Oh, that looks fun! I have a work dinner tomorrow and probably won’t have the energy to do both. But thanks for letting me know.

Declination rates are near zero in Philadelphia and zero in Harris County (since the police call into the DA’s office for approval before making an arrest) so you could also work with court data sets from those counties and be confident you’re not missing much

www.jstor.org/stable/10.10...

I think then you would just need police data linked to court data since a prosecutor’s decision to charge would result in a charge in the court records. The data used in the paper above should do the trick.

If you want something vaguely nationally representative, but a bit outdated, the SCPS data has that for a number of large jurisdictions from 2009

Not totally sure what you are asking? Most court data will have that except for arrests that did not lead to a charge. If court data is what you want, there are lots of data sets out there and I’m happy to share some with you if you want.

But just look at the literature. It is chock full of completely implausible claims.

Agreed that judge IV is not worse than many other IV settings, both are BAD.

A number of studies show IV estimates ~10 times larger than OLS even when the theory predicts OLS to be biased away from zero.

I’m saying that power tends to be extremely low with this research design and the bias induced by significance filtering goes up as bias goes down. There’s also lots of degrees of freedom which enables cherry picking as well as potential monotonicity and exclusion violations.

When the literature systematically produces massive, unrealistic estimates, I feel like we should approach the research design as if there are flashing red lights and blaring sirens warning danger, not a "keep calm and ujive on."

It’s not an equal concern with all research designs. It is a particular concern with low power research designs and judge IV tends to be very low power. I honestly think it has been a bad thing for our field to embrace it as the majority of estimates it produces are extremely biased.

In practice, I think judge IV is a hugely biased and unreliable estimator. It is typically an order of magnitude larger than OLS estimates, even when theory would predict that OLS is biased away from zero.

In practice I think this is really important. Judge IV estimates tend to be an order of magnitude larger than OLS even when theory would suggest OLS is biased away from zero.

Strong disagree. The paper does not consider issues of publication bias/specification search. In the presence of any sort of significance filtering (which we know is rampant) judge IV tends to be an extremely biased estimator since only the extreme estimates are statistically significant.

Paper link: papers.ssrn.com/sol3/papers....

With multiple reasonable methods of calculating racially disparate impact the researcher needs to think carefully about what they want to convey.

And probably report multiple metrics for full transparency. Fin. 14/14

We look at racial disparities in impact for a Philly bail reform. Even though Black defendants benefited most in an absolute sense, White defendants were disproportionately selected to benefit (had higher BRAI).

Sounds complicated? That’s the point. 13/

In our setting we use it to show how characteristics of beneficiaries (i.e. compliers) differ from the pool of potential beneficiaries (i.e. compliers + never takers). It’s a cool method but probably too much to explain here. Below is a little teaser graph! 12/