Coyote Blog

So a $15 national minimum wage will almost certainly be on the table in Congress this year, and if past such legislative efforts are any guide the Republicans will probably eventually go along in exchange for reducing the $15 to a lower number and slowing the rollout.

I have talked a lot about the negative effects of higher minimum wages on low-skill workers.  Two good example background posts are here and here.  I covered how a broad range of labor regulation hurts unskilled workers in a cover story for Regulation magazine a few years back.  Unfortunately, in a country where the average American buys about $1000 in lottery tickets each year, the willingness to believe we can get something for nothing is strong.

But I want to talk specifically about a Federal minimum wage increase, where one other problem emerges.  The best way to state this is -- how can one possibly set the same minimum wage for San Francisco at the same rate as one does for rural Mississippi?  Here is one source for comparative state cost of living.  Doing this by county would make the curve even wider.

Cost of living in Hawaii is more than 2x that of Mississippi.  CA and NY are not far behind.  A minimum wage that might comfortably be accommodated in San Francisco (and note even there the rise to $15 was ending service jobs in that city long before COVID), would be an economic disaster for rural Alabama.  I don't tend to think primarily along racial lines as seems to be the case on the Left today, but basically this is a policy driven by rich white tech guys in San Francisco that is going to devastate the employment prospects of rural blacks.

Whatever one's misgivings about minimum wages, it is certainly true that allowing states to take the lead on setting minimum wages (counties would make even more sense) makes a lot more sense that trying to take action at the national level.  Even with state action there are disparities.  Look at this unemployment map of the rural counties in CA vs. the tech. enclaves in the Bay Area (the chart below is January 2020, I wanted to dial back pre-COVID).  Those rural counties are being slaughtered by the $15 minimum wage.  Just think about rural areas in states less costly than CA.

So a state by state approach is WAY better. There are three reasons, in increasing order of cynicism, why Democrats in Congress will still insist on a higher national minimum wage.

1. Democrats in Congress believe they are carrying salvation to the powerless masses in Alabama who are held in slavery by state Republicans.  Democracy and the "will of the people as expressed at the ballot box" are only sacred when it's our team in charge.
2. Folks in Congress did not spend a career fighting to win a seat in that body only to find that there is something that would be best done by state legislators.  They didn't work to get promoted to have LESS power (though that is exactly how the country was originally designed).  This is the enemy of all Federalist notions
3. The fact that a $15 minimum wage will devastate many rural areas, mostly Republican bastions, may be a feature and not a bug for some.  Think of the $15 minimum wage as Democratic revenge at the heart of red states for the Trump reductions of SALT deductions in the tax code (which were a dagger at the blue state model).

Long-time readers will know that I am interested, to the point of obsession, in incentives.   One should always be suspicious of bad outcomes described as irrational or the nefarious actions of bad people.  In both cases, if one looks carefully, the outcomes usually turn out to be the perfectly rational outcomes of perfectly normal people responding to bad incentives, assumptions, and/or information.

I personally believe the COVID response in this country (and others) is exaggerated and counter-productive.  But for this post I am not going to ask you to agree or disagree with my skepticism.  Instead, I am going to focus on incentives, and show how media, academia, and government all have incentives, assumptions, and information asymmetries that push them towards exaggerated COVID responses.

The following list is not necessarily complete and the items here are not independent of each other.  Having completed this post, they now look a little random but this is sometimes the way I clarify my thinking on things -- to write and publish and get feedback and maybe be more structured the next time.

Incentives

• Political incentives to "do something" about the issue of the moment.  We see this after every high-profile "bad thing" that happens.  There is immense pressure on politicians to do something -- pass some law (often with a person's name in it) or, if the legislative process is perceived as to slow, fire off some executive order.  In the heat of battle these actions are often taken without regard to efficacy, cost, or unintended consequences.  In the heat of these frenzies, a multi-dimensional decision is magically redefined as having only one dimension that matters.  Anyone who focuses on costs or unintended consequences or even efficacy problems of the proposed solution are cast as heartless and uncaring, potentially even evil and nefarious.
• Politicians always legislate to first-order metrics, never second-order metrics.  Politicians know that the public and the media is looking at their country or state every day and publishing the number of COVID cases and deaths.  No one is publishing the number of additional suicides, or cancer deaths from people too scared to go to the hospital, or increased starvation and disease deaths in poorer countries as food prices rise and aid from rich countries dries up. These second order effects are real but hard to prove or measure.  They are what we call "unintended consequences" but should instead call "ignored but entirely predictable consequences."
• Political incentives to expand power.  Every politician in every branch of government is always working to expand their own power (this is not unique to government, you can say the same thing of executives and functional departments in many large corporations).  When the public is scared and panicky, politicians are able to break through past limits and norms and establish new precedents.  The best example of this is that governments in Western democracies all expanded their power during the 20th century wars, expansions that largely stuck and were not reversed in peace time (except for a few fortunate examples like locking up whole ethnic groups in internment camps).  When the public is scared, power is to be had and it is the unusual politician that will say in such a situation that the right solution is to do nothing.
• Political incentives not to admit error.  Politicians simply cannot admit error.  To some extent this is due to the personality and ego traits that the political process sorts for, and to some extent this is based on day to day political incentives.  But think about any President in your lifetime and try to think of even the smallest issue on which they said something like "I tried X, over time X has not worked and now I realize we should do something other than X."  We would actually hope this is the kind of person we have leading the country, but simultaneously our own behaviors don't allow it.  Presidents frequently admit past errors of others (eg, a current President saying the war in Afghanistan was a mistake) but they can never turn against any policy of their own.  So if, say, lockdowns were the response to wave 1 of the virus, lockdowns are damn well going to be the response in successive waves.  Because not doing so is essentially an admission that it was a mistake the first time.
• If it bleeds, it leads.  This one takes little explanation, because I think most of us understand the strong incentives of news organizations to create and amplify emergencies to increase the attention and viewership they get.  Cable news had a huge spike in viewership after 9/11 and again in the early days of the Gulf War, and they are constantly jonesing for the same sort of hit.  Remember that the media has accurately called 11 of the last 2 pandemics, earlier predicting disaster from swine flu (dating myself here), bird flu, ebola, zika, mad cow, and probably several I can't remember.
• Reference to personal circumstances when making national trade-offs.  I would say that the number 1 thing that drives me crazy about statists on the Left and Right and which makes me a libertarian is the tendency to impose solutions to tradeoffs on everyone in the country based on how you would personally make decisions for yourself.   If one-size-fits-all public policy decisions are going to be made, I want them to be made in a way that suits me.  For example, a politician in Chicago might say they would never feel comfortable letting thier kids walk to school on their own, so no parent should be allowed to let their kids walk to school alone.  Applying this to COVID, we know there is a large contingent in media, academia, and politics who will say that is is wrong to consider economic damage when evaluating COVID lockdowns.  What do all these folks have in common who tend to be advocating strongest for lockdowns?  They still have their jobs, are still getting paid, can still be productive over the Internet, and are comfortable getting their social interaction over zoom.  Note that these are the same folks that constantly tell us to check our privilege, but then tell us to ignore the economic hardships of lockdowns that they are too privileged to experience.  Only by the most extreme action do the voices of the less privileged who are suffering the most under lock-downs get heard (and even then, like the hair dresser in TX and later in SF, they get mocked by the elite).

Assumptions

• Trump is so bad that no price is too high to get rid of him.  I have told folks for years that every generation thinks their current era is uniquely politically toxic.  I don't think we have yet risen even to 1968 levels of discord, but one exception is the hatred for Trump that exists in some quarters.  I personally have never seen anything like it.  The nadir was when Trump mentioned that HCQ looked like a promising COVID treatment and the governors of MI and NV immediately banned HCQ without evidence to make Trump look bad (a desire I assume stems from a perception that Tump is so dangerous and represents such an existential threat that any action to undermine him or make his re-election less likely should be pursued).  A prominent study was essentially made up out of whole cloth to prove HCQ was dangerous and thus Trump bad, a conclusion that should have made zero sense to everyone as HCQ is used by millions every day as a malaria prophylactic.   I find Trump distasteful but trust the American system to limit the damage of tyrants, but many are working from a very different assumption.
• Humans have conquered nature.  I will confess to having an almost Victorian confidence in progress, but even I accept that sometimes nature throws things at us that are a) not our fault and b) we can't yet stop.  But throughout our COVID responses there seems to be, particularly in Western nations, an assumption that we should be able to prevent death from this thing -- ie that any death should be judged as a failure of our response.  But diseases still kill people.  Last year communicable diseases killed at least 15 million people in the world.  And many of our Western deaths have been among the very old in care facilities where the average life expectancy pre-COVID was numbered in months.

Information

• Good cause skewing of data, or "fake but accurate."  Decades ago, there was a stat that there were a million homeless people in the US.  Everyone repeated it as gospel.  Someone tracked it down, and eventually discovered that it was just made up by a homeless advocate who just picked a round large number.  When this was presented to a well-respected reporter on NPR, that the "fact" she was quoting was no such thing, she just shrugged.  She said homelessness was clearly a problem and if the number she was quoting (as a reporter!) was exaggerated, then it was in the good cause of increasing attention to homelessness.  This was the first example I can remember of something that was considered fake but accurate, but there have been many more since.   During COVID, this has caused outlets like Goggle and Facebook to actually censor opinions the tend to be skeptical of the severity of the disease or efficacy of mitigation steps like lockdowns.  They claim to be doing so for a good cause, believing it is better to err on the side of having the public too cautious rather than insufficiently cautious.
• Asymmetric public exposure to experts.  Throughout COVID we have been told that the experts all say X, that there is a consensus for X.  And sure enough, we mostly only hear X on the news.  But anyone in academia can tell you that this sort of homogeneity of opinion can't possibly be true.  As in other science, on issues such as mask or lockdown effectiveness or herd immunity thresholds, academics hold a wide range of opinions and there are a wide range of findings in the literature.  But this heterodoxy in opinions never really gets full public view due to media incentives, political incentives, and good cause skewing.  The most extreme voices on the end of the academic scale that support the media's and politicians' desire to create fear are selected for public exposure.  Then, these selected academics are retroactively crafted into leading experts.  Any of you folks every heard of Anthony Fauci before this started?  How about whatever expert your governor is using?  No, you had not -- these are prominent people in their field but just one of ten or twenty equally qualified persons who could have been selected and presented as experts.  They are then retroactively reinvented not as one of ten folks with a wide variety of opinions but as the one leading true unassailable expert.
• Social media amplification of tail-of-the-distribution events.  One of the features of social media independent of these incentives is that it tends to spread and amplify tail of the distribution events/risks.  The problem is that there seems to be two personality types in people -- one, and I would include myself in this -- who are knee-jerk skeptical of such stories.  Did it really happen?  Did A really cause B?  Is this really anything more than one bizarre outlier?  But there is a second type of person, and I would say that they are WAY more prevalent than I would have believed a year ago, who sees a story that someone's gynecologist's hairdresser's uncle claimed to have had heart issues after getting COVID and suddenly "everyone who gets COVID has permanent heart damage!"  Even before the Internet, Americans were very bad at parsing relative risks and now they just seem terrible at it.

