1 Concentrated Poverty and Regional Equity PORTLAND NNIP MEETING March 1, 2012 Tom Kingsley and Rob Pitingolo National Neighborhood Indicators Partnership.

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Presentation transcript:

1 Concentrated Poverty and Regional Equity PORTLAND NNIP MEETING March 1, 2012 Tom Kingsley and Rob Pitingolo National Neighborhood Indicators Partnership

2 NNIP SHARED INDICATORS The Shared Indicators agenda Completed framework paper, indicator selection Have national data now; plan assemble local data next few years Chicago/Minneapolis partners working on system & protocols for collection (McKnight grant) Using national data for first analysis National analysis 100 largest metros (UI) Analysis for individual metros by partners

3 THIS ANALYSIS 2000 Census to 2005/09 ACS Imperfections: period and sample size Concentrated poverty Low income neigh. defined as >20% poverty Conditions low-income neighborhoods Taking into account composition change Regional equity (disparity gaps) Between low- and higher-income neighborhoods

4 Stark contrasts – low- and higher- income neighborhoods

5 Concentrated Poverty Went Up Tracts Poor Population

6 Huge variation across metros – level and change in concentrated poverty

7 Except for income, indicators in low income neigh. improved modestly

8 But composition changed – some tracts improved, some worsened

9 Overall condition changes, in part due to shifts in composition

10 In Cleveland, city tracts more likely to improve, suburban tracts more likely to worsen

11 Similar Pattern in Baltimore

12 Changes in tracts that stayed low income throughout the period – modest differences in results

13 Large disparities (gaps): low- vs. higher- income neighborhoods

14 Small changes to gaps, /09 Some narrowed, some widened Ave Income ($000) % LF Employed % Homeowners% Access to Car % College Degree

15 Regional disparity, not closely correlated with concentrated poverty

16 Next Steps Analyze/present standard errors Examine variations with metro size Explore developing an overall disparity index More indicators; z-scores to normalize Examine contrasts between metros More maps – varying change trajectories Consider policy implications