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Is racial bias in police stops necessarily bad?

The idea that persons belonging to some groups are more disposed to criminal activity than others is not new. Under the Criminal Tribes Act of 1871, certain groups in India were deemed to be Criminal Tribes because their members were “addicted to the systematic commission of non-bailable offences”. Once a tribe became “notified” as criminal, all its members were required to register with the local magistrate, failing which they would be charged with a crime under the Indian Penal Code. This act was repealed in 1952 but replaced by the Habitual Offenders Act 1952 which gave police the power to investigate a suspect’s criminal tendencies and whether his occupation was “conducive to [a] settled way of life” (Resist Initiative International, 2007).

In the West, the study of the relation between race and crime has a long history in the discipline of criminology and the study of criminal justice (Greene and Gabbidon, 2009). Summarising the evidence for the US, Sampson and Wilson (2009, p. 37) argue that “the evidence is clear that African Americans face dismal and worsening odds when it comes to crime in the street and incarceration”. A parallel field of study - comprehensively surveyed in Piquero et al. (2003) - has been of “criminal careers”: the longitudinal sequence of offences committed by an individual offender. They make the point that research on criminal careers has generated a wealth of information about the relationship between past and future criminal activity and helped isolate important life circumstances and events related to changes in criminal activity over time. However, notwithstanding this research, little is known about how criminal careers vary across race and gender.

Elliot (1994), using National Youth Survey data to study violent offenders, found that in the US, at the age of 17, 36% of African American males and 25% of White males reported committing one or more violent offences and that twice as many African American males continued their violent careers into their 20s and were thus likely to have longer criminal careers. However, in Britain the evidence points in a different direction. Sharp and Budd (2003) in their analysis of the Offending, Crime, and Justice Survey, found that, after controlling for age, White and Mixed Race respondents had, in 2003, higher rates of (self-reported) offending than did Black and Asian respondents; nonetheless, people from ethnic minorities were more likely to have contact with the CJS than persons who were White.

A justification for racial disparity in stops might lie in racial disparity in offences. If persons belonging to racial minorities offended, or were thought to offend, disproportionately to their numbers in the population, then “targeting” such persons as candidates for stops could, arguably, be construed as efficient, rather than biased, policing: the efficient deployment of police resources in preventing crime requires racial disparity in stops. On this argument, racial disparity in the selection of persons who are stopped does not necessarily mean that such selection is underpinned by bias.

The efficiency argument has been made most strongly by Smith (1997), although not, it should be emphasised, in the context of race. He argues that the police responded to “cues that were objectively related to offending when making stop decisions”17 and that, in making this response, the police were necessarily selective in their targets since “the relationships between age and sex and offending were extremely robust and strong ... if the police were to stop the same proportion of old ladies as young men, that would be evidence of bias because old ladies are far less likely to be law-breakers” (p. 330).

This argument translates into the context of this chapter by substituting “Whites” for “old ladies” and “Blacks” for “young men”. This is similar to the “statistical discrimination” hypothesis for explaining the racial disparity in the labour market (Phelps, 1972). Employers believe that, on average, productivity levels differ between racial groups, although not necessarily for racial reasons. However, since employers cannot observe everything they wish to know about the productivity of individual job candidates, they use race as a predictor of the candidates’ abilities.18

Farrell and McDevitt (2010) have stated that “one of the most challenging concepts in racial profiling research has been the relation between disparity and discrimination” and suggested that researchers must “ultimately decide what level of disparity is sufficient to indicate discrimination” (p. 82). The implication of these observations is that even if one accepts the argument that, for reasons of efficiency, there is racial disparity in stops, it still leaves open the possibility that this exceeds (or falls short of) the disparity required by efficiency considerations. There is consensus among researchers on racial profiling that disparity of treatment does not necessarily equate to biased treatment (Farrell and McDevitt, 2006). However, important questions which the protagonists in the debate have not been able to answer satisfactorily are these: How much of the racial disparity in stops can be justified on efficiency grounds and how much is the result of bias? Furthermore, does the efficiency/bias composition of stops vary by racial group so that some groups suffer relatively more than others?

