Macro Roundup

Susan Edelman, Selim Algar and Aaron Feis, "Richard Carranza held ‘white-supremacy culture’ training for school admins,"New York Post, May 20, 2019, https://nypost.com/2019/05/20/richard-carranza-held-doe-white-supremacy-culture-training/

Richard Carranza held ‘white-supremacy culture’ training for school admins

City Department of Education brass are targeting a “white-supremacy culture” among school administrators — by disparaging ideas like “individualism,” “objectivity” and “worship of the written word,” The Post has learned.

A presentation slide obtained by The Post offers a bullet-point description of the systemic, supposedly pro-white favoritism that Schools Chancellor Richard Carranza claims must be eradicated from the DOE, and provides just one insight into his anti-bias training efforts.

The list — derived from “Dismantling Racism: A Workbook for Social Change Groups” by Kenneth Jones and Tema Okun — names more than a dozen hallmarks of “white-supremacy culture” that school administrators are expected to steer clear of.

“They include such dynamics as “paternalism,” a “sense of urgency” and “power hoarding,” according to the slide, which an insider said was part of mandatory training sponsored and funded by the department’s Office of Equity and Access and recently administered to principals, central office supervisors and superintendent teams.

The seminar is concurrent with Carranza’s larger push to root out “implicit bias” in the school system — an effort that some veteran DOE members blasted as creating a view of “toxic whiteness” detailed in a front-page story in Sunday’s Post.

“The training is not focused on white supremacy and white privilege,” Carranza said after a City Council budget hearing on Monday, referring to his larger campaign.

“It’s about what are our biases and how we work with them.”

The two slides were shown to top managers but were not part of a $23 million city wide implicit bias training, officials said.

The mandatory session for higher-ups included a “White Privilege Exercise” sheet in which attendees were asked to score the personal relevance of certain statements on a scale of 0 to 5.

“If a police officer pulls me over, I can be sure I haven’t been singled out because of my race,” one scenario reads.

“I can be pretty sure that if I ask to talk to ‘the person in charge,’ I will be facing a person of my race,” another says.

The DOE did not immediately respond to a request for comment about the materials used for the administrators’ training, but one adviser said that if the program’s frankness is making people uncomfortable, that’s because it’s working.

“It requires discomfort,” said Matt Gonzales, who serves as an outside adviser on the DOE’s school diversity task force and is a director of New York Appleseed, an advocacy group for school integration.

“Having to talk about someone’s own whiteness is a requirement for them to become liberated.”

Several recent attendees of the DOE’s overarching implicit-bias training sessions — mandatory for all, including teachers — have bristled at the program’s emphasis on the inherent insidiousness of “white” culture.

White employees who object when accused of harboring deep-seated bias are branded “fragile” and “defensive,” one insider who received the training has said.

But Carranza said on Monday that such skeptics often don’t realize their own biases until they are forced to confront them and that they are likely the ones who need the training the most.

“It’s good work. It’s hard work,” Carranza said. “And I would hope that anybody that feels that somehow that process is not beneficial to them, I would very respectfully say they are the ones that need to reflect even harder upon what they believe.”

Carranza also waved off allegations by at least four white DOE administrators who are poised to sue the department over their claims that, under his watch, they were demoted or stripped of duties in favor of less qualified persons of color.

“It’s always been my experience that anyone that comes in as a CEO of an organization takes a look at the organization and, based on their experience, makes some changes,” he said. “This is no different.”

The schools boss insisted that there was room on his staff for people of any race who share his emphasis on equality.

“I have some deputy chancellors that are white, but have an incredible equity lens as well... for making sure that historically underrepresented communities are being served,” Carranza said.

But one Manhattan middle school teacher who underwent mandatory implicit-bias training in December said she left feeling as though everything she had learned about “colorblindness” was being uprooted.

“I say they’re my students whether they’re green, purple, orange or black,” the educator, who asked not to be identified, told The Post. “We’re being told if you’re not recognizing students as African American, Haitian, Puerto Rican, Dominican, et cetera, you’re wrong.”

“It feels like I’m in a dystopian novel where all of a sudden being white is bad. All of a sudden, I’m the enemy.”

“…Bottom Line: The AFL-CIO can only get a distorted and wildly inflated CEO-to-worker pay ratio of something like 360-to-1 every year with a flawed apples-to-oranges analysis that compares the total annual compensation of a small, select group of CEOs in their prime earning years heading America’s largest multi-national corporations, who probably typically work 50-60 hours per week or more, to the average annual cash wages of part-time rank-and-file employees who work less than 34 hours per week on average, often for small companies that aren’t anywhere close to the size of S&P 500 companies. According to this 2015 report from Business Insider, S&P 500 companies employ only 17% of workers in the US, so it’s not really fair to compare the salaries to S&P 500 CEOs to all of the rank-and-file US workers, since a large of majority of those workers aren’t employed by large multinational companies in the S&P 500.Once we make a more statistically valid apples-to-apples comparison, the CEO-to-worker compensation ratio falls by two-thirds from the AFL-CIO’s expected 364.5-to-1 ratio to 124-to-1 once we consider total (median) compensation for both CEOs and full-time (40 hours per week) rank-and-file workers employed by companies with 500 or more workers. If we assume a 60-hour work week for the average worker (to be comparable to the workweek of an average CEO), the CEO-to-worker compensation ratio falls by two-thirds to only 83-to-1 for median CEO pay. Further, even if we could confiscate 100% of the compensation of all S&P 500 CEOs, the typical rank-and-file worker would probably get about $1.34 per week in additional after-tax earnings. Big deal….”

