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The Murder Rate – A Regression with Many Variables

In this post, I want to look at the murder rate, by state. I ran a regression with the state murder rate for 2015 as the dependent variable, and literally threw the kitchen sink at it: demographics, weaponry, income, education, population density, etc. Basically, if its something some reasonable percentage of the population believes matters, and I could find data for it, I threw it into the hopper.

I also included variables relating to immigration status. The latter stems from some from some debate in the comments section to other posts in which I stated my belief that illegal immigrants drive up the crime rate. Several detractors insisted that illegal immigrants have lower, not higher crime rates than the rest of the population, and that I am racist to boot. Before presenting results, I will note – I am not too proud to admit the regression results did not fit with my preconceptions. I am also not too proud to admit the regression results did not fit with the preconceptions of my detractors. Finally, while I am always interested in whatever the data has to say, I suspect my detractors will really, really not the results.

So… without further ado, the output from R:

r output 20170402a

What does this all mean? Simply put, only two variables are statistically significant at the 5% (or even 10%) level: percent of the population made up of non-Hispanic Whites, and population density. The greater the share of the population made up of non-Hispanic Whites, the lower the murder rate. On the other hand, the greater the population density, the higher the murder rate. To those who don’t use statistics very often, remember – this is taking into account all other variables.

Now, there are a few variables that come close to being statistically significant at the 10% level. In other words, it is possible (not necessarily likely, just possible) that under other circumstances – with a better defined model, or more precise variables – these variables would prove to be statistically significant as well. These variables are:

1. Percent of the population made up foreign citizens here legally. That variable would have a negative effect on the murder rate if it were statistically significant.
2. Percent of the population that is Asian. This variable also would have a negative effect on the murder rate if it were statistically significant.
3. Percent of the population age 18 to 64. Obviously, most of the murders are committed by people within a subset of this range – probably around 18 to 30. If I had the data to separate out this cohort, I believe we would find that the more people in this cohort, the greater the murder rate.

So… what doesn’t matter? First, the percentage of the population made up of illegal immigrants. Ditto the percentage of the population made up of naturalized citizens. These did not increase the murder rate nor lower it. If the murder rate parallels the crime rate in general, then the media narrative that illegal immigrants have lower crime rates than the population as a whole is not supported and to some extent contradicted by the data.

Second, race & ethnicity don’t matter, at least once you pull out non-Hispanic Whites and maybe Asians. Holding all other variables (including education and income) constant, it doesn’t appear that the murder rate differs in a statistically significant way from one non-Hispanic White or Asian racial/ethnic group to another.

Median income doesn’t matter. Neither does the percentage of the population with an income under 20K. Or the percentage of the population with an income over 100K. Or education level. The murder rate is not affected by these variables.

Another thing that doesn’t matter is the degree to which the population happens to be armed. And Lord knows, there are all sorts of variables here. These include “destructive devices” (think grenades, rockets, missiles, mines, poison gas, explosives, or incendiary devices – apparently all these and more are registered by the ATF), machine guns, silencers, short barreled rifles, short barreled shotguns, or other. The innocuous sounding other group includes your garden variety revolvers and pistols.

So essentially, in summary – accounting for education, income, nativity. immigration status, the regression suggests that having more non-Hispanic Whites decreases the murder rate, and having a greater population density increases the murder rate. No other variables in this regression are statistically significant.

Anyway, I can babble on about the results. For example, it would be interesting to see immigrants (both legal and illegal) broken up with enough granularity to see if the results of non-Hispanic Whites and Asians apply to immigrants as well.

But enough of my prattling. What are your thoughts?

As always, if you want my spreadsheet, drop me a line. If you contact me within a month of the publication of this post, I will send it to you and possibly make some sort of witty remark. Since I am adorable, I probably will send you my spreadsheet after that date as well, but I reserve the right to have a file crash, lose my computer, acquire dementia, or die if too much has elapsed. My contact info is my first name (mike) and a dot, then my last name (kimel – only one m there) at gmail dot com.

Links and details to the data are in my spreadsheet.  But if you want to replicate it yourself (it was a pain in the butt, but who am I to stop you?) the data are listed below. Where possible (which was the case for only a few exceptions, as noted below), I tried to use 2015 data to match the murder rate.

2014 data on firearms came from Exhibit 8 from this document produced by the Bureau of Alcohol, Tobacco and Firearms.

Population from the Census. 2015 data was used for most purposes. 2014 data was used for firearms per capita data.

