Macro Roundup

Justin Fox reports superstar cities are still gaining population but their share of population seems to be shrinking, "... Not one of the country’s 10 largest cities lost population between 2010 and 2020, the first time that’s happened since the 1940s. All of them now have more than a million residents for the first time ever. The top-10’s share of the U.S. population did shrink, as it has with every Census since 1930. But the decline — to 7.87% in 2020 from 7.94% of the population in 2010 — was the smallest since then....The 10 biggest U.S. cities, while large and growing, now account for less than 7.9% of the country’s population. London alone houses 13.4% of the U.K. population, Toronto 7.9% of Canada’s. In 1930 and 1940, 5.6% of Americans lived in New York City (it was 2.7% in 2020). The modern U.S. is thus a decentralized nation, where despite an urban revival in recent years the periphery has kept growing faster than the center. Rural areas aren’t growing; most American counties actually lost population in the 2010s. But low-density suburban counties attached to large metropolitan areas grew faster than either high-density suburbs or urban counties, economist Jed Kolko calculated recently, while the fastest-growing major metro areas (Austin, Orlando, Raleigh, Nashville) aren’t among the largest...."

Justin Fox, "America’s Superstar Cities Aren’t What They Used to Be,"Bloomberg, October 4, 2021, https://www.bloomberg.com/opinion/articles/2021-10-04/new-york-la-and-other-superstar-cities-aren-t-what-they-used-to-be

America’s Superstar Cities Aren’t What They Used to Be

The first big data release from the 2020 Census in August contained some positive news about America’s biggest cities. The biggest of them, New York, turned out to have hundreds of thousands more people than the annual population estimates made by the Census Bureau had projected. Not one of the country’s 10 largest cities lost population between 2010 and 2020, the first time that’s happened since the 1940s. All of them now have more than a million residents for the first time ever.

The top-10’s share of the U.S. population did shrink, as it has with every Census since 1930. But the decline — to 7.87% in 2020 from 7.94% of the population in 2010 — was the smallest since then.

Then again, the boundaries of metropolitan areas — the main alternative to cities in rankings like this — are changing all the time. The White House Office of Management and Budget adjusts them every couple of years to reflect shifts in commuting patterns and occasionally redefines their very meaning. These changes usually add territory to metro areas but sometimes break them apart, as happened in 2003 when new OMB definitions split the San Francisco-Oakland-San Jose metro area, at the time the nation’s fifth-largest, into six different areas, none in the top 10, and the Washington-Baltimore area, then the fourth largest, into three. Not coincidentally, the share of U.S. population accounted for by the 10 biggest metro areas fell from 31.5% in the 2000 Census to 26.1% in 2010. It was 26.4% in 2020.

The Census Bureau also divides the U.S. into urban and rural areas, with the urban share of the nation’s population increasing steadily over time, to 80.7% as of 2010 (the 2020 numbers aren’t out yet). But its definition of urban casts an awfully wide net, including every settlement of 2,500 people or more, and is subject to change from time to time (the 2,500 is about to be upped to 10,000, for example).

All of which is a long way of saying that while the population of the 10 largest cities is a flawed measure of urbanization in the U.S., all the other measures are flawed too — and the top-10-city total, available all the way back to 1790, conveys some useful pieces of information. One is that, as already mentioned, the 2010s were not a decade of urban decline. The 10 biggest cities added 1.6 million residents, the third biggest such gain since 1930.

Still, that long era of decline left its mark. The 10 biggest U.S. cities, while large and growing, now account for less than 7.9% of the country’s population. London alone houses 13.4% of the U.K. population, Toronto 7.9% of Canada’s. In 1930 and 1940, 5.6% of Americans lived in New York City (it was 2.7% in 2020).

The modern U.S. is thus a decentralized nation, where despite an urban revival in recent years the periphery has kept growing faster than the center. Rural areas aren’t growing; most American counties actually lost population in the 2010s. But low-density suburban counties attached to large metropolitan areas grew faster than either high-density suburbs or urban counties, economist Jed Kolko calculated recently, while the fastest-growing major metro areas (Austin, Orlando, Raleigh, Nashville) aren’t among the largest.

This is a little hard to square with claims that large cities continue to wield great political clout. If it weren’t for the Electoral College, according to one oft-heard argument, voters in New York, Los Angeles and/or Chicago would choose every president. How they would manage to do this with only 4.7% of the nation’s population is a bit of a mystery. True, the three cities’ metro-area populations added up to 13% of the U.S. total in 2020, but that was down from 13.3% in 2010 and traditionally suburbs and cities largely canceled each other out politically — although that has been changing lately.

