All posts in category scholarly

The sausage making behind peer review

Even though I am not on Twitter, I still lurk every now and then. In particular I can see webtraffic referrals to the blog, so I will go and use nitter to look it up when I get new traffic.

Recently my work about why I publish preprints was referenced in a thread. That blog post was from the perspective of why I think individual scholars should post preprints. The thread that post was tagged in was not saying from a perspective of an individual writer – it was saying the whole idea of preprints is “a BIG problem” (Twitter thread, Nitter Thread).

That is, Dan thinks it is a problem other people post preprints before they have been peer reviewed.

Dan’s point is one held by multiple scholars in the field (have had similar interactions with Travis Pratt back when I was on Twitter). Dan does not explicitly say it in that thread, but I take this as pretty strong indication Dan thinks posting preprints without peer review is unethical (Dan thinks postprints are ok). The prior conversations I had with Pratt on Twitter he explicitly said it was unethical.

The logic goes like this – you can make errors, so you should wait until colleagues have peer reviewed your work to make sure it is “OK” to publish. Otherwise, it is misleading to readers of the work. In particular people often mention the media uncritically reporting preprint articles.

There are several reasons I think this opinion is misguided.

One, the peer review system itself is quite fallible. Having received, delivered, and read hundreds of peer review reports, I can confidently say that the entire peer review system is horribly unreliable. It has both a false negative and a false positive problem – in that things that should be published get rejected, and things that should not be published get through. Both happen all the time.

Now, it may be the case that the average preprint is lower quality than a peer reviewed journal article (given selection of who posts preprints I am actually not sure this is the case!) In the end though, you need to read the article and judge the article for yourself – you cannot just assume an article is valid simply because it was published in peer review. Nor can you assume the opposite – something not peer reviewed is not valid.

Two, the peer review system is vast currently. To dramatically oversimplify, there are “low quality” (paid for journals, some humanities journals, whatever journals publish the “a square of chocolate and a glass of red wine a day increases your life expectancy” garbage), and “high quality” journals. The people who Dan wants to protect from preprints are exactly the people who are unlikely to know the difference.

I use scare quotes around low and high quality in that paragraph on purpose, because really those superficial labels are not fair. BMC probably publishes plenty of high quality articles, it just happened to also publish an a paper that used a ridiculous methodology that dramatically overestimated vaccine adverse effects (where the peer reviewers just phoned in superficial reviews). Simultaneously high quality journals publish junk all the time, (see Crim, Pysch, Econ, Medical examples).

Part of the issue is that the peer review system is a black box. From a journalists perspective you don’t know what papers had reviewers phone it in (or had their buddies give it a thumbs up) versus ones that had rigorous reviews. The only way to know is to judge the paper yourself (even having the reviews is not informative relative to just reading the paper directly).

To me the answer is not “journalists should only report on peer reviewed papers” (or the same, no academic should post preprints without peer review) – all consumers need to read the work for themselves to understand its quality. Suggesting that something that is peer reviewed is intrinsically higher quality is bad advice. Even if on average this is true (relative to non-peer reviewed work), any particular paper you pick up may be junk. There is no difference from the consumer perspective in evaluating the quality of a preprint vs a peer reviewed article.

The final point I want to make, three, is that people publish things that are not peer reviewed all the time. This blog is not peer reviewed. I would actually argue the content I post here is often higher quality than many journal articles in criminology (due to transparent, reproducible code I often share). But you don’t need to take my word for it, you can read the posts and judge that for yourself. Ditto for many other popular blogs. I find it pretty absurd for someone to think me publishing a blog is unethical – ditto for preprints.

No point in arguing with peoples personal opinions about what is ethical vs what is not though. But thinking that you are protecting the public by only allowing peer reviewed articles to be reported on is incredibly naive as well as paternalistic.

We would be better off, not worse, if more academics posted preprints, peer review be damned.

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by Andy Wheeler on November 12, 2023  •  Permalink
Posted in Criminal Justice, scholarly, writing
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Posted by Andy Wheeler on November 12, 2023

https://andrewpwheeler.com/2023/11/12/the-sausage-making-behind-peer-review/

Some notes on synthetic control and Hogan/Kaplan

This will be a long one, but I have some notes on synthetic control and the back-and-forth between two groups. So first if you aren’t familiar, Tom Hogan published an article on how the progressive District Attorney (DA) in Philadelphia, Larry Krasner, in which Hogan estimates that Krasner’s time in office contributed to a large increase in the number of homicides. The control homicides are estimated using a statistical technique called synthetic control, in which you derive estimates of the trend in homicides to compare Philly to based on a weighted average of comparison cities.

Kaplan and colleagues (KNS from here on) then published a critique of various methods Hogan used to come up with his estimate. KNS provided estimates using different data and a different method to derive the weights, showing that Philadelphia did not have increased homicides post Krasner being elected. For reference:

Part of the reason I am writing this is if people care enough, you could probably make similar back and forths on every synth paper. There are many researcher degrees of freedom in the process, and in turn you can make reasonable choices that lead to different results.

I think it is worthwhile digging into those in more detail though. For a summary of the method notes I discuss for this particular back and forth:

  • Researchers determine the treatment estimate they want (counts vs rates) – solvers misbehaving is not a reason to change your treatment effect of interest
  • The default synth estimator when matching on counts and pop can have some likely unintended side-effects (NYC pretty much has to be one of the donor cities in this dataset)
  • Covariate balancing is probably a red-herring (so the data issues Hogan critiques in response to KNS are mostly immaterial)

In my original draft I had a note that this post would not be in favor of Hogan nor KNS, but in reviewing the sources more closely, nothing I say here conflicts with KNS (and I will bring a few more critiques of Hogan’s estimates that KNS do not mention). So I can’t argue much with KNS’s headline that Hogan’s estimates are fatally flawed.

An overview of synthetic control estimates

To back up and give an overview of what synth is for general readers, imagine we have a hypothetical city A with homicide counts 10 15 30, where the 30 is after a new DA has been elected. Is the 30 more homicides than you would have expected absent that new DA? To answer this, we need to estimate a counterfactual trend – what the homicide count would have been in a hypothetical world in which a new progressive DA was not elected. You can see the city homicides increased the prior two years, from 10 to 15, so you may say “ok, I expected it to continue to increase at the same linear trend”, in which case you would have expected it to increase to 20. So the counterfactual estimated increase in that scenario is observed - counterfactual, here 30 - 20 = 10, an estimated increase of 10 homicides that can be causally attributed to the progressive DA.

