Yes, of course, but I wouldn't underestimate the difficult of gathering such a large data set; this is a very labor-intensive, time-intensive process and I know because I've conducted studies myself. It took me a very long time to gather the data. I'm mostly happy that someone did this.
Pedraza has a PhD from University of Washington and Wilcox-Archuleta is a doctoral candidate at UCLA. UW and UCLA are reputable schools. This is in their field of supposed expertise. Their article is crap.
Are you familiar with the expression "soft bigotry of low expectations"?
Uh, this isn't an academic publication, it is a study for an organization that's probably a side-project for these people.
Unless you have a critique of the estimation itself, what you're doing is nitpicky and childish. I fail to see how their categorization of counties matters. It's totally irrelevant to the goal of this study, which is to estimate how Latinos in Texas voted in 2016.
Further, unless you're a social scientist, this sort of criticism smacks of a dilettante perspective. It's actually very difficult to gather precinct data for many localities, which probably explains the, as you described it, "truncated" nature of the data. Maybe they didn't use Travis County data because, as everyone knows, Travis County Latino neighborhoods are gentrifying/morphing at a rapid clip. Have you thought about emailing the people who conducted the study to ask them about their decisions/choices? This might literally boil down to "we used this population cut-off to categorize a county" and "we could not match this Census data to these precincts" or "we lacked the time to do this and were under a deadline".
Just so we're clear, my argument is that these people are academics who conducted a very time-intensive project within the span of one month as they were fufilling other obligations, including working on dissertations or teaching classes etc. This wasn't for an academic journal, it was an independent project. So yeah, my expectations are pretty low. The nature of this type of work demands, what, at least 3 months? I'm not going to snipe at people for messing up on such short notice.
edit: introducing many explanatory variables in social scientific research, particularly w/r/t voting behavior is a fraught process because there are multicollinearity problems that are difficult to address in a satisfying manner. Black % is going to correlate with ultra-Latino precincts and poor/immigrant precincts. Income is going to correlate with assimilation. I know this because I've actually tried to do this sort of analysis and it doesn't really work. I assume that there are statistical techniques that can finesse these issues (I don't know them because I am an undergrad) but, really, there's no way to get around these problems. This is social science. Statistics isn't an oracle, it can only point to tendencies and make very broad estimates that are up for dispute because there are assumptions involved.
a further addition: it appears that statisticians do not think that partial multicollinearity is much of a problem arguing that it might only make estimates "more ambiguous" but, considering that social science deals with the magnitude of effects and wants to produce fairly precise estimates, this is actually very important.
