2006
DOI: 10.1007/s11113-006-9007-4
|View full text |Cite
|
Sign up to set email alerts
|

County child poverty rates in the US: a spatial regression approach

Abstract: We apply methods of exploratory spatial data analysis (ESDA) and spatial regression analysis to examine intercounty variation in child poverty rates in the US. Such spatial analyses are important because regression models that exclude explicit specification of spatial effects, when they exist, can lead to inaccurate inferences about predictor variables. Using county-level data for 1990, we re-examine earlier published results [Friedman and Lichter (Popul Res Policy Rev 17:91–109, 1998)]. We find that formal te… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1
1

Citation Types

4
128
0
3

Year Published

2012
2012
2022
2022

Publication Types

Select...
5
3

Relationship

0
8

Authors

Journals

citations
Cited by 161 publications
(135 citation statements)
references
References 59 publications
4
128
0
3
Order By: Relevance
“…(Cutts and Webber, 2010). Such non-independencemay be due to, amongst other things, feedback forces, 2 as well as grouping forces (Voss et al, 2006;Anselin, 2001;Wrigley et al, 1996). 3 The assumption that geographically contiguous parliamentary constituencies with similar party vote shares might be influenced by grouping forces, such as intense party activism,is not entirely new, however.Previous work had found that the Liberal Democrats improved their local election support following intensive activism in areas surrounding those where they won at the previous contest (Dorling, Rallings and Thrasher, 1998).…”
Section: The Geography Of Voting In Great Britainmentioning
confidence: 99%
“…(Cutts and Webber, 2010). Such non-independencemay be due to, amongst other things, feedback forces, 2 as well as grouping forces (Voss et al, 2006;Anselin, 2001;Wrigley et al, 1996). 3 The assumption that geographically contiguous parliamentary constituencies with similar party vote shares might be influenced by grouping forces, such as intense party activism,is not entirely new, however.Previous work had found that the Liberal Democrats improved their local election support following intensive activism in areas surrounding those where they won at the previous contest (Dorling, Rallings and Thrasher, 1998).…”
Section: The Geography Of Voting In Great Britainmentioning
confidence: 99%
“…Generally, poverty has attracted a lot o f attention fro m the academia and non-academia globally. Few recent studies are based on the premise that individuals and households with co mmon characteristics sometimes are found clustered together either by choice or because they are constrained to co-locate by coercive operation of social, econo mic, geographic or political forces [14]. Identification of these households has been made possible through the advancement in spatial analytical techniques; which has also enables spatial pattern of poverty (concentration of poverty rates and outliers) to be quantified [15], [16], [17].…”
Section: Review Of Previous Literaturementioning
confidence: 99%
“…The categorization of spatial concentration into high or lo w poverty rate neighbourhood is in relat ionship with average national poverty rate. Most counties in US are found in the high-high and lo w-low subregions [14]. That is, the counties whose poverty rate is above (below) the average poverty rate are surrounded by counties with poverty rate above (below) average national poverty rate.…”
Section: Review Of Previous Literaturementioning
confidence: 99%
See 1 more Smart Citation
“…10 In a similarly important synthesis, Castro (2007) provided detailed reviews of spatial perspectives and model applications structured around demography's core areas-fertility, mortality, migration, and population models-all with a focus on the implications for population policy research. 11 Many of the early adopters of spatial analysis in demography drew upon specific geospatial data sets, utilized non-census data/units of analysis (as well as census data/units), and adopted fairly rudimentary forms of spatial analysis-from overlay, buffering, and spatial joins (for building contextual databases and descriptive analysis)-but only a relatively small number had adopted more advanced geostatistics and point pattern analysis (e.g., Castro et al 2006) and spatial regression (e.g., Morenoff 2003;Voss et al 2006;Chi and Zhu 2008). Increasingly, researchers across the population sciences are developing innovative ways to both harness and analyze geospatial data on the social, built, and physical environment contexts of individual lives.…”
Section: Introductionmentioning
confidence: 99%