I have taken down this post because there was an error in the data. Thanks to reader Mike B for spotting it. My apologies to readers. I am embarassed.
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):
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.
by Mike Kimel
The Top 10 Performing Immigrant Groups in the US – Lessons Learned
First, I’d like to apologize. An earlier version of this post was taken down because I sent it with the wrong table. This version has the correct table, and I added a bit of verbage as well.
In my last post, I noted that that on aggregate, immigrants’ per capita income in the US was correlated with the GDP per capita in their native land. The following two graphs were generated:
The graph on the left shows that on aggregate, immigrants from richer countries do better in the US than immigrants from poorer countries. The graph on the right shows that the effect is magnified for immigrant groups which have been in the US for the longest.
In this post, I want to look at the top ten countries (for which data is available) ranked by earnings of a country’s immigrants to the US to see if there are obvious lessons to be learned:
The first thing to note is that most of the countries on the top ten list are relatively wealthy to start with. This make them examples of the rich stay rich, and not examples of the poor making good.
Economic Outcomes of Immigrants v. Their Stay at Home Counterparts: What the Data Shows
In this post, I will test whether people from countries with relatively poor economies also tend to do poorly when they relocate to the United States. As an example, GDP per capita for Haiti is much lower than GDP per capita for Hong Kong. Does the available data also say Haitian immigrants to the US generate lower income per capita than immigrants from Hong Kong? In general, as we will see below, the answer is yes.
Data used in this post comes from two sources. The first is GDP per capita, by country, obtained from the World Bank. The post also uses information obtained from the Census Department’s 2014 American Community Survey. In particular, the post uses the 2014 per capita income of immigrants to the US by nation of origin. It also uses the percentage of the immigrants from a given country that have arrived in the US prior to the year 2000. That data is kind of unwieldy to find, but the starting point is here. To be clear, immigrants in this source are foreign born, which is to say first generation only. Only immigrants alive at the time of the survey are included.
There are 72 countries for which there is data on per capita income for that country’s immigrants to the US (from the Census) and for which 2014 GDP per capita is available (from the World Bank). The graph below shows per capita income for those immigrants along the X axis, and GDP per capita along the Y axis.
The correlation is 0.7, which is fairly high. That is to say, in general, the poorer a country is, the worse its immigrants fare in the US. This is because on aggregate the skills and culture of people living in poor countries do not command a high price on the world market. Such a combination of skills and culture will also not command a high price in a large open economy like the US. (Yes, there are exceptions. Some will be covered in later posts.)
How Does Diversity Affect Economic Growth? A Look at Data on Immigration, Tax Rates and Real GDP per Capita
Authored by Mike Kimel
In this post, I will explain the annualized growth rate in real GDP per capita using tax rates and the percentage of the population that is foreign born using data for the United States. The data shows the following:
A. the tax rate that maximizes economic growth is higher than you think
B. immigration from countries with advanced economies whose population resembles the US correlates with faster economic growth over subsequent years
C. increasing rates of immigration from countries with diverse populations
You can skip ahead to the results of the regression if you want. Otherwise, if you care about the details, here goes…
I used to occasionally write posts where I would build models based on fitting models of the following form:
growth in real GDP per capita, t to t+1 = f(tax rate at t, tax rate squared at t)
In that formulation, tax rate = top marginal income tax rate. The quadratic form allows us to find a tax rate that maximizes growth rates if the real world has such a thing (which, happily, it does as we will see below).
More recently, I have been looking at how immigration has affected the economy. In particular, I had a few posts looking at this relationship:
job growth, t to t+10 = f(foreign born % at t)
Why the ten year look-ahead? To be frank, I just got tired of arguing with readers about causality. Comparing X today to Y over the next ten years puts a halt to chicken and egg arguments tout suite.
I’ve also had a few essays speculating about the effect of changes in immigration law in 1965 on the economy but in those posts, I relied only on logical and not on data.
