1. Heather O’Connell University of Wisconsin Center for Demography and Ecology Institute for Research on Poverty Introduction. Researchers acknowledge the uniqueness of the South by including regional dummy variables in analyses. However such an approach does not help explain the processes behind this uniqueness. In addition, such studies have addressed overall poverty rates, but have not focused on racial inequality in poverty. The racial inequality nuance seems important given the historical significance of the South in shaping US race relations. ▪What is it that makes the South different from the rest of the US? ▫One possible answer is the legacy of slavery. The Legacy of Slavery and Racial Inequality in Poverty in the US South ▪ How should it be operationalized? ▫ The response in the poverty literature is the proportion black (Swanson et al, 1994). I suggest employing a historical measure and two separate operationalizations suggested by the racial state literature (James, 1988). Measures. 1860 Slave Concentration in Southern Counties 2) The number of slaves relative to the total population reported in the 1860 Census. Legend Proportion Black 0.0002 - 0.0594 0.0594 - 0.1607 0.1608 - 0.3006 0.3007 - 0.4826 0.4827 - 0.8608 2000 Black Concentration in Southern Counties 1) The number of African Americans relative to the total population reported in the 2000 Census SF3. Legend Proportion of slaves 0.0000 - 0.1002 0.1003 - 0.2310 0.2311 - 0.3916 0.3917 - 0.5706 0.5707 - 0.9085 Legend % Total Land Operated by Blacks Black Land Ownership in Southern Counties 3) The proportion of total farm acres for black operators relative to the proportion for white operators reported in the 2002 Agricultural Census. Black Advantage in Voter Registration in 5 States 4) The proportion of the voting age black population registered to vote relative to the proportion of the white population registered to vote ( The registration counts are taken from states’ websites for the time closest to 2000; only 5 states report them by race. This subsample is used to better understand how black land ownership, which is available for all states, is related to the racial state literature ). Results. A bivairiate correlation confirms the association suggested in maps 3 and 4: black land ownership is not highly related to voter registration (r =.23). However, regressions were conducted for the five state sample to compare patterns with those from the overall South. Results vary between the two samples, for both the main explanatory variables and control variables. 0.0000 - 0.0134 0.0134 - 0.0364 0.0364 - 0.0703 0.0703 - 0.1434 0.1435 - 0.2492 Five States OLS Models Census South OLS Models (AL, GA, LA, NC and SC) Contact: hoconnel@ssc.wisc.edu ▪ The control variables found in the poverty literature are inconsistently related to racial inequality in poverty. ▪ Contemporary concentrations of blacks is only related to racial inequality in poverty when the whole South is considered. ▪ The historical concentration of slaves in a place explains the relationship of the contemporary concentration of blacks with inequality in poverty. ▪ Neither operationalization of the racial state is related to racial inequality in poverty, nor do they have the expected mediating relationship with slave concentration. Conclusions. The legacy of slavery is not simply blacks’ disadvantage in accumulating wealth, as suggested by the contemporary concentration operationalization. I argue the legacy of slavery is the normative structure of a place that has continued to affect racialized outcomes, including poverty, to this day. However, my results suggest this legacy cannot be explained by a formalized contemporary racial state. Further investigation into the mechanisms behind the legacy are needed. In addition, the model fit suggests more work is needed to identify explanatory variables related to racial inequality in poverty rates, because the traditional covariates of poverty explain only a fraction of the variation. Legend % 18+ Black Population Registered to Vote 0.0950 - 0.5362 0.5363 - 0.8321 0.8322 - 0.9427 0.9428 - 1.0414 1.0423 - 1.2462 Model 1Model 2Model 3Model 4 β SE β β β _const5.443.165.473.157.323.257.953.33 metropolitan status-0.100.20-0.060.20-0.110.20-0.130.20 high school only3.652.183.422.181.772.301.562.31 high school or more4.74*2.244.54*2.243.942.243.912.25 unemployment-7.526.19-8.846.26-10.646.26-11.446.45 agriculture industry-2.104.03-2.124.02-3.224.02-4.434.24 manufacturing-2.562.58-2.382.58-3.242.59-3.932.72 service industry-6.023.87-4.863.97-6.774.03-7.444.17 educ/health industry-2.433.12-2.193.12-3.533.16-4.093.23 fire industry5.707.535.497.524.737.463.237.64 single moms8.57*3.363.215.355.965.466.575.51 families with kids-4.10*2.02-3.862.02-4.77*2.05-4.97*2.07 middle class-8.01**2.86-7.92**2.86-7.46**2.84-7.77**2.88 proportion black 1.441.120.231.250.301.27 proportion slave 1.21*0.571.16*0.57 black land ownership -1.942.17 voter registration 0.290.56 R²R² 0.210.220.24 Model 1Model 2Model 3Model 4 β SE β β β _constant3.961.533.161.523.481.513.661.52 metropolitan status-0.010.110.020.110.000.110.010.11 high school only-2.61*0.99-2.03*0.98-2.07*0.98-2.17*0.98 high school or more1.090.961.210.941.220.931.250.93 unemployment-6.75*3.10-6.19*3.05-5.103.04-4.963.04 agriculture industry-1.281.98-0.971.95-1.441.94-1.661.95 manufacturing-2.661.37-1.721.36-1.891.35-2.021.36 service industry-4.80*2.08-2.092.13-2.312.11-2.392.11 educ/health industry-3.46*1.75-2.511.74-3.041.73-3.201.74 fire industry6.104.246.604.186.914.146.344.17 single moms17.33*1.755.95*3.008.37**3.058.17**3.05 families with kids-2.18*1.10-0.381.15-1.211.17-1.301.17 middle class1.671.450.101.47-0.141.46-0.151.46 proportion black 2.28**0.490.820.631.020.66 proportion slave 1.15**0.321.17**0.32 black land ownership -1.481.38 R²R² 0.210.240.26 Moran I0.050.03