For almost 40 years, inequality within the top percentile of the income distribution, measured as the ratio of income share of top 0:1% to the income share of top 1%, has been increasing in the US. The income of super-rich people increased more than the income of rich people. In this paper, we show that improvements in automation technology (the number of tasks for which capital can be used) is an important factor contributing to this inequality. We consider a model in which labor has a convex cost and capital has a linear cost. This leads to a decreasing returns to scale profit function for entrepreneurs. As capital replaces labor in more and more tasks, the severity of diseconomies of scale diminishes, hence the market share of top-skilled entrepreneurs increases. If entrepreneurial skill is distributed according to a Pareto distribution, then top income distribution can be approximated by a Pareto distribution. We show that the shape parameter of this distribution is inversely related to the level of automation. Finally, we rationalize convex cost of labor using the theory of efficiency wage.
That is my piece in the Globe and Mail, excerpted with edits from my new Big Business: A Love Letter to an American Anti-Hero, here is one excerpt:
Furthermore, it is striking just how effective the major tech companies have been as innovators. Other than providing the best free search in the world, Alphabet – the umbrella corporation under which Google is a subsidiary – gave us Gmail, one of the best and biggest e-mail services in the world, for free. Google Maps, which is also free, is pretty neat, too.
Then, despite the risks identified by critics of the deal – that YouTube appeared to be a bottomless pit for copyright-violation suits and nasty comments – Google bought the streaming-video service for US$1.65 billion, and dramatically upgraded it. Google cleaned up the legal issues, using its advanced software capabilities to spot copyright violations while enforcing takedown requests, improving search and heavily investing in the technology that has helped make video so widely used on the internet today.
In 2005, Google purchased Android and elevated the company’s open-source system to the most commonly used cellphone software in the entire world. Because of the Google-Android combination, hundreds of millions of people have enjoyed better and cheaper smartphones. More generally, Google has made most of its software open-source, enabling others to build upon it with additional advances, with entire companies now devoted to helping other companies build upon that infrastructure – meaning Google has not likely been the major beneficiary of its own actions.
Google, by way of Alphabet, has taken a lead role in developing self-driving vehicles and the underlying artificial intelligence, now being developed through Waymo; by throwing its weight behind this, Alphabet made the concept more publicly acceptable, and it could potentially save many lives on the road. After Hurricane Maria devastated Puerto Rico, Alphabet also stepped in to do good, deploying its work-in-progress Project Loon to restore internet access, which may eventually be integral for remote areas in Africa. It’s a bold attempt to create a better and more connected living situation for some of the world’s more vulnerable people.
All that from a company that is just a little more than 20 years old. Is this really the kind of company we should be punishing?
There are other points of interest at the link.
Here’s a good interview by the Richmond Fed of Preston McAfee. McAfee was one of the designers of the FCC’s spectrum auctions and used that experience to move from academia to technology firms. He has held top positions at Yahoo, Google and Microsoft. Here’s one issue that I have discussed before, tacit collusion among AIs..
EF: What are the implications of machine learning, if any, for regulators?
McAfee: It is likely to get a lot harder to say why a firm made a particular decision when that decision was driven by machine learning. As companies come more and more to be run by what amount to black box mechanisms, the government needs more capability to deconstruct what those black box mechanisms are doing. Are they illegally colluding? Are they engaging in predatory pricing? Are they committing illegal discrimination and redlining?
So the government’s going to have to develop the capability to take some of those black box mechanisms and simulate them. This, by the way, is a nontrivial thing. It’s not like a flight recorder; it’s distributed among potentially thousands of machines, it could be hundreds of interacting algorithms, and there might be hidden places where thumbs can be put on the scale.
I think another interesting issue now is that price-fixing historically has been the making of an agreement. In fact, what’s specifically illegal is the agreement. You don’t have to actually succeed in rigging the prices, you just have to agree to rig the prices.
The courts have recognized that a wink and a nod is an agreement. That is, we can agree without writing out a contract. So what’s the wink and a nod equivalent for machines? I think this is going somewhat into uncharted territory.
One of the highest ransoms ever paid — US $60 million for the two Born brothers in Argentina in 1975 — was negotiated by one of the captives himself: Jorge Born. As a company insider, he knew how much money could be raised, but it still took seven months before his captors were convinced that they had truly squeezed him dry. When it finally arrived, the father felt he had no option but to accede to the memorandum signed by his son and his kidnappers. So negotiators work extremely hard to avoid parallel negotiations and bat away unhelpful interventions from the hostage. It is not surprising that some victims despair.
That is from the new and interesting Kidnap: Inside the Ransom Business, by Anja Shortland.