There’s a stronger argument to be made that economic power and cultural clout remain concentrated in a few places. Gross domestic product grew more slowly in the 10 largest metro areas than the country as a whole from 2010 to 2019 (2020 data aren’t out yet), but per-capita personal income grew faster. New York still dominates finance and the news media, Washington dominates government, Los Angeles rules entertainment and San Jose and San Francisco technology.

Or at least that’s the history. The great dispersal brought on by the pandemic and the subsequent turn to remote work threatens this status quo — and it isn’t really reflected in the Census data, which is supposed to count people in their permanent place of residence only as of April 2020, which was about when the pandemic really got rolling. Even if these developments don’t spell doom for big cities, they pose challenges. Then again, big U.S. cities have faced some awfully big challenges over the past century, and they’re still standing.

Matt Klein reports that while the Federal government is currently financing the Federal government foreign demand is up ticking, "...The U.S. federal government has sold about $5.4 trillion in bills, notes, and bonds since the end of 2019. Who has been on the other side? In practice: the U.S. federal government. Most of the borrowing has been effectively self-financed. The reason is that federal deficits have facilitated a surge in household and business holdings of bank deposits, money-market fund shares, and other safe liquid assets. Much of those savings have then been recycled into purchases of Treasury debt. At the same time, generous federal aid to states and localities—plus unexpected budgetary windfalls due to soaring housing and stock markets, robust income growth for top earners, and sustained consumer spending, which in turn can be explained in part by the federal government’s extraordinary support programs—have left America’s subnational governments with huge surpluses that have been recycled into Treasury bills, notes, and bonds. The net effect is that most of the money that the Treasury borrowed to disburse to the rest of the economy has been lent back to the federal government.... The main change is that demand from “rest of the world” has soared, which is why average quarterly demand for all of 2020Q1-2021Q2 was almost identical to 2015Q1-2019Q4 despite the unprecedented selling that occurred in the first half of 2020. More Treasury notes and bonds were bought by investors domiciled abroad in the first half of 2021 ($364 billion) than in any other two-quarter period outside of 2010. That’s more than offset the ongoing liquidation of bills accumulated during the peak of the panic. For better or worse, the rebound of foreign purchases represents a return to normalcy. It also helps explain the persistent strength of the dollar in the face of a rising current account deficit. How much of this buying can be attributed to explicit or surreptitious currency intervention by foreign governments is a subject for another time...."

Matt Klein, "Who Is Financing the Federal Government?,"The Overshoot, October 1, 2021, https://theovershoot.co/p/who-is-financing-the-federal-government

Who Is Financing the Federal Government?

The U.S. federal government has sold about $5.4 trillion in bills, notes, and bonds since the end of 2019. Who has been on the other side? In practice: the U.S. federal government. Most of the borrowing has been effectively self-financed.

The reason is that federal deficits have facilitated a surge in household and business holdings of bank deposits, money-market fund shares, and other safe liquid assets. Much of those savings have then been recycled into purchases of Treasury debt.

At the same time, generous federal aid to states and localities—plus unexpected budgetary windfalls due to soaring housing and stock markets, robust income growth for top earners, and sustained consumer spending, which in turn can be explained in part by the federal government’s extraordinary support programs—have left America’s subnational governments with huge surpluses that have been recycled into Treasury bills, notes, and bonds.

The net effect is that most of the money that the Treasury borrowed to disburse to the rest of the economy has been lent back to the federal government.

According to the latest release of the U.S. Financial Accounts, the major buyers of marketable Treasury securities since the end of 2019 have been, in descending order of importance: the Federal Reserve (about $3 trillion), money-market mutual funds ($1.2 trillion), state and local government treasuries ($555 billion), and U.S. banks and credit unions ($535 billion).1

That’s a big change from before the pandemic.

From 2015Q1—just after the Fed had fully tapered its bond purchases from LSAP3—through 2019Q4, the U.S. Treasury issued about $207 billion in bills, notes, and bonds more than it repaid each quarter, on average.

Pension plans, insurers, money funds, mutual funds, and ETFs bought about $84 billion each quarter on behalf of savers, on average, while U.S. banks, credit unions, and U.S. branches of foreign banks bought about $20 billion.

