Authors: Chris Barnum, Nick Manrique, Tracy Payne and Tiffany Miller

St. Ambrose University, Davenport, Iowa

This is a preliminary examination. Please do not quote or cite any

information in this document without the consent of the lead author.

Davenport Police Department (especially)

◦ Chief Frank Donchez◦ Maj. Donald Schaeffer◦ Lt. Scott Sievert◦ Dan DeFauw

The Criminal Justice students at St. Ambrose University

We used trained observers to determine the racial and gender breakdowns of drivers on the streets of Davenport.

Over 50 trained observers watched traffic in 29 locations in Davenport

The observers were recurrently deployed from September 2010 – May 2011.

The observers worked 7 days a week and watched vehicles in intervals from 8:00 am – 2:00 am

These observers recorded information for more than 16,500 vehicles.

In order to determine if the data generated by the observers were valid, we compared them with 2010 census information for Davenport provided by the US Census Bureau

The Davenport Police use “reporting areas” to locate traffic stops. Each traffic stop is assigned to one of these reporting areas.

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Each of our observation zones subsumes a number of these “reporting areas.”

Information for stops occurring between January 2010 and September 2011

A total of over 15, 000 traffic stops.

For each stop officers record information about: (1) the driver including race, gender, age (2) the stop including the reason, date, time and location and (3) the outcome including citation, arrest, warning, search, field interview or vehicle exit.

Comparison of 2010 data to 2011 data.

Very Similar

Zone 2010 20111 0.367913 0.4180122 0.406685 0.3738193 0.220472 0.202975 0.099567 0.1269847 0.102941 0.076199 0.176471 0.241228

10 0.311688 0.28758211 0.1875 0.23129312 0.283088 0.2872513 0.263736 0.27457614 0.241379 0.2415 0.22807 0.24460416 0.276265 0.39664817 0.366167 0.38744918 0.390572 0.50144119 0.345336 0.38709720 0.117347 0.2458121 0.333333 0.37411822 0.311475 0.35828923 0.193798 0.20370424 0.045455 0.14035125 0.333333 0.17567626 0.283737 0.23614528 0.1875 0.1829 0.190722 0.171296

Totals 0.293 0.325835

The initial traffic stop comparisons consist of data for total stops compared to census data and day observations

The goal of this analysis is to determine whether White and Black drivers were treated differently once a stop occurred.

Arrests

Citations

Consent Searches

Exiting the Vehicle

Logistic Regression

This method enables a researcher to predict the likelihood of a given event (such as an arrest being made) on the basis of several other factors (such as the officer or driver’s age, gender, race, or the area of town or time of day).

This method also allows a researcher to isolate and assess the relative strength of each of the predictor variables used in the analysis.

Only 2011 data is used in the following analyses.

Independent Variable B S.E. Wald df Sig. Exp(B)

Black_White .197 .051 14.778 1 .000 1.218

off_sex -.205 .085 5.826 1 .016 .814

off_race .452 .080 31.734 1 .000 1.571

yrs_serv -.043 .007 39.524 1 .000 .958

off_age .021 .006 13.363 1 .000 1.022

day_night .412 .050 67.138 1 .000 1.509

Observ zone .000 .001 .311 1 .577 .999

Patrol -.844 .063 180.963 1 .000 .430

Traffic 1.587 .107 221.218 1 .000 4.889

moving_vio .104 .059 3.133 1 .077 1.110

equip_vio -.286 .059 23.929 1 .000 .751

Drive gender -.058 .049 1.421 1 .233 .944

Drive age -.012 .002 48.459 1 .000 .988

Constant .167 .226 .549 1 .459 1.182

Model Summary

-2 Log likelihood

Cox & Snell R

Square

Nagelkerke R

Square

10304.126a .164 .220

Net of the other independent variables included in the model, the odds are 1.28 times greater that a White driver will be issued a citation during a traffic stop than will be a Black driver.

