Macro Roundup

“…For several years after the 2007-09 recession, roughly a million people moved to the U.S. annually. That pace started to slow during the Trump administration and fell to a trickle after the Covid-19 pandemic started. The slowdown has left the U.S. with 2.4 million fewer immigrants of working age—about 1% of the working-age population—than if pre-2017 immigration trends had continued, according to Giovanni Peri, a labor economist at the University of California, Davis. The change is being felt as the economy rebounds and many employers struggle to replace workers who were laid off or quit since early 2020, contributing to wage pressure and inflation…”

In long-term care, the outlook is especially striking. Demand for home health aides and nursing home care is increasing as baby boomers age. Over the past decade, an average 18% of the sector’s workforce has been foreign born. That is close to the national average, but a disproportionate share of those with hands-on jobs such as orderlies are foreign born, according to nursing home operators.

Employment in nursing-care facilities fell to 1.34 million in March from 1.6 million in mid-2019. While the U.S. has some visa programs to help foreign doctors or engineers immigrate, operators of long-term-care homes can’t recruit from abroad because the jobs they are filling wouldn’t qualify for any existing visa program.

A September survey by the American Health Care Association and the National Center for Assisted Living, sister trade groups, found 99% of nursing homes and 96% of assisted-living facilities reporting staffing shortages.

Because of those shortages, 58% of nursing homes and 28% of assisted-living facilities limited how many new clients they could take on. With fewer caregivers, industry officials say, people looking for homes or health aides sometimes can’t even get their calls to agencies returned.

Adam Lampert, the chief executive of Mr. Nwaokolo’s Dallas employer, Manchester Care Homes, and a sister company employing home health aides, said that African immigrants make up roughly 80% of his workforce, many referred to him through a church.

Recently, when his team has tried to recruit through employee networks, it has come up short. There are fewer new arrivals, the people likeliest to take caregiving jobs.

“We’re not trying to discriminate against citizens when we look for employees—quite the opposite,” Mr. Lampert said. “We’re just looking for people to fill the roles that we need. And time and again, we find it is the immigrant population that tends to respond to us.”

Mr. Nwaokolo said caregiving can be demanding, such as when he needs to change clients’ adult diapers or help them go to the bathroom. It can be emotionally taxing, too, particularly working with those who have dementia.

“You have to learn how to be patient,” he said. “You have to watch your anger, because sometimes they can hit you or maybe spit in your face.”

Mr. Nwaokolo, who had been working as a local health official in Nigeria, came to the U.S. in 2015 for a health conference in Boston, where he met his future wife. That meant finding a new job. He wanted to work in healthcare but knew his experience likely wouldn’t transfer. Caregiving appealed to him, he said, because all the children in his village were taught to care for their elders.

Among Mr. Nwaokolo’s colleagues at the Dallas company, many came to the U.S. on tourist visas and ended up staying, typically by marrying an American. Others were sponsored by immediate family members who are U.S. citizens.

His cousin Anthony Chidera Nwakor, who lives in Lagos, applied for a visa to the U.S. in 2019 and was rejected. He applied again last year and is still waiting for an appointment at the U.S. consulate in Lagos. “I want to come there and make some connections, maybe work or go to school,” he said.

Mr. Nwaokolo said he has received offers to work as a traveling home nurse for as much as triple the $15 an hour he currently earns. He has decided to stay put for now because he feels some loyalty to Mr. Lampert, the chief executive of Manchester Care Homes, who paid for his nursing training and has lent him money for his family’s medical care in Nigeria.

“This place is like a family to me,” Mr. Nwaokolo said.

Michelle Hackman, "Add Declining Immigration to Problems Weighing on the Labor Market,"Wall Street Journal, April 5, 2022, https://www.wsj.com/articles/add-declining-immigration-to-problems-weighing-on-the-labor-market-11649174837

Add Declining Immigration to Problems Weighing on the Labor Market

DALLAS—In an economy where nearly every industry is short of labor, nursing homes represent an extreme. Their employment has fallen 16% since 2019, and 99% of homes said last fall they didn’t have enough staff.

Burnout and stress are leading causes, but so is a less-known contributor: fewer immigrants.

Emeka Nwaokolo, 36 years old, came to the U.S. from Nigeria for a business trip and met a woman from Dallas he eventually married. He ended up working as a manager for Manchester Care Homes, which runs a network of nursing homes around Dallas.

Over the years, he has referred other Nigerian immigrants from his church to Manchester. In the past few years, though, it has become much tougher for Nigerians to emigrate to the U.S. because of more-stringent visa screening at American consulates abroad. The situation has worsened with pandemic-related backups in immigration processing.

