Macro Roundup

From 1997 to 2021, US securities analysts systematically overestimated 2-year cumulative revenue growth forecasts by 4.3% and 3-year forecasts by 4.6%, but their forecast errors were even larger for EBT: 28.4% high for 2-year and 36.8% for 3-year forecasts

Using a database of all US stocks from 1997 to 2021, we measured the median analyst estimate for growth over the next two to three years across a range of earnings metrics. We then compared these forecasts against the actual median outcomes over the next two to three years. While the estimated and actual growth rates in the table below are annualized for simplicity, the error is shown on a cumulative basis to reflect the magnitude of the mistakes at the end of each horizon. Analysts’ forecast errors seem to be bigger precisely in the areas that should matter most for stock prices: earnings further down the income statement that go to equity investors, and earnings further out in time.

Brian Chingono and Greg Obenshain, "The Sages of Wall Street," Verdad, October 2022, https://mailchi.mp/verdadcap/the-sages-of-wall-street

The Sages of Wall Street

Last week, we showed that past earnings growth does not predict future earnings growth. When earnings growth is measured over long horizons, there is little to no evidence of persistence beyond chance, even among the high-fliers that have grown the fastest in the past.

But of course, investors don’t simply look in the rearview mirror when projecting future earnings growth. They also evaluate additional information, including interviews with management, studies of firms’ competitive landscapes, and forensic reviews of any clues that are hidden in the footnotes of firms’ financial statements.

But how good are security analysts at predicting growth? In the paper we referenced last week by Chan et al. (2001), the authors state that “it is commonly suggested that one group of informed participants, security analysts, may have some ability to predict growth.... Over long horizons, however, there is little forecastability in earnings, and analysts’ estimates tend to be overly optimistic.”

We tested this finding for ourselves and came to a similar conclusion, as shown in the table below. Using a database of all US stocks from 1997 to 2021, we measured the median analyst estimate for growth over the next two to three years across a range of earnings metrics. We then compared these forecasts against the actual median outcomes over the next two to three years. While the estimated and actual growth rates in the table below are annualized for simplicity, the error is shown on a cumulative basis to reflect the magnitude of the mistakes at the end of each horizon.

While analyst estimates of future revenue are relatively close to actual outcomes, their accuracy deteriorates considerably as you move further down the income statement toward measures like Earnings Before Tax (EBT) that better reflect the earnings that are available to equity investors. And in each case, the cumulative error widens as you move further out in time from two years to three years in the future.

What if we replaced the analyst forecasts with a naïve estimate that corresponds with long-term GDP growth? Since 1947, US GDP growth has averaged 6.3% per year on a nominal basis (including inflation of around 3.5% per year). In the table below, we show the cumulative errors from forecasting 6.3% annualized growth for every company, compared to the cumulative errors from analyst forecasts.

This finding confirms Chan et al.’s observation that “it is difficult to see how the profitability of the business sector over the long term can grow much faster than overall gross domestic product.”

Fair enough, but fundamental investors might object that they don’t invest in average companies. What we want to understand is the distribution of forecasts versus actual results. Are analysts effective at separating high- and low-growth firms more broadly?

And here, we find some evidence of analyst forecasting skill. Below we show a table where we sorted analysts’ estimates of revenue and EBITDA growth into quintiles, and actual growth outcomes into quintiles. We then evaluate how accurate analysts are at selecting which growth quintile a company belongs to.

An accurate prediction would be a company where analysts expect top quintile growth, and actual growth over the next three years is indeed in the top quintile. These accurate predictions are tagged with a one in our data, and all other outcomes are tagged with a zero. The average of this binary column gives us the sorting accuracy of analyst forecasts. As shown in the table below, analysts are up to 50% accurate in the highest-expectation quintile, whereas they are around 30-40% accurate in other forecast quintiles, a significant improvement over random chance (20%).

But there’s a wrinkle to this analysis.

If a stock is richly priced because analysts expect 20% annualized earnings growth over the next three years, investors in that firm generally won’t earn a significant return premium unless its realized earnings growth exceeds 20% per year. On the other hand, if realized growth comes in at 10% annualized, the price decline could be catastrophic, as investors have seen when the pandemic-era “stay-at-home” trades like video conferencing and home exercise reversed dramatically in 2022. So we really care about the errors relative to the forecasts.

In the table below, we show median three-year forecasts and outcomes within each quintile of analyst expectations. The results indicate that analysts are generally directionally correct (the highest-expectation stocks grow faster than the lowest-expectation stocks), but the errors are significantly bigger in the higher-expectation quintiles than the lower-expectation quintiles, and errors are also significantly bigger the further down we look on the income statement.

Perhaps these results simply reflect human bias toward overconfidence, and more accurate predictions could be made by algorithmically weighting metrics from the financial statements? After all, the superiority of mechanical (statistical) predictions over clinical forecasts has been well documented across a range of disciplines in numerous studies. In the next installment of our research series, we’ll explore whether better growth forecasts can be made using quantitative measures.

rock-bottom interest rates, low taxes, and stagnant wages helped drive US corporate profits to an all-time high of 12% of GNP in Q2 ’22, but that these structural may be reversing

“…Powerful structural forces have been propelling earnings to one record after another, relative to GDP, important engines of corporate profits—rock-bottom interest rates, low taxes and stagnant wages—may be going into reverse. Although floating-rate debt accounts for just 11% of S&P 500 companies’ total borrowing, a slug of the remaining 89% will also need to be refinanced sooner or later—almost certainly at much higher cost. That includes $1trn-plus of investment-grade bonds issued in 2020. According to UBS, a bank, labour-intensive sectors such as retail could see operating profits decline by 2% for every additional one-percentage rise in wages …”

Economist Staff, "Have profits peaked at American businesses?,"The Economist, October 9, 2022, https://www.economist.com/business/2022/10/09/have-profits-peaked-at-american-businesses

Have profits peaked at American businesses?

Fedex nearly failed to get its wheels off the ground. Months after it first began delivering packages overnight in 1973, the first oil shock buffeted the global economy and the young logistics firm looked destined to crumble. Now, as the Organisation of the Petroleum Exporting Countries once again sent shock waves through the already wobbly world economy with an announcement on October 5th of a sharp cut in output, fuel prices are just one of the firm’s worries. Weak package volumes and persistently high costs caused FedEx to withdraw next year’s profit guidance in September, knocking more than a fifth, or $11bn, off its market value.

FedEx has long been regarded as a bellwether for the broader economy. In a sign that this reputation is well-earned, corporate titans everywhere are now warning of profit hits as Wall Street gears up for America’s earnings season, which began this week. On October 6th Shell, a British oil supermajor, said it expected margins in its refining and chemicals businesses to plummet. The next day Samsung, a South Korean electronics giant, cautioned that its operating profits will decline for the first time in three years.