A couple of years ago I wrote about the 1994 Myra Sadker book "Failing at Fairness,"  a book that tried to make the case that girls were getting hosed (as compared to boys) by the US educational system.  Leaving aside my general sense that all kids of all races and genders are getting hosed by the current public school system, the idea that girls were doing worse in education was already wrong in 1994 and is demonstrably ridiculous today.  I showed these charts at the time.  First from the indispensable Mark Perry:

In this second chart, even the New York Times has noticed

Really, one only needs to go look at the unemployed knuckleheads living in their parents' house and rioting for Antifa to know we have a boy problem today, not a girl problem.  But I wanted to add one more piece of data, from some research I cannot vouch for in Self.  The income results for young women are about what you would expect from the education data above, and the crossover (somewhat coincidentally because it is dependent on start date chose) almost exactly matches the crossover in the education data.

Perhaps it has always been so, but we live in a time when our social science mythology is really divorced from social science data.

A few weeks ago Matt Yglesias published a tweet (since deleted, which I don't totally understand as I thought it was pretty innocuous from a Progressive viewpoint) saying that he wanted to spend more time focusing on "relative child poverty."  What the heck is "relative" child poverty?  I want to spend a bit of time discussing why this is a useless metric, helpful only if one want to try to sell socialism in the US.

Relative child poverty is a metric based on the country's median income -- how many kids live in families with income that is X% of the median.  Here is an example (source):

If you click on the source, the headline presents this as "These rich countries have high levels of child poverty."   The implication is that the US has more child poverty than Latvia or Poland or Cyprus or Korea and only slightly less child poverty than Mexico and Turkey.  But does it really mean this?  No.  This chart is a measure of income equality, NOT the absolute well-being of children.

Many of the countries ahead of the US are there not because their poor are well off, but because their median income is so much lower than ours. In fact, you will notice the lack of African and Asian countries in this. I will bet a lot of money that certain countries in Africa and Asia everyone knows to be dirt poor would beat out the US in this, thus making the bankruptcy of this metric obvious.

Take Denmark in the #1 spot. It looks like 20% more kids in the US live in poverty than in Denmark. But per the OECD, the US has a median income 41% higher than Denmark. So what it really means is the US has 20% more kids living under an income bar that is set 41% higher.  How can this possibly have any meaning whatsoever, except to someone who wants to make the US look bad?

The chart below does the same thing -- it has nothing to do with absolute well-being, but defines poverty as living below some percentage of that country's median income. In this metric, a country where everyone equally made only $1000 or even $10 a year would have 0% poverty!

Within the US, the same game is being played with poverty stats.  Despite decades of government income distribution and poverty programs, the stats appear to show that the US has nearly unchanged poverty rates.   But this is because the census data on which the poverty stats are based EXCLUDE government transfers -- in other words, they exclude the effect of many or most of these poverty programs.  When this and other issues are corrected for, US poverty rates have dropped to all-time historic lows (source)

Here is another study coming to a very similar conclusion.

One thing you never, ever, ever see is comparisons of the poor in the US to poor in other countries on an absolute well-being basis after transfer payments. That is because the bottom 10 or 20 percentile in the US are among the top half of richest people in the world, and in many nations they would be among the top 10%. It is possible to make these comparisons, though. I did so several years ago from a data set I saw Kevin Drum using (ironically to try to make the point the US is worse than Europe, again by using relative poverty numbers).  I am sorry this data is old, but there is a long time-delay in the data source itself and I have not updated the analysis for a couple of years (on my to-do list, though).

Here are the US Bernie-Socialist favorites Denmark and Sweden:

I know progressives would argue that if you take more from the right end and give it to the left end, our poor would be even better off. But we have a control group for this -- Including Sweden and Denmark -- and that is clearly NOT the result one gets.  The problem with this theory is that forcible income redistribution policy and economic growth / prosperity are not independent variables. When you redistribute the pie, you get a smaller pie.

If one wishes to compare poverty across countries, the way to do it should be to compare the disposable incomes after taxes and transfers (adjusted for PPP) of the 10th or 20th income deciles in each country.  This seems obvious to me, after all we use the median (50th decile) income to compare prosperity across nations, so why not the same approach for poverty? But no one ever does it. My guess is the point is to exaggerate poverty in the US and understate it in socialist nations.

Update:  In related news:

Well, in 1820, 94 percent of the world’s population lived in extreme poverty (less than $1.90 per day adjusted for purchasing power). In 1990 this figure was 34.8 percent, and in 2015, just 9.6 percent.

In the last quarter century, more than 1.25 billion people escaped extreme poverty - that equates to over 138,000 people (i.e., 38,000 more than the Parisian crowd that greeted Father Wresinski in 1987) being lifted out of poverty every day. If it takes you five minutes to read this article, another 480 people will have escaped the shackles of extreme of poverty by the time you finish. Progress is awesome. In 1820, only 60 million people didn’t live in extreme poverty. In 2015, 6.6 billion did not.

I will refer you to my post last December on how much of society seems to hate short-sellers, and on some of the virtues of short selling.

For this post I just wanted to make a more narrow point -- one reason that unicorns (private startups with valuations north of $1 billion) like WeWork and Uber and Peloton had their valuations get so out of whack is because there is no way to short stocks in the private equity world.

Companies like Lyft and Uber and WeWork have seen private funding rounds at ever-increasing valuations.  These are done outside the accountability of the broader market and untethered to any sort of normal valuation metrics like earnings or even revenues.

Lots and lots of investors, perhaps the vast majority of them, believed that the last private round that valued WeWork at $45 billion was insane.  Many folks, including myself, would have gladly shorted the stock at this price had we been able.  Heck, many of us would have shorted back at $10 and $20 billion valuations for the company.  Because there is no short selling in this private equity world, unicorn valuations are +based on information from a very limited number of the most optimistic company supporters.  And because of this faulty price discovery, billions of capital that could be doing something more productive have been wasted in many of these companies, poured into business models that don't work or, worse, the tequila and drug fueled Gulfstream flights of the founders.

As I wrote over a decade ago, short selling broadens the group of people who can "vote" on a company's value

At the start of the bubble, a particular asset (be it an equity or a commodity like oil) is owned by a mix of people who have different expectations about future price movements.  For whatever reasons, in a bubble, a subset of the market develops rapidly rising expectations about the value of the asset.  They start buying the asset, and the price starts rising.  As the price rises, and these bulls buy in, folks who owned the asset previously and are less bullish about the future will sell to the new buyers.  The very fact of the rising price of the asset from this buying reinforces the bulls' feeling that the sky is the limit for prices, and bulls buy in even more.

Let's fast forward to a point where the price has risen to some stratospheric levels vs. the previous pricing as well as historical norms or ratios.  The ownership base for the asset is now disproportionately made up of those sky-is-the-limit bulls, while everyone who thought these guys were overly optimistic and a bit wonky have sold out. 99.9% of the world now thinks the asset is grossly overvalued.  But how does it come to earth?  After all, the only way the price can drop is if some owners sell, and all the owners are super-bulls who are unlikely to do so.  As a result, the bubble might continue and grow long after most of the world has seen the insanity of it.

Thus, we have short-selling.  Short-selling allows the other 99.9% who are not owners to sell part of the asset anyway, casting their financial vote for the value of the company.  Short-selling shortens bubbles, hastens the reckoning, and in the process generally reduces the wreckage on the back end.

I am not advocating some goofy plan to bring short-selling to private equity.  What I am saying is that prices set in markets with a robust ability to sell short are going to be much more trustworthy than prices set where short-selling is not an option.

From an interview the other day

Q I can read you the tweet, Mr. President. You said that, “Badly run and weak companies are smartly blaming these small tariffs instead of themselves…”

THE PRESIDENT: Yeah. A lot of badly run companies are trying to blame tariffs. In other words, if they’re running badly and they’re having a bad quarter, or if they’re just unlucky in some way, they’re likely to blame the tariffs. It’s not the tariffs. It’s called “bad management.”

The first answer to this is, LOL.  This is the man with a string of failed businesses (steaks, college) and multiple bankruptcies in his core business.  In fact, I would list one of Trump's most useful business skills is his ability to get other players in the capital structure to take the losses for his bad business decisions and management.

But as far as trade is concerned, if one is worried about bad management in US businesses, then the right thing is certainly not to protect those businesses from competition.  The US auto business in the 60's and 70's as well as almost the entirety of the British industrial base in the 20th century are good examples of the problem.  Protecting businesses from international competition, as is Trump's objective, only shelters those businesses from accountability and reduces the pressure to fix whatever bad management may exist.

As a special bonus, I would argue that many of the bad habits of large US companies today are directly attributable to the stimulative Federal Reserve policy which Trump wants to increase.  Returning profits to shareholders in the form of share buybacks rather than dividends is a perfectly valid strategy, particularly when the tax code favors capital gains over dividends.  But when companies borrow billions just to buy back more of their own stock, rather than reinvest it in new opportunities, something is broken.  A large part of the blame are twin Federal Reserve policies of low interest rates and a QE-created equity and asset price bubble.