The idea of comparing disparities in stops and offences to arrive at a measure of bias is well established in the criminology literature. For example, Lamberth (1998), in his study of police stops on the New Jersey Turnpike, found that while African Americans composed 13.5% of the turnpike’s driving population and 15% of the turnpike’s speeders, they constituted 35% of the drivers pulled over: from this disjoint between disparity in stops and speeding, he concluded that the offence of “Driving While Black” was alive and well on the New Jersey Turnpike.

Table 5.6 shows the proportion of stops which lead to an arrest, first, for all Areas in E&W and then in the Metropolitan Area. This proportion

Table 5.6 Percentage of stops that result in arrests, by ethnicity: all Police Areas in England and Wales'' and Metropolitan Area**

All Police Areas

All

Persons

British

Whites

Black

African

Black

Caribbean

Indian

Bangladeshi

Pakistani

2006-07

11.2

11.0

13.2

13.0

8.6

9.1

9.2

2007-08

10.3

10.6

11.5

11.6

7.4

9.0

8.2

2008-09

8.0

9.0

7.1

7.7

5.2

5.1

6.2

2009-10

8.1

9.0

7.1

7.4

5.4

4.7

6.0

2010-11

9.0

9.7

8.3

8.4

6.5

5.8

6.4

2011-12

9.3

9.5

9.2

9.8

7.5

6.6

6.6

2012-13

10.5

9.9

13.4

13.3

9.6

9.2

7.9

2013-14

12.2

11.0

17.5

16.5

13.0

12.5

9.7

2014-15

14.0

12.3

21.0

20.4

14.8

15.9

12.2

2015-16

15.9

13.9

22.0

22.9

15.6

15.4

14.0

2016-17

17.4

15.5

21.6

23.0

17.3

17.0

15.6

2017-18

17.4

15.7

20.3

22.8

17.6

16.3

17.7

2018-19

15.8

15.4

16.7

19.4

15.7

13.4

14.8

Metropolitan Area

2006-07

11.1

9.8

13.9

13.6

8.4

9.0

9.9

2007-08

8.8

7.7

11.5

11.5

6.9

8.9

7.6

2008-09

5.8

5.6

6.7

7.3

4.3

4.7

4.4

2009-10

6.3

6.3

6.8

7.1

4.9

4.4

4.6

2010-11

7.5

7.5

8.1

8.1

6.2

5.2

5.3

2011-12

8.5

8.3

9.0

9.7

7.6

6.0

6.3

2012-13

12.1

11.7

13.5

14.1

10.5

8.8

9.9

2013-14

16.2

15.8

18.7

18.1

15.3

12.6

15.1

2014-15

19.5

18.6

22.6

22.8

18.4

16.4

17.4

2015-16

19.6

18.8

22.8

24.1

18.1

15.2

15.6

2016-17

20.0

19.3

21.9

23.8

17.9

16.9

17.4

2017-18

19.1

19.2

20.6

22.9

17.4

15.6

17.3

2018-19

15.6

16.6

16.1

19.0

15.1

12.8

12.6

Source: Own calculations from MoJ (2019).

* Excluding British Transport Police. " Excluding City of London.

can be identified as the “arrest rate” of stops as an instrument of policing. What is clear from Table 5.6 is that the arrest rate of stops has increased both in E&W and in the Metropolitan Area: it was 11.2% in 2006/07 (meaning that 11.2% of all stops were followed by an arrest) in E&W, dipping to between 8% and 9% in the period 2008-09 to 2011-12, before rising to around 16% between 2015-16 and 2018-19. Table 5.6 also shows that Black Caribbeans were more likely to be arrested after being stopped than other ethnicities: in E&W in 2018-19, 19.4% of Black Caribbeans, compared to 15.8% of all ethnicities, were arrested following a stop and this difference in arrest rates has existed since 2011-12 in E&W and since 2006-07 in the Metropolitan Area. The next section presents an analysis, using Bayes’ Theorem, of police stops and arrests in the Metropolitan Area based on data for the 13-year period 2006-07 to 2018-19.

 
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