In this post, I want to combine those three (well, really two and a half) issues. I will fit the following model:
1. Dependent variable: annualized growth in real GDP per capita, t to t+10
2. Explanatory variables i and ii are tax rate and tax rate squared, both at time t
3. The next explanatory variable is the % of the population that is foreign born at time t. But… if immigration, and its impact on the economy, changed as a result of the 1965 Immigration Act as I’ve stated in earlier posts, what we really need is two variables:
3a. Foreign Born as a % of the Pop Until 1965 (and zero otherwise)
3b. Foreign Born as a % of the Pop After 1965 (and zero otherwise)
Tax rates came from the IRS’s historical table # 23. The foreign born percentage was obtained from the Migration Policy Institute (MPI). The MPI’s data originated with the Census, but they organized it a bit better so I downloaded it from them rather than the Census. Data is available in ten year increments from 1850 to 2010, and annually from 2010 to 2015. I annualized the decennial data by simply assuming a linear annual change between every tenth year’s figures.
The entire set of data was organized in Excel, but the regression itself was run in R. The output (click on figure for larger size) appears below:
The first thing to note is that the model explains about half of the variation in the ten year economic growth rate. Not bad for tax rates and immigration alone.
Next, the coefficient on tax rates is positive, the coefficient on tax rates squared is negative, and both are significant. (That’s the quadratic relationship mentioned earlier.) If you do the math, it turns out that the rate that maximizes the ten year annualized growth in real GDP per capita is 55%. This is about what most of my previous estimates over the years have come up with as well.
Moving on, the next variable is the percentage of the population that is foreign born in years prior to 1965. That variable is positive and significant even at the 1% level. In plain English, before 1965, more immigrants -> faster economic growth.
But the next variable is problematic, and spits out a result that is, at a minimum, politically incorrect. That variable, the percentage of the population that is foreign in years after 1965 is negative though not quite significant. If we are worried about being reported by the neighbors, we could with a straight face, stop here and state from this that immigration has not not affected growth since 1965. For the moment, let’s do that.
The coefficients and relative significance of the last two variables essentially restate what I have been writing in the last few weeks. As a result, I can explain what is going on by more or less plagiarizing myself. So, at a high level, why does pre-1965 immigration clearly boost economic growth and post-1965 immigration clearly not? As I noted in earlier posts, from 1921 to 1965, about 70% of the immigrants came from Germany, Great Britain and Ireland. The 1965 Immigration Act was designed to allow more immigration from the rest of the world.
Before 1965 immigrants would have fit in more seamlessly. After all, the US had been strongly shaped by previous immigrants from the very same countries where the new immigrants had just left. Furthermore, most of the people the immigrants would encounter in their new land would have experience with other immigrants from the same culture. Additionally, in the last century technology was an important driver of growth, and the countries which supplied the most immigrants before 1965 also happened to be fairly technological advanced countries. One more thing to keep in mind – the percentage of the population that was foreign born shrunk steadily from close to 12% in 1929 to about 5% in 1965.
Since 1965, of course, the story is very different. The foreign born population has been increasing, reaching 13.5% in 2015. Post-1965 immigrants have been far more heterogeneous in ethnic composition and skillset than the earlier group. May have come from poorer, less technologically advanced societies. Some have cultural traits that are not entirely compatible with accepted norms in the US which results in a variety of frictions.
My guess, from the results, is that if more granular data was available on post 1965 immigration (say, by country of origin, or better still, by education level and education quality), it would turn out that some subsamples of post 1965 immigration had positive and significant effects on growth, but proportionately larger subsamples would have negative and significant coefficients. I will dig a bit harder to see if I can find data that can confirm or repudiate my guess.
A few closing comments. Given the election is coming up, it is worth noting that Hilary and Trump are on opposite sides of both the tax and immigration issues. Hilary’s proposed tax changes are likely to generate faster economic growth, Trump’s proposed tax changes are likely to slow the economy. On the other hand, Trump’s immigration proposals (to the extent that they can be coherently defined) suggest an interest in pre-1965 style policies. Hilary, though, will probably accelerate the path we are already following.