Apparently California is close to a new law mandating that Uber drivers (and other "gig" economy workers) be treated as employees rather than independent contractors.  Progressives are cheering this as a victory for the drivers:

Billionaires who say they can’t pay minimum wages to their workers say they will spend tens of millions to avoid labor laws. Just pay your damn workers! https://t.co/PtyP4JZ7W0

— Lorena (@LorenaSGonzalez) August 29, 2019

I have explained before why this will likely kill Uber (e.g. here) but let me summarize quickly the argument of why this is bad for most drivers (self-plagiarized from a Twitter thread).  The key issues are driver productivity and driver agency.

Let's define worker productivity as far as Uber is concerned as the amount of customer revenue a driver brings in per paid hour. In the current model, this is not a real concern for Uber as they are only paying Uber drivers when they are actually driving customers.  Essentially, Uber drivers and Uber have a revenue share agreement to split customer revenue. Uber has set the share low enough to maximize its revenue (of course) but high enough to still attract drivers. It tweaks this formula fairly frequently.  Uber driver productivity as we have defined it is essentially locked in by the formulas in this revenue share agreement.

Given this arrangement, note what Uber does NOT have to worry about. It does not have to worry that drivers are working hard enough or are positioning themselves in productive locations and productive times of day.  Uber drivers can drive anywhere they want at any time they want.  An Uber driver currently can turn on the app at 4am in the suburbs of Peoria and Uber does not care, even if this positioning is unlikely to get many rides. Why? Because Uber only pays if there is a ride.  It doesn't care if the driver is sitting around unproductively, because it is not paying the driver for that time.

So today, it is left up to the driver to make trade-offs between the most productive time & positioning and the demands of their own personal schedule & life choices. This sort of flexibility has real value to many drivers. It is agency that many hourly workers don't have, and that has attracted many people to become Uber drivers.  My neighbor, for example, sits in his living room all day with the app on and runs out to the car whenever he accepts a ride (and then turns the app off so he can come back home).  He gets few rides in our area but he is happy with the lifestyle and the little bit of extra money he makes from Uber.

But this all changes if drivers must be Uber employees and subject to wage and hour laws.  The key difference under such wage and hour laws is that Uber would have to pay drivers whether they have a passenger or not, as long as the app is turned on.  Suddenly, forced to pay for labor whether the labor is working or not, Uber is going to get real interested in driver productivity.

If Uber pays by the hour, my neighbor's preferred way to drive is a dead loser for the company. In fact, if I am a driver and paid by the hour, I could go find a library in an out of the way place at an odd time of day and sit and read and collect hourly paychecks -- All without having to drive much. Now, instead of productivity choices being in the driver's hands because it's the driver that makes more or less money with greater or lesser productivity, these choices now land in Uber's lap. Uber can no longer allow so much driver agency.

If making Uber drivers hourly workers does not kill Uber altogether, then Uber is going to be forced to monitor driver productivity and do one or both of two things:

1. Establish productivity rules, such as driving time windows and allowed geographic ranges and/or
2. Set a minimum productivity threshold below which Uber will have to let those drivers go

Interestingly, like a lot of labor regulation, this one will benefit the middle while hurting the lower-paid drivers.

1. Top drivers will be unaffected, because they already make the minimum
2. Middle drivers may get a small boost
3. Lower-earning drivers will lose their driving jobs entirely

A better way to characterize this law is that it will greatly reduce the flexibility many Uber drivers love, while causing the lowest paid drivers not to make more, but to lose their driving gig altogether.

I wrote a great deal more about how much of labor regulation actually hurts the lowest rungs of unskilled workers in an article here for Regulation Magazine.

Tyler Cowen pointed out this article on the widening gap between white and black home ownership rates.  Black home ownership rates have fallen pretty steadily since the financial crisis -- apparently when banks are castigated by activists and government officials for "exploiting" blacks by giving them easy credit, blacks no longer get as much easy credit.

For people trying to rise in their economic status, there are a lot of things wrong with home ownership.  The most important is that it limits geographic flexibility.  Home owners have much higher costs to pick up and move, making it harder and less likely to exploit opportunities for better work and/or lower living costs in other parts of the country.   And as someone who just had an $8000 air conditioning unit fail in 110 degree heat, I can testify that home ownership also involves more risk of large unexpected expenses than does renting.  All things considered, in a free market, there are a lot of reasons home ownership might be a bad idea for folks trying to rise in income.

The complicating factor, as usual, is it is not a free market.  Public policy has tipped the scales such that home ownership has become probably the most important of all middle class savings vehicles.  Part of this is a human behavioral issue -- people contribute to homes every month because the bank makes damn sure that they do so (sort of like having a really tough personal trainer).  No other savings vehicle has such strong incentives not to cheat on monthly contributions.  But even so homes would still not be such a great investment vehicle.  In a 30 year mortgage, the percentage of your monthly payment in the early years that goes to equity is trivial.  There is really no reason that a home should be anything more than a depreciating asset, like a car or a boat.

Which brings us to the public policy angle -- a myriad of policy interventions all conspire to make sure that home prices rise continuously.  On the demand side, demand is subsidized via special government mortgage programs, special treatment for mortgages on bank balance sheets, the mortgage interest tax deduction, as well as a number of direct subsidies for lower income folks.  We even had QE where the government was buying up mortgage bonds to keep interest rates low.  On the supply side, supply is constrained through growth boundaries, density limits, zoning restrictions and a zillion other local regulations.  The net effect of this subsidized demand and constrained supply is (with a few interruptions) ever-rising prices.

While many of us decry crony capitalism, most every homeowner in this country (including me) is a crony.  We benefit from this program that like most all other crony capitalist programs, benefits incumbents at the expense of new entrants.  In this case, those of us with houses get to enjoy a good rate of return on our home investment while those without homes are shut out of the market by rising prices.

I want to put a couple of caveats on this post.  1) Though there are utilitarian arguments related to slavery in this post, by no means do I think they ever come close in magnitude to the basic fact of the moral outrage of slavery. 2) Though there are utilitarian arguments about reparations in this post, by no means do I think they ever come close in magnitude to the moral outrage of penalizing people for sins of their grandfathers.

The other day, in what I thought was a quick throwaway comment on Twitter to an NBC article that seemed to be supporting slavery reparations, I wrote

Hmm, pretty sure my family in Hamburg, who were pre-occupied in the late 19th century with getting out from under Prussian rule and heading for America, did not benefit from US slavery

— Coyoteblog (@Coyoteblog) July 8, 2019

One response I got which I want to address was this one:

Yes they did. They left their home country and came to America because the abundance of opportunities. Opportunities made possible by the years of free labor take a wild guess from who?

— Zavi (@Zaviuc) July 9, 2019

This notion that slavery somehow benefited the entire economy is a surprisingly common one and I want to briefly refute it.  This is related to the ridiculously bad academic study (discussed here) that slave-harvested cotton accounted for nearly half of the US's economic activity, when in fact the number was well under 10%.  I assume that activists in support of reparations are using this argument to make the case that all Americans, not just slaveholders, benefited from slavery.  But this simply is not the case.

At the end of the day, economies grow and become wealthier as labor and capital are employed more productively.  Slavery does exactly the opposite.

Slaves are far less productive that free laborers.  They have no incentive to do any more work than the absolute minimum to avoid punishment, and have zero incentive (and a number of disincentives) to use their brain to perform tasks more intelligently.  So every slave is a potentially productive worker converted into an unproductive one.  Thus, every dollar of capital invested in a slave was a dollar invested in reducing worker productivity.

As a bit of background, the US in the early 19th century had a resource profile opposite from the old country.  In Europe, labor was over-abundant and land and resources like timber were scarce.  In the US, land and resources were plentiful but labor was scarce.  For landowners, it was really hard to get farm labor because everyone who came over here would quickly quit their job and headed out to the edge of settlement and grabbed some land to cultivate for themselves.

In this environment the market was sending pretty clear pricing signals -- that it was simply not a good use of scarce labor resources to grow low margin crops on huge plantations requiring scores or hundreds of laborers.  Slave-owners circumvented this pricing signal by finding workers they could force to work for free.  Force was used to apply high-value labor to lower-value tasks.  This does not create prosperity, it destroys it.

As a result, whereas $1000 invested in the North likely improved worker productivity, $1000 invested in the South destroyed it.  The North poured capital into future prosperity. The South poured it into supporting a dead-end feudal plantation economy.  As a result the south was impoverished for a century, really until northern companies began investing in the South after WWII.  If slavery really made for so much of an abundance of opportunities, then why did very few immigrants in the 19th century go to the South?  They went to the industrial northeast or (as did my grandparents) to the midwest.  The US in the 19th century was prosperous despite slavery in the south, not because of it.

Apparently there is a new CBO report on the effect of a Federal minimum wage hike to $15.  Before I get into the economic impacts, I want to observe that the $15 Federal minimum wage is a political smart bomb that hits mostly red states in much the same way as the reduced Federal tax deductions for SALT (state and local taxes) was a smart bomb that mostly hit blue states.  From an equity standpoint it is insane to have the same minimum wage in rural Alabama as in San Francisco, but since its main negative employment effects will be in red states I think this may be a feature rather than a bug for Democrats.

Anyway, for years folks have made the argument that government-mandated minimum wages are necessary because of the power imbalance between employers and low-skill workers which allows employers to exercise monopsony power and keep wages below some theoretical market clearing price (which is a total laugh -- if you really believe this you can come to my company and try to hire for unskilled positions at the top of the economic cycle and see how much power we have).   The progressive theory is that companies therefore earn excess profits due to this power.

But that is almost impossible.  To actually profit from such power, a company would have to have a consumer monopoly and monopsony hiring power and those two are Venn diagrams that don't overlap much.  As I wrote before (excerpt from a much longer piece)

Let’s consider a company paying minimum wage to most of its employees.  At least at current minimum wage levels, minimum wage employees will likely be in low-skill positions, ones that require little beyond a high school education.  Almost by definition, firms that depend on low-skill workers to deliver their product or service have difficulty establishing barriers to competition. One can’t be doing anything particularly tricky or hard to copy relying on workers with limited skills. As soon as one firm demonstrates there is money to be made using low-skill workers in a certain way, it is far too easy to copy that model.    As a result, most businesses that hire low-skill workers will have had their margins competed down to the lowest tolerable level.  Firms that rely mainly on low-skill workers almost all have single digit profit margins probably averaging around 5% of revenues (for comparison, last year Microsoft had a pre-tax net income margin of over 23%).