“Households and nonprofits”, a category that includes hedge funds,2 bought about $56 billion each quarter on average, with the biggest purchases in 2018 and early 2019. The “rest of the world”, which includes U.S. hedge funds legally domiciled in the Caribbean, the offshore subsidiaries of U.S. multinationals that recycle retained earnings into U.S. fixed income to avoid taxes, as well as foreign insurers, pension plans, and central banks, bought about $29 billion each quarter, on average. The Fed was letting its portfolio shrink by about $10 billion each quarter, on average.

From 2020Q1 through 2021Q2, the Treasury issued about $898 billion of marketable debt securities each quarter, with about half of all that borrowing taking place in 2020Q2. That extra issuance has been absorbed entirely by the Fed, money-market funds, state and local governments, and U.S. banks and credit unions.

The surprise windfall

State and local government tax receipts have surged 10% compared to before the pandemic. Personal income tax revenue is running 20% above pre-pandemic levels, corporate profit tax revenue is running 27% above, and sales tax revenue is 11% above.

Residential and commercial property taxes are the single biggest revenue source for states and localities. Collections are up only 3% so far, but the recent 20% surge in house prices should get baked into assessments and provide another tailwind to government revenues in the years ahead.

State and local government policymakers hadn’t expected any of this when the pandemic began. They remembered the last downturn, when tax revenues fell 9% from 2008Q2 to 2009Q2 and took years to recover in nominal terms. The initial reaction to the pandemic was therefore to slash investment budgets and to lay off workers quickly. Capital spending net of depreciation was 9% lower in 2021Q2 than in 2019Q4 on a seasonally-adjusted basis, while payrolls are still about down about 1 million from February 2020.

The net effect is that the underlying budget deficit of the aggregate state and local government sector was 19% smaller in 2021Q2 than in 2019Q4 on a seasonally-adjusted basis. On top of all this, state and local governments got about $350 billion in general purpose aid from the federal government ($150 billion in 2020Q2 and $200 billion in 2021Q2), as well as around $20 billion in additional Medicaid funding.

So far, treasurers of the states and localities have used their windfalls to buy Treasury debt in unprecedented size, although it’s likely that these surplus holdings will be liquidated in the years ahead to provide additional public goods and services.

With the exception of state and local governments, the big buyers of Treasury debt—the Federal Reserve, money-market funds, banks, and credit unions—are intermediaries. They buy Treasury bills, notes, and bonds only to the extent that others are willing to hold their liabilities. So if we want to answer the question of who is financing the Treasury, we must therefore look at who is financing these other entities.

A brief tour of the Fed’s balance sheet

The biggest and most obvious buyer of Treasury securities during the pandemic has been the Fed. After buying about $820 billion in March 2020 and $675 billion in April 2020, the Fed has settled into a purchase schedule of about $80 billion a month, or $240 billion each quarter. That’s about half of what the Treasury has borrowed, on average, in the first half of the year. (Note that the Fed is likely going to start “tapering” these purchases by the end of this year and could be entirely out of the bond-buying business by next summer.)

The Fed has financed its purchases—as well as its purchases of $1.1 trillion of mortgage-backed securities—by creating offsetting liabilities. The yields on these liabilities currently range from 0% to 0.25%, which is substantially less than the yields on the securities that the Fed buys. That difference explains why the Fed pays about $100 billion a year to the Treasury in profits. The central bank’s purchases effectively transform longer-duration debts yielding 1-3% into money-like claims yielding roughly zero. The government’s debt doesn’t disappear, but the effective interest costs paid by the government are reduced by about a fifth.

Since the end of February 2020, the Fed has created about $2.4 trillion in additional deposits held by banks as reserves (of which about $700 billion are additional reserves held by the U.S. branches of foreign banks), about $1.5 trillion in repo liabilities3 (mostly to money-market funds) and about $400 billion in physical currency.

Reserves

Banks hold reserves at the Fed to settle payments with other banks, to manage their cash needs, and to satisfy regulatory requirements to back their short-term debts (mostly deposits held by households and businesses) with high-quality liquid assets. The Fed creates and destroys reserves by buying and selling securities and by adjusting the size of its lending programs. The banking system as a whole can only get rid of reserves by transforming them into other Fed liabilities, which generally requires the cooperation of other sectors of the economy.4

Problems can flare up when banks need to hold more reserves than are available to them. That’s why the Fed bought about $300 billion in Treasury bills in October 2019-February 2020—before the pandemic—to ensure that there were enough reserves to keep the banking system stable. It also did about $200 billion in repo lending to securities dealers during that period to ensure there was enough money available to prevent interest rates from spiking.