For several years after the 2007-09 recession, roughly a million people moved to the U.S. annually. That pace started to slow during the Trump administration and fell to a trickle after the Covid-19 pandemic started.

The slowdown has left the U.S. with 2.4 million fewer immigrants of working age—about 1% of the working-age population—than if pre-2017 immigration trends had continued, according to Giovanni Peri, a labor economist at the University of California, Davis. The change is being felt as the economy rebounds and many employers struggle to replace workers who were laid off or quit since early 2020, contributing to wage pressure and inflation.

Labor shortages stem from a wide variety of factors, including early retirement, Covid-19 sickness or fear of it, lack of child care and a desire to start a business. Additionally, Mr. Peri noted, fewer immigrants are a factor. Industries with above-average levels of foreign-born workers are likelier to have high job-opening rates.

Now, no one is coming, Mr. Berlanga said, and he faces more competition for his subcontractors’ time as home demand booms. He is also paying more for almost every service. Paint jobs, which routinely cost him $6 a square foot before the pandemic, now cost $7.50.

The smaller number of immigrants appears to be one reason wages are growing briskly in industries where vacancies are high, from hospitality to long-term care. “The numbers suggest it’s a small contributing factor to what’s happening with wage growth,” though likely dwarfed by factors such as the opportunities low-wage employees have to switch to different lines of work, said Daniel Costa, director of immigration law and policy research at the Economic Policy Institute, a left-leaning think tank.

The slowdown in immigration began in 2017, when the Trump administration adopted a broad range of policies to curb both illegal and legal immigrants, including refugees, foreign recruits, international students and family members of American citizens.

Those already-reduced flows were then cut in half in 2020 by the onset of the pandemic and the related restrictions. The U.S. closed consulates around the world, stopped processing refugees and began turning away most adult migrants at the Southern border under a new public-health authority known as Title 42.

In the 12 months ended last June 30, about 247,000 people moved to the U.S., less than a quarter of the 2016 level and half that of 2019, according to U.S. census data. Those figures don’t distinguish between people who came to the U.S. legally and those who didn’t.

Further crimping the availability of foreign-born workers are pandemic-related delays in processing work permits for those already in the U.S. There were 1.6 million pending work-permit applications at the U.S. Citizenship and Immigration Services at the end of December, up from 646,000 in 2019. It takes an average of nine to 11 months to process applications, versus three to four months at the end of 2019, according to government data.

Guito Tatawho fled violence in Haiti and now lives in Indianapolis, received a work permit in 2017 as part of his asylum application and became certified as a commercial truck driver. In 2020 he bought his own truck and began working for an Illinois trucking company, where he estimates he made $7,000 a month after truck payments.

When he applied to renew his work permit in February 2021, the immigration agency didn’t get to his case before his permit—and driver’s license—expired in August.

“I’m sitting at home right now doing nothing,” Mr. Tata said in February. “My truck is just parked somewhere. I’m losing money.”

On March 18, some seven months after the expiration, he finally got his new work permit.

A spokesman for the immigration agency said it couldn’t comment on an individual case but is aware of unprecedented processing delays and is working to fix them. Last week, the Biden administration said it plans to make expired work permits valid for many immigrants who rely on them.

The administration, in a move it said was to address labor shortages, this year made 55,000 additional visas available for temporary seasonal employers such as landscapers, hotels and ski resorts, nearly doubling the total available.

A political backlash to the administration’s handling of illegal immigration at the Southern border this year has made officials reluctant to take bigger steps to increase legal immigration, according to people familiar with the administration’s thinking.

About a fifth of consulates still aren’t processing most visas, and even those that are open are buried under a backlog of 7.5 million applications, according to State Department data.

Core finding, "...This paper is the first to examine changes in poverty over time using a comprehensive set of linked survey and administrative data, implementing recommendations of the Interagency Technical Working Group on Evaluating Alternative Measures of Poverty. Using the Comprehensive Income Dataset (CID), we correct for measurement error in survey-reported incomes, focusing on single parent families from 1995 to 2016. Our preferred estimates indicate that single parent family poverty declined by 62% over time, while it fell by only 45% using survey data alone. Moreover, survey-reported deep poverty among single parent families increased over time, while it fell using the CID...."