Ladders runs an excerpt from my book Big Business: A Love Letter to an American Anti-Hero, here is one part:
Another way to think about the non-pay-related benefits of having a job is to consider the well-known and indeed sky-high personal costs of unemployment. Not having a job when you want to be working damages happiness and health well beyond what the lost income alone would account for. For instance, the unemployed are more likely to have mental health problems, are more likely to commit suicide, and are significantly less happy. Sometimes there is a causality problem behind any inference—for instance, do people kill themselves because they are unemployed, or are they unemployed because possible suicidal tendencies make them less well suited to do well in a job interview? Still, as best we can tell, unemployment makes a lot of individual lives much, much worse. In the well-known study by economists Andrew E. Clark and Andrew J. Oswald, involuntary unemployment is worse for individual happiness than divorce or separation. Often it is more valuable to watch what people do rather than what they say or how they report their momentary moods.
There is much more at the link.
One of the goals of the Swachh Bharat or Clean India mission was to achieve an “open-defecation free” (ODF) India by 2 October 2019 (the 150th anniversary of Gandhi’s birth). OD is a big problem in India contributing to child sickness, stunting and a host of permanent problems including lower IQs. As of 2011, half of Indian households didn’t have access to a latrine but since that time millions of latrines have been built and the government has encouraged (sometimes “vigorously”) latrine use.
Unfortunately, the close connection between the Swachh Bharat mission and Prime Minister Modi has made achieving the mission, or claiming to have achieved the mission, not just a political goal but a test of patriotism and support for Modi. The Swachh Bharat website, for example, proclaims that India is now 99% open defecation free, including 100% coverage in Rajasthan, Madhya Pradesh, Utter Pradesh and Bihar.
In Rajasthan and Madhya Pradesh, states that had been declared ODF by the time of the survey, we found rural open defecation rates of about 50% and about 25%, respectively. The vast majority of villages in Uttar Pradesh and Bihar have also been declared ODF; the quantitative survey found open defecation rates of approximately 40% and 60%, respectively, in these states (Gupta et al 2019).
How do villages, and eventually blocks, districts, and states get declared ODF despite high levels of open defecation? One reason is that ODF status is often declared where latrine coverage is, in fact, incomplete: about 30% of households in the four states we studied did not own a latrine. Another reason is that many people who own a latrine still defecate in the open. In fact, latrine use among latrine owners has not changed since 2014: one in four people who own a latrine in the 2018 survey do not use it (Gupta et al 2019).
Ambitious program need not reach their goals to be successful–progress has been made and Modi can take credit–but it’s dangerous when problems are declared solved in order to meet political timelines and narratives. Work remains to be done.
Somehow I had missed this earlier paper by John Charles Bradbury:
Since the early-2000s, the share of revenue going to Major League Baseball players has been diminishing similar to the decline of labor’s share of revenue observed in the US economy. This study examines potential explanations for the decline in baseball, which may result from related factors and provide information relevant to explaining this macroeconomic trend. The results indicate that the value-added from non-player inputs, collective bargaining agreement terms, and related changes in the returns to winning contributed to the decline of players’ share of income. Competition from substitute foreign labor and physical capital are not associated with the decline in labor’s share of income in baseball.
There is also this sentence:
The decline in labor’s revenue share in MLB is consistent with changes in revenue share in the hospitality and leisure industry that experienced a decrease in labor’s share of income from 65.7 percent to 62.1 percent between 1987 and 2011 (Elsby, Hobijn, and Şahin 2013).
Another hypothesis I have heard is that baseball players are not nearly as good at, or as well-suited for, the use of social media, as compared say to the more visible basketball players. Another (quite speculative) claim is that sabermetrics has commoditized a lot of players and in turn lowered their bargaining power.
No, I do not favor a gold standard, for reasons explained in this Bloomberg column. Still, it is sad/funny to watch the mood affiliation circus of those trying to suggest, in more or less the same breath, that Trump’s Fed picks are dangerous and terrible, and also that the gold standard is the worst idea ever. Here is one point of mine:
Historical data indicates that industrial production volatility was not higher before 1914, when the U.S. was on the gold standard, compared to after 1947, when it mostly wasn’t. And there are similar results for the volatility of unemployment. That’s not quite an argument for the gold standard, but it should cause opponents of the gold standard to think twice. Whatever the imperfections of a gold standard might be, monetary authorities make a lot of mistakes, too.
And here is the closer:
Most generally, I still think central bank governance can do a better job than a gold-based system that sometimes creates excess deflationary pressures.
Nonetheless, the contemporary world is always testing my belief in central banking. Exactly how will matters unfold when so many world leaders are not behaving as responsibly as they should? Might that irresponsibility seep into monetary policy? After all, populations are aging and debt is accumulating. Surely it is reasonable to worry that some of these governments will seek to monetize their debts and move toward excessively easy money.
