The Guardian newspaper has a story about wages in England:
A shortage of factory workers is starting to push up pay rates but wage rises in the services sector remain rooted at around 2%, according to the latest feedback from the Bank of England’s regional agents.
The central bank said its agents, which are based in offices across the country, found that shortages this month across the manufacturing sector were leading to a “slight increase in pay growth” that would take average rate of pay rises up by half a percent, from 2-3% this year to 2.5%-3.5% in 2018.
The report appeared to justify Threadneedle Street’s move last week to increase interest rates, which officials at the bank said was needed to dampen the inflationary effects of wage rises.
A survey of employers in October by the Recruitment and Employment Confederation chimed with the BoE report after it found firms were having to raise their pay offers to hire new staff.
The REC said the increase, the second quickest rise in wages since November 2015, followed a fall in unemployment to the lowest level in 40 years that had restricted the number of workers available to take up new positions. It warned that higher pay offers were also needed to counter a growing shortage of EU workers ahead of Brexit.
“We already know that EU workers are leaving because of the uncertainties they are facing right now,” said Kevin Green, REC’s chief executive. “We therefore need clarity around what future immigration systems will look like. Otherwise, the situation will get worse and employers will face even more staff shortages.”
Official data shows that in August net migration fell to its lowest level in three years, with more than half the drop caused by EU citizens leaving and fewer arriving since the Brexit vote.
I find it truly shocking that employment and wages are determined by the market forces, or that the supply of labor is affected by whether foreigners can freely enter and exit a market. Who could have imagined such absurd chains of events? Fortunately, we can rest assured that this is an aberration and can’t possibly apply in the US.
A presidential candidate like Donald Trump should not be viable. Candidates he supports should not be viable. The existence of Donald Trump should be a boon for the Democrats. And, in fact, it has been.
But it hasn’t been enough. Perhaps four (or eight?) years worth of results will tip the balance for Democrats, but it is reasonable to ask: why have Democrats been coming up short against Trump, both in the Presidential election and in special elections since?
The reason is that the Democrats have abandoned their traditional base (i.e., the working class). So why the change?
I would suggest it is because the middle class intelligentsia from which most leaders and volunteers of the Party spring is increasingly reliant on people who have believe in nonsense.
This research explored political motivations underlying resistance to evolutionary psychology. Data were collected from 268 adults who varied in terms of academic employment and parental status. Dependent variables represented whether participants believed that several attributes are primarily the result of biological evolution versus socialization. Variables addressed attitudes about: (a) sex differences in adults, (b) sex differences in children, (c) sex differences in chickens, (d) human universals, and (e) differences between dogs and cats. Using a Likert-scale, participants were asked to rate the degree to which they believed items were due to “nature” versus “nurture.” For instance, one of the items from the cat/dog subscale was “Dogs are more pack-oriented than cats.” Independent variables included political orientation, parental status, and academic employment status. Political liberalism corresponded to endorsing “nurture” as influential – but primarily for the two human sex-difference variables. Academic employment status was independently predictive of the belief that sex differences are the result of “nurture.” This effect was exacerbated for academics who came from sociology or women’s studies backgrounds. The effect of academic employment status also corresponded to seeing behavioral differences between roosters and hens as caused by “nurture.” Further, parents were more likely than non-parents to endorse “nature” for the sex-difference variables. Beliefs about differences between cats and dogs and beliefs about causes of human universals (that are not tied to sex differences) were not related to these independent variables, suggesting that the political resistance to evolutionary psychology is specifically targeted at work on sex differences.
While the paper deserves its own post, for our purposes, a quick summary is this: a person’s tendency to attribute differences between the behavior of roosters and hens to nurture rather than biology increases if the person is either an academic or not a parent. The paper also notes that this effect seems especially pronounced among Gender Studies scholars. The sample size is a bit small, but meshes with what can be observed on the evening news or twitter.
Conservatives have more children than liberals, and academics tend to lean left, so the particular brand of crazy discussed in the paper above is a Democrat rather than a Republican phenomenon. More than that – the childless and academics have the time to set the agenda for causes and organizations in which they get involved.
The adoption of the an anti-Biology stance (and yes, the Republicans have their own, different and long-standing anti-Biology stance… and it has them cost them) comes at the same time as the Democrats have been jettisoning Labor as their cause. This is not a coincidence. The historical image of Labor is of men trudging off to work every day at the crack of dawn to support their nuclear family. In today’s lexicon, those are oppressors who maintain the toxic male patriarchy.
Once you identify the problem, the solution is easy: toss those fat cats who lord their privilege with sweat stained undershirts and grime under their finger nails under the bus. And don’ t stop there. Oppose their elitist attitude by finding common cause with other ideas that are anathema to them. Labor worries about unrestricted unskilled immigration, fearing it will lower wages, cost jobs, and making the country less safe? The obvious solution is to bring in Sayfullo Saipov and pretty much anyone for whom Saipov cares to vouch. The US taxpayer will be happy to spring for the bill.
