Spatial Patterns Of Poverty Research Articles

Spatial patterns of poverty and forest cover dynamics in the Vietnamese Highlands

Spatial patterns of poverty and forest cover dynamics in the Vietnamese Highlands

Contrasting monetary and multidimensional poverty in India: a spatial perspective

The present study makes a comparative analysis on the spatial pattern of poverty by considering both monetary and multidimensional approach and assesses the variation or gap in poverty estimation. Two latest available datasets i.e. NSSO data of planning commission report regarding monetary or consumption poverty and the baseline report on multidimensional poverty by NITI Aayog have been used. Our analysis shows that significant interstate disparities are observed in the spatial concentration of poverty applying two different approaches. Extreme monetary poverty persist in states like Chhattisgarh (39.9 percent), Karnataka (37.0 percent), Meghalaya (36.9 percent) etc. whereas extreme multidimensional poverty exists in Bihar (51.9 percent) followed by Jharkhand (42.2 percent). The result reveals the fact that though monetary poverty and multidimensional poverty are associated with each other the strength of relationship between these two approaches is not strong enough. The study strongly recommends the incorporation of the multidimensional approach along with the consumption approach to measure poverty which has high policy relevance.

Factors affecting poverty using a geographically weighted regression approach (case study of Java Island, 2020)

Poverty is still the main problem in development both at the national and regional levels. The poverty reduction program carried out has not paid attention to spatial aspects so that the policies taken are often not on target. This study aims to see the spatial pattern of poverty in Java Island which includes Banten, DKI Jakarta, West Java, Central Java, DI Yogyakarta and East Java. The method used is geographically weighted regression (GWR) with addiptive weighting of the Gaussian Kernel which is processed with QGIS, Geoda and GWR4 software. This approach can identify spatial patterns that cannot be identified in ordinary regression analysis as found in previous studies. The data used in this study is secondary data in 2020 sourced from the Badan Pusat Statistik (BPS) and government website. The results of the study showed positive and group spatial autocorrelation in 34 districts/cities. There are 65 districts/cities in Java Island only affected by HDI, 4 districts/cities affected by TPT and HDI, 47 districts/cities affected by MSEs and HDI, and 3 districts/cities affected by TPT, UMK and HDI. The government can improve the quality of education, the level of public health services, and provide job training to reduce poverty.

Evaluating the access of slum residents to healthcare centers in Kermanshah Metropolis, Iran (1996–2016): A spatial justice analysis

BackgroundProper access to health care centres and services is one of the key indicators of health justice, and it is more than ever important in slums. ObjectiveThis aim of this research is to evaluate the accessibility of health care centres to slum residents in the Kermanshah metropolis, Iran during the period 1996–2016. MethodsIn this cross-sectional study, data was obtained from the Census of Iran for the periods 1996, 2006 and 2016. Information on the number and location of health care centres was collected from the Kermanshah University of Medical Sciences. Network Analysis modelling method in Arc/GIS10.6 software was used to evaluate the accessibility of people to health centres. ResultsThe results show that the spatial pattern of health centres in Kermanshah was random during 1996, 2006 and 2016, but the spatial pattern of poverty in the metropolis was clustered. In addition, the distribution of health centres was not consistent with the population densities. However, the overall population with inappropriate access to health centres in the slums of Kermanshah metropolis decreased over the study period (1996–54.02%, 2006–51.09%, and 2016–34.71%). ConclusionsThe findings of the study reveal that access to health care services by the slum population is not consistent with the increase of health care centres. This means that health policymakers were unsuccessful to provide the required health care services for the slums.

Spatial Autoregressive Model and Spatial Patterns of Poverty In Lampung Province

Many research in economics only focus on the independence of a region while neglecting the effects of space and the interaction that occurs between mutually adjacent areas. The purpose of this study is to measure the multidimensional poverty concept in 15 districts/cities in the province of Lampung in 2015-2019. Spatial analysis such as moran i statistics, LISA clustered map, and lisa signification are used to analyze spatial patterns and spatial autocorrelation. Spatial modeling with spatial autoregressive model, geoda and geographical information systems are used as explanatory spatial data and spatial modeling. The results show that the percentage of poor people between districts/cities in Lampung Province have positive Moran's I values, there is a clustered pattern in 2015-2019, Moran scatter plot depicts 4 quadrants, LISA Cluster map indicates high-high and low-low areas, and LISA map has 4 significant areas. Spatial regression results show that per capita expenditure for nonfood has a negative effect, per capita expenditure for food has a positive effect, population growth rate has a positive effect, household clean water has a positive effect, life expectancy has a negative effect, mean years of schooling has a negative effect, and simultaneously the independent variables have a significant influence on the percentage of poor people. Poverty in Lampung Province is spatially related to each other between regions, the findings suggest that the variables used affect spatially. The implication of this result is one of the basis for inter-regional policies in the interests of multi-dimensional poverty alleviation between regions.Keywords: Poverty, Spatial analysis, Spatial Autoregressive Model (SAR)

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.