Social scientists tend to not prefer to just extrapolate prior trends from the same location into the future. There could be widespread changes that occur everywhere that caused the increase in city A. If homicide rates accelerated in every city in the country, even those without a new progressive DA, it is likely something else is causing those increases. So say we compare city A to city B, and city B had a homicide count trend during the same time period 10 15 35. Before the new DA in city A, cities A/B had the same pre-trend (both 10 15). The post time period City B increased to 35 homicides. So if using City B as the counterfactual estimate, we have the progressive DA reduced 5 homicides, again observed - counterfactual = 30 - 35 = -5. So even though city A increased, it increased less than we expected based on the comparison city B.

Note that this is not a hypothetical concern, it is pretty basic one that you should always be concerned about when examining macro level crime data. There has been national level homicide increases over the time period when Krasner has been in office (Yim et al, 2020, and see this blog post for updates. U.S. city homicide rates tend to be very correlated with each other (McDowall & Loftin, 2009).

So even though Philly has increased in homicide counts/rates when Krasner has been in office, the question is are those increases higher or lower than we would expect. That is where the synthetic control method comes in, we don’t have a perfect city B to compare to Philadelphia, so we create our own “synthetic” counter-factual, based on a weighted average of many different comparison cities.

To make the example simple, imagine we have two potential control cities and homicide trends, city C1 0 30 20, and city C2 20 0 30. Neither looks like a good comparison to city A that has trends 10 15 30. But if we do a weighted average of C1 and C2, with the weights 0.5 for each city, when combined they are a perfect match for the two pre-treatment periods:

C0  C1 Average cityA
 0  20   10     10
30   0   15     15
20  30   25     30

This is what the synthetic control estimator does, although instead of giving equal weights it determines the optimal weights to match the pre-treatment time period given many potential donors. In real data for example C0 and C1 may be given weights of 0.2 and 0.8 to give the correct balance based on the prior to treatment time periods.

The fundamental problem with synth

The rub with estimating the synth weights is that there is no one correct way to estimate the weights – you have more numbers to estimate than data points. In the Hogan paper, he has 5 pre time periods, 2010-2014, and he has 82 potential donors (99 other of the largest cities in the US minus 17 progressive prosecutors). So you need to learn 82 numbers (the weights) based on 5 data points.

Side note: you can also consider matching on covariates additional data points, although I will go into more detail on how matching on covariates is potentially a red-herring. Hogan I think uses an additional 5*3=15 time varying points (pop, cleared homicide, homicide clearance rates), and maybe 3 additional time invariant (median income, 1 prosecutor categorization, and homicides again!). So maybe has 5 + 15 + 3 = 23 data points to match on (so same fundamental problem, 23 numbers to learn 82 weights). I am just going to quote the full passage on Hogan (2022a) here where he discusses covariate matching:

The number of homicides per year is the dependent variable. The challenge with this synthetic control model is to use variables that both produce parallel trends in the pre-period and are sufficiently robust to power the post-period results. The model that ultimately delivered the best fit for the data has population, cleared homicide cases, and homicide clearance rates as regular predictors. Median household income is passed in as the first special predictor. The categorization of the prosecutors and the number of homicides are used as additional special predictors. For homicides, the raw values are passed into the model. Abadie (2021) notes that the underlying permutation distribution is designed to work with raw data; using log values, rates, or other scaling techniques may invalidate results.

This is the reason why replication code is necessary – it is very difficult for me to translate this to what Hogan actually did. “Special” predictors here are code words for the R synth package for time-invariant predictors. (I don’t know based on verbal descriptions how Hogan used time-invariant for the prosecutor categorization for example, just treats it as a dummy variable?) Also only using median income – was this the only covariate, or did he do a bunch of models and choose the one with the “best” fit (it seems maybe he did do a search, but doesn’t describe the search, only the end selected result).

I don’t know what Hogan did or did not do to fit his models. The solution isn’t to have people like me and KNS guess or have Hogan just do a better job verbally describing what he did, it is to release the code so it is transparent for everyone to see what he did.

So how do we estimate those 82 weights? Well, we typically have restrictions on the potential weights – such as the weights need to be positive numbers, and the weights should sum to 1. These are for a mix of technical and theoretical reasons (having the weights not be too large can reduce the variance of the estimator is a technical reason, we don’t want negative weights as we don’t think there are bizzaro comparison areas that have opposite world trends is a theoretical one).

These are reasonable but ultimately arbitrary – there are many different ways to accomplish this weight estimation. Hogan (2022a) uses the R synth package, KNS use a newer method also advocated by Abadie & L’Hour (2021) (very similar, but tries to match to the closest single city, instead of weights for multiple cities). Abadie (2021) lists probably over a dozen different procedures researchers have suggested over the past decade to estimate the synth weights.

The reason I bring this up is because when you have a problem with 82 parameters and 5 data points, the problem isn’t “what estimator provides good fit to in-sample data” – you should be able to figure out a estimator that accomplishes good in-sample fit. The issue is whether that estimator is any good out-of-sample.

Rates vs Counts

So besides the estimator used, you can break down 3 different arbitrary researcher data decisions that likely impact the final inferences:

  • outcome variable (homicide counts vs homicide per capita rates)
  • pre-intervention time periods (Hogan uses 2010-2014, KNS go back to 2000)
  • covariates used to match on

Lets start with the outcome variable question, counts vs rates. So first, as quoted above, Hogan cites Abadie (2021) for saying you should prefer counts to rates, “Abadie (2021) notes that the underlying permutation distribution is designed to work with raw data; using log values, rates, or other scaling techniques may invalidate results.”

This has it backwards though – the researcher chooses whether it makes sense to estimate treatment effects on the count scale vs rates. You don’t goal switch your outcome because you think the computer can’t give you a good estimate for one outcome. So imagine I show you a single city over time:

        Y0    Y1    Y2
Count   10    15    20
Pop   1000  1500  2000

You can see although the counts are increasing, the rate is consistent over the time period. There are times I think counts make more sense than rates (such as cost-benefit analysis), but probably in this scenario the researcher would want to look at rates (as the shifting denominator is a simple explanation causing the increase in the counts).

Hogan (2022b) is correct in saying that the population is not shifting over time in Philly very much, but this isn’t a reason to prefer counts. It suggests the estimator should not make a difference when using counts vs rates, which just points to the problematic findings in KNS (that making different decisions results in different inferences).

Now onto the point that Abadie (2021) says using rates is wrong for the permutation distribution – I don’t understand what Hogan is talking about here. You can read Abadie (2021) for yourself if you want. I don’t see anything about the permutation inferences and rates.

So maybe Hogan mis-cited and meant another Abadie paper – Abadie himself uses rates for various projects (he uses per-capita rates in the 2021 cited paper, Abadie et al., (2010) uses rates for another example), so I don’t think Abadie thinks rates are intrinsically problematic! Let me know if there is some other paper I am unaware of. I honestly can’t steelman any reasonable source where Hogan (2022a) came up with the idea that counts are good and rates are bad though.