The Fed’s pandemic-induced bond-buying fixed the reserve shortage, but it had the potential to create a new problem: banks were getting stuffed with low-yielding assets relative to their shareholder capital. That threatened banks’ ability to comply with the supplementary leverage ratio (SLR): a hard limit on the ratio of bank assets/equity, with no allowances for the quality of the assets banks hold. The leverage ratio was created as a backstop to prevent banks from gaming the risk-weighted capital requirements, as they had before the financial crisis.

The Fed and the world’s other major banking regulators had two workarounds. First, they created exemptions to the leverage ratio rules. Reserves would no longer be counted as assets against shareholder equity. (The Fed, unlike other regulators, also allowed banks to hold as much U.S. Treasury debt as they wanted without triggering the SLR.) Second, the regulators curtailed dividends and buybacks, forcing the banks to retain earnings. That lifted the equity side of the ratio, especially after factoring in the impact of government income support on loan losses.

But then the Fed allowed the SLR exemption to expire on March 31, 2021. At the time, I wrote—correctly, as it turns out—that this wouldn’t be a big deal. Banks might be constrained in their ability to grow their balance sheets by adding deposits and reserves, but the financial system as a whole would be fine as money-market funds expanded to fill the gap. And it turns out that the banks weren’t even that constrained, with reserves rising roughly $300 billion since mid-March, although the pace of increase has been slower than before.

The Treasury General Account and the Reverse Repo Facility

To understand the rise and fall of the Treasury General Account (TGA) balance—and the subsequent surge in the Fed’s repo borrowing—we need to look at Treasury bill issuance and at the behavior of money-market mutual funds.

On the eve of the pandemic, the face value of Treasury debt maturing in under a year was $4.9 trillion. By June, that number had jumped to $7.4 trillion. About 90% of the total increase in federal debt was financed by issuing extra Treasury bills, rather than notes or bonds.

This borrowing proved to be excessive. Despite the surge in spending associated with pandemic relief, the Treasury ended up raising a lot more cash than it needed. The result was a $1.4 trillion increase in the Treasury’s own deposits held at the Fed in the span of a few months. In other words, the Treasury was selling bills to the public and then depositing the proceeds at the Fed, which then went out and bought notes, bonds, and mortgages. That helped push down the supply of bank reserves as the Treasury effectively “sterilized” some of the Fed’s asset purchases.

The TGA balance has been liquidated in 2021 to finance the American Rescue Plan Act and, more recently, to cover the government’s spending needs as the debt ceiling strangled new debt issuance. (Once the debt ceiling is raised, the TGA should rise again.)

As the Treasury spent down its TGA balance into the broader economy, it provided cash to the private sector. Some of the money has ended up in the banking system as deposits, thereby re-entering the Fed as reserves.

But banks don’t want more deposits and reserves if it means bumping into the SLR, which could mean limits on dividends and buybacks. So they’ve been encouraging depositors to take their savings elsewhere by charging punitive fees. As a result, most of the money released from the TGA has gone outside of the banks and into money-market mutual funds, which have turned around and lent to the Fed via the repo market.

Money-market funds and banks

Money-market mutual funds bought about $1.2 in Treasury bills in March-May 2020, which was about half of the total amount issued. From then through February 2021, direct holdings of Treasury securities were basically flat. More recently, money funds have sold about $370 billion of direct holdings of Treasury debt and added about $800 billion in repo exposure to Treasury securities, with the Fed almost certainly on the other side of those transactions. (The main reason is that the Fed’s repo facility offers better yields and liquidity for money funds than Treasury securities.)

Banks have mostly added reserves to their balance sheet, rather than Treasury securities, but they have still bought a decent chunk of bills, notes, and bonds since the pandemic began. At least, they did plenty of buying after having sold off some of their holdings during the worst of the financial crunch in March and April.

But banks and money funds are intermediaries just like the Fed. So how have they been financing their own balance sheets?

In both cases, the answer is: household and business savings accumulated thanks to the combination of cash infusions from the federal government and spending constraints. Households and nonprofits have boosted their holdings of bank deposits, money fund shares, and physical currency by $3.8 trillion since the end of 2019, while U.S. businesses (corporate and noncorporate) have lifted their holdings of deposits and money fund shares by $1.5 trillion. Not all of that cash has gone into Treasury debt or to the Fed, but most of it has.6

The bulk of the household saving boom can be attributed to the combination of nearly $2 trillion in federal government income support plus the rise in forced saving due to the pandemic’s impact on consumer services. As for companies, they drew on bank credit lines and sold bonds to raise cash early in the pandemic, but ended up not needing the money as sales and profits snapped back. That snapback in turn was a function in part of the government spending that supported both businesses directly as well as their customers.