A Feasibility Study of Implementing the Policies on Increasing Birth Rate with an Emphasis on Socio-economic Status: A Case Study of Kermanshah Metropolis, Western Iran

Population and development have a mutual relationship, and more to the point, lack of sufficient knowledge on population development and fertility can lead to the presentation of inappropriate population plans in such a way that they may turn out to be useless and ineffective even in the event of implementation. So, the present study aimed to delve into the feasibility of implementing the new governmental policies on increasing birth rate with an emphasis on the socioeconomic status in Kermanshah Metropolis, Iran. To evaluate poverty and fertility in Kermanshah Metropolis, the present cross-sectional study used the latest statistics obtained from the Statistical Center of Iran (2011), and as for the data analysis, such tests in Arc/GIS and SPSS 20 software. The results of the present study showed that the spatial patterns of poverty and fertility were formed in clusters across the city, and the results of variance analysis of the relationship between the variables of fertility and poverty demonstrated that there was a significant difference between the selected neighborhoods in terms of the variables of poverty and total fertility (P < 0.001). Furthermore, the spatial autocorrelations of fertility and poverty in Kermanshah were positive and showed that the spatial means and standard distances of poverty and fertility were overlapping. Not all districts and urban neighborhoods were equal in the total fertility rate. So, given the societal classes’ different socioeconomic and cultural status across different neighborhoods, the government’s new approaches to increasing birthrate cannot be applied to all classes throughout Kermanshah Metropolis.

Review: Confronting Suburban Poverty in America

This book synthesizes research undertaken over a number of years by the Brookings Institution as part of its Metropolitan Opportunity series, and funded inter alia by the Ford and Annie E. Casey Foundations. It is intended to make a serious and substantial contribution to public policy debates in North America about the changing nature of poverty, in particular its spatial manifestation, and what might be done about it. In this respect, it follows in the long tradition of excellent work coming out of the Institution and supported by Foundations such as these. But the book also sets out to provide a much-needed update on the more recent causes and spatial patterns of poverty in America and to highlight the significance of more recent processes of suburbanizing poverty. Kneebone and Berube have achieved their goal of bringing together in a clear and accessible form a rigorous analysis of the new dynamics of American poverty and its increasing concentration in suburban areas rather than the traditional foci of inner city areas and more remote rural communities.

An Application of Bayesian Methods to Small Area Poverty Rate Estimates

Efforts to estimate various sociodemographic variables in small geographic areas are proving difficult with the replacement of the Census long-form with the American Community Survey (ACS). Researchers interested in subnational demographic processes have previously relied on Census 2000 long-form data products in order to answer research questions. ACS data products promise to begin providing up-to-date profiles of the nation’s population and economy; however, unit- and item-level nonresponse in the ACS have left researchers with gaps in subnational coverage resulting in unstable and unreliable estimates for basic demographic measures. Borrowing information from neighboring areas and across time with a spatiotemporal smoothing process based on Bayesian statistical methods, it is possible to generate more stable and accurate estimates of rates for geographic areas not represented in the ACS. This research evaluates this spatiotemporal smoothing process in its ability to derive estimates of poverty rates at the county level for the contiguous United States. These estimates are then compared to more traditional estimates produced by the US Census Bureau, and comparisons between the two methods of estimation are carried out to evaluate the practical application of this smoothing method. Our findings suggest that by using available data from the ACS only, we are able to recreate temporal and spatial patterns of poverty in US counties even in years where data are sparse. Results show that the Bayesian methodology strongly agrees with the estimates produced by the SAIPE program, even in years with little data. This methodology can be expanded to other demographic and socioeconomic data with ease.

Vietnam's Coastal Communities: An Assessment of Poverty

A Vietnamese geographer examines factors leading to the persistence of poverty in the coastal regions of Vietnam, against the backdrop of the country's overall recent success in poverty amelioration. After reviewing the spatial pattern of poverty for Vietnam as a whole, based on current government statistics, the author contrasts the situation in the country's interior relative to coastal locations, based on a detailed survey of over 200 households in Khanhhoa Province, located in south-central Vietnam. The changing labor market conditions in East Asia are discussed briefly in light of continued poverty reduction in the country. Journal of Economic Literature, Classification Numbers: D31, I31, O15, P20. 4 figures, 5 tables, 33 references.