Oh, but wait — I forgot one big new argument in favor of a gold standard: President Trump himself. Perhaps his management of central bank affairs is somewhat … erratic? Might it not be a good idea to have the operation of monetary policy protected by a greater reliance on rules? My personal preference is for a nominal GDP rule, but the irony is this: At the end of the day, the advocates of the gold standard, and their possible presence on the Federal Reserve Board, are themselves the best argument for … the gold standard.
By George Melloan, here is one bit:
So Mr. Cowen’s book is timely, and his writing style is a refreshing contrast to the strident left-wing declamations that are so common today. He is calm and conversational, splashing cool water on the firebrands. He writes: “All of the criticisms one might mount against the corporate form—some of which are valid—pale in contrast to two straightforward and indeed essential virtues. First, business makes most of the stuff we enjoy and consume. Second, business is what gives most of us jobs. The two words that follow most immediately from the world of business are ‘prosperity’ and ‘opportunity.’”
Here is the full review, very well done in my admittedly biased view.
Camden NJ has thrice been named the most dangerous city in America. Camden suffered not only from high crime but from poor policing under a rigid union contract. Jim Epstein described the situation in 2014:
Camden’s old city-run police force abused its power and abrogated its duties. It took Camden cops one hour on average to respond to 911 calls, or more than six times the national average. They didn’t show up for work 30 percent of the time, and an inordinate number of Camden police were working desk jobs. A union contract required the city to entice officers with extra pay to get them to accept crime-fighting shifts outside regular business hours. Last year, the city paid $3.5 million in damages to 88 citizens who saw their convictions overturned because of planted evidence, fabricated reports, and other forms of police misconduct.
In 2012, the murder rate in Camden was about five times that of neighboring Philadelphia—and about 18 times the murder rate in New York City.
In May of 2013, however, the entire police department was disbanded nullifying the union contract and an entirely new county police department was put into place.
The old city-run force was rife with cops working desk jobs, which Cordero saw as a waste of money and manpower. He and Thomson hired civilians to replace them and put all uniformed officers on crime fighting duty. Boogaard says she didn’t see a single cop during the first year she lived in the city. “Now I see them all the time and they make friendly conversation.” Pastor Merrill says the old city-run force gave off a “disgruntled” air, and the morale of Metro police is noticeably better. “I want my police to be happy,” he says.
Without the expensive union contracts the new force added officers and also introduced more technology such as Shotspotter. So what has been the result? Violent crime is down and clearances are up (charts from Daniel Bier, who also notes that the fall in violent crime and increase in convictions far exceed that in comparison to New Jersey more generally or Philadelphia.)
As I have long argued, we need more police and better policing in America.
I view this work as an antidote to many of the less than stellar arguments circulating today. It looks like this:
Table of contents
1. A new pro-business manifesto
2. Are businesses more fraudulent than the rest of us?
3. Are CEOs paid too much?
4. Is work fun?
5. How monopolistic is American big business?
6. Are the big tech companies evil?
7. What is Wall Street good for, anyway?
8. Crony capitalism: How much does big business control the American government?
9. If business is so good, why is it disliked?
Here is part of the Amazon description:
An against-the-grain polemic on American capitalism from New York Times bestselling author Tyler Cowen.
We love to hate the 800-pound gorilla. Walmart and Amazon destroy communities and small businesses. Facebook turns us into addicts while putting our personal data at risk. From skeptical politicians like Bernie Sanders who, at a 2016 presidential campaign rally said, “If a bank is too big to fail, it is too big to exist,” to millennials, only 42 percent of whom support capitalism, belief in big business is at an all-time low. But are big companies inherently evil? If business is so bad, why does it remain so integral to the basic functioning of America? Economist and bestselling author Tyler Cowen says our biggest problem is that we don’t love business enough.
In Big Business, Cowen puts forth an impassioned defense of corporations and their essential role in a balanced, productive, and progressive society. He dismantles common misconceptions and untangles conflicting intuitions.
Here is the publisher’s home page. Definitely recommended…and if you are a regular MR reader, no more than five to ten percent of this book has already appeared on this blog.
An excellent new working paper uses genetic markers for educational attainment to track students through the high school math curriculum to better understand the role of nature, nurture and their interaction in math attainment. The paper begins with an earlier genome wide association study (GWAS) of 1.1 million people that found that a polygenic score could be used to (modestly) predict college completion rates. Panel (a) in the figure at right shows how college completion is five times higher in individuals with an education polygenic score (ed-PGS) in the highest quintile compared to individuals with scores in the lowest quintile; panel b shows that ed-PGS is at least as good as household income at predicting college attainment but not quite as good as knowing the educational level of the parents.
Of the million plus individuals with ed-PGS, some 3,635 came from European-heritage individuals who were entering US high school students in 1994-1995 (the Add Health sample). Harden, Domingue et al. take the ed-PGS of these individuals and match them up with data from their high school curricula and their student transcripts.