What’s changed in 2021?

I’ve spent most of this piece looking at the total amount of debt the Treasury has sold since the pandemic began. But there have been some interesting shifts more recently that warrant some attention. Note that we only have comprehensive data for the first half of the year.

Most of the big buyers—the Fed, state and local governments, and U.S. banks—continued to be big buyers in 2021H1. “Households”, which likely means hedge funds, continued to be big sellers. Money funds largely stopped direct purchases, although their rising repo lending to the Fed means they are still big sources of indirect financing, as noted above.

The main change is that demand from “rest of the world” has soared, which is why average quarterly demand for all of 2020Q1-2021Q2 was almost identical to 2015Q1-2019Q4 despite the unprecedented selling that occurred in the first half of 2020. More Treasury notes and bonds were bought by investors domiciled abroad in the first half of 2021 ($364 billion) than in any other two-quarter period outside of 2010. That’s more than offset the ongoing liquidation of bills accumulated during the peak of the panic.

1 There have been other buyers, such as mutual funds, ETFs, pensions, and insurers, but those purchases have been largely offset by sales from hedge funds, U.S. offices of foreign banks, and U.S. securities broker-dealers. There has been a reallocation of Treasury holdings from more speculative buyers to “real money” investors, but these transactions aren’t financing the aggregate increase in issuance.

2 Hedge funds are lumped in with “households and nonprofits” and with “rest of the world”, depending on their legal domicile. The Fed publishes information on hedge funds’ consolidated balance sheet with a lag in the Enhanced Financial Accounts, but this includes changes in valuations/ So far, the data on hedge funds only go through the end of 2020. From 2019Q4 through 2020Q4, hedge fund holdings of Treasury debt fell by more than $300 billion even as the prices of individual bonds rose. The quarterly data on transactions shows that “households and nonprofits” sold $380 billion in U.S. Treasury debt from 2020Q1 through 2021Q2.

3 The Fed sells bonds to its counterparties for cash and promises to buy (repurchase) the bonds back the next day at a slightly higher price. The result is extremely similar to a deposit that pays interest. The main difference is that savings held at the Fed as repos can only be accessed once a day.

4 For example, an old-fashioned bank run where depositors demanded physical currency from their ATM en masse would destroy reserves without changing the size of the Fed’s balance sheet. The Treasury could destroy reserves by selling T-bills, taking deposits from the banking system and generating proceeds that could then be deposited in the Treasury’s account at the Fed. That’s more or less what happened with the Supplementary Financing Program in 2008, as well as in March-July 2020. The TGA, discussed in the next section, has a similar effect.

5 Bank reserves are up by a little more than $2.4 trillion. As of the end of August, money-market repo lending to the Fed was up around $800 billion. Total usage of the Fed’s reverse repo facility has gone up another $250 billion since then. But some of that lending might be from counterparties other than money funds.

6 About 72% of the total increase in bank deposits since the start of the pandemic has been matched by a rise in holdings of reserves and Treasury securities. Banks have also bought about $800 billion of agency MBS while money funds have sold about $300 billion.

Steady as she goes, "Studies that investigate the effects of socioeconomic background (SES) on student achievement tend to find stronger SES effects with age, although there is much inconsistency between studies. There is also a large academic literature on cumulative advantage arguing that SES inequalities increase as children age, a type of Matthew Effect. This study analysing data from the children of NLSY79 mothers (N ≈ 9000, Obs ≈ 27,000) investigates the relationship of SES by children's age for two cognitive domains (Peabody Picture Vocabulary test and digit span memory) and three achievement domains (reading comprehension, reading recognition and math). There are small increases in the SES-test score correlations for several domains, but there are more substantial increases in the test score correlations with mother's ability and prior ability. Regression analyses found linear increases in SES effects for all domains except digit memory. However, when considering mother's ability, the substantially reduced SES effects did not increase with children's age. Much of the effects of SES on children's domain scores are accounted for by mother's ability. The effects of prior ability also increase with age and SES effects are small. Therefore, there is no evidence for cumulative socioeconomic advantage for these domains. Generally, increases in SES effects on children's cognitive development and student achievement are likely to be spurious because of the importance of parents' abilities and their transmission from parents to children..."
Takeaway, "...The main conclusion from this study is that the increase in the SES effects on cognitive development and student achievement as children grow older observed in these data is spurious, confounded by parental and child's abilities. Increases in SES effects with age for some domains disappear with the inclusion of mother's ability and mother's ability age interaction terms, or with analogous measures of prior ability. Net of mother's and child's abilities, SES effects on children's test scores are small, so changes in its effects with age are unimportant..."
Evidence

