The Economic Policy Institute had a very useful analysis of this data just before the pandemic. Between 1979 and 2000, there was a rough match between growth in one measure of overall inequality — the gap between wages at the 95th percentile and those of the median worker — and its estimate of the average wage premium for college-educated workers. Since 2000, however, wage inequality has continued to rise, while the college premium has barely changed... my own version of this observation, comparing growth of incomes of households at the 95th percentile with those of the median male college graduate. Now, Americans at the 95th percentile don’t consider themselves rich, because they aren’t, surely as compared with C.E.O.s, hedge funders and so on. Nonetheless, they have seen substantial gains. On the other hand, the typical college graduate — who is, remember, someone who made it through and received an accredited degree — hasn’t.

Paul Krugman, "Education Has Less to Do With Inequality Than You Think,"Krugman Wonks Out, April 29, 2022, https://www.nytimes.com/2022/04/29/opinion/college-student-loan-debt.html

Education Has Less to Do With Inequality Than You Think

President Biden says that he is taking a “hard look” at student debt relief, which probably means that some significant relief is coming. For one thing, Biden promised relief during the 2020 campaign. For another, it’s one progressive priority he can address by executive action, which is important given the extreme difficulty of getting anything through an evenly divided Senate.

How much relief will he offer? I have no idea. How much relief should he offer? I’m for going as big as political realities allow, but I understand that too generous a debt write-off might produce a backlash. And I have no confidence that I know where the line should be drawn.

What I think I do know is that much of the backlash to proposals for student debt relief is based on a false premise: the belief that Americans who have gone to college are, in general, members of the economic elite.

The falsity of this proposition is obvious for those who were exploited by predatory for-profit institutions that encouraged them to go into debt to get more or less worthless credentials. The same applies to those who took on educational debt but never managed to get a degree — not a small group. In fact, around 40 percent of student loan borrowers never finish their education.

But even among those who make it through, a college degree is hardly a guarantee of economic success. And I’m not sure how widely that reality is understood.

What is widely understood is that America has become a far more unequal society over the past 40 years or so. The nature of rising inequality, however, isn’t as broadly known. I keep encountering seemingly well-informed people who believe that we’re mainly looking at a widening gap between the college-educated and everyone else.

This story had some truth to it in the 1980s and 1990s, although even then it didn’t account for the huge income gains at the top of the distribution — the rise of the 1 percent and even more among the 0.01 percent. Since 2000, however, most college graduates have actually seen their real incomes stagnate or even decline.

The Economic Policy Institute had a very useful analysis of this data just before the pandemic. Between 1979 and 2000, there was a rough match between growth in one measure of overall inequality — the gap between wages at the 95th percentile and those of the median worker — and its estimate of the average wage premium for college-educated workers. Since 2000, however, wage inequality has continued to rise, while the college premium has barely changed:

So here’s how I see it: Much of the student debt weighing down millions of Americans can be attributed to false promises.

Some of these promises were scams pure and simple; think Trump University. Even those who weren’t outright cheated, however, were pulled in by elite messaging assuring them that a college degree was a ticket to financial success. Too many didn’t realize that their life circumstances might make it impossible to finish their education — it’s hard for comfortable, upper-middle-class Americans to realize how difficult staying in school can be for young people from poorer families with unstable incomes. Many of those who did manage to finish found that the financial rewards were far smaller than they expected.

And all too many of those who fell victim to these false promises ended up saddled with large debts.

Of course, there are many Americans who have suffered from rising inequality. I wouldn’t argue that college debtors are greater victims than, say, truck drivers who have seen their real wages plunge or families stuck in declining rural areas and small towns. And we should be helping all of these people.

Unfortunately, most things we could and should be doing for Americans in need — like extending the expanded child tax credit — can’t be done in the face of 50 Republican senators, plus Joe Manchin. Student debt relief, by contrast, is something President Biden can do. So he should.

Women...now make up 44% of college-educated workers in science, technology, engineering, and mathematics (STEM) occupations. However, a closer look at the underlying data reveals divergent patterns of gender (in)equality within STEM. Men continue to make up the vast majority of degree recipients in STEM fields that are spatially and mathematically intensive — geoscience, engineering, economics, mathematics, computer science, and physical science — while women are overrepresented in life sciences, psychology, and social sciences....In 2017, 49% of all bachelor’s degrees in science and engineering were awarded to women. This number masks substantial gender disparities across fields, however. Women earned two-thirds of bachelor’s degrees in life sciences, psychology and social science fields — 62% in biology, 78% in psychology, and 61% in social science. In contrast,women were only slightly over one quarter of degree recipients in the combined math-intensive fields of geoscience (39%), engineering (22%), economics (32%), mathematics (42%), computer science (19%), and the physical sciences (40%)....Gender disparities within science, technology, engineering, and mathematics bachelor’s degrees have become more pronounced since 2000...Gender differences carry over from education to the STEM workforce.