Many icons of America Inc are making similar noises. Ford has blamed its expected profit squeeze on, among other things, shortages of parts for its cars. Nike is struggling to clear its bursting inventory of unsold sportswear. Even America’s tech behemoths, which are freezing hiring as advertisers tighten digital-marketing budgets and inflation-weary consumers put off buying a new smartphone, are no longer looking invulnerable. The odd set of upbeat results, such as PepsiCo’s on October 12th, only highlight how unfizzy the broader picture looks.

All told, forecasts for third-quarter profits for the s&p 500 index of big American firms have so far been revised down by 6.8% since June. That is more than twice as big as the average revision in the past decade. Expectations for next year are bound to fall. Some of the pain is down to the strong greenback, which makes foreign revenues, accounting for almost a third of the s&p 500’s total, worth less in dollars. A bigger reason is the economic slowdown. If this turns into a recession, as seems likely, bottom lines will almost certainly suffer more, which they tend to whenever gdp contracts. Since the second world war earnings per share fell by an average of 13% around recessions, calculates Goldman Sachs, a bank.

More worrying for ceos, other important engines of corporate profits—rock-bottom interest rates, low taxes and stagnant wages—may be going into reverse. After years of receiving a small share of companies’ takings, lenders, governments and labour are demanding more.

Historically low rates of interest and tax have contributed one-third of the s&p 500’s profit growth (excluding financial firms) in the past two decades, according to a study by Michael Smolyansky of the Federal Reserve. Both are now rising. Higher interest rates will make it costlier for companies to service their debts, which will eat into the bottom line. To begin with, this will affect those companies—typically riskier ones—that borrowed at a floating rate. Although floating-rate debt accounts for just 11% of s&p 500 companies’ total borrowing, a slug of the remaining 89% will also need to be refinanced sooner or later—almost certainly at much higher cost. That includes $1trn-plus of investment-grade bonds issued in 2020.

Just as financiers become more demanding, so too is the taxman. As appetite for deficit-funded tax cuts wanes, another Tax Cuts and Jobs Act, which was signed into law by Donald Trump in 2017 and slashed the statutory corporate rate from 35% to 21%, looks unlikely. The Inflation Reduction Act (ira), passed recently under Mr Trump’s Democratic successor, Joe Biden, includes a 15% minimum corporate-tax rate on profits of businesses with more than $1bn in pre-tax income. In addition, earlier this year interest-expense deductions became less lenient. Goldman Sachs reckons that the new rules will reduce overall s&p 500 earnings by a modest 1% in 2023, with technology and health-care sectors hit hardest. Still, strained public finances make it likely that taxes will rise in the medium term. Adding insult to injury, the ira introduces a 1% tax on share buy-backs, indicating a political appetite to squeeze companies with nothing better to do with their profits than fork them over to shareholders.

American chief executives are less squeamish than their European counterparts about countering the combination of rising labour costs and weakening demand with lay-offs. Some are already trimming payrolls. On October 6th it was reported that General Electric was planning to sack 20% of staff at its American wind-turbine business. Intel, a chipmaker, is also rumoured to be cutting thousands of jobs.

Yet businesses may find it harder than in the past to wield the axe. The balance of power between labour and capital is shifting. A new rule proposed by the Department of Labour on October 11th would make it harder for firms to classify their workers as contractors rather than employees, raising costs. Union membership, which spent the second half of the 20th century in decline, is enjoying a small but significant revival. A Gallup poll puts public support for organised labour at its highest level since 1965.

Many businesses are already feeling the heat. A walkout of 90,000 railroad workers was narrowly averted in September after unions threatened to bring railways to a standstill, which could have done $2bn-worth of damage per day to the economy. Younger workers are discovering a taste for organising—even wage rises this summer have not stopped Starbucks baristas from joining union efforts in growing numbers. ceos may not be able to keep them, the lenders and the government out of the profit pool for much longer.

American’s $800B investment in corporate R&D, venture-capital investment, and government funding/subsidies represents 3.8% of GDP, maintaining an edge over China’s $660B (2.7% of GDP)

“…The Economist has totted up corporate spending on R&D, venture-capital investment, direct government funding and, for advanced technologies, implicit funding through subsidies, and subtracted the overlap among these categories. This calculation confirms that America maintains a slight edge, spending about $800bn or 3.8% of GDP in 2020. That compares to about $660bn in China after adjusting for differences in the cost of living, or 2.7% of GDP….”

Economist Staff, "China and the West are in a race to foster innovation,"The Economist, October 13, 2022, https://www.economist.com/briefing/2022/10/13/china-and-the-west-are-in-a-race-to-foster-innovation

China and the West are in a race to foster innovation

China’s government is planning on winning the ai race, winning future wars and winning the future,” warned Todd Young, an American senator, in July. “At stake” in the West’s technological competition with China, echoed a report from American officials and businessmen in September, “is the future of free societies, open markets, democratic government, and a world order rooted in freedom not coercion.” This week the head of a British intelligence agency joined the chorus, urging “deep investments” in new technology to counter China’s growing prowess.

The anxiety is easy to understand. In 2008 China spent a third as much as America did on research and development (r&d) and about half as much as Europe, after adjusting for differences in the cost of living. By 2014 it had surpassed Europe. By 2020 its spending was 85% of America’s.

The fruits of this investment are becoming apparent: in August a Japanese research institute calculated that China now produces more of the world’s most highly cited academic research than America does. Since 2015 more patents have been issued in China than in America. China’s output of a basket of sophisticated goods including information technology, pharmaceuticals and electronics is expected to surpass America’s this year, according to a report published by the Information Technology and Innovation Foundation, an American think-tank. “China has become a serious competitor in the foundational technologies of the 21st century,” concluded another report last year from the Belfer Centre at Harvard University.

Small wonder, then, that Western countries are embarking on a frantic effort to retain or regain their technological edge. On October 7th America issued fierce new restrictions on exports to China of advanced semiconductors and related equipment. The new rules could be as crippling to the Chinese chip industry as previous American sanctions were to Huawei, a Chinese telecoms firm, says Greg Allen, who used to head the artificial-intelligence (ai) unit at America’s Department of Defence. “It’s a total clamp down, trying to cut off every head of the hydra of China’s chip industry.”

As well as trying to disrupt the flow of technology abroad, America’s government is investing more in innovation. In August Congress approved $370bn of spending on green energy, including lots of money for research. The month before it passed the Chips and Science Act, which provides $52bn over five years for the semiconductor industry, some of which will incentivise private r&d.

The act also revamps the National Science Foundation (nsf) to put more emphasis on applied science and technology and potentially doubles its funding. Germany, Japan and South Korea are making multi-billion-dollar investments in computer chips. Last year Britain announced the $1bn Advanced Research and Invention Agency (aria) to supercharge high-risk, high-reward science.

The result of all this is a global boom in investment in innovation. In 2020 the world’s spending on r&d exceeded $2.1trn, over 2.5% of global gdp, a record. The binge has three notable features. The first is the heavy involvement of governments, which are unwilling to leave investment to capital markets and are instead both funding r&d and subsidising production of certain high-tech goods. Both China and the West explicitly link such spending to geopolitical competition. “Technological innovation has become the main battlefield of the international strategic game,” said China’s president, Xi Jinping, in a speech last year to Chinese scientists. “We’re in a multi-generation era-defining competition against the ccp,” rhymes Mr Young, one of the sponsors of the Chips Act.