Their Poverty Thresholds, "...To construct absolute poverty thresholds in 1995 and 2016, we use as a starting point the official threshold in each year for a family of two adults and two children multiplied by the SPM 3-parameter equivalence scale to account for family size and composition. Note that the official thresholds are updated for inflation using the CPI-U, which has a widely documented upward bias (see Moulton 2018 for a recent review and additional citations). We therefore also construct alternative poverty thresholds in 2016, which use an adjusted inflation measure that subtracts 0.8
percentage points off the annual growth in the CPI-U-RS each year starting from the 1995 baseline (Meyer and Sullivan 2012). We also show poverty estimates that use the Personal Consumption Expenditure (PCE) price index to adjust for inflation, which has been used by several prior studies to update thresholds over time and corrects for some but not all of the bias in the CPI-U. While the level of the baseline thresholds is fundamentally arbitrary,we use the 1995 official thresholds because they allow us to show how the poverty rate in 1995 falls after using a broader income definition and correcting for survey misreporting. We focus on poverty (income below 100% of the threshold) and deep poverty (income below 50% of the threshold). We also focus on single parent families rather than single mother families, although - as Appendix Table A2 shows - the percentage of single parent families headed
by a female remains largely consistent over time (85% in 1995 and 84% in 2016). We define single parent families as sharing units with a single adult (aged 18 or over) and at least one child (under age 18), and we estimate that there were 7.2 million single parent families in 1995 and 6.4 million single parent families in 2016..”

The Evidence

“…Table 2 shows poverty rates in 1995 and 2016 Focusing first on 1995 survey levels, we find that the poverty rate for single parent families using survey-reported pre-tax money income is 47.4% (Column 1). While this rate is calculated using the reweighted sample of individuals in PIKed and non-whole imputed families, it is virtually identical to the rate (47.0%) calculated using the full survey sample and original survey weights (Appendix Table A3). Incorporating tax liabilities and credits in the resource measure leads to a reduction of 3.8 percentage points (8%), while the combination of taxes and non-medical in-kind transfers reduces survey poverty by 17.1 percentage points (36%) to a rate of 30.3%. Switching to the CID further reduces the poverty rate (relative to the survey baseline) for each income concept, as it falls by 5% for pre-tax money income, 2% for post-tax money income, and 14% for post-tax/in-kind transfer income - yielding a final rate of 26.1%. In 2016, using thresholds adjusted by the CPI-U, the poverty rate for single parent families is 39.9% using survey-reported pre-tax money income (Column 2). The poverty rate falls by 7.6 percentage points (19%) after incorporating taxes, and by 18.3 percentage points (46%) after incorporating both taxes and in-kind transfers. Again, switching to the CID causes the poverty rate to fall for each income concept, with post-tax/in-kind transfer poverty falling the most by 28% to a final rate of 15.5%. Not only is the percent decline in poverty due to changes in the income concept larger in 2016 (reflecting increased spending on tax credits and in-kind transfers over time), but the percent decline due to employing the CID is also larger in 2016 than in 1995. The latter result is consistent with increased underreporting of survey incomes, indicating that survey estimates of changes in poverty - regardless of income concept - will be biased upward. Changes in poverty from 1995 to 2016 are explicitly shown in Column 5, 6, and 7 of Table 2. When updating thresholds based on the CPI-U, the percent decline in poverty is greater using the CID than using the survey alone. Poverty based on pre-tax money income fell by 21% using the CID, compared to 16% using the survey. Poverty based on post-tax money income fell by 38% using the CID, compared to 26% using the survey. And poverty based on post-tax/in-kind transfer income fell by 41% using the CID, compared to 29% using the survey. The poverty reductions are even larger after correcting for the upward bias in the CPI-U by updating thresholds using either the PCE price index or the adjusted CPI-U-RS. Using the PCE price index, CID poverty fell by 28% using pre-tax money income, 44% using post-tax money income, and 49% using post-tax/in-kind transfer income. Using the adjusted CPI-U-RS (our preferred inflation adjustment), CID poverty fell by 37% using pre-tax money income, 51% using post-tax money income, and 56% using post-tax/in-kind transfer income. Further accounting for administrative SNAP in 2016 leads to a 62% decrease in poverty over time and a 2016 poverty rate among single parent families of 9.8%.This final estimate of the change in poverty is our preferred estimate because it reflects the broadest set of income sources available for consumption, corrects for survey misreporting using the CID, and corrects for bias in the inflation measure used to adjust official poverty thresholds. Given the large increase in aggregate SNAP underreporting and its bias toward poverty rate increases, we prefer estimates that include administrative SNAP data for 2016 even though we do not have administrative data available for 1995. Table 2 also reports results for deep poverty. Based on pre-tax money income, deep poverty between 1995 and 2016 fell by 8 to 21% (depending on the inflation measure used to update thresholds) when relying only on the survey and by 31 to 46% when relying on the CID. After incorporating both taxes and in-kind transfers into the resource measure, survey-reported deep poverty rose between 1995 and 2016 by 9 to 23% (depending on the inflation measure used to update thresholds). In contrast, CID deep poverty fell either slightly (when using the CPI-U) or by more than 20% when using the adjusted CPI-U-RS…”

