Spatial Distribution Of Poverty Research Articles

The Spatial Effects of Regional Poverty: Spatial Dependence, Spatial Heterogeneity and Scale Effects

Recognizing the spatial effects of regional poverty is essential for achieving sustainable poverty alleviation. This study investigates these spatial effects and their determinants across three distinct administrative levels within Hubei Province, China. To analyze the spatial patterns and heterogeneity of multi-scale regional poverty, we employed various spatial analysis techniques, including the global and local Moran’s I statistics, the Lineman, Merenda, and Gold (LMG) method, as well as Multiscale Geographically Weighted Regression (MGWR). We found that: (1) Regional poverty exhibits significant spatial dependence across various scales, with a higher level of spatial dependence observed at higher administrative levels. (2) The spatial distribution of poverty is primarily influenced by geographical factors, encompassing first-, second-, and third-nature geographical elements. Notably, first-nature geographical factors make substantial contributions, accounting for 36.99%, 42.23%, and 23.79% at the county, township, and village levels, respectively. (3) The influence of geographical factors varies with scale. Global effects of various factors may transcend scales or remain confined to specific scales, while the local impacts of different factors also exhibit variations across scales. These results underscore the necessity for collaborative efforts among government entities at different levels with the anti-poverty measures tailored to local contexts.

Multidimensional Poverty Measurement in Segregated Cities

This paper illustrates the insights that can be gained from using a multidimensional poverty measurement method based on the capability approach to analyse the scale, characteristics, and spatial distribution of poverty in segregated cities. We assess the poverty gaps between formal and informal neighbourhoods and across districts, compare the results with income-based poverty estimates, and analyse the dimensional composition of poverty and disparities across population subgroups. The results show a weaker correlation between multidimensional and income-based measures and less overlap in terms of who is identified as poor in informal settlements than in the rest of the city. After adjusting for underrepresentation of the slum population in the household survey, informal settlements account for 10% of the city's population but nearly half of the multidimensionally poor. The results highlight the need to design poverty reduction policies that target informal neighbourhoods.

Spatial distribution of poverty in Pakistan: an asset-based approach

The main objective of this study is to construct a valid and reliable asset index at household level by using NSER-BISP data in order to compute asset poverty for provinces, districts, and tehsils of the Pakistan. An asset index may be better measure than current income or expenditure for gauging household’s long-term capacity for buying goods and services and its potential resilience to economic shocks. The study employs multiple correspondence analysis (MCA) to construct asset index contrary to principal component analysis (PCA), as MCA provides us weights and contributions of each dimension of binary variable separately. The average MCA score is showing the level of asset-based poverty wherein higher values of index are representing higher level of poverty. The findings indicate that incidence of asset-based poverty is differently observed across provinces and within provinces through disaggregation of the MCA score at district and tehsil levels. By and large, the poorest districts of Pakistan are belonging to Baluchistan (i.e., Sherani, Kohlu, Chaghi, and Dera Bugti) and Sindh (i.e., Badin, Umerkot, Tando Muhammad Khan, and Tharparker) provinces; however, districts of Punjab (i.e., Lahore, Rawalpindi, and Gujranwala) province are found relatively lower asset-based poverty. Further, the analysis highlights the prevalence of asset-based poverty at tehsils level as well where again the tehsils of Baluchistan and Sindh provinces are bearing the highest asset-based poverty. Furthermore, the study also contributes by visualizing the prevalence of geographical asset-based poverty at district level for all four provinces of Pakistan by GIS mapping.

Using Satellite Data to Guide Urban Poverty Reduction

Poverty reduction in low‐ and middle‐income countries is increasingly an urban challenge, and a challenge that continues to be constrained by lack of data, including data on the spatial distribution of poverty within cities. Utilizing existing household survey data in combination with Convolutional Neural Networks (CNN) applied to high‐resolution satellite images of cities, this study shows that existing data can generate detailed neighborhood‐level maps providing key targeting information for an anti‐poverty program. The approach is highly automatic, applicable at scale, and cost‐effective. The method also provides direct support for policy development, as illustrated by the case study, where the Government of Mozambique is implementing an urban social safety net program, targeting poor urban neighborhoods, utilizing the estimated poverty maps.

