the use of data to identify racial and ethnic disparities

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Jack McDevitt Associate Dean and Director Institute on Race and Justice Northeastern University

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The Use of Data to Identify Racial and Ethnic Disparities. Jack McDevitt Associate Dean and Director Institute on Race and Justice Northeastern University. Background. Worked on the following areas of racial disparity Capital Punishment Sentencing Use of mandatory Sentences - PowerPoint PPT Presentation

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Page 1: The Use of Data to Identify Racial and Ethnic Disparities

Jack McDevitt Associate Dean and Director

Institute on Race and Justice Northeastern University

Page 2: The Use of Data to Identify Racial and Ethnic Disparities

Worked on the following areas of racial disparity ◦Capital Punishment◦Sentencing

Use of mandatory Sentences◦Police Decision to Arrest ◦Police Misconduct (including use of Force)◦Hate Crime ◦Racial Profiling◦Anti-gang enforcement programs

Page 3: The Use of Data to Identify Racial and Ethnic Disparities

Data is not available Court is looking for data that exactly

matches the situation of the defendant Disparity does not always equal

discrimination How Much Disparity is Too Much? Much Disparity is Cumulative The “Baseline” Question

Page 4: The Use of Data to Identify Racial and Ethnic Disparities

Data is generally available for the individuals who are subject to formal decisions◦Sentencing◦ Arrest

Data is less often available for those who are eligible but not subject to formal sanctions

Data is seldom available about how the decision was made ◦School zone ……“good kids and bad kids”

Page 5: The Use of Data to Identify Racial and Ethnic Disparities

All agencies collect data on “what they do” generally as part of the an MIS system

Most Agencies can not manipulate the data within their system

Partner with a local academic Make a request that is supported by

“academic research”

Page 6: The Use of Data to Identify Racial and Ethnic Disparities

In racial profiling If we note that African American drivers are 27% of those who are stopped to what should we compare it ◦Census population data◦ Adjusted census data◦Estimated driving population ◦Observed driving population

Internal Benchmarking ◦Similarly Situated Officers