It’s true that income inequality declined in the early part of the 20th century, but the cause had more to do with the economic devastation of the Great Depression than the New Deal tax regime.To see how, we must first turn to Messrs. Piketty and Saez’s inequality statistics. Their data show a rapid decline in top income shares between the 1929 stock-market crash and the end of World War II—a period economists have dubbed the “Great Leveling.” In their version, the sharpest decline took place between 1940 and 1945, just as the 91% top marginal rate schedule became a fixture of midcentury tax policy. Their statistics imply that more than 34% of the decline in the top 1%’s income share occurred in this brief period, as did an astounding 73% of the decline in the top 10% of earners.Our investigationof the Piketty-Saez data reveals that they failed to account properly for historical changes in how the Internal Revenue Service reported income-tax statistics. As a result, their numbers systematically overstate the levels of top income concentrations by as much as a third, while also distorting the trend line during the “Great Leveling” period. The combination of these errors creates an illusion that FDR’s tax hikes caused inequality to fall. Messrs. Piketty and Saez’s mistakes arise from how the IRS tabulates income. Between 1943 and 1944 the tax collection agency shifted from tracking “net income” to “adjusted gross income,” or AGI. The latter category, a truer depiction of annual earnings, includes both taxable earnings and deductible income such as charitable giving and state and local tax payments. Yet Messrs. Piketty and Saez didn’t bring pre-1944 IRS records into line with AGI accounting standards. Instead, they applied a fixed and arbitrary adjustment to all years before the AGI accounting change that conveniently scaled upward to the highest income brackets. At the same time, Messrs. Piketty and Saez mishandled how they estimate the top 1%’s income shares. In addition to IRS records of tax payments, this calculation requires a measure for all personal income earnings. We found that in every year prior to 1960, the IRS’s numerator is mismatched to the total income denominator used by Messrs. Piketty and Saez. They used the wrong accounting definition for personal income and neglected to adjust their data for wartime distortions on tax reporting. When we corrected these problems, something stunning happened. The overall level of top income concentration flattened, and the timing of its leveling shifted away from the World War II-era tax rates that Messrs. Piketty and Saez place at the center of their story. The nearby chart shows the results for the top 1% of income earners. In our series, inequality rose between 1917 and 1928, confirming the “Roaring ’20s” boom. The crash of 1929 emerges as the precipitating event of the “great leveling”—mainly due to severe capital losses among the wealthy during the Depression....instead of an inequality U-curve, in which income concentrations sharply fall then rise again as a direct inverse of federal income tax rates, we find a pattern that resembles a shallow saucer. Inequality does change over time, but at a much more subdued level. The causes of the shifts are related to macroeconomic events, such as the Depression, and conventional economic factors, such as increased labor and capital mobility.

Phillip Magness and Vincent Geloso, "Inequality and the Piketty Accounting Error,"Wall Street Journal, April 11, 2022, https://www.wsj.com/articles/inequality-picketty-saez-accounting-error-wealth-income-tax-capital-warren-aoc-ocasio-cortez-11649706037

Inequality and the Piketty Accounting Error

The political left’s love affair with steep progressive taxation got an academic boost with the publication of Thomas Piketty’s bestselling 2014 book, “Capital in the Twenty-First Century.” Appealing to the New Deal era, Mr. Piketty proposed a simple explanation and remedy for rising economic inequality: The concentration of income among the top 1% could be mitigated by strategically targeting wealth with the tax system.

Mr. Piketty based his theory on a historical argument taken from his own empirical work with fellow economist Emmanuel Saez. When Congress and President Franklin D. Roosevelt hiked the top marginal income-tax rate to 91% during the New Deal and World War II, they allegedly broke up the concentration of the capital stock at the top of the income ladder. Inequality declined to a midcentury trough, where it remained until the Reagan tax cuts in the 1980s. Inequality then rebounded to form a centurylong U-shaped pattern. The solution, then, is to restore tax rates to their FDR levels.

But the Piketty-Saez theory is less a matter of history than an accounting error caused by their misunderstanding of World War II-era tax statistics. That’s the main conclusion of a Analysis of top income concentration in the U.S. between 1917 and 1960, which we recently published in the Economic Journal.

Progressives embraced Messrs. Piketty and Saez’s historical account after it appeared in an influential academic paper in 2003. Their story undergirds the wealth-tax proposals of Sen. Elizabeth Warren and Rep. Alexandria Ocasio-Cortez. Heather Boushey, a member of President Biden’s Council of Economic Advisers, is also a fan. Even the New York Times’s “1619 Project” draws on Messrs. Piketty and Saez to proclaim confidently that “progressive taxation remains among the best ways to limit economic inequality.”