The second feature of the new era is experimentation with different types of funding that couple industrial policy with efforts to promote risk-taking or private-sector rigour. America and Britain, for instance, are reviving research missions akin to America’s cold-war quest to put a man on the Moon. China, meanwhile, is using “guidance funds”, in which the state takes a stake alongside private investors, to steer money to startups in ai and chips, among other advanced technologies.

Third, governments are trying to ensure their country captures more of the benefits of innovation. That can mean both preventing exports of some goods and using industrial policy to promote domestic production.

But there remain big differences in approach between China and the West—most notably the far more muscular role the state still plays in directing innovation in China to favoured industries. The West, in contrast, relies on a more diffuse network of universities, non-profits and private businesses that have more freedom to set their own priorities. There is little doubt that China’s system has helped it catch up with the West in some existing technologies, but analysts question whether it will be as good at generating future breakthroughs. The answer will determine the outcome of the global battle for technological dominion.

America’s government invested lavishly in innovation during the cold war, through such organisations as the nsf and the Defence Advanced Research Projects Agency (darpa). Its spending peaked at 1.86% of gdp in 1964. But after the fall of the Berlin Wall federal spending on r&d fell well below 1% of gdp. Private investment, meanwhile, doubled from 1% of gdp in 1979 to 2% in 2017. Giant tech firms such as Google, Facebook (now Meta), Amazon and Apple sprouted in America. China spawned similar titans, such as Alibaba, Baidu, JD.com and Tencent.

But on both sides of the Pacific the age of free-flowing private capital left many disappointed. The Communist Party has called the spread of big consumer-tech firms a “disorderly expansion of capital”. It has obliged China’s internet giants to follow its priorities, blocking share sales and issuing abrupt regulations to cow wayward firms. It seems to want less video-gaming and online commerce and more ai, chips and green tech.

Many Americans have similar misgivings. Peter Thiel, a fabled investor, has argued that there has been too much investment in “bits” (software and analytics) and not enough in “atoms” (hardware and manufacturing). “With chips we were caught behind the eight ball,” says Eddie Bernice Johnson, a Democrat from Texas who chairs the committee that drafted the Chips Act, “It was a national security imperative.” Mr Young of Indiana agrees: “The totally free-market theories of Friedman, Hayek—they don’t make sense when you’re facing an existential threat that plays with market forces.”

Buying breakthroughs

To allow a proper comparison of the sums devoted to innovation on the two sides of the Pacific, The Economist has totted up corporate spending on r&d, venture-capital investment, direct government funding and, for advanced technologies, implicit funding through subsidies, and subtracted the overlap among these categories. This calculation confirms that America maintains a slight edge (see chart 1), spending about $800bn or 3.8% of gdp in 2020. That compares to about $660bn in China after adjusting for differences in the cost of living, or 2.7% of gdp.

What is more, juguo tizhi can also lead to misallocation of funds. A paper published in the journal Econometrica in July suggests that Chinese spending on r&d spurs less growth in productivity than that of neighbouring Taiwan. That is in part because the state often supports soes, even if they are less productive. Several studies suggest that corporate r&d in China is about half as productive as in America (although they do not focus exclusively on advanced technology).

America’s expenditure, meanwhile, is much more diffuse. Private businesses account for about 60%, venture capital for nearly 20% and foundations, charities and universities more than 5%. In a recent presentation, Pierre Azoulay, a professor at mit, notes that the “Cambrian explosion of philanthropic funders” is a “silver lining” compared with the perceived sclerosis in government funding. From 2010 to 2019, research funding from non-profits nearly doubled, from $12bn to $22bn. “Our system is unique because its more distributed and bottom-up; not top-down,” says Maria Cantwell, another senator.

America may also be more daring in its investments. The Institute for Progress (ifp), an American think-tank, is helping government agencies distribute grants more effectively, says Caleb Watney, a co-founder. Erwin Gianchandani of the nsf cites “golden tickets” as an example. Rather than the standard consensus-based process to allocate funding, a single reviewer can champion a project.

Michael Lauer, head of extramural research at America’s National Institutes of Health (nih), lists a handful of new programs where labs receive funding with far fewer strings attached than normal. The Other Transactions Authorities, a recent nih program that quickly funds unconventional projects, disbursed over $2bn in 2020 and 2021.

Hunting for lightbulbs

America is also creating more “moonshot” programs in an effort to replicate the success of darpa. Last year it launched the $1bn Advanced Research Projects Agency for Health (arpa-h) to focus on ambitious biomedical research. The total amount of funding in this category increased from about $4bn in 2021 to nearly $6bn in 2022. Tom Kalil of Schmidt Futures, another organisation that aims to shape policy on innovation, says this heralds a shift towards more risk-taking. China lacks any equivalent funding agencies, notes Mr Cao of Nottingham University China.

It is possible to exaggerate the strengths of both the American and Chinese systems. For all the talk of moonshots, notes a former White House official, nih was still slow to fund research on covid-19 at the beginning of the pandemic. And researchers are still often buried in paperwork. The new funding does little to address the administrative burden in the current system—researchers lose about 40% of their time to that, notes Tony Mills of the American Enterprise Institute, another think-tank. By the same token, although thousands of new firms have sprung up in China in favoured industries, such as ai and semiconductors, most of them do not seem to have achieved much.

Neither system has a monopoly on results. According to a report published in September by the Special Competitive Studies Project, a research group organised by Eric Schmidt, a former ceo of Google (and a former member of the board of The Economist), China is dominant in some industries, such as 5g telecoms. It makes some 80% of the world’s lithium batteries. But the West is ahead on biotech, cloud computing and ai. It has been the source of most fundamental advances in these fields, such as crispr (a gene-editing technology) and the transformer architecture that underpins many big ai models.

Although few advanced computer chips are made in America, American firms tend to design them. asml, a Dutch manufacturer of chipmaking equipment, has a monopoly in the advanced lithography needed to make the fanciest ones. And the Chips Act is prompting Intel and tsmc, two of the biggest chipmaking firms, to build new semiconductor fabs in America. Intel is also set to spend nearly $20bn on new chip factories in Germany.

China has built first-rate ai models by throwing money at researchers and firms. Wu Dao, its version of gpt-3, an American ai model that can write like a human, uses ten times more parameters to train itself. Yet the chips used for much of this sort of machine-learning, gpus, were originally developed to generate graphics for video games—one of the industries on which the Chinese government has cracked down most ferociously. By the same token, it was not until September that a Chinese firm developed a vaccine against covid-19 that is as effective as Western ones—a recent high-stakes test of its capacity to innovate. “China does best in products where manufacturing is complex but the science is mature, for example in batteries,” says Dan Wang, an analyst at Gavekal Dragonomics, a research group.