Again, even if they were, it is not a reason to prefer counts vs rates, you would change your estimator to give you the treatment effect estimate you wanted.

Side note: Where I thought the idea with the problem with rates was going (before digging in and not finding any Abadie work actually saying there is issues with rates), was increased variance estimates with homicide data. So Hogan (2022a) estimates for the synth weights Detroit (0.468), New Orleans (NO) (0.334), and New York City (NYC) (0.198), here are those cities homicide rates graphed (spreadsheet with data + notes on sources).

You can see NO’s rate is very volatile, so is not a great choice for a matched estimator if using rates. (I have NO as an example in Wheeler & Kovandzic (2018), that much variance though is fairly normal for high crime not too large cities in the US, see Baltimore for example for even more volatility.) I could forsee someone wanting to make a weighted synth estimator for rates, either make the estimator a population weighted average, or penalize the variance for small rates. Maybe you can trick microsynth to do a pop weighted average out of the box (Robbins et al., 2017).

To discuss the Hogan results specifically, I suspect for example NYC being a control city with high weight in the Hogan paper, which superficially may seem good (both large cities on the east coast), actually isn’t a very good control area considering the differences in homicide trends (either rates or counts) over time. (I am also not so sure about describing NYC and New Orlean’s as “post-industrial” by Hogan (2022a) either. I mean this is true to the extent that all urban areas in the US are basically post-industrial, but they are not rust belt cities like Detroit.)

Here is for reference counts of homicides in Philly, Detroit, New Orleans, and NYC going back further in time:

NYC is such a crazy drop in the 90s, lets use the post 2000 data that KNS used to zoom in on the graph.

I think KNS are reasonable here to use 2000 as a cut point – it is more empirical based (post crime drop), in which you could argue the 90’s are a “structural break”, and that homicides settled down in most cities around 2000 (but still typically had a gradual decline). Given the strong national homicide trends though across cities (here is an example I use for class, superimposing Dallas/NYC/Chicago), I think using even back to the 60’s is easily defensible (moreso than limiting to post 2010).

It depends on how strict you want to be whether you consider these 3 cities “good” matches for the counts post 2010 in Hogan’s data. Detroit seems a good match on the levels and ok match on trends. NO is ok match on trends. NYC and NO balance each other in terms of matching levels, NYC has steeper declines though (even during the 2010-2014 period).

The last graph though shows where the estimated increases from Hogan (2022a) come from. Philly went up and those 3 other cities went down from 2015-2018 (and had small upward bumps in 2019).

Final point in this section, careful what you wish for with sparse weights and sum to 1 in the synth estimate. What this means in practice when using counts and matching on pop size, is that you need lines that are above and below Philly on those dimensions. So to get a good match on Pop, it needs to select at least one of NYC/LA/Houston (Chicago was eliminated due to having a progressive prosecutor). To get a good match on homicide counts, it also has to pick at least one city with more homicides per year as well, which limits the options to New York and Detroit (LA/Houston have lower overall homicide counts to Philly).

You can’t do the default Abadie approach for NYC for example (matching on counts and pop) – it will always have a bad fit when using comparison cities in the US as the donor pool. You either need to allow the weights to sum to larger than 1, or the lasso approach with an intercept is another option (so you only match on trend, not levels).

Because matching on trends is what matters for proper identification in this design, not levels, this is all sorts of problematic with the data at hand. (This is also a potential problem with the KNS estimator as well. KNS note though they don’t trust their estimate offhand, their reasonable point is that small changes in the design result in totally different inferences.)

Covariates and Out of Sample Estimates

For sake of argument, say I said Hogan (2022a) is bunk, because it did not match on “per-capita annual number of cheese-steaks consumed”. Even though on its face this covariate is non-sense, how do you know it is non-sense? In the synthetic control approach, there is no empirical, falsifiable way to know whether an covariate is a correct one to match on. There is no way to know that median income is better than cheese-steaks.

If you wish for more relevant examples, Philly has obviously more issues with street consumption of opioids than Detroit/NOLA/NYC, which others have shown relationships to homicide and has been getting worse over the time Krasner has been in office (Rosenfeld et al., 2023). (Or more simply social disorganization is the more common way that criminologists think about demographic trends and crime.)

This uncertainty in “what demographics to control for” is ok though, because matching on covariates is neither necessary nor sufficient to ensure you have estimated a good counter-factual trend. Abadie in his writings intended for covariates to be more like fuzzy guide-rails – they are qualitative things that you think the comparison areas should be similar on.

Because there are effectively an infinite pool of potential covariates to match on, I prefer the approach of simply limiting the donor pool apriori – Hogan limiting to large cities is on its face reasonable. Including other covariates is not necessary, and does not make the synth estimate more or less robust. Whether KNS used good or bad data for covariates is entirely a red-herring as to the quality of the final synth estimate.

Side note: I don’t doubt that Hogan got advice to not share data and code. It is certainly not normative in criminology to do this. It creates a bizarre situation though, in which someone can try to replicate Hogan by collating original sources, and then Hogan always comes back and says “no, the data you have are wrong” or “the approach you did is not exactly replicating my work”.

I get that collating data takes a long time, and people want to protect their ability to publish in the future. (Or maybe just limit their exposure to their work being criticized.) It is blatantly antithetical to verifying the scientific integrity of peoples work though.

Even if Hogan is correct though in the covariates that KNS used are wrong, it is mostly immaterial to the quality of the synth estimates. It is a waste of time for outside researchers to even bother to replicate Hogan’s covariates he used.

So I used the idea of empirical/falsifiable – can anything associated with synth be falsifiable? Why yes it can – the typical approach is to do some type of leave-one-out estimate. It may seem odd because synth estimates an underlying match to a temporal trend in the treated location, but there is nothing temporal about the synth estimate. You could jumble up the years in the pre-treatment sample and still would estimate the same weights.

Because of this, you can leave-a-year-out in the pre-treatment time period, run your synth algorithm, and then predict that left out year. A good synth estimator will be close to the observed value for the out of sample estimates in the pre-treated time period (and as a side bonus, you can use that variance estimate to estimate the error in the post-trend years).

That is a relatively simple way to determine if the Hogan 5 year vs KNS 15 year time periods are “better” synth controls (my money is on KNS for that one). Because Hogan has not released data/code, I am not going to go through that trouble. As I said in the side note earlier, I could try to do that, and Hogan could simply come back and say “you didn’t do it right”.

This also would settle the issue of “over-fit”. You actually cannot just look at the synth weights, and say that if they are sparse they are not over-fit and if not sparse are over-fit. So for reference, you have in Hogan essentially fitting 82 weights based on 5 datapoints, and he identified a fit with 3 non-zero weights. Flip this around, and say I had 5 data points and fit a model with 3 parameters, it is easily possible that the 3 parameter model in that scenario is overfit.

