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 “Immigration and Job Creation at the State Level”

In this post, I was planning to look at how the percentage of a state’s population that is made up of immigrants affects job creation.  After all, we hear from some quarters that immigrants create jobs, and from others that immigrants take away jobs that would otherwise go to Americans.  Obviously, the truth is sometimes one and sometimes the other, and mostly somewhere in between.  It depends on many factors, including the nature of the immigrants themselves, who, like anyone else, vary in attitude, capability, sociability, etc.  Even so, understanding whether in aggregate, immigrants help or hinder, and under what conditions, is worth knowing.  

As I was pulling data, it occurred to me that I had phrased the question poorly.  The relevant issue is not whether immigrants create jobs.  It is whether immigrants create jobs for the native population.  If you doubt that, I’ve got an experiment for you to perform.  Go to Chetumal.  From the pictures I’ve seen, it seems like a very pretty town in Mexico on the border with Belize.   Meet with the mayor, and tell him (at this time the mayor is a man) you will renovate a factory and put 100 people to work in his town.  I bet he will be ecstatic.  Now explain that your plan involves hiring 110 Belizeans and firing 10 Mexicans who are currently employed.   My hypothesis is that the mayor’s mood will noticeably sour at this point.  You don’t actually have to go with Chetumal to run this experiment.  Pretty much any jurisdiction not run by a US politician will probably do.  

Having established the question, here’s how I tackled it.  

1.  I found data on the immigrant share of each state from Pew Research.  Data is available in 10 year increments from 1960 to 2010, and then for 2014.  Pew references the American Community Surveys, so I am 99.97% certain that all of the data originates with the Census, but I couldn’t find it there
 
2.  I found data on monthly reports containing, among other things, “employees on nonfarm payrolls by state” for every month going back to December 1993 at the BLS

3.  There are three years for which the immigrant data can be matched to employment data:  2000, 2010, and 2014.  For each of those years, I used employment figures for December and the immigrant share of the population to calculate the “native born employment.”  That is, the number of jobs held by native born people. 

3a.  Note the implicit assumption that the native born share of employment was equal to the native born share of the population.  That may be an underestimate in places where immigrants are go-getters and bring strong competitive advantages.  Conversely, it will be an over-estimate where the immigrants are less industrious and less competitive than the natives.  I believe this effect will be small.  

4.  I computed the growth rate in native employment from 2000 to 2010, and from 2000 to 2014.

5.  I computed the correlation between the immigrant share of the population in 2000, and the growth rate from 2000 to 2010 as well as the growth rate from 2000 to 2014.  They were -0.047 and 0.032, respectively.  In other words, very close to zero. 

So, at first glance, immigrants today don’t affect the job market tomorrow.  But the problem is, this analysis lumps Texas with West Virginia.  That’s like comparing oranges and bicycles.  So how do we do this differently?  One way is to use geographic groupings developed by the Census.  For each of those regions, I found the correlation between immigrant share of the population in 2000, and the growth from 2000 to 2014, and also the median immigrant share for the states in the region.  And since a picture is worth a thousand words, I overlaid that on a reasonable looking map showing the Census regions that I found here.

invisible hand

So what do we see here?  The red numbers, which are the correlation between the immigrant share of the population in 2000 and the growth rate from 2000 to 2014 are positive in the three Census regions that mostly correspond to the old Confederacy, and negative everywhere else.  That is to say, in the old Confederacy, the more immigrants there were in a given state in the year 2000, the more jobs were created over the next 14 years.  But, in general, outside that region, the more immigrants there were in the year 2000, the fewer jobs were created over the next 14 years.  

Why might that be?  Assuming the relationship is not spurious, I can think of a few reasons for the relationship. One scores OK on my personal self-censorship index (i.e., the ratio of how well an explanation fits the facts divided by the expected amount of trouble I will get into for pointing it out) so that’s the one I’m sticking with.  The gray highlighted numbers shows the median percentage of  immigrants for the states in each Census region.  And it does seem there is a rough correspondence between the likelihood that regions with a smaller share of immigrants are those most likely to benefit from more of them.   Put another way  - like just about any other variable, there is an optimal number of immigrants.  If you have too few, added some will generate benefits.  But there is a converse to that statement too – if you have above the optimal percentage of immigrants, adding more immigrants can put locals out of work.  