But China is trying to mend some of the failings of its system. It boosted funding for basic research by 16% last year, in an attempt to foster more breakthrough discoveries. It is also trying to reduce centralisation. In July the party announced new rules to increase scientists’ autonomy. “There is evidence that China has recognised the limits of using a blunt metric to evaluate scientists,” adds Mr Wang. “Thus universities are starting to move towards the peer-review system of the West.”

There are some mistakes, however, from which China shows little sign of retreating. Mr Xi’s decision to rein in the tech industry contributed to an 11% contraction in venture-capital investment from the first three quarters of 2019 to the same period in 2022. In America vc investment grew by 70% in that time. China’s stubborn zero-covid policy, meanwhile, is driving foreign capital and talent out of the country. A survey by the German Chamber of Commerce in May found that nearly a third of foreign workers plan to leave.

Such talent is crucial to competitiveness. In the past China benefited from both foreign investment and large cohorts of “sea turtles”, students and researchers who work or study abroad and later return. The share of Chinese students studying abroad who eventually returned home rose from 25% in 2004 to 65% in 2019. And from 2015 to 2019, the number of academic papers published involving co-operation between American and Chinese researchers grew by over 10% a year, according to Nature.

Yet in 2020 this growth in academic collaboration between the two countries abruptly stalled. Less than half as many Chinese received visas to study abroad in the first half of 2022 as in the first half of 2019. This pulling apart is bad for the world, but China may suffer more. It does not have as diverse a pool of researchers as the West. According to data from MacroPolo, a think-tank, although 60% of the world’s best ai researchers work in America, over two-thirds of them are foreign (and over a quarter of them Chinese). In contrast, China draws overwhelmingly on domestic talent: almost all its best ai researchers are Chinese, and 70% of them have studied only in China.

America is not only open to foreign expertise, it also benefits from a big network of alliances with other technologically advanced countries. Collectively, America, Britain, France, Germany, Japan and South Korea spend over twice as much on r&d as China. China, by contrast, has few allies, and none that are powerhouses of research and innovation.

Bulbs blown

American politicians do not seem to understand the advantage conferred by their country’s openness, however. The original draft of the Chips bill included a provision to boost skilled immigration. Although some politicians, including Mr Young, gave it cautious support, it had to be removed to ensure the support of more Republicans, in particular. (America’s allies, happily, are doing better. Britain has devised a scheme to provide visas to graduates of top universities. Australia and Canada, already home to lots of immigrants, are increasing immigration further.)

Whatever the limits to America’s openness, however, China’s growing isolation—both self-imposed and enforced by restrictions like America’s new rules on tech exports—is far more severe. After several years when its technological rise seemed unstoppable, the outlook suddenly seems much less clear. In the coming days Mr Xi will preside over China’s 20th Party Congress. Across the Pacific, America’s Congress will be debating how much money to devote to the Research initiatives at agencies like the nsf. You can be sure each will be on the other’s mind.

Jason Riley, "Biden’s Marijuana Pardon Won’t Release a Single Inmate,"Wall Street Journal, October 11, 2022, https://www.wsj.com/articles/biden-marijuana-pardon-prison-midterms-crime-drug-offenders-racial-disparities-systemic-racism-war-on-drugs-pot-legalization-11665520780

Biden’s Marijuana Pardon Won’t Release a Single Inmate

Even Democrats who sympathize with President Biden’s decision to pardon thousands of people convicted of marijuana possession under federal law might question his timing.

Republican candidates across the country have made crime control a major issue in this year’s midterm elections, and Mr. Biden’s order could inadvertently help the GOP advance a narrative that Democrats are preoccupied with coddling criminals.

What trumps that concern for the White House, however, is that Democrats have their own narrative to push, which is that the war on drugs has led to the mass incarceration of people who are disproportionately black and is therefore racist. Following the White House announcement, an NAACP official told National Public Radio, “We’ve seen since the 1970s that marijuana policy was intentionally and malevolently constructed to target the African-American community.” President Obama made a similar claim in 2015. “Over the last few decades, we’ve also locked up more and more nonviolent drug offenders than ever before, for longer than ever before,” he said. “And that is the real reason our prison population is so high.”

What civil-rights activists often conveniently omit from this history is the role that black community leaders and the black press played a half-century ago in getting tougher drug laws passed. They also leave out the key role of black lawmakers in advancing legislation that created sentencing disparities for drug offenses. The fight in Congress in the 1980s and ’90s was led by black liberal Democrats, including Harlem’s Rep. Charles Rangel and Brooklyn’s Rep. Major Owens. A majority of the Congressional Black Caucus voted in favor of the law that created much harsher penalties for crack-cocaine offenses than for powder-cocaine offenses.

Whatever you think of the wisdom of this approach in hindsight, the claim that the initial war on drugs and subsequent escalations were motivated by racial animus toward blacks is nonsense.

There are strong libertarian arguments that the drug war, much like alcohol prohibition in the past, has on balance been a failure and that it’s time to cut our losses. Given that polls now show a majority of Americans, including about half of Republican voters, favor decriminalizing pot, the White House announcement might resonate with people in both parties. But if the goal is to address mass incarceration and racial imbalance in the prison system, then focusing on drug offenders is the wrong approach.

Even Mr. Biden had to concede that no one is in federal prison for simple marijuana possession. What he didn’t say is that even among those housed in state prisons—which hold about 90% of the country’s incarcerated population—a relatively small percentage is there on drug offenses, and almost all of those were convicted of trafficking, not for being caught with small amounts of drugs for personal use. “As a percentage of our nation’s incarcerated population, those possessing small amounts of marijuana barely register,” writes Yale law professor and former District of Columbia public defender James Forman in his book, “Locking Up Our Own.” “For every ten thousand people behind bars in America, only six are there because of marijuana possession.”

The reality is that what drives incarceration rates is violent crimes, not drug crimes. According to the Justice Department, as of 2019 some 58% of people imprisoned by states had been sentenced for violent offenses, 15% for property offenses, 14% for drug offenses and only 4% for drug possession. Put another way, drug offenders are less than a quarter of the violent offenders, and more than 85% of the prison population is there for something other than a drug offense. The U.S. regularly tops the list of advanced countries with the largest prison population, but if we sent home every incarcerated drug offender tomorrow, we’d still top the list.

Nor would releasing drug offenders do much of anything to address the racial imbalance among inmates, which results primarily from the fact that blacks are about 13% of the U.S. population yet around one-third of those arrested for violent crimes. So long as this racial gap in violent offenses persists, so will the racial gap in incarceration rates. “The racial disparities in prison populations would barely budge if all the people serving time for drug crimes were immediately released,” writes John Pfaff, a professor of criminal law at Fordham University, in his book, “Locked In.” And “it seems likely that scaling back the drug war would not on its own necessarily alter offending or enforcement patterns enough to bring about real change.”