Simultaneously, it is not necessary to have a sparse weights matrix. Several alternative methods to estimate synth will not have sparse weights (I am pretty sure Xu (2017) will not have sparse weights, and microsynth estimates are not sparse either for just two examples). Because US cities have such clear national level trends, a good estimator in this scenario may have many tiny weights (where good here is low bias and variance out of sample). Abadie thinks sparse weights are good to make the model more interpretable (and prevent poor extrapolation), but that doesn’t mean by default a not sparse solution is bad.

To be clear, KNS admit that their alternative results are maybe not trustworthy due to not sparse weights, but this doesn’t imply Hogan’s original estimates are themselves “OK”. I think maybe a correct approach with city level homicide rate data will have non-sparse weights, due to the national level homicide trend that is common across many cities.

Wrapping Up

If Crim and Public Policy still did response pieces maybe I would go through that trouble of doing the cross validation and making a different estimator (although I would unlikely be an invited commenter). But wanted to at least do this write up, as like I said at the start I think you could do this type of critique with the majority of synth papers in criminology being published at the moment.

To just give my generic (hopefully practical) advice to future crim work:

  • don’t worry about matching on covariates, worry about having a long pre-period
  • the default methods you need to worry about if you have enough “comparable” units – this is in terms of levels, not just trends
  • the only way to know the quality of the modeling procedure in synth is to do out of sample estimates.

Bullet points 2/3 are perhaps not practical – most criminologists won’t have the capability to modify the optimization procedure to the situation at hand (I spent a few days trying without much luck to do my penalized variants suggested, sharing so others can try out themselves, I need to move onto other projects!) Also takes a bit of custom coding to do the out of sample estimates.

For many realistic situations though, I think criminologists need to go beyond just point and clicking in software, especially for this overdetermined system of equations synthetic control scenario. I did a prior blog post on how I think many state level synth designs are effectively underpowered (and suggested using lasso estimates with conformal intervals). I think that is a better default in this scenario as well compared to the typical synth estimators, although you have plenty of choices.

Again I had initially written this as trying to two side the argument, and not being for or against either set of researchers. But sitting down and really reading all the sources and arguments, KNS are correct in their critique. Hogan is essentially hiding behind not releasing data and code, and in that scenario can make an endless set of (ultimately trivial) responses of anyone who publishes a replication/critique.

Even if some of the the numbers KNS collated are wrong, it does not make Hogan’s estimates right.

References

2 Comments
by Andy Wheeler on July 9, 2023  •  Permalink
Posted in Crime Analysis, data science, Papers, Python, R, Regression, scholarly
Tagged ,

Posted by Andy Wheeler on July 9, 2023

https://andrewpwheeler.com/2023/07/09/some-notes-on-synthetic-control-and-hogan-kaplan/

Make more money

So I enjoy Ramit Sethi’s Netflix series on money management – fundamentally it is about money coming in and money going out and the ability to balance a budget. On occasion I see other budget coaches focus on trivial expenses (the money going out) whereas for me (and I suspect the majority of folks reading this blog with higher degrees and technical backgrounds) you should almost always be focused on finding a higher paying job.

Lets go with a common example people use as unnecessary discretionary spending – getting a $10 drink at Starbucks every day. If you do this, over the course of a 365 day year, you will have spent $3650 additional dollars. If you read my blog about coding and statistics and that expense bothers you, you are probably not making as much money as you should be.

Ramit regularly talks about asking for raises – I am guessing most people reading this blog if you got a raise it would be well over that Starbucks expense. But part of the motivation to write this post is in reference to formerly being a professor. I think many criminal justice (CJ) professors are underemployed, and should consider better paying jobs. I am regularly starting to see public sector jobs in CJ that have substantially better pay than being a professor. This morning was shared a position for an entry level crime analyst at the Reno Police Department with pay range from $84,000 to $102,000:

The low end of that starting pay range is competitive with the majority of starting assistant professor salaries in CJ. You can go check out what the CJ professors at Reno make (which is pretty par for the course for CJ departments in the US) in comparison. If I had stayed as a CJ professor, even with moving from Dallas to other universities and trying to negotiate raises, I would be lucky to be making over $100k at this point in time. Again, that Reno position is an entry level crime analyst – asking for a BA + 2 years of experience or a Masters degree.

Private sector data science jobs in comparison, in DFW area in 2019 entry level were often starting at $105k salary (based on personal experience). You can check out BLS data to examine average salaries in data science if you want to look at your particular metro area (it is good to see the total number in that category in an area as well).

While academic CJ salaries can sometimes be very high (over $200k), these are quite rare. There are a few things going against professor jobs, and CJ ones in particular, that depress CJ professor wages overall. Social scientists in general make less than STEM fields, and CJ departments are almost entirely in state schools that tend to have wage compression. Getting an offer at Harvard or Duke is probably not in the cards if you have a CJ degree.

In addition to this, with the increase in the number of PhDs being granted, competition is stiff. There are many qualified PhDs, making it very difficult to negotiate your salary as an early career professor – the university could hire 5 people who are just as qualified in your stead who aren’t asking for that raise.

So even if you are lucky enough to have negotiating power to ask for a raise as a CJ professor (which most people don’t have), you often could make more money by getting a public sector CJ job anyway. If you have quant skills, you can definitely make more money in the private sector.

At this point, most people go back to the idea that being a professor is the ultimate job in terms of freedom. Yes, you can pursue whatever research line you want, but you still need to teach courses, supervise students, and occasionally do service to the university. These responsibilities all by themselves are a job (the entry level crime analyst at Reno will work less overall than the assistant professor who needs to hustle to make tenure).

To me the trade off in freedom is worth it because you get to work directly with individuals who actually care what you do – you lose freedom because you need to make things within the constraints of the real world that real people will use. To me being able to work directly on real problems and implement my work in real life is a positive, not a negative.

Final point to make in this blog, because of the stiff competition for professor positions, I often see people suggesting there are too many PhDs. I don’t think this is the case though, you can apply the skills you learned in getting your CJ PhD to those public and private sector jobs. I think CJ PhD programs just need small tweaks to better prepare students for those roles, in addition to just letting people know different types of positions are available.

It is pretty much at the point that alt-academic jobs are better careers than the majority of CJ academic professor positions. If you had the choice to be an assistant professor in CJ at University of Nevada Reno, or be a crime analyst at Reno PD, the crime analyst is the better choice.

