Macro Roundup

“…A few dozen career criminals known to the cops are responsible for a lion’s share of the thefts across the Upper East Side, according to police.The 41 repeat offenders are responsible for a staggering 147 thefts across the upscale neighborhood, according to 19th Precinct commander Melissa Eger. “This is really driven by shoplift related larcenies and robberies,” she told the precinct’s community council recently. The recidivists include Michelle Mckelley, 42, a shoplifter with a whopping 96 prior arrests who was recently caught stealing from a Target on Third Avenue; and serial thief Jamel Pringle, 39, who had a jaw-dropping 167 arrests under his belt when he was nabbed swiping from a Rite Aid on Second Avenue, cops said….”

Griffin Kelly, "41 repeat offenders account for 147 thefts across Upper East Side: NYPD,"New York Post, April 16, 2022, https://nypost.com/2022/04/16/41-repeat-offenders-account-for-147-thefts-across-upper-east-side-nypd/

41 repeat offenders account for 147 thefts across Upper East Side: NYPD

A few dozen career criminals known to the cops are responsible for a lion’s share of the thefts across the Upper East Side, according to police.

The 41 repeat offenders are responsible for a staggering 147 thefts across the upscale neighborhood, according to 19th Precinct commander Melissa Eger.

“This is really driven by shoplift related larcenies and robberies,” she told the precinct’s community council recently.

The recidivists include Michelle Mckelley, 42, a shoplifter with a whopping 96 prior arrests who was recently caught stealing from a Target on Third Avenue; and serial thief Jamel Pringle, 39, who had a jaw-dropping 167 arrests under his belt when he was nabbed swiping from a Rite Aid on Second Avenue, cops said.

“I’m broke, I’m homeless and I steal for a living,” Pringle told The Post in February.

Major crimes in the 19th, which stretches from East 59th Street to East 96th, are up 37 percent from last year, NYPD data shows. In 2022, the precinct has recorded:

402 grand larcenies — up 42 percent
950 petit larcenies — up 52 percent
57 robberies — up 46 percent
35 car thefts — up 52 percent

An employee at a CVS on East 86th Street told The Post they deal with shoplifters everyday, and have been targeted by at least four repeat offenders they recognize.

“They steal a lot of cough and cold products and makeup,” said the employee. “The minute we see it happening, we tell them to leave and then call the cops, who take forever. Sometimes people get mad, throw stuff and destroy products.”

A shoplifter became belligerent at the pharmacy recently, tossing groceries and smashing open bottles of NyQuil and DayQuil, employees said.

A representative from Manhattan District Attorney Alvin Bragg’s office made excuses for the troubling catch and release trend, claiming to residents at the precinct community council meeting that strict discovery laws make it easy for criminals to avoid consequences and steal again.

“There’s a very short time window we have to turn over a lot of information to the defense attorney and their client, and if it’s not done in ‘X’ amount of time, that case can be dismissed,” said Kevin JeanBaptiste, Bragg’s supervisor of community engagement.

But residents said repeat offenders are tearing the neighborhood apart.

“I’m born and raised in Yorkville, and I’ve never had any fear walking the streets, but now I’m beginning to reconsider that,” Kathryn Jolowics, 82, who keeps her keys at the ready to fight off would-be muggers, told The Post. “I’m nervous coming home late in the evening. Some of the blocks are just totally dark where people can just come up and attack innocent bystanders or steal from them.”

Shopkeepers said state bail reform laws passed in 2019 drive thefts, and only inconvenience victims.

“It’s discouraging to go through the trouble, report to the police department, fill out paperwork, answer questions, cooperate as much as possible, only to hear from the officers that there’s a very good chance, if the perp is ever caught, they’ll be back out on the streets in a very short time,” said Dave Goodside, who owns Beach Cafe on 2nd Avenue.

He doesn’t even waste time calling the cops on dine-and-dashers, he said.

Goodside said he’s noticed regular shoplifting at stores like Target, Walgreens and CVS -brand name monoliths that make for easy marks.

“Corporations have done the math. It’s far less expensive to just stay out of the way and let the perp leave,” he said. “If a confrontation turns physical and an employee or customer gets hurt, the corporation has a whole other host of legal issues and medical bills to contend with.”

Community Board 8 chairman Russel Squire lamented, “If people see others committing these crimes and getting away with it, it makes a real sense of lawlessness for the community. We need to give judges more discretion.”

In 2018 Heritage estimated that total government support for the poor and lower income families with children was $695.7 billion implying $65,200 per year. They estimate that the combined resources available to these families (so earning) summed to $83,300 per year. If the Build Back Better act as written in the fall of 2021 had/does become law, "... Total government spending on the average poor family will rise from $65,200 per year to more than $76,400. When limited private earnings are added to this massive government spending, combined total resources will reach nearly $94,600 per year for the average poor family..."
Breakdown of resources

“…Government resources are the fiscal resources devoted by federal, state, and local governmentsto meet the physical, medical, and educational needs and wants of families with children.10 Specifically, this means government spending to provide cash, food, and housing benefits as well as medical care, direct social services for the family, and public education for the children….…The total lower-income government support system represents all taxpayer funding channeledto meet the medical, educational, and physical needs and wants of poor and lower-income families with children……Self-support resources are the resources a family itself provides to meet needs and wants. These resources include primarily earnings, but they also include business income, asset income such as interest and dividends, and employer-provided medical insurance….…The combined total resources of a family represent the sum of the government resources and self-support resources provided directly or indirectly to the family……The self-support ratio represents the ratio of self-support resources of a family as a share of their combined total resources..”

Poor and Lower-Income Families with Children

“..The total taxpayer support system for poor and lower-income families with children represents total government spending utilized to meet medical needs, educational needs, and physical needs and wants of those families. In 2018, these two groups combined totaled 12.5 million families and 26.3 million children—roughly a third of all children in the U.S….”