Messrs. Forman and Pfaff are not indifferent to the problems of mass incarceration and racial inequality. What distinguishes both scholars is a desire to have an honest debate about what’s driving the phenomenon and, as important, what isn’t. Which is more than can be said about the Biden administration.

Brad Setser, “Brad Sester On Current Accounts Surpluses And FX Reserves,”@Brad_Setser, October 10, 2022, https://twitter.com/Brad_Setser/status/1579659816372666368

“As @BobEUnlimited notes, one big global shift has come from the change in the U.S. petrol balance. I think there has been another: a shift from a world where big current account surpluses show up in fx reserves to one where they don’t. Prior to the global financial crisis — and in the few years after it – reserve growth in China, Saudi Arabia and Russia more or less tracked to their combined current account surplus. That isn’t the case now…”

“… This matters because the global current account surplus today is almost entirely in China (on track for at least $400b), Russia (on track for $250b or so), and the Gulf (on track for $400b or more), and none are showing in any reserve growth. The reasons differ (rate differentials and exporters hoarding dollars for China, sanctions for Russia, the Saudi shift away from SAMA toward a sovereign fund) but the net effect is the same — none are likely to add to their formal reserves in 2022. This matters for financial flows (less demand for bonds, probably less demand for euros too — for strange reasons), but it also simply makes the global flow of funds hard to track. It also has led to a broad misunderstanding about what is driving the big reported fall in global reserves. That fall is mostly from valuation changes – combined with real selling by EM Asian countries other than China. The big surplus countries (China, Russia and Saudi Arabia, which is representative of the Gulf) aren’t selling (unlike in 15), but they also aren’t adding. Their missing accumulation thus is helping to drive the large reported fall in global reserves. Last thought: Russia’s reported current account surplus in the first part of 2022 is almost equal to that of China. That reflects high oil and low Russian oil imports. But it also reflects the big gap between China’s customs surplus and its BoP surplus…”

“…Bottom line: there hasn’t been this kind of surge in the aggregate surplus of the big authoritarian global powers that hasn’t been primarily intermediated through their relatively conservative central banks in recent financial history.”

Russel 3000 data from 2016-21 shows that ROICs for the high-return businesses go down on average and the ROICs for the low-return businesses go up on average as results regress towards the mean

“…Historical results show that ROIC tends to regress toward the mean. Regression toward the mean says that outcomes that are far from average are followed by outcomes with expected values closer to the average. Exhibit 5 breaks the population of companies into quintiles based on their ROICs in 2016 and follows each cohort for five years. It shows that the ROICs for the high-return businesses go down on average and the ROICs for the low-return businesses go up on average. This simple picture belies a lot of richness in the data. Some businesses start with high ROICs and get even better. Others have low ROICs that don’t improve. But regression is powerful…”

Michael Mauboussin and Dan Callahan, "Return on Invested Capital,"Morgan Stanley, October 6, 2022, https://www.morganstanley.com/im/publication/insights/articles/article_returnoninvestedcapital.pdf

Inventors in East Asia now file significantly more international patents than their North American counterparts to protect their intellectual property around the world

“…The figure above shows the evolution in the number of foreign patent applications filed by different regions of the world in foreign jurisdictions. By 2003, East Asia (mainland China, Hong Kong, Japan, Macao, Mongolia, South Korea and Taiwan) had surpassed North America.According to this measure of foreign patents, Japan, accounting for 61% of the region’s foreign patent applications from 2010 to 2019. South Korea, China and Taiwan followed, filing 18%, 12% and 7%, respectively. East Asia has risen as the main destination where inventors around the world seek patent protection. Of all the patents filed abroad by applicants in North America during the 2010s, 41% went to East Asia, while just 13% stayed in the region, and 11% went to Western Europe…”

Ana Maria Santacreu and Jesse LaBelle, "East Asia Outpaces Other Regions in International Patenting," Federal Reserve Bank Of St. Louis, October 3, 2022, https://www.stlouisfed.org/on-the-economy/2022/oct/east-asia-outpaces-other-regions-international-patenting

East Asia Outpaces Other Regions in International Patenting

The global need for patent protection has increased substantially over the last couple of decades. Because patents are territorial (they offer protection only in the jurisdiction where they are granted), innovators typically seek patent protection in multiple countries where they are interested in doing business to prevent the theft of ideas or to keep technological competition at bay. That was the case of the United States in Asia during the decades following the formation of the World Trade Organization, as we documented in a December 2021 essay in Economic Synopses.

In this blog post, we document changes in the patterns of patenting across regions around the world. We use data on patent applications filed by 213 jurisdictions, grouped into 14 regions.

The figure below shows the evolution in the number of foreign patent applications filed by different regions of the world in foreign jurisdictions. By 2003, East Asia (mainland China, Hong Kong, Japan, Macao, Mongolia, South Korea and Taiwan) had surpassed North America (Canada, Greenland and the United States) in terms of filing patents in other jurisdictions.

East Asia Rises as a Patent Application Destination

Exploring patenting activity in East Asia further, we found that the main innovator, according to this measure of foreign patents, was Japan, accounting for 61% of the region’s foreign patent applications from 2010 to 2019. South Korea, China and Taiwan followed, filing 18%, 12% and 7%, respectively, of patent applications abroad.

East Asia has risen not only as an innovation hub (i.e., filing patents abroad) but also as the main destination where inventors around the world seek patent protection.

Of all the patents filed abroad by applicants in North America during the 2010s, 41% went to East Asia, while just 13% stayed in the region, and 11% went to Western Europe. Those in Western Europe showed a slight preference for East Asia, with 37% of their patents filed abroad going to the region, compared with 34% filed in North America.

Even East Asian countries have a preference for their own region. Of all the patent applications filed by East Asian countries to other jurisdictions, 43% went to North America, while 36% stayed within the region, and 11% went to Western Europe.

These patterns of international patenting indicate a bias for seeking patent protection in East Asia, not only by countries outside the region but also by intraregional countries. The shift toward Asia has not always been the case. Instead, in the 1980s and early 1990s, the other most innovative regions were filing for patents with one another, while Asia was behind in both the origin and the destination of patent applications.

High-Tech Competition Builds in East Asia

What explains these new trends? There could be several reasons why East Asia has become the main destination where countries around the world seek patent protection.

First, Asia is an important destination of technology-intensive goods imports and foreign direct investment from North America and Western Europe. Hence, inventors in these developed regions may want to protect their exported technologies from theft and misappropriation by competitors in Asian countries.

Second, Asian economies are becoming innovation powerhouses, increasing competition for high-tech goods within the region. For instance, the Japanese and South Korean technology sectors have grown substantially, whereas China and Taiwan have been dominating the manufacturing of semiconductors. This regionally strong growth may have led to a “race to patent,” as each country attempts to become the dominant innovative force in that region and ward off competition.

In summary, foreign patenting has been increasing substantially in recent decades. While the U.S. and Western Europe have historically been seen as innovation powerhouses, East Asia has quickly established itself as both the main origin of innovations and the main destination for patent protection.