Do remote areas benefit from economic growth? Evidence from Uganda

AbstractOne of the most universal patterns in the spatial distribution of poverty in developing countries is that the incidence of poverty is lower in urban areas than in rural areas. It is widely accepted, though less well documented, that remote rural areas tend to be poorer than areas with good market access. Furthermore, there is concern that remote rural areas may not benefit equally from economic growth. In this paper, we examine poverty trends in rural Uganda to test whether remote rural areas benefit from economic growth to the same degree as better connected rural areas. Applying a variant of small‐area estimation methods to household survey data and several Demographic and Health Surveys carried out over 10 years, we confirm that remote rural areas are poorer than better connected rural areas, but find no evidence that they have fallen further behind over this period.

Uma análise espacial da pobreza no Estado de Alagoas

No presente trabalho buscou-se analisar a distribuição espacial da pobreza nos municípios alagoanos, a partir do conceito de pobreza absoluta. Para esse fim, utilizou-se a técnica de análise exploratória de dados espaciais e os modelos de econometria espacial. Os dados utilizados nesta pesquisa foram obtidos junto ao Programa das Nações Unidas para o Desenvolvimento (PNUD). Dentre os resultados obtidos, pode-se destacar três: a) existem fortes discrepâncias intrarregionais no estado, principalmente entre as mesorregiões Leste e Sertão; b) há uma relação inversa entre a pobreza e as variáveis renda per capita e taxa de urbanização, enquanto que ocorre o contrário com relação ao índice de Gini e c) há uma dependência espacial da pobreza, ou seja, há um certo grau de similaridade entre os municípios analisados, de modo que a pobreza em um município é explicada, em parte, pela pobreza nos municípios vizinhos. Esses resultados são relevantes, pois fornecem evidências relacionadas à importância da dependência espacial sobre pobreza dos municípios alagoanos, sendo útil para subsidiar políticas públicas que busquem promover o desenvolvimento dessas localidades.

Spatial poverty traps in rural China: Aggregation, persistence, and reinforcement

Poverty is a challenge faced by all countries across the world. How to effectively reduce poverty has always been an issue of scholars and governments, and understanding poverty traps and the causes is the basis for understanding poverty governance. Current research has no longer only attributed poverty to “lack of capital formation,” but put more emphasis on the significance of spatial factors. By using China's county‐level large sample panel data, this paper identifies poverty traps in rural China from three dimensions: aggregation, persistence and reinforcement. Research in this paper shows that from 2006 to 2015, there was nearly no change in spatial pattern of poverty in rural China, and the spatial distribution of poverty is not synchronised with economic growth. Distribution of poverty‐stricken counties is mainly related to topographic factors (slope and elevation). In addition, rural poverty in China is persistent, and there is not only a low‐level equilibrium of income in poverty‐stricken counties, but also a “club convergence” of income between poverty‐stricken and non‐poverty counties. Further research has also found that income in poverty‐stricken counties has a “spatial spillover” effect, so poverty also manifests itself as spatial self‐reinforcement. Research in this paper reveals the relationship between spatial externalities and rural poverty. It not only provides empirical evidence for correctly assessing the relationship between development and poverty, it also offers support for reasonably formulating a regional anti‐poverty targeting mechanism.

Spatial analysis of urban poverty in urban spaces (case study: Noorabad city)