For what it is worth, both tax and immigration policies have consequences. However, it is easier to change direction, or to reverse the effects of earlier policies if those relate to the fiscal rather than the immigration arena. That’s why the Roman Empire could survive crazy behavior by madmen like Caligula and Nero, but one mistake by a dry technocrat like Valens led inexorably to the sacking of Rome.
In future posts, I will try to understand what some of the impediments have been to integration of post-1965 immigrants. I am also interested in whether and how those impediments can be reduced.
Finally, as always – if you want my data, drop me a line at my first name (mike) dot my last name (kimel – and that’s with one m, not two) at gmail which of course is followed by a dot com. I’d be happy to share my Excel spreadsheets and if you want it, the trivial amount of R code that went into this. If you contact me within a month of this post going up, I’ll send it to you. Beyond that, I will probably send it to you but no guarantees. I reserve the right to have my computer stolen, go into a coma, move on with my life, etc. But of course, the data is pretty easy to recreate.
One postscript… This post kind of reminds of me of
Authored by Mike Kimel
The way the immigration process was structured from 1921 to 1965, 70% of immigrants to the US came from Britain, Germany and Ireland. In a recent post I noted that the proportion of great writers to scientists in Ireland tended to be a lot higher than for Britain and Germany. I also noted that these cultural traits persisted for a long time, and even survived immigration; the ratio of great Irish-American writers to scientists is higher than the ratio of great British-American or German-American writers to scientists. I also noted some evidence that the same is true in Argentina among populations descended from Ireland, Britain, and Germany.
Assume for this post that STEM has been as important to economic growth as it appears. Then, as a country, we probably would have grown more quickly if there had been fewer immigrants from Ireland and more from the UK and Germany. Alternatively, as a country, we probably would have grown more quickly if more immigrants arrived in the US with a STEM background, which of course would have required more vetting of the immigrants. Another way we could have grown more quickly would be if immigrant children, as well as the native born population, grew up with increased likelihood of going into STEM vocations.
Since the writer to scientist proportion is lower among Irish Americans than among British and German-Americans, the lower hanging fruit, so to speak, probably lies there. At the margin, German-Americans are already picking STEM over writing, whereas their Irish-American counterparts are more likely to have gone the other way. Even making an assumption that seems entirely unwarranted to me, namely that there is some intrinsic reason why the descendants of Irish will, on average, do more poorly at STEM vocations than the descendants of British or German people, it is still likely that Irish-Americans constitute the lower hanging fruit when it comes to STEM.
To put things a different way, growth would have been faster had we, as a population figured out how to reduce the writer to scientist ratio, and doing so among Irish-Americans would have been more beneficial than doing so among British- and German-Americans.
So what drives more people to do X rather than Y for a living? Sometimes it just happens, courtesy of progress and technology. For instance, the American labor force working in agriculture has gone from upwards of 90% around the time of the Revolution to below 2% today. In turn, the share of American kids who plan to become farmers when they grow up has dropped at roughly the same pace.
But such changes can also be engineered. Through the careful application of petro-dollars, liberally marinated over a few decades, our Saudi allies have gotten a lot of people to live a fundamentalist lifestyle. They did so, in part, by creating a lot of employment for clerics throughout the Middle East and the rest of the world. Similarly, the Soviet Union generated a fair amount of demand for political commissars (and at one point, for biologists steeped in the Lysenkoist school). And in today’s world, when your reservoir of Juche runs low, just head on over to the, ahem, Democratic People’s Republic of Korea. The DPRK has got plenty of experts who can fill absolutely all your Juche needs, from applied to theoretical.
There are plenty of levers – money, time, religion, politics, outright decrees – that have been used to change cultures throughout the world. Any and all of them can be used to change the ratio of writers to scientists among the Irish diaspora or in most any other group of people you can name.
In follow-up posts, I want to look at factors that affect whether a given attempted cultural change of this sort succeeds or fails. After all, many such attempts have failed, and the consequences are often disastrous.
authored by Mike Kimel
Earlier this month, I wrote a post noting that countries where punctuality is more highly prized in the year 1999 tend to have higher GDP per capita in both the year 2000 and in 2015. I would like to follow up with a bit about how that happens, which I had I intended to post in the comments to that piece.