Mark Perry, "Any day now, expect the AFL-CIO to produce another hugely inflated CEO-to-worker pay ratio, debunked in advance," American Enterprise Institute, May 20, 2019, https://www.aei.org/carpe-diem/its-that-time-of-year-expect-afl-cio-to-produce-another-hugely-inflated-ceo-to-worker-pay-ratio-any-day-debunked-in-advance-2/

Any day now, expect the AFL-CIO to produce another hugely inflated CEO-to-worker pay ratio, debunked in advance

Ahead of this year’s annual AFL-CIO report on executive pay, the Wall Street Journal last week published its second new annual report on CEO compensation in 2018 for the S&P 500 companies along with several related articles here and here. Therefore, like last year, I’m able to preempt the AFL-CIO and publish a rebuttal to its anticipated massively inflated 360+ CEO-to-worker pay ratio before its analysis is released, instead of responding to their report after it has gone public and gets widely and blindly reported by the media without anyone (except John Merline of IBD in 2015 ) ever questioning “the statistical legerdemain used to produce it” (see below).

As I have reported annually every May since 2014, here is a summary of the main flaws in the “methodology” used by the AFL-CIO to produce its wildly inflated 360+ CEO-to-worker pay ratios year after year:

The AFL-CIO uses the mean (average) CEO compensation figure each year, which is always much higher than the more realistic and more typical median CEO compensation. Based on the Wall Street Journal‘s CEO pay database in 2017, the mean compensation for S&P 500 CEOs was $13.5 million, which was 13.4% higher than the more realistic median pay of $11.9 million for the typical CEO in that year. For 2018, the Wall Street Journal is reporting that the median compensation for S&P 500 CEOs last year was $12.4 million, a 4.2% increase from the median compensation in 2017. (The Wall Street Journal did not provide a complete database of CEO compensation for 2018, so it’s not possible to report the mean compensation this year for S&P 500 CEOs using WSJ data.)
Applying the 4.2% increase in median pay in 2018 for median compensation, I predict that the AFL-CIO will be reporting average CEO compensation in 2018 of something like $14.5 million (it reported $13.94 million for CEO compensation in 2017, which was higher than the $13.5 million average according to the Wall Street Journal). In that case, the expected average CEO compensation reported by the AFL-CIO of $14.5 million will be about 17% higher than the more realistic compensation of $12.4 million for a typical S&P 500 CEO as reported by the WSJ using median pay.
The AFL-CIO compares total CEO compensation (base salary plus all additional forms of compensation including cash bonuses, stock awards, option awards, pension benefits, etc.) for executives in their prime earning years managing the world’s largest corporations to cash-only pay for part-time rank-and-file workers of all ages working for companies of all sizes (including small grocery stores, independent small restaurants, etc.).

The analysis below summarizes my corrections for those statistical flaws above that are used by the AFL-CIO annually to get an exaggerated, inflated CEO-to-worker pay ratio:

In previous years, the AFL-CIO reported the methodology it uses to calculate annual worker pay as follows:

The average annual income earned by rank-and-file workers is taken from the U.S. Bureau of Labor Statistics Current Employment Statistics survey. Specifically, it is the average hours and earnings of production and nonsupervisory employees on private nonfarm payrolls. The average weekly pay is multiplied by 52.

Based on the AFL-CIO’s methodology, here’s how average worker pay will be updated and calculated for 2018. The AFL-CIO will use an average hourly wage of $22.70 for production and non-supervisory workers last year (BLS data here) and an average workweek of only 33.7 hours (BLS data here) for the average rank-and-file worker. Assuming 52 weeks of work per year: $22.70 per hour x 33.7 hours per week x 52 weeks ≈ $39,779 average annual cash-only earnings for the typical part-time worker in 2018. Conveniently, the fringe benefits that all rank-and-file workers, even part-time workers, receive as part of their total compensation are always completely ignored by the AFL-CIO.

Therefore, the AFL-CIO engages in some questionable statistical chicanery by sloppily reporting an apples-to-oranges comparison of: a) total CEO compensation for only about 500 CEOs working full-time in their prime earning years to b) the cash wages only for 1o4.3 million rank-and-file workers of all ages working for companies of all sizes, who work an average of less than 34 hours per week, and are therefore working part-time, not full-time, on average. But you would never know that from the AFL-CIO’s website because the average hourly pay and average hourly workweek of rank-and-file workers are never reported, and I guess nobody has ever bothered to check to find out that the AFL-CIO is using average annual cash-only income (excluding fringe benefits) for a mix of full-time and part-time employees whose average workweek was only 33.7 hours last year.

For 2018, the CEO-to-worker pay would be about 312-to-1 ($12,400,000 ÷ $38,640) comparing the Wall Street Journal‘s reported median compensation of $12.4 million for S&P 500 CEOs to the annual cash wages for part-time workers using the AFL-CIO’s methodology, see graph above. Assuming that the AFL-CIO reports average CEO compensation of $14.5 million in 2018, we can expect its new forthcoming CEO-to-worker pay ratio to be around 364.5-to-1 for 2018 (see graph above), slightly higher than the 361-to-1 ratio it reported for 2017. Using a more realistic estimate of the compensation for an S&P 500 CEO (median instead of mean) reduces the CEO-to-worker pay ratio by more than 14%. But that’s not the most important correction to make, so let’s now adjust for hours worked and add fringe benefits for rank-and-file workers to make a more accurate comparison since CEO pay is based on total compensation including all fringe benefits.