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by Andy Wheeler on June 22, 2023  •  Permalink
Posted in Crime Analysis, Criminal Justice, data science, scholarly
Tagged

Posted by Andy Wheeler on June 22, 2023

https://andrewpwheeler.com/2023/06/22/make-more-money/

Criminology not on the brink

I enjoy reading Jukka Savolainen’s hot takes, most recently Give Criminology a Chance: Notes from a discipline on the brink. I think Jukka is wrong on a few points, but if you are a criminologist who goes to ASC conferences, definitely go and read it! To be specific, in addition to the title here are two penultimate paragraphs in full that I mostly disagree with:

I arrived in Atlanta with a pessimistic view of academic criminology. During my 30 years in the field, the scholarship has become increasingly political and intolerant of evidence that contradicts the progressive narrative. The past few years have been particularly discouraging for those who care about scientific rigor and truth. Despite these reservations, I approached the ASC meeting with an open mind.

The situation is far from hopeless. True, criminology possesses precious little viewpoint diversity. Much of the scholarship is more interested in pursuing a political agenda than objective truth. The ASC’s outward stance as a politically neutral arbiter of scientific evidence is at odds with its recent history as an activist organization.

Although his take on a generic American Society of Criminology experience is again not misleading and accurate, I am not so sure about the assessment of the trend over time, e.g. “increasingly political and intolerant”. Nor do I think criminology has too “little viewpoint diversity”.

The latter statement is to be frank absurd. For those who haven’t been to an ASC conference, there are no restrictions to who can become a member of the American Society of Criminology. The yearly conference is essentially open as well – you have to submit an abstract for review, but I have never heard of an abstract being turned down (let me know if you are aware of an example!) So you really get the kaleidoscope (as Jukka articulated). Policing scholars, abolitionists, quantitative, qualitative, ghost criminology – criminologists are a heterogeneous bunch.

About the only way to steelman the statement “precious little viewpoint diversity” is to say something more like certain opinions in the field are rewarded/punished, such as being in advanced positions at ASC, or limiting what gets published in the ASC journals (Criminology or Criminology and Public Policy). Or maybe that the average mix of the field slants one way or another (say between pro criminal justice or critical criminal justice).

I have not been around 30 years like Jukka, and I suppose I lost my card carrying criminologist privileges when I went to the private sector, but I haven’t seen any clear change in the nature of field, the ASC conference, or what has been published, in the last ~10 years I have been in a reasonable position to make that judgment. I think Jukka (or anyone) would be hard pressed to quantify his perception – but certainly open to real evidence if I am wrong here (again just my opinion based on fewer years of experience than Jukka).

As a side story, I have heard many of my friends who do work in policing state that they have been criticized for that by colleagues, and subsequently argue our field is “biased against cops”. I don’t doubt my friends personal experiences, but I have personally never been criticized for working with the police. I have been criticized by fellow policing scholars as “downloading datasets” and “not being a real policing scholar”. I know qualitative criminologists who think they are biased against in the field (based on rates of qualitative publishing). I know quantitative criminologists who have given examples of bias in the field against more rigorous empirical methods. I know Europeans who think the field is biased towards Americans. I bet the ghost criminologists think the living are biased against the (un)dead.

I think saying “Much of the scholarship is more interested in pursuing a political agenda than objective truth” is a tinge strong, but sure it happens (I am not quite sure how to map “much” to a numeric value so the statement can be confirmed or refuted). I would say being critical of some work, but then uncritically sharing equally unrigorous work that confirms your pre-conceived notions is an example of this! So if you think one or more is “much”, then I guess I don’t disagree with Jukka here – to be clear though I think the majority of criminologists I have met are interested in pursuing the truth (even if I disagree with the methods they use).

So onto the last sentence of Jukka’s I disagree with, “The ASC’s outward stance as a politically neutral arbiter of scientific evidence is at odds with its recent history as an activist organization.”. But I disagree with this because I personally have a non-normative take on science – I don’t think science is wholly defined by being a neutral arbiter of truth, and doing science in the real world literally involves things that are “activist”.

I believe if you asked most people with Phds what defines science, they would say that science is defined via the scientific method. I personally think that is wrong though. I think about the only thing we share as scientists are being critique-y assholes. The way I do my work is so different from many other criminologists (both quantitative and qualitative), let alone researchers in other scientific fields (like theoretical physics or history), that I think saying we “share a common research method” is a bit of a stretch.

When my son was younger and had science fairs, they were broken into two different types of submissions; traditional science experiments, like measure a plants growth in sunlight vs without, or engineering “build things”. The academic work I am most proud of is in the engineering “build things” camp. These modest contributions in various algorithms – a few have been implemented in major software, and I know some crime analysis units using that work as well – really have nothing to do with the scientific method. Me deriving standard errors for control charts for crime trends is only finding truth in a very tautological way – I think they are useful though.

There is no bright line between my work and “activism” – I don’t think that is a bad thing though and it was the point of the work. You could probably say Janet Lauritsen is an activist for more useful national level CJ statistics. Jukka appears to me to be making normative opinions about he thinks Janet’s activism is more rigorously motivated than Vitale’s – which I agree with, but doesn’t say much if anything about the field of criminology as a whole or recent changes in the field. (If anything it is evidence against Jukka’s opinion, I posit Janet is clearly more influential in the field than Vitale.)

To end with the note “on the brink” – it may be unfair to Jukka (sometimes you don’t get to pick your titles in magazine articles). Part of the way I view being an academic and critiquing work I imagine people find irksome – it involves taking real words people say, trying to reasonably map them to statements that can be confirmed or refuted (often people say things that are quite fuzzy), and then articulating why those statements are maybe right/maybe wrong. It can seem pedantic, but I am a Popper kind-of-guy, and being able to confirm or refute statements I think is the only way we can get closer to objective truth.

To do this with “on the brink” takes more leaps than statements such as “increasingly political and intolerant”. “Criminology” is the general study of criminal behavior – which I am pretty confident will continue on as long as people commit crimes with or without the ASC yearly conference. We can probably limit the “on the brink” statement to something more specific like the American Society of Criminology on the brink. I don’t know about the ASC financials, but I am going to guess Jukka meant by this statement more of a proclamation about the legitimacy of the organization to outside groups.

I am not so sure this is the point of ASC though – it derives its value by being a social club for people who do criminology research. At least that is my impression of going to ASC conferences from my decade as a criminologist. Part of Jukka’s point is that things are getting worse more recently – you can’t lose something you never had to begin with though.

4 Comments
by Andy Wheeler on May 14, 2023  •  Permalink
Posted in Criminal Justice, scholarly
Tagged ,

Posted by Andy Wheeler on May 14, 2023

https://andrewpwheeler.com/2023/05/14/criminology-not-on-the-brink/

This one simple change will dramatically improve reproducibility in journals

So Eric Stewart is back in the news, and it appears a new investigation has prompted him to resign from Florida State. For a background on the story, I suggest reading Justin Pickett’s EconWatch article. In short, Justin did analysis of his own papers he co-authored with Stewart to show what is likely data fabrication. Various involved parties had superficial responses at first, but after some prodding many of Stewart’s papers were subsequently retracted.