Normally this would be the end of the post, but I feel I should add a few additional comments below.

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Comment 1:  Frankly, I don’t think the Census geographic divisions are the right way to do this analysis.    Sure, the Great Lakes states (a.k.a., East North Central) makes sense.  And we aren’t comparing Texas and West Virginia, but we are comparing Florida and West Virginia, which is just as bad.    And worse, we are weighting Florida and West Virginia the same.  Personally, I’d like to see “Large Coastal Economic Powerhouses” – California, Texas, Florida, and New York – which make up a third of the country’s population (correlation = -0.59) and other logical groupings.

Comment 2.   This analysis biases down the negative effect of immigration on jobs, and biases up the positive effect of immigration on jobs.  Assume for simplicity that any state that manages to crack the nut will generate jobs for a 15 year period, no matter what else happens.  If a state pulls that stunt off in 1997, by 2000 its share of the immigrant population will be up, but it will continue generating jobs for at least a decade.  The way I set up this analysis, the newly arrived immigrants “get the credit” for job generation.  

Comment 3.  Perhaps this analysis shouldn’t be on the immigrant share of the population as much as on newly arrived immigrants as a share of the population.  After all, most immigrants who have been here at least X years have fully assimilated and are part of “us.”

Comment 4.  As always, I will mail a copy of my spreadsheet to anyone who wants it provided they email within two weeks of the date this post goes up.  You may be in luck beyond that point, but as time goes on, I may switch computers, die, etc.  If you want my spreadsheet, I am at my first name (that is “mike”), dot, my last name (that is “kimel” with only one m) at gmail dot com.  

Comment 5.  This one is mostly irrelevant, but provides a bit of the background to this post.  A few weeks ago I had a post using national level data which showed a a negative correlation between the immigrant share of the population and the growth in jobs over the next decade going back to 1950.  In a more recent post I tried to provide a few explanations for why.  The comment section got rather testy and led to what is, as far as I can see, a rather odd post.  

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Hillary Clinton was right to separate Trump from the GOP on racism, xenophobia, and sheer meanness.*

She was wrong to separate him from the GOP on fiscal and regulatory policy and on court and administrative-agency appointees. It wasn’t a package deal, or rather, it should not have been. She could have made the distinction, but she didn’t; not with specifics and not even generically on any regular basis, anyway.

Washington Post blogger Paul Waldman yesterday posted a lengthy post titled “Why Hillary Clinton was right to separate Donald Trump from the GOP” in which he makes the same mistake that Clinton herself has made since she secured the nomination in early June: conflating the five-decades-long Republican racial/xenophobic/culture-wars Southern-and-blue-collar-white strategy with economic, fiscal and regulatory policy.

For Clinton this explains her decision to highlight to the Democratic Convention delegates her embrace of so much of Bernie Sanders policy agenda by agreeing to incorporate it into the Party platform—and then never mention most of it again.  And to never mention (until very recently, and then only generically and only very sporadically) that Trump’s fiscal and regulatory policy is Paul Ryan’s on steroids, that that his economic advisers are the Koch brothers’ and other Republican donors’ dream-come-true, as will be his Supreme Court and lower-bench nominees and key federal-agency heads.  Trump is the far-right-libertarian billionaire’s Trojan Horse.**

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On The new heavyweight macro critics

I strongly advise you to read this post by Noah Smith and then click his links and read all of Romer (I have) and Kocherlakota (I haven’t yet but advise myself to do it now).
update: Kocherlakota’s post is very brief non-technical and brilliant. I advise you to read it now.
end update.

