Police Shootings: How Bad Are Things?

Epistemic Status: rough, back-of-envelope

How many people are killed by police in the US? How does this compare to death rates from other causes?

In 2015, the Washington Post counted 990 Americans shot by police, the Guardian counted 1146 killed, and Fatal Encounters reported 1357, while the FBI and BJS’s 7-year average number of police killings per year were 418 and 380, respectively.

In 2012, an estimated 55,400 people were killed or hospitalized by police; 1 in 291 stops or arrests resulted in hospital-treated injury or death.  1063 suffered fatal injuries. Beatings were by far the most common cause of injury, while shooting was the most common cause of death.

I’m inclined to believe the reporters’ numbers over the FBI and BJS’s numbers, and estimate something like 1000-1500 police killings a year, and tens of thousands of police-caused hospitalizations a year.

Comparison to Total Homicides

According to the CDC, there were 15,809 homicides in America in 2014, and 2.1 million emergency room visits for assault in 2011.

This means that 5-10% of all homicides are committed by police.  3% of all severe assaults are committed by police.

There are about 765,000 police in the US. There are about 152 million men, who commit about 90% of homicides; there were 9972 male homicide perpetrators in 2010.  Thus, roughly, a policeman is 30x as likely to kill you as a randomly chosen man is.

Breakdown by Race

According to the Washington Post, 48% of people killed by police are white, while 25% were black. (The remainder were of a different or unknown race.)  This represents an overrepresentation of black people and underrepresentation of white people, since the US is 62% white and 13% black.  Black people are 2.5x as likely as white people to be killed by police.

There’s some research showing that there is no racial disparity in the rate of police killing per encounter, but researching “per encounter” rates of violence hides a lot under the rug.  If police are biased against black people, they are more likely to “encounter” them, looking for a reason to arrest them, and thus are more likely to escalate to violence. On the other hand, black people commit more crimes (per population) than white people.  Teasing out what constitutes police bias and what constitutes justifiable increased policing intensity is a tough subject.  What’s not in doubt is that the burden of police killings falls disproportionately on black people.

Comparison to Lynching

While this may seem an inflammatory comparison, a lynching, like a police killing, is an extrajudicial killing of a suspected or alleged criminal.

According to the Tuskegee Institute, the year with the highest number of lynchings, 1892, saw 61 whites lynched and 161 blacks lynched.

Given that the US population in 1892 was only about 20% of its current size, this means that, adjusted for population, about as many people are killed by police today as were lynched in the 1890s.

Looking at black people specifically, who were 12% of the US population in 1890, just as they are today, the risk of being lynched for a black person was about twice as high in the 1890s than the risk of being shot by a cop for a black person is today. Lynchings were notably more skewed towards black people than police shootings are.

Comparison to Police States

There is absolutely no comparison in magnitude between anything happening in the US criminal justice system and Stalin’s Great Purge, which killed between 600,000 and 1.2 million people, out of a population of roughly 100 million.

As we noticed with hate crimes, looking at serious problems of violence in the US can put into perspective how terrifyingly, unimaginably bad Hitler and Stalin were. Our problems are not trivial, but totalitarian regimes are…a fundamentally different kind of thing.

Augusto Pinochet had an estimated 40,018 people killed, tortured, or forcibly disappeared between 1973 and 1990, or an estimated 2354 per year, out of a population of 10-13 million.  His regime was at least 50x as deadly as US police are.

South Africa under apartheid tried and executed about 134 political prisoners between 1961 and 1989, which is not quite comparable to police killings, but is a lower rate than exists in the US.  However, South African “deaths in police custody” in 1997-2004, immediately after apartheid, averaged 434 deaths a year, while 763 people were killed by the apartheid government’s police in 1985, an unusually violent year.  Police killings in apartheid South Africa were roughly 5x as common per population as they are in the present-day US, while police killings in 1990’s South Africa were roughly 2.5x as common as they are in the present-day US.

According to a recent human rights agency’s report, 323 people have died in Egyptian prison facilities since 2013 after the recent coup, as well as 624 protesters killed.   This is comparable to the number of police killings in the US.

245 people were killed by Venezuelan security and police forces in 2015; per population, this is about twice as many as police killings in America.

Thailand’s war on drugs, which involved 2800 extrajudicial killings in the first three months after it began in 2003, is at least 10x as deadly as police in America are.

200 people died in police custody last year in Russia, about half the rate of police shootings in America per population.

The US is generally, but not always, less deadly to its citizens than typical authoritarian regimes.  The US has similar rates of death due to police as present-day South Africa, Russia, Venezuela, and Egypt.

Comparisons to other causes of death

Like all kinds of homicide, the number of police homicides pales in comparison to the number of deaths due to disease. Cancer kills more than hundreds of times as many people per year than police do. Suicide kills 30-40x as many.  Infant mortality kills more than 15 times as many.  HIV kills six times as many people.   Doctors and medical researchers are still on the front lines against death.

And prison itself probably causes quite a bit more humanitarian damage than police killings do.

However, justice matters too. An innocent person killed by police is wronged, in a way that a person who succumbs to a disease is not. Police killings count towards the vaguely defined but important category of “evidence that we don’t live in a free and just society”, in the same way that torture, detention without trial, mass surveillance, and other civil liberties violations do.

 

If Prison Were a Disease, How Bad Would It Be?

 

Epistemic status: highly uncertain

As of 2013, 2,220,300 adults were incarcerated in US state and federal prisons and county jails.

The majority of these people –about 60% — are incarcerated for nonviolent offenses such as theft, drugs, or public order violations.

How bad is this, in terms of years of life lost?  How much damage is due to being imprisoned?  (ETA: of course, in this context, I am only looking at the harms of prisons, not the benefits due to the deterrent effect of prisons, or the harms of crime. This should not be read as a claim that prison has zero deterrent effect!)

One article attempts to quantify:

African American males can expect to spend 3.09 years lifetime in prison, on average, and Hispanic and Caucasian males will spend on average 1.06 and 0.50 years, respectively.

Comparing life expectancies of people who have and have not gone to prison, as if “prison” were a disability, they compute that white males lose 19,665 person-years of life to prison per 100,000, black males lose 139,507 person-years, and Hispanic males lose 45,766 person-years.

For comparison purposes, here is a table of person-years of life lost to the most common diseases in the US.  Cancer, the top killer, only appears to cost 2882 person-years of life per 100,000.  All causes together only cost 38,211 person-years of life per 100,000.