Potential Impact Of Build Back Better

“…This group covers families with children and incomes (before receipt of means-tested aid) above 100 percent of FPL but below 200 percent of FPL (twice the FPL for a given-size family). Thus, 200 percent of FPL would be twice the 100 percent of the FPL numbers given above. The threshold of 200 percent of FPL in 2018 was $40,468 for a single parent with two children and $50,903 for a married couple with two children. Thus, the lower- income group would cover families with annual incomes between 100 percent of FPL and 200 percent of FPL (roughly between $26,354 and $52,708 on average). There were 7.33 million families with children with incomes in this range in 2018. These families included 15.1 million children and 15.4 million adults. The average family size was 4.2 persons with 2.1 children. Half of these families were married couples; half included an added adult relative of the family head…”

“…Table 1 and Chart 2 show the average of these resources per family. The combined total resources per family among poor families averaged $83,304 per year. Post-tax earnings and other private resources were worth $18,148 Government cash, food, and housing benefits cost $20,757 Governmental medical benefits added an average of $16,960 Direct social services cost $4,967; and Public education cost $22,472…”

“…The government’s official poverty measure cutoff representing 100 percent of FPL in 2018 was $20,231 for a single parent with two children and $25,465 for a married couple with two children.28 The government’s official poverty measure cutoff averaged $26,354 across all poor families with children. There were 5.18 million such families with pre-welfare incomes below these levels. These families included 11.1 million children and 9.2 million adults. The average family size is 3.9 persons with an average of 2.2 children per family. Three-tenths of these families were married couples; a third included an added adult relative of the family head within the family. One in seven included a cohabiting adult partner. This population corresponds very closely to the population defined as poor in the annual Census official poverty reports…”

Lower-income families with children.

The Poor

“…At its pre-crisis peak, the private-capital industry had some $2.2trn under management. Today it manages four to five times as much, a little over half of it in North America....As private markets have grown, more young firms have chosen to delay going public. The average age of companies doing an ipo in America was eight years in the 1980s and 1990s. The average since 2001 has been 11 years….”

Matthew Valencia, "Private markets have grown exponentially,"The Economist, February 26, 2022, https://www.economist.com/special-report/2022/02/23/private-markets-have-grown-exponentially

Private markets have grown exponentially

When john connaughton left consulting for private equity (pe) by joining Bain Capital in 1989, “my mentors counselled against it,” he recalls. “They said it wouldn’t last.” Now he heads Bain’s global pe business. He has helped assemble such huge deals as the formation of iqvia, a life-sciences group valued at $47bn. Bain Capital manages $155bn of assets. The target for its 13th buy-out fund last year was $9bn; it closed just short of $12bn.

pe has been on a tear for three decades. Other firms set their sights even higher than Bain Capital. Blackstone, the biggest, wants to raise a record $30bn for its next fund. cvc, Hellman & Friedman and Apollo Global Management have launched vehicles of $20bn or more. Funds are not just bigger but also being formed more quickly. The cycle between general partners (gps), who manage pe funds, closing one fund and starting the next has shortened from five years to half that, says David Perdue of pjt Partners, an investment bank. Institutional investors such as university endowments, sovereign-wealth funds and pension plans are increasingly keen on pe and other alternatives to public markets. The attraction is understandable: in the latest fiscal year, many large American endowments enjoyed returns of 30-60% mainly thanks to private markets.

The pe industry has been “supersizing”, says Hugh MacArthur of Bain & Company, a consultancy no longer affiliated with Bain Capital. By most measures, from fundraising to “dry powder” (committed capital awaiting deployment), it is three times larger than a decade ago. In just five years, the number of pe funds registered in America has jumped by more than half, to over 18,000.

Dealmaking is at record levels. The global value of disclosed leveraged buy-outs reached $1.2trn in 2021, far above the previous record of $800bn in 2006. pe made up a fifth of all mergers and acquisitions, its highest share for at least a decade. This deal splurge has supercharged activity in high-yield (junk) bond and leveraged-loan markets. Junk-bond issuance surpassed $600bn for the first time last year. So hungry were pe funds in 2021 that the bidding process sped up dramatically. Kem Ihenacho of Latham & Watkins, a law firm, says that, just as buyers gazump when housing markets are red-hot, many bidders are “pre-empting the auction” by offering to sign less than halfway through the process.

Besides buying assets from corporate owners and founders, private funds buy from each other. Some firms have been through three or four pe funds’ hands. In America, secondary buy-outs can exceed the volume of initial public offerings (ipos), the usual route for investors to cash out, says the Bank for International Settlements (bis), the central bankers’ bank.

The pe boom is part of a broader expansion of private markets. Top-tier firms that once focused on leveraged buy-outs, such as Blackstone, kkr and Carlyle, now look just as keenly for opportunities in private debt, real assets such as property and infrastructure, and “growth equity”, which sits between venture capital and buy-outs. More than two-thirds of the industry’s dry powder is earmarked for investments other than buy-outs. Since 2010 buy-outs have gone from 80% of kkr’s business to less than half.

These market leaders are now “one-stop capital providers” for firms less able to tap traditional sources such as banks and public markets, says the bis. Such diversification (along with stratospheric pay) has cemented their reputation as the new kings of Wall Street. Today’s business-school graduates may now be more likely to seek a career in private markets than in investment banking. Last year Blackstone had 29,000 applicants for just over 100 analyst jobs.

The private-markets party has boosted profits and share prices. In 2021 the industry’s upper ranks posted record results, and publicly listed pe firms enjoyed benchmark-beating share-price gains. Blackstone’s and kkr’s share prices doubled. The average profitability of alternative managers is well above that of banks (albeit more volatile). The Boston Consulting Group reckons alternative managers took in more than 40% of global asset-management revenues in 2020.