“…Five childhood outcomes are investigated. Two are cognitive: the Peabody Picture Vocabulary Test (PPVT) and the Wechsler digit memory span scores; and three are achievement measures: the Peabody Individual Achievement Tests (PIAT) for reading comprehension, reading recognition, and math. At ages 3 and 4, children were tested only by theppVT, at age 5 only by theppVT and PIAT math, and at age 6 in all domains but digit memory. From ages 7 to 12, children were tested in all domains, and from age 13 to 15 only in the two PIAT reading domains and PIAT math…Mother's cognitive ability is measured by AFQT score from parts of the Armed Services Vocational Aptitude Battery; a special survey administered in 1980 to NLSY79 respondents. The raw AFQT score in the NLSY79 data is the sum of scores in the arithmetic reasoning, word knowledge and paragraph comprehension subtests and one-half of the score in the numeric operations subtest…At ages 3 and 4 there were onlyppVT items, so the multiple-domain factor was the same as the single-domain factor. Between ages 7 and 12 when students were tested in all 5 domains, the variation accounted for by g increased from 25% to 37% consistent with the increasing heritability of cognitive ability with age. If theppVT and digit memory items are excluded, the percentage of variance explained by the principal factor increases by about 10 percentage points. Pooling all the data, mother's ability correlates at 0.26 with digit memory and between 0.4 and 0.5 with the other four domains. Cognitive ability, g, correlates most strongly withppVT scores (r = 0.78), most weakly with digit memory (r = 0.47) and between 0.66 and 0.73 with the other domains. Mother's ability and g correlate at 0.46 consistent with studies cited earlier that have reported intergenerational correlations for ability.Family SES correlates at 0.62 with mother's ability which is higher than Torres's estimate (Torres, 2013, p. 166). SES and g correlate at 0.41. The average correlation between ability and prior ability is 0.6 (Table A4). The correlations of ability and prior ability were generally higher at older ages (Table A5) again consistent with the increasing heritability of cognitive ability….”

Results

“…children's age for the five domains. TheppVT (blue diamond, solid line) and math (purple triangle, long-dash short-dash line) show the highest correlations and digit memory (red squares, dashed line), the lowest correlations. For reading comprehension (green star, dotted line) there is dramatic increase in the SES-achievement correlation with age. There are shallower increases in the correlations with age for theppVT and math. For reading recognition (brown circle, short-dash dot short-dash line) there is an anomalous correlation (0.44) at age 5 but for older ages the correlations are stable at around 0.35. The correlations between SES and digit memory decline from ages 7 to 9 and then increase. So even without considering mother's or child's ability, the evidence for cumulating socioeconomic inequalities is not unequivocable. Fig. 2 presents the correlations between mother's ability (AFQT) score and test scores. There is a substantial increase in the correlation between mother's ability and reading comprehension with children's age. The AFQT correlations with theppVT, math and reading recognition show clear but smaller increases. For digit memory, the correlations increase between ages 8 and 12. The LOESS curves show largely linear or curvilinear relationships. The correlations between prior ability and children's test scores show clear increases with age for all five domains (Fig. 3). For theppVT, there is an anomalous very large correlation at age 6 (≈0.7) and very low rapidly until age 10 and then stabilizes. The correlations between prior ability and digit memory are weaker but also show an increase with age…”

Gary Marks and Michael O'Connell, "No evidence for cumulating socioeconomic advantage. Ability explains increasing SES effects with age on children's domain test scores,"Intelligence, September/October 2021, https://www.sciencedirect.com/science/article/abs/pii/S0160289621000660

Emily Willoughby, Matt McGue, William Iacono and James Lee, "Genetic and environmental contributions to IQ in adoptive and biological families with 30-year-old offspring," Intelligence, September/October 2021
https://www.gwern.net/docs/iq/2021-willoughby.pdf

Bottomline, “.. Our biometric decomposition of variance is consistent with this figure: the parental environment contributing 4% of the variance in full scale IQs at age ~15 (Table 3), with a standard deviation of 14.2 for fullscale IQ in adopted offspring, indicates that a 1-SD increase in the quality of the parental environment would increase IQ by approximately 2.83 points (i.e., 14.2.04 √; Burks, 1938)...."