Note

This region comprises the Aland Islands, the Channel Islands, Denmark, Estonia, the Faroe Islands, Finland, Iceland, Ireland, the Isle of Man, Latvia, Lithuania, Norway, the Svalbard and Jan Mayen islands, Sweden and the U.K.

“…Over the next five years, the first cohort of people who became parents during the “one-child policy” era that began in 1980 will increasingly advance from their 60s and 70s — or what sociologists describe as being “young-old” — into their 80s. Wang Feng, an expert on Chinese demographic change at the University of California, Irvine said this growing group of “old-old”, with a higher likelihood of developing costly chronic diseases, would make greater care demands both on their children and the state. “If this continues, how can China sustain the pension payments for this enlarging elderly population,” said Wang…”

Eleanor Olcott, "China’s demographic crisis looms over Xi Jinping’s third term,"Financial Times, October 2, 2022, https://www.ft.com/content/199c92ca-4f1e-4898-9152-d021936f6ab4

China’s demographic crisis looms over Xi Jinping’s third term

China’s president Xi Jinping is poised to secure an unprecedented third term as leader at a congress of his Communist party this month, but behind the political theatre a broader shift is playing out.

Demographers predict the world’s most populous country will start to shrink in 2022, a turning point with profound ramifications for its future.

“That China’s population decline has coincided with the start of Xi’s third term is symbolically and practically significant,” said Wang Feng, an expert on Chinese demographic change at the University of California, Irvine.

While Xi is clearly in the political ascendancy, the economic tools at his disposal are increasingly constrained by a property sector meltdown, the damage to consumer confidence caused by successive coronavirus lockdowns, and local government fiscal shortfalls.

The rapid ageing of China’s population — a process that will accelerate during Xi’s third term — will further chip away at Beijing’s powers to stimulate growth and manage economic crises.

Wang said this growing group of “old-old”, with a higher likelihood of developing costly chronic diseases, would make greater care demands both on their children and the state.

Local governments are already struggling to meet the rising cost of health and social care. Their spending on China’s sprawling zero-Covid infrastructure has ballooned, while tax receipts from the battered property sector have plummeted.

“If this continues, how can China sustain the pension payments for this enlarging elderly population,” said Wang.

Not all experts are pessimistic about the economic impact of the ageing population.

“A smaller workforce means reduced labour supply, which means workers can demand higher wages,” Golley said. Growth in gross domestic product per capita would outpace overall GDP expansion as China’s population shrank, she said.

But other experts warned that China could lose its cost advantage in manufacturing without having transitioned to the consumption-led economic model of developed economies.

China’s birth rate has been in decline for many years. Between the start of Xi’s rule in 2012 and 2021, the number of babies born each year fell by more than 45 per cent, to 10.6mn.

China, which has the highest abortion rate among large economies, last year moved to tighten controls over abortions for non-medical purposes. Experts warned that officials could take more drastic measures to limit women’s access to abortion.

But Beijing’s measures to stimulate fertility have had little impact, particularly in the wake of the coronavirus pandemic and a strict zero-Covid policy that has disincentivised the young from getting married and having babies.

Yi Fuxian, an obstetrics expert at the University of Wisconsin-Madison and a long-term critic of the one-child policy, said rising unemployment and fear of persistent lockdowns have prompted young couples to delay marriage and childbearing. He estimated this would reduce the number of births in China by 1mn in both 2021 and 2022.

“The zero-Covid policy has greatly reduced people’s willingness to have children,” Yi said.

For a country’s population to remain stable, 2.1 children on average need to be born to each couple. Ms Li, a 34-year-old mother in the north-eastern province of Jilin, is one of the tens of millions of Chinese parents who have decided to stop at one.

Li, who declined to give her personal name, has a three-year-old and recently returned to part-time work selling tickets for local tourist attractions. “The expense of raising a child is like a bottomless pit,” said Li.

High property prices and heavy spending on education are major factors. In China, the average total cost of raising a child is nearly seven times per capita GDP, compared with four times in the US, according to YuWa Population Research, a Chinese think-tank.

Concerns about expensive childcare have been exacerbated by the economic insecurity wrought by lockdowns. Li’s hairdresser husband suffered a salary cut when his salon was forced to shut down during Jilin’s prolonged spring lockdown.

“One child is enough. We have no money or energy to raise a second,” she said.

Rising assets values reduced labor force participation for 51-65 year olds by 0.7mm people as of Sept. ‘21, 15% of overall LFP reduction since Jan. ‘20. It’s unclear whether recent declines in asset prices will reverse this trend. “…As of September 2021, the labor force participation rate was 61.6%, down from a pre-pandemic peak of 63.4% in January 2020. I try to quantify how much of the 1.8-percentage-point drop in LFP during 2020 and 2021 may have been related to rising asset values during that same period. I focus on households with members between 51 and 65 years of age who are thus more likely to retire. The computed change in the value of wealth leads to an estimated drop in LFP of 0.92 percentage points for this age group, which corresponds to 0.7 million people—about 15% of the total drop.Nor is it clear whether these labor supply effects are symmetric: Would people increase their LFP by the same amount if their wealth fell…”

Miguel Faria e Castro, "Asset Returns and Labor Force Participation During COVID-19,"Federal Reserve Bank of St. Louis, January 6, 2022, https://research.stlouisfed.org/publications/economic-synopses/2022/01/06/asset-returns-and-labor-force-participation-during-covid-19

Asset Returns and Labor Force Participation During COVID-19

As of September 2021, the labor force participation (LFP) rate1 was 61.6%, down from a pre-pandemic peak of 63.4% in January 2020. This 1.8-percentage-­point decline in the LFP rate corresponds roughly to a net decrease of 4.7 million workers in the U.S. economy, compared with the pre-pandemic period.2

In a previous essay, I discussed the large increase in excess retirements during the pandemic period relative to previous trends.3 An increase in retirements or a decrease in the number of retired people returning to work are among the reasons the LFP may have decreased and remained low. In that essay, I explained that there are many reasons older people may have chosen to accelerate their retirement plans, including greater risk of death conditional on infection and the need to care for family members. Another factor that may have enabled these retirements was rising asset values during the pandemic period. The figure shows that some of the most common asset classes, such as stocks or housing, posted historically high cumulative real returns during 2020 and 2021.4 Thus, these years were associated with significant increases in wealth for households exposed to these assets.

Second, I impute the realized returns on several asset classes during the 2020-2021 period, which allows me to estimate the evolution in the value of net worth for households in the sample (assuming the composition of their portfolio remained fixed).

In the final step, I use standard estimates for the elasticity of LFP rates to changes in wealth; that is, I estimate how much the LFP rate is predicted to change for each household in the sample, given the estimated change in net worth. Standard theories of household behavior predict that people tend to work less in response to increases in wealth, and many studies have tried to empirically measure the magnitude of these effects. I combine the results of one such study5 with my estimates for the evolution of net worth to obtain estimates for the change in LFP of individual households, which can then be aggregated into an estimated change of the LFP rate.