These numbers are really weird.  They would place prison as being responsible for nearly half of all person-years of life lost.  That would be an utterly shocking result. I’m skeptical.

(ETA: it turns out that the authors of this study were looking at a stock, not a flow, of person-years lost to prison, as Ben notes below. Do not use this study’s numbers to estimate the harms of prison, they don’t make a lot of sense.)

Epidemiologist Ernest Drucker, in his book A Plague of Prisonstried to quantify the years of life lost to imprisonment for drug offenses in New York State.   He estimated a total of 360,000 years of life in prison between 1973 and 2008. This isn’t a fair comparison to diseases, though, because a year living in prison is not as bad as being dead, and prison has harms outside the time actually spent in prison.  If we were to count years in prison as “years of life lost”, however, then, given that there are roughly 19 million people in New York, drug offenses alone cost 55 person-years of life per 100,000, which is a more modest number.  

A study of the dose-response effect of years of prison on mortality found that each additional year in prison (compared to being released on parole) produced a 2-year decline in life expectancy.  For comparison purposes, smokers lose on average 11-12 years of life expectancy compared to nonsmokers.  Getting a diagnosis of colon cancer means losing about 10 years of life expectancy, while getting a diagnosis of testicular cancer means losing 1.3 years of life expectancy.

If we combine these numbers, assume each year in prison is roughly equivalent to two years of life lost, then New York State’s drug incarceration is responsible for about 110 person-years of life per 100,000, which is about half the death rate due to HIV.  This is a more believable number, though it would still make the list of the top 15 causes of death by years of life lost. But it’s only for drug incarceration, which is responsible for only about 1/5 of all incarceration.

If we look at the total number of people incarcerated in New York State, or 77,227, we get an estimated 810 person-years of life lost to prison in New York per 100,000 population, which is more than the national YLL of homicide.  And if we extrapolate to the full 2,220,300 Americans incarcerated, assume 2 years of life lost per year in prison, we get a rate of person-years of life lost due to prison per 100,000 population of 1396, which would make “prison”, if it counted as a cause of death, the sixth worst public health problem in terms of person-years of life lost.

The deadliness of prison, depending on which numbers you use, seems to range from “truly implausibly bad” to “one of the most serious public health problems in America.”

The leading causes of death among former inmates are drug overdose, cardiovascular disease, homicide, and suicide; the highest elevated risks, at 10-12x the population expected rates, were drug overdose and homicide, especially at 0-2 weeks after release. Prison puts people in more danger than they were before.

Some suggested mechanisms for why prison is so dangerous include poor conditions such as overcrowding that expose prisoners to infectious disease; violence within prisons; poor medical care inside prisons; and increased risky behaviors, due to trauma or psychological harm or lack of material opportunities for ex-cons.

For US-centric and present-day-centric utilitarian calculations, prison looks really, really bad. Reducing the prison population seems potentially important on a level comparable to working on Big Problems like cancer, heart disease, diabetes, car accidents, etc.

If nobody were imprisoned for drug crimes, then (aside from any additional risks incurred from the resulting increased drug use) the drop in incarceration alone would save more American lives than eradicating HIV from the US today.

 

 

 

Hate Crimes: A Fact Post

CW: violence, rape, murder, racism.

Epistemic status: a few days’ worth of background reading, way outside my field. This is me “showing my work” in how I orient myself, not a substitute for social science.

Since the Trump election, I’ve been concerned about what, concretely, a resurgence in racism might mean, and how likely it is.  There are people I respect saying “anything could happen” and warning us to stay vigilant and prepared to resist acts of fascist tyranny. There are also people I respect telling everyone to calm down because it’s probably not that bad.

As a grandchild of a resistance fighter against the Nazis, I was raised to believe that it can happen here, and we have to be prepared.  Part of preparation, though, is realism.  What exactly are we facing, and what kind of preparation is appropriate?  The first step is trying to assess the situation accurately.

It may seem naive to start by reviewing hate crime statistics.  The numbers probably aren’t all that accurate; and recorded hate crimes are nowhere near all the harms due to racism.  I’ll be making some attempts to deal with the first issue later in this post.  As for the second, well, this is a very primitive attempt to come to my own conclusions. I would need to be an economist with far more resources and time, in order to, say, estimate the cumulative economic damage of redlining.  For the moment, I want to do the exercise of looking at some numbers and coming to my own conclusions — not because I expect to do that better than social scientists do, but to practice original seeing, which I think is important for getting outside the sway of others’ opinions.

Why hate crime? Because racial violence is one of the concrete “bad outcomes” that we implicitly fear, when we fear a “rise in racism”.  So it makes sense to ask things like how common it is now, and how common it was in the past, or how common it is in other countries, to get a sense of the range of where things can go.

Overview

There are two major data sources in the US for information on hate crimes. One is the Department of Justice’s National Crime Victimization Survey, which is taken from a sample of about 100,000 households, and asks them detailed questions about crimes they’ve been the victims of.  The other is the FBI’s Uniform Crime Reporting database, which collects recorded crimes from police departments across the country. These sources conflict quite a bit.

The US only began recognizing “hate crime” as a legal category in the 90’s, so older information on hate crime is mostly unavailable. For (very rough) comparison purposes, I’m also going to look at statistics on lynching and on race riots, to get a sense of past levels of racial violence. I’ll also briefly compare these to contemporary Russian hate crime statistics, for an example of a country which famously has a severe problem with racial violence.

UCR Data

In 2015, there were 5850 hate crime incidents reported to the FBI by police departments.  

Of these, 36% were motivated by anti-black bias, 13% by anti-gay (male) bias, 12% by anti-white bias, 12% by anti-Jewish bias, 5% by anti-Muslim bias, 4% by anti-LGBT bias excluding gay males, and 2% by anti-Hispanic bias.

(I was surprised to see so low a rate of hate crimes against Hispanics, and so high a rate against whites.)

There were 18 murders and 13 rapes. There were 4482 crimes against persons, of which 41.3% were intimidation, 37.8% assault, and 19.7% aggravated assault.  (That is, a total of 2577 assaults.) The majority of the 2338 hate crimes directed against property were acts of vandalism.

The states with the highest number of hate crimes per capita were:

  • DC
  • Massachusetts
  • North Dakota
  • Montana
  • Kentucky

Southern states in general have lower per-capita rates of hate crime than northern states, according to the UCR; and Mississippi has a grand total of zero hate crimes reported, which is highly suspicious.  There is a serious underreporting problem with hate crimes — several major Southern cities never report hate crimes at all, such as Birmingham, Alabama; Jackson, Mississippi; and Baton Rouge, Louisiana.  So it’s quite likely that these numbers are underestimates.