Questions: a) How would the CEO-to-worker pay ratio change if we: a) calculate average worker pay for full-time workers, b) compare the average pay for a rank-and-file worker who works the same number of hours that a typical CEO works, e.g., 50 or 60 hours per week, c) compare total compensation of both CEOs and rank-and-file workers working full-time including fringe benefits for the rank-and-file, and d) consider only those workers who are employed by companies with 500 or more workers to consider the workers most likely to actually work for an S&P 500 company? The graph above summarizes how the CEO-to-worker pay ratio would change, here are the details:

1. Correcting for the AFL-CIO’s Statistical Shortcomings/Legerdemain

a. Using the WSJ data, a typical S&P 500 CEO earned $12.4 million last year. Assuming a 40-hour workweek for full-time workers employed by private companies with 500 or more workers, who received average hourly compensation of $49.85 last year including fringe benefits (BLS data here, Table 8), and annual total compensation of $99,700, we would get CEO-to-worker compensation ratio of only 124-to-1, or less than half of the previously calculated 312-to-1 ratio. In other words, expect the AFL-CIO’s CEO-to-worker pay ratio to be conveniently inflated again this year by a factor of more than 2X by using cash wages only for part-time workers and comparing it to the total compensation for S&P CEOs! Talk about bogus statistical chicanery! And year after year, the media never questions the AFL-CIO’s totally flawed methodology and parrots the inflated and wildly exaggerated CEO-to-worker pay ratios.

b. Since we can realistically assume that no S&P 500 CEO works only 40 hours per week, let’s assume a 50-hour workweek for full-time rank-and-file workers to make a more realistic comparison, and use hourly compensation of $49.85 like above and annual total compensation of $124,625 for workers employed by companies with 500 or more employees. That would result in a CEO-to-worker compensation ratio of only 99.5-to-1 see graph above.

c. To make it an even more realistic comparison to the average workweek of an S&P 500 CEO, let’s assume a 60-hour workweek for full-time rank-and-file workers and annual total compensation of $114,348 we would get CEO-to-worker compensation ratios of 83-to-1.

Conclusion: By considering both total compensation (including fringe benefits) for both CEOs (and using median CEO pay) and rank-and-file workers working for large companies with more than 500 workers, and by considering longer workweeks for rank-and-file workers that would be more comparable to the hours worked by a typical CEO of a major multi-national corporation in the S&P 500, we can get a more accurate apples-to-apples comparison. Those more accurate comparisons result in CEO-to-worker compensation ratios of between 124-to-1 (for rank-and-file workers averaging 40-hour workweeks) to as low as 83-to-1 for rank-and-file workers putting in 60-hour weeks that might be the most comparable to the workweek of a typical S&P 500 CEO.

Let’s keep this analysis in mind in the coming weeks when the AFL-CIO’s reports something like last year’s 361-to-1 CEO-to-worker pay ratio that will generate sensationalized media coverage even though it is hugely exaggerated by a factor of more than 2X and probably more realistically inflated by a factor of more than 4-to-1!

2. Confiscation and Redistribution of CEO Pay

And what’s the whole point of the AFL-CIO’s annual reports on wildly inflated CEO-to-worker pay ratios? The sub-title of the AFL-CIO’s Executive Paywatch report in 2017 pretty much sums it up: “More for Them, Less for Us.”

The AFL-CIO’s message seems to be that if CEOs weren’t being so generously over-compensated, then the rank-and-file workers would be doing much better and making higher wages. For example, according to the AFL-CIO in 2015:

America is supposed to be the land of opportunity, a country where hard work and playing by the rules would provide working families with a middle-class standard of living. But in recent decades, corporate CEOs have been taking a greater share of the economic pie while workers’ wages have stagnated.

The AFL-CIO has fallen here hook, line and sinker for the zero-sum, fixed pie fallacy, one of the most common economic mistakes that falsely assumes that there’s a static fixed pie and therefore one party can gain (get a bigger slice) only at the expense of another (who’s left with a smaller slice). But let’s assume that there is a “fixed pie of wages” and do some confiscation and redistribution of CEO compensation to see how that would affect average rank-and-file worker pay.

Q: If the average CEO of an S&P 500 company received $14.5 million last year, then as a group, those 500 CEOs received about $7.25 billion in total compensation in 2018. If the AFL-CIO could wave a magic wand and confiscate that entire amount and redistribute $7.25 billion to the current 104.3 million rank-and-file workers, what would each one get?

A: An annual increase in pay of $69.50 for each rank-and-file worker before taxes, or about $1.34 more per week and about only 3 cents per hour. In other words, complete confiscation and redistribution of S&P 500 CEO compensation would make almost no difference for the average rank-and-file worker.

Bottom Line: The AFL-CIO can only get a distorted and wildly inflated CEO-to-worker pay ratio of something like 360-to-1 every year with a flawed apples-to-oranges analysis that compares the total annual compensation of a small, select group of CEOs in their prime earning years heading America’s largest multi-national corporations, who probably typically work 50-60 hours per week or more, to the average annual cash wages of part-time rank-and-file employees who work less than 34 hours per week on average, often for small companies that aren’t anywhere close to the size of S&P 500 companies. According to this 2015 report from Business Insider, S&P 500 companies employ only 17% of workers in the US, so it’s not really fair to compare the salaries to S&P 500 CEOs to all of the rank-and-file US workers, since a large of majority of those workers aren’t employed by large multinational companies in the S&P 500.

Once we make a more statistically valid apples-to-apples comparison, the CEO-to-worker compensation ratio falls by two-thirds from the AFL-CIO’s expected 364.5-to-1 ratio to 124-to-1 once we consider total (median) compensation for both CEOs and full-time (40 hours per week) rank-and-file workers employed by companies with 500 or more workers. If we assume a 60-hour work week for the average worker (to be comparable to the workweek of an average CEO), the CEO-to-worker compensation ratio falls by two-thirds to only 83-to-1 for median CEO pay. Further, even if we could confiscate 100% of the compensation of all S&P 500 CEOs, the typical rank-and-file worker would probably get about $1.34 per week in additional after-tax earnings. Big deal.

When it’s released, the 2018 CEO-to-worker pay ratio reported by the AFL-CIO gets my annual “Biggest Blindly Accepted Statistical Fairy Tale of the Year Award.” Well no, it’s actually a tie with the gender wage gap myth and the incessantly repeated “77 cents on the dollar” statistical falsehood. What’s disappointing is that much of the mainstream media seem to blindly accept both of these statistical falsehoods without ever challenging the “statistical legerdemain” that are used to produce and perpetuate these statistical myths. One exception was this excellent article in 2015 by Investor’s Business Daily’s John Merline, (“Do CEOs Make 300 Times What Workers Get? Not Even Close“) who concluded:

What’s not understandable is why the mainstream press keeps repeating the massively inflated 300-to-1 number without noting the statistical legerdemain that produced it.