So there is quite a bit of human messiness in the responses to accusations of error/fraud, but I just want to focus on one thing. In many of these instances, the flow goes something like:

  1. individual points out clear numeric flaws in a paper
  2. original author says “I need time to investigate”
  3. multiple months later, original author has still not responded
  4. parties move on (no resolution) OR conflict (people push for retraction)

My solution here is a step that mostly fixes the time lag in steps 2/3. Authors who submit quantitative results should be required to submit statistical software log files along with their article to the journal from the start.

So there is a push in social sciences to submit fully reproducible results, where an outside party can replicate 100% of the analysis. This is difficult – I work full time as a software engineer – it requires coding skills most scientists don’t have, as well as outside firms to devote resources to the validation. (Offhand, if you hired me to do this, I would probably charge something like $5k to $10k I am guessing given the scope of most journal articles in social sciences.)

An additional problem with this in criminology research, we are often working with sensitive data that cannot easily be shared.

I agree a fully 100% reproducible would be great – lets not make the perfect the enemy of the good though. What I am suggesting is that authors should directly submit the log files that they used to produce tables/regression results.

Many authors currently are running code interactively in Stata/R/SPSS/whatever, and copy-pasting the results into tables. So in response to 1) above (the finding of a data error), many parties assume it is a data transcription error, and allow the original authors leeway to go and “investigate”. If journals have the log files, it is trivial to see if a data error is a transcription error, and then can move into a more thorough forensic investigation stage if the logs don’t immediately resolve any discrepancies.

If you are asking “Andy, I don’t know how to save a log file from my statistical analysis”, here is how below. It is a very simple thing – a single action or line of code.

This is under the assumption people are doing interactive style analysis. (It is trivial to save a log file if you have created a script that is 100% reproducible, e.g. in R it would then just be something like Rscript Analysis.R > logfile.txt.) So is my advice to save a log file when doing interactive partly code/partly GUI type work.

In Stata, at the beginning of your session use the command:

log using "logfile.txt", text replace

In R, at the beginning of your session:

sink("logfile.txt")
...your code here...
# then before you exit the R session
sink()

In SPSS, at the end of your session:

OUTPUT EXPORT /PDF DOCUMENTFILE="local_path\logfile.pdf".

Or you can go to the output file and use the GUI to export the results.

In python, if you are doing an interactive REPL session, can do something like:

python > logfile.txt
...inside REPL here...

Or if you are using Jupyter notebooks can just save the notebook a html file.

If interested in learning how to code in more detail for regression analysis, I have PhD course notes on R/SPSS/Stata.

This solution is additional work from the authors perspective, but a very tiny amount. I am not asking for 100% reproducible code front to back, I just want a log file that shows the tables. These log files will not show sensitive data (just summaries), so can be shared.

This solution is not perfect. These log files can be edited. Requiring these files will also not prevent someone from doctoring data outside of the program and then running real analysis on faked data.

It ups the level of effort for faking results though by a large amount compared to the current status quo. Currently it just requires authors to doctor results in one location, this at a minimum requires two locations (and to keep the two sources equivalent is additional work). Often the outputs themselves have additional statistical summaries though, so it will be clearer if someone doctored the results than it would be from a simpler table in a peer reviewed article.

This does not 100% solve the reproducibility crisis in social sciences. It does however solve the problem of “I identified errors in your work” and “Well I need 15 months to go and check my work”. Initial checks for transcription vs more serious errors with the log files can be done by the journal or any reasonable outsider in at most a few hours of work.

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by Andy Wheeler on April 14, 2023  •  Permalink
Posted in Criminal Justice, Papers, Python, R, scholarly, SPSS, Stata, writing
Tagged

Posted by Andy Wheeler on April 14, 2023

https://andrewpwheeler.com/2023/04/14/this-one-simple-change-will-dramatically-improve-reproducibility-in-journals/

ASEBP blog posts, and auto screenshotting websites

I wanted to give an update here on the Criminal Justician series of blogs I have posted on the American Society of Evidence Based Policing (ASEBP) website. These include:

  • Denver’s STAR Program and Disorder Crime Reductions
    • Assessing whether Denver’s STAR alternative mental health responders can be expected to decrease a large number of low-level disorder crimes.
  • Violent crime interventions that are worth it
    • Two well-vetted methods – hot spots policing and focused deterrence – are worth the cost for police to implement to reduce violent crime.
  • Evidence Based Oversight on Police Use of Force
    • Collecting data in conjunction with clear administrative policies has strong evidence it overall reduces officer use of force.
  • We don’t know what causes widespread crime trends
    • While we can identify whether crime is rising or falling, retrospectively identifying what caused those ups and downs is much more difficult.
  • I think scoop and run is a good idea
    • Keeping your options open is typically better than restricting them. Police should have the option to take gun shot wound victims directly to the emergency room when appropriate.
  • One (well done) intervention is likely better than many
    • Piling on multiple interventions at once makes it impossible to tell if a single component is working, and is likely to have diminishing returns.

Going forward I will do a snippet on here, and refer folks to the ASEBP website. You need to sign up to be able to read that content – but it is an organization that is worth joining (besides for just reading my takes on science around policing topics).

So my CRIME De-Coder LLC has a focus on the merger of data science and policing. But I have a bit of wider potential application. Besides statistical analysis in different subject areas, one application I think will be of wider interest to public and private sector agencies is my experience in process automation. These often look like boring things – automating generating a report, sending an email, updating a dashboard, etc. But they can take substantial human labor, and automating also has the added benefit of making a process more robust.

As an example, I needed to submit my website as a PDF file to obtain a copyright. To do this, you need to take screenshots of your website and all its subsequent pages. Googling on this for selenium and python, the majority of the current solutions are out of date (due to changes in the Chrome driver in selenium over time). So here is the solution I scripted up the morning I wanted to submit the copyright – it took about 2 hours total in debugging. Note that this produces real screenshots of the website, not the print to pdf (which looks different).