It is where you are that matters: the spatial determinants of rural poverty in India

AbstractThe spatial patterns of poverty in India are of considerable importance in themselves and for development theory and practice. This article examines the determinants of rural poverty in India using spatial econometric methods. It finds that while agricultural growth is the key determinant of rural poverty declines, there is significant spatial dependence in the growth rates of agricultural output. Irrigation is the primary driver of agricultural growth, and spatial variations in irrigation development seem to be associated with agro‐ecological conditions which may be vastly different within Indian states, parts of which may be similar to those prevailing in geographically contiguous states. Poverty reduction strategies need to be designed in the light of spatial factors and using spatial methods.

Spatial patterns of poverty in Vietnam and their implications for policy

This study examines the geographic distribution of poverty in Vietnam by applying small area estimation methods to household budget data and population census data. The resulting district-level poverty estimates suggest that the incidence of poverty is highest in the remote northern and central highlands and lowest in the south-east and in large urban centres. However, mapping the density of poverty reveals that most poor people do not live in the poorest districts but in the two lowland deltas, where poverty incidence is intermediate. The policy implications of these findings present an important trade-off between targeting poor areas and poor people that can only be resolved with better information on the relative costs of delivering different programmes and their expected impact. Existing government estimates of poverty at the district level are not closely correlated with our poverty estimates, perhaps because of regional variation in their methods of collecting poverty data.

World poverty: a reference handbook

World Poverty provides an authoritative and balanced examination of the many facets of world poverty and the policy issues surrounding it. World Poverty: A Reference Handbook provides an authoritative overview of world poverty as it stands today. Economic expert Geoffrey Gilbert offers a balanced examination of the controversies and policies surrounding world poverty and addresses such fundamental issues as the definition of poverty and the construction of indicators and indices. In clear terms, this reference work sheds light on spatial patterns of poverty around the globe; the quality of health, food, shelter; and the commitments of the international community. Issues of special interest such as globalization, effectiveness of foreign aid, corruption, and goals for poverty reduction are presented from diverse angles. As with all volumes in the series, this essential reference includes biographical profiles, pivotal documents, and detailed listings of organizations and resources. * Annotated timeline of significant events, conferences, and declarations, dating from the 8th century to the present * Diverse biographical sketches of key individuals, including Jeffrey Sachs and James Wolfensohn

Regional or National Poverty Lines? The Case of Uganda in the 1990s

Absolute poverty lines are often derived from the cost of obtaining sufficient calories. Where staples vary across regions, such poverty lines may differ depending on whether they are set using national or regional food baskets. Regional poverty lines are open to the objection that they may be contaminated by income effects. This paper explores this issue by focussing on Uganda, a country where widening spatial inequalities in the 1990s have caused concern. Conflicting results from earlier studies have suggested that the spatial pattern of poverty in Uganda is very sensitive to whether national or regional food baskets are used in setting poverty lines. We confirm this suggestion by comparing the spatial profile of poverty in 1993 using national and regional poverty lines. However, since the regions consuming the more expensive staple sources of calories are also those with higher incomes, using simple regional poverty lines is problematic. Instead, a method of setting regional poverty lines is considered that adjusts for income differentials between regions. Even with this adjustment, the use of regional food baskets implies a markedly different

Life and death of the people of London: a historical GIS of Charles Booth's inquiry

Social reformer Charles Booth undertook a massive survey into the social and economic conditions of the people of London at the end of the 19th century. An important innovation of his Inquiry was the construction of large, detailed maps displaying social class of inner London on a street-by-street basis. These provide a detailed and vivid picture of the geography of poverty and affluence at this time. These maps have been digitised, georeferenced and linked to contemporary ward boundaries allowing Booth's measurement of social class to be matched to the measurement of social class in the 1991 census of population and standardised mortality ratios derived for all causes of death in the survey area between 1991 and 1995. The social class data were used to derive an index of relative poverty for both time periods and a comparison of the geographies of relative poverty and their relationship with contemporary mortality was made. Although the overall standard of living had increased, the geography of poverty at the end of the 19th century was very similar to that at the end of the 20th century. Moreover, the geography of all causes of death for people over the age of 65 was more strongly related to the geography of poverty in the late 19th century than contemporary patterns of poverty. This relationship was also true for mortality for specific diseases that are related to deprivation in early life. The paper concludes that the spatial patterns of poverty in inner London are extremely robust and a century of change has failed to disrupt it.

Rural Welfare in Fujian, 1976–1978: The Maoist Legacy

The literature on interregional disparities in China has become quite extensive, as has that on absolute poverty and its regional incidence. But most of this work deals with huge “regions” – entire provinces or even groups of provinces (“coastal” versus “western” China) – each of which is comparable, in area and population, to a good-sized country. Needless to say, there may well be large variations within such regions at any point in time, and large spatial shifts within them over time. Many studies suggest, for example, that, contrary to the “Maoist model,” relative disparities among provinces did not narrow significantly during the Maoist era. Can the same be said of disparities among counties, within individual provinces? And are the broad spatial patterns of poverty, as observed across provinces, somehow replicated in microcosm within particular provinces?