Source: HBR, Nov 2019
Source: Obama White House archives, Jul 2015
The third level of mystery is explaining the conceptual drivers of productivity growth. Even if we agreed on the facts of historical productivity growth, explaining those facts is more difficult still. Moses Abramovitz famously called TFP a “measure of our ignorance,” the unexplained gap between input and output.1 And a rigorous conceptual understanding of that gap continues to elude economists
Figure 2—and all subsequent references to annual U.S. labor productivity in these remarks—references real output per hour worked in the private nonfarm business sector (excluding government enterprises) as reported by the Bureau of Labor Statistics (BLS). In other contexts, I have referenced the BLS’ labor productivity series for the nonfarm business sector (including government enterprises). The two series are closely correlated and exhibit the same trends, but excluding government enterprises permits the analysis of total factor productivity (TFP) that follows.
a simple thought experiment provides a sense of how important productivity is to incomes: what if productivity growth from 1973 to 2013 had continued at its pace from the previous 25 years? In this scenario, incomes would have been 58 percent higher in 2013. If these gains were distributed proportionately in 2013, the median household would have had an additional $30,000 in income. Had income inequality and labor force participation not worsened markedly, middle-class incomes would be nearly twice as high.
Virtually all the variation in labor productivity growth is accounted for by variation in TFP.
Source: IMF, Apr 2017
The paper reiterates many of the arguments concerning advanced economies referenced in this post, such as total factor productivity (TFP) hysteresis due to the boom-bust financial cycle and resulting capital misallocation, “an adverse feedback loop of weak aggregate demand, investment, and capital-embodied technological change”, elevated economic and policy uncertainty.
Source: City-Journal, Autumn 2016
Each of these abuses is far more significant than anything done by conservatives, and there are plenty of others. The only successful war on science is the one waged by the Left.
… two huge threats to science are peculiar to the Left—and they’re getting worse.
The first threat is confirmation bias, the well-documented tendency of people to seek out and accept information that confirms their beliefs and prejudices. In a classic study of peer review, 75 psychologists were asked to referee a paper about the mental health of left-wing student activists. Some referees saw a version of the paper showing that the student activists’ mental health was above normal; others saw different data, showing it to be below normal. Sure enough, the more liberal referees were more likely to recommend publishing the paper favorable to the left-wing activists. When the conclusion went the other way, they quickly found problems with its methodology.
Scientists try to avoid confirmation bias by exposing their work to peer review by critics with different views, but it’s increasingly difficult for liberals to find such critics. Academics have traditionally leaned left politically, and many fields have essentially become monocultures, especially in the social sciences, where Democrats now outnumber Republicans by at least 8 to 1. (In sociology, where the ratio is 44 to 1, a student is much likelier to be taught by a Marxist than by a Republican.) The lopsided ratio has led to another well-documented phenomenon: people’s beliefs become more extreme when they’re surrounded by like-minded colleagues. They come to assume that their opinions are not only the norm but also the truth.
Groupthink has become so routine that many scientists aren’t even aware of it. Social psychologists, who have extensively studied conscious and unconscious biases against out-groups, are quick to blame these biases for the underrepresentation of women or minorities in the business world and other institutions. But they’ve been mostly oblivious to their own diversity problem, which is vastly larger. Democrats outnumber Republicans at least 12 to 1 (perhaps 40 to 1) in social psychology, creating what Jonathan Haidt calls a “tribal-moral community” with its own “sacred values” about what’s worth studying and what’s taboo.
The narrative that Republicans are antiscience has been fed by well-publicized studies reporting that conservatives are more close-minded and dogmatic than liberals are. But these conclusions have been based on questions asking people how strongly they cling to traditional morality and religion—dogmas that matter a lot more to conservatives than to liberals.
A few other studies—not well-publicized—have shown that liberals can be just as close-minded when their own beliefs, such as their feelings about the environment or Barack Obama, are challenged.
Social psychologists have often reported that conservatives are more prejudiced against other social groups than liberals are. But one of Haidt’s coauthors, Jarret Crawford of the College of New Jersey, recently noted a glaring problem with these studies: they typically involve attitudes toward groups that lean left, like African-Americans and communists. When Crawford (who is a liberal) did his own study involving a wider range of groups, he found that prejudice is bipartisan. Liberals display strong prejudice against religious Christians and other groups they perceive as right of center.
the second great threat from the Left: its long tradition of mixing science and politics. To conservatives, the fundamental problem with the Left is what Friedrich Hayek called the fatal conceit: the delusion that experts are wise enough to redesign society. Conservatives distrust central planners, preferring to rely on traditional institutions that protect individuals’ “natural rights” against the power of the state. Leftists have much more confidence in experts and the state.
… The Right cited scientific work when useful, but it didn’t enlist science to remake society—it still preferred guidance from traditional moralists and clerics. The Left saw scientists as the new high priests, offering them prestige, money, and power. The power too often corrupted. Over and over, scientists yielded to the temptation to exaggerate their expertise and moral authority, sometimes for horrendous purposes.