It is short enough for me to just post the entire script here in a blog post:

from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.chrome.options import Options
import time
from PIL import Image
import os

home = 'https://crimede-coder.com/'

url_list = [home,
            home + 'about',
            home + 'blog',
            home + 'contact',
            home + 'services/ProgramAnalysis',
            home + 'services/PredictiveAnalytics',
            home + 'services/ProcessAutomation',
            home + 'services/WorkloadAnalysis',
            home + 'services/CrimeAnalysisTraining',
            home + 'services/CivilLitigation',
            home + 'blogposts/2023/ServicesComparisons']

res_png = []

def save_screenshot(driver, url, path, width):
    driver.get(url)
    # Ref: https://stackoverflow.com/a/52572919/
    original_size = driver.get_window_size()
    #required_width = driver.execute_script('return document.body.parentNode.scrollWidth')
    required_width = width
    required_height = driver.execute_script('return document.body.parentNode.scrollHeight')
    driver.set_window_size(required_width,required_height)
    #driver.save_screenshot(path)  # has scrollbar
    driver.find_element(By.TAG_NAME, 'body').screenshot(path)  # avoids scrollbar
    driver.set_window_size(original_size['width'], original_size['height'])

options = Options()
options.headless = True
driver = webdriver.Chrome(options=options)

for url in url_list:
    driver.get(url)
    if url == home:
        name = "index.png"
    else:
        res_url = url.replace(home,"").replace("/","_")
        name = res_url + ".png"
    time.sleep(1)
    res_png.append(name)
    save_screenshot(driver,url,name,width=1400)

driver.quit()

# Now appending to PDF file
images = [Image.open(f).convert('RGB') for f in res_png if f[-3:] == 'png']
i1 = images.pop(0)
i1.save(r'Website.pdf', save_all=True, append_images=images)

# Now removing old PNG files
for f in res_png:
    os.remove(f)

One of the reasons I want to expand knowledge of coding practices into policing (as well as other public sector fields) is that this simple of a thing doesn’t make sense for me to package up and try to monetize. The IP involved in a 2 hour script is not worth that much. I realize most police departments won’t be able to take the code above and actually use it – it is better for your agency to simply do a small contract with me to help you automate the boring stuff.

I believe this is in large part a better path forward for many public sector agencies, as opposed to buying very expensive Software-as-a-Service solutions. It is better to have a consultant to provide a custom solution for your specific agency, than to spend money on some big tool and hope your specific problems fit their mold.

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by Andy Wheeler on March 14, 2023  •  Permalink
Posted in Crime Analysis, Criminal Justice, data science, Python, scholarly
Tagged ,

Posted by Andy Wheeler on March 14, 2023

https://andrewpwheeler.com/2023/03/14/asebp-blog-posts-and-auto-screenshotting-websites/

Getting access to paywalled newspaper and journal articles

So recently several individuals have asked about obtaining articles they do not have access to that I cite in my blog posts. (Here or on the American Society of Evidence Based Policing.) This is perfectly fine, but I want to share a few tricks I have learned on accessing paywalled newspaper articles and journal articles over the years.

I currently only pay for a physical Sunday newspaper for the Raleigh News & Observer (and get the online content for free because of that). Besides that I have never paid for a newspaper article or a journal article.

Newspaper paywalls

Two techniques for dealing with newspaper paywalls. 1) Some newspapers you get a free number of articles per month. To skirt this, you can open up the article in a private/incognito window on your preferred browser (or open up the article in another browser entirely, e.g. you use Chrome most of the time, but have Firefox just for this on occasion.)

If that does not work, and you have the exact address, you can check the WayBack machine. For example, here is a search for a WaPo article I linked to in last post. This works for very recent articles, so if you can stand being a few days behind, it is often listed on the WayBack machine.

Journal paywalls

Single piece of advice here, use Google Scholar. Here for example is searching for the first Braga POP Criminology article in the last post. Google scholar will tell you if a free pre or post-print URL exists somewhere. See the PDF link on the right here. (You can click around to “All 8 Versions” below the article as well, and that will sometimes lead to other open links as well.)

Quite a few papers have PDFs available, and don’t worry if it is a pre-print, they rarely substance when going into print.1

For my personal papers, I have a google spreadsheet that lists all of the pre-print URLs (as well as the replication materials for those publications).

If those do not work, you can see if your local library has access to the journal, but that is not as likely. And I still have a Uni affiliation that I can use for this (the library and getting some software cheap are the main benefits!). But if you are at that point and need access to a paper I cite, feel free to email and ask for a copy (it is not that much work).

Most academics are happy to know you want to read their work, and so it is nice to be asked to forward a copy of their paper. So feel free to email other academics as well to ask for copies (and slip in a note for them to post their post-prints to let more people have access).

The Criminal Justician and ASEBP

If you like my blog topics, please consider joining the American Society of Evidence Based Policing. To be clear I do not get paid for referrals, I just think it is a worthwhile organization doing good work. I have started a blog series (that you need a membership for to read), and post once a month. The current articles I have written are:

So if you want to read more of my work on criminal justice topics, please join the ASEBP. And it is of course a good networking resource and training center you should be interested in as well.

  1. You can also sign up for email alerts on Google Scholar for papers if you find yourself reading a particular author quite often.↩︎
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by Andy Wheeler on February 14, 2023  •  Permalink
Posted in ask me anything, Criminal Justice, Personal Productivity, scholarly, writing
Tagged

Posted by Andy Wheeler on February 14, 2023

https://andrewpwheeler.com/2023/02/14/getting-access-to-paywalled-newspaper-and-journal-articles/

Where are they now? Job outcomes for recent SUNY crim Phds

The other day I noticed one of my PhD cohort mates, like me, took a private sector data science job. So of the 6 that finished their Phds in my cohort, 2 of us are now in private sector and the rest are professors. I was curious the overall rate for a larger sample.

There is probably some better official source, but I was able to do a search in Proquest dissertations (SUNY we needed to submit it there), for "State University of New York at Albany" AND "School of Criminal Justice" published between 2010 through 2020 and it scooped up a pretty good sample (with a few false positives I eliminated). I then added in a few people I noticed missing in that set, in the end 69 total over the 11 years (6 defenses per year actually seemed high to me). (Using the WayBack machine you can look at old Phd profiles or the old list of dissertations, but I am not sure of the completeness of either.) Then I filled in their current main job best I could into professor, private sector, university research center, think tank, government (and a few I did not even hazard a guess), based on LinkedIn/google searches/personal knowledge.

Here is the spreadsheet, let me know if you think I miscategorized you or your dissertation is missing altogether. Filtering based on the year of the dissertation is not the same as cohort (you could have started along time ago and more recently defended), but looks to me a pretty reasonable sample of “recent” Phd’s from SUNY Albany Criminal Justice program. Also missing at this Proquest search phase is likely to be missing at random (the few who were not scooped up in my search I see no reason to think are systematic based on Proquest’s idiosyncratic search). But missing in terms of me being able to look once you are in the sample is not (since if you are a professor you probably come up in a general google search for your university).

I tended to be liberal for who I listed as professor (this includes temp teaching jobs and postdocs, but not people who are adjuncts). Many people not in the professor list though were formerly professors (myself included), but tried to figure out the current main job for individuals.

The breakdown for the 69 dissertations is then:

Prof          34  49%
Gov           18  26%
Private        6   9%
Univ Research  3   4%
Think Tank     1   1%
Don't Know     7  10%

So private sector is lower overall than in my cohort, only 10% over the time period (and highest possible sample estimate is 19%, if all 7 don’t know are actually in private sector). Government jobs being at 26% I don’t find surprising, think tank and private is lower than I would have guessed though.