If there were some margin windfall to be obtained from labor market power that allowed a company to hire people for far less than their labor was worth to it, and thus earn well above this lowest tolerable margin,  new companies would try to enter the market, probably by lowering prices to consumers using some of that labor premium.  Eventually, even if the monopsony premium exists, it is given away to consumers in the form of lower prices.  If the wholesale price of gasoline suddenly falls sharply, gasoline retailers don't get to earn a much higher margin, at least not for very long.  Competition quickly causes the retailer's lowered costs to be passed on to consumers in the form of lower retail prices.  The same goes for any lowering of labor costs due to monopsony power  -- if such a windfall exists, it is quickly passed on to consumers.

As a result, the least likely response to increasing labor costs due to regulation is that such costs will be offset out of profits, because for most of these firms, profits have already been competed down to the minimum necessary to cover capital investment and the minimum returns to keep owners interested in the business.

I have not read the CBO report.  Interestingly, apparently both Kevin Drum and CNBC have and they summarize the findings differently -- not just draw ideologically different conclusions but report the key data differently.  I have not made any attempt to reconcile this (my guess is that Drum has picked the most optimistic case).  But I will take this from Kevin Drum's version:

• Total wages for workers would rise by $44 billion (accounting for both higher wages and increased joblessness). Income for business owners would fall $14 billion.
• Consumers would pay higher prices amounting to a total of $39 billion. That’s an increase of about 0.3 percent.

You can see that the CBO obviously does not buy the progressive argument about excess corporate profits.  90% of the wage increase is paid for by consumers in the form of higher prices.  My bet is that most of the business income loss is not margin compression as much as lost sales due to higher prices.  Note also the inefficiency of the minimum wage even in these optimistic numbers -- consumers and businesses contribute $53 billion in value to increase wages by $44 billion.  The rest is a net loss to the economy and my bet is that these numbers underestimate this loss.

The other problem with minimum wage increases as an anti-poverty program is that people are in the bottom 20 percentile of earnings mostly due to insufficient work hours, not due to wage rates.  It turns out that increasing the wage rates of the poorest 20% to middle class levels yields $6,335 a year in gains for a person in the poorest 20% while increasing that same poor person's amount of work done to middle class levels yields $28,844 a year in gains (government data here).  If you want to help poor people, economic growth and reducing barriers to hiring low-skill workers (combined with efficient transfer programs) is the way to go -- in this context the minimum wage increase can actually be counter-productive.

One other reason minimum wage increases are a bad anti-poverty program is that most of the data I have seen points to about a third of minimum wage jobs held by earners in families below the poverty line. So 2/3 of the increased wages from a minimum wage increase go to non-poor households

Last summer I had the cover story in Regulation Magazine titled, "How Labor Regulation Harms Unskilled Workers." I fear we are heading to a European model of very high minimum costs of employing anyone, which tends to result in a two-tier system of well-paying jobs for skilled and educated employees and lifetime government relief for the unskilled and under-educated.

From a very dedicated reader (and Boing Boing)

East West Market in Vancouver, B.C. had a terrific idea to get people to start bringing their own reusable shopping bags: design plastic bags with messages too embarrassing to carry. Unfortunately, while hilarious, it's backfiring. They made them too good and now everyone wants a set of them! Collect all three: the Colon Care Co-op, Into The Weird Adult Video Emporium, and Dr. Toews' Wart Ointment Wholesale.

The bags are great, I will let you click through to see them

It's shrinking, but only because folks are getting wealthier.

via the always terrific Mark Perry

I thought this study was very interesting, as highlighted by Alex Tabarrok.  I usually try not to publish highlights from research papers without actually reading them (the divergence between press releases on papers and the papers themselves is shocking and constitutes what may be one of the worst current academic practices).  But in this case I have learned to trust Mr. Tabarrok's judgement:

The best paper I have read in a long time is Hopenhayn, Neira and Singhania’s From Population Growth to Firm Demographics: Implications for Concentration, Entrepreneurship and the Labor Share. HNS do a great job at combining empirics and theory to explain an important fact about the world in an innovative and surprising way. The question the paper addresses is, Why is dynamism declining? As you may recall, my paper with Nathan Goldschlag, Is regulation to blame for the decline in American entrepreneurship?, somewhat surprisingly answered that the decline in dynamism was too widespread across too many industries to be explained by regulation. HNS point to a factor which is widespread across the entire economy, declining labor force growth.

Figure Two of the paper (at right) looks complicated but it tells a consistent and significant story. The top row of the figure shows three measures of declining dynamism: the rise in concentration which is measured as the share of employment accounted for by large (250+) firms, the increase in average firm size, and the declining exit rate. The bottom row of the figure shows the same measures but this time conditional on firm age. What we see in the bottom figure is two things. First, most of the lines jump around a bit but are generally flat or not increasing. In other words, once we control for firm age we do not see, for example, increasing concentration. Peering closer at the bottom row the second thing it shows is that older firms account for a larger share of employment, are bigger and have lower exit rates. Putting these two facts together suggests that we might be able to explain all the trends in the top row by one fact, aging firms.

So what explains aging firms? Changes in labor force growth have a big influence on the age distribution of firms. Assume, for example, that labor force growth increases. An increase in labor force growth means we need more firms. Current firms cannot absorb all new workers because of diminishing returns to scale. Thus, new workers lead to new firms. New firms are small and young. In contrast, declining labor force growth means fewer new firms. Thus, the average firm is bigger and older.

Today I want to come back to a topic I have not covered for a while, which is what I call knowledge or certainty "laundering" via computer models.  I will explain this term more in a moment, but I use it to describe the use of computer models (by scientists and economists but with strong media/government/activist collusion) to magically convert an imperfect understanding of a complex process into apparently certain results and predictions to two-decimal place precision.

The initial impetus to revisit this topic was reading "Chameleons: The Misuse of Theoretical Models in Finance and Economics" by Paul Pfleiderer of Stanford University (which I found referenced in a paper by Anat R. Admati on dangers in the banking system).  I will except this paper in a moment, and though he is talking more generically about theoretical models (whether embodied in code or not), I think a lot of his paper is relevant to this topic.

Before we dig into it, let's look at the other impetus for this post, which was my seeing this chart in the "Southwest" section of the recent Fourth National Climate Assessment.

The labelling of the chart actually understates the heroic feat the authors achieved as their conclusion actually models wildfire with and without anthropogenic climate change.  This means that first they had to model the counterfactual of what the climate could have been like without the 30ppm (0.003% of the atmosphere) CO2 added in the period.  Then, they had to model the counterfactual of what the wildfire burn acreage would have been under the counter-factual climate vs. what actually occurred.   All while teasing out the effects of climate change from other variables like forest management and fuel reduction policy (which --oddly enough -- despite substantial changes in this period apparently goes entirely unmentioned in the underlying study and does not seem to be a variable in their model).  And they do all this for every year back to the mid-1980's.

Don't get me wrong -- this is a perfectly reasonable analysis to attempt, even if I believe they did it poorly and am skeptical you can get good results in any case (and even given the obvious fact that the conclusions are absolutely not testable in any way).  But any critique I might have is a normal part of the scientific process.  I critique, then if folks think it is valid they redo the analysis fixing the critique, and the findings might hold or be changed.  The problem comes further down the food chain:

1. When the media, and in this case the US government, uses this analysis completely uncritically and without any error bars to pretend at certainty -- in this case that half of the recent wildfire damage is due to climate change -- that simply does not exist
2. And when anything that supports the general theory that man-made climate change is catastrophic immediately becomes -- without challenge or further analysis -- part of the "consensus" and therefore immune from criticism.

I like to compare climate models to economic models, because economics is the one other major field of study where I think the underlying system is as nearly complex as the climate.  Readers know I accept that man is causing some warming via CO2 -- I am a lukewarmer who has proposed a carbon tax.  However, as an engineer whose undergraduate work focused on the dynamics of complex systems, I go nuts with anti-scientific statements like "Co2 is the control knob for the Earth's climate."  It is simply absurd to say that an entire complex system like climate is controlled by a single variable, particularly one that is 0.04% of the atmosphere.  If a sugar farmer looking for a higher tariff told you that sugar production was the single control knob for the US climate, you would call BS on them in a second (sugar being just 0.015% by dollars of a tremendously complex economy).

But in fact, economists play at these same sorts of counterfactuals.  I wrote about economic analysis of the effects of the stimulus way back in 2010.  It is very similar to the wildfire analysis above in that it posits a counter-factual and then asserts the difference between the modeled counterfactual and reality is due to one variable.

Last week the Council of Economic Advisors (CEA) released its congressionally commissioned study on the effects of the 2009 stimulus. The panel concluded that the stimulus had created as many as 3.6 million jobs, an odd result given the economy as a whole actually lost something like 1.5 million jobs in the same period. To reach its conclusions, the panel ran a series of complex macroeconomic models to estimate economic growth assuming the stimulus had not been passed. Their results showed employment falling by over 5 million jobs in this hypothetical scenario, an eyebrow-raising result that is impossible to verify with actual observations.

Most of us are familiar with using computer models to predict the future, but this use of complex models to write history is relatively new. Researchers have begun to use computer models for this sort of retrospective analysis because they struggle to isolate the effect of a single variable (like stimulus spending) in their observational data. Unless we are willing to, say, give stimulus to South Dakota but not North Dakota, controlled experiments are difficult in the macro-economic realm.

But the efficacy of conducting experiments within computer models, rather than with real-world observation, is open to debate. After all, anyone can mine data and tweak coefficients to create a model that accurately depicts history. One is reminded of algorithms based on skirt lengths that correlated with stock market performance, or on Washington Redskins victories that predicted past presidential election results.