Population density from 2010 was obtained from the Census.

2015 median hh income came from the Census.

A number of other variables came from the Census CPS Table Creator. This was used for data on race, income, native v. naturalized citizens v. foreigner, educational attainment, age, and gender.

Pew estimates on illegal immigrants, including Mexican v. non-Mexican, were available for 2014.

Finally, the number of 2015 murders originated with the FBI, but was present in this handy dandy file compiled by the Murder Accountability Project.


Update…  April 2, 2017  4:01 PM

I forgot to mention a couple corrections to the data:

1. The Pew data on % of illegal aliens that come from Mexico included a few NAs, in each case for states with a very low percentage of the population being made up of illegal immigrants.  In those instances, I assigned the national average share (i.e., 52% of the unauthorized aliens are from Mexico).

2.  The CPS table information on race and ethnicity had a few examples where no information was given for a given combination of race & ethnicity.  In each case, it was possible to determine that the number was very small because the sum total of the other race & ethnicity combinations came close to 100%.  In those instances, I simply replaced the NA with a zero.

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Education and Externalities

Some years ago I read this NBER working paper. (Note – a couple years later a slightly modified version appeared in the American Economic Journal but I will quote from the earlier, non-paywalled version since it is available to everyone.)

Here’s the issue, in a nutshell:

In this paper, we use administrative data from the Houston Independent School District and the Louisiana Department of Education to examine whether the influx of Katrina and Rita students adversely affected the academic performance, attendance and discipline of their new peers.

Later in the paper:

…the arrival of low achieving peers hurts all native students, but this effect is more negative for low achieving natives in elementary and high achieving natives in secondary schools. By contrast, the arrival of high achieving evacuees benefits everyone, though the biggest benefit is for the low achieving natives.

If you missed that, later on the same page they write:

…we find that high achieving evacuees increase native performance and low achieving evacuees reduce native performance.

But it isn’t just performance…

By contrast, the results for discipline and attendance do show that it is enough to have 1 or 2 misbehaving evacuee children to worsen the attendance and behavior of native kids in elementary schools. In middle- and high-schools, only having many undisciplined kids in a classroom worsens native behavior.

And it isn’t just because more kids = less resources:

These results show no statistically significant effect of the fraction of evacuees on class-size in elementary schools. In middle and high-schools there is little evidence that the influx of evacuees significantly increased class-size, except for class-sizes in social studies which shows a marginally significant effect…. The results once again show no statistically significant effect of the influx of evacuees on either operating or instructional expenditures per student. This is likely because the Federal and State Governments seemed to have reimbursed schools and districts almost fully. Also, interviews with principals in Houston, suggested that schools received substantial aid from a number of foundations around the country.

Jumping to the conclusion, just to repeat the findings in case someone is tempted to misread them:

Non-linear models show that high achieving natives are significantly positively affected by high achieving evacuees and significantly negatively impacted by low achieving evacuees. Low achieving natives also generally benefit from high achieving evacuees and are hurt by low achieving evacuees in terms of their own test scores…

Of course, any parent who isn’t blind knows that a big determinant of the quality of his/her kids’ education is the quality of his/her kids’ peers. Still, its a well constructed and well executed paper. I also happen to think this situation makes a fine allegory for immigration.

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Net Migration and Economic Growth Around the World – 1958 to the Present

In my last post, I used World Bank data to look at the effect of net migration on economic growth. Net migration is defined by the World Bank as the number of immigrants (coming into a country) less the number of emigrants (leaving the country). I showed that net migration as a share of the population in 2012 (the last year with for which this data has been reported so far) is negatively correlated with growth of PPP GDP per capita from 2012 to 2015. In other words, countries where the share of immigrants as a percent of the population was larger grew more slowly than countries with a smaller proportion of immigrants.

The natural question is… does this relationship hold over a longer period of time? In this post, I will show that the answer is yes.

As to data… I will use three series compiled by the World Bank: net migration, population, and PPP GDP per capita. Net migration data is reported every fifth year beginning in 1962, and it covers five years of activity. In other words, the net migration figure for 1962 is the sum total net migration for the years 1958 through 1962. Similarly, the net migration figure for 1967 is the total for the years from 1963 through 1967. Population is available annually going back to 1960. PPP GDP per capita is available annually, but only begins in 1990. To maximize the use of the available data, and still avoid situations where growth could be leading immigration, I looked at total migration from 1958 to 1992 as a share of the population in 1992, and compared it to growth in PPP GDP per capita from 1993 to 2015.