Our findings paint a different picture. It’s true that income inequality declined in the early part of the 20th century, but the cause had more to do with the economic devastation of the Great Depression than the New Deal tax regime.

To see how, we must first turn to Messrs. Piketty and Saez’s inequality statistics. Their data show a rapid decline in top income shares between the 1929 stock-market crash and the end of World War II—a period economists have dubbed the “Great Leveling.” In their version, the sharpest decline took place between 1940 and 1945, just as the 91% top marginal rate schedule became a fixture of midcentury tax policy. Their statistics imply that more than 34% of the decline in the top 1%’s income share occurred in this brief period, as did an astounding 73% of the decline in the top 10% of earners.

Messrs. Piketty and Saez’s mistakes arise from how the IRS tabulates income. Between 1943 and 1944 the tax collection agency shifted from tracking “net income” to “adjusted gross income,” or AGI. The latter category, a truer depiction of annual earnings, includes both taxable earnings and deductible income such as charitable giving and state and local tax payments. Yet Messrs. Piketty and Saez didn’t bring pre-1944 IRS records into line with AGI accounting standards. Instead, they applied a fixed and arbitrary adjustment to all years before the AGI accounting change that conveniently scaled upward to the highest income brackets.

At the same time, Messrs. Piketty and Saez mishandled how they estimate the top 1%’s income shares. In addition to IRS records of tax payments, this calculation requires a measure for all personal income earnings. We found that in every year prior to 1960, the IRS’s numerator is mismatched to the total income denominator used by Messrs. Piketty and Saez. They used the wrong accounting definition for personal income and neglected to adjust their data for wartime distortions on tax reporting. When we corrected these problems, something stunning happened. The overall level of top income concentration flattened, and the timing of its leveling shifted away from the World War II-era tax rates that Messrs. Piketty and Saez place at the center of their story.

The nearby chart shows the results for the top 1% of income earners. In our series, inequality rose between 1917 and 1928, confirming the “Roaring ’20s” boom. The crash of 1929 emerges as the precipitating event of the “great leveling”—mainly due to severe capital losses among the wealthy during the Depression.

These events predate the alleged inequality-reducing effects of high progressive taxation. The first Depression-era income tax hikes weren’t enacted until 1932. The policies that Messrs. Piketty and Saez wish to emulate became permanent during World War II, when Congress expanded federal income-tax eligibility and aggressively ramped up enforcement through automatic payroll withholding.

The 1929 drop is hard to celebrate, as it involved a downward leveling for everyone. It’s possible that FDR-era tax policies contributed to a leveling that was already under way, but these effects are muted in comparison with what Messrs. Piketty and Saez assume. When combined with other revisions to questionable claims in the post-1960 portion of the Piketty-Saez inequality data, a different centurylong pattern emerges. Instead of an inequality U-curve, in which income concentrations sharply fall then rise again as a direct inverse of federal income tax rates, we find a pattern that resembles a shallow saucer. Inequality does change over time, but at a much more subdued level. The causes of the shifts are related to macroeconomic events, such as the Depression, and conventional economic factors, such as increased labor and capital mobility.

These findings should provide a warning to policy makers who imagine that they can design a more equitable distribution of income by simply pulling the levers of fiscal policy. The assumed relationship between high rates and low inequality has attained a position of dogma among those who seek to rationalize a preference for tax hikes. In reality, it may be an artifact of poor statistical methodology and careless data treatment.

Mr. Magness is director of research at the American Institute for Economic Research. Mr. Geloso is an assistant professor of economics at George Mason University.