The UCR has been keeping hate crime stats since 1995. Hate crime rates have been slowly declining in that period.  Anti-black hate crimes are about ⅔ their 90’s level, anti-Jewish hate crimes are about 60% of their 90’s level, anti-white hate crimes are about half, etc.

The number of anti-Muslim hate crimes spiked in 2001, from negligible to about 500, and then declined to a stable but higher-than-before level.

So, clearly, it is possible for current events to cause a spike in hate crimes.  This is a special type of a spike in hate crimes, though: Muslims may have been so small and new a population in the US that they just weren’t a habitual target of bigotry before September 11.  The September 11th hate crimes spike tells us that current events can rapidly create new targets of bigotry, even when they were largely left alone before.

NCVS Data

From 2004-2012, the rate of hate crime victimization in the population, according to the self-reports in the NCVS survey, remained steady at roughly 1 per 1000 persons 12 or older.

This would imply a much higher rate of hate crime than the UCR reports — roughly 260,000 a year — and even if we only count those crimes which survey respondents said they reported to police, that’s still 120,000.  However, according to the NCVS, only 14,380 hate crimes were confirmed by police investigators. Most reports of hate crimes do not result in the police concluding there was a hate crime.  And, of those, we might infer, only a fraction are reported to the UCR, given that the UCR’s hate crime numbers are less than half the number that the NCVS says were confirmed by police.

This low rate of police recording and police reporting is specific to hate crimes, not common across all crime.  The UCR and NCVS also include reporting on non-hate crimes like rape, robbery, aggravated assault, etc. In most of these cases, the number of crimes that the NCVS says were reported to police is comparable to the number of crimes that the UCR says were recorded by police.

Crime # (NCVS 2012) % reported to police (NCVS 2012) # recorded by police (UCR 2012) recording rate
Rape 431840 32.5 90185 0.64
Robbery 578580 60.9 327374 0.93
Aggravated assault 816760 58.4 764449 1.6
Simple assault 3179440 40 n/a n/a
Burglary 2904570 60 1579527 0.91
Motor vehicle theft 564160 83.3 707758 1.5
Theft 11142310 29 5706346 1.76
All hate crime 293790 34 6718 0.067
Violent hate crime 263540 34 4810 0.053

The one exception is hate crime, where only about 5% of hate crimes reported to police are recorded in the UCR.

That this discrepancy exists specifically in hate crimes suggests that police preferentially take hate crimes less seriously than other crimes.  And, indeed, according to the NCVS, police were more likely to take reports and make arrests in non-hate crimes vs. hate crimes.

However, the fact that there are cases where the NCVS numbers significantly undershoot the UCR numbers — giving the nonsensical result that the police record more e.g. motor vehicle thefts than victims report to police — suggests that the NCVS may have some serious sampling bias.

In the NCVS 2012 data, 52% of hate crime victims were white, 13% were black, and 30% were Latino.  This throws some doubt on the much lower rate of anti-Hispanic hate crimes in the UCR data — maybe Latinos/Hispanics are less likely to report hate crimes to the police, or less likely to be taken seriously by the police.

According to the NCVS, 16% of hate crimes were “serious violent crimes” (robbery, aggravated assault, or rape), 44% were “simple assault”, and 22% were property crime.

So How Many Hate Crimes Are There?

The unfortunate fact is that we don’t know how many hate crimes there are, because both our major data sources seem to have serious flaws.

How big a deal is hate crime, in terms of damage to human life? What are the casualty rates?

According to the NCVS,  5% of hate crimes were aggravated assault causing injury, and 10.6% were simple assault causing injury, giving roughly 32,760 injuries a year due to hate crime.

The NCVS doesn’t report murders.  The UCR’s numbers of 18 hate-crime murders a year are probably an underestimate, but also probably not as much of an underestimate as the other types of crime, since I would expect that people are more likely to report murders to the police than other crimes. There were a total of 15,809 homicides in the US in 2011.  If 0.1% of all crimes are hate crimes, as the NCVS reports, and homicide is a representative crime, then this would predict 15 hate-crime homicides a year, which is comparable to the UCR’s numbers.

My tentative order-of-magnitude estimates are that there are 10-20 hate-crime murders a year, and tens of thousands of hate-crime injuries.

Lynchings

According to the Tuskeegee Institute archives, lynchings in 1882-1968 were at most one or two hundred killings a year.

At the peak in 1892, the total number of lynchings in the US was 230, with 161 blacks and 61 whites killed.

Controlling for population growth, and comparing lynchings of black people directly to all hate-crime murders, (yes, obviously this is an imperfect comparison), this means that “hate-crime killings” were roughly 45x as common per population in the late 19th century as they are today.

The NAACP numbers claim there were 3436 people lynched between 1889 and 1922, or an average of 104 lynchings per year.

Lynchings began to decline in the 1920’s, potentially due to a variety of causes: the urbanization of the South, more active anti-lynching efforts by state police and the National Guard, the activism of the NAACP, and the attempt to pass the Dyer Anti-Lynching Bill in 1922. (It passed in the House but failed in the Senate.)

tstr21

(Image from Harry Truman’s report on civil rights.)

It’s worth noting that this is what a climate of lawless terror looks like.

It wasn’t that black people had to use a separate drinking fountain or couldn’t sit at lunch counters, or had to sit in the back of the bus.

You really must disabuse yourself of this idea. Lunch counters and buses were crucial symbolic planes of struggle that the civil rights movement used to dramatize the issue, but the main suffering in the south did not come from our inability to drink from the same fountain, ride in the front of the bus or eat lunch at Woolworth’s.

It was that white people, mostly white men, occasionally went berserk, and grabbed random black people, usually men, and lynched them. You all know about lynching. But you may forget or not know that white people also randomly beat black people, and the black people could not fight back, for fear of even worse punishment.

This constant low level dread of atavistic violence is what kept the system running. It made life miserable, stressful and terrifying for black people.

White people also occasionally tried black people, especially black men, for crimes for which they could not conceivably be guilty. With the willing participation of white women, they often accused black men of “assault,” which could be anything from rape to not taking off one’s hat, to “reckless eyeballing.”

This is going to sound awful and perhaps a stain on my late father’s memory, but when I was little, before the civil rights movement, my father taught me many, many humiliating practices in order to prevent the random, terroristic, berserk behavior of white people. The one I remember most is that when walking down the street in New York City side by side, hand in hand with my hero-father, if a white woman approached on the same sidewalk, I was to take off my hat and walk behind my father, because he had been taught in the south that black males for some reason were supposed to walk single file in the presence of any white lady.

