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Testimony Regarding the Potential for Profiling in Connecticuts Towns

Orlando J. Rodriguez, M.A.U.S. Commission on Civil RightsDecember 6, 2011

Members of the U.S. Commission on Civil Rights:

I am a Senior Policy Fellow with Connecticut Voices for Children (Connecticut Voices), aresearch-based public education and advocacy organization that works statewide to promote the

well-being of Connecticuts children , youth, and families. I am here today at the request of the U.S.Commission on Civil Rights

I have created a composite demographic measure called the Non-European Residents Index(NERI). It is one possible alternative for capturing the potential for p rofiling in Connecticuts 169towns by quantifying the concentration of non-European residents in a given town, and the changein their concentration over the past 10 years. 1,2 The NERI does not incorporate data on policedetentions, arrests, or any proxy for profiling. As such, the NERI is a purely demographic measureof the non-European population residing in a town. Use of the NERI infers the relativeconcentration of non-Europeans in a town as a proxy for potential profiling. Consequently, theNERI measures the population that could be subject to profiling and is one possible indicator of thepotential for profiling.

The NERI cannot account for potential profiling in towns where minority populations(non-Europeans) are transient (seasonal workers) or when minorities are traveling through a town in

route to another destination.

Map 1 shows the concentration of non-European residents in Connecticut towns in 2009/2010. 3 The highest concentrations are in urban centers and surrounding towns, as well as towns with acollege or university. The highest concentrations are in the following towns: Hartford, Bridgeport,Bloomfield, Stamford, New Haven, East Hartford, Danbury, Windsor, Norwalk, Waterbury, New Britain, and New London.

Map 2 shows the change in concentration of non-European residents between 2000 and 2009/2010. This is the change in the NERI during the 2000 to 2009/2010 period. Towns with the highestincrease in the concentration of non-Europeans are: Norwich, East Hartford, Manchester, Windsor,

Stratford, Rocky Hill, Meriden, Bridgeport, Stamford, West Haven, Waterbury, Danbury, East Windsor, Ansonia, New Britain, Windsor Locks, Derby, Deep River, and Hamden.

1 See Appendix B for NERI values and U.S. Census Bureau variables.2 See Appendix C for methodology.3 See Appendix A for map with town names.


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Map 2: Increase in Town Non-European Residents2000 to 2009/2010

Chart 2: Increase in Non-European Residents Index2000 to 2009/2010

Note: Based on the N on- E uropean R esidents Index

Hartford


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Map 3: Percent Increase in Town Non-European Residents2000 to 2009/2010

Chart 3: Percent Increase in Non-European Residents Index2000 to 2009/2010

Note: Based on the N on- E uropean R esidents Index

Hartford


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Appendix A: Map of Connecticut Towns


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Appendix B: Non-European Residents Index (NERI) Values


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Appendix C: Methodology Used to Calculate theNon-European Residents Index (NERI)

Data Sources from the U.S. Census Bureau

The Non-European Residents Index (NERI) is derived from U.S. Census Bureau data on (1) thepercentage of the population that are naturalized citizens, (2) the percentage of the population that isforeign-born (not born in the U.S.) outside of Europe, (3) the percentage of the population that isHispanic, (4) the percentage of the population that is non-Hispanic African American, (5) and thepercentage of the population that is non-Hispanic Asian.

2000 Decennial Censuso SF1 table P4

Total population Hispanic population Non-Hispanic African American population Non-Hispanic Asian population

o SF2 table PCT19 Population born in Asia, Oceania, Africa, and Latin America. Excludes Europe,

Canada, Australia, and New Zealand.o SF2 table P21

Naturalized population 2005-2009 American Community Survey

o Table B05006 Population born in Asia, Oceania, Africa, and Latin America. Excludes Europe,

Canada, Australia, and New Zealand.o Table B05001

Naturalized population 2010 Decennial Census

o SF1 table P9 Total population Hispanic population Non-Hispanic African American population Non-Hispanic Asian population


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Excluding Highly Correlated Variables

Eight variables were considered for inclusion in the NERI. Four variables were excluded because of their high correlation ( 0.8) to other variables. The variables %Hispanic and %African

American(NH) were combined into the single variable %Hispanic or African American(NH). The

table below lists the correlation coefficients for each pair of variables.Included in NERI: N Y N Y Y N N Y

Starting List of Potential Variables

% Born in PR or

US Territory

% Naturalized

% Not A

Citizen

% Non- European

Foreign Born

% Hispanic or African

American(NH)

% Hispanic

% African American

(NH)

% Asian(NH)

% Born in PR or US Territory 1.000

% Naturalized 0.247 1.000

% Not A Citizen 0.502 0.666 1.000% Non-European ForeignBorn 0.476 0.733 0.931 1.000% Hispanic or AfricanAmerican(NH) 0.817 0.520 0.732 0.786 1.000

% Hispanic 0.881 0.439 0.758 0.721 0.910 1.000

% African American(NH) 0.599 0.507 0.569 0.706 0.906 0.648 1.000

% Asian(NH) 0.091 0.630 0.529 0.585 0.282 0.258 0.253 1.000

NERI Calculation

The NERI is a simply calculation with a value ranging from 0 to 1.0. It is the sum of four variables(percentages) divided by the sum of the maximum values of all four variables. This results in asingle variable in which equal weight is given to each underlying variable.

%Naturalized: range 0 to 1.0 (100%)%Non-European Foreign Born: range 0 to 1.0 (100%)%Non-European Race = %Hispanic or African American(NH) + %Asian(NH): range 0 to 1.0 (100%)

Example:

NERI for Andover = (%Naturalized + %Non-European Foreign Born + %Hispanic or African American(NH) + %Asian(NH))/3

= (3.24% + 1.25% + 2.6% + 1.12%)/3= 0.082/3= 0.027

Map Categories

There are four map categories: High, Above Average, Average, and Below Average based on NERI values or changes in NERI values between 2000 and 2009/2010. Initially, five map categories(High, Above Average, Average, Below Average, and Low) werecalculated using ArcMap (ESRI) software using the natural breaks (Jenks) classification method. The Below Average andLow categories were combined into a single category called Below Average.

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120611 USCommissionCivilRights Rodriguez