These same sneer-and-smear techniques predominate in the debate over climate change. President Obama promotes his green agenda by announcing that “the debate is settled,” and he denounces “climate deniers” by claiming that 97 percent of scientists believe that global warming is dangerous. His statements are false.
While the greenhouse effect is undeniably real, and while most scientists agree that there has been a rise in global temperatures caused in some part by human emissions of carbon dioxide, no one knows how much more warming will occur this century or whether it will be dangerous. How could the science be settled when there have been dozens of computer models of how carbon dioxide affects the climate? And when most of the models overestimated how much warming should have occurred by now? These failed predictions, as well as recent research into the effects of water vapor on temperatures, have caused many scientists to lower their projections of future warming. Some “luke-warmists” suggest that future temperature increases will be relatively modest and prove to be a net benefit, at least in the short term.
The most vocal critics of climate dogma are a half-dozen think tanks that together spend less than $15 million annually on environmental issues. The half-dozen major green groups spend more than $500 million, and the federal government spends $10 billion on climate research and technology to reduce emissions.
Add it up, and it’s clear that scientists face tremendous pressure to support the “consensus” on reducing carbon emissions, as Judith Curry, a climatologist at Georgia Tech, testified last year at a Senate hearing.
“This pressure comes not only from politicians but also from federal funding agencies, universities and professional societies, and scientists themselves who are green activists,” Curry said. “This advocacy extends to the professional societies that publish journals and organize conferences. Policy advocacy, combined with understating the uncertainties, risks destroying science’s reputation for honesty and objectivity—without which scientists become regarded as merely another lobbyist group.”
To preserve their integrity, scientists should avoid politics and embrace the skeptical rigor that their profession requires. They need to start welcoming conservatives and others who will spot their biases and violate their taboos. Making these changes won’t be easy, but the first step is simple: stop pretending that the threats to science are coming from the Right. Look in the other direction—or in the mirror.
Source: ZeroHedge, Nov 2016
After a recession-signalling three straight quarters of decline, Q3 prleminary productivity data showed a huge 3.1% surge QoQ – the biggest jump since Q3 2014. However, the jump was not enough to regain annual gains as year-over-year productivity declined 0.04%. This is the first consecutive annual decline since 1993.
Source: HBR, May 2015
Brynjolfsson and McAfee explain that while digital technologies will help economies grow faster, not everyone will benefit equally—as the latest data already shows. Compared with the Industrial Revolution, digital technologies are more likely to create winner-take-all markets.
once you adjust for inflation, an American household at the 50th percentile of income distribution earns less today than it did in 1998, even after accounting for changes in household size.
… the Great Decoupling. The two halves of the cycle of prosperity are no longer married: Economic abundance, as exemplified by GDP and productivity, has remained on an upward trajectory, but the income and job prospects for typical workers have faltered.
Workers’ prospects are deteriorating in the developing world, too. A recent study by Loukas Karabarbounis and Brent Neiman found that labor’s share of GDP had declined in 42 out of 59 countries, including China, Mexico, and India. The researchers concluded that as advances in information technology caused the price of plants, machinery, and equipment to drop, companies shifted investment away from labor and toward capital.
The net effect has been to decrease the demand for low-skilled information workers while increasing the demand for highly skilled ones. … skill-biased technical change. By definition, it favors people with more education, training, or experience.
What if we were to reframe the situation? What if, rather than asking the traditional question—What tasks currently performed by humans will soon be done more cheaply and rapidly by machines?—we ask a new one: What new feats might people achieve if they had better thinking machines to assist them? Instead of seeing work as a zero-sum game with machines taking an ever greater share, we might see growing possibilities for employment. We could reframe the threat of automation as an opportunity for augmentation.
Brynjolfsson: You could break the Second Machine Age into stages. In stage II-A, humans teach machines what we know painstakingly, step-by-step. That’s how traditional software programming works. Stage II-B is when machines learn on their own, developing knowledge and skills that we can’t even explain. Machine learning techniques have had some success doing that in areas as diverse as understanding speech, detecting fraud, and playing video games.
Is there a third stage?
Brynjolfsson: Maybe. It might be when machines understand emotions and interpersonal reactions, an area where humans still have the edge.
humans are still far superior in three skill areas. One is high-end creativity that generates things like great new business ideas, scientific breakthroughs, novels that grip you, and so on. Technology will only amplify the abilities of people who are good at these things.
The second category is emotion, interpersonal relations, caring, nurturing, coaching, motivating, leading, and so on. Through millions of years of evolution, we’ve gotten good at deciphering other people’s body language…
Brynjolfsson: …and signals, and finishing people’s sentences. Machines are way behind there.
The third is dexterity, mobility.
The intellectually easy thing to do is to look at an existing process and say, How can I have a machine do part of that job? It does take a certain amount of creativity and a little bit of work to do that, and it does create value. However, it takes a lot more creativity to say, How can I have this machine and this human work together to do something never done before and create something that will be more valuable in the marketplace?