Since taking off in the 1980s, pe has seen two boom-and-bust cycles. The first boom was driven by swashbuckling dealmakers, epitomised by kkr co-founders Henry Kravis and George Roberts. Its emblematic deal was the highly leveraged $25bn takeover of rjr Nabisco in 1988. The first bust soon followed. The second boom, starting in the late 1990s, saw the industry scale up and expand beyond equity and outside America. Several managers, starting with Blackstone, took advantage of it to list their own shares, monetising fee income and giving gps more exit options. More have since done so. tpg, a San Francisco leveraged-buy-out firm with some $110bn of assets, listed on the Nasdaq in January.

Boom back bigger

The financial crisis hit pe, but it bounced back, fuelled by cheap debt as interest rates fell. Even the arrival of covid-19 in 2020 did not knock it for long. Dealmaking froze briefly, but pe firms moved to shore up portfolio firms that needed help or as an opportunity to buy cheap assets. m&a activity took off again later that year.

Private markets have been propelled by push and pull factors, says Mohamed El-Erian, chief economic adviser at Allianz, an insurer, and a former boss of pimco, a bond-fund manager. The main push factor was ultra-loose monetary policy, which drove investors towards illiquid markets that offered higher yields. Another was the retreat of banks in response to tougher capital requirements and post-crisis laws (such as Dodd-Frank in America) that discouraged or prohibited them from betting with their own balance-sheets. Private funds gleefully took up the slack. Among pull factors are innovations such as private-debt and property-investment funds that were designed to appeal to wealthy individuals and institutions.

As companies stay private longer, “more investors are looking to get in at that pre-ipo stage, as that’s when most of the wealth creation happens,” says Ben Meng of Franklin Templeton, a fund manager. Some firms opt not to go public at all, confident of raising enough capital privately, says Byron Trott, head of bdt Capital Partners, a merchant bank for family firms. Of the 40 companies bdt has invested in since 2009, only three have gone public.

Not that the public markets are down and out. Last year was a record one for ipo listings. Firms going public also have other routes, such as direct listings or mergers with special-purpose acquisition companies (spacs), which landed with a bump after a boom in 2020-21 but are unlikely to disappear. Yet at around 4,000, the number of publicly listed American firms is far below its peak of nearly 6,000 in the mid-1990s. One reason is that investors see disadvantages in public ownership, including onerous disclosure requirements, quarterly earnings pressure and attacks by activist investors.

At pe-owned firms, the activists are on the inside. Owners work closely with managers to shape strategy and capital structure. They reward success handsomely while punishing failure faster than the owners of public firms. Done correctly, this can increase value by narrowing the information gap between shareholders and management, reducing agency costs. The endowments, pension funds and other institutional investors that fuel private markets are believers. They think large allocations to alternative investments offer the best hope of hitting annual-return targets for their portfolios that are typically in the high single digits.

But as the industry enters its third age, it faces challenges. One is the prospect of sustained higher inflation and interest rates. Cheap debt is pe’s lifeblood. A rise of a couple of percentage points in the cost of borrowing is unlikely to lead to surging bankruptcies. But more than that might. As more capital has flowed into private markets, prices for assets have risen so far that “there is little room for error,” says Bain & Company’s Mr MacArthur. For buy-outs the average price tag relative to earnings is at an all-time high.

A second worry is greater scrutiny. Private markets are lightly regulated and opaque. Regulators want more transparency, especially on fees and performance measures. Supervisors want to know how private markets might affect financial stability. The industry must also navigate geopolitics, notably the reassessment of the virtues of foreign capital by China, until recently a big part of many managers’ plans. The collapse in September of Blackstone’s $3bn deal to buy soho China, a property developer, augurs ill.

The final test comes from within: generational change. Many who shaped the industry are leaving. Mr Kravis and Mr Roberts handed over to new co-heads last year. Stephen Schwarzman, Blackstone’s 75-year-old co-founder, remains the boss, but more day-to-day responsibility rests with its 52-year-old president, Jonathan Gray. Apollo’s co-founder, Leon Black, quit in March 2021 after an inquiry into his ties to Jeffrey Epstein. At Carlyle, one co-ceo quit in 2020 after losing a power struggle. Can new leaders keep the magic going? The challenge is tougher when, as Stan Miranda of Partners Capital, an investment firm, puts it, “We’ve been through a golden 40-year period in which conditions grew ever more benign. It’s been incredible—and it may well be over.”

This special report looks at the risks as the tailwinds of the past decade drop, and at the opportunities as private markets win new investors. It considers a future in which scoring big with buy-outs is no longer enough. It explores what institutional investors want and the burgeoning market for private debt; and it looks at regulatory and reputational landmines. The report focuses on America. Private markets have become more global, but it remains true that today’s trend in New York is tomorrow’s in London or Shanghai.

Core Finding, “…we analyze labor market underutilization and labor supply behavior of workers during the Covid pandemic, building on the Aggregate Hours Gap developed by Faberman, Mueller, Şahin, and Topa (2020). We find diverging patterns between the AHG and the unemployment rate, though the nature of the divergence is not the same during the Covid pandemic as it was following the Great Recession. The Covid pandemic is characterized by a relatively tight labor market, with the AHG already below its February 2020 level by the end of 2021. We find that this is driven by individuals out of the labor force and part-time workers whose desired work hours dropped substantially during the Covid pandemic. The decline in desired work hours is more than double the decline in the labor force participation rate and is just as persistent throughout the pandemic. We perform a counterfactual exercise that holds desired work hours constant at their pre-pandemic average within detailed labor force and demographic groups and recalculates the AHG and associated potential work hours from March 2020 forward. We find that the decline in desired work hours reduced the AHG by 2.5 percentage points (12.5 percent) relative to its estimated value at the end of 2021, and it accounts for essentially all of the discrepancy between potential work hours and the labor force participation rate. Our evidence does not support the notion that the contraction in labor supply is driven mostly by women responding to child care demands. Instead, the drop in desired hours is pervasive across most demographic groups, with somewhat larger declines among those with less than a college degree. The decline is also concentrated among what we refer to as the intensive margin of labor force participation. This margin represents individuals who prefer to work infrequently, and when they do, they generally prefer part-time work hours.Finally, we find a range of suggestive evidence that shows that the Covid pandemic likely plays a considerable role in the observed reduction in desired work hours—including lower desired hours among those in jobs with a higher potential exposure to Covid—but that its effects on job search behavior mostly dissipate by the end of 2021. We also find that most individuals have persistently higher reservation wages throughout the pandemic, consistent with a reassessment of their labor supply decisions. Our findings suggest that an overall lower willingness to work has led to a contraction in labor supply that persists throughout the Covid pandemic. This decline contributed to the rapid tightening of the labor market following the onset of the pandemic…”