In other words, I took a look at (roughly) the percentage of the population that had migrated over 34 years, and compared that to the growth rate from the following year to 2015, which is a period of 22 years.

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Net Migration and Economic Growth Around the World

This post uses data from the World Bank to look at the effect of migration on countries around the world. I will begin by looking at all countries for which the World Bank has data, then drill down.

So to begin, the data used in this post:
1. Net migration, by country available here. The most recent data is from 2012. Net migration is defined as the “total number of immigrants less the annual number of emigrants, including both citizens and noncitizens. Data are five-year estimates.” As an example, the US reportedly had net migration of 5,007,887 (i.e., positive) in 2007 through 2012, while Bangladesh had a figure of -2,226,481 (i.e., negative) in the same years. That should fit with your intuition.

2. PPP GDP per capita. Data available here. The last year for which data is available is 2015.

3. Data on population through 2015.

I started by looking at immigration relative to the size of the population. I assumed that the net migration figure was the same in each of the five years. (I know – not correct, but reasonable.) I then divided the Net Migration from 2012 by Population from 2012. I then compared that to the annualized growth in PPP GDP per capita from 2012 to 2015. In other words, I looked at the Net Migration as a share of the Population in 2012 and the growth rate in the subsequent three years. I put both series up on a scatter plot.

Before I put up the graph, I would also note that I did leave some data out. It goes without saying that if a country did not report information, I did not include it. Additionally, countries reporting zero net migration were left out. After all, even North Korea has escapees, er, migrants, even if they won’t admit to it. Otherwise, everything went into the pot leaving a sample of 176 countries. Here’s what the relationship between Net Migration (from as a share of the Population in 2012 and the growth in PPP GDP per capita from 2012 to 2015 looks like for them:

Figure 1.  Net Migration div Pop 2012 v. Growth from 2012 to 2015, 176 Countries 20170112
Figure 1

The correlation is -0.32. That is, countries with higher Net Migration as a share of their Population tended to perform less well over the subsequent three years. In other words, it is better to give than to receive, at least when it comes to migrants.

Of course, if we want to understand the effect of Net Migration in the US and other Western Countries, perhaps it makes sense to narrow things down. The next graph uses only countries deemed to be “High Income” by the World Bank. I also restricted the sample to countries with populations exceeding 1 million people to avoid trying to learn life lessons based on recent happenings in Monaco or Andorra. Here’s what that looks like:

Figure 2.  Net Migration div Pop 2012 v. Growth from 2012 to 2015, 44 Countries 20170112
Figure 2

The population sample dropped from 176 countries to 44, and the correlation tightened up a bit to -0.48.

Frankly, I think the sample still needs cleaning up. Most of the points on the graph look bunched up because there are a few countries with very, very high Net Migration. For example, Oman is at 6.8%(!!!!), Qatar 3.6%, Kuwait 3.0% and Singapore 1.5%. These are mostly special cases, even for high income countries, and I would venture to say, provide very few lessons on immigration that are applicable to the US or most of the West. Limiting the sample to countries with Net Migrants to Population under 1.4%, the graph now looks like this:

Figure 3.  Net Migration div Pop 2012 v. Growth from 2012 to 2015, 40 Countries 20170112 - with corrected axis
Figure 3

This doesn’t change the outcome much, but it makes things easier to see. If desired, we can cut out one more outlier – this one on account of excessive economic growth. The point on the far right side of the graph is Ireland, bouncing back (in PPP GDP) from the monster collapse in 2007-8. Removing Ireland as well gives us this:

Figure 4.  Net Migration div Pop 2012 v. Growth from 2012 to 2015, 39 Countries 20170112 - with axis corrected
Figure 4

The absolute value of the correlation drops, but the fact remains: we are still left with a negative correlation between Net Migration as a percentage of the Population in 2012 and the growth in PPP GDP per capita between 2012 and 2015. We can do a bit more pruning, but frankly, the data simply refuses to support Holy Writ. Sure, these graphs don’t prove that immigration is bad for growth. However, they make it very, very hard to argue that immigration had a positive effect on growth during the past few years. Of course, that isn’t what we hear from our betters.

I will follow up this post with looks at other periods for which data is available from the World Bank. Meanwhile, I put together a spreadsheet that allows the user to make changes to the dates or downselect the data through income level, population, etc. It’s a bit large, but I will send it to anyone who contacts me for it within a month of the publication of this post.  I can be reached at mike and a dot and my last name (note – just one “m” in my last name) and the whole thing is at


Updated about fifteen minutes after original posting.  Figures 3 and 4 needed an additional significant digit on the Y-axis.

