matrix, selecting the optimal performing spatial distance matrix as the input data set of SVNN. Then, the SVDNN model was used for the MPI measurement of the Yangtze River Economic Belt, after which the results were compared with three baseline models of DNN, the back propagation neural network (BPNN), and artificial neural network (ANN). Experiments demonstrate that the SVDNN model can obtain spatial correlation from the spatial distance dataset between counties and its poverty identification accuracy is better than other baseline models. The spatio-temporal characteristics of MPI measured by SVDNN were also highly consistent with the distribution of urban aggregations and national-level poverty counties in the Yangtze River Economic Belt. The SVDNN model proposed in this paper could effectively improve the accuracy of poverty identification, thus reducing the misallocation of resources in tracking and targeting poverty in developing countries.

Lo necesario, lo superfluo y la medición de la pobreza

El objetivo de este trabajo es unir la aportación de la filosofía escolástica con la técnica de la medición de la pobreza a fin de clarificar cuánto ingreso debe ser normativamente considerado para adquirir “lo necesario” para vivir. La escolástica ha diferenciado –desde Tomás de Aquino- los bienes necesarios, los socialmente necesarios y los superfluos. Sobre los dos primeros se reconocieron derechos de propiedad usufructuaria, pero no sobre los superfluos. Lo que el trabajo investiga es saber si la línea de pobreza absoluta, nacional o internacional (actualmente establecida en $1,90 diarios en PPP de 2011) da buena cuenta de la capacidad para adquirir “lo necesario”. Rechazada esta opción se propone un Índice de Acceso a lo Necesario y se analizan las consecuencias de políticas públicas que conlleva la ambigüedad de “lo necesario”. The goal of this work is to combine the contribution of scholastic philosophy with the technique of poverty measurement in order to clarify how much income should be normatively considered in order to acquire "what is necessary" to live. Scholasticism has differentiated - from Thomas Aquinas - the necessary, socially necessary and superfluous goods. On the first two rights of usufruct property were recognized, but not on the superfluous ones. What the research investigates is whether the absolute poverty line, national or international (currently set at $ 1.90 per day in PPP 2011) gives a good account of the capability to acquire "what is necessary". Once this option is rejected, an Index of Access to the Necessary is proposed and the consequences of public policies that entail the ambiguity of "what is necessary" are analyzed.

Poverty and its Alleviating Strategies among Rural Farming Households in Benue State, Nigeria

The study analysed rural farming households’ poverty status and alleviating strategies in Benue State, Nigeria. The specific objectives of the study were to: describes the rural household heads’ socio-economic characteristics; determine the poverty status of the respondents and its determinants; and identify poverty alleviating strategies of the respondents. Data for the study was collected from 420 respondents selected using a multi-stage sampling technique. Data collected were analysed using descriptive statistics, the Foster-Greer-Thorbecke poverty measurement index, and the Binary Logistic regression model. The findings of the study revealed a very high incidence of poverty (70%), having a gap of 0.34, and severity of 0.17. Poverty in the area is positively associated with the age of the household head and household size, while gender, educational level, off-farm activity, membership of a group, farm size, and land ownership are negatively associated with poverty. The common poverty alleviation strategies identified were agricultural wage labour (48.6%), rental services (45.0%), and transportation business (36.7%). Therefore, it was recommended that the government and other stakeholders should initiate sustainable social protection schemes that can assist rural residents in alleviating poverty until their condition improves.

Leaving No One Behind: An Individual-Level Approach to Measuring Multidimensional Poverty in Botswana

The Leave No One Behind principle is at the core of the 2030 Agenda for sustainable development and acknowledges that poverty is multidimensional and should be examined at individual level. Notwithstanding this, most empirical studies use the household as the unit of analysis for multidimensional poverty measurement. However, estimation of poverty levels at household-level underestimates poverty levels of the society and does not capture intra-household inequalities. The objective of this study is two-fold: (1) developing a country-specific individual-level multidimensional poverty measure; and (2) providing estimates of multidimensional poverty for Botswana. This study contributes to the limited literature on individual-level multidimensional poverty measurement. Empirically, this study offers the first attempt to estimate a nationally relevant and context-specific multidimensional poverty index for Botswana using the individual as a unit of analysis. The results reveal that an estimated 46.2% of individuals are considered multidimensionally poor based on individual-level analysis. This figure is higher than the household-level estimate of 36.5%, which indicates that using the household as a unit of analysis leads to underestimating poverty levels in the society. The results show that on average, the multidimensionally poor are deprived in 47.4% of all indicators under consideration. This finding indicates that multidimensional poverty intensity is also a considerable concern in Botswana. These findings warrant policy interventions.

Trends in Subjective Income Poverty Rates in the European Union

When developing anti-poverty policies, policymakers need accurate data on the prevalence of poverty. In this paper, we focus on subjective poverty, a concept which has been largely neglected in the literature, though it remains a conceptually appealing way to define poverty. The primary goal of this study is to re-examine the concept of subjective poverty measurement and to estimate trends in subjective income poverty rates in the European Union. Our estimations are based on a Minimum Income Question using data from a representative survey, EU-SILC. We find robust empirical evidence of decreasing trends in subjective poverty in 16 of 28 EU countries. We conjecture that trends in subjective poverty may reflect changes in societies which are not captured by official poverty indicators, and our results thus enrich the existing data on general poverty trends in the EU.

Urban poverty: Measurement theory and evidence from American cities

We characterize axiomatically a new index of urban poverty that i) captures aspects of the incidence and distribution of poverty across neighborhoods of a city, ii) is related to the Gini index and iii) is consistent with empirical evidence that living in a high poverty neighborhood is detrimental for many dimensions of residents’ well-being. Widely adopted measures of urban poverty, such as the concentrated poverty index, may violate some of the desirable properties we outline. Furthermore, we show that changes of urban poverty within the same city are additively decomposable into the contribution of demographic, convergence, re-ranking and spatial effects. We collect new evidence of heterogeneous patterns and trends of urban poverty across American metro areas over the last 35 years.

RURAL HOUSEHOLD POVERTY AND ITS DETERMINING FACTORS: A POVERTY ANALYSIS USING ALTERNATIVE MEASUREMENT APPROACHES

Poverty analysis studies in Ethiopia are dominated by measures determined by the subjectivity of the researchers and not with the involvement of households in understanding and measuring what is meant to the people. Studies conducted to take into considerations the knowledge of the poor are very scant and limited to rural participatory projects. This study is motivated to bridge the literature gap of comparing the objective measuring of poverty with a measure that accounts the perception of households about poverty. Hence, this study aims at examining rural household poverty and its determining factors using alternative poverty measurement approaches inGozamin district of East Gojjam Zone, Amhara Region. It used both objective and subjective based poverty analysis approaches, where the poverty line of the study area is estimated as 19.16 Birr per day using cost of basic needs approach. The study indicates that 35.12 % of the population lives under poverty and it is closely estimated as 33.33% using Participatory Poverty Assessment (PPA). Poverty is rampant and a sever challenge in Dega(cold) agro-ecology of the District, where 57.37% of the population lives under poverty compared to Kolla(hot), where it is down to 8.4%. Among others, family size and being in Kolla agro-ecology have significant negative effect on consumption expenditure, but positively affect poverty incidence, gap and severity, while access to credit, cooperative, health extension services and off-farm activities have significant but exactly opposite results. PPA findings revealed that, perception of the community towards poverty is beyond the conventional, income/consumption based definition. Therefore, development policies and poverty reduction strategies should emphasie on local level poverty understanding and measures.