Macro Roundup

new Gutiérrez on the decline of labor's share finds once they include dwellings and self employed workers the labor share has been stable since the 1970's in advanced economies with the exception of the United States whose decline was driven by manufacturing post 2000 (see figure 1)

"...We identify two undocumented measurement challenges affecting corporate sector labor shares outside the United States: the inclusion of dwellings and the inclusion of self-employed workers in the corresponding sectoral accounts. Both issues have become more important over time, biasing corporate labor shares downward. We propose two methods to correct for these challenges and obtain 'true' non-housing labor share series. Contrary to common wisdom, the corrected series exhibit stable labor shares across all major economies, except the US, where the corrected labor share declines by 6 percentage points since 1980.....The corrected series suggest that non-housing labor shares have remained broadly stable since 1970 for all advanced economies but the US.....illustrated by the dashed line in Figure 1.A corollary of this result is thathousing explains all of the decline in European total economy labor shares.The US NFC labor share is largely unaffected by housing or self-employment, so it still exhibits a sharp decline - particularly after 2000 (black solid line). To conclude, we look within regions, across industries. We find that labor shares have remained largely stable across EU industries and US non-manufacturing industries - on average. Thus, most of the decline in the US labor share is due to manufacturing. The uniqueness of US trends and the critical role of manufacturing cast doubts on most technological explanations for the labor share decline, which apply both across regions and across industries...."

Germán Gutiérrez and Sophie Piton, "Revisiting the Global Decline of the (Non-Housing) Labor Share," May 17, 2019, https://drive.google.com/file/d/1RMTqNNJFUSxeMx9UsObAgqUeb3HU2K8i/view

"....In 1979, the startup rate—measured as the share of new employers as a fraction of all employers—was 13 percent. In 2007, before the onset of the Great Recession, it was roughly 10 percent, an almost 25 percent decline.....We propose a simple and novel explanation for these patterns. The startup rate is linked in general equilibrium to the pace of labor supply growth, which for reasons largely pre-determined by demographics, slowed dramatically in the late 1970s. Why should labor supply growth affect the startup rate at all? Ultimately, growing labor supply requires growing labor demand, and this can only happen through a decline in the real wage that allows incumbent firms to expand or through entry of new firms. A lesson from models of firm dynamics, starting with Hopenhayn (1992), is that in the long run, free entry ensures that shifts in labor supply are absorbed entirely at the entry margin. Along the balanced growth path of a standard model of firm dynamics extended to incorporate labor supply growth, slower growth in labor supply requires slower growth in the number of firms and thus a lower startup rate.....This explanation fits the data very well. The slowdown in U.S. labor supply growth since the late 1970s explains roughly two-thirds of the declining startup rate; it explains the widespread declines across markets since the labor force growth slowdown affected them nearly universally; and it explains the stability of average incumbent dynamics conditional on their age...."

Fatih Karahan, Benjamin Pugsley and Ayşegül Şahin, "Demographic Origins of the Startup Deficit," Federal Reserve Bank of New York, May 2019, https://www.newyorkfed.org/medialibrary/media/research/staff_reports/sr888.pdf

Love how they paint a success story, more low skilled single mothers working as a problem

"...The share of young single mothers in the work force has climbed about four percentage points, driven by those without college degrees, according to a New York Times analysis of Current Population Survey data. It’s a striking rise even compared with other groups of women who have increased their labor force participation during this period of very low unemployment.The last time single mothers’ labor force participation grew so rapidly was during the 1990s, driven by a thriving economy and major federal policy changes, including welfare overhaul and tax incentives. In recent years, though, there has been no new federal policy that would obviously encourage single mothers to work in large numbers. Instead, they seem to be responding to a patchwork of policies, both carrots and sticks. At the federal level, the safety net has become less reliable, so working for pay is increasingly their only option.But at the local one, new policies like paid leave and minimum wage increases have made it more feasible for single mothers to work. Together, these appear to have primed them to take advantage of the biggest driver of all: a highly competitive labor market....."