Noah recalls that he has long argued that current mainstream academic macroeconomics is no good at all. I too have argued this. So have the somewhat better known Larry Summers and Paul Krugman. Relatively recently Paul Romer denounced his former field. Most surprisingly so has Narayana Kocherlakota (links in the post). Olivier Blanchard (pdf) and Simon Wren-Lewis are very polite but clearly agree (actually now that I re-read it Wren-Lewis is (finally) getting pretty frank). Robert Shiller, George Akerlof and Janet Yellen have long been at least as harsh as Kockerlakota (although no one is quite as snarky as Romer).

I have really one question. Given the extreme prominence of the critics of the current orthodoxy, how can the orthodoxy remain an orthodoxy ? In particular, all of the linked authors agree that the current mainstream consists of New Keynesian DSGE models. But collectively they are much more prominent than all New Keynesian DSGE modelers put together.

more after the jump

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2nd Amendment/Open Carry vs Police “I feared for my life”

Supporters of Open Carry and ‘Must Issue’ Concealed Carry insist that no one should be afraid of someone exercising his or her 2nd Amendment Rights whether that be in some public park or the aisles of your local Wal-Mart. Yet right along side that we have a doctrine that everyone should comply with every request made by a Peace Officer without question and without hesitation and if refusal to comply ends up with the application of force up to and including deadly force then a sufficient defense is “I feared for my life”.

Rarely have those two doctrines collided so openly as in North Carolina this week. North Carolina is an Open Carry State. Anyone has the right to carry a handgun in or out of a holster as long as they are not actively threatening someone. Which you think at a minimum would mean pointing the weapon at someone with some apparent hostile intent. But instead a man who was NOT the subject of the particular police search action stepped out of his car while visibly armed and after a disputed set of events was gunned down. Because police “feared for their lives”.

In another incident a man was gunned down in disputed circumstances by a woman police officer, also in fear for her life. But somewhat disturbingly a police officer in a helicopter, who oddly enough was the spouse of that shooting officer, described the shooting victim as “looks like a big bad dude”. From 500 feet in the air. Was he also “in fear for his life” and if so on what grounds? This man was not even visibly armed.

On balance I am a 2nd Amendment supporter, mostly because I am from a gun family and everyone but me owns at least one (well maybe not my 87 year old Mom). But if you are going to support Open Carry it has to apply equally to the guy wearing camo and carrying an assault rifle in a Luby’s in Texas and wearing a Gadsden flag on his hat and a Confederate flag on his vest and to some guy wearing a Black Lives Matter T-Shirt and carrying a pistol in a Church’s Chicken in North Carolina. We just can’t have a society be one where a cop just decides he is more scared of “big bad dude” than “good ol’ boy”.

Pick a side.

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Who ARE those other people, Mr. Trump? You know; the ones whose money you said on Tuesday that you use rather than your own? The OP in the OPM? And what if, say, those OP call their loans during your presidency? Do tell.

I am now going to the brand new Trump International Hotel D.C. for a major statement.

– Donald Trump, twitter, Sept. 16 at 9:23 a.m.

And so he did, as all the world will remember.  That’s when, and where, he made his trumpeted “birther”-renouncement statement—er, his internationally televised ad for his new D.C. hotel.

The “where” being the operative word in that sentence.  And the “for his new D.C. hotel” being not an accurate reflection of who actually owns it.

At a campaign appearance in North Carolina on Tuesday, in comments that should have received wide attention, not just from the news media but also from Clinton, Trump elaborated on whom the Trump International Hotel D.C actually belongs to: “other people”.  Here’s how CNN began its online text report on that campaign appearance:

Kenansville, North Carolina (CNN)Donald Trump bragged Tuesday there’s “nothing like” using other people’s money, hours after a report said he used more than $250,000 from his charitable organization to litigate lawsuits against his business interests.

Trump, while calling for building safe zones in Syria financed by Gulf states, vaunted the benefits of doing business with “OPM.”

“It’s called OPM. I do it all the time in business. It’s called other people’s money,” Trump said. “There’s nothing like doing things with other people’s money because it takes the risk — you get a good chunk out of it and it takes the risk.”