Independent Variable B S.E. Wald df Sig. Exp(B)

Black_White -.562 .104 29.005 1 .000 .570

off_sex .314 .197 2.552 1 .110 1.369

off_race .199 .194 1.051 1 .305 1.220

yrs_serv .022 .017 1.788 1 .181 1.023

off_age -.030 .014 4.750 1 .029 .970

day_night -.204 .108 3.580 1 .058 .815

Observe zone .003 .002 1.216 1 .270 1.003

Patrol -.297 .124 5.746 1 .017 .743

Traffic -.789 .202 15.220 1 .000 .454

moving_vio -.048 .125 .144 1 .704 .954

equip_vio -.250 .124 4.092 1 .043 .778

Drive Gender .276 .108 6.560 1 .010 1.318

Drive Age -.017 .004 15.779 1 .000 .983

Constant -1.442 .520 7.689 1 .006 .237

.

Model Summary

-2 Log likelihood

Cox & Snell R

Square

Nagelkerke R

Square

3211.912a .014 .043

Net of the other independent variables included in the model, the odds are 1.75 times greater that a Black driver will be arrested during a traffic stop than will be a White driver.

Independent Variable B S.E. Wald df Sig. Exp(B)

Black_White -.794 .171 21.498 1 .000 .452

off_sex .241 .327 .544 1 .461 1.273

off_race .555 .329 2.843 1 .092 1.742

yrs_serv -.013 .024 .288 1 .592 .987

off_age .020 .020 1.040 1 .308 1.020

day_night -.068 .177 .150 1 .699 .934

Observe zone .005 .004 1.608 1 .205 1.005

Patrol .069 .207 .113 1 .737 1.072

Traffic -.969 .395 6.032 1 .014 .379

moving_vio -.292 .208 1.972 1 .160 .747

equip_vio -.253 .199 1.625 1 .202 .776

Drive Gender .252 .177 2.038 1 .153 1.287

Drive A -.025 .007 11.110 1 .001 .976

Constant -4.061 .815 24.845 1 .000 .017

Model Summary

-2 Log likelihood

Cox & Snell R

Square

Nagelkerke R

Square

1434.327a .007 .046

Net of the other independent variables included in the model, the odds are 2.21 times greater that a Black driver will be asked for consent to search during a traffic stop than will be a White driver.

Model Summary

-2 Log likelihood

Cox & Snell R

Square

Nagelkerke R

Square

4127.181a .030 .076

Independent Variable B S.E. Wald df Sig. Exp(B)

Black_White -.512 .089 33.277 1 .000 .599

off_sex .625 .195 10.325 1 .001 1.869

off_race .616 .167 13.590 1 .000 1.851

yrs_serv .021 .013 2.587 1 .108 1.022

off_age -.020 .011 2.985 1 .084 .981

day_night -.227 .092 6.101 1 .014 .797

Observe Zone .004 .002 5.711 1 .017 1.004

Patrol .665 .126 27.992 1 .000 1.944

Traffic -.175 .191 .845 1 .358 .839

moving_vio .144 .108 1.778 1 .182 1.154

equip_vio .072 .107 .444 1 .505 1.074

Drive Gender .516 .095 29.852 1 .000 1.676

Drive Age -.029 .004 59.273 1 .000 .971

Constant -2.809 .463 36.752 1 .000 .060

Net of the other independent variables included in the model, the odds are 1.67 times greater that a Black driver will be asked to step out of the vehicle during a traffic stop than will be a White driver.

Next step is to analyze disparity at the officer level

Variable Arrests Searches Citations

2007 Exp(B) 2010 Exp(B) 2007 Exp(B) 2010 Exp(B) 2007 Exp(B) 2010 Exp(B)

Diver’s Age .988 0.98* .967* 0.967* .984* 0.980*

Male Driver 2.29* 1.42* 4.64* 2.281* .988 1.072

DL Out-County 1.03* 1.49* .225* 1.418 1..95 0.943

In-State Reg. 1.99 1.96* .807 0.837 1..06 1.360*

Equip. Violation .632* 0.47* 1.11 0.874 .626* .5072*

Seizure 43.22* 61.8* 48.14* 55.96* 1.33 2.104*

Black officer .378* 0.48* 1.43 5.131 .679 0.979

Male officer .848* 1.03 .325* 0.671 2.015* 0.929

Years of Service .961* .098* 1.02 0.971* 1.07* 1.029*

Working days .355* 1.58* .208* 1.74* 3.17* 0.366*

Minority Driver 2.10* 2.91* 3.94* 2.51* 1.31* 1.46*

Nagelkerke R2 .186 .164 .308 .180 .247 .135