Claire Cain Miller and Ernie Tedeschi, "Single Mothers Are Surging Into the Work Force,"New York Times, May 29, 2019, https://www.nytimes.com/2019/05/29/upshot/single-mothers-surge-employment.html

"...The result: The experts were, by and large, horrific forecasters. Their areas of specialty, years of experience, and (for some) access to classified information made no difference. They were bad at short-term forecasting and bad at long-term forecasting. They were bad at forecasting in every domain. When experts declared that future events were impossible or nearly impossible, 15 percent of them occurred nonetheless. When they declared events to be a sure thing, more than one-quarter of them failed to transpire. As the Danish proverb warns, “It is difficult to make predictions, especially about the future.”...Tetlock, along with his wife and collaborator, the psychologist Barbara Mellers, ran a team named the Good Judgment Project. Rather than recruit decorated experts, they issued an open call for volunteers. After a simple screening, they invited 3,200 people to start forecasting. Among those, they identified a small group of the foxiest forecasters—bright people with extremely wide-ranging interests and unusually expansive reading habits, but no particular relevant background—and weighted team forecasts toward their predictions. They destroyed the competition.Tetlock and Mellers found that not only were the best forecasters foxy as individuals, but they tended to have qualities that made them particularly effective collaborators. They were “curious about, well, really everything,” as one of the top forecasters told me. They crossed disciplines, and viewed their teammates as sources for learning, rather than peers to be convinced. When those foxes were later grouped into much smaller teams—12 members each—they became even more accurate.They outperformed—by a lot—a group of experienced intelligence analysts with access to classified data.,,:

David Epstein, "The Peculiar Blindness of Experts,"The Atlantic, June 2019, https://www.theatlantic.com/magazine/archive/2019/06/how-to-predict-the-future/588040/

Credentialed authorities are comically bad at predicting the future. But reliable forecasting is possible.

The bet was on, and it was over the fate of humanity. On one side was the Stanford biologist Paul R. Ehrlich. In his 1968 best seller, The Population Bomb, Ehrlich insisted that it was too late to prevent a doomsday apocalypse resulting from overpopulation. Resource shortages would cause hundreds of millions of starvation deaths within a decade. It was cold, hard math: The human population was growing exponentially; the food supply was not. Ehrlich was an accomplished butterfly specialist. He knew that nature did not regulate animal populations delicately. Populations exploded, blowing past the available resources, and then crashed.

In his book, Ehrlich played out hypothetical scenarios that represented “the kinds of disasters that will occur.” In the worst-case scenario, famine rages across the planet. Russia, China, and the United States are dragged into nuclear war, and the resulting environmental degradation soon extinguishes the human race. In the “cheerful” scenario, population controls begin. Famine spreads, and countries teeter, but the major death wave ends in the mid-1980s. Only half a billion or so people die of starvation. “I challenge you to create one more optimistic,” Ehrlich wrote, adding that he would not count scenarios involving benevolent aliens bearing care packages.

The economist Julian Simon took up Ehrlich’s challenge. Technology—water-control techniques, hybridized seeds, management strategies—had revolutionized agriculture, and global crop yields were increasing. To Simon, more people meant more good ideas about how to achieve a sustainable future. So he proposed a wager. Ehrlich could choose five metals that he expected to become more expensive as resources were depleted and chaos ensued over the next decade. Both men agreed that commodity prices were a fine proxy for the effects of population growth, and they set the stakes at $1,000 worth of Ehrlich’s five metals. If, 10 years hence, prices had gone down, Ehrlich would have to pay the difference in value to Simon. If prices went up, Simon would be on the hook for the difference. The bet was made official in 1980.

In October 1990, Simon found a check for $576.07 in his mailbox. Ehrlich got smoked. The price of every one of the metals had declined. In the 1960s, 50 out of every 100,000 global citizens died annually from famine; by the 1990s, that number was 2.6.

Ehrlich’s starvation predictions were almost comically bad. And yet, the very same year he conceded the bet, Ehrlich doubled down in another book, with another prediction that would prove untrue: Sure, his timeline had been a little off, he wrote, but “now the population bomb has detonated.” Despite one erroneous prediction after another, Ehrlich amassed an enormous following and received prestigious awards. Simon, meanwhile, became a standard-bearer for scholars who felt that Ehrlich had ignored economic principles. The kind of excessive regulations Ehrlich advocated, the Simon camp argued, would quell the very innovation that had delivered humanity from catastrophe. Both men became luminaries in their respective domains. Both were mistaken.