But the real test of such models is to accurately predict future events, and the same complex economic models that are being used to demonstrate the supposed potency of the stimulus program perform miserably on this critical test. We only have to remember that the Obama administration originally used these same models barely a year ago to predict that unemployment would remain under 8% with the stimulus, when in reality it peaked over 10%. As it turns out, the experts' hugely imperfect understanding of our complex economy is not improved merely by coding it into a computer model. Garbage in, garbage out.

Thus we get to my concept I call knowledge laundering or certainty laundering.  I described what I mean by this back in the blogging dinosaur days (note this is from 2007 so my thoughts on climate have likely evolved since then).

Remember what I said earlier: The models produce the result that there will be a lot of anthropogenic global warming in the future because they are programmed to reach this result. In the media, the models are used as a sort of scientific money laundering scheme. In money laundering, cash from illegal origins (such as smuggling narcotics) is fed into a business that then repays the money back to the criminal as a salary or consulting fee or some other type of seemingly legitimate transaction. The money he gets
back is exactly the same money, but instead of just appearing out of nowhere, it now has a paper-trail and appears more legitimate. The money has been laundered.

In the same way, assumptions of dubious quality or certainty that presuppose AGW beyond the bounds of anything we have see historically are plugged into the models, and, shazam, the models say that there will be a lot of anthropogenic global warming. These dubious assumptions, which are pulled out of thin air, are laundered by being passed through these complex black boxes we call climate models and suddenly the results are somehow scientific proof of AGW. The quality hasn't changed, but the paper trail looks better, at least in the press. The assumptions begin as guesses of dubious quality and come out laundered at "settled science."

Back in 2011, I highlighted a climate study that virtually admitted to this laundering via model by saying:

These question cannot be answered using observations alone, as the available time series are too short and the data not accurate enough. We therefore used climate model output generated in the ESSENCE project, a collaboration of KNMI and Utrecht University that generated 17 simulations of the climate with the ECHAM5/MPI-OM model to sample the natural variability of the climate system. When compared to the available observations, the model describes the ocean temperature rise and variability well.”

I wrote in response:

[Note the first and last sentences of this paragraph]  First, that there is not sufficiently extensive and accurate observational data to test a hypothesis. BUT, then we will create a model, and this model is validated against this same observational data. Then the model is used to draw all kinds of conclusions about the problem being studied.

This is the clearest, simplest example of certainty laundering I have ever seen. If there is not sufficient data to draw conclusions about how a system operates, then how can there be enough data to validate a computer model which, in code, just embodies a series of hypotheses about how a system operates?

A model is no different than a hypothesis embodied in code. If I have a hypothesis that the average width of neckties in this year’s Armani collection drives stock market prices, creating a computer program that predicts stock market prices falling as ties get thinner does nothing to increase my certainty of this hypothesis (though it may be enough to get me media attention). The model is merely a software implementation of my original hypothesis. In fact, the model likely has to embody even more unproven assumptions than my hypothesis, because in addition to assuming a causal relationship, it also has to be programmed with specific values for this correlation.

This brings me to the paper by Paul Pfleiderer of Stanford University.  I don't want to overstate the congruence between his paper and my thoughts on this, but it is the first work I have seen to discuss this kind of certainty laundering (there may be a ton of literature on this but if so I am not familiar with it).  His abstract begins:

In this essay I discuss how theoretical models in finance and economics are used in ways that make them “chameleons” and how chameleons devalue the intellectual currency and muddy policy debates. A model becomes a chameleon when it is built on assumptions with dubious connections to the real world but nevertheless has conclusions that are uncritically (or not critically enough) applied to understanding our economy.

The paper is long and nuanced but let me try to summarize his thinking:

In this essay I discuss how theoretical models in finance and economics are used in ways that make them “chameleons” and how chameleons devalue the intellectual currency and muddy policy debates. A model becomes a chameleon when it is built on assumptions with dubious connections to the real world but nevertheless has conclusions that are uncritically (or not critically enough) applied to understanding our economy....

My reason for introducing the notion of theoretical cherry picking is to emphasize that since a given result can almost always be supported by a theoretical model, the existence of a theoretical model that leads to a given result in and of itself tells us nothing definitive about the real world. Though this is obvious when stated baldly like this, in practice various claims are often given credence — certainly more than they deserve — simply because there are theoretical models in the literature that “back up” these claims. In other words, the results of theoretical models are given an ontological status they do not deserve. In my view this occurs because models and specifically their assumptions are not always subjected to the critical evaluation necessary to see whether and how they apply to the real world...

As discussed above one can develop theoretical models supporting all kinds of results, but many of these models will be based on dubious assumptions. This means that when we take a bookshelf model off of the bookshelf and consider applying it to the real world, we need to pass it through a filter, asking straightforward questions about the reasonableness of the assumptions and whether the model ignores or fails to capture forces that we know or have good reason to believe are important.

I know we see a lot of this in climate:

A chameleon model asserts that it has implications for policy, but when challenged about the reasonableness of its assumptions and its connection with the real world, it changes its color and retreats to being a simply a theoretical (bookshelf) model that has diplomatic immunity when it comes to questioning its assumptions....

Chameleons arise and are often nurtured by the following dynamic. First a bookshelf model is constructed that involves terms and elements that seem to have some relation to the real world and assumptions that are not so unrealistic that they would be dismissed out of hand. The intention of the author, let’s call him or her “Q,” in developing the model may be to say something about the real world or the goal may simply be to explore the implications of making a certain set of assumptions. Once Q’s model and results become known, references are made to it, with statements such as “Q shows that X.” This should be taken as short-hand way of saying “Q shows that under a certain set of assumptions it follows (deductively) that X,” but some people start taking X as a plausible statement about the real world. If someone skeptical about X challenges the assumptions made by Q, some will say that a model shouldn’t be judged by the realism of its assumptions, since all models have assumptions that are unrealistic. Another rejoinder made by those supporting X as something plausibly applying to the real world might be that the truth or falsity of X is an empirical matter and until the appropriate empirical tests or analyses have been conducted and have rejected X, X must be taken seriously. In other words, X is innocent until proven guilty. Now these statements may not be made in quite the stark manner that I have made them here, but the underlying notion still prevails that because there is a model for X, because questioning the assumptions behind X is not appropriate, and because the testable implications of the model supporting X have not been empirically rejected, we must take X seriously. Q’s model (with X as a result) becomes a chameleon that avoids the real world filters.

Check it out if you are interested.  I seldom trust a computer model I did not build and I NEVER trust a model I did build (because I know the flaws and assumptions and plug variables all too well).

By the way, the mention of plug variables reminds me of one of the most interesting studies I have seen on climate modeling, by Kiel in 2007.  It was so damning that I haven't seen anyone do it since (at least get published doing it).  I wrote about it in 2011 at Forbes:

My skepticism was increased when several skeptics pointed out a problem that should have been obvious. The ten or twelve IPCC climate models all had very different climate sensitivities -- how, if they have different climate sensitivities, do they all nearly exactly model past temperatures? If each embodies a correct model of the climate, and each has a different climate sensitivity, only one (at most) should replicate observed data. But they all do. It is like someone saying she has ten clocks all showing a different time but asserting that all are correct (or worse, as the IPCC does, claiming that the average must be the right time).

The answer to this paradox came in a 2007 study by climate modeler Jeffrey Kiehl. To understand his findings, we need to understand a bit of background on aerosols. Aerosols are man-made pollutants, mainly combustion products, that are thought to have the effect of cooling the Earth's climate.

What Kiehl demonstrated was that these aerosols are likely the answer to my old question about how models with high sensitivities are able to accurately model historic temperatures. When simulating history, scientists add aerosols to their high-sensitivity models in sufficient quantities to cool them to match historic temperatures. Then, since such aerosols are much easier to eliminate as combustion products than is CO2, they assume these aerosols go away in the future, allowing their models to produce enormous amounts of future warming.

Specifically, when he looked at the climate models used by the IPCC, Kiehl found they all used very different assumptions for aerosol cooling and, most significantly, he found that each of these varying assumptions were exactly what was required to combine with that model's unique sensitivity assumptions to reproduce historical temperatures. In my terminology, aerosol cooling was the plug variable.

When I was active doing computer models for markets and economics, we used the term "plug variable."  Now, I think "goal-seeking" is the hip word, but it is all the same phenomenon.

Postscript, An example with the partisans reversed:  It strikes me that in our tribalized political culture my having criticised models by a) climate alarmists and b) the Obama Administration might cause the point to be lost on the more defensive members of the Left side of the political spectrum.  So let's discuss a hypothetical with the parties reversed.  Let's say that a group of economists working for the Trump Administration came out and said that half of the 4% economic growth we were experiencing (or whatever the exact number was) was due to actions taken by the Trump Administration and the Republican Congress.  I can assure you they would have a sophisticated computer model that would spit out this result -- there would be a counterfactual model of "with Hillary" that had 2% growth compared to the actual 4% actual under Trump.

Would you believe this?  After all, its science.  There is a model.  Made by experts ("top men" as they say in Raiders of the Lost Ark).  Do would you buy it?  NO!  I sure would not.  No way.  For the same reasons that we shouldn't uncritically buy into any of the other model results discussed -- they are building counterfactuals of a complex process we do not fully understand and which cannot be tested or verified in any way.  Just because someone has embodied their imperfect understanding, or worse their pre-existing pet answer, into code does not make it science.  But I guarantee you have nodded your head or even quoted the results from models that likely were not a bit better than the imaginary Trump model above.

A while back I wrote:

Is it possible that inflation exists but it shows up mainly in financial assets (stocks, bonds, perhaps real estate) that don't really factor into standard inflation metrics?  Every step the Fed has taken, as well as other western central banks, appears to me to be crafted to pump money into securities markets rather than into main street.  Certainly we have seen a huge inflation in the value of financial assets and real estate over the past several years.

It was an honest question -- I am not an economist.  Business school gives one a pretty good working knowledge of micro but macro is usually outside my ken.  However, I see this is not a new idea and others make the same point.  Saw this chart on the Zero Hedge Twitter feed

I will add that progressives want to use this data to make some sort of fairness / income inequality point about wages vs. rich people's asset holdings, but this chart is not a natural result of unbridled capitalism.  It is the predictable result, even the desired result by its creators, of Fed policy in general and quantitative easing in particular.

