John R. Lott Jr. responded to my criticism of his working paper where he claims to have found that illegal immigrants are more likely to be admitted to Arizona state adult correctional facilities than other Arizona residents. Lott did not respond to my main criticism directly, which is that the Arizona Department of Corrections (ADC) data do not allow him to identify illegal immigrants with nearly as much precision as he claimed in his paper.
Praising the supposedly precise ADC data, Lott claimed that the “huge advantage of using the data that will be presented here from the Arizona Department of Corrections is that over our 32.5-year period we know each prisoner who entered the prison system, their criminal convictions history, and whether he is a documented or undocumented immigrant [emphasis added].”
Lott was so confident in his data’s ability to identify illegal immigrants that he wrote: “It is the entire universe of cases, not a sample, and thus there are no issues of statistical significance.”
The rest of his paper depends upon the immigrants in the “non-US citizen and deportable” variable in his dataset being illegal immigrants. We showed that that variable does not exclusively contain illegal immigrants. Thus, Lott’s characterization of the utility of his data is false. This is the only point in my criticism of his paper that counts and Lott did not challenge me on it in his rebuttal, except to point out that the description of the variable in question has a comma in the codebook rather than a conjunction. I dispute that, but it is irrelevant. I’ll take his non-response to my main point as an admission that he misinterpreted the variable and that his paper does not accurately describe illegal immigrant admissions to ADC facilities.
Lott then spends a lot of time attempting to rebut my back-of-the-envelope (BOE) calculation for 2017, which is what I called it in my blog. It’s hard to take any BOE calculation seriously and I even included a note at the end of the blog about how my BOE calculation is probably wrong.