“…The results by demographics show that desired work hours fell for all groups save for the college educated. Men had slightly smaller reductions in desired hours compared to women, likely because of added child-rearing burdens falling disproportionately upon women during the Covid pandemic….The fraction of those out of the labor force who prefer zero work hours rises from 41 percent to 50 percent during the pandemic…”
R. Jason Faberman, Andreas Mueller and Ayşegül Şahin, "Has the Willingness to Work Fallen during the Covid Pandemic?"National Bureau Of Economic Research, February 2022, https://www.nber.org/papers/w29784

The Evidence

The key to our analysis is the Job Search Supplement of the Survey of Consumer Expectations (SCE)...The supplement asks a wide range of questions on an individual’s employment situation, work preferences, and job search behavior. We focus on questions in the supplement that ask respondents about the number of hours they desire to work and their reservation wage... Quantitatively, we find that the decline in desired work hours across all individuals is 4.6 percent, compared to a decline in the labor force participation rate of 2.3 percent, over this period. The decline occurs with a sharp drop at the onset of the pandemic that remains persistently low thereafter

“…Table 1 reports the mean estimates of desired work hours for 2013-17, 2018-19, and 2020-21, along with the difference in desired hours between 2018-19 and 2020-2021, by detailed labor force state and selected demographics.15 The table shows that, prior to 2020, those who work full-time generally prefer full-time hours and those working part-time generally prefer fewer hours. The unemployed, regardless of their duration, generally prefer full-time hours, on average. Those out of the labor force that report “wanting work” (but either did not actively search or were not available for work) prefer close to full-time hours (about 30 hours per week), but fewer hours than the unemployed, on average. Those who are retired or out of the labor force for other reasons prefer a small but nontrivial amount of work hours, generally between one and two days per week of work. Table 1 also shows that desired hours fell, on average, during the Covid pandemic across nearly all labor force states and for all reported demographic groups.16 Within labor force states, the declines were largest among part-time workers (3.1 hours, or 11.5 percent) and those out of the labor force who either want work (2.5 hours, or 8.4 percent), or were otherwise out of the labor force for reasons besides retirement (3.3 hours, or 22.8 percent). The full-time employed and the unemployed exhibit almost no change in their desired work hours….”

we provide a back-of-the-envelope calculation of how much the U.S. government gains and loses from inflation shocks. With total U.S. government debt increasing to more than 100% of U.S. GDP last year and with the large surprise increase in inflation in 2021, the U.S. government is a big winner. The decrease in the real value of debt amounts to 3.3% of U.S. GDP—at the expense of the debtholders, who were effectively hit by a 6.5% tax on the wealth held in nominal Treasury....In the next figure, we plotted the year-end nominal value of Treasury debt as a percentage of nominal GDP by payment horizons, for all Treasury debt due in one year or further. As shown in the figure, the largest share of current outstanding Treasury debt is debt maturing in three to five years, followed closely by two-year maturities. This means that the two-year and five-year inflation expectation shocks will have the largest redistributive impact on the government budget....In the next figure, we plotted how much inflation surprises have decreased the real value of U.S. government debt as a percentage of U.S. GDP each year over the previous four decades. A positive number means a redistribution from debtholders to the U.S. government, an inflation tax on the debt holders. Historically, the effects of inflation shocks, both positive and negative, have been small—barely exceeding 1% of U.S. GDP in the most extreme cases. However, the large inflation surprises we experienced last year, together with the ever-increasing Treasury debt in recent years, has led to an unprecedented, large-scale redistribution. This has resulted in an inflation tax that amounts to 3.3% of U.S. GDP imposed on Treasury debtholders—that is, a tax rate of 6.5% on wealth invested in Treasury securities due in one year or further. While 38% of the Treasury debt, not counting that which is held by the Federal Reserve, is owned by foreigners, nearly 62% of this debt is held by U.S. households, either directly or indirectly, according to the most recent release of the Financial Accounts of the United States.

Yu-Ting Chiang and Jesse LaBelle, "How Much Are We “Taxed” by Surprise Inflation?"Federal Reserve Bank Of St Louis, March 2, 2022, https://www.stlouisfed.org/on-the-economy/2022/mar/how-much-are-we-taxed-surprise-inflation

In an economy, borrowing and lending are typically conducted in nominal terms: A lender provides a loan for a certain sum of money and a borrower repays that exact sum plus interest. This means that, whenever the price level of goods and services is different from what people expected—an “inflation shock”—each transaction has a “winner” and a “loser” in terms of the real value delivered (in comparison to what was agreed upon).

In particular, when inflation turns out to be higher than expected (a positive inflation shock), then what the borrower needs to repay will be much less valuable than what was expected when the agreement was made, that is, a positive inflation shock benefits nominal borrowers at the expense of lenders.

The U.S. government is one of the largest of these nominal borrowers in the world. Over the past 40 years, the nominal U.S. government outstanding debt averaged around 57% of U.S. gross domestic product (GDP). In this article, we used inflation expectation data from the Cleveland Fed to calculate inflation surprises over the past four decades, and together with data on U.S. Treasury debt from the Center for Research in Security Prices, we provide a back-of-the-envelope calculation of how much the U.S. government gains and loses from inflation shocks.