MP: Post will updated appropriately once the AFL-CIO releases its Executive Paywatch 2019 report based on 2018 data.

SF Fed note implies the high cost of low skilled labor

"....With these rates, I can calculate the separate contributions of inflows and outflows to the level of the overall participation rate. Figure 3 shows the contribution of the inflows (blue line) and the contribution of the outflows (green line). The sum of these two reflects the deviations of the prime-age participation from its 1978-2018 average (yellow line). For instance, the fall in the LFP rate during the Great Recession caused by the sharp increase in the outflow rate (green line) would have been even more pronounced if the rate of inflows (blue line) had not also increased. With overall participation closely following the changes in outflows during the period of the recovery and the recent increase in labor force participation, the figure clearly shows that the predominant force is the slowdown in the outflows from participation. The inflow probability has remained flat over the past three years, suggesting that the tight labor market is not bringing new workers into the labor force. Instead, the participation rate is rising because fewer workers are leaving the labor force.... contrary to a popular and appealing hypothesis, the tight labor market is not bringing new workers into the labor force. Using micro data to construct the flows of workers into and out of the labor force, I show that the recent increase in the prime-age labor force participation rate is not driven by an inflow of new workers. Instead, the increase is a mechanical byproduct of the tight labor market: because unemployed workers are finding jobs faster, they are less likely to leave the labor force.... "

Regis Barnichon, "Is the Hot Economy Pulling New Workers into the Labor Force?" Federal Reserve Bank of San Francisco, May 20, 2019, https://www.frbsf.org/economic-research/publications/economic-letter/2019/may/is-hot-economy-pulling-new-workers-into-labor-force/

Daron Acemoğlu and Pascual Restrepo, "The race between machines and humans: Implications for growth, factor shares and jobs," Center for Economic and Policy Research, July 5, 2016, https://voxeu.org/article/job-race-machines-versus-humans
Daron Acemoğlu and Pascual Restrepo, "Robots and Jobs: Evidence from US Labor Markets,"National Bureau of Economic Research, July 16, 2018, https://economics.mit.edu/files/15254Daron Acemoğlu and Pascual Restrepo, "Robots and jobs: Evidence from the US," Center for Economic and Policy Research, April 10, 2017, https://voxeu.org/article/robots-and-jobs-evidence-usDaron Acemoğlu and Pascual Restrepo, "The Race Between Machine and Man: Implications of Technology for Growth, Factor Shares and Employment," National Bureau of Economic Research, June 2017, https://www.nber.org/papers/w22252Daron Acemoğlu and Pascual Restrepo, "Automation and New Tasks: How Technology Displaces and Reinstates Labor," National Bureau of Economic Research, March 5, 2019, https://www.nber.org/papers/w25684Daron Acemoğlu and Pascual Restrepo, "Demographics and Automation," National Bureau of Economic Research, March 2018, http://www.nber.org/papers/w24421Daron Acemoğlu and Pascual Restrepo, "Artificial Intelligence, Automation and Work," National Bureau of Economic Research April 19, 2018, https://economics.mit.edu/files/14641
Daron Acemoğlu and Pascual Restrepo, "Low Skill and High Skill Automation,"Journal of Human Capital, 2018, https://economics.mit.edu/files/15118Core of Restrepo's argument, evidence based, as long as the rate of automation of jobs by machines and the creation of new complex tasks for workers are balanced, there will be no major labor market decline, parallels your analogy that you won't automate farms if you can't move workers to higher productivity sectors so they will be able to buy the corn

"...The stabilising forces in the model stem from ‘price effects’. Because automation tends to reduce payments to labour, it also increases the profitability of the creation of new complex tasks relative to further automation. This stabilising force implies that rapid automation tends to self-correct itself, provided that it takes place within an environment in which the technology for creating future innovations and R&D of different types remains unchanged.Under these circumstances, the economy will ultimately return back to its state before the arrival of these automation technologies. If so, the current difficulties of workers in the face of new technologies notwithstanding, the future may not be bleak for labour. Nevertheless, this stabilising force does not imply that all sorts of changes will necessarily reverse themselves.If what has changed is the technology for creating future innovations, and in particular, if automation-related innovations have become easier than creating new tasks, then the wave of new automation technologies we are now seeing will be just the first stage before the economy settles into a new long-run equilibrium with worse prospects for labour. Overall, the extent to which the future will validate concerns about rising technological non-employment will depend on whether we are witnessing a period of rapid discovery of new automation technologies or a fundamental shift in how we are able to produce technologies for the future...."
The race between machines and humans: Implications for growth, factor shares and jobs

Concerns that new digital technologies, artificial intelligence, and robotics will create widespread technological non-employment are now widespread. Various recent labour market trends, ranging from declines in US labour force participation to increases in wage inequality and the share of capital in national income, are seen as harbingers of this new normal (e.g. Brynjolfsson and McAfee 2012, Akst 2014, Autor 2015, Karabarbounis and Neiman 2014, Oberfield and Raval 2014). A major shortcoming of the typical arguments about technological non-employment is that there is no clear reason why the effect of new technologies will be different this time than in the past, when they did not create such widespread reductions in employment.

It is not that new technologies weren’t predicted to be equally calamitous. John Maynard Keynes stated in 1930:

“We are being afflicted with a new disease of which some readers may not have heard the name, but of which they will hear a great deal in the years to come — namely, technological unemployment.” (Keynes 1930).

In 1965, economic historian Robert Heilbroner confidently asserted:

“As machines continue to invade society, duplicating greater and greater numbers of social tasks, it is human labour itself — at least, as we now think of ‘labour’ — that is gradually rendered redundant." (quoted in Akst 2014).