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Issues Affecting Economic Growth – Gored Oxen Edition

I write about issues I believe affect economic growth. For example, over the years, I have written a lot about taxes. And here’s a simple graph showing why:

Tax Rates v Growth in Real GDP per capita

What we see is that tax rates at any given time seem to be related to the growth rate of real GDP per capita over the next decade. What is more, the correlation is positive. That is to say, growth tends to be faster when tax rates are higher, and not lower. This of course contradicts popular belief, particularly among Republicans. However, since economic growth is important for the quality of life of all Americans, getting this right matters. Unfortunately, over the past few decades, government policy has gradually moved us in a direction that inhibits growth.

Of course, it could be the relationship between tax rates and future growth shown in the graph is a spurious correlation. But that is unlikely, since it is very easy to explain why (up to a certain point) higher tax rates would lead to faster economic growth. Additionally, even people who get the direction of the correlation wrong are certain a correlation is there. But… if it ever does turn out that the relationship is spurious, we won’t find that out by keeping our head in the sand.

Another topic I have been writing on a lot lately is immigration. Here’s what a graph looking at the foreign born population in certain years and the growth rate of real GDP per capita over the next ten years:

Foreign Born Population v. Growth in Real GDP per capita

The correlation between the share of the population that is foreign born and the growth rate is negative, which indicates that as the foreign born share rises, growth falls. The correlation between these two variables, at least in the post WW2 era, is stronger than the correlation between tax rates and growth. This of course contradicts popular belief, particularly among Democrats. However, since economic growth is important for the quality of life of all Americans, getting this right matters. Unfortunately, over the past few decades, government policy has gradually moved us in a direction that inhibits growth.

Of course, it could be the relationship between the percentage of the population that is foreign born and future growth shown in the graph is a spurious correlation. But that is unlikely, since it is very easy to explain why (up to a certain point) having less immigration would lead to faster economic growth. Additionally, even people who get the direction of the correlation wrong are certain a correlation is there. But… if it ever does turn out that the relationship is spurious, we won’t find that out by keeping our head in the sand.

If it seems to you that I have written almost exactly the same thing about taxation and immigration, it isn’t your imagination.  I did a copy and paste of a big chunk of the first half of the post to the second half and changed a few words.  The fact is, the analysis is very similar. The only difference is whose ox is getting gored. A grown up is willing to look the data in the eyes and follow it where it goes.

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Immigration, Democrats, Republicans and the NY Times

Tom Cotton, the junior United States Senator from Arkansas had a piece in the NY Times:

President-elect Trump now has a clear mandate not only to stop illegal immigration, but also to finally cut the generation-long influx of low-skilled immigrants that undermines American workers.

Yet many powerful industries benefit from such immigration. They’re arguing that immigration controls are creating a low-skilled labor shortage.

“We’re pretty much begging for workers,” Tom Nassif, the chief executive of Western Growers, a trade organization that represents farmers, said on CNN. A fast-food chain founder warned, “Our industry can’t survive without Mexican workers.”

These same industries contend that stricter immigration enforcement will further shrink the pool of workers and raise their wages. They argue that closing our borders to inexpensive foreign labor will force employers to add benefits and improve workplace conditions to attract and keep workers already here.

I have an answer to these charges: Exactly.

Higher wages, better benefits and more security for American workers are features, not bugs, of sound immigration reform. For too long, our immigration policy has skewed toward the interests of the wealthy and powerful: Employers get cheaper labor, and professionals get cheaper personal services like housekeeping. We now need an immigration policy that focuses less on the most powerful and more on everyone else.

Wasn’t this the Democrat’s position not long ago? When and why did that change?



1.  If it isn’t clear, Cotton is a Republican

2. The bolded section was part of Cotton’s piece, but I chose to bold it as I felt it was worth a special highlight.

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The Bottom 10 Performing Immigrant Groups in the US – Lessons Learned

In my last post, I looked at the top ten immigrant groups by country of origin to the US, ranked by their per capita income in the US. In this post, I want to look at the bottom ten countries. Here’s some information on those countries (data sources mentioned at the bottom of the post):

Post 3, Figure 1 - Bottom 10 Countries by Immigrant Income - Corrected
(click to embiggen)
(note – this is a corrected table. Thanks to reader Mike B for pointing out the error in the last column of the table.)

