You can’t spell causal without ACLU……(in the wild)

Stats in the wild: AP article about racial profiling in LA, ACLU press release, and full report by Ian Ayres.

Ayres finds that minorites, including African-Americans and Hispanics, are stopped and searched at disproportionately high rates. He is quoted in the AP article: “The results of this study raise grave concerns that African-Americans and Hispanics are over-stopped, over-frisked, over-searched, and over-arrested,” said report author Ian Ayres, a Yale Law School economist and professor.

Some observations:
1.) It appears that African-Americans and Hispanics are definately “over-stopped, over-frisked, over-searched, and over-arrested”. But is it because of their race? This is hard to prove. We have correlation, but not causation. To do this we would need to randomly assign people to live across the city and randomly assign socio-economic stauses to each person. Clearly, this cannot be done. In reality, often certain races live together in neighborhoods and often have similar socio-economic statuses which can confound the analysis.

2.One statistic he offers and uses in his analysis is “stops per resident of a certain race”. He says, “African Americans were much more likely to be stopped than non-minorities. In the single-year of data, there were more than 4,500 stops for every 10,000 African Americans residents but only 1,750 stops for every 10,000 non-minority residents. In two divisions (Central and Hollywood), there were more stops of African Americans in one year than there were African American residents, meaning that the average number of stops per resident was greater than one.12 See Table 1.”

Is stops per resident a good metric for testing racial disparity? If there is no racial disparity, should we assume that the “stops per resident” would be about the same for each race? If there is an area where resdients are mostly White or mostly African-American, any stop in that area will affect this measure greatly. What we really want is something like “stops per driver” because the demographics of the drivers may be different than the residents. This isn’t hard to believe.

For example, say that in a certain area there are 100,000 residents. 90,000 are white and 10,000 are black. Now say there is a mall in this area and plenty of people drive in from surrounding area. Further assume that there are an equal number of white and black drivers on the road. Now say, that in a given year, cops stop 1000 people, 500 white and 500 black. The stop rates per 10,000 resdients for whites is about 56 per 10,000 and for blacks it is 500 per 10,000. This may not be the case in LA, but this relatively simple example shows how this metric could easily lead to skewed results.

3.) The regression that is done is using a rate as a response variable. This would lend itself nicely to logistic regression, which may be more appropriate.

Note: I have no affiliation with the LAPD or ACLU.

Cheers.

Posted on October 23, 2008, in Uncategorized. Bookmark the permalink. Leave a comment.

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