I focus on households with members between 51 and 65 years of age who are thus more likely to retire. The computed change in the value of wealth leads to an estimated drop in LFP of 0.92 percentage points for this age group, which corresponds to 0.7 million people—about 15% of the total drop.

It is important to emphasize that rising asset valuations may not have necessarily caused people to exit the labor force (or remain out of it). These resulting increases in wealth may have, however, compounded other reasons to stop working, as mentioned in the second paragraph of this essay (i.e., risk of death from infection and increased domestic responsibilities).6 Finally, it is not clear whether these reductions in LFP are permanent, as people could eventually return from retirement, which is particularly likely for younger households. Nor is it clear whether these labor supply effects are symmetric: Would people increase their LFP by the same amount if their wealth fell? These questions are left for further research.

Notes

1 The labor force participation rate is the percentage of the civilian noninstitutional population 16 years and older that is working or actively looking for work.

2 This calculation uses as a reference point the civilian noninstitutional population as of September 2021, which was equal to 261.8 million people.

3 Faria-e-Castro, M. "The COVID Retirement Boom." Federal Reserve Bank of St. Louis Economic Synopses, No. 25, 2021;https://doi.org/10.20955/es.2021.25.

4 For reference, the cumulative real returns on stocks and housing for a period of the same length ending in December 2019 were 14.8% and 2.4%, respectively.

5 Benson, D. and French, E. "How Do Sudden Large Losses in Wealth Affect Labor Force Participation?" Federal Reserve Bank of Chicago Letter No. 282, January 2011;https://www.chicagofed.org/publications/chicago-fed-letter/2011/january-282.

6 See, for example, the following NPR story: "These Older Workers Hadn't Planned To Retire So Soon. The Pandemic Sped Things Up";https://www.npr.org/2021/08/23/1028993124/these-older-workers-hadnt-planned-to-retire-so-soon-the-pandemic-sped-things-up.

Over the past 5 years California's state-level GDP has grown by 18%, the 4thfastest rate in the country and outpacing Texas and Florida. Even subtracting tech California’s growth was above average. “…Peter Thiel suggested that California suffers from a “tech curse”: a play on the “resource curse”, the notion that countries with abundant natural resources often have weak economies and corrupt political systems. In the past five years, California’s state-level gdp has grown by 18%, the fourth-fastest rate in the country and a better performance than either Florida or Texas. Even subtracting tech, California’s growth was above average, according to our calculations…”

Economist Staff, "Peter Thiel says California suffers from a “tech curse”. Is he right?,"The Economist, September 2022, https://www.economist.com/finance-and-economics/2022/09/22/peter-thiel-says-california-suffers-from-a-tech-curse-is-he-right

Peter Thiel says California suffers from a “tech curse”. Is he right?

Speaking recently at the National Conservatism Conference in Miami, Peter Thiel, an investor and intellectual, made a provocative argument. He suggested that California suffers from a “tech curse”: a play on the “resource curse”, the notion that countries with abundant natural resources often have weak economies and corrupt political systems. If data is the new oil, then California is the new Saudi Arabia—even, he said, if things aren’t quite “as bad as Equatorial Guinea”.

Mr Thiel made the Equatorial Guinea comparison with tongue firmly in cheek, but he was deadly serious about the tech-curse theory. At first glance it seems plausible. California’s tech industry has in recent years produced astonishing wealth. The state is also in many ways dysfunctional. Parts of downtown San Francisco resemble an open-air drug den. Many of the state’s public schools seem keener on talking about social justice than teaching children. Each year, one in every 100 Californians, on net, leaves for another state.

Mr Thiel thinks that California’s poverty and prosperity are two sides of the same coin, with state and local governments providing the link. Public-sector employees draw on tech’s enormous tax revenues to overpay themselves and do no work, he says. The state’s tech moguls in effect buy off politicians, ensuring, for example, that they enact super-restrictive planning regulations to keep house prices high.

It is in vogue to criticise both California and tech: doing both at the same time left the audience in raptures. There is also a grain of truth to what Mr Thiel says. But there are two big problems with his theory.

Many of the proceeds of this growth have gone on enormous mansions in Atherton and Los Altos, but they have also trickled down to a greater extent than Mr Thiel appreciates. Just over a decade ago the median Californian household had an income 7% higher than the median American one. Now their income is 15% higher. The unemployment rate, relative to the national average, has fallen. So has poverty. And there is little to suggest that the decline in joblessness or poverty is caused by poor people leaving the state.

Mr Thiel also overstates tech’s costs. It is true that some of California’s politicians behave with nearly as much impunity as the Saudi elite. Yet anyone with a passing knowledge of Californian history knows that dirty dealing in politics long predates tech. San Francisco’s politics today is tame in comparison with the 1970s.

It is similarly hard to blame tech for California’s housing market. The ratio of California’s average house price to America’s is much lower than in the mid-2000s. Meanwhile, California’s anti-building rules, the cause of sky-high prices, emerged with the environmentalist movement of the 1970s, not Mark Zuckerberg and Elon Musk. There is a lot to dislike about Big Tech, but it is not as malign as Mr Thiel believes.

The Verdad team shows that from 2001-22, firms with the highest analyst growth forecasts actually delivered the lowest total returns, as EBITDA multiple contraction more than offset high EBITDA growth that fell short of initial analyst forecasts.

“…The above chart shows the total returns of each quintile of growth prediction broken out into categories. The data above suggests that while firms with high forecast growth indeed grew at impressive rates, they also experienced the greatest contractions in their multiples. In fact, the same three factors that predicted growth—analyst forecast EBITDA growth, improvement in the TEV/EBITDA multiple, and trailing EBITDA growth—were the exact same factors that predicted multiple contraction. As a result, higher growth net of capital consumption for these firms was more than offset by higher multiple contraction. This is the cruel result of a lack of persistence in growth. Valuation multiples contract because growth does not persist….”

Greg Obenshain and Brian Chingono, "Predicting Growth,"Verdad, October 2022, https://mailchi.mp/verdadcap/predicting-growth
Predicting Growth

Two weeks ago, we extended the 2001 findings in the paper The Level and Persistence of Growth Rates through June 2022 and confirmed that, indeed, there is no persistence in long-term earnings growth beyond chance. The chart below shows this graphically. The straight black line at the top is what we’d expect if growth perfectly persisted, meaning that companies that were above the median growth rate in year 1 were again above that rate in years 2, 3, 4 and 5. The gray line is what we would expect if growth does not persist at all, meaning that a company that was above median growth in year 1 had a coin flip of a chance at being above median growth in year 2, so that that probability of being above median growth for two years in a row is 25% and just 3.215% for five years running. The blue line is Chan et al.’s original finding from that paper and the dotted black line is ours.

This result led us to quip that analysts might as well plug in the same long-term growth assumptions for a SaaS company as a coal mine. And we stand by this. The data shows that we need to be very humble when plugging a terminal growth assumption into a discounted cash flow model. A good assumption is the GDP growth rate.