This was just one of many humiliating practices we were taught to prevent white people from going berserk.

I remember a huge family reunion one August with my aunts and uncles and cousins gathered around my grandparents’ vast breakfast table laden with food from the farm, and the state troopers drove up to the house with a car full of rifles and shotguns, and everyone went kind of weirdly blank. They put on the masks that black people used back then to not provoke white berserkness. My strong, valiant, self-educated, articulate uncles, whom I adored, became shuffling, Step-N-Fetchits to avoid provoking the white men. Fortunately the troopers were only looking for an escaped convict. Afterward, the women, my aunts, were furious at the humiliating performance of the men, and said so, something that even a child could understand.

This is the climate of fear that Dr. King ended.

To get that experience, you only need a few dozen actual recorded lynchings per year.  The indirect impact of living under threat of violence far exceeds the literal death count.

This is what it looks like, historically, to have 45x the rate of racial violence of today.

Race Riots

For most of US history before the 1960’s, a “race riot” was racial violence by white people against nonwhite people (usually black, sometimes immigrants such as Filipinos or Mexicans).  Whole towns might be attacked and burned. In the early 20th century, these were extremely bloody: in the Tulsa race riot of 1921, whites literally bombed a black neighborhood from private airplanes, killing about 300 and forcing thousands from their homes.

While lynchings were largely a rural Southern activity, race riots were urban and nationwide.

There is no central repository of race riot casualty statistics that I could find, so I have some quick-and-dirty Internet numbers here; this is not an exhaustive list.

A return to the levels of racial violence of the 1910’s-1920’s would mean, relative to population, roughly a 50x increase in the number of “hate crime” murders compared to today. As with lynching, this is what a climate of terror looks like.

A return to the levels of racial violence of the 1960’s would constitute a roughly 5x jump, compared to the number of hate crime homicides of today. That’s what it looks like to live in what we now remember as a “turbulent” time.

Pre-1963, only 10% of race riots could be attributed to escalation by blacks. Afterwards, most race riots were still started by whites but the proportion became closer to 60/40.

Mass racial violence dissipated through the 70’s and never again reached its 60s peak, with a few exceptions such as the Rodney King riots of 1992, which killed 50 people and caused $1B in property damage.

Hate crimes in Russia

There are estimated tens of thousands of neo-Nazi skinheads in Russia. In 2008, Amnesty International estimated 85,000 neo-Nazis in Russia.  Over the past ten years, there are an average of 56 hate-crime-related deaths a year, and 378 injuries.    Source, from SOVA, a Russian think tank that studies racism and xenophobia in Russia.

Racism in Russia is most commonly directed against Africans, Central Asians, Jews, and Vietnamese. Only a few percent of the Russian population are peoples of the Caucasus, and there are only 186,000 Jews in Russia, so this is a much more intensive campaign of violence than it would be in the US; the US is about 23% nonwhite; so, conservatively, accounting for Russia’s lower total population and lower non-Russian population than the US, racial violence in Russia is maybe 31x as deadly, in terms of risk of being victimized, as it is in the US.  

Once again, this is what a climate of fear and widespread mob violence looks like. Dozens of hate-crime murders per year, more than an order of magnitude more common than hate-crime murder is in the US.

(Anti-LGBT violence is also a serious problem in Russia but we don’t seem to have good statistics on how common it is; one report says 300 attacks per year.)

Did hate crimes in the US increase post-election?

UCR and NCVS numbers come out yearly, so it’s clearly too soon to tell from those sources.

There’s allegedly a 30% jump in NYC hate crimes this year, and the NYPD has instituted a special police unit to fight the uptick.

The SPLC has set up an opportunity for people to report hate crime incidents around the election, but all those they cited were “intimidation” — verbal harassment and threats.  The most common type was anti-immigrant intimidation.  The most common locations were schools.  

Another tracking site for hate crimes reports 79 self-reported incidents of “violence”, but I noted several errors (duplicates, shootings that were apparently non-hate-related, non-violent crimes).

I think that it’s important to be watchful to see if a post-election rise in hate crimes holds up, but we don’t have enough evidence to be confident that there’s been one.

Scenario Planning

The “really bad” scenario for hate crime in the US is a rise of 30-50x in serious mob violence motivated by bigotry and tacitly condoned by the state. This, we know from historical and international evidence, feels from the inside like living in a dangerous, lawless, oppressive place.

I do not think mob violence alone will cause genocide on a much larger scale. The twenty million murders committed by the Nazis are a different, alien, unthinkable scale of operation.  I suspect you need governments for that. Governments that actively want to exterminate a population, not just keep it fearful and subordinate.  Mob violence is much more common than official campaigns of extermination, and is a more likely threat scenario.

One good thing is that it’s probably not possible to jump to 1890’s-1920’s levels of racial mob violence all at once. If that were to happen, we’d see a smaller uptick before it gets that bad.  If we’re watchful, we’ll have warning, and we may be able to counteract the problem.

The SPLC has advice on how to prevent hate crime. I’m not sure how well validated this is, but what they emphasize is community response. Churches and town councils can organize things like prayer meetings, candlelight vigils, public gatherings with marches and speeches, and other public, communal displays of support for the victims of hate crimes and refusal to tolerate hate in the community.  Forming “coalitions for tolerance” to protest hate crimes and support victims of hate can send a forceful message to hate groups that they are not welcome, and potentially prevent future crimes.

I don’t know much about this topic, but I’d probably want to read more on the psychology and dynamics of mob violence, and whether there are known techniques for defusing or preventing it. I’d very much appreciate if more knowledgeable people shared info about this.

 

 

Industry Matters 2: Partial Retraction

Epistemic status: still tentative

Some useful comments on the last post on manufacturing have convinced me of some weaknesses in my argument.

First of all, I think I was wrong that most manufacturing job loss is due to trade. There are several economic analyses, using different methods, that come to the conclusion that a minority of manufacturing jobs are lost to trade, with most of the remainder lost to labor productivity increases.

Second of all, I want to refine my argument about productivity.

Labor productivity and multifactor productivity in manufacturing, as well as output, have grown steadily throughout the 20th century — but they are slowing down. The claim “we are making more things than ever before in America” is literally true, but there is also stagnation.

It’s also true that manufacturing employment has dropped slowly through the 70’s and 80’s until today.  This is plausibly due to improvements in labor productivity.