My sister is currently in Northeastern Brazil, which is not unusual. By my reckoning, she has spent between twenty and twenty-five years in the country and has a condo in Natal. She loves the country and the people. (As do I, I might add.) At the moment, she is helping a Brazilian friend rebuild/expand a small hotel he owns close to the beach.
I get periodic updates via “WhatsApp,” including videos of geese and pictures of monkeys. A lot of her stories being told would surprise the average American. To a Brazilian, or anyone with experience in South America for that matter; it is the usual litany:
1. Almost nothing happens on time. Most of the crew is late every day. Sometimes hours late.
2. Less periodic events (e.g., delivery of materials) are also usually late. Sometimes by more than 24 hours.
3. The result is there are always people standing around, which results in wasted capacity and resource. You need the crew there in case the cement mixer rolls in; but in reality, it might not actually arrive until the day after it was scheduled. Or maybe, it may arrive the day after that. Of course you plan around it or have contingencies; but no matter what, the result could be less wasted time, energy, and resource if everything happened as planned. Put another way, the lack of punctuality reduces the amount of work being done and production produced.
As I noted last month, my wife is in a similar line of business . . . she buys, rehabs, and rents-out residential rental properties in Northeast Ohio. She also deals with crews and the schedules she builds always have contingencies and room for such events as painters not showing up or electricians arriving stumbling drunk and belligerent. The reason tor the contingencies is because it happens. (In the post describing that, I contrasted with my own white collar world, where in twenty years I never once had to plan around whether someone was going to show up drunk, and my failure to plan for that eventuality has never mattered.)
And yet, my wife alternates between horrified and humored by my sister’s stories. For my wife, a person or delivery showing up late enough or material being defective enough to affect the workflow usually happens once per job. (As an FYI, it takes between two to six weeks to rehab a single family home in Northeastern Ohio, depending on the state of the house when we buy it. I cannot imagine a similar timeline happening in Northeastern Brazil.)
In my sister’s world, that’s how things are all the time. It is the difference between standing on the pier and being a fish.
by Mike Kimel
The British, the Germans and the Irish: A Look at How Traditions Survive Emigration
Between 1921 and the passage of the 1965 Immigration Act, about 70% of those admitted into the US came from the UK, Ireland, and Germany. In a roundabout way, I want to discuss what that has meant for the US. But first, I’d like to regale you, the reader, with a paragraph long digression.
Because I never really developed an attention span until I was about 40, I didn’t quite learned to study properly and ended up having to wing my way through school. So while I enjoy what I do tremendously, a part of me occasionally thinks wistfully that in an alternate universe, I might have become a physicist. Perhaps as a result, I pay a dilettante’s attention to the hard and life sciences. Here or there, it’s enough for me to recite the journalist’s summary of what is going on in a field of research though I won’t pretend that conveys any real understanding.
Authored by Mike Kimel
Here’s a story widely reported in England from earlier this year:
A black former soldier is suing the Ministry of Defence after he was injured when his hands were exposed to temperatures of -30C during training.
Abdoulie Bojang, 30, is suing for £200,000 after he suffered career-ending injuries when he was exposed to below-zero temperatures in Banff, Canada during training.
He says the army ‘failed to take into account his ethnicity’, and is suing over non-freezing cold injuries…
A spokesman for solicitors Bolt Burdon Kemp said: ‘Service personnel of African and Afro-Caribbean descent, including those of mixed race, are particularly vulnerable in low temperatures.’
‘The MoD has acknowledged research indicating that these groups are 30 times more likely to contract an NFCI (non-freezing cold injury) than Caucasian service personnel.
I assume that if the MoD has acknowledged such differences are real, they probably are, but beyond that, I will not pretend that I am qualified to have an opinion on medical issues. However, I do have some questions:
1. Given this article, is it racism to treat one group of soldiers differently in cold weather based on that group’s ancestry?