With total U.S. government debt increasing to more than 100% of U.S. GDP last year and with the large surprise increase in inflation in 2021, the U.S. government is a big winner. The decrease in the real value of debt amounts to 3.3% of U.S. GDP—at the expense of the debtholders, who were effectively hit by a 6.5% tax on the wealth held in nominal Treasury.

Inflation Shocks: From 1984 to 2021

The Cleveland Fed provides estimates of inflation expectations each month over different horizons.1 For each year, we calculate how much the initial expectation has changed over the course of one year because of new information about the economy. For example, if last year you expected inflation to be 2%, but actual inflation over the year turns out to be 4%, then that is an inflation shock of 2 percentage points.

Similar logic can be applied to longer horizons as well. For example, if last year (say, January 2021) you expected inflation to be 2% per year on average for the next five years (the period January 2021 to January 2026), but with new information that arrived over the previous year, now today, in January 2022, you update your inflation expectations for the same period (January 2021 to January 2026) to average 4% instead. That represents an inflation shock of 2 percentage points annually for the five-year horizon.

In the next figure, we plotted the annualized inflation shocks in the United States over five different horizons.

To illustrate how inflation shocks redistribute between borrowers and lenders of nominal contracts, consider the following example: Suppose you are wanting to buy a car that costs $10,000 today. You agree to borrow the $10,000 from your bank today and pay it back, with 5% interest—for a total of $10,500—one year from today. Suppose the bank and you both expect inflation to be zero percent. However, if after a year, realized inflation turns out to be 10%, then the $10,500 you promised to pay is worth only $9,450 in today’s dollars, and you are actually paying back less than the principle you borrowed.

This example illustrates the redistributive effect of surprises in inflation. This phenomenon also holds for longer horizon shocks—the original value of contracts changes when agreements are made based on the expected cumulative inflation rate over the agreed horizon, and the market updates its expectation over time with the arrival of new information.

U.S. Treasury Debt

The U.S. government is one of the largest nominal borrowers in the world. This means the same redistribution happens between the U.S. government and its debtholders when the U.S. government borrows money in nominal terms today and pays it back at agreed-upon dates in the future.

Rather than a single lump-sum payment, as in the earlier example, Treasury securities mature in a wide range of horizons. To understand how Treasury debts of different maturities are exposed to inflation shocks, we calculate, for each year, the year-end nominal value of Treasury debt that is outstanding and priced at the beginning of each year.2

Consider an example: If in January 2021, the U.S. government needs to make a payment of $10,500 in one year, in January 2022, and $10,500 in two years, in January 2023, and the market nominal interest rate (the forward rate) is expected to be 5% between January 2022 and January 2023; then in January 2021, the year-end value for the one-year horizon is $10,500, and the year-end value for the two-year horizon is $10,000 ($10,500/1.05).

These values represent the year-end amounts the U.S. government agrees to pay in January 2021 that are exposed to inflation shocks, and they allow us to understand how changes in the real value of nominal amounts from over the year affect the real value of outstanding debt of different horizons that the U.S. government had promised to pay—that is, how inflation shocks of different horizons affect the total real value of the U.S. government debt.

In the next figure, we plotted the year-end nominal value of Treasury debt as a percentage of nominal GDP by payment horizons, for all Treasury debt due in one year or further. As shown in the figure, the largest share of current outstanding Treasury debt is debt maturing in three to five years, followed closely by two-year maturities. This means that the two-year and five-year inflation expectation shocks will have the largest redistributive impact on the government budget.

How do inflation surprises affect the real value of Treasury debt? Suppose that in January 2021, when people agreed to lend money to the government, inflation was expected to be zero percent for the next two years; to keep this example simple, we also assume a forward rate of zero. However, over the first year, inflation was actually 5%, and people now expect inflation to average 3% in the two-year horizon: an inflation shock of 5% for the one-year horizon and 1% for the second year. As a result, payment of $1 at the end of the first year loses around 4.76% of its originally expected value (-4.76% ≈ 1/1.05 − 1), and a payment at the end of the second year with value of $1 loses around 5.7% of its originally expected value (-5.7% ≈ 1/1.032 − 1).

If we apply the logic from the previous example to a larger scale, in January 2021, the government promised two payments with one- and two-year maturities to bond holders that have nominal value equivalent to $1.6 trillion and $3.15 trillion respectively in January 2022. With these amounts, the inflation shocks considered above for the one- and two-year horizons will lead to a 4.76% decrease in real value for the first payment—a loss of $76.2 billion ($1.6 trillion ×4.76%)—and a decrease of 5.7% for the second payment—for a loss of $179.55 billion ($3.15 trillion ×5.7%)—relative to their original values. Following this logic, we calculate how inflation shocks redistribute between the U.S. Treasury and its debtholders each year.

In the next figure, we plotted how much inflation surprises have decreased the real value of U.S. government debt as a percentage of U.S. GDP each year over the previous four decades. A positive number means a redistribution from debtholders to the U.S. government, an inflation tax on the debt holders.

Conclusion

The federal government is the largest nominal debtor in the economy. This means it is either the biggest winner or loser from every inflation shock. With the unprecedented positive inflation shock we have experienced over the last year, the federal government has come out as a big winner, with an inflation tax that amounts to around 3.3% of GDP, equivalent to a 6.5% tax on wealth held in Treasury securities. These numbers can be expected to change if there are more surprises in inflation.

Notes and References

These values are computed by a model based on Treasury yields, inflation data, inflation swaps, and survey-based measures of inflation expectations.
We exclude debt held by the Federal Reserve.

