Merging Data to Understand the Relationship between Drug Legalization and Violent Crime

In recent years, many US states have decided to legalize the use of marijuana.

When these ideas were first proposed, there were many theories about the relationship between crime and the “War on Drugs” (the term given to US efforts to arrest drug users and dealers over the past several decades).

In this exercise, we’re going to test a few of those theories using drug arrest data from the state of California.

Though California has passed a number of laws lessening penalities for marijuana possession over the years, arguably the biggest changes were in 2010, when the state changed the penalty for possessing a small amount of marijuana from a criminal crime to a “civil” penality (meaning those found guilty only had to pay a fine, not go to jail), though possessing, selling, or producing larger quantities remained illegal. Then in 2016, the state fully legalized marijuana for recreational use, not only making possession of small amounts legal, but also creating a regulatory system for producing marijuana for sale.

Proponents of drug legalization have long argued that the war on drugs contributes to violent crime by creating an opportunity for drug dealers and organized crime to sell and distribute drugs, a business which tends to generate violence when gangs battle over territory. According to this theory, with drug legalization, we should see violent crime decrease after legalization in places where drug arrests had previously been common.

To be clear, this is far from the only argument for drug legalization! It is simply the argument we are well positioned to analyze today.

Pre-Legalization Analysis

(1) We will begin by examining county-level data on arrests from California in 2009, which is derived from data provided by the Office of the California State Attorney General here. Download and import the file ca_arrests_2009.csv.

(2) Use your data exploration skills to get a feel for this data. If you need to, you can find the original codebook here (This data is similar, but has been collapsed to one observation per county.)

(3) Figuring out what county has the most violent arrests isn’t very meaningful if we don’t normalize for size. A county with 10 people and 10 arrests for violent crimes is obviously worse than a county with 1,000,000 people an 11 arrests for violent crime.

To address this, also import nhgis_county_populations.csv from the directory we’re working from.

(4) Use your data exploration skills to get used to this data, and figure out how it relates to your 2009 arrest data.

(5) Once you feel like you have a good sense of the relation between our arrest and population data, merge the two datasets.

Checking Your Merges

(6) When you merge data, you have to make some assumptions about the nature of the data you’re working with. For example, you have to assume it’s OK to connect variables that share the same value of your merging variables. Similarly, you have to make assumptions about whether your merge a 1-to-1 merge (meaning there is only one observation of the variable you’re merging on in both datasets), a 1-to-many merge (meaning there is only one observation of the variable you’re merging on in the first dataset, but multiple observations in the second). So before running a merge, answer the following questions:

  1. What variable do you think will be consistent across these two datasets you can use for merging?

  2. Do you think there will be exactly 1 observation for each value in your arrest data?

  3. Do you think there will be exactly 1 observation for each value in your population data?

Being correct in your assumptions about these things is very important. If you think there’s only one observation per value of your merging variable in each dataset, but there are in fact 2, you’ll end up with two observations for each value after the merge. Moreover, not only is the structure of your data now a mess, but the fact you were wrong means you didn’t understand something about your data.

Because of the importance of this, pandas provides a utility for testing these assumptions when you do a merge: the validate keyword! Validate will accept "1:1", "1:m", "m:1", and "m:m". It will then check to make sure your merge matches the type of merge you think it is. I highly recommend always using this option (…and not just because I’m the one who added validate to pandas).

Repeat the merge you conducted above, but this time use the validate to make sure your assumptions about the data were correct.

(7) Checking whether you are doing a 1-to-1, many-to-1, 1-to-many, or many-to-many merge is only the first type of diagnostic test you should run on every merge you conduct. The second test is to see if you data merged successfully!

To help with this, the merge function in pandas offers a keyword option called indicator. If you set indicator to True, then pandas will add a column to the result of your merge called _merge. This variable will tell you, for each observation in your merged data, whether: (a) that observation came from a successful merge of both datasets, (b) if that observation was in the left dataset (the first one you passed) but not the right dataset, or (c) if that observation was in the right dataset but not the left). This allows you to quickly identify failed merges!

For example, suppose you had the following data:

[2]:
import pandas as pd
df1 = pd.DataFrame({'key':['key1', 'key2'], 'df1_var':[1, 2]})
df1
[2]:
key df1_var
0 key1 1
1 key2 2
[3]:
df2 = pd.DataFrame({'key':['key1', 'Key2'], 'df2_var':['a', 'b']})
df2
[3]:
key df2_var
0 key1 a
1 Key2 b

Now suppose you expected that all observations should merge when you merge these datasets (because you hadn’t noticed the typo in df2 where key2 has a capital Key2. If you just run a merge, it works without any problems:

[4]:
new_data = pd.merge(df1, df2, on='key', how='outer')

And so you might carry on in life unaware your data is now corrupted: instead of two merged rows, you now have 3, only 1 of which merged correctly!

[5]:
new_data
[5]:
key df1_var df2_var
0 key1 1.0 a
1 key2 2.0 NaN
2 Key2 NaN b

When what you really wanted was:

[6]:
df2_correct = df2.copy()
df2_correct.loc[df2.key == "Key2", "key"] = 'key2'
pd.merge(df1, df2_correct, on='key', how='outer')
[6]:
key df1_var df2_var
0 key1 1 a
1 key2 2 b

(in a small dataset, you’d quickly see you have 1 row instead of 2, but if you have millions of rows, a couple missing won’t be evident).