In part 1, we discussed different ways of measuring income mobility and income growth for the poor, and discovered that many traditional measurement approaches are overly pessimistic -- when one focuses on actual individuals, instead of income quintiles, income for the poor has improved substantially.

Unlike some libertarians, I don't have a problem with intelligently structured income transfer and safety net programs to help the very poor. In fact, I believe that such income transfer programs can be far less distortive and economically inefficient that many other anti-poverty programs.  One of the latter I will focus on in this article is the minimum wage.

Each year, Mark Perry puts together an awesome demographic snapshot of how various income quintiles differ from each other.  Here is his latest:

I want to first call your attention to the figures at the top for mean number of earners per household and household income per earner.  Much of anti-poverty policy seems to be based on the assumption that poor people, because they lack bargaining power, get hosed on wages and other work rules.  Public policy thus tends to focus on minimum wage and overtime rules and a myriad of other workplace interventions.

But in fact, if we compare the lowest quintile with the middle quintile in the chart above, we see something very different.  What we see is the main difference is hours worked, not the relative wage rates.  Let's consider two scenarios

1. We keep the amount of work done the same, but raise the wage rates of the poorest quintile to the middle quintile.  In this case, their average income would go up by about 50% from $12,319 to $18,654  (calculated as 0.41 mean earners times middle income per earner of $45,497).
2. We keep wage rates the same but raise the amount of work done in the poorest quintile household to that of the middle quintile. In this case, their average income more than triples from $12,319 to $41,163 (calculated as 1.37 middle income earners times poor income per earner of $30,046)

So in this example, increasing the poor's wage rates to middle class levels yields $6,335 a year while increasing the poor's amount of work done to middle class levels yields $28,844 a year.  Public policy that focuses on increasing work hours for the poor has 4.5 times the effect of public policy focused on wage rates.  A corollary to this is that any public intervention on wage rates for the poor that has negative employment effects is likely to have little net effect on poverty.  

But in fact this understates the relative benefits of approach #2. Look at the education levels in the poorest quintile vs. the middle.  The poorest quintile has 2.5 times as many people without even a high school degree as in the middle.   For these folks to progress, the only way they can develop skills is on a job and they can't do this without a job.  Or said another way, another advantage of approach #2 and getting them more hours of work is that they gain more skills to overcome their starting disadvantage in education.

I wrote about this in the summer issue of Regulation magazine, in a article entitled "How Labor Regulation Harms Unskilled Labor."  I argued that while likely intended to help the very poor, most labor regulation may be harming the poor, particularly those without skills or much experience, by making it harder and harder for them to find work.  This not only impoverishes them, but makes it harder for them to progress to better jobs and higher income levels.

In my business,which staffs and operates public campgrounds, I employ about 350 people in unskilled labor positions, most at wages close to the minimum wage. I had perhaps 40 job openings last year and over 25,000 applications for those jobs. I am flooded with people begging to work and I have many people asking for our services. But I have turned away customers and cut back on operations in certain states like California. Why? Because labor regulation is making it almost impossible to run a profitable, innovative business based on unskilled labor.

Why is this important? Why can’t everyone just go to college and be a programmer at Google? Higher education has indeed been one path by which people gain skills and opportunity, but until recently it has never been the most common. Most skilled workers started as unskilled workers and gained their skills through work. But this work-based learning and advancement path is broken without that initial unskilled job. For people unwilling, unable, or unsuited to college, the loss of unskilled work removes the only route to prosperity.

...the mass of government labor regulation is making it harder and harder to create profitable business models that employ unskilled labor. For those without the interest or ability to get a college degree, the avoidance of the unskilled by employers is undermining those workers’ bridge to future success, both in this generation and the next.

Public policy could best help the poor by lowering the regulatory barriers to hiring unskilled labor and promoting economic growth that will help keep us close to full employment.

Part 1 of this series was here.

Postscript:  This update on the Seattle minimum wage study is interesting.  Note that this study is occuring near peak employment, a time when one would expect the minimum employment impact from a minimum wage increase.  However, I do think the findings are roughly consistent with the discussion above:

In their latest paper, which has not been formally peer reviewed, Mr. Vigdor and his colleagues considered how the minimum-wage increases affected three broad groups: People in low-wage jobs who worked the most during the nine months leading up to and including the quarter in which the increase took effect (more than about 600 or 700 hours, depending on the year); people who worked less during that nine-month period (fewer than 600 or 700 hours); and people who didn’t work at all and hadn’t during several previous years, but might later work. The latter were potential “new entrants” to the ranks of the employed, in the authors’ words.

The workers who worked the most ahead of the minimum-wage increase appeared to do the best. They saw a significant increase in their wages and only a small percentage decrease in their hours, leading to a healthy bump in overall pay — an average of $84 a month for the nine months that followed the 2016 minimum-wage increase.

The workers who worked less in the months before the minimum-wage increase saw almost no improvement in overall pay — $4 a month on average over the same period, although the result was not statistically significant. While their hourly wage increased, their hours fell substantially. (That doesn’t mean they were no better off, however. Earning roughly the same wage while working fewer hours is a trade most workers would accept.)

It’s the final group of workers — the potential new entrants who were not employed at the time of the first minimum-wage increase — that Mr. Vigdor and his colleagues believe fared the worst. They note that, at the time of the first increase, the growth rate in new workers in Seattle making less than $15 an hour flattened out and was lagging behind the growth rate in new workers making less than $15 outside Seattle’s county. This suggests that the minimum wage had priced some workers out of the labor market, according to the authors.

“For folks trying to get a job with no prior experience, it might have been worth hiring and training them when the going rate for them was $10 an hour,” Mr. Vigdor speculated, but perhaps not at $13 an hour.

I would add as an aside that I think the NYT is being a bit arrogant an narrowly focused on money (vs. other benefits of employment) when they added the parenthetical phrase at the end of the third paragraph.

This is part 1 of a 2 part series.  Part 2 is here. 

The typical way of talking about income mobility and inequality is to look at the relative income and growth rates of different income quartiles.  Here is a chart used recently by Kevin Drum over at Mother Jones:

As you can see, since the three lines here have their steepest upwards slopes at different times, there are a myriad of possibilities for cherry-picking endpoints to make whatever point you want to make.  I would say this is a pretty healthy picture, with real income growth for everyone, though the general flatness of the middle income brackets since 2007 seems to get the most notice.

A healthy and growing economy should cause all of these quintiles to grow. But income growth and mobility for individuals is about more than just the quintiles.  As a small business owner over the last 20 years, I have had tax returns in all five quintiles -- I have had blowout years when I was "rich" and I have had years when my taxable income would qualify me for food stamps.  In other words, I move between the lines -- and so do a lot of other people.

Young people gaining experience and promotions move from lower to higher quintiles as they age.  New immigrants often come in at the bottom and progress over time.  When people retire, they may fall down a few quintiles.  Marriage might kick someone up to a higher quintile, and divorce may lead to them falling down a few.  There is a constant ebb and flow that is hidden by merely looking at quintiles.

Russ Roberts, who seems to be the token non-socialist at Medium, writes:

but the biggest problem with the pessimistic studies is that they rarely follow the same people to see how they do over time. Instead, they rely on a snapshot at two points in time. So for example, researchers look at the median income of the middle quintile in 1975 and compare that to the median income of the median quintile in 2014, say. When they find little or no change, they conclude that the average American is making no progress.

But the people in the snapshots are not the same people. These snapshots fail to correct for changes in the composition of workers and changes in household structure that distort the measurement of economic progress. There is immigration. There are large changes in the marriage rate over the period being examined. And there is economic mobility as people move up and down the economic ladder as their luck and opportunities fluctuate.

Roberts describes several studies that follow actual people, not quintiles, and finds that the American ideal of income mobility is still alive and well

This first study, from the Pew Charitable Trusts, conducted by Leonard Lopoo and Thomas DeLeire uses the Panel Study of Income Dynamics (PSID) and compares the family incomes of children to the income of their parents.⁴ Parents income is taken from a series of years in the 1960s. Children’s income is taken from a series of years in the early 2000s. As shown in Figure 1, 84% earned more than their parents, corrected for inflation. But 93% of the children in the poorest households, the bottom 20%, surpassed their parents. Only 70% of those raised in the top quintile exceeded their parent’s income.

... Julia Isaacs’s study for the Pew Charitable Trusts finds that children raised in the poorest families made the largest gains as adults relative to children born into richer families.

The children from the poorest families ended up twice as well-off as their parents when they became adults. The children from the poorest families had the largest absolute gains as well. Children raised in the top quintile did no better or worse than their parents once those children became adults.

He has a lot more at the link.

In part 2, I will discuss why public policy is missing the boat, and in some cases doing exactly the wrong thing, to promote income mobility particularly for the poor.

From a fascinating article on Amazon and its automation vision:

After a customer places an order, a robot carrying the desired item scoots over to a worker, who reads on a screen what item to pick and what cubby it’s located in, scans a bar code and places the item in a bright-yellow bin that travels by conveyor belt to a packing station. AI suggests an appropriate box size; a worker places the item in the box, which a robot tapes shut and, after applying a shipping label, sends on its way. Humans are needed mostly for grasping and placing, tasks that robots haven’t mastered yet.

Amazon’s robots signal a sea change in how the things we buy will be aggregated, stored and delivered. The company requires one minute of human labor to get a package onto a truck, but that number is headed to zero. Autonomous warehouses will merge with autonomous manufacturing and delivery to form a fully automated supply chain.

I got some cr*p on twitter a while back about writing this, but I think it is pretty much vindicated by the "one minute" factoid above:

Amazon likely is being pressured by the tightening labor market to raise wages anyway.  But its call for a general $15 minimum wage is strategically brilliant.  The largest employers of labor below $15 are Amazon's retail competitors.  If Amazon is successful in getting a $15 minimum wage passed, all retailers will see their costs rise but Amazon's competition will be hit much harder.