"...Expansionary monetary policy has heterogeneous effects on the labor force, with labor market tightness playing an important mediating role. We show empirically that expansionary monetary policy benefits the employment of workers with weak labor force attachment more in tight labor markets than in slack ones. This pattern holds across racial, education, and sex categories, as the employment benefits for Blacks, high school dropouts, and women increase with labor market tightness. The beneficial impact of monetary policy on less-attached workers is economically sizeable and long lasting. Using a New Keynesian model with workers of heterogeneous types, we analyze how labor market tightness transmits changes in monetary policy into employment growth of workers of different types. The model predicts that the expansionary effect of monetary policy on the employment of less-attached workers is stronger in tighter labor markets. We further show that a monetary policy that follows an average inflation targeting rule particularly benefits less-attached workers. By keeping rates low for longer, employment becomes more inclusive. Similarly, a flatter Philips curve enables the central bank to maintain low rates, implying that expansionary monetary shocks lead to larger and more persistent increases in the employment of low labor force participation workers. Our empirical and theoretical results both suggest that sustained expansionary monetary policy, which tightens labor markets, facilitates robust employment growth among less attached workers. Our findings thus imply that the Federal Reserve’s recent change in its conduct of monetary policy from strict to average inflation targeting will benefit the employment of female, minority, and low skilled workers. At the same time, expansionary monetary policy increases inflationary pressure and may also foster wealth inequality by raising asset prices (Amberg et al., 2021; Peydró et al., 2021). Managing the tradeoff between broad-based employment goals, inflation targets, and wealth inequality is an important topic of further research...."

Nittai Bergman, David Matsa, and Michael Weber," Inclusive Monetary Policy: How Tight Labor Markets Facilitate Broad-Based Employment Growth,"Becker Friedman Institute, January 2022, https://bfi.uchicago.edu/wp-content/uploads/2022/01/BFI_WP_2022-03.pdf

“…The employment response of Whites, however, differs from that of Blacks. Column 2 of Table 3 reports estimates of equation (3) for Whites. In contrast to Blacks, the b1 coefficient for Whites is much smaller and not statistically significant...The difference in the Black and White coefficient estimates is highly statistically significant (p < 0.01)...."

“..Panel B of Table 3 presents a similar heterogeneity analysis with respect to educational attainment, reporting results for those who did not complete high school in column 3, high school graduates in column 4, those with some college education in column 5, and bachelor’s degree holders in column 6.8 We find that in response to monetary easing, the increase in employment growth among workers who did not complete high school is larger when labor markets are tight than when they are slack (column 3). The b1 coefficient implies that a one standard deviation drop in the federal funds rate is associated with 0.39 percentage point greater two-year employment growth in tight labor markets (90th percentile) than in slack ones (10th percentile). This magnitude corresponds to approximately 18% of unskilled workers’ mean two-year employment growth...For workers with greater educational attainment, in contrast, the b1 coefficient estimates are close to zero and not statistically significant (columns 4-6)…”

“…Our empirical analysis explores monetary policy’s heterogeneous effects with respect to workers’ race, education, and sex. We investigate how expansionary monetary policy promotes employment growth for each group across local labor markets with different tightness. We find that for demographic groups with lower average labor market attachment—Blacks, the least educated, and women—expansionary monetary policy has a larger effect on employment growth in tighter labor markets. Because expansionary monetary policy tightens labor markets...this finding implies that sustaining expansionary monetary policy over longer time periods is particularly helpful to these demographic groups...Our results show that for demographic groups with low average labor market attachment—Blacks, the least educated, and women—monetary expansions have a larger effect on employment growth in tight labor markets, which we measure using the market’s aggregate prime-age employment-to-population ratio. This effect is economically large. For example, we find that a one standard deviation drop in the federal funds rate increases subsequent two-year Black employment growth by 0.91 percentage points more in tight labor markets (90th percentile) than in slack labor markets (10th percentile).Similarly, for workers who did not complete high school, a one standard deviation drop in the federal funds rate increases employment growth over the subsequent two years by 0.39 percentage points more in tight labor markets than in slack ones. This additional impact of monetary policy in tight labor markets is sizable, corresponding to 9% and 18% of the mean employment growth rates for Blacks and high school non-completers over the sample period, respectively....

The effect is seemingly persistent

“..The effects on less-attached workers are persistent. We find that monetary policy’s incremental effect on less-attached workers’ employment growth in tight labor markets peaks 7 to 9 quarters after interest rates decreases. Although monetary policy’s incremental effect wanes over time, its cumulative effect is long lasting. For example, the differential effect of monetary policy on cumulative Black employment growth in tight versus slack labor markets persists even four years after the federal funds rate decreases…”

The Evidence

“…Table 3 presents OLS estimates of equation (3). Each column in Table 3 examines the employment growth of a different demographic group. Panel A of the table examines heterogeneity with respect to workers’ race, presenting results for Blacks in column 1 and Whites in column 2. For Blacks, the coefficient on the interaction between the federal funds rate and local labor market tightness, b1, is negative, sizable, and statistically significant. This coefficient implies that a monetary easing is associated with greater Black employment growth in tight labor markets as compared to in slack ones.To assess the magnitude of this estimate, consider the effect of a one standard deviation (2.25 percentage point) decrease in the federal funds rate. Our estimate implies that, over the subsequent two years, this drop in the federal funds rate is associated with a 0.91 percentage point larger increase in Black employment growth in labor markets at the 90th percentile of employment-to-population (86%) than in labor markets at the 10th percentile of employment-to-population (49%).This additional boost in employment growth in tighter labor markets is sizable, corresponding to 9% of the mean two-year Black employment growth over the sample period…”

Developed economies are offering increasing amounts in subsidies to encourage citizens to have more children. But there is little evidence that they are having an effect: fertility rates are falling rapidly across the rich world. “Pronatalist policies haven’t really been shown to consistently change fertility levels,” said Bernice Kuang, a demographer at the University of Southampton, in the UK....As evidence in favour of subsidies, demographers cite surveys indicating that couples in some countries are not having as many children as they would like; generally women would like to have around two children, above the actual birth rate in many countries.

“Governments shouldn’t be making explicit incentives to get women to have more babies than they wish just to fulfil some national quota or their desire to create a small baby boom,” he said.

But even so, he questioned whether governments should be trying to intervene in such personal choices.

