The Geography of American Poverty: Is There a Need for Place-Based Policies?

Partridge, Mark D.; Rickman, Dan S.

doi:10.17848/9781429454872

Cited by 81 publications

(78 citation statements)

References 2 publications

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“…This is particularly true in analyses related to healthcare (e.g., incidence of diseases and access to medical services e.g., [40][41][42][43]), environmental protection (e.g., [44][45][46]), the real estate market (e.g., [47,48]), poverty (e.g., [49,50]) and migration (e.g., [51][52][53][54]). The GWR tool is also used in labour market analyses (e.g., [55][56][57][58]).…”

Section: Methodsmentioning

confidence: 99%

Geographically Weighted Regression in the Analysis of Unemployment in Poland

Lewandowska-Gwarda

2018

IJGI

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Abstract:The main aim of this paper is an application of Geographically Weighted Regression (which enables the identification of the variability of regression coefficients in the geographical space) in the analysis of unemployment in Poland 2015. The study is conducted using 2015 statistical data for 380 districts (LAU 1) in Poland. The research results show that the determinants of unemployment are diverse in the geographic space and do not have a significant impact on unemployment rates in all spatial units (LAU 1). The existence of clusters of districts, characterised by the influence of the variables and a similar strength of interactions, is confirmed. Geographically Weighted Regression (GWR) proved to be an extremely effective instrument of spatial data analysis. The model had a considerably better fit with empirical data than the global model, and it enabled the drawing of detailed conclusions concerning the local determinants of unemployment in Poland.

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Section: Methodsmentioning

confidence: 99%

Geographically Weighted Regression in the Analysis of Unemployment in Poland

Lewandowska-Gwarda

2018

IJGI

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“…Many factors have been reported in cultural studies, economics, psychology and sociology to examine residents' perceptions of tourism development (Blake, Arbache, Sinclair, & Teles, 2008;Eadington & Smith, 1992;Echtner, 1995;Deller, Marcouiller, English, & Lledo, 2005;Johnson & Stewart, 2005;McGranahan, 1999;Partridge & Rickman, 2006). In spite of the positive economic impacts of tourism development, the effect of local culture on it has not been studied extensively.…”

Section: Conceptual Backgroundmentioning

confidence: 99%

Residents’ perceptions of tourism development – implications for small economies business: the case study of Ali Sadr, Iran

Ahadian

2013

Current Issues in Tourism

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“…The increasing poverty rate in both urban and rural areas is due to the low quality of human resources, of which education is an important indicator [3], [4]. Additionally, poverty is also associated with interregional spatial interactions associated with population mobility and spatial impoverishment [5]. Crandall and Weber [6] explained that poverty has a spatial interaction.…”

Section: Introductionmentioning

confidence: 99%

Application of spatial error model using GMM estimation in impact of education on poverty alleviation in Java, Indonesia

Januardi¹,

Utomo²

2017

CST

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Java Island is the center of development in Indonesia, and yet poverty remains its major problem. The pockets of poverty in Java are often located in urban and rural areas, dominated by productive age group population with low education. Taking into account spatial factors in determining policy, policy efficiency in poverty alleviation can be improved. This paper presents a Spatial Error Model (SEM) approach to determine the impact of education on poverty alleviation in Java. It not only focuses on the specification of empirical models but also in the selection of parameter estimation methods. Most studies use Maximum Likelihood Estimator (MLE) as a parameter estimation method, but in the presence of normality disturbances, MLE is generally biased. The assumption test on the poverty data of Java showed that the model error was not normally distributed and there was spatial autocorrelation on the error terms. In this study we used SEM using Generalized Methods of Moment (GMM) estimation to overcome the biases associated with MLE. Our results indicate that GMM is as efficient as MLE in determining the impact of education on poverty alleviation in Java and robust to non-normality. Education indicators that have significant impact on poverty alleviation are literacy rate, average length of school year, and percentage of high schools and university graduates.

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The Geography of American Poverty: Is There a Need for Place-Based Policies?

Cited by 81 publications

References 2 publications

Geographically Weighted Regression in the Analysis of Unemployment in Poland

Geographically Weighted Regression in the Analysis of Unemployment in Poland

Residents’ perceptions of tourism development – implications for small economies business: the case study of Ali Sadr, Iran

Application of spatial error model using GMM estimation in impact of education on poverty alleviation in Java, Indonesia

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