However, the striking, very rapid decline of manufacturing employment post-2000, in which half of all manufacturing jobs were lost in fifteen years, looks like a different phenomenon. And it does correspond temporally to a drop in output growth and productivity growth.  It also corresponds temporally to the establishment of normal trade relations with China, and there is more detailed evidence that there’s a causal link between job loss and competition with China.

My current belief is that the long-term secular decline in manufacturing employment is probably just due to the standard phenomenon where better efficiency leads to employing fewer workers in a field, the same reason that there are fewer farmers than there used to be.

However, something weird seems to have happened in 2000, something that hurt productivity growth.  It might be trade.  It might be some kind of “stickiness” effect where external shocks are hard to recover from, because there’s a lot of interdependence in industry, and if you lose one firm you might lose the whole ecosystem.  It might be some completely different thing. But I believe that there is a post-2000 phenomenon which is not adequately explained by just “higher productivity causes job loss.”

Most manufacturing job loss is due to productivity; only a minority is due to trade

David Autor‘s economic analysis concluded that trade with China contributed 16% of the US manufacturing employment decline between 1990 and 2000, 26% of the decline between 2000 and 2007, and 21% over the full period.  He came to this conclusion by looking at particular manufacturing regions in the US, looking at their exposure to Chinese imports in the local industry, and seeing how much employment declined post-2000.  Regions with more import exposure had higher job loss.

Researchers at Ball State University also concluded that trade was responsible for a minority of manufacturing job loss during the period 2000-2010: 13.4% due to trade, and 87.8% due to manufacturing productivity growth.  This was calculated using import numbers and productivity numbers from the U.S. Census and the Bureau of Labor Statistics, under the simple model that the change in employment is a linear combination of the change in domestic consumption, the change in imports, the change in exports, and the change in labor productivity.

Josh Bivens of the Economic Policy Institute, using the same model as the Ball State economists, computes that imports were responsible for 21.15% of job losses between 2000 and 2003, while productivity growth was responsible for 84.32%.

Justin Pierce and Peter Schott of the Federal Reserve Board observe that industries where the 2000 normalization of trade relations with China would have increased imports the most were those that had the most job loss. Comparing job loss in above-median impact-from-China industries vs. below-median impact-from-China industries, the difference in job loss accounts for about 29% of the drop in manufacturing employment from 2000 to 2006.

I wasn’t able to find any economic analyses that argued that trade was responsible for a majority of manufacturing job losses. It seems safe to conclude that most manufacturing job loss is due to productivity gains, not trade.

It’s also worth noting that NAFTA doesn’t seem to have cost manufacturing jobs at all.

Productivity and output are growing, but have slowed since 2000.

Real output in manufacturing is growing, and has been since the 1980’s, but there are some signs of a slowdown.

Researchers at the Economic Policy Institute claim that slowing manufacturing productivity growth and output growth around 2000 led to the sharp drop in employment.  If real value added in manufacturing had continued growing at the rate it had been in 2000, it would be 1.4x as high today.

Manufacturing output aside from computers and electronic products has been slow-growing since the 90’s.  The average annual output growth rate, 1997-2015, in manufacturing, was 12% in computers, but under 4% in all other manufacturing sectors. (The next best was motor vehicles, at 3% output growth rate.)

US motor vehicle production has been growing far more slowly than global motor vehicle production.

Here are some BLS numbers on output in selected manufacturing industries:

As an average over the time period, this growth rate represents about 2.5%-3.5% annual growth, which is roughly in line with GDP growth.  So manufacturing output growth averaged since the late 80’s isn’t unusually bad.

Labor productivity has also been rising in various industries:

However, when we look at the first and second derivatives of output and productivity, especially post-2000, the picture looks worse.

Multifactor productivity seems to have flattened in the mid-2000’s, and multifactor productivity growth has dropped sharply.  Currently, multifactor productivity is actually dropping.

Manufacturing labor productivity growth is positive, but lower than it’s been historically, at about 0.45% in 2014, and a 4-year moving average of 2.1%, compared to 3-4% growth in the 90’s.

Multifactor productivity in durable goods is down in absolute terms since about 2000 and hasn’t fully recovered.

(Multifactor productivity refers to the returns to labor and capital. If multifactor productivity isn’t growing, then while we may be investing in more capital, it’s not necessarily better capital.)

Labor productivity growth in electronics is dropping and has just become negative.

Labor productivity growth in the auto industry is staying flat at about 2%.

Manufacturing output growth has dropped very recently, post-recession, to about 0. From the 80’s to the present, it was about steady, at roughly 1%.  By contrast, global manufacturing growth is much higher: 6.5% in China, 1.9% globally.  And US GDP growth is about 2.5% on average.

In some industries, like auto parts and textiles,  raw output has dropped since 2000. (Although, arguably, these are lower-value industries and losing output there could just be a sign that the US is moving up the value chain.)

Looking back even farther, there is a slowdown in multifactor productivity growth in manufacturing, beginning in the early 70’s. Multifactor productivity grew by 1.5% annually from 1949-1973, and only by 0.3% in 1973-1983.  Multifactor productivity growth today isn’t clearly unprecedentedly low, but it’s dropping to the levels of stagnation we saw in the 1970’s, or even below.

Basically, recent labor productivity is positive but not growing and in some cases dropping; output is growing slower than GDP; and multifactor productivity is dropping. This points to there being something to worry about.

What might be going on?

Economist Jared Bernstein argues that automation doesn’t explain the whole story of manufacturing job loss. If you exclude the computer industry, manufacturing output is only about 8% higher than it was in 1997, and lower than it was before the Great Recession.  The growth in manufacturing output has been “anemic.”  He says that factory closures have large spillover effects. Shocks like the rise of China, or a global glut of steel in the 1980’s, lead to US factory closures; and then when demand recovers, the US industries don’t.

This model also fits with the fact that proximity matters a lot.  It’s valuable, for knowledge-transfer reasons, to build factories near suppliers.  So if parts manufacturing moves overseas, the factories that assemble those parts are likely to relocate as well. It’s also valuable, due to shipping costs, to locate manufacturing near to expensive-to-ship materials like steel or petroleum.  And, also as a result of shipping costs, it’s valuable to locate manufacturing in places with good transportation infrastructure. So there can be stickiness/spillover effects, where, once global trade makes it cheaper to make parts and raw materials in China, there’s incentives pushing higher-value manufacturing to relocate there as well.