The famous economist, Wassily Leontief, was equally pessimistic about the implications of new machines. By drawing an analogy with the technologies of the early 20th century that made horses redundant, he speculated that:

“Labour will become less and less important… More and more workers will be replaced by machines. I do not see that new industries can employ everybody who wants a job” (Leontief 1952).

So why did these previous dire predictions not come true? And why should it be different this time?

Our recent work attempts to answer these questions (Acemoglu and Restrepo 2016). Our approach is built on two key ideas. First, during most times, there is a continuous process of tasks previously performed by labour being mechanised and automated, while at the same time, new employment opportunities for labour are created. Second, new employment opportunities come mostly from the introduction of new and more complex tasks in which labour has a comparative advantage relative to capital. Herein lies our answer to Leontief's puzzle - the difference between human labour and horses is that humans have a comparative advantage in new and more complex activities. Horses did not.

The importance of these new complex tasks is well illustrated by the technological and organisational changes during the Second Industrial Revolution, which involved not only the replacement of the stagecoach by the railroad, sailboats by steamboats, and of manual dock workers by cranes, but also the creation of new labour-intensive tasks. These new tasks generated jobs for a new class of engineers, machinists, repairers, and conductors, as well as of modern managers and financiers involved with the introduction and operation of new technologies (e.g. Landes 1969, Chandler 1977, Mokyr 1990).

The importance of new complex tasks can also be seen in recent US labour market dynamics. Employment figures document not just the automation of existing labour-intensive jobs, but also the rise of new occupations, ranging from engineering and programming jobs to those performed by audio-visual specialists, executive assistants, data administrators and analysts, meeting planners, or computer support specialists. Indeed, during the last 30 years, new tasks and new job titles account for a large fraction of US employment growth. To document this fact, we use data from Lin (2011) that measures the share of new job titles — in which workers perform newer tasks than those employed in more traditional jobs — within each occupation. In 2000, about 70% of the workers employed as computer software developers (an occupation employing one million people at the time) held new job titles. Similarly, in 1990 a radiology technician and in 1980 a management analyst were new job titles.
Figure 1 shows that for each decade since 1980, employment growth has been greater in occupations with more new job titles. The regression line shows that occupations with 10 percentage points more new job titles at the beginning of each decade grow 5.05% faster over the next ten years (standard error = 1.3%). From 1980 to 2007, total employment in the US grew by 17.5%. About half (8.84%) of this growth is explained by the additional employment growth in occupations with new job titles, relative to a benchmark category with no new job titles.1

These two key building blocks imply that one should consider the dynamics of modern labour markets in advanced economies as being characterised by a race between two technological forces: automation on the side of machines, and the creation of new complex tasks on the side of man. While automation is an ongoing process which, all else equal, takes jobs away from labour, the creation of new complex tasks is also an ongoing process which adds new jobs for labour. If the first force outpaces the second, there will be a declining share of labour in national income and technological non-employment. If the second force outpaces the first, the reverse will happen - there will be a greater share of labour in national income and rising employment. Our task-based framework further shows that automation, though it corresponds to a technological improvement and increases GDP, may also reduce the real wages of workers, not just their share in national income. This last result is relevant for understanding a key pillar of the concerns about the effect of new technology on wages, which is generally hard to reconcile with existing models (in which technological improvements always increase wages).

Viewed from the perspective of our theoretical framework, the reason why illustrious commentators of the past, including Keynes and Leontief, did not turn out to be right is that the second force in the race between machine and man was every bit the first one’s equal. Looking into the future, whether the wave of new technologies will spell doom for labour will similarly depend on whether this second force can keep up with the increased pace of the first.

But in this framework, leaving as exogenous the rates at which automation and the creation of new complex tasks proceed is not fully satisfactory. Though it helps us understand the forces at work, it poses an equally deep question: Why was it that in the past the two forces turned out to be balanced? Is there any reason why we should expect the same from today’s technological developments?

To answer this even more fundamental question, we develop the full version of our framework in which the rates at which automation and the creation of new complex tasks proceed is endogenised, and responds to whichever of these two activities are more profitable. For example, the cheaper capital is, the more profitable automation is, which replaces the relatively expensive labour with cheaper capital. In the endogenous technology version of the model, this greater profitability triggers further automation. This conceptual structure is useful for two related reasons. First, it helps us identify the forces that act as stabilisers — so that once automation pulls ahead of the creation of new labour-intensive tasks, there will be economic forces that induce a faster creation of new tasks as well. Second, it helps us delineate conditions under which the torrent of new automation technologies that we are currently witnessing will not self-correct and will thus have long-term adverse consequences for the prospects of labour.

The stabilising forces in the model stem from ‘price effects’. Because automation tends to reduce payments to labour, it also increases the profitability of the creation of new complex tasks relative to further automation. This stabilising force implies that rapid automation tends to self-correct itself, provided that it takes place within an environment in which the technology for creating future innovations and R&D of different types remains unchanged. Under these circumstances, the economy will ultimately return back to its state before the arrival of these automation technologies. If so, the current difficulties of workers in the face of new technologies notwithstanding, the future may not be bleak for labour. Nevertheless, this stabilising force does not imply that all sorts of changes will necessarily reverse themselves. If what has changed is the technology for creating future innovations, and in particular, if automation-related innovations have become easier than creating new tasks, then the wave of new automation technologies we are now seeing will be just the first stage before the economy settles into a new long-run equilibrium with worse prospects for labour. Overall, the extent to which the future will validate concerns about rising technological non-employment will depend on whether we are witnessing a period of rapid discovery of new automation technologies or a fundamental shift in how we are able to produce technologies for the future.