Could increased substance abuse during the pandemic be a factor contributing to the fall in labor-force participation?..The dramatic drop and sluggish recovery in the labor supply of prime-age workers can be seen in Figure 1. Early on in the pandemic, the LFPR dropped by about 3 percentage points, and although two-thirds of that decline has recovered,the rate is still about 1 percentage point below its pre-pandemic level.After its steady decline since the late 1990s, the LFPR of prime-age workers was trending upward before the pandemic.1 The straight dashed line in the figure shows the trend in labor-force participation over the January 2015 to February 2020 period. Assuming the prime-age LFPR would have increased at the same rate absent the COVID-19 pandemic, the LFPR is about 1.6 percentage points below trend….The first row of Table 1 reports the cumulative number of deaths from opioids, meth, and alcohol between April 2020 and June 2021 for individuals ages 25 to 54.13 The second row reports the number of deaths predicted using the pre-pandemic trend and seasonal components in Figure 2. Subtracting the second row from the first row gives an estimate of the additional number of deaths that occurred due to the COVID-19 pandemic. These are reported in the third row. There were 69,342 opioid deaths between April 2020 and June 2021, 14,820 of which were above the level predicted by the pre-pandemic trends. Similarly there were 2,702 excess meth-related deaths and 7,835 excess alcohol-related deaths. In total, during this period there were an additional 25,356 drug and alcohol deaths.... As the bottom three rows of Table 1 report, more than 27 million prime-age individuals are estimated to have a substance abuse disorder with more than 5 million of these individuals, 23%, being additional substance abusersabove the expected level based on past trend and seasonal effects…The LFPRs are calculated from the 2015-2018 NSDUH for prime-age individuals who either exhibit a disorder from using a particular substance or do not have a substance-disorder. The LFPR of individuals with an opioid-use disorder is 69.7 which is 13.2 percentage points below the prime-age rate for individuals who do not have a substanceuse disorder of 82.9%.Similarly, the LFPR for individuals with meth-use disorder is 66.7% which is 16.3 percentage points below the rate for individuals without a substance-use disorder. By contrast, there is no difference in LFPRs between alcohol abusers and non-abusers. Since the data shows no impact of alcohol abuse on labor-force participation, the increase in alcohol abusers will not have any impact on the number of additional individuals out of the labor force. For each substance, multiplying the number of abusers in Table 1 by the difference between the LFPR of abusers and non-abusers gives an estimate of the number of prime-age individuals who are out of the labor force (LF) due to substance abuse. For example, multiplying the 1,96 million additional opioid abusers reported in Table 1 by 13.2 percentage points implies that about 259,000 additional people are not in the labor force due to elevated levels of opioid abuse. The estimates indicate that all together there are over 2.7 million prime-age individuals not participating in the labor market due to substance abuse,391 thousand of which are additional non-participants due to the elevated levels of abuse during the COVID-19 pandemic. Table 3 summarizes the impact of the COVID-19 epidemic on labor-force participation of prime-age workers. Due to a 1.3 percentage point lower participation rate, there were 1.51 million fewer prime-age individuals in the labor force in June 2021 than in February 2020. The sixth row of the table reports the number of individuals predicted to be in the labor force in June 2021 using the pre-pandemic trend. Relative to the predicted number of 105.04 million, there were 2.02 million fewer participants...Under the assumption of no change in the death rate of abusers, the increase in opioid and meth usage during the pandemic accounts for a sizable fraction of the decline in LFPRs. The results are reported as the upper bound estimates in Table 4. These shares are determined by taking the estimated 391 thousand additional individuals not participating due to substance abuse reported in Table 2 and dividing it by the declines in the size of the labor force reported in Table 3. Additional non-participants due to elevated substance abuse are 25.8 percent of the 1.51 million person decline in the labor force between February 2020 and June 2021 and 19.4 percent of the decline from the predicted June 2021 levels based on the pre-pandemic trend. Next, we provide a lower-bound estimate assuming that the rise in opioid deaths during the pandemic was due to increased opioid use of existing abusers rather than a surge in the number of abusers….Using data on substance abuse deaths, increased substance abuse during the COVID-19 pandemic is estimated to account for between 9 and 26 percent of the decline in prime-age labor-force participation between February 2020 and June 2021. A decline that, as of January 2022, has still not fully recovered. Relative to levels predicted using the pre-pandemic trend, estimates indicate that increased substance abuse accounts for 7 to 19 percent of the decline. Interestingly, the falls in labor-force participation in early 2020 were larger for those without a college degree. The LFPR of non-college individuals dropped by 3.9 percentage points compared to a 2.8 percentage point drop for those with a college degree. Non-college labor-force participation has also been slower to rebound. As of January 2022, more than 70 percent of the initial fall in the LFPR of college graduates has recovered with the rate now less than 1 percentage point below levels predicted by the pre-pandemic trend. In contrast, less than half of the initial drop for non-college has recovered. As of January 2022, the non-college rate is still 2.4 percentage points below trend. The large initial drop in labor-force participation by non-college individuals and its slow recovery may be due to differential rates of increase in substance abuse by education during the pandemic. Rates of opioid abuse are more than three times higher, and rates of meth abuse nearly six times higher, among those without a college degree. Given their higher prevalence for illicit drug use, the extent of drug abuse may have increased more for noncollege individuals during the pandemic leading to larger negative effects on their labor-force participation.