It doesn’t seem to be entirely coincidence that the productivity slowdown coincided with the opening of trade with China. The industries where employment dropped most after 2000 were those where the risk of tariffs on Chinese goods dropped the most.

However, this story is still consistent with the true claim that most lost manufacturing jobs are lost to productivity, not trade. Multifactor productivity may be down and output and labor productivity may be slowing, but output is still growing, and that growth is still big enough to drive most job loss.

Industry Matters

Epistemic status: tentative

In the wake of the election, I’ve been thinking about the decline of manufacturing in America.

The conventional story, the one I’d been told by the news, goes as follows. Cheap labor abroad competes with US manufacturing jobs; those jobs aren’t coming back; most manufacturing jobs are lost to robots, not trade, anyhow; this is tragic for factory workers who lose their jobs, and perhaps they should be compensated with more generous social services, but overall the US’s shift towards a service economy is for the best.  Opposition to outsourcing, while perhaps an understandable emotional reaction from the hard-hit working class, is simply bad economics.  At best, the goal of keeping manufacturing jobs at home is a concession to the dignity and self-image of workers; at worst, it’s wooly-headed socialism or xenophobia.

But what if that story were not true?

Here’s an alternative story, which I think there’s some data to suggest.

Industry — as in, factories in the US making things like cars and trains — is important to long-run technological innovation, because most commercial R&D is in the manufacturing sector, and because factories and research facilities tend to physically co-locate.

High-tech, high-cost-per-unit industries in particular, like the auto industry, are like keystone species in an industrial ecosystem, because you need many different kinds of technology to support them, and because the high cost per unit makes them the first industries where it’s worth it to invest in new process improvements like robotics.  If you don’t have heavy industry at home, eventually you won’t have innovation at home.

And if you don’t have innovation at home, your economy may eventually stagnate. Foundational technologies, things like integrated circuits or metallurgy, have high fabricatory depth; better microchips give rise to more computing power which gives rise to untold multitudes of software applications. If your economy lives exclusively on the “leaves” of the tech tree, you aren’t going to be able to capture the value from a long future of continued inventions.  There may be high-paying jobs in the service economy, but an entire economy built on services will eventually flatten out.

In other words: maybe industry matters.

And, while industrial jobs may initially leave the US because they’re cheaper elsewhere, foreign labor doesn’t stay cheap forever. As countries industrialize and become wealthier, they gain expertise and advance technologically, and eventually compete on quality, not just on price.  Rich countries hope to “move up the value chain”, outsourcing cheap and crude tasks to poorer countries while focusing their own efforts on higher-tech, higher-priced tasks. The problem is that this doesn’t always work — since collocation matters, it may be that you need at least some of the basic factory work to stay at home in order to be able to do the high-tech work, especially in the long run.

“Industry matters”, if true, might be an argument in favor of tariffs, in a vaguely Hamiltonian industrial policy.  Now, the laws of economics still hold; tariffs will always cause some degree of damage.  I’m not confident that the numbers work out such that even an ideal tariff would be worth it, let alone the trade policy likely to be administered by the actually-existing USG.

“Industry matters” might also be an argument in favor of deregulation designed around making it easier to move around  “atoms not just bits.”  If environmental and labor regulations make it extremely difficult to build factories in the US, and if industry has an outsized impact on long-run growth, then the cost of regulation is even higher than previously assumed. If a factory doesn’t open, the cost is not only borne by the people today who could have worked in or profited from that factory, but by future generations who won’t be able to work at the new companies which would have been produced from innovations downstream of that factory.

If industry matters, it might be worth it to trade a bit of efficiency today for long-run growth. Not as a concession to Rust Belt voters, but as a genuine value-creating move.

The US is transitioning to a service economy

According to the Bureau of Labor Statistics’ Employment Outlook Handbook, occupations with declining employment include:

  • Agricultural workers
  • Clerks (file, correspondence, accounting, etc)
  • Cooks (fast food and short order)
  • Various manufacturing occupations like “machine tool setters” and “electronic equipment assemblers”
  • Railroad-related occupations
  • Drafters, medical transcriptionists
  • Secretaries and administrative assistants
  • Broadcasters, editors, reporters, radio and television announcers
  • Travel agents

while the jobs with the fastest growth rates include:

  • Nurses, home health aides, physician’s assistants, physical therapists
  • Financial advisors
  • Statisticians, mathematicians
  • Wind turbine service technicians, solar photovoltaic installers
  • Photogrammetry (i.e. mapping) specialists
  • Surgeons, biomedical engineers, nurse midwives, anaesthesiologists, medical sonographers
  • Athletic trainers, massage therapists, interpreters, psychological counselors
  • Bartenders, restaurant cooks, food preparers, waiters and waitresses
  • Cashiers, customer service representatives, hairdressers, childcare workers, teachers
  • Carpenters, construction laborers, electricians, rebar workers, masons

Basically, medicine, education, customer service, construction, and the “helping professions” are growing; factory work, farming, and routine office tasks are shrinking, as are industries like news and travel agents that have been disrupted by the internet.

As far as mass layoffs go, in May 2013 the largest sector by number of mass layoffs was manufacturing, where the largest number of people laid off were in “machinery” and “transportation equipment.”  Construction followed, where most layoffs were in “heavy and civil engineering” construction.

By sector, mining and manufacturing are losing employment, while construction, leisure and hospitality, education and health, and financial services, are gaining employment.

This part of the conventional story is true: manufacturing jobs really are disappearing.

US manufacturing productivity and output are stagnating

It’s not just jobs, but also productivity and output, where manufacturing in the US is weakening.  US manufacturing still produces a lot, but its growth is slowing.  We’re not getting better at making things the way we used to.

In the US, the biggest output gains per industry, in billions of dollars, between 2002 and 2012, were in the federal government, healthcare and social assistance, and professional services, at 2.6%, 2.6%, and 2.4% respectively. Manufacturing only grew by 0.2%.

Manufacturing output as a whole between 1997 and 2015 was only growing at 0.8% a year, meaning that it’s slowed down in the last 20 years.  Broken down by subsector, the highest manufacturing growth rates were in motor vehicles and other transportation equipment, at an average of about 2% yearly growth; other kinds of manufacturing, such as textiles and apparel, were stagnant or even declined in output.  By contrast, the largest output growth between 1997 and 2015 was in information tech, at an average of 5.6% yearly growth, probably coinciding with the rise of the Internet economy.

In other words, US manufacturing isn’t shedding jobs merely because it’s becoming ultra-automated and efficient. US manufacturing growth has slowed down a lot in output as well.