The first thing to note is that Saudi Arabia is an outlier. Based on median age, education, and income, the composite Saudi immigrant described by this table is either a graduate student, a layabout, or something in between. Many Saudis may be receiving income from sources that are not accounted for in this table; from my limited experience, Saudi expats I have come across seem to receive a stipend, which is born out from other sources, though it seems those stipends are getting cut due to falling oil prices.

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Mike Kimel vs. Yves Smith

I and many others, including Yves Smith, waded into the thicket of Mike Kimel’s provocative generic and specific-reasons-for-the-generic claims in his post here earlier this week titled “Negative Effects of Immigration on the Economy.”  I and others, including academic economist Barkley Rosser, commented in replies in the Comments thread to Mike’s post.  Yves did so on her Naked Capitalism blog, in prefatory comment to her republication of Mike Kimel’s post.

Yves is an expert on such matters as the effects of immigration on the economy.  I, suffice it to say, am not.   But Angry Bear Bruce Webb and I both invoked Yves’ prefatory comment as refutation of Mike’s specific reasons for his generic claims.  But Mike begs to differ, claiming that Yves’ preface is in agreement with his post.

To which I replied:

You and Yves Smith make the same point? Really? Actually, Yves was trying gently to refute your main point, which was your claim that immigration has a negative impact on job creation and your attributing this to the fact that so many immigrants aren’t white and from Europe and therefore, culturally (and intellectually) don’t sufficiently appreciate the importance of such things as time schedules and promptness.

Yves’ point was the opposite: that lower rates of job creation comes from lower rates of increase in GDP, which has nothing to do with the entrepreneurial, timekeeping and English-language skills of the current wave of immigrants and everything to do with the highly successful corporate efforts in the last nearly four decades to suppress wages–one (but only one) tactic of which has been the use of immigrants to keep wages down, thus reducing DEMAND FOR GOODS AND SERVICES. The effect on job creation is, contrary to your claim, not direct and is not the result of what you say it is, and is the indirect result of deliberate corporate goals.

Funny, y’know, but Germany, Holland, Scandinavia and Canada all have had very large non-white immigration in recent decades. All have strong laws supporting worker power, as well as corporate cultures that favor long-term investment and rational executive-suite compensation, and … voila! They have economies that work well.

Obviously, Yves can speak for herself on this if she cares to.  But it’s clear from the comments in the lengthy Comments thread to Mike’s post that I’m far from the only one who takes strong issue with what is at the heart of Mike’s premise.

This is by no stretch the type of thing I normally post about, but it gets to the heart of what others do, here and elsewhere, and certainly concerns issues at the forefront of this election cycle.  And therefore it’s worth my walking out onto this limb here.


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Irish austerity exodus lingers on

August brings us the annual Irish immigration data, so it’s time to look at what has happened in their statistical reporting “year” that ended in April 2014. While better than last year, it’s still not pretty.

According to the Central Statistics Office, net emigration continued in 2013-14, with net emigration of 21,400. a decline of just over 1/3 compared to net emigration of 33,100 in 2012-13. Of the new total, once again, the Irish themselves accounted for over 100% of the net departures, with 29,200 more Irish nationals leaving the country than returning.

This continued out-migration continues to diminish any published improvements in Irish employment numbers and unemployment rate. In the year to the second quarter of 2014 (the closest quarter to April 2014 immigration figures), employment  increased to 1,901,600, a rise of 31,600 over a year previous. Unemployment fell by even more, 46,200, in the year to Q2 2014. So, while there is definite improvement even accounting for emigration, Ireland is nowhere near back to its peak 2007 employment figure of about 2.15 million. So employment is still 11.6% below its peak.

In Iceland (create a custom table here), by contrast, despite (but also in part because of of) the almost 50% decline in the value of the kronor, the sharp dip in unemployment has been almost completely erased, with July 2014′s value of 179,000 employed being a mere 1.7% below May 2008′s maximum of 182,100. Indeed, Iceland’s unemployment rate has fallen to a mere 4.4% in July 2014, compared with 6.2% in the United States — and 11.5% in Ireland.

So the lesson, if I have haven’t pounded it into your head enough already, is that Ireland’s austerity measures are not paying off, as it has failed to regain its pre-crisis employment level  and has seen its unemployment rate fall only by reverting to its historical solution of exporting people, as in the 1980s.

Cross-posted from Middle Class Political Economist.

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