But the observation that growth rates do not persist is not the same as saying they are not predictable in the short run. And last week we showed that, while analysts are wildly over-optimistic in their growth estimates, they do, in fact, sort companies correctly into the high and low growers. While it may indeed be true that growth does not persist, surely we can predict with some confidence that the IT services firm with a growing customer backlog is likely to grow faster next year than the auto parts supplier who just lost their largest contract.

So can we make some reasonable predictions about growth? One way to do this is just to select those variables that have predicted growth in the past. When we run regressions on EBITDA growth, the three variables that are most significant and have the highest magnitude of impact are, in order, analyst expected EBITDA growth, followed closely by both the trailing improvement in the TEV/EBITDA multiple and trailing EBITDA growth.

We can build a simple growth factor that equal weights these three variables. And this factor works very well to predict the relative magnitude of short-term growth. Below we show the same persistence chart, except this time we show the persistence of growth for the top quintile of our growth factor and the bottom quintile of our growth factor. The black line is again random chance.

But, alas, there is a problem: predicting growth isn’t the same as predicting returns. And it turns out that our growth prediction is actually negatively correlated with total returns. Below we show the realized EBITDA growth by predicted growth quintile and the realized total return.

The key to understanding this apparent paradox is to decompose total returns into the contributions of growth, changes in multiple, leverage, and distributions. The below chart shows the total returns of each quintile of our growth prediction broken out into these categories.

This is the cruel result of a lack of persistence in growth. Valuation multiples contract because growth does not persist. In fact, the lack of dispersion of returns between the high growers and the low growers points to the efficiency of markets. Growth is more or less correctly priced. And that’s what we’d expect. As every good analyst learns, it’s not the forecast that matters, but the forecast relative to what is already priced in.

Stocks that price high growth are particularly susceptible to multiple contraction. So an analyst building a DCF model should remind themselves that the higher the growth rate, the lower the exit multiple relative to the entry multiple should be. Analysts should assume that growth mean reverts to the market within five years. This is the argument we have made from the beginning and an eminently reasonable approach, even for the fastest growers, as we showed in Figure 2. To give a sense of what this should look like, we provide a cheat sheet below. For each projected growth quintile, we show the starting annualized two-year growth projection and the subsequent realized median annual growth rates.

We’ve spent three weeks now looking at growth, only to come to the conclusion that predicting growth well doesn’t lead to being able to forecast total returns any better. Next week we plan to look at what fair multiples should be for a terminal value assumption and why some companies deserve higher valuations, independent of their growth rates.

Analysis from New York Fed shows that Americans spend 60 million fewer commuting hours each day vs. pre-pandemic. They allocated this time primarily toward leisure activities and sleeping, while reducing overall work hours.

“…In the aggregate, Americans now spend 60 million fewer hours traveling to work each day. We investigate how people spend this saved time on other activities. The chart above shows how one hour of foregone commute time is allocated toward other activities. First, we find a substantial fall in time spent working. Overall paid-work hours fell because of substitution toward other activities throughout the day: we see notable increases in leisure time and sleeping. The rise in leisure was particularly pronounced among younger Americans, who reported spending more time at social events, eating at restaurants or bars, and exercising….”

David Dam, Davide Melcangi, Laura Pilossoph, and Aidan Toner-Rodgers, "What Have Workers Done with the Time Freed up by Commuting Less?,"Federal Reserve Bank Of New York, https://libertystreeteconomics.newyorkfed.org/2022/10/what-have-workers-done-with-the-time-freed-up-by-commuting-less/

What Have Workers Done with the Time Freed up by Commuting Less?

The COVID-19 pandemic has dramatically changed the way Americans spend their time. One of the most enduring shifts has occurred in the workplace, with millions of employees making the switch to work from home. Even as the pandemic has waned, more than 15 percent of full-time employees remain fully remote and an additional 30 percent work in hybrid arrangements (Barrero, Bloom, and Davis). These changes have substantially reduced time spent commuting to work; in the aggregate, Americans now spend 60 million fewer hours traveling to work each day. In this post, we investigate how people spend this saved time on other activities. Using detailed data from the American Time Use Survey (ATUS), we find that employed individuals allocate their saved commute time toward leisure activities and sleeping, while reducing overall work hours.

Measuring Time Spent

The ATUS is a nationally representative survey that measures both the amount of time people spend on various activities and where these activities take place. ATUS respondents are randomly selected from a subset of households in the Current Population Survey, allowing us to link demographic and employment information to respondents’ time use information. Because COVID-19 disrupted the survey’s data collection between March 18 and May 9 of 2020, the survey results for 2020 reflect data only from May 10 to December 31. To make comparisons with prior years, we restrict the data to this period in the other years as well.

To examine how workers allocate their saved commute time, we apply the methodology of Aguiar, Hurst, and Karabarbounis to the COVID-19 period. For those interested in the details, we aggregate individual ATUS responses to the state-level and exploit variation across states in the extent of commute time reduction. After categorizing more than 400 time use activities into nine major categories, we regress yearly changes in time use for each category on changes in commute time. Our first-differences approach enables us to account for time-invariant state characteristics, and we also include a variety of state-level control variables to account for worker composition. Across specifications, we restrict our analysis to employed individuals in order to exclude reductions in commute times due to job loss. Additionally, to account for the possibility that COVID-19 induced firms to cut hours just as employees were shifting to remote work, we also consider a sample of only full-time employees and find similar results.

Changes in Time Use

The main results are presented in the chart below in which we show how one hour of foregone commute time is allocated toward other activities. First, we find a substantial fall in time spent working; the decrease in hours worked away from home is only partially offset by an increase in working at home. This is in line with results from Bloom, Han, and Liang, who find that remote work led to a decrease in overall hours worked. Moreover, our findings help reconcile earlier work on commute times, which found that even though employees reported allocating 35 percent of their saved commute time to work, they spent fewer total hours in paid employment (Barrero, Bloom, and Davis). Our results from the ATUS suggest that although individuals may have increased time working in the precise time-slot they used to commute, overall paid-work hours fell because of substitution toward other activities throughout the day.

Second, we see notable increases in leisure time and sleeping. The rise in leisure was particularly pronounced among younger Americans, who reported spending more time at social events, eating at restaurants or bars, and exercising. Older age groups, on the other hand, tended to allocate more time to nonmarket work, such as activities related to childcare, the maintenance of the household, repairs, and meal preparation.

The ATUS also includes information on who physically accompanied the respondent during each activity. We use this variable to classify which activities were done with people outside of the immediate household. We categorize solitary activities and those with household members into one category, and activities with friends, co-workers, clients, and non-household family members into the other. As we would expect, the next chart shows that time spent on activities done alone or with household members increased, such as leisure (+2.30 hours) and nonmarket activities (+0.69). Differentiating by age, we see that younger people were the only group to significantly increase their leisure time with non-household members (+1.13), likely reflecting the differential risks of COVID-19.