And after all of this, the misogynist racist pigs prove their perfidy by refusing to give their votes to the Democrats who despise them and want them dead. They begin voting Republican. Sure, Republican economic policies not only don’t work, the benefits they do manage to generate don’t trickle down to the working class. But at least Republicans aren’t purposely screwing them over, and the Republican Party is willing to give them some hope along with the bad economic policy. Hope is free, after all.
The good news is that insanity isn’t completely entrenched in the Democratic Party. It hasn’t had control long – less than a decade, in fact. It can be reversed. I’m just afraid that it isn’t going to happen.
George Borjas, perhaps the US’ pre-eminent immigration economist notes:
Maybe it’s just me because I instinctively read in between the lines whenever I read anything about immigration, but I’m beginning to detect such a seismic shift in the immigration debate. We all know the party line by now: Immigrants do jobs that natives don’t want to do. As a result, natives do not lose jobs, and natives do not see their wages reduced. And anyone who claims otherwise is obviously a racist xenophobic moron. They obviously don’t like immigrants, and they obviously are not educated/credentialed enough to understand and appreciate expert opinion.
The flurry of immigration restrictions proposed by the Trump administration demands a switch in tactics–with a corresponding switch in the argument linking immigration and wages. The party line must now be that less immigration is bad. But how can one show that in simple-to-grasp economic terms that can be mass-marketed to the masses? By far the simplest way is to come up with examples that less immigration raises labor costs and makes us miserable because everything becomes more expensive.
Borjas goes on:
There is no upper bound to the hypocrisy of experts. It might be a lot of fun to keep track of this over the next few years, watching the dominos fall and all those “immigration-does-not-affect-wages” experts fall all over themselves as they switch to proving the economic awfulness of Trump’s actions because fewer immigrants mean higher labor costs, higher prices, more inflation.
But don’t hold your breath for any admission that they were wrong in the past. They will instantly switch to the former party line the minute the Trump immigration restrictions fade into history.
Economics is a simple field. Just about everything can be described in terms of supply and demand. If the supply of something is scarce but the demand for it is strong, its price rises. On the other hand, if there is a lot of supply but little demand, its price will go down.
Now, buyers and sellers can engage in certain strategies to weight the scales. For example, sellers of a product can band together (perhaps by buying each other out) to achieve some amount of monopoly power. Conversely, buyers of a product can collude to bid down the cost of purchasing.
This is, of course, true for the market for labor. And in the labor market, one classic way for sellers of labor (i.e., workers) to raise their bargaining power, and therefore their pay, is to band together into unions. What makes unions effective is that:
1. Union members commit to acting in concert
2. While it is easy for a company with a 1,000 person assembly line to replace a few people at a time without missing a beat, replacing all 1,000 at once would seriously crimp operations.
As a result, the cost of workforce dissatisfaction to a company with a unionized workforce is greater than the cost of workforce of dissatisfaction to a company without a unionized workforce. Therefore, a company with unionized workforce will, all else being equal, be willing to make greater concessions on pay and working conditions than the same company would be if its workforce was not unionized.
But a union is not a guarantee of anything. After all, a union can be broken. And all you need to break is to make sure there is a sufficiently large, inexpensive workforce capable of replacing the unionized workforce. There might be short term pain, but on paper at least, after that its all profit.
Which brings me to this story in the NY Times. Its about a small town in Iowa heavily reliant on the meat packing industry. Despite the Times’ clear and omnipresent bias that more immigration is always a positive thing, the following paragraph provides a good summary of the entire piece:
At that point, Mr. Smith returned to do night cleanup, earning $5.50 an hour with no benefits, but a vast majority of his former co-workers were turned away, he said, because the new owner did not want to hire union supporters. Instead, the company began actively recruiting in Mexico and immigrant communities in Texas and California.
If there are enough low-skilled immigrants, unions cannot compete. They chose to turn a blind eye toward illegal immigration because they felt it was good for business. Democrats also understood that decades ago and sided with unions. This is because Democrats felt it was good for society if factory workers could enjoy a middle class lifestyle. In the past decade, Democrats have changed. (The reason for this may be the subject of a future post.)
But regardless of politics, the facts are simple: except in very limited circumstances, one cannot simultaneously have strong both unions and virtually unrestricted immigration.
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:
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.
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.
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.
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.
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.
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:
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:
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:
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:
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 gmail.com.
Updated about fifteen minutes after original posting. Figures 3 and 4 needed an additional significant digit on the Y-axis.
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:
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:
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.