Core Finding "...The labor-force participation rates of prime-age U.S. workers dropped in March 2020—the start of the COVID-19 pandemic—and have still not fully recovered. At the same time, substance abuse deaths were elevated during the pandemic relative to trend indicating an increase in the number of substance abusers, and abusers of opioids and crystal methamphetamine have lower labor-force participation rates than non-abusers. Could increased substance abuse during the pandemic be a factor contributing to the fall in labor-force participation? Estimates of the number of additional substance abusers during the pandemic presented here suggest that increased substance abuse accounts for between 9 and 26 percent of the decline in prime-age labor-force participation between February 2020 and June 2021...."

The Evidence

“…Figure 2 shows the deaths rates associated with overdoses from different substances The solid blue line in the figure displays monthly deaths from narcotics, which covers various types of opioids such as opium, heroin, fentanyl, and other natural and synthetic opioids, as well as deaths from cocaine. The blue dashed line shows the trend and seasonal narcotic deaths based on the pre-pandemic period The red solid and dashed lines show the death rates, actual and predicted, from alcohol (excluding those associated to drunk driving). Finally, the black lines are deaths from psychostimulants, primarily crystal methamphetamine (meth). Meth deaths have also been higher during the pandemic, although they have not been as elevated relative to their exponentially increasing trend as narcotic and alcohol deaths…”

“…Next turn to how much substance abuse lowers labor-force participation. The first row of Table 2 shows the difference between the LFPRs of opioid, meth, and alcohol non-abusers and abusers…”

Jeremy Greenwood, Nezih Guner and Karen Kopecky, "Substance Abuse during the Pandemic: Implications for Labor-Force Participation,"National Bureau Of Economic Research, April 2022, https://www.nber.org/papers/w29932

“….These shares are determined by taking the estimated 391 thousand additional individuals not participating due to substance abuse reported in Table 2 and dividing it by the declines in the size of the labor force reported in Table 3…. …Additional non-participants due to elevated substance abuse are 25.8 percent of the 1.51 million person decline in the labor force between February 2020 and June 2021 and 19.4 percent of the decline from the predicted June 2021 levels based on the pre-pandemic trend….”

“…Due to a 1.3 percentage point lower participation rate, there were 1.51 million fewer prime-age individuals in the labor force in June 2021 than in February 2020.The sixth row of the table reports the number of individuals predicted to be in the labor force in June 2021 using the pre-pandemic trend. Relative to the predicted number of 105.04 million, there were 2.02 million fewer participants. How many of these missing workers might be attributed to drug abuse?Under the assumption of no change in the death rate of abusers, the increase in opioid and meth usage during the pandemic accounts for a sizable fraction of the decline in LFPRs. The results are reported as the upper bound estimates in Table 4….”

“… The LFPRs are calculated from the 2015-2018 NSDUH for prime-age individuals who either exhibit a disorder from using a particular substance or do not have a substance-disorder. The LFPR of individuals with an opioid-use disorder is 69.7 which is 13.2 percentage points below the prime-age rate for individuals who do not have a substanceuse disorder of 82.9%. Similarly, the LFPR for individuals with meth-use disorder is 66.7% which is 16.3 percentage points below the rate for individuals without a substance-use disorder. By contrast, there is no difference in LFPRs between alcohol abusers and non-abusers. Since the data shows no impact of alcohol abuse on labor-force participation, the increase in alcohol abusers will not have any impact on the number of additional individuals out of the labor force. For each substance, multiplying the number of abusers in Table 1 by the difference between the LFPR of abusers and non-abusers gives an estimate of the number of prime-age individuals who are out of the labor force (LF) due to substance abuse. For example, multiplying the 1,96 million additional opioid abusers reported in Table 1 by 13.2. percentage points implies that about 259,000 additional people are not in the labor force due to elevated levels of opioid abuse. The estimates indicate that all together there are over 2.7 million prime-age individuals not participating in the labor market due to substance abuse, 391 thousand of which are additional non-participants due to the elevated levels of abuse during the COVID-19 pandemic. Table 3 summarizes the impact of the COVID-19 epidemic on labor-force participation of prime-age workers.

Robert Armstrong, "Did QE Cause Inflation,"Financial Times, July 19, 2022, https://www.ft.com/content/2c3fc086-b543-4efe-b696-8c84aa919b69

Did QE cause inflation?

QE, bank lending and inflation Did quantitative easing contribute to inflation, and how much? Will quantitative tightening have the opposite effect?

Important questions: it is generally thought that low rates did contribute to inflation, and the Federal Reserve is hoping like hell that higher rates will slow it. The role of QE and QT, as we’ve noted before, is trickier. There is little consensus among practitioners, academics and Fed officials about what central bank bond buying does to the economy and how it does it. It would be a lot better if we had a clear idea of what was going on.