Simply pointing out, again and again, that Trump is breaking with four decades of tradition in refusing to make public any of his or his company’s tax returns; simply pointing out Trump’s companies’ six bankruptcies; pointing out that his comments about Putin (not least that he has a reciprocal-compliments relationship with Putin, and Putin’s relationship with Russia’s oligarchs who invest in Trump real estate (or whatever it was that Donald Jr. was saying at the 2008 seminar)?  These, independently, don’t register with most of the public, apparently.

But how about running ads in swing states tying all these together with the bowtie called OPM, and Trump’s Sept. 16 personal ad for the new hotel that bears his name but to which the in-name-only label applies?  Russian oligarchs, after all, could tie President Trump in knots—should they threaten to, say, pull their financing from “his” real estate properties.

Unless of course the other people’s money comes without strings attached. Or balloon loan repayments that can be called at any time.

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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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One Man’s Profit is Another’s Loss

There is this fixed quantity of whatever it is and if you get more, I get less. One man’s profit is another’s loss.

This dogma was already advanced by some ancient authors. Among modern writers Montaigne was the first to restate it; we may fairly call it the Montaigne dogma. It was the quintessence of the doctrines of Mercantilism, old and new. — Ludwig von Mises

Add “the Montaigne dogma” to the collection of pejorative phantoms: mercantilism and Malthusianisme, image of limited good, zero-sum fallacy, Luddite fallacy, fixed Work-fund and the theory of the lump of (labor, labour, work, jobs, output).

Except… it really ought to be the Seneca dogma since Seneca was the ancient author whose de Beneficiis Montaigne faithfully borrowed from for ‘his’ essay (find “Demades“). Even Seneca was elaborating on an older maxim by Publilius.

Is it ever true that one man’s profit is another’s loss? You bet! I just gave an example — gambling and other contests of skill or luck are typically zero sum. Your loss is my gain. Our loss is the house’s gain.

But there is a more historically-pertinent operation of the zero-sum game: bills of exchange. As I remarked in that earlier post, one of the prime motivations for early modern merchant bankers to adopt the novel and challenging technique of double-entry bookkeeping was to “prove an alibi” against suspicions of usury. The way that bills of exchange were accounted for made them one of the favorite financial instruments for avoiding an appearance of usury. Raymond de Roover explained:

As a result of the usury prohibition, bills [of exchange] were never discounted but were bought at a rate of exchange which fluctuated up and down according to the conditions prevailing in the money market. There is no doubt that interest was received by the banker who invested his money in the purchase of bills, for a hidden interest was included in the rate of exchange. Because of this subterfuge, the structure of the money market was such that exchange fluctuations were caused either by a change in the rate of interest or by a change in the terms of international trade.

Interest was thus concealed in the exchange rate charged by the banker. As a consequence, the profit on any given transaction was uncertain. A banker, however, could rely on his long-run observation of the fluctuations in the terms of international trade to achieve a high degree of predictability covering a large number of transactions.

By the middle of the 16th century, the use of bills of exchange had become common enough in trade between England and the Low Countries to raise suspicions about manipulation of exchange rates by bankers. This suspicion was articulated in the memorandum prepared for the 1564 Royal Commission on the Exchange, “For the Understanding of the Exchange,” which first noted the ‘usurious’ undercurrents of different exchange rates prevailing simultaneously in London and Antwerp:

…when the English pound is paid for a month before hand [in London], then the price thereof in reason ought to be the less; and when the English pound is not paid for in Flemish money until a month after hand [in Antwerp], then the price in reason ought to be the more. But here you may perceive that this necessary and fair name Exchange might be truly termed by the odious name of buying and selling of money for time, otherwise called usury.

The memorandum then went on to describe “how private gains may be made when the Exchange goeth too low” and “how the bankers do cunningly fall [or raise] the exchange at Antwerp.” Among the remedies proposed for such manipulation of exchange rates was to “govern this realm by good policy” such that would “temper and forbear the superfluous delicacies” of imported goods and cause English exports “to be wrought to the best value before they are vented.” The resulting trade surplus would raise and maintain the value of the English pound.

Of course not every country can run a trade surplus all the time. For the world as a whole, the balance of trade is indeed a zero-sum game.