But now suppose we use the indicator function:

[7]:
new_data = pd.merge(df1, df2, on='key', how='outer', indicator=True)
new_data._merge.value_counts()
[7]:
both          1
right_only    1
left_only     1
Name: _merge, dtype: int64

We could immediately see that only one observation merged correct, and that one row from each dataset failed to merge!

Moreover, we can look at the failed merges:

[8]:
new_data[new_data._merge != "both"]
[8]:
key df1_var df2_var _merge
1 key2 2.0 NaN left_only
2 Key2 NaN b right_only

Allowing us to easily diagnose the problem.

Note: The pandas merge function allows users to decide whether to keep only observations that merge (how='inner'), all the observations from the first dataset pasted to merge (how='left'), all the observations from the second dataset passed to merge (how='right'), or all observations (how='outer'):

join_types

But one danger to using the more restrictive options (like the default, how='inner') is that the merge throws away all the observations that fail to merge, and while this may be the eventual goal of your analysis, it means that you don’t get to see all the observations that failed to merge that maybe you thought would merge. In other words, it throws away the errors so you can’t look at them!

So to use indicator effectively, you have to:

  • Not use how="inner", and

  • Check the values of _merge after your merge.

(8) Now repeat your previous merge using both the validate keyword and the indicator keyword with how='outer'.

(9) You should be able to get to the point that all counties in our arrest data merge with population data. Can you figure out why that did not happen? Can you fix the data so that they all merge to population data?

Comparing Arrest Rates

(10) Now that we have arrest counts and population data, we can calculate arrest rates. For each county, create a new variable called violent_arrest_rate_2009 that is the number of violent arrests for 2009 divided by the population of the county from 2005-2009, and an analogous variable for drug offenses (F_DRUGOFF).

(11) Make a scatter plot that shows the relationship between each county’s violent arrest rate and it’s drug arrest rate.

Comparing with 2018 Arrests

And now the part that will likely be homework!

Showing that drug arrests and violent crime arrests tend to be positively correlated does not tell us much about whether they are causally relation. It could be the case that people dealing drugs causes more violent crime, but it could also be that certain communities, for some other reason, tend to have both more drug sales and more violent crime.

So to test for this, we went to see if the same communities that had violent crime in 2009 also have violent crime in 2019 (after marijuana legalization). If these communities have just as much crime in 2018, that would suggest that violent crime is being driven by a third factor, and not drug sales of marijuana.

(12) Just as we created violent arrest rates and drug arrest rates for 2009, now we want to do it for 2018. Using the data on 2018 arrests (also in the same repository we used before) and the same dataset of population data (you’ll have to use population from 2013-2017, as 2018 population data has yet to be released), create a dataset of arrest rates.

As before, be careful with your merges!!!

(13) Now merge the two county-level datasets so you have one row for every county, and variables for violent arrest rates in 2018, violent arrest rates in 2009, felony drug arrest rates in 2018, and felony drug arrest rates in 2009.

(14) Did drug arrests go down from 2009 to 2018? (they sure better! This is what’s called a “sanity check” of your data and analysis. If you find drug arrests went up, you know something went wrong with your code or your understanding of the situations.

(15) Now we want to look at whether violent crime decreased following drug legalization. Did the average violent arrest rate decrease? By how much? (Note: We’re assuming that arrest rates are proportionate to crime rates. If policing increased so that there were more arrests per crime committed, that would impact our interpretation of these results. But this is just an exercise, so…)

(16) So we’ve determined that both drug arrests and violent crime arrests were decreasing over this period. But maybe all crime was just falling, and this isn’t about drug legalization.

This is the problem with a “pre-to-post” analysis: yes, our results are consistent with the idea that drug legalization reduced violent crime, but lots of things happened between 2009 and 2018, not just drug legalization, so we don’t know that drug legalization caused the decline in violent crime.

So let’s do a kind of difference-in-difference analysis. We know that drug legalization should have had a bigger effect on counties that had higher drug arrest rates prior to drug legalization. After all, in a county that had no drug arrests, legalization wouldn’t do anything, would it?

So let’s split our sample into two groups: high drug arrests in 2009, and low drug arrests in 2009 (cut the sample at the average drug arrest rate in 2009).

Now we can ask: did violent crime fall more from 2009 to 2018 in the counties that had lots of drug arrests in 2009 (where legalization likely had more of an effect) than in counties with fewer drug arrests in 2009 (where legalization likely mattered less)? Calculate this difference-in-differences:

(the change in violent crime rate for counties with lots of drug arrests in 2009) - (the change in violent crime rate for counties with few drug arrests in 2009)

(17) Hmmm… we showed that there was a greater absolute decline in violent arrest rates in counties more impacted by drug legalization. But was there also a greater proportionate decline?

Calculate:

(the percentage change in violent crime rate for counties with lots of drug arrests in 2009) - (the percentage change in violent crime rate for counties with few drug arrests in 2009)