US manufacturing also stagnated in labor productivity and multifactor productivity. Multifactor productivity (the efficiency of labor & capital) in manufacturing has declined at an 0.5% rate from 2007-2014, while it was increasing at a 1.7% rate in 2000-2007, 1.9% in 1995-2000, and 1.1% in 1990-1995.  Manufacturing productivity was roughly flat from the 1970’s through 2000.

Manufacturing total factor productivity is still increasing, but has been leveling off.

Manufacturing output, similarly, is still increasing, but has been leveling off in recent decades.

While overall manufacturing productivity is still growing  over the period 1987-2010, manufacturing output flattened in about 2000.

While manufacturing output seems to have grown roughly steadily since the 1950s, with a slow decline or stagnation in employment from about 1970-2000, note how the output curve seems to be bending at around 2000, just as manufacturing employment plummets.

You can also see this slight bend in the curve, beginning in around 2000, in manufacturing value added.

The story of “we’re getting more efficient and thus using fewer workers” is only part true.  We’re getting more efficient, but at a slowing rate. We’re producing more output than we did in the 70’s, but that seems to have leveled off in around 2000. Yes, there’s more output and fewer workers, but it looks like recently, since about 2000, multifactor productivity and output are slowing down.

The Big Three auto manufacturers in the US, between 1987 and 2002, had dropping market share and stock price, largely due to international competition.  They lagged the competition in durability and vehicle quality, so were forced to cut prices. They also had a labor productivity disadvantage relative to Japan.  It took nearly two decades for US car manufacturers to catch up to Japanese production process improvements.

In other words, the story of the decline in US manufacturing jobs is not merely that we’re a rich country with expensive labor, or a high-tech country that uses automation in place of workers.  If that were true, output and productivity would be continuing to grow, and they’re not.  US manufacturing is stagnating in quality and efficiency.

Robots aren’t taking American jobs

The decline in US manufacturing began in the 1970’s and 1980’s, as trade liberalization made it easier to move production abroad, and new corporate governance rules made US managers focus on stock prices and short-term performance (which could be boosted by moving factories to cheaper countries.)

Manufacturing automation, by contrast, is much newer, and can’t account for anywhere near that much job loss.  There are only 1.6 million industrial robots worldwide, mostly in the auto and electronics industries; an automotive company has 10x the roboticization of the average manufacturing company.  That is to say, robots are only being used in the highest-tech sectors of the manufacturing world, and not very widely at that. Industrial robots are a rapidly growing but very recent development; there was a 15% increase in the world’s supply of robots just in 2015.

Moreover, countries with more growth in industrial robotics don’t have more job loss.  Most new robots are actually abroad rather than in the US. The largest market is in China, with 27% of global supply; the second largest market is in Europe.  The US boosted its purchases of robots by only 5% this year, at well below the global rate of robotics growth.

It is simply false that robots are causing any significant part of US manufacturing unemployment. There aren’t very many, they haven’t been around very long, they’re mostly in other countries, and they don’t hurt employment in those countries.

According to the Bureau of Labor Statistics, no US manufacturing layoffs in 2013 were due to automation.

Most of the news articles about the dangers of technological unemployment are based on projections about which jobs are in principle automatable. This is speculative, and doesn’t take into account new industries that may open up as technology improves (basically the argument from Say’s law.)  The “post-work future” is largely science fiction at this point. Lost manufacturing jobs are real — but they weren’t lost to robots.

Trade caused manufacturing job loss

The US-China Relations act in 2000 that normalized trade relations permanently was a “shock” to US manufacturing that US jobs were slow to recover from.  Not only did employment plummet, but manufacturing productivity also dropped steeply.

Only 2% of job losses are due to offshoring. But this understates the true amount: if plants close in the US while companies buy from foreign affiliates, that’s effectively “jobs moving overseas” under a different name.  Foreign affiliates now make up 37% of the total employees of US multinational companies, a figure that has been steadily rising since the 80’s; it was 26% in 1982.

Moreover, trade can also cause US job losses if foreign-owned companies outcompete US companies. The most common reason given for manufacturing layoffs in 2013 was “business demand”, mostly contract completion.  Restructuring and financial problems such as bankruptcy were also common reasons.  The main reason for manufacturing layoffs seems to be failure of US factories — poor demand or poor company performance.  Some portion of this is probably due to international competition.

In short, it’s freer trade and poor competitiveness on the international market, not automation, that has hurt American manufacturing.  It’s not the robots that are the problem — if anything, we don’t have enough robots.

Manufacturing drives the future, and location matters

A McKinsey report on manufacturing notes that while manufacturing is only 16% of US GDP, it’s a full 37% of productivity growth.  77% of commercial research and development comes from manufacturing.  Manufacturing, in other words, is where new technology comes from, and new technology drives growth.  If you care about the future economy, you care about manufacturing.

R&D, especially later-stage development rather than basic academic research, must be physically proximate to the lead factory even if some production is globalized, for reasons of communication and feedback between research and production.  You can’t outsource or trade all your manufacturing without losing your ability to innovate.

Moreover, globalized supply chains have real costs: as trade and outsourcing increase, transportation costs and supply chain risks have also been increasing. Physical proximity places some limits on how widely dispersed manufacturing can be.  Trade growth has outpaced infrastructure growth in the US, driving transportation costs up.  The cost of freight for steel and iron ore is almost as high as the material itself.

Steel production, in particular, has plummeted in industrialized countries since the 70’s and 80’s, as part of the switch to a service economy. China’s steel and cement production since the 80s seems to have grown rapidly, while its car production seems to be growing roughly linearly.  South Korea’s steel production is growing steadily. US car production, by contrast, has been shrinking (in terms of number of units), as has its steel production.  Because (due to their weight) metals have unusually high transportation costs, proximity matters an unusual amount, and so a fall in steel production might mean a fall in heavy industry output generally, which is difficult to recover from.

The main theory here is that, once you cease to be an industrial economy, it’s hard to profitably keep factories at home, which means it’s hard to innovate technologically, which means long-run GDP growth is threatened.

The largest manufacturing industries are machines, electronics, and metals

The largest manufacturing companies in China make cars (SAIC, Dongchen, China South Industries Group), chemicals (Sinochem, Chemchina), metals (Minmetals, Hesteel, Shougang, Wuhan), various engineering (Norinco, China Metallurgical group, Sinomach), electronics (Lenovo), phones (Huawei), ships (China Shipbuilding).