IntroductionIncreased urbanization has led to urban poverty. In developing countries, rapid urban growth is typically greater than the capacity of local governments in providing services and infrastructure. This leads to an increase in urban poverty and inequality within the city. In recent decades, due to the significant concentration of poverty in the city, discussions and conversations about the causes and consequences of poverty have become prevalent with a renewed importance.The city of Noorabad has witnessed the formation of slum areas in some urban spaces, due to the rapid population growth over the past decades and the inability to provide proper services and facilities to citizens. In order to plan and reintegrate the situation of the poor, initially, the location and spatial distribution of poverty must be exactly identified in the city. Then with careful planning, this challenging urban problem may be solved.Materials and MethodsThe research method is based on descriptive-analytic approaches and detailed data were obtained at the level of statistical blocks of Noorabad city in 2011. Also some relevant information collected from secondary studies were used. First, the 32 indicators of urban poverty are un-scaled with fuzzy method in Excel and then, by combining the indicators, the combined indexes of urban poverty were extracted. For spatial analysis of the integrated urban poverty index, several spatial cluster analysis models such as spatial Moran spatial, local Moran statistics, hot spots, profile graphs and trend analysis in ArcGIS software were used.Results and Discussion The results of multi-distance spatial cluster analysis and Moran spatial autocorrelation show that amounts of the combined index of urban poverty with clustered pattern is distributed in Noorabad city. Based on the results of local Moran and hotspot methods, poverty clusters are concentrated in the northern parts of the city and rich clusters are in the southern parts. Also, based on the combined index of urban poverty, 51 percent of the population of the city are poor and very poor, and 31 percent of its population are rich and very rich, and 18 percent of its population are located in the middle income areas. Based on the profile of the combined index of urban poverty, the west and south of the city are considered to be the prosperous area and the eastern and northern parts of the city are deprived zones. The trend analysis of the combined index of urban poverty also showed that the trend in the data of the combined index of urban poverty in the east-west direction and in the north-south direction has increased significantly.ConclusionPoverty is one of the socially undesirable phenomenon that planners and policymakers are always seeking to reduce. The success of poverty alleviation programs depend on the exact identification of the phenomenon of poverty and its indicators. Urban areas are very diverse and there are significant differences in different regions and in particular urban areas. Hence, measuring urban poverty is also important in urban areas. In this research, by using available tools in Arc GIS, spatial analysis of urban poverty in the city Noorabad was performed. The results of the analyzing patterns show that the combined indexes of urban poverty have been distributed with cluster patterns across the city. According to the results of the mapped clusters, poverty clusters are concentrated in northern parts of city and rich clusters in the southern parts. Also, based on the zoning the combined index of urban poverty, 51 percent of the population of the city are poor and very poor. According to the results, it can be stated that the results of this study are based on the clustering pattern of urban poverty distribution corresponding with the results of most researches presented in the study, including Gio et al (2018), Nikpour et al (2019), Nikpour and Hasanalizadeh (2018), Farhadikhah et al (2017), Rustai et al (2016), and Movahed et al (2016).

Análise da dimensão espacial da pobreza em Minas Gerais

Dadas as disparidades regionais que caracterizam o estado de Minas Gerais, torna-se cada vez mais relevante investigar o comportamento da taxa de pobreza e a forma como os municípios pobres se distribuem no estado. Nesse sentido, o objetivo desse trabalho é investigar a distribuição espacial da pobreza no estado de Minas Gerais. Para isso, utilizou-se do método econométrico espacial para averiguar a presença de autocorrelação espacial. A partir da análise exploratória dos dados foi possível constatar a presença de dependência espacial entre as variáveis e a formação de clusters de pobreza. Por meio da regressão estimada pelo Método das Defasagens Espaciais foi possível ratificar a presença de dependência espacial, confirmada pela relevância da variável pobreza na vizinhança (variável pobreza defasada) no modelo. Portanto, conclui-se que a pobreza de um município é explicada, em parte, pela pobreza de seus municípios vizinhos.

Determinants of Economic Segregation and Spatial Distribution of Poverty

Purpose - While many related prior studies have focused on the segregation by race and ethnicity, the academic interest in the separation of residence...

The Spatial Distribution of Poverty and the Long Reach of the Industrial Makeup of Places: New Evidence on Spatial and Temporal Regimes.

A wealth of research identifies industrial structure as a central correlate of place-level poverty and suggests that changes in and the clustering of industry contribute to the spatial clustering of poverty over time. However, few studies have investigated the spatial and temporal dimensions simultaneously, and none have effectively examined spatio-temporal interactions. Consequently, a core tenet of theory on poverty in place has not been adequately examined. To address this limitation, we explicitly test hypotheses about systematic variation in the poverty-industry relationship over time and across space using a new method to quantify dynamic associations by simultaneously accounting for spatial and temporal autocorrelation and relationship heterogeneity. The Upper Midwest is our study site given dramatic regional changes in dominant industries (i.e., manufacturing, services, and agriculture) and poverty during the past several decades. We find that the specific character of the poverty-industry relationship systematically varies along both the temporal and spatial dimensions: industry is more protective in certain periods than in others according to sector trends, and is more protective in certain places than others conditional on sector dependence. Our approach yields a more precise and reliable understanding of the long reach of local industrial structure on the spatial clustering of poverty.