But from this I take away around 50% of recent PhDs in criminal justice from SUNY go on to be professors. For prospective PhDs, this estimate is also conditional on completing the PhD (they aren’t in the sample if they did not finish). If you include those individuals Gov/Private would go up in overall proportions.

Again if missing in the list or miscategorized let me know and I will update the post.

4 Comments
by Andy Wheeler on January 8, 2023  •  Permalink
Posted in Criminal Justice, scholarly
Tagged

Posted by Andy Wheeler on January 8, 2023

https://andrewpwheeler.com/2023/01/08/where-are-they-now-job-outcomes-for-recent-suny-crim-phds/

Preprint: Analysis of LED street light conversions on firearm crimes in Dallas, Texas

I have a new pre-print out, Analysis of LED street light conversions on firearm crimes in Dallas, Texas. This work was conducted in collaboration with the Child Poverty Action Lab, in reference to the Dallas Taskforce report. Instead of installing the new lights though at hotspots that CPAL suggested, Dallas stepped up conversion of street lamps to LED. Here is the temporal number of conversions over time:

And here is an aggregated quadrat map at quarter square mile grid cells (of the total number of LED conversions):

I use a diff-in-diff design (compare firearm crimes in daytime vs nighttime) to test whether the cumulative LED conversions led to reduced firearm crimes at nighttime. Overall I don’t find any compelling evidence that firearm crimes were reduced post LED installs (for a single effect or looking at spatial heterogeneity). This graph shows in the aggregate the DiD parallel trends assumption holds citywide (on the log scale), but the identification strategy really relies on the DiD assumption within each grid cell (any good advice for graphically showing that with noisy low count data for many units I am all ears!).

For now just wanted to share the pre-print. To publish in peer-review I would need to do a bunch more work to get the lit review where most CJ reviewers would want it. Also want to work on spatial covariance adjustments (similar to here, but for GLM models). Have some R code started for that, but needs much more work/testing before ready for primetime. (Although as I say in the pre-print, these should just make standard errors larger, they won’t impact the point estimates.)

So no guarantees that will be done in anytime in the near future. But no reason to not share the pre-print in the meantime.

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by Andy Wheeler on November 17, 2022  •  Permalink
Posted in Crime Analysis, Crime Mapping, Criminal Justice, Mapping, scholarly
Tagged , ,

Posted by Andy Wheeler on November 17, 2022

https://andrewpwheeler.com/2022/11/17/preprint-analysis-of-led-street-light-conversions-on-firearm-crimes-in-dallas-texas/

NIJ grants funding gun violence research

Before I get into the nitty gritty of this post, a few notes. First, my next post in the Criminal Justician series on ASEBP is up, Violent Crime Interventions That are Worth it. I discuss more of the costs with implementing hot spots policing and focussed deterrence from the police departments perspective, and why they are clearly worthwhile investments for many police departments facing violence problems.

Second, I want to point folks to Jacob Kaplan’s blog, most recent post The Covid Kings of Salami. Some of Jacob’s thoughts I disagree with (I think smaller papers are OK, or that policing what is big enough is a waste of time). But if you like my posts on CJ topics, you should check out Jacob’s as well.

Now onto the title – a work in progress at the moment, but working with Scott Jacques on the openness of funded US criminology research. A short post in response to the oft mistaken idea that gun violence research is banned in the US. This is confused logic related to the Dickey act saying awards for gun control advocacy are banned as being federally funded by the CDC.

There are other agencies who fund gun violence research, in particular here I have scraped data from the National Institute of Justice (what I think is likely to be the largest funder in this area). Here is some python code showing some analyses of those awards.

So first, here you can download and see the size of the scraped dataset of NIJ awards:

import pandas as pd

# award data scraped, stay tuned for code for that!
award_url = 'https://dl.dropbox.com/s/eon4iokv0qpllgl/NIJ_Awards.csv?dl=0'
award_df = pd.read_csv(award_url)
print(award_df.shape)
print(award_df['award_text'][0])

So as a first blush check for awards related to gun violence, we can just search the text for the award narrative for relevant terms, here I just search for GUN VIOLENCE and FIREARM. A more thorough investigation would either code the 7k awards or the original solicitations, but I think this will likely be largely accurate (probably slightly more false positives than false negatives).

award_df['award_textU'] = award_df['award_text'].str.upper()

# Lets try to find any of these (other text?)
word_list = ['GUN VIOLENCE','FIREARM']

for w in word_list:
    award_df[w] = 1*(award_df['award_textU'].str.find(w) > -1)

award_df['AnyGun'] = 1*(award_df[word_list].sum(axis=1) > 0)
print(award_df['AnyGun'].sum())

So we can see that we have 1,082 awards related to gun violence (out of 7,215 listed by the NIJ). Lets check out the total funding for these awards:

# Lets figure out the total allocated
award_df['AwardVal'] = award_df['field-award-amount'].str.strip()
award_df['AwardVal'] = award_df['AwardVal'].replace('[\$,]', '', regex=True)
award_df['AwardVal'] = pd.to_numeric(award_df['AwardVal'])
award_df['Tot'] = 1

cf = ['Tot','AwardVal']
award_df.groupby('AnyGun',as_index=False)[cf].sum()

So we have in the listed awards (that go back to 1998 but appear more consistently filled in starting in 2002), over 300 million in grant awards related to gun violence/firearm research. Here we can see the breakdown over time.

# See awards over time
gun_awards = award_df[award_df['AnyGun'] == 1].copy()
gun_awards.groupby('field-fiscal-year',as_index=False)[cf].sum()

So the awards gifted by NIJ no doubt have a different flavor/orientation than if you had the same money from CDC. (There are other orgs though, like NSF, who I am sure have funded research projects relevant to gun violence over time as well.) Sometimes people distinguish between “public health” vs “criminal justice” approaches, but this is a pretty superficial dichotomy (plenty of people in public health have gotten NIJ awards).

So you certainly could argue the Dickey amendment changed the nature of gun violence research being conducted. And since the CDC budget is so massive, I suppose you could argue that it reduced the overall amounts of gun violence research being funded (although it is likely 0 sum, more for firearm research would have slashed some other area). You could use the same argument to say NIJ though is underfunded instead of advocating for the CDC to write the checks though.

But the stronger statement I often see stated, that firearm research is entirely banned in the US, is not even close to being correct.

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by Andy Wheeler on November 15, 2022  •  Permalink
Posted in Criminal Justice, Python, scholarly
Tagged , ,

Posted by Andy Wheeler on November 15, 2022

https://andrewpwheeler.com/2022/11/15/nij-grants-funding-gun-violence-research/

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