When economists later examined metal prices for every 10-year window from 1900 to 2008, during which time the world population quadrupled, they saw that Ehrlich would have won the bet 62 percent of the time. The catch: Commodity prices are a poor gauge of population effects, particularly over a single decade. The variable that both men were certain would vindicate their worldviews actually had little to do with those views. Prices waxed and waned with macroeconomic cycles.

Yet both men dug in. Each declared his faith in science and the undisputed primacy of facts. And each continued to miss the value of the other’s ideas. Ehrlich was wrong about the apocalypse, but right on aspects of environmental degradation. Simon was right about the influence of human ingenuity on food and energy supplies, but wrong in claiming that improvements in air and water quality validated his theories. Ironically, those improvements were bolstered through regulations pressed by Ehrlich and others.

Ideally, intellectual sparring partners “hone each other’s arguments so that they are sharper and better,” the Yale historian Paul Sabin wrote in The Bet. “The opposite happened with Paul Ehrlich and Julian Simon.” As each man amassed more information for his own view, each became more dogmatic, and the inadequacies in his model of the world grew ever more stark.

The pattern is by now familiar. In the 30 years since Ehrlich sent Simon a check, the track record of expert forecasters—in science, in economics, in politics—is as dismal as ever. In business, esteemed (and lavishly compensated) forecasters routinely are wildly wrong in their predictions of everything from the next stock-market correction to the next housing boom. Reliable insight into the future is possible, however. It just requires a style of thinking that’s uncommon among experts who are certain that their deep knowledge has granted them a special grasp of what is to come.

The idea for the most important study ever conducted of expert predictions was sparked in 1984, at a meeting of a National Research Council committee on American-Soviet relations. The psychologist and political scientist Philip E. Tetlock was 30 years old, by far the most junior committee member. He listened intently as other members discussed Soviet intentions and American policies. Renowned experts delivered authoritative predictions, and Tetlock was struck by how many perfectly contradicted one another and were impervious to counterarguments.

Tetlock decided to put expert political and economic predictions to the test. With the Cold War in full swing, he collected forecasts from 284 highly educated experts who averaged more than 12 years of experience in their specialties. To ensure that the predictions were concrete, experts had to give specific probabilities of future events. Tetlock had to collect enough predictions that he could separate lucky and unlucky streaks from true skill. The project lasted 20 years, and comprised 82,361 probability estimates about the future.

The result: The experts were, by and large, horrific forecasters. Their areas of specialty, years of experience, and (for some) access to classified information made no difference. They were bad at short-term forecasting and bad at long-term forecasting. They were bad at forecasting in every domain. When experts declared that future events were impossible or nearly impossible, 15 percent of them occurred nonetheless. When they declared events to be a sure thing, more than one-quarter of them failed to transpire. As the Danish proverb warns, “It is difficult to make predictions, especially about the future.”

Read: What was the worst prediction of all time?

Even faced with their results, many experts never admitted systematic flaws in their judgment. When they missed wildly, it was a near miss; if just one little thing had gone differently, they would have nailed it. “There is often a curiously inverse relationship,” Tetlock concluded, “between how well forecasters thought they were doing and how well they did.”

Early predictions in Tetlock’s research pertained to the future of the Soviet Union. Some experts (usually liberals) saw Mikhail Gorbachev as an earnest reformer who would be able to change the Soviet Union and keep it intact for a while, and other experts (usually conservatives) felt that the Soviet Union was immune to reform and losing legitimacy. Both sides were partly right and partly wrong. Gorbachev did bring real reform, opening the Soviet Union to the world and empowering citizens. But those reforms unleashed pent-up forces in the republics outside Russia, where the system had lost legitimacy. The forces blew the Soviet Union apart. Both camps of experts were blindsided by the swift demise of the U.S.S.R.

One subgroup of scholars, however, did manage to see more of what was coming. Unlike Ehrlich and Simon, they were not vested in a single discipline. They took from each argument and integrated apparently contradictory worldviews. They agreed that Gorbachev was a real reformer and that the Soviet Union had lost legitimacy outside Russia. A few of those integrators saw that the end of the Soviet Union was close at hand and that real reforms would be the catalyst.