We also highlight a new implication of our conceptual structure regarding the efficiency of the market equilibrium. It is well-known that models with endogenous technology have various sources of inefficiencies resulting from monopoly markups charged by firms with market power (which are typically those firms that introduce new products and technologies to the market). In addition to these well-known sources of inefficiency, we identify a new type of inefficiency, which leads to too much automation and too few new complex tasks being created. This inefficiency arises because automation, which enables firms to economise on wage payments, responds to high wages. When some of the wage payments accruing to workers are rents (e.g. efficiency wages or quasi-rents created by labour market frictions), there will be more automation than what the social planner would desire, and technology becomes inefficiently biased towards replacing labour.

Finally, we use our framework to explore the implications of automation for inequality. When different workers have different amounts of skills, both automation and the creation of new tasks may lead to greater inequality — in the first case, because machines compete more strongly against less skilled labour; and in the second, because the more skilled workers have greater competitive advantage than the less skilled in new complex tasks. However, we show that as long as over time, tasks become standardised and are more easily performed by less skilled labour (e.g. as in Acemoglu et al. 2010), the introduction of new complex tasks benefits those workers as well as the more skilled ones. Depending on how rapidly this standardisation process takes place, the economy might generate powerful forces self-correcting the inequality implications of automation technologies as well.

We view our work as a first step towards a systematic investigation of different types of technological changes that impact capital and labour differentially. Several areas of research appear fruitful based on this step. First, a more systematic analysis of the efficiency implications and how this interplays with different types of labour market imperfections (which create wedges between the opportunity cost of labour and wages) is an important area for future work. Second, a richer analysis of tasks at different parts of the complexity distribution being automated is an important area for research, especially in light of much evidence that automation will affect not just low-skilled but increasingly also high-skilled workers in the near future. Third, since there may be major differences in the ability of technology to automate and also to create new tasks across industries (e.g. Polanyi 1966, Autor et al. 2003), the extent to which these differences become constraining factors needs to be investigated. Finally, and most importantly, there is great need for empirical evidence on the impact of automation and robotics on employment. Indeed, whether rapid automation does act as an impetus for the creation of new complex tasks is of the utmost importance to provide greater empirical content to the framework developed here. Daron Acemoğlu and Pascual Restrepo, "The race between machines and humans: Implications for growth, factor shares and jobs," Center for Economic and Policy Research, July 5, 2016, https://voxeu.org/article/job-race-machines-versus-humans
You've read this, contradictory to their theoretical model finds that, "... one more robot per thousand workers reduces the employment to population ratio by about 0.2 percentage points and wages by 0.37 percent..."however they suspect it's a short term thing, and only at the margins. "... As robots and automation technologies take over tasks performed by labor, there is increasing concern about the future of jobs and wages. We analyze the effect of the increase in industrial robot usage between 1990 and 2007 on US labor markets. Using a model in which robots compete against human labor in the production of tasks, we show that advances in robotics technology may reduce employment and wages, and that the local labor market impacts of robots can be estimated by regressing the change in employment and wages on the exposure to robots in each local labor market—defined from advances in robotics technology in each industry and the local distribution of employment across industries. Using this approach, we estimate robust negative effects of robots on employment and wages across commuting zones. We bolster this evidence by showing that the commuting zones most exposed to robots in the post-1990 era do not exhibit any differential trends before 1990. The impact of robots is distinct from the effects of overall productivity increases, other types of IT capital and the total capital stock. According to our estimates, one more robot per thousand workers reduces the employment to population ratio by about 0.2 percentage points and wages by 0.37 percent....Overall, our summary of the quantitative magnitudes implied by our analysis is that one more robot reduces employment in a commuting zone with the average exposure to robots by 6.2 workers and one more robot per thousand employees reduce wages in such a commuting zone by about 0.73 percent (relative to a commuting zone with no exposure). Translating this into aggregate effects requires a range of assumptions, and our best-case range puts these aggregate effects between 3 and 5.6 workers losing their jobs as a result of the introduction of one more robot in the national economy, and wages declining between 0.25 percent to 0.5 percent as a result of one more robot per thousand employees."..."Daron Acemoğlu and Pascual Restrepo, "Robots and Jobs: Evidence from US Labor Markets,"National Bureau of Economic Research, July 16, 2018, https://economics.mit.edu/files/15254Good blog post on the "Robots and Jobs" paper putting it's findings into context, just a rounding error in the grand arch of things"....We believe as well that the negative effects we estimate are both interesting and surprising, because of the small offsetting employment increases in other industries and occupations. So far, there are relatively few robots in the US economy, and so the number of jobs lost due to robots has been limited to between 360,000 and 670,000 jobs. If the robots spread as predicted, future aggregate job losses will be much larger. For example, BCG (2015) has an 'aggressive' scenario in which the world stock of industrial robots would quadruple by 2025. In our estimates, that would imply a 0.94-1.76 percentage points lower employment to population ratio, and 1.3-2.6% lower wage growth between 2015 and 2025. These are sizable effects. But it should also be noted that even under the most aggressive scenario, we are talking about a relatively small fraction of employment in the US economy being affected by robots.There is nothing here to support the view that new technologies will make most jobs disappear and humans largely redundant...."Robots and jobs: Evidence from the US “We are being afflicted with a new disease of which some readers may not have heard the name, but of which they will hear a great deal in the years to come," John Maynard Keynes wrote almost a century ago, "namely, technological unemployment.”
(Keynes 1930)

If Keynes' original readers found those worries were misplaced, today there is concern that these gloomy predictions may soon come true (Brynjolfsson and McAfee 2012, Ford 2016). Bolstering such concerns, a range of low-skill and medium-skill occupations exposed to automation have suffered employment declines and sluggish or even negative wage growth (Autor et al. 2003, Goos and Manning 2007, Michaels et al. 2014).

Plenty of research speculates on what might happen when the robots arrive. Frey and Osborne (2013), classified occupations by how susceptible they are to automation and concluded that 47% of US workers are at risk in the next 20 years. McKinsey (2016) claims the same statistic is 45%, and The World Bank estimates that this number for the OECD as a whole is 57% of workers. Arntz et al. (2016), however, disagree. They argue that, within an occupation, many workers specialise in tasks that cannot be automated easily, and so their estimate for OECD jobs at risk is only 9%.