The US’s largest manufacturers are general engineering (GE), automotive (GM, Ford), electronics (HP, Apple, IBM, Dell, Intel), pharmaceuticals (Cardinal Health, Pfizer), consumer goods (Procter & Gamble, Johnson&Johnson), aerospace (Boeing, Lockheed Martin), food and beverage (Pepsi, Kraft, Coca-Cola), construction equipment (Caterpillar), and chemicals (Dow).

Germany’s largest manufacturing companies are automotive (Volkswagen, Daimler, BMW), chemicals (BASF), engineering (Siemens, Bosch, Heraeus), steel (ThyssenKrupp), pharmaceuticals (Bayer), and tires (Continental).

Japan’s largest manufacturers are automotive (Toyota, Nissan, Honda), engineering (Hitachi, Panasonic, Toshiba, Mitsubishi, Mitsui, Sumitomo, Denso), electronics (Sony, Fujitsu, Canon), steel (Nippon Steel, JFE), and tires (Bridgestone).

Korea’s largest manufacturers are electronics (Samsung, LG), automotive (Hyundai, Kia), and steel (POSCO).

Machinery and appliances, and electronics and parts, are by far the largest exports from Mexico.

Top exports from China, at a coarse level of granularity, are machines (48%), textiles (11%), and metals (7.8%).  At a more granular level, this involves computers, broadcasting equipment, telephones, integrated circuits, and office machine parts.

US‘s top exports are machines (24%), transportation (15%), chemicals (13%), minerals (11%), and instruments (6.3%). More granularly, this is integrated circuits, gas turbines, cars, planes and helicopters, vehicle and aircraft parts, pharmaceuticals, and refined petroleum.

Germany‘s top exports are machines (27%), transportation (23%), chemicals (13%), metals (8.1%), or in more detail: cars, vehicle parts, pharmaceuticals, and a variety of smaller machine things (valves, air pumps, gas turbines, etc).

Japan’s exports are machines (37%), transportation (22%), metals (9.8%), chemicals (8.5%), and instruments (7.8%). Or, in more detail: cars, vehicle parts, integrated circuits, and a variety of machines like industrial printers.

South Korea’s exports are machines (37%), transportation (19%), minerals (8.9%), metals (8.5%), plastics (7.1%). In more detail, integrated circuits, phones, cars, ships, vehicle parts, broadcasting equipment, and petroleum.

“Heavy industry” — that is, machines, engineering, automobiles, electronics, and metals — is the cornerstone of an industrial economy.  Integrated circuits are a true “root” of the tech tree, the foundation on which the information economy is built. Capital-intensive heavy industries like automobiles are a “keystone” which is deeply interwoven with the production of machines, parts, robots, electronics, and steel.

It’s a relevant warning sign for Americans that many current developments that seem likely to improve “heavy industry” are not concentrated in the US.

Of the top 5 semiconductor companies, only 2 are American. Some electronics innovations, like flat-screens (developed by Sony) and laser TV’s (developed by LG) were developed by Asian companies, and Mexico is the biggest exporter of flat screen TVs.  Robotics, as discussed above, is being pursued much more intensively in Asia and Europe than in the US. “Smart factories”, in which automation, sensors, and QA data analysis are integrated seamlessly, are being pioneered in Germany by Siemens.  The majority of drones worldwide are produced by Israel.  The Japanese companies Canon and Ricoh, as well as the American HP, are expected to launch 3d printers this year; meanwhile the largest manufacturer of desktop 3d printers, XYZprinting, is Taiwanese.

A positive sign, from a US-centric perspective, is that self-driving cars are being developed by American companies (Tesla and Google.)  Another positive sign is that basic research in physics and materials science — the fundamentals that make a continuation of Moore’s law possible — is still quite concentrated in American universities.

But, to have a strong industrial economy, it’s not enough to be good at software and basic research; it remains important to make machines.

Non-xenophobic, economically literate, pro-industry

Globalization has been a humanitarian triumph; Asia’s new prosperity has vastly reduced global poverty in recent decades. To acknowledge that global competition has been hard on Americans doesn’t preclude appreciating that it’s been good for foreigners, and that foreigners have equal moral worth to ourselves.

Acknowledging harms from trade also doesn’t require one to be a fan of planned economies or a believer in a “zero-sum world.” Trade is always locally a win-win; restricting it always has costs.  But it may also be true that short-term gains from trade can be counterweighted by long-term losses in productivity, especially due to loss of the gains in local skill and knowledge that come from being a manufacturing center.

If you want to live in a vibrantly growing country, you have to make sure it remains a place where things are made.

That’s not mere protectionism, and it’s certainly not Luddite.

I don’t think this is true of, say, agriculture, where vast increases in efficiency have reduced the number of farmers needed to support the global population, but where that’s not really a problem for overall growth. US farming has not lost ground — we produce more food than ever.  We are not getting worse at farming, we just need fewer people to do it.  I suspect we are getting worse at manufacturing.  And since manufacturing has so disproportionate an effect on downstream growth and innovation, that’s a problem for all of us, in a way that it’s not a problem if farmers or travel agents lose their jobs to new technologies.

Pro-Industry, Anti-Corruption

The truly obvious gains from capitalism are actually gains from industry. Cheap, varied, abundant food. Electricity and electric appliances. Fast transportation. The sort of things described in Landsailor.

Other things that show up in GDP are less obviously good for humans. If real estate prices rise, are we really better housed? If stock prices rise, do we really have more stuff?  If we spend more on medicine and education but don’t have better health outcomes or educational outcomes, are we really better cared for and better educated?

The value of firms has dramatically shifted, since 1975, towards the “dark matter” of intangibles — things like brands, customer goodwill, regulatory favoritism, company culture, and other things that can’t be easily measured or copied. US S&P 500 firms are now 5/6’ths dark matter.  How much of the growth in their value really corresponds to getting better at making stuff?  And how much of it is something more like “accounting formalism” or “corruption”?

If you are suspicious of things that cost more money but don’t create obvious Good Things for humans, then you will not consider a shift to a service economy a good outcome, even if formally it doesn’t look too bad in GDP terms. If you take a jaundiced view of medicine, education, the “helping” professions, government, and management — if you see them as frequently doing expensive but unhelpful things — then it is not good news if these sectors grow while manufacturing declines.

If your ideal vision of the future is a science-fiction one, where we cure new diseases, find new fuel sources, and colonize the solar system, then manufacturing is really important.  

The old slogans like “what’s good for GM is good for America” are not as far from the truth as you’d think.