Partisan Bias in Federal Public Corruption Prosecutions

Sandy Gordon sent me this paper, which begins:

The 2007 U.S. Attorney firing scandal has raised the specter of political bias in the prosecution of officials under federal corruption laws. Has prosecutorial discretion been employed to persecute enemies or shield allies? To answer this question, I [Gordon] develop a model of the interaction between officials deciding whether to engage in corruption and a prosecutor deciding whether to pursue cases against them. Biased prosecutors will be willing to file weaker cases against political opponents than against allies. Consequently, the model anticipates that in the presence of partisan bias, sentences of prosecuted opponents will tend to be lower than those of co-partisans. Employing newly collected data on public corruption prosecutions, I find evidence of partisan bias under both the Bush and Clinton Justice Departments. However, additional analysis suggests that these results may understate the extent of bias under Bush, while overstating it under Clinton.

Interesting. This reminds me of Bill James’s comment that Major League Baseball’s discrimination against blacks could be seen by the fact that black players had much better statistics than whites: under a discriminatory regime, they were taking marginal white players who were worse than the marginal black players. It’s also similar to what we found in Section 5.3 of our stop-and-frisk paper: the whites who were stopped were more likely than the blacks to be arrested, which suggests that police were disproportionally stopping minorities, at least with regard to this measure.

Gordon writes,

Employing an approach from economic models of discrimination, I [Gordon] treat partisan bias as a “taste” or preference for prosecuting one’s political opponents (or for not prosecuting allies). This approach, pioneered by Becker (1957), has been employed recently to study discrimination against minorities in setting bail (Ayres and Waldfogel 1994; Ayres 2001), racial pro ling (Knowles, Persico, and Todd 2001), and discrimination against female candidates in congressional elections (Anzia and Berry 2007).

I actually think this model makes more sense for studying prosecutors (as in the current paper) than for studying racial profiling of police (the subject of my paper with Jeff Fagan and Alex Kiss). Without any direct knowledge of prosecutors or police, I’m only speculating, but the idea of a “taste” or political pressure to prosecute one side or the other makes sense to me, whereas the idea of a police officer having a “taste” for stopping one race or the other sounds a little silly. My impression of police stops is that the police use whatever cues they have, and many of these cues are correlated with race. That to me doesn’t seem like the same thing as having a preference for stopping racial minorities per se.

Gordon writes, “The 2007 U.S. Attorney firing scandal raised the possibility that federal corruption laws could be deployed for partisan ends. In this paper, I have sought to move beyond anecdotes to construct a systematic test of partisan bias in corruption prosecutions.” This makes sense to me. What I’d also like to see is some work bridging the anecdotes to the quantitative results, giving a sense of who are the people being prosecuted that are driving these results.

Little things

I find Table 1 confusing (also, the numbers can be rounded to the nearest percent). Perhaps a flowchart like Figures 2 and 3 here would be clearer?

I’d also recommend that the captions of the tables be expanded. I don’t know if many other people are this way, but when I read an article I flip through to the graphs (or, if there are no graphs, the tables). So, for example, when I got to Table 2, I couldn’t figure out what was meant by “Public” and “Private.” I also couldn’t figure out why there were data from 1998-2000 and 2004-2006, but nothing from 1993-1997 or 2001-2003. I’m sure this is described in the article somewhere, but it would be good to see in right there in the table.

The numbers in Table 3 can be rounded. For example, there’s no way that “21.86 months” is informative. “22 months” would be fine. A graph would be better, but if it’s a table, please round! Similarly for Tables 4 and 6. You certainly don’t need to present things such as p-values to 3 decimal places. The usual asterisks would be fine! And for Table 5, please use a graph such as in Chapter 10 of our book.