The integrators outperformed their colleagues in pretty much every way, but especially trounced them on long-term predictions. Eventually, Tetlock bestowed nicknames (borrowed from the philosopher Isaiah Berlin) on the experts he’d observed: The highly specialized hedgehogs knew “one big thing,” while the integrator foxes knew “many little things.”

Hedgehogs are deeply and tightly focused. Some have spent their career studying one problem. Like Ehrlich and Simon, they fashion tidy theories of how the world works based on observations through the single lens of their specialty. Foxes, meanwhile, “draw from an eclectic array of traditions, and accept ambiguity and contradiction,” Tetlock wrote. Where hedgehogs represent narrowness, foxes embody breadth.

Incredibly, the hedgehogs performed especially poorly on long-term predictions within their specialty. They got worse as they accumulated experience and credentials in their field. The more information they had to work with, the more easily they could fit any story into their worldview.

Unfortunately, the world’s most prominent specialists are rarely held accountable for their predictions, so we continue to rely on them even when their track records make clear that we should not. One study compiled a decade of annual dollar-to-euro exchange-rate predictions made by 22 international banks: Barclays, Citigroup, JPMorgan Chase, and others. Each year, every bank predicted the end-of-year exchange rate. The banks missed every single change of direction in the exchange rate. In six of the 10 years, the true exchange rate fell outside the entire range of all 22 bank forecasts.

In 2005, tetlock published his results, and they caught the attention of the Intelligence Advanced Research Projects Activity, or IARPA, a government organization that supports research on the U.S. intelligence community’s most difficult challenges. In 2011, IARPA launched a four-year prediction tournament in which five researcher-led teams competed. Each team could recruit, train, and experiment however it saw fit. Predictions were due at 9 a.m. every day. The questions were hard: Will a European Union member withdraw by a target date? Will the Nikkei close above 9,500?

Tetlock, along with his wife and collaborator, the psychologist Barbara Mellers, ran a team named the Good Judgment Project. Rather than recruit decorated experts, they issued an open call for volunteers. After a simple screening, they invited 3,200 people to start forecasting. Among those, they identified a small group of the foxiest forecasters—bright people with extremely wide-ranging interests and unusually expansive reading habits, but no particular relevant background—and weighted team forecasts toward their predictions. They destroyed the competition.

Tetlock and Mellers found that not only were the best forecasters foxy as individuals, but they tended to have qualities that made them particularly effective collaborators. They were “curious about, well, really everything,” as one of the top forecasters told me. They crossed disciplines, and viewed their teammates as sources for learning, rather than peers to be convinced. When those foxes were later grouped into much smaller teams—12 members each—they became even more accurate. They outperformed—by a lot—a group of experienced intelligence analysts with access to classified data.

One forecast discussion involved a team trying to predict the highest single-day close for the exchange rate between the Ukrainian hryvnia and the U.S. dollar during an extremely volatile stretch in 2014. Would the rate be less than 10 hryvnia to a dollar, between 10 and 13, or more than 13? The discussion started with a team member offering percentages for each possibility, and sharing an Economist article. Another team member chimed in with historical data he’d found online, a Bloomberg link, and a bet that the rate would land between 10 and 13. A third teammate was convinced by the second’s argument. A fourth shared information about the dire state of Ukrainian finances, which he feared would devalue the hryvnia. A fifth noted that the United Nations Security Council was considering sending peacekeepers to the region, which he believed would buoy the currency.

Two days later, a team member with experience in finance saw that the hryvnia was strengthening amid events he’d thought would surely weaken it. He informed his teammates that this was exactly the opposite of what he’d expected, and that they should take it as a sign of something wrong in his understanding. (Tetlock told me that, when making an argument, foxes often use the word however, while hedgehogs favor moreover.) The team members finally homed in on “between 10 and 13” as the heavy favorite, and they were correct.

In Tetlock’s 20-year study, both the broad foxes and the narrow hedgehogs were quick to let a successful prediction reinforce their beliefs. But when an outcome took them by surprise, foxes were much more likely to adjust their ideas. Hedgehogs barely budged. Some made authoritative predictions that turned out to be wildly wrong—then updated their theories in the wrong direction. They became even more convinced of the original beliefs that had led them astray. The best forecasters, by contrast, view their own ideas as hypotheses in need of testing. If they make a bet and lose, they embrace the logic of a loss just as they would the reinforcement of a win. This is called, in a word, learning.