09-30-2017 Note from the authors A more complete manuscript will be - PDF document

09-30-2017 Note from the authors A more complete manuscript will be shared with the discussant and session participants on or around Monday Oct 16, 2017 (available on request from the lead author). This is a work in progress and we apologize for the delay in its dissemination. The material contained in this document is an extended abstract that includes background on, and motivation for, the project, includes the presentation of some descriptive findings but at this time excludes formal model results. T HE SCALE AND SPATIAL PATTERNING OF RACIAL / ETHNIC SEGREGATION BASED ON BOTH HOME AND WORKPLACE ENVIRONMENTS : T HE CASE OF THE A TLANTA MSA Robert J. Zuchowski (rxz155@psu.edu) Stephen A. Matthews (sxm27@psu.edu) both Department of Sociology & Population Research Institute Pennsylvania State University, University Park, PA 16802, USA September 30, 2017 Paper prepared for the International Population Conference XXVIII International Union for the Scientific Study of Population (IUSSP), Cape Town, South Africa October 29-November 4, 2017 Acknowledgements: This work was supported by the National Science Foundation under IGERT Award #DGE-1144860, Big Data Social Science. Words: 4000 approx. 1

Abstract Motivated by Ellis, Wright and Parks (2004) emphasis on measuring both residential and workplace segregation we utilize a relatively new data product to provide a micro-geographic lens on the study of segregation and spatial mismatch by race/ethnic groups in the Atlanta-Sandy Springs-Marietta Metropolitan Statistical Area (MSA). We examine the spatial clustering of both residence and workplace segregation using exploratory spatial data analysis (ESDA) approaches such as Local Indicators of Spatial Association (LISA) measures. We examine segregation at different census hierarchical scales within Atlanta, and utilize the race/ethnic diversity of Atlanta to compare and contrast the segregation and spatial mismatch experiences of African American, Hispanic and Asian residents in different counties within the MSA. We will close our paper with a discussion of housing and labor market policy implications based off of findings. Our intent is to showcase LODES and to demonstrate their utility to demographers. Key words: spatial mismatch theory; segregation, exploratory spatial data analysis, local analysis, spatial heterogeneity and nonstationarity. 2

Introduction In recent years profound changes have occurred in the political, economic, social and demographic structures of U.S. metropolitan areas. These changes include increased functional specialization, spatial economic differentiation and inequality within metropolitan areas including edge cities, gentrified neighborhoods, ethnic enclaves, new immigrant destinations, Black ghettos, etc. The changing urban racial/ethnic landscape and patterns of segregation are seemingly driven not by a single process but potentially many. Our work falls under the spatial segregation research, which over the past decade has promoted attention to ‘spatial’ issues and has introduced both more nuanced and more sophisticated methods of analysis (Reardon et al, 2008). Some of this literature has been case- study description and informed by exploratory spatial data analysis (ESDA) methods (see for example Brown and Chung, 2006). Following Brown and Chung (2006) but motivated by Ellis, Wright and Parks (2004) emphasis on measuring both residential and workplace segregation we utilize a new data product to provide a micro-geographic lens on the study of segregation based on where people live and where they work — and spatial mismatch by race/ethnic groups — in the Atlanta-Sandy Springs-Marietta Metropolitan Statistical Area (MSA), henceforth Atlanta. Contribution This paper offers three improvements on conventional practice used in studies of segregation and can potentially contribute to empirical research on the spatial mismatch hypothesis (Kain, 1968; Wilson, 1987, Ihlanfeldt and Sjoquist, 1991; 1998), and at the very least provide a different lens on an examination of non-residential segregation. 3

First, we take advantage of an innovative, high-resolution geospatial data set; the Longitudinal Employer-Household Dynamics (LEHD) Origin-Destination Employment Statistics; known as LODES data (see LEHD program: http://lehd.ces.census.gov; Graham, Kutzbach & McKenzie, 2014). LODES includes information on the characteristics of the labor force population for every home and workplace census block (for the Atlanta MSA there are data on 88,527 blocks); LODES also includes a limited dataset on origin-destination (not used in this paper). LODES are available annually 2002-2014, which permits an analysis of change (note, race/ethnicity only available post 2009). Using LODES allows us to move beyond the conventional residential focus within the segregation literature. Our research on non-residential segregation (workplace segregation), is not unique in its framing. Indeed, our work in this area stems from a broader interest in non-residential places across the social and health sciences (e.g., Matthews, 2011, Siordia and Matthews, 2016), but we also acknowledge a debt to the earlier work of Ellis, Wright and Parks (2004) and as noted above the spatial mismatch literature. It is important to note, that while the focus is on race/ethnicity we are also examining ‘segregation’ using other dimensions of stratification of workers based on gender, age, income and education. In the full paper we will be expanding on these other dimensions. Second, the high spatial resolution data in LODES provides the building blocks of our analysis of the effects of scale and larger spatial contexts (tracts and counties). Specifically, we examine the spatial clustering of both home and workplace segregation using exploratory spatial data analysis (ESDA) approaches such as Local Indicators of Spatial Association (LISA) measures focusing on the block level data. In addition, we will (a) examine segregation at different census hierarchical scales (census blockgroup, census tract) within Atlanta, and (b) utilize the diversity of Atlanta to compare and contrast the segregation and spatial mismatch experiences of the 4

residents of different counties within the MSA. A key theme of our ESDA is the description of the variability of results across scales and across different subdivisions of the MSA (e.g., by county). We will leverage this heterogeneity in patterns by scale and in the full paper/presentation will include vignettes that focus on specific counties (rather than the entire Atlanta MSA – see below). These county vignettes provide an illustration of the complexity of urban systems and also hint at the potential for the relationships between variables in any formal model to vary across contexts (i.e., for their to be non-stationarity in the processes generating the patterns we observe). Ongoing work includes examining for the presence of non-stationarity via the use of geographically weighted regressions and we will include examples of this work in the full paper/presentation. Third, as just alluded, Atlanta is especially suited for a study of racial/ethnic segregation. The Atlanta MSA is a 30-county region that contains almost 6 million residents (2015); making it the ninth largest MSA in the U.S. and second fastest growing MSA in the past decade (a locator map is provided in Figure 1). More importantly Atlanta has a rich Black history, a growing Black middle class, an increasingly mixed-race/ethnic population (49% minority), is one of the largest of the new immigrant gateway regions of the country and yet is still an area beset with extreme racial and economic inequalities (Ingwerson 1987, Sjoquist 2000, Baylor 2000, Liu 2012, Aka 2012). More recently, Atlanta was hit particularly badly by the Great Recession (2007-2012), with elevated rates of foreclosure (Hall et al, 2015) that impacted the housing markets, especially for minority homeowners. (Dis)Aggregating Atlanta The question “ why Atlanta? ” will become evident as we present our work and especially as we zoom in-and zoom-out to compare results for the MSA by different scales but also in our focus 5