John Hinderaker of Powerline writes approvingly of Trump's apparent trade deal with Mexico.  First, he quotes the New York Times celebrating the higher taxes:

Under the changes agreed to by Mexico and the United States, car companies would be required to manufacture at least 75 percent of an automobile’s value in North America under the new rules, up from 62.5 percent, to qualify for Nafta’s zero tariffs. They will also be required to use more local steel, aluminum and auto parts, and have 40 to 45 percent of the car made by workers earning at least $16 an hour, a boon to both the United States and Canada and a win for labor unions, which have been among Nafta’s biggest critics.

I am not sure how narrowing the scope of products subject to lower taxes is a "boon" to this country, though I suppose labor unions might be happy and one is suspicious that this is sufficient reason for the NYT to support it.  My suspicion is that these numbers are incredibly carefully tailored by Ford and GM lobbyists to hit a couple of their competitors while missing themselves -- this has all the fingerprints of a classic crony deal that benefits very few powerful groups to the detriment of most consumers.

So the NYT can be expected to cheer for bad crony economics that helps a few unions, but what about Conservatives, who are supposed to understand markets and trade.  Hinderaker writes:

So, from 62.5% to 75% to qualify for zero tariffs. Not exactly radical, but positive.

So broadening a US government tax on US consumers is "positive."  Powerline in the past has rightfully chided Paul Krugman for abandoning his understanding of economics in favor of cheerleading the Democratic team.  Now Powerline is doing the same for Trump.

Tyler Cowen asks, "Why isn’t inflation higher?"  I have wondered that for a while, but monetary policy and related topics in macro are one of the areas I admit that I simply do not understand so I don't write about it.  So rather than offering any hypotheses to Cowen's question, I will ask my own:

1. Is it possible that inflation exists but it shows up mainly in financial assets (stocks, bonds, perhaps real estate) that don't really factor into standard inflation metrics?  Every step the Fed has taken, as well as other western central banks, appears to me to be crafted to pump money into securities markets rather than into main street.  Certainly we have seen a huge inflation in the value of financial assets and real estate over the past several years.
2. Expansion of the economy above the rate of productivity improvement should drive inflation, unless there was a lot of excess capacity to soak up.   That may have been partly the case in the US since 2008, but surely that is gone.  Does the still greatly underutilized Chinese and Indian labor force act as excess capacity that prevents inflation from heating up here?  If so, might Trump's trade restrictions interfere with this going forward?

From an article about how China's decision to restrict imports of recyclable materials is throwing the recycling industry for a loop:

The trash crunch is compounded by the fact that many cities across the country are already pursuing ambitious recycling goals. Washington D.C., for example, wants to see 80% of household waste recycled, up from 23%.

D.C. already pays $75 a ton for recycling vs. $46 for waste burned to generate electricity.

"There was a time a few years ago when it was cheaper to recycle. It's just not the case anymore," said Christopher Shorter, director of public works for the city of Washington.

"It will be more and more expensive for us to recycle," he said.

Which raises the obvious question:  If it is more expensive, why do you do it?  The one word answer would be "sustainability" -- but does that really make sense?

Sustainability is about using resources in a way that can be reasonably maintained into the future.  This is pretty much impossible to really model, but that is not necessary for a decision at the margin such as recycling in Washington DC.  When people say "sustainable" at the margin, they generally mean that fewer scarce resources are used, whether those resources be petroleum or landfill space.

Gosh, if only we had some sort of simple metric that summarized the value of the time and resources that go into a service like recycling or garbage disposal.  Wait, we do!  This metric is called "price".  Now, we could have a nice long conversation about pricing theory and whether or not prices always mirror costs.  But in a free competitive market, most prices will be a good proxy for the relative scarcity (or projected scarcity) of resources.  Now, I am going to assume the numbers for DC are correct and are worked out intelligently (ie the cost of recycling should be net of the value of materials recovered, and the cost of burning the trash should be net of the value of the electricity generated).   Given this, recycling at $75 a ton HAS to be less "sustainable" than burning trash at $46 since it either consumes more resources or it consumes resources with a higher relative scarcity or both.

Postscript:  I have had students object to this by saying, well, those costs include a lot of labor and that doesn't count, sustainability is just about materials.  If this is really how sustainability is defined, then it is an insane definition.  NOTHING is more scarce or valuable than human time.  We have no idea, really, how much recoverable iron or oil there is in the world (and in fact history shows we systematically always tend to underestimate the amount).  But we do know for an absolute fact that there are 182.4 billion human hours lived in a given day. Period.  Labor is if anything more important than material in any sustainability question (after all, would you be willing to die a year earlier in exchange for there being more iron in the world?  I thought not.)

In fact, it is probably the changing scarcity and value of labor in China that is driving the issues in this article in the first place.  China can't afford the labor any more to re-sort badly sorted American recyclables, likely because the economic boom in China has created much more useful and valuable things for Chinese workers to do than separate cardboard boxes from foam peanuts.  Another way to think of the market wage rate is as the opportunity cost for labor, ie if you use an hour of labor for to do X, what is the value of production you are giving up somewhere else by their no longer having access to this hour of labor.

I have written the cover story for the Summer 2018 issue of Regulation magazine, titled "How Labor Regulation Harms Unskilled Workers."  The link to the Summer 2018 issue is here and the article can be downloaded as a pdf here.  I meant to be a bit more prepared for this but it was originally slated for the Spring issue and it (rightly) got kicked to the later issue to add a more timely article on tariffs and trade.  The summer publication date sort of snuck up on me until I saw that Walter Olson linked it.

FAQ  (I will keep adding to this as I get questions)

How did a random non-academic dude get published in a magazine for policy wonks? This piece started well over  a year ago, back when my friend Brink Lindsey was still at Cato (he has since moved to the Niskanen Center).   I had told him once that I was spending so much of my personal time responding to regulatory changes affecting my company that I had little time to actually focus on improving my business.  I joked that we were approaching the regulatory singularity when regulations were added faster than I could comply with them.

Brink asked that I write something on small business and regulation.  After about 10 minutes staring at a blank document in Word, I realized that was way too broad a topic.  I decided that the one area I knew well, at least in terms of compliance costs, was labor regulation.  After some work, I eventually narrowed that to the final topic, the effect (from a business owner's perspective who had to manage compliance) of labor regulation on unskilled labor.

Once I finished, I was ready to just give up and publish the piece on my blog.  I sent it to Brink but told him I thought it was way too rough for publication.  He told me that he had seen many good published pieces that looked far worse in their early drafts, so I buckled down and cleaned it up.  My editor at Regulation took on the heroic task of getting the original monstrosity tightened down to something about half the length.  As with most good editing processes, the piece was much better with half the words gone.

The real turning point for me was advice I got from Walter Olson of Cato.  I "know" Walter purely from blogging but I love his work and had been a substitute blogger at Overlawyered in its early years.  At one point, I was really struggling with this article because I kept feeling the need to address the broader viability of the minimum wage and the academic literature that surrounds it.  But I am not an academic, and I have not done the research and I was not even familiar with the full body of literature on the subject.  Walter's advice boiled down to the age-old adage of "write what you know."  He encouraged me to focus narrowly on how a business has to respond to labor regulation, and how these responses might effect the employment and advancement prospects of unskilled workers.  As such, then, the paper evolved away from a comprehensive evaluation of minimum wages as a policy choice (a topic I have opinions about but I don't have the skills to publish on) into a (useful, I think) review of one aspect of minimum wage policy, a contribution to the discussion, so to speak.

Update:  Eek, I forgot since I started this so long ago.  I also owe a debt of gratitude to about 8 of our blog readers who own businesses and volunteered to be interviewed for this article so I could make sure I was being comprehensive.

There are many positive (or negative) aspects of labor regulation you have excluded!  Yes, as discussed above this paper is aimed narrowly at one aspect of labor regulations -- understanding how businesses that employ unskilled workers respond to these regulations and how those responses affect workers and their employment and advancement prospects

Everyone knows employer monopsony power means there are no employment or price effects to minimum wage increases.  Some studies claim to have proved this, others dispute this.  I would say that this statement has always seemed insane from my perspective as a small business owner.  It sure doesn't feel like I have a power imbalance in my favor with my workers.   I address this with a real example in the article but also address it in much more depth here.  The short answer is that for minimum wages to have no employment or price effects, a company has to have both monopsony power in the labor market AND monopoly power in its customer markets.  Without the latter, all gains from "underpaying" a worker due to monopsony power get competed away and benefit consumers (in the form of lower prices) rather than increase a company's profit.

The costs of these regulations are supposed to come out of your bloated profits.  Perhaps that is what happens at Google, where compliance costs are a tiny percentage of what their highly-compensated employees earn and where the company enjoys monopoly profits in its core businesses.  For those of us in highly-competitive businesses that employ unskilled workers, our profit margins are really thin (as explained in more depth here).  When profits are close to the minimum that supports further investment and participation in the business, then labor regulatory costs are going to get paid by consumers and workers.

Then maybe the best thing for workers is to create monopolies.  Funny enough, this idea was actually one of the centerpieces of Mussolini's corporatist economic model, a model that was copied approvingly by FDR in the centerpiece New Deal legislation the National Industrial Recovery Act (NRA).  The NRA sought to create cartels in major industries that would fix prices, wages, and working conditions, among other things.   The Supreme Court struck the legislation down, a good thing since it would have been a disaster for consumers and for innovation and probably for most workers too.  As a bit of trivia, this year's Superbowl winner the Philadelphia Eagles was named in honor of this law.  More here.

So do you think minimum wages are a good policy overall or not?  Hmm, mostly not.  For a variety of reasons, minimum wages are a very inefficient way to tackle poverty (and also here), and tend to have cronyist effects that help one class of worker at the expense of other classes (this latter should be unsurprising since many original supporters of the first federal minimum wages were explicitly hoping to disadvantage black workers competing with whites).