“Higher equality allows especially better-educated women an easier combination of career and family life,” Sobotka said. “Why not allow some of the men to retreat from work for two or three years to take parental leave and then head back to work?”

Federica Cocco, Edward White, Kang Buseong and Anna Gross, "Baby bust: Can policymakers boost dwindling world fertility rates?"Financial Times, April 20, 2022, https://www.ft.com/content/530cde70-eea2-47ff-8b4e-5efa8b9630d5

Baby bust: Can policymakers boost dwindling world fertility rates?

When Choi Yoon-seo became pregnant in 2020, South Korea paid her $500 — a reward for helping the country address its dire demographic crunch.

That year it hit its lowest fertility rate on record at 0.84 children per woman — the lowest in the world.

The government in Seoul has since increased the one-off pregnancy gift to around $1,700. But Choi said the idea that the payments would encourage her to have another baby was laughable.

“I used up the whole amount on prenatal tests before the halfway point of my pregnancy,” she said.

Since her son was born in January 2021, the 40-year-old has struggled with family, societal and economic pressures. She had to give up her job as a social worker and became overwhelmed with “so much guilt over not being able to be a good mother”.

The state’s childcare subsidies are “unrealistic” given the cost of living and education in the Korean capital, she said.

Seoul’s efforts are not unusual. Developed economies are offering increasing amounts in subsidies to encourage citizens to have more children. But there is little evidence that they are having an effect: fertility rates are falling rapidly across the rich world.

“Pronatalist policies haven’t really been shown to consistently change fertility levels,” said Bernice Kuang, a demographer at the University of Southampton, in the UK.

More recently, other European countries have followed France’s lead. In 2020, Hungary designated fertility clinics a “strategic” sector and made IVF treatment available for all women.

And last year the Italian government introduced incentives aimed at reversing demographic decline, which included longer parental leave for fathers. “An Italy without children is... destined to slowly grow old and disappear,” prime minister Mario Draghi warned.

Despite this, European fertility rates are still falling.

In France, the rate has dropped from above two children per woman in 2010 to 1.83 in 2021, according to data from the country’s statistics office Insee. A recent bill put forward by politicians from the Republicans party blamed cuts to family support by successive presidents François Hollande and Emmanuel Macron.

As evidence in favour of subsidies, demographers cite surveys indicating that couples in some countries are not having as many children as they would like; generally women would like to have around two children, above the actual birth rate in many countries.

Kuang said that lower-income couples needed “financial support and affordable childcare” while higher-income couples “need policies make it easier for working people to balance childcare and, say, school pick-up, with their jobs”.

But Kuang cited South Korea’s situation as evidence that other factors were also at play, “such as rigid gender norms and the unequal division of labour within the household”.

“Governments want to make it so that women can do childcare as well as work, but some women don’t want to work that hard and some families don’t want to operate that way,” said Kuang. “Can you make it so that men can do more? Like find a way for them to work all day and take care of their kids.”

That is a new and quite interesting paper by Thomas Philippon. Here is the abstract:"Growth theory is based on the assumption of exponential total factor productivity (TFP) growth. Across countries and time periods I find that TFP growth is actually linear. Unlike the exponential model, the additive growth model provides useful medium-term forecasts of TFP. It also explains the TFP slowdown and the volatility puzzle, and predicts falling real interest rates. For the distant future the model predicts ever increasing increments in standards of living but with growth rates that converge to zero. For the distant past the model suggests that the size of TFP increments has changed in the late 1600’s, the early 1800’s, and around 1930."Or consider this presentation:"Initial trend growth is around 2.5%. After 40 years, TFP doubles, and since increments are constant, the trend growth rate is half of what it used to be. After 60 years later, it is only one percent."And:"…the process of US TFP increments has only one break over the past 130 years, around 1930, following the large-scale implementation of the electricity revolution…For the UK, I find two breaks between 1600 and 1914. The first is between 1650 and 1700, when growth becomes positive. The second is around 1830. These breaks are consistent with historical research on the first and second industrial revolutions…"The author argues that linear TFP growth holds for Thailand, Taiwan, and Korea as well, and indeed for all recent countries with data for TFP. As Philippon puts it informally“New ideas add to our stock of knowledge; they do not multiply it.”As stated above a very interesting paper,but I do have some worries. First, his model fits “TFP” better than gdp growth per se, which (at least until recently) does appear to be exponential in advanced economies. If I read the author’spp.21-22 correctly, he is suggesting (speculatively) that 20th century gdp growth received an artificial inflation from improvements in educational achievement that perhaps are unlikely to be replicated. Maybe, but the broader predictions of the theory — including on gdp growth — require further consideration. Second, is TFP even “a real thing”? Or is it a meaning-poor residual, based on arbitrary distinctions between “innovation” and “investment”? Maybe the ongoing trend is simply that more innovation is embodied in concrete investments, thus causing TFP to measure lower? Too much of the paper takes the TFP concept for granted. Nonetheless worth a real ponder.

Tyler Cowen, “Additive Growth,” Marginal Revolutions, April 20, 2022, https://marginalrevolution.com/marginalrevolution/2022/04/additive-growth.html

Thomas Philippon, "Additive Growth,"National Bureau Of Economic Research, April 2022, https://www.nber.org/papers/w29950