What they find is math attainment is a combination of nature and nurture. First, students with higher ed-PGS are more likely to be tracked into advanced math classes beginning in grade 9. (Higher ed-PGS scores are also associated with higher socio-economic status families and schools but these differences persist even after controlling for family and school SES or looking only at variation within schools.) Higher ed-PGS also predicts math persistence in the following years. The following diagram tracks high ed-PGS (blue) with lower ed-PGS (brown) over high school curricula/years and post high-school. Note that by grade 9 there is substantial tracking and some cross-over but mostly (it appears to me) in high-PGS students who fall off-track (note in particular the big drop off of blue students from Pre-Calculus to None in Grade 12).
Nature, however, is modified by nurture. “Students had higher returns to their genetic propensities for educational attainment in higher-status schools.” Higher ed-PGS students in lower SES schools were less likely to be tracked into higher-math classes and lower-SES students were less likely to persist in such classes.
It would be a mistake, however, to conclude that higher-SES schools are uniformly better without understanding the tradoffs. Lower SES schools have fewer high-ability students which makes it difficult to run advanced math classes. Perhaps the lesson here is that bigger schools are better, particularly bigger schools in poorer SES districts. A big school in a low SES district can still afford an advanced math curriculum.
The authors also suggest that more students could take advanced math classes. Even among the top 2% of students as measured by ed-PGS only 31% took Calculus in the high-SES schools and only 24% in the low SES schools.It’s not clear to me, however, that high-PGS necessitates high math achievement. Notice that many high-PGS students take pre-calc in Grade 11 but then no math in Grade 12 but they still go on to college and masters degrees. Lots of highly educated people are not highly-educated in math. Still it wouldn’t be a surprise if there were more math talent in the pool.
There is plenty to criticize in the paper. The measure of SES status by school (average mother’s educational attainment) leaves something to be desired. Moreover, there are indirect genetic effects, which the authors understand and discuss but don’t have the data to test. An indirect genetic effect occurs when a gene shared by parent and child has no direct effect on educational capacity (i.e. it’s not a gene for say neuronal development) but has an indirect “effect” because it is correlated with something that parent’s with that gene do to modify the environment of their children. Nevertheless, genes do have direct effects and this paper forces us to acknowledge that behavioral genetics has implications for policy.
Should every student be genotyped and tracked? On the one hand, that sounds horrible. On the other hand, it would identify more students of high ability, especially from low SES backgrounds. Genetics tells us something about a student’s potential and shouldn’t we try to maximize potential?
For homework, work out the equilibrium for inequality, rewatch the criminally underrated GATTACA and for an even more horrifying picture of the future, pay careful attention to the Mirrlees model of optimal income taxation.
Dear friends: Monday, April 8 isn’t just my birthday. It’s also the official launch date for *Open Borders*!
URL for ordering the book: https://www.amazon.com/gp/product/1250316960/ref=as_li_tl?ie=UTF8&camp=1789&creative=9325&creativeASIN=1250316960&linkCode=as2&tag=bryacaplwebp-20&linkId=1ed2cdfe4a1c0cd2a62e942a39f87b9d
URL for an introductory post on the book: https://www.econlib.org/pre-order-open-borders-the-science-and-ethics-of-immigration/
The United States, as of 2014, spends 160 times as much exploring space as it does exploring the oceans.
That is from the new and interesting Jump-Starting America: How Breakthrough Science Can Revive Economic Growth and the American Dream, by Jonathan Gruber and Simon Johnson, two very eminent economists. And if you are wondering, I believe those numbers are referring to government efforts, not the private sector. I am myself much more optimistic about the economic prospects for the oceans than for outer space.
Most of all this book is a plea for radically expanded government research and development, and a return to “big science” projects.
Overall, books on this topic tend to be cliche-ridden paperweights, but I found enough substance in this one to keep me interested. I do, however, have two complaints. First, the book promotes a “side tune” of a naive regionalism: “here are all the areas that could be brought back by science subsidies.” Well, maybe, but it isn’t demonstrated that such areas could be brought back in general, as opposed to reshuffling funds and resources, and besides isn’t that a separate book topic anyway? Second, too often the book accepts the conventional wisdom about too many topics. Was the decline of science funding really just a matter of will? Is it not at least possible that federal funding of science fell because the return to science fell? Curing cancer seems to be really hard. Furthermore, some of the underlying problems are institutional: how do we undo the bureaucratization of society so that the social returns to science can rise higher again? Will a big government money-throwing program achieve that end? Maybe, but the answers on that one are far from obvious. This is too much a book of levers — money levers at that — rather than a book on complex systems. I would prefer a real discussion of how today science has somehow become culturally weird, compared say to Mr. Spock and The Professor on Gilligan’s Island. The grants keep on going to older and older people, and we are throwing more and more inputs at problems to get at best diminishing returns. Help!
Still, I read the whole thing through with great interest, and it covers some of the very most important topics.