Whether 9% or 57% or jobs are at risk, there is no guarantee that firms would replace those workers with robots. That would depend on the costs of automation, and how much wages change in response to this threat. Additionally, even if an industry introduces robots to do specific jobs, productivity improvements may create new jobs in the firm, or other occupations might be able to expand.

Research

In Research, we go beyond these feasibility studies, and estimate what is already happening due to the introduction of robots (Acemoglu and Restrepo 2017). We investigate the equilibrium impact of industrial robots in local US labour markets. These machines are defined by The International Federation of Robotics as “an automatically controlled, reprogrammable, and multipurpose” (IFR 2014; see also Graetz and Michaels, 2015). Industrial robots are fully autonomous machines that can be programmed to perform several manual tasks such as welding, painting, assembling, handling materials, or packaging. This definition excludes other types of capital, such as software, that may also replace labour, and machines designed for a single function, such as cranes, circuit printers, textile looms or coffee machines.

In 2007 there were four times as many industrial robots in the US and Western Europe as in 1993 (Figure 1). The IFR estimates that there are currently between 1.5 and 1.75 million industrial robots in operation, a number that could increase to 4 to 6 million by 2025 (Boston Consulting Group 2015).
Robot usage is far from evenly spread across the economy. The automotive industry employs 39% of existing industrial robots, followed by the electronics industry (19%), metal products (9%), and the plastic and chemicals industry (9%). Some of these industries were also exposed to import competition from Mexico; whereas Chinese imports and offshoring affected a different set of industries. The actual facts are shown in Figure 2.
Basic theory guiding the empirics

We use a simple model in which robots and workers compete in the production of different tasks (after Acemoglu and Autor 2011, Acemoglu and Restrepo 2016). In this model, the share of tasks performed by robots varies across industries, and there is trade between labour markets specialising in different industries. The model decomposes the aggregate impact on employment and wages into a negative displacement effect on workers who lose their jobs, and a positive productivity effect across the wider economy.

The model also shows that we can summarize the net impact of robots on local labour markets - commuting zones in our application - through a measure of the exposure of these areas to robots. The exposure is defined as the sum over industries of the national penetration of robots into 19 industries, multiplied by the employment share of that industry in that labour market. To compute it, we calculate employment shares by industry in each zone from Census data. Figure 3 shows that US commuting zones differ substantially in their exposure to robots. This variation allows us to estimate the impact of industrial robots on wages and employment in these local labour markets.
When computing our measure of exposure to robots, one concern is that the adoption of robots by US industries might be a response to problems (or opportunities) in these industries or the local economy of commuting zones that house them. To remove the influence of these confounding factors, we focus on the spread of robots coming from advances in technology, which we proxy using the industry-level spread of robots in other advanced economies (this is similar to what Autor et al. 2013 and Bloom et al. 2016 do for the increase in imports from China).

The impact on employment and wages

Our results show a strong relationship between a commuting zone’s exposure to robots and employment. In the areas most exposed to robots, between 1990 and 2007 both employment and wages declined in a robust and significant way. During this period, we estimate that, relative to other areas, the introduction of a new robot per 1,000 workers in a commuting zone reduced the local employment-to-population ratio by 0.37 percentage points and local wages by 0.73%. This is equivalent to 6.2 workers losing their jobs for every robot.

Although these numbers suggest that exposed commuting zones are doing worse than the rest in terms of employment and wages, they do not necessarily reflect the US-wide effects of robots. The adoption of robots in one commuting zone could lower production costs, and via trade, enable other industries to create employment in the rest of the economy. Such indirect benefits would be missed in our cross-sectional comparisons. To account for these gains, we use our model to recompute the impact of robots allowing for trade between commuting zones, and found that the employment and wage effects were smaller - but not by much. The exact impact depends on how easy it was to substitute between goods produced in different places, the cost savings from robots and the elasticity of the local labour supply. Using reasonable estimates for these elasticities and our estimates to discipline the calibration of the remaining parameters in our model, we found that allowing for trade still implies each new robot per thousand workers reduced employment to population ratio by 0.34 percentage points and cut wages by about 0.5% (as opposed to 0.73%). If we further take account of the potential spillovers from reduced employment and wages in industries adopting the robots onto other local non-tradable industries, these numbers might be even smaller: about 0.18 percentage points lower employment to population ratio and 0.25% slower wage growth for every one robot per thousand workers (corresponding to about three workers losing their jobs because of one more robot).

Clearly industrial robots are not the only innovations being implemented at any time, and we might have erroneously attributed to robots the effects of many other technologies that could also displace labour. This is not the case: our results are the same when we control for the increase in overall capital intensity and IT capital by industry. Note also that exposure to robots is only weakly correlated with various other trends. Nor is our measure of exposure to robots related to past trends in employment and wages in the pre-robot era from 1970 to 1990. Indeed, controlling for broad industry composition (shares of manufacturing, durables, construction and so on), for detailed demographics, for exposure to Chinese and Mexican imports, for the decline in routine jobs, and for offshoring opportunities does not change our results.

In the commuting zones most exposed to robots, the employment effect is strongest for routine manual, blue collar, assembly and related occupations, and for workers with less than college education. But no one, it seems, has escaped entirely. Slicing the data by type of employment or education (Figure 2), we find no overall positive impact on any group.
Future impacts

This is only a first step. Alternative strategies for estimating the aggregate implications of the spread of robots and other labour-replacing technologies would complement our approach.