There are, however, not one but three issues bound up together in the memorandum on exchange. The first is usury and its concealment in the exchange instrument. The second is the effect of exchange fluctuations on the profits and losses of bankers and merchants. And the third is the manipulation of exchange rates, either by bankers for the private gain or by government to counter the cunning tricks of bankers.

Nowadays, we no longer have to worry about fraud by bankers. The old superstitious prejudices against usury have been supplanted by an enlightened embrace of the unequivocal blessings of credit and debt. Comparative advantage has proven that it is economically illiterate to question the universal benefit of globalization.

Verily, we can embrace the von Mise-erly wisdom that “There are in the market economy no conflicts between the interests of the buyers and sellers.” One man’s gain is clearly the alleviation of another’s pain.

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Unskewing 538

Different data journalists have different estimates of the probability that Hillary Clinton will be elected. The numbers will be updates, so I type the current ones before discussing.

Five Thirty Eight polls plus 55.7%
Five Thirty Eight polls only 57.1 %
Daily Kos 63%
Upshot 75%
Princeton Election Consortium (Sam Wang) (random drift) 71%
(Bayesian) 81%

As usual Nate Silver and Sam Wang are the extremes with Silver estimating probabilities closer to 50%. This happens mostly because Silver estimates distributions of parameters which Wang assumes to be known constants. I have to admit I generally agree with Silver.

Silver explained at least part of the difference with other aggregators

High numbers of undecided and third-party voters are associated with higher volatility and larger polling errors. Put another way, elections are harder to predict when fewer people have made up their minds. Because FiveThirtyEight’s models account for this property, we show a relatively wide range of possible outcomes, giving Trump better odds of winning than most other statistically based models, but also a significant chance of a Clinton landslide if those undecideds break in her favor.

This is a bit reassuring to me. I think there are a lot of #NeverTrump voters who are very unenthusiastic about Clinton. These are voters who say he is unqualified and temperamentally unsuited to be President. I tend to guess many of them will reluctantly vote for Clinton if and only if it seems necessary and otherwise stay home of vote 3rd or 4th party. I remember 2000 (and some of these voters don’t) but I am not as alarmed as I would be without this argument.

The point (if any) of this post is that fivethirtyeight normalizes polls in which only Trump and Clinton are named to the standard of polls in which Johnson & Stein are also named. They will be on the ballot, but this seems to me to be a mistake. Respondents can volunteer that they will vote for another person if asked to choose between Trump and Clinton. I think the pressure due to naming only Trump and Clinton is weaker than the pressure of an upcoming election and fear of wasting a vote. So I’d guess polls which name only 2 candidates give more accurate forecasts. I think this is historically true (sorry no link). Certainly declared support for 3rd party candidates in September polls regularly vastly exceeds actual votes for 3rd party candidates.

I don’t know the fivethirtyeight correction term (sorry I could probably find it there if I looked). My impression is that Clinton averages 1 or 2% better in polls which name only Trump and her. Currently The Huffington Post says 1% nationwide (Clinton 4% ahead in 2 name polls 3% ahead in 3 name polls including Johnson (including Stein has to hurt Clinton)). Given the confidence interval and the fact that all aggregators assume normality, a 1% difference in means corresponds to about the difference between 57% and 75% (this is a very rough BS pseudo calculation).

An even more striking pattern over at The Huffington Post is that the fitted curve for the Clinton Vs Trump Vs Johnson is much smoother than the fitted curve for Clinton-Trump. This is partly due to their smoothing algorithm which smooths more if there are few data points (it is a compromise between don’t want to use very few points and don’t want to use very old data). But eyeballing, I am fairly sure it isn’t just that. Also the moderately smoothed Clinton support in 2 way polls varies more (including conventions roughly 44-48 for Clinton and 40-42 for Trump). I think this shows a lot of the variance is in the willingness of #NeverTrumpers to say they will vote for Clinton if pressed.

So after pychoanalyzing data ananylis, I conclude that the key issue is whether people who think Trump should not be elected, but don’t want to vote for Clinton end up reluctantly voting for Clinton. What an original thought. Bet no one has written that already in 2016.

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