Study published in Intelligence finds that adoptive parents had no impact on their (adopted) children's adult intelligence, "...While adoption studies have provided key insights into the influence of the familial environment on IQ scores of adolescents and children, few have followed adopted offspring long past the time spent living in the family home. To improve confidence about the extent to which shared environment exerts enduring effects on IQ, we estimated genetic and environmental effects on adulthood IQ in a unique sample of 486 biological and adoptive families. These families, tested previously on measures of IQ when offspring averaged age 15, were assessed a second time nearly two decades later (M offspring age = 32 years). We estimated the proportions of the variance in IQ attributable to environmentally mediated effects of parental IQs, sibling-specific shared environment, and gene-environment covariance to be 0.01, 0.04, and 0.03 respectively; these components jointly accounted for 8% of the IQ variance in adulthood. The heritability was estimated to be 0.42. Together, these findings provide further evidence for the predominance of genetic influences on adult intelligence over any other systematic source of variation...."

New Bruce Meyer looks at the likely impact of the child tax credit expansion - he estimates the employment effect of the 1990's EITC expansion would be undone by the CTCT expansion. Basically the CTC work disincentives are significant (which will result in a lower fall in child poverty than proponents ae estimating) core finding, "...The proposed change under the American Families Plan (AFP) to the Tax Cuts and Jobs Act (TCJA) Child Tax Credit (CTC) would increase maximum benefit amounts to $3,000 or $3,600 per child (up from $2,000 per child) and make the full credit available to all low and middle-income families regardless of earnings or income. We estimate the anti-poverty, targeting, and labor supply effects of the expansion by linking survey data with administrative tax and government program data which form part of the Comprehensive Income Dataset (CID). Initially ignoring any behavioral responses, we estimate that the expansion of the CTC would reduce child poverty by 34% and deep child poverty by 39%. The expansion of the CTC would have a larger anti-poverty effect on children than any existing government program, though at a higher cost per child raised above the poverty line than any other means-tested program. Relatedly, the CTC expansion would allocate a smaller share of its total dollars to families at the bottom of the income distribution—as well as families with the lowest levels of long-term income, education, or health—than any existing means-tested program with the exception of housing assistance. We then simulate anti-poverty effects accounting for labor supply responses. By replacing the TCJA CTC (which contained substantial work incentives akin to the EITC) with a universal basic income-type benefit, the CTC expansion reduces the return to working at all by at least $2,000 per child for most workers with children. Relying on elasticity estimates consistent with mainstream simulation models and the academic literature, we estimate that this change in policy would lead 1.5 million workers (constituting 2.6% of all working parents) to exit the labor force. The decline in employment and the consequent earnings loss would mean that child poverty would only fall by 22% and deep child poverty would not fall at all with the CTC expansion...."
Likely employment effects, “…These static calculations ignore any changes in behavior, in particular employment and hours decisions. The AFP CTC would replace the TCJA CTC—which like the EITC has substantial work incentives—with a program akin to a universal basic income that provides benefits regardless of earnings. Consequently, the expansion would reduce the return to work for most working parents by at least $2,000 per child. Among all working parents with earnings below $100,000, the reduced incentive to work at all due to the CTC reform is 88% as large as the reduced incentive to work at all due to a hypothetical elimination of the EITC. We estimate that the CTC expansion would lead 1.5 million working parents to exit the labor force. The vast majority of the effect (1.3 million) is due to the decrease in the return to work.Our estimate is comparable in magnitude to that implied by a National Academy of Sciences simulation of the EITC and to the change in the employment of single mothers during welfare reform. When incorporating the estimated employment reduction into our poverty simulations, we find that the CTC expansion would reduce child poverty by 22% instead of the 34% reduction we found based on our static simulation. The CTC expansion would not decrease deep child poverty, reversing the 39% reduction we estimated based on a static simulation….”

“… Workers with earnings below $50,000 account for 72% of the employment loss (Appendix Figure A8). Most the of the employment reduction (1.32 million) is the result of the substitution effect from a decreased return to work. The remaining portion (0.14 million) is the result of the income effect from increasing incomes of working families. Table 4 reports employment reductions under other labor supply elasticity assumptions. We do not account for the reduction in work hours among current workers who continue working under the CTC expansion. Since the implicit marginal tax rate rises for workers on the phase-in portion of the TCJA CTC, there will likely be a reduction in hours worked among those who continue to work. Whereas the TCJA CTC rewards an additional dollar of earnings with approximately $0.15 of benefits for these workers, the AFP CTC provides no reward for an additional dollar of earnings. We estimate that 10.4 million workers on the phase-in portion of the TCJA CTC face on average a 14.6 percentage point increase in their implicit marginal tax rate due to the CTC expansion (Table 5).30F31 Not accounting for reductions in earnings of these workers facing higher implicit marginal tax rates will lead us to understate poverty in our dynamic simulations. The implicit marginal tax rate also rises for workers on the phase-out portion of the incremental CTC. Because the incremental CTC phases out at a 5% rate, the implicit marginal tax rate of these workers rises by 5 percentage points. However, the hours reductions of these workers are unlikely to lead their families into poverty because the phase-out begins at $112,500 of AGI for head of household tax units and $150,000 for married tax units filing jointly…..”