Improving the moringa value chain in Bangladesh and linking growers with the market

The demand for food in Bangladesh and around the world is changing rapidly. Driven by economic growth, rising incomes, and urbanization, demand is shifting away from traditional staples toward high-value vegetable commodities. In Bangladesh, additional demand for these commodities is projected to be worth about $10 billion by 2020. More than 80% of people living on less than $ 2.5 a day in Bangladesh live in rural areas. This spatial distribution of poverty makes capitalizing on the opportunities afforded by high value vegetables like moringa production an important strategic priority for those seeking to reduce poverty in the country. Insufficient processing capacity, the lack of cold storage facilities or a functioning cold chain, and the persistence of transport bottlenecks are significant constraints to high value moringa production in Bangladesh. The promise of generating higher income and increased export revenues by accessing international markets is matched by the challenges of meeting the exacting quality and safety standards that apply in those markets and by the prospect of having to compete with high quality imports from those markets. There is limited processing of moringa in Bangladesh leading to value addition. The majority of moringa produce is not processed at all and, thus lacks any value addition. Most farmers do not have adequate knowledge of moringa processing and value addition. This paper mainly deals with the present status, value addition, processing and marketing of moringa in Bangladesh. Suggestions were also made on the improvement of the moringa value chain, market access and generation of additional household income.

Mapping poverty using mobile phone and satellite data.

Poverty is one of the most important determinants of adverse health outcomes globally, a major cause of societal instability and one of the largest causes of lost human potential. Traditional approaches to measuring and targeting poverty rely heavily on census data, which in most low- and middle-income countries (LMICs) are unavailable or out-of-date. Alternate measures are needed to complement and update estimates between censuses. This study demonstrates how public and private data sources that are commonly available for LMICs can be used to provide novel insight into the spatial distribution of poverty. We evaluate the relative value of modelling three traditional poverty measures using aggregate data from mobile operators and widely available geospatial data. Taken together, models combining these data sources provide the best predictive power (highest r2 = 0.78) and lowest error, but generally models employing mobile data only yield comparable results, offering the potential to measure poverty more frequently and at finer granularity. Stratifying models into urban and rural areas highlights the advantage of using mobile data in urban areas and different data in different contexts. The findings indicate the possibility to estimate and continually monitor poverty rates at high spatial resolution in countries with limited capacity to support traditional methods of data collection.

The suburbanization of poverty? An alternative perspective

A popular and powerful narrative focuses on a crisis of suburban decline in the United States. However, a consensus regarding the scope and scale of one dimension of suburban decline—poverty—is hindered by the use of contradictory definitions of suburban space. This research presents an alternative approach to measuring suburban poverty that is less computationally intensive yet capable of capturing complex shifts in the spatial distribution of poverty within metropolitan areas. An analysis of the distribution of poverty in the largest 100 metropolitan areas between 1990 and 2007–11 concludes that while poverty is increasing in the low-density suburbs of a handful of large metropolitan areas, the more general trend in most other metropolitan areas is an increase in poverty in moderately dense residential areas. Implicated in these trends are long-term trends in metropolitan area economic growth, a secular decline in inner-ring suburbs, and the impact of gentrification on housing opportunities for at-risk populations in large cities.

Impact of Equivalence Scales on The Spatial Distribution of Poverty in Cameroon: A Dynamic Approach

The purpose of this study is to investigate the integration of the relative cost of children to adults and of economies of scale in the context of household’s welfare evaluation. Results derived with empirically-estimated scales show that female-headed households are poorer than male-headed households. Poverty is also found to be more prevalent in rural areas, in households whose heads are illiterate, and in households whose heads work in the informal sector. As for poverty dynamics, the results show that poverty decreased in Cameroon between 1996 and 2001. Despite the fact that some results are the same as those derived from the Recommended Dietary Allowances (RDA) scales used by Cameroon’s National Institute of Statistics, many stand in stark contrast to these. The results of the present study are used to recommend specific policies in favor of poverty alleviation.