We believe as well that the negative effects we estimate are both interesting and surprising, because of the small offsetting employment increases in other industries and occupations. So far, there are relatively few robots in the US economy, and so the number of jobs lost due to robots has been limited to between 360,000 and 670,000 jobs. If the robots spread as predicted, future aggregate job losses will be much larger. For example, BCG (2015) has an 'aggressive' scenario in which the world stock of industrial robots would quadruple by 2025. In our estimates, that would imply a 0.94-1.76 percentage points lower employment to population ratio, and 1.3-2.6% lower wage growth between 2015 and 2025. These are sizable effects. But it should also be noted that even under the most aggressive scenario, we are talking about a relatively small fraction of employment in the US economy being affected by robots. There is nothing here to support the view that new technologies will make most jobs disappear and humans largely redundant.

Daron Acemoğlu and Pascual Restrepo, "Robots and jobs: Evidence from the US," Center for Economic and Policy Research, April 10, 2017, https://voxeu.org/article/robots-and-jobs-evidence-usNote they assume labor has a comparative advantage in new tasks

"...We examine the concerns that new technologies will render labor redundant in a framework in which tasks previously performed by labor can be automated and new versions of existing tasks, in which labor has a comparative advantage, can be created. In a static version where capital is fixed and technology is exogenous, automation reduces employment and the labor share, and may even reduce wages, while the creation of new tasks has the opposite effects. Our full model endogenizes capital accumulation and the direction of research towards automation and the creation of new tasks. If the long-run rental rate of capital relative to the wage is sufficiently low, the long-run equilibrium involves automation of all tasks. Otherwise, there exists a stable balanced growth path in which the two types of innovations go hand-in-hand. Stability is a consequence of the fact that automation reduces the cost of producing using labor, and thus discourages further automation and encourages the creation of new tasks. In an extension with heterogeneous skills, we show that inequality increases during transitions driven both by faster automation and introduction of new tasks, and characterize the conditions under which inequality is increasing or stable in the long run...."
Daron Acemoğlu and Pascual Restrepo, "The Race Between Machine and Man: Implications of Technology for Growth, Factor Shares and Employment," National Bureau of Economic Growth, June 2017, https://www.nber.org/papers/w22252
framework that implies "automation always reduces the labor share in value added and may reduce labor demand even as it raises productivity"....This paper develops a task-based model to study the effects of different technologies on labor demand.....Our conceptual framework offers several lessons. First, the presumption that all technologies increase (aggregate) labor demand simply because they raise productivity is wrong. Some automation technologies may in fact reduce labor demand because they bring sizable displacement effects but modest productivity gains (especially when substituted workers were cheap to begin with and the automated technology is only marginally better than them). Second, because of the displacement effect, we should not expect au tomation to create wage increases commensurate with productivity growth. In fact, as we noted already, automation by itself always reduces the labor share in industry value added and tends to reduce the overall labor share in the economy (meaning that it leads to slower wage growth than productivity growth). The reason why we have had rapid wage growth and stable labor shares in the past is a consequence of other technologicalchanges that generated new tasks for labor and counterbalanced the effects of automationon the task content of production. Some technologies displaced labor from automatedtasks while others reinstated labor into new ones. On net, labor retained a key role inproduction. By the same token, our framework suggests that the future of work dependson the mixture of new technologies and how these change the task content of production.....At the center of our framework is the task content of production—measuring the allocation of tasks to factors of production. Automation, by creating a displacement effect, shifts the task content of production against labor, while the introduction of new tasks in which labor has a comparative advantage improves it via the reinstatement effect. These technologies are qualitatively different from factor-augmenting ones which do not impact the task content of production.For example, automation always reduces the labor share and may reduce labor demand, and new tasks always increase the labor share.We then show how changes in the task content of production and other contributors to labor demand can be inferred from data on labor shares, value added and factor prices at the industry level.The main implication of our empirical exercise using this methodology is that the recent stagnation of labor demand is explained by an acceleration of automation, particularly in manufacturing, and a deceleration in the creation of new tasks. In addition, and perhaps reflecting this shift in the composition of technological advances, the economy also experienced a marked slowdown in productivity growth, contributing to sluggish labor demand.Our framework has clear implications for the future of work too. Our evidence and conceptual approach support neither the claims that the end of human work is imminent nor the presumption that technological change will always and everywhere be favorable to labor.Rather, it suggests that if the origin of productivity growth in the future continues to be automation, the relative standing of labor, together with the task content of production, will decline. The creation of new tasks and other technologies raising the labor intensity of production and the labor share are vital for continued wage growth commensurate with productivity growth.Whether such technologies will be forthcoming depends not just on our innovation capabilities but also on the supply of different skills, demographic changes, labor market institutions, tax and R&D policies of governments, market competition, corporate strategies and the ecosystem of innovative clusters. We have also pointed out a number of reasons why the balance between automation and new tasks may have become inefficiently tilted in favor of the former, with potentially adverse implications for jobs and productivity and some directions for policy interventions to redress this imbalance.
Daron Acemoğlu and Pascual Restrepo, "Automation and New Tasks: How Technology Displaces and Reinstates Labor," National Bureau of Economic Research, March 5, 2019, https://www.nber.org/papers/w25684Restrepo makes the point that the cure for high prices remains high prices
"We argue theoretically and document empirically that aging leads to greater (industrial) automation, and in particular, to more intensive use and development of robots. Using US data,we document that robots substitute for middle-aged workers (those between the ages of 36 and 55). We then show thatdemographic change—corresponding to an increasing ratio of older to middle-aged workers—is associated with greater adoption of robots and other automation technologies across countries and with more robotics-related activities across US commuting zones. We also provide evidence of more rapid development of automation technologies in countries undergoing greater demographic change. Our directed technological change model further predicts that the induced adoption of automation technology should be more pronounced in industries that rely more on middle-aged workers and those that present greater opportunities for automation. Both of these predictions receive support from country-industry variation in the adoption of robots. Our model also implies that the productivity implications of aging are ambiguous when technology responds to demographic change, but we should expect productivity to increase and labor share to decline relatively in industries that are most amenable to automation, and this is indeed the pattern we find in the data."