2. Given this article, is it racism not to treat one group of soldiers differently in cold weather based on that group’s ancestry?
3. Do answers to 1 & 2 change if the notion that a person’s ancestry affects that person’s vulnerability to the cold is true?
4. Do answers to 1 & 2 change if the notion that a person’s ancestry affects that person’s vulnerability to the cold is not true?
5. If people of a particular ancestry are disproportionately likely to be affected by cold, what is the likelihood that groups of people with a different ancestry are disproportionately likely to be affected (positively or negatively) by other conditions?
6. Under what conditions would it be racist to account for issues described in question 5?
7. Under what conditions would it be racist not to account for issues described in question 5?
Please share your answers in comments.
(Note – this post is not a follow up to this one, but the two posts do reside in the same world.)
Authored by Mike Kimel
In this post, I want to demonstrate the importance of a specific cultural trait, namely punctuality, on the economy. The difficulty, of course, is coming up with a good measure of punctuality, and in particular, one that isn’t regularly gamed.
Digging around, I found a paper entitled The Pace of Life in 31 Countries by Robert V Levine and Ara Norenzayan in the Journal of Cross Cultural Psychology in 1999. For the purposes of this post, the most interesting thing about this paper was this measurement:
As a sample of concern with clock time, the accuracy of 15 clocks, in randomly selected downtown banks, were checked in each country. The criterion for the correct time was that reported by the telephone company.
The 31 countries span the globe, and seem to encompass every inhabited continent, though it should be said, the list is Europe-heavy; unless I miscounted, 14 of the 31 countries are in Europe. The clock accuracy results for the countries, as well as several other measures of less relevance to this post, are returned in a table in the paper. I then compared those results (compiled in or before 1999, I remind the reader) with the real GDP per capita in US dollars for those same countries in 2015. That data was pulled from World Bank tables. The World Bank data excluded two of the countries in the Levine & Norenzayan paper, Taiwan and Syria.
Here’s what the data looks like, graphed:
I took the liberty of highlighting and labeling the three points at either end of the curve.
The figure shows that the correlation between the natural log of clock inaccuracy, as measured in average seconds of clock error in or before 1999 and real GDP per capita in 2015 is -0.56. That is, countries with more accurate clocks in or before 1999 tend to be wealthier in 2015. Note also that the correlation is a bit lower (-0.50) when data from the year 2000 is used. This suggests that if there is a causation, it isn’t running from wealth to clock accuracy.
Frankly, there are a few anomalies with the graph, and they tend to be where my intuition doesn’t match the accuracy ranking provided by Levine and Norenzayan. Having stated that, I should note that my intuition is informed primarily from having lived abroad for about a decade and a half, and from having a fair number of interactions (professional and personal). For example, Italy ranks second in clock accuracy, but my experience is that there are a fair number of Italians who tend to be relatively tardy to meetings, etc., relative to people from a number of other European countries. My admittedly snide hypothesis is that the Italian post office’s clock is simply just as late as the clocks in private Italian banks. Not surprisingly (to me, anyway), Italian GDP per capita is 9th among European countries included by Levine and Norenzayan, not 2nd.
Nevertheless, despite the anomalies, at a high level, this data seems right to me, and it provides a bit of confirmation to the idea that punctuality is tied to positive economic outcomes.
The backstory, for those interested:
A few weeks ago, I had a post showing that at the national level, over the past few decades, there is a negative correlation between immigration and subsequent job creation. In a more recent post, I looked at state level data to determine whether states with a greater percentage of immigrants created more or fewer jobs for the native born population. The results showed that outside the old Confederacy, the more immigrants as a share of the population, the less jobs were created for the native born population. In between the two posts, I tried to provide a few explanations for why the observed relationship exists.
In the “explanations” post, I mentioned cultural traits as issues that make a difference in whether immigrants contribute positively or negatively. In the comments to the post, I mentioned timeliness (i.e., punctuality) as one such trait. That statement met with resistance from other commenters. It was even suggested that such a view might be racist. This post is intended to support my comment.