An Assessment of the Incidence of Poverty Among Rural Active Population in Mangu Local Government Area, Plateau State, Nigeria

The study investigated the incidence and spatial distribution of poverty among rural active population in Mangu Local Government of Plateau State, Nigeria. The objectives of the study were to determine the socio-demographic and economic characteristics of the respondents; the incidence and spatial distribution of poverty and the causes of poverty in the study area. A sample size of 500 was adopted for the study. The study area comprised of 11 districts and over 260 settlements. Five districts and 25 settlements were selected using the random and systematic sampling techniques. The questionnaire, focus group discussions and in-depth interviews were used as tools for data collection from the respondents. The data were analyzed using different statistical techniques such as descriptive, correlation analyses as well as ANOVA. The finding reveals that there is high and spatial variation in level of poverty in the study area. Many reasons were identified to have influenced poverty in the study area such as failure of agriculture due to high cost of farm inputs, crises, decline of tin mining activity in the study area as well as low level of education, and low income among others. Recommendations were made for policy makers and implementers to empower the rural areas and enhance the development of the country side.Keyword: Incidence, Poverty, Rural, Active Population

When the Remedy is Worse than the Disease: Adjusting Survey Income Data for Price Differentials, with Special Reference to Mozambique

In using survey data for money metric analysis of poverty and well-being, it is customary to adjust either the data or the poverty line for spatial prices differentials where data exist to make such adjustment. In developing countries where recorded price differentials between regions or provinces are large, using the remedy of adjusting for price differentials may sometimes lead to very wrong conclusions about the spatial distribution of poverty. This may have severe consequences for policy and may be detrimental to the poor. The paper deals with a specific situation, that of Mozambique, where large price differentials are said to exist between the capital (Maputo City) on the one hand, and the rest of the country. Official data that adjust for this may heavily over-estimate poverty in Maputo City, with consequences for the targeting of poverty. We use an asset index based on Multiple Correspondence Analysis (MCA) to show that the spatial poverty profile derived from the price-adjusted income data exaggerates poverty in Maputo City, and undertake further empirical analysis to show that not adjusting for the estimated spatial price differentials may have given more reliable estimates of well-being, judging by asset holdings.

The place of 'place' in theories of poverty: mobility, social capital and well-being

This article argues that theories about the spatial distribution of poverty since World War II have always combined analyses of the impact of the built environment, of culture and of mobility of populations. However, the form taken by these combinations has varied with the logic of the underlying political economy adopted by governments in the USA, UK and continental Europe. The author argues that a new version may be emerging, reflecting ideas from theory on social capital and well-being.

Impact of Equivalence Scales on the Spatial Distribution of Poverty in Cameroon: A Dynamic Approach

The purpose of this study is to investigate the integration of the relative cost of children to adults and of economies of scale in the context of the evaluation of household welfare. Results derived with empirically-estimated scales show that female-headed households are poorer than male-headed households. Poverty is also found to be more prevalent in rural areas, in households whose heads are illiterate, and in households whose heads work in the informal sector. As for poverty dynamics, the results show that poverty increased in Cameroon between 1996 and 2001. Despite the fact that some results are the same as those derived from the Recommended Dietary Allowances (RDA) scales used by Cameroon's National Institute of Statistics, many stand in stark contrast to these. The results of the present study are used to recommend specific policies in favour of poverty alleviation.

Changing poverty distribution in Bolivia: the role of rural–urban migration and urban services

Bolivia is a country with high levels of poverty and inequality among its peoples and regions. For the nation and its urban and rural areas, trends in the social and spatial distribution of poverty (and extreme poverty) are identified from 1976 to 2003 using UBN data with minor support where appropriate from poverty lines. The main survey between 1992 and 2001 uses composite and selected UBN to track detailed poverty change for the country’s nine departments, its ten largest cities and a selection of other smaller urban and rural municipalities. Because of rising background increases in population in the various surveyed administrative units, many instances of relative reductions in poverty are accompanied by rising absolute increases. Marked spatial variations in poverty and development in the country over the last several decades are identified as the main driver for the country’s quickening pace of rural–urban migration. As a result, the paper concludes by assessing two different but closely related views. One investigation tests the notion that because more poor people have been living in Bolivia’s cities than in its rural areas since the mid to late 1990s, rapid rural–urban migration has simply shifted the locus of poverty from the countryside to the cities in a process called, the ‘urbanisation of poverty.’ A second, more challenging, investigation assesses the view that the flow of poor rural people to the better serviced urban areas of Bolivia has actually acted to alleviate national poverty levels.