“…Model D, unlike model G, appears to have only one break over the period 1890-2019. We can formally test this idea following Bai and Perron (2003). The unconstrained test finds one break in the series around 1930 (the point estimate is 1933). We cantest H0: no breaks versus H1: break in 1933. The W statistic is 21.72 and the p-value is 0.0. I emphasize, however, that while the existence of a break is clear, the date is really an interval between the late 1920s and WW2. The date of the break is consistent with Field (2003)’s argument that “the years 1929- 1941 were, in the aggregate, the most technologically progressive of any comparable period in U.S. economic history.” This period corresponds to the large scale implementation of the discoveries of the second industrial revolution: electric light, electric power, and the internal combustion engine, as discussed in Jovanovic and Rousseau (2005). Gordon (2016) points out that it is somewhat surprising that “much of the progress occurred between 1928 and 1950,” several decades after the discoveries were made. Following David (1990), he explains the paradox by showing that the 1930s were a period of follow-on inventions, such as the perfection of the piston power-powered aircraft and television, and the increasing quality of machinery made possible by the large increases in available horsepowers and kilowatt-hours of electricity. Following these historical insights, figure 5 proposes an interpretation of US TFP from 1890 to 2019, using linear growth with one structural break in 1933 after the electrification revolution. We can summarize this idea in the following remark, keeping in mind that we normalize US TFP to 1 in 1890. Fact 5. From 1890 to 1933, TFP increases by.017 each year until it reaches a level around 1.75 in the early 1930s. From 1933 to 2019 TFP increases by.057 each year (3.3 p.p. of its level in 1933) to reach a level around 8 at the end of the sample….”

The outlier

Core Finding:"....There is no TFP slowdown, or, to put it differently, the perceived TFP slowdown is the result of using a misspecified model as a benchmark. Initial trend growth is around 2.5%. After 40 year, TFP doubles, and since increments are constant, the trend growth rate is half of what it used to be. After 60 years later, it is only one percent...."

Evidence from the United States, "...Figure 1 reveals a new fact and makes an important empirical point. The new fact is that there is no TFP slowdown in the US according to model D. The important empirical point is that, with realistic values for TFP growth rates, the distinction between models D and G requires at least 10 years of out-of-sample forecasts....US growth is better described as additive rather than multiplicative. Instead of stating that the average growth rate of TFP is 1.45%, which is correct but not particularly useful, it is more relevant to say that TFP increases by 0.0245 points each year starting from a normalized value of 1 in 1947. For labor productivity, both the additive growth model and the multiplicative growth model predict an increasing size of productivity increments, but at different speeds. The additive model D predicts that labor productivity increments increase with the square of the time horizon, while the geometric model G predicts exponentially increasing increments. Model G does not describe the data with a constant growth rate. Model D describes the data relatively well with year-on-year increments of about $1560 per full time worker ($0.87 per hour, assuming 1800 hours worked in a year) around 2010…”

"...Figure 3 shows the raw and smoothed series....The data is from Bergeaud et al. (2016) and winsorized in the first and last percentiles to remove limit extreme outliers during WW2. The model is initiated over the first 10 observations, 1891 to 1900. As expected the trend growth of the economy changes over this long sample.....Table 1 shows that the volatility of TFP growth rates declines significantly over time...."

TFP growth is not exponential. New ideas add to our stock of knowledge; they do not multiply it. TFP has been growing linearly over the past 90 years in the US and the additive model beats the exponential model for every single country, developed or catching up, where TFP data is available. The TFP frontier appears to grow linearly within broad historical periods: 1650 to 1830, 1830 to 1930, and 1930 until today. Additive TFP growth predicts increasing growth of labor productivity and GDP per capita thanks to capital accumulation. This prediction also appears to be empirically accurate... The additive growth model explains the observed TFP slowdown as a simple side effect of model misspecification. We should not have expected growth rates to be constant in the first place. The additive model does not necessarily solve the research productivity puzzle....... since this puzzle is not about the stochastic process for TFP but rather about the specification of the production function for ideas.Models where ideas are non-rival often imply a tight connection between growth and the quantity of research. These models predict accelerating growth - whether of the linear kind or not - from an increasing number of researchers....I start my investigation with post-war US data. The empirical justification is that this is the most widely used and reliable data. The theoretical justification is that one might expect different TFP dynamics between countries at the frontier and countries catching up to the frontier. The main advantage of post-war US data, then, is that one can reasonably argue that the US was at the technological frontier during the entire period....My primary sources for TFP are Fernald (2012) (Fernald)and Bergeaud et al. (2016) (BCL).... Fernald’s series cover only the US business sector, while BCL include households and the government....The simplest way to start comparing model D and model G is to consider the following experiment. Suppose that two agents, George and Daniela, are asked in the middle of the sample (1983) to predict the level of TFP in the second half of the sample (1984- 2019). The agents have access to data from the end of World War 2 until 1983. The two agents have dogmatic beliefs regarding the correct model of economic growth. George believes in model G from equation (2) while Daniela believes in model D from equation (3). George therefore fits a log linear model over the years 1947: 1983 and predicts future (log) TFP as log Aˆ (G) t = ˆag + ˆgt for t = 1984: 2019. Daniela instead fits a linear model and predicts future TFP as Aˆ (D) t = ˆa + ˆbt. Figure 1 shows that Daniela would have made a much better forecast than George. George is puzzled by the TFP slowdown while Daniela does not perceive an obvious long term break in her model (although there are some meaningful medium term deviations).....Figure 1 reveals a new fact and makes an important empirical point. The new fact is that there is no TFP slowdown in the US according to model D. The important empirical point is that, with realistic values for TFP growth rates, the distinction between models D and G requires at least 10 years of out-of-sample forecasts...Fact 1. There is no TFP slowdown in the US according to model D....US growth is better described as additive rather than multiplicative. Instead of stating that the average growth rate of TFP is 1.45%, which is correct but not particularly useful, it is more relevant to say that TFP increases by 0.0245 points each year starting from a normalized value of 1 in 1947. For labor productivity, both the additive growth model and the multiplicative growth model predict an increasing size of productivity increments, but at different speeds. The additive model D predicts that labor productivity increments increase with the square of the time horizon, while the geometric model G predicts exponentially increasing increments. Model G does not describe the data with a constant growth rate. Model D describes the data relatively well with year-on-year increments of about $1560 per full time worker ($0.87 per hour, assuming 1800 hours worked in a year) around 2010...Fact 2. Postwar US TFP growth is well described by Model D with increments of ∆ = 0.0245 points each year starting from a normalized value of 1 in 1947. Model D also predicts the correct non-linear evolution of labor productivity.