Why are you opposed to all these worker protections?  Or, more directly, why do you hate workers?  This is silly -- I am not and I don't.  However, this sort of critique, which you can find in the comments below, is typical of how public policy discussion is broken nowadays.  When I grew up, public policy discussion meant projecting the benefits of a policy and balancing them against the costs and unintended consequences.  In this context, I am merely attempting to air some of the costs of these regulations for unskilled workers that are not often discussed.  Nowadays, however, public policy is judged solely on its intentions.  If a law is intended to help workers (whether or not it will every reasonably achieve its objectives), then it is good, and anyone who opposed this law has bad intentions.  This is what you see in public policy debates all the time -- not arguments about the logic of a law itself but arguments that the opposition are bad people with bad intentions.  For example, just look in the comments of this and other posts I have linked -- because Coyote points out underappreciated costs to laws that are intended to help workers, his intentions must be to harm workers.  It is grossly illogical but characteristic of our post-modernistic age.

I will retell a story about Obamacare or the PPACA.  Most of my employees are over 60 and qualify for Medicare.  As such, no private insurer will write a policy for them -- why should they?  Well, along comes Obamacare, and it says that my business has to pay a $2000-$3000 penalty for every employee who is not offered health insurance, and Medicare does not count!  I was in a position of paying nearly a million dollars in fines (many times my annual profits) for not providing insurance coverage to my over-60 employees that was impossible to obtain -- we were facing bankruptcy and the loss of everything I own.  The only way out we had was that this penalty only applied to full-time workers, so we were forced to reduce everyone's hours to make them all part-time.  It is a real flaw in the PPACA that caused real harm to our workers.  Do I hate workers and hope they all get sick and die just because I point out this flaw with the PPACA and its unintended consequence?

I've heard that raising the minimum wage increases worker productivity so much that businesses are better off.    I know there is academic literature on this and I am frankly just not that familiar with it.  I can say that I have never, ever seen workers suddenly and sustainably work harder after getting a wage increase.  What I see instead is employers doing things like cutting back employee hours and demanding the same amount of work gets done.  This could result in more productivity if there was fat in the system beforehand but it also can result in things like lower service levels (e.g. the bathrooms get cleaned less frequently).   Without careful measurement, these changes could appear to an outsider to be productivity gains.  In addition, as discussed in the article, with higher minimum wages employers can substitute more skilled for less skilled workers, which can result in productivity gains but leave unskilled workers without a job.

Workers are human beings.  It is wrong to think of them as "costs" or "resources".  The most surprised I think I have ever been on my blog is when I got so much negative feedback for writing that the best thing that could happen to unskilled workers is for someone to figure out how to make a fortune hiring them.  I thought this was absolutely obvious, but the statement was criticized as being heartless and exploitative.  My workers are my friends and are sometimes like family.  I hire hundreds of people over 60 years of age, people that the rest of society casts aside as no longer useful.  They take pride in their ability to continue to be productive.   You don't have to tell me they are human beings.  Just this week I have helped modify an employee's job responsibilities to help them manage their newly diagnosed MS, found temporary coverage for a manager who needs to get to a relative's funeral, found a replacement for a manager that wants to take a sabbatical, and loaned two different employees money to help them through some tough financial times.  From a self-interested point of view, I need my employees to be happy and satisfied in their work or they will provide bad, grumpy service.  But at the end of the day I can only keep these people employed if customers are willing to pay more for the services they provide than the employees cost me.  If the cost of employing people goes up, then either customers have to pay more or I can hire fewer employees.

You probably support child labor too.   Child labor laws are an entirely reasonable zone of government regulation.  The reason this is true stems from the definition of a child -- a child is someone considered under the law to lack agency or the ability to make adult decisions due to their age.   We generally give parents, rightly, a lot of the responsibility for protecting their children from bad decisions, but I am fine with the government backstopping this with modest regulations.  In other words, I have no problem with the law treating children like children.  Instead, I have a problem with the law treating adults like children.

Aren't you just begging to get audited?  Hah!  That's what my wife says.  To me, the logical response of a regulator should be, "wow, this guy knows the law way better than most of the business folks we deal with, so he probably is not a compliance risk" -- but you never know.  Actually, we have been audited many times on many of these laws.  So much so that practically the first series of posts I did on this blog, way back in the blog pleistocene era of 2004, was 3 part series on surviving a Department of Labor audit.  Looking back on the series, everything in it (which included experience from a number of different audits) still seems valid and timely.

The City of San Francisco seems to have odd litter preferences.  After a huge program to ban and/or charge for plastic bags to get them out of the litter stream ( a program that for all my whining about it seems to have achieved that goal pretty well), the city seems to have substituted used syringes:

City Health Director Barbara Garcia estimated in 2016 that there were 22,000 intravenous drug users in San Francisco - around one for every 38.9 residents, while the city hands out roughly 400,000 needles per month.

Of the 400,000 needles distributed monthly, San Francisco receives around 246,000 back - meaning that there are roughly 150,000 discarded needles floating around each month - or nearly 2 million per year, according to Curbed.

I am pretty sure if they were to divert some of their plastic bag tax revenue to paying 5 cents per needle on return (or about $20,000 a month) that the needles would disappear from the streets in a matter of days.  Not sure if this would create some problems with safety or new crime incentives, but I would l think it would be worth a try.

Via Don Boudreaux:

Greg Ip writes that “The U.S. runs a trade deficit because it consumes more than it produces while its trading partners, collectively, do the opposite” (“How the Tax Cut President Trump Loves Will Deepen Trade Deficits He Hates,” April 19).

Here is how I like to explain why this is wrong.  The trade deficit exists in large part because foreigners are more likely to consume the American-made goods and services they buy right here in the US, rather than take them back to their home country, while US consumers tend to bring foreign goods back to America to consume them.  Let me unpack this.

First, over any reasonable length of time, payments between countries are going to balance.  If this were not true, there would be some mattress in China that has trillions of dollar bills stuffed in it, and no reasonable person nowadays just lets money sit around lying fallow.  There are some payments between countries for each others' goods.   And there are some payments for each others' services.   And there are some payments for various investments.  All these ultimately balance, which makes fixating on just one part of this circular flow, the payments for physical goods, sort of insane.  If we have a trade "deficit" in physical goods, then we must have a trade surplus in services (which we do) and in investments (which we do) to balance things out.

But what do we mean by an investment surplus?  It means that, for example, folks from China are spending more money in the US for things like real estate and buildings and equipment -- either directly or through purchases of American equity and debt securities -- than US citizens are buying in China.  But note that another name for investment is just stuff that foreigners buy in this country that stays in this country and they don't take back home.  If a Chinese citizen buys a house in Los Angeles (something that apparently happens quite a bit), that is just as much "consumption" as when I buy a TV made in China.  But unlike my TV purchase (which counts as an import), because of the arbitrary way trade statistics are calculated, selling a Chinese citizen a house in LA does not count as an export because they keep and use the house here.  Let's say one Chinese person sells 10,000 TV's to Americans, and then uses the proceeds to build a multi-million dollar house in Hawaii.  This would show up as a huge trade deficit, but there is no asymmetry of consumption or production -- Chinese and American citizens involved in this example are producing and consuming the same amounts.  The same is true when the Chinese build a manufacturing plant here.  Or when then invest capital in a company like Tesla and it builds a manufacturing plant here.

Our bizarre fixation on the trade deficit number would imply that, if trade deficits are inherently bad, then we would be better off if the Chinese person who bought the house in LA dismantled it and then shipped the material back to China.  Then it would show up as an export.  Same with the factory -- if we fixated on reducing the trade deficit then we should prefer that the Chinese buy the equipment for their factory here but have it all shipped home and built in China rather than built here.   Is this really what you want?

I am willing to concede one exception -- when Chinese use trade proceeds to buy US government debt securities.   This is where my lack of formal economics training may lead me astray, but I would say that the US government is the one major American institution that is able to consume more than it produces.  Specifically, by running enormous deficits it is able to -- year in and year out -- allow people to consume more than they produce.  Trade proceeds from foreigners that buy this debt in some sense help subsidize this.

However, I don't think one can blame trade for this situation.  Government deficits are enabled by feckless politicians who pander to the electorate in order to be re-elected, a dynamic that has little to do with trade.  I suppose one could argue that by increasing the demand for government securities, foreigners are reducing the cost of debt and thus perhaps enabling more spending, though I am not sure politicians are at all price sensitive to interest rates when they run up debt -- as a minimum their demand curve is really, really steep.   There is a relation between government borrowing and trade but the relationship is reversed -- Increased borrowing will tend, all things being equal, to increase the value of the dollar which will in turn make imports cheaper and exports more expensive, perhaps increasing the trade deficit.

I am always amazed that folks, say those in government in places like San Francisco, consistently support restricting the supply of new housing while subsidizing home buyers and then are surprised when prices and rents keeps rising.  From the Market Urbanism Report via Walter Olson.

But a look at the numbers shows that, on the contrary, housing construction (or lack thereof) seems to be the driving factor behind whether or not large U.S. metros remain affordable.

This would be the conclusion from 7 years of data from the Census Bureau, which publishes annual lists on the number of new privately-owned housing units authorized in each metro area. Between 2010 and 2016, when overall national housing permits ticked up each year following the recession, most major metros have issued housing permit numbers in the high 4- or low 5-figures annually. But three metros have stood far above the rest.

The Dallas-Fort Worth-Arlington MSA issued 273,853 housing permits over this 7-year period; New York-Newark-Jersey City issued 283,814; and Houston-The Woodlands-Sugar Land topped every metro with 316,639 permits. Combined, the 3 metros accounted for 13.5% of the nation’s approved housing units.

Other metros weren’t even close to these three....

These statistics are glaring, and show that the urban housing affordability crisis, and its solution, is far simpler than many pundits suspect. In their ongoing quest to satisfy their anti-growth biases, they’ve settled on demand-side responses (read: government subsidies) that ignore or worsen the fundamental problem of under-supply; while they continue to blame various third party boogeymen, including developers, landlords, Airbnb hosts, techies, hipsters, Asian families buying second homes, and migrants in general.

But, again, the Census data sheds light on the actual nature of the issue: some metros in America are building a LOT of housing. Other metros may think they are, but actually are not. And housing prices within given metros are either stabilizing or skyrocketing based on this decision.