Here is the sexy stuff, “… The extent to which the decrease in the return to work affects labor supply depends on the baseline return to work. If the baseline return to work is lower, a given decrease in the return to work will reduce labor supply more. Appendix Figure A7 shows the percent decrease in the return to work due to the CTC expansion, relative to the baseline return to work under the TCJA CTC. Workers with earnings between $0 and $30,000 face a mean percent decrease in the return to work between 7% and 10%. The percent decrease in the return to work falls as earnings rise beyond $30,000, reflecting the higher baseline return to work (in dollars) for those with higher earnings. We multiply the percent change in the return to work by the relevant labor supply elasticity for each worker (0.75 for single mother EITC recipients and 0.25 for all other workers with children), and we multiply the percent change in income by the relevant income elasticity (0.085 for single mother EITC recipients and 0.05 for all other workers with children). As a result of the CTC expansion, we estimate that employment falls by 1.46 million workers, representing 2.6% of all working parents...”

“…Of those adults, 23% (13 million) had tax unit earnings of less than $30,000, and 43% had tax unit earnings of between $30,000 and $100,000. For workers with earnings between $30,000 and $100,000, the mean return to work falls by approximately $2,900 to $3,300 (Appendix Figure A6).For workers with earnings below $30,000, the return to work falls by less—with cell means between $450 and $2,400—because their TCJA CTC benefit had not yet fully phased in. Notably, the binned estimates of the decrease in the return to work that we empirically estimate using the CID align closely with the changes in the return to work across current earnings calculated for a hypothetical family in Appendix Figure A1…”

Dynamic Results Accounting for Changes in Labor Supply

"...We start by calculating the percent change in the return to work for each tax unit, which is the change in the return to work due to the CTC expansion divided by the current return to work under the TCJA CTC. The change in the return to work is the incremental CTC benefit when working at the current earnings level minus the incremental CTC benefit when not working (as described in Section 2). The current return to work under the TCJA CTC is current earnings minus the additional tax liability accrued due to working minus the transfer benefits lost due to working. To calculate the percent change in the probability of working for each tax unit that is currently working, we multiply the percent change in the tax unit’s return to work by the relevant elasticity for the tax unit. We apply an elasticity of 0.75 for single mother tax units currently receiving the EITC and 0.25 for all other tax units. The 0.75 elasticity for single mother tax units receiving the EITC is equal to the midpoint of the 0.3 to 1.2 range recommended for EITC-eligible workers based on the literature review relied on by the CBO (McClelland and Mok 2012).26F27 The 0.25 elasticity is consistent with those used by other simulation models and the academic literature (Congressional Budget Office 2012; Chetty et al. 2013).27F28 As we show later, our elasticity assumptions produce employment effects consistent with the NAS (2019) simulation of an expansion of the EITC. In addition to the effects of a decreased return to work, the increase in incomes due to the CTC expansion would be expected to further reduce labor force participation through an income effect. To estimate the reduction in labor force participation due to higher incomes, we apply elasticities that indicate the percent change in the probability of participation due to a one percent change in income. We follow NAS (2019) in their simulation of a child allowance, which uses an elasticity of -0.085 for single-mother tax units. We assign an elasticity of -0.05 for all other tax units.28F29 We multiply these elasticities by the increase in income due to the CTC expansion divided by income under the TCJA CTC for the tax unit’s family. To estimate the total number of current workers exiting the labor force due to the CTC expansion, we multiply each individual worker’s weight in the CPS ASEC by the percent change in the probability of the worker exiting the labor force, either due to the decrease in the return to work or to higher incomes. We sum these products over all workers with children in the CPS ASEC to estimate the number of current workers exiting the labor force. See Appendix C for further details of our methodology. We report changes in work incentives and employment for workers based on the earnings of their tax unit, in intervals of $10,000. We estimate that there were 56 million adults with children who worked during the year and were a member of a tax unit with nonzero earnings (Appendix Figure A5)…”

How they get there.