Benn Steil and Benjamin Della Rocca, economists at the Council on Foreign Relations, have argued in a recent post that QE had a very important, perhaps even determining, role in creating inflation. I suspect they’re wrong, but it’s an important argument to consider.

Here’s how the argument goes (picky financial details incoming!).

When, in the course of QE, the Fed buys a Treasury bond from investors, the transaction is completed through an intermediary — a bank. The proceeds from the sale become a customer deposit at that bank, a liability. At the same time, the bank is credited with a reserve deposit at the Fed for the same amount, an asset.

Because a bank will always have fewer reserve assets at the Fed than deposit liabilities, the addition of the same amount to each will push the bank’s reserve/deposit ratio up. QE improved the bank’s liquidity in that sense.

A bank that has more liquidity has an incentive to lend. They don’t have to lend more, but they have reason to, in order to optimise their balance sheet. This is what Steil and Della Roca call the “credit channel” of QE.

In 2020, the Fed bought many billions in Treasuries, but banks’ reserves fell (they call them “excess reserves”; more on this shortly). This, Steil and Della Rocca say, is evidence that “QE was working as hoped” — that is, it was encouraging lending. When lending goes up, they argue, excess reserves fall.

But the gap between the Fed’s holding of Treasuries and reserves grew wider and wider, allowing Steil and Della Rocca to predict that inflation was coming. Here is their chart:

I suspect that this argument is wrong for two reasons, one involving how banks behave, and one about financial plumbing.

I’ve been talking to bankers for several years, as an analyst and a reporter, and I have never heard of talk about the decision to lend or not in terms of having ample liquidity or being liquidity constrained. Instead, the effective constraint is loan demand — the availability of creditworthy borrowers who want money.

I asked my favourite bank analyst, Brian Foran of Autonomous Research, about this, and he confirmed my suspicions. “Ninety per cent of bank lending decisions are, do my customers want to borrow and are they in good shape to do so,” he told me. “I’ve never sat in a meeting with a bank CEO who said ‘I’ve got all these deposits and I have to figure out a way to lend them out’. They might say — I have a high loan to deposit ratio and I have to work that out over the long run,” for example by retaining more of the loans the bank makes rather than selling them on to the secondary market.

It could be that bankers either are unaware or hesitant to admit the role that liquidity plays in lending decisions. But there is another point, made to me by former Fed trader and the “Fed Guy” blogger Joseph Wang. Banks’ lending isn’t constrained by the volume of liquidity, but rather by its cost. If banks need cash they can always borrow it at some price. Reserves at the Fed, if they ever did matter, don’t matter now, because as Steil and Della Rocca point out in a footnote, the Fed eliminated all reserve requirements in March of 2020.

Now the financial plumbing point. Here is a version of the Steil and Della Rocca chart, with two other series added: total bank credit creation, and the balances in the Fed’s reverse repo programme (more on what that is momentarily).

Here’s the problem, though. Lending does not track reserves (blue line) at all. Now, as argued above, I don’t think there is much reason that it should. And if those two don’t track, the Steil/Della Rocca argument does not work, because it depends on the idea that higher reserves, driven up by QE, incentivise lending.

And there is another explanation, other than higher lending, for why banks’ reserves have come down. It was, again, explained to me by Joseph Wang. The reserves are being funnelled, somewhat circuitously, into the Fed’s growing reverse repo programme (yellow line).

Here is how that funnelling would take place (now we are getting really technical, so feel free to skip the next two paragraphs). The Fed uses the reverse repo programme to sop up excess liquidity in the banking system that would otherwise force the overnight rate below the Fed’s target. Market participants, mostly money-market funds, can give the Fed their cash and receive an interest-paying Treasury security in return. It’s a collateralised overnight loan to the Fed.

In recent years banks have had more deposits than they wanted, which caused problems with their capital requirements. So they have pushed clients towards money market funds. The money market funds have, in turn, put more and more money into the RRP. The way that transaction takes place is that the money market fund that wants to participate in the RRP makes (another) deposit at a bank, and then that same sum is taken out of the bank’s reserve account at the Fed, and placed in a Fed RRP account. The bank’s reserves at the Fed fall.

I’m not totally confident in any of this, and look forward to hearing what Steil and Della Rocca have to say in response. But it seems to me that if QE encourages lending, the mechanism is much more indirect than the one they suggest.

Zooming back out to why all this matters. Unhedged’s best guess is that QE works primarily by injecting/sopping up liquidity in financial markets, not by encouraging lending. So QT will have its effect by withdrawing liquidity from markets, making them more volatile, diminishing investor risk appetites and increasing demand for cash and risk-free assets. That, rather than weaker lending, is what we should prepare for as QT proceeds.