Spatio-temporal dimensions of child poverty in America, 1990–2010

Abstract

The persistence of childhood poverty in the United States, a wealthy and developed country, continues to pose both an analytical dilemma and public policy challenge, despite many decades of research and remedial policy implementation. In this paper, our goals are twofold, though our primary focus is methodological. We attempt both to examine the relationship between space, time, and previously established factors correlated with childhood poverty at the county level in the continental United States as well as to provide an empirical case study to demonstrate an underutilized methodological approach. We analyze a spatially consistent dataset built from the 1990 and 2000 U.S. Censuses, and the 2006–2010 American Community Survey. Our analytic approach includes cross-sectional spatial models to estimate the reproduction of poverty for each of the reference years as well as a fixed effects panel data model, to analyze change in child poverty over time. In addition, we estimate a full space-time interaction model, which adjusts for spatial and temporal variation in these data. These models reinforce our understanding of the strong regional persistence of childhood poverty in the U.S. over time and suggest that the factors impacting childhood poverty remain much the same today as they have in past decades.

Environmental inequality and pollution advantage among immigrants in the United States

“…Ignoring spatial autocorrelation may lead to biased inferential statistics and increases the chance of committing a Type I error (rejecting the null hypothesis if it is true) (Anselin et al, 2000). As such, we employed spatial panel models that effectively account for spatial autocorrelation as well as heterogeneity across time (Call & Voss, 2016; Millo & Piras, 2012). We fitted spatial panel models using the package splm within the R Statistical Environment (Call & Voss, 2016; Millo & Piras, 2012).…”

“…As such, we employed spatial panel models that effectively account for spatial autocorrelation as well as heterogeneity across time (Call & Voss, 2016; Millo & Piras, 2012). We fitted spatial panel models using the package splm within the R Statistical Environment (Call & Voss, 2016; Millo & Piras, 2012). The splm package has been frequently used in the analysis of time-space dependent phenomena (Call & Voss, 2016; Millo & Piras, 2012).…”

“…We fitted spatial panel models using the package splm within the R Statistical Environment (Call & Voss, 2016; Millo & Piras, 2012). The splm package has been frequently used in the analysis of time-space dependent phenomena (Call & Voss, 2016; Millo & Piras, 2012). …”

The Spatial Concentration of America's Rural Poor Population: A Postrecession Update

“…Poverty and other forms of economic disadvantage are not evenly distributed across space (Call and Voss 2016;Curtis, Voss, and Long 2012;Voss et al 2006), and these patterns are the product of historical processes of uneven development (e.g., due to political marginalization) and the unequal distribution of "at risk" populations across the country. Questions about space are particularly salient for understanding rural poverty in the United States for at least two reasons.…”

COVID-19 incidence and mortality in the Metropolitan Region, Chile: time, space, and structural factors

“…As shown in Fig 1, several municipalities share a small area around the center of the region, surrounding downtown Santiago (the clearer area in the middle). (15)(16)(17)(18)(19). In Chile, several studies show marked patterns of residential segregation and geographical concentration of income, particularly in the case of the MR (19)(20)(21)(22)(23).…”