A leaky pipeline reduces the share of women rising up the ranks of career hierarchy in academia. Women were 45% of junior full-time faculty but only 33% of senior full-time faculty in 2019. However, the pipeline leaks more in fields where women are overrepresented in the lower ranks. In psychology, for instance, the share of women was 68% among junior full-time faculty and 53% among senior faculty, a 15 percentage-point difference. With a 6 percentage-point difference, pipeline leakage is smallest in computer science: the share of women was 25% among junior full-time computer science faculty and 19% among senior faculty. What explains the differences in pipeline leakage by field? It is possible that the smaller number of women who choose to enter the male-dominated fields may be highly positively selected — especially motivated — compared to those who enter the more female-friendly fields. This is consistent with research showing that a large number of female entrants into STEM fields has a negative impact on gender equality in that field in terms of career outcomes. In psychology and the social sciences, it may be that the higher degree of female representation in the lower ranks indicates a shift in the preferences of men: the number of junior male faculty has declined since 1981. This may be due to occupational “tipping”, whereby men start to exit an occupation when the fraction of women in that occupation rises above a certain threshold.

What this Means:

As a smaller proportion of women enter geoscience, engineering, economics, mathematics, computer science, and physical science at the undergraduate level, the career pipeline in these fields will become even smaller. Less gender diversity could be detrimental to the quality and relevance of knowledge generated in these fields, by narrowing the range of ideas generated and reducing success in problem solving. We should then expect the same outcome in life sciences, psychology and social sciences if women continue to saturate them while men exit. A widening of the uneven gender distribution between STEM fields helps perpetuate the gender pay gap. This, in turn, can have negative consequences for gender equality outside the labor market if, for example, lower female-male wage ratios erode women’s decision-making power in the family and reduce women’s well-being.

The share of women holding academic positions has increased substantially but also varies greatly by field. In 2019, women comprised 39% of all academic positions held by science, engineering and health doctorate holders at 2- and 4-year colleges or universities. This reflects a substantial increase from just 9% in 1973 and 22% in 1993. Gender gaps vary noticeably by fields. Women held only 19% of academic positions in engineering, 26% in physical and geosciences, 26% in mathematics and statistics, and 23% in computer science. This is in contrast to 60% of positions in psychology, 47% in life sciences, and 42% in social sciences including economics. Among academic position types, women are more likely than men to hold “other” full-time positions (instructor, adjunct, lecturer, research associate, administrator) as well as post-doc and part-time positions.

The STEM fields in which women are underrepresented typically pay higher salaries than fields with high shares of women. For example, the median salary in 2019 for full-time workers holding the highest degree in their field was $105,000 for computer scientists and $100,000 for engineers, both male-dominated occupations. Among female-heavy occupations, median salary was $74,000 for psychologists and $64,000 for biological, agricultural and environmental scientists.

Gender differences carry over from education to the STEM workforce. While the college-educated STEM workforce had almost reached gender parity in 2019, with a female share of 44%, large gender gaps remain between types of STEM jobs. Although only 29% of the science and engineering (S&E) workforce in 2019 was female, women were 57% of S&E-related workers (e.g. nurse, dental hygienist). The representation of women coincides with the patterns of gender balance in STEM degrees. Women scientists and engineers comprise 48% of biological, agricultural and environmental life scientists, 81% of psychologists, and 64% of social scientists; and much smaller shares of geoscientists (30%), engineers (16%), economists (24%), mathematical scientists (42%), computer scientists (24%), and physical scientists (35%). Even within the S&E-related workforce, women comprise 70% of health-related occupations — such as nurses, pharmacists, and physician assistants — but only 21% of computer and information systems managers.

Female attrition along the STEM educational hierarchy is driven by the very disciplines that, paradoxically, seem to attract large numbers of women at the entry level. In general, women earned a smaller share of doctoral degrees than bachelor’s degrees in 2017, reflecting the well-known leaky pipeline in women’s progress up the career hierarchy. In STEM fields, the pipeline is leakiest in life science, psychology and social science fields, which are female-dominated at the undergraduate level — the female share of degree recipients in these fields was 58% at the doctoral level compared with 66% at the bachelor’s level in 2017. In contrast, the four fields with the lowest female shares among bachelor’s degrees recipients — geoscience, engineering, economics, and computer science — have higher female representation among PhD recipients (see here).

Gender disparities within science, technology, engineering, and mathematics bachelor’s degrees have become more pronounced since 2000. The number of women earning bachelor’s degrees in life sciences, psychology and social sciences grew faster than the number of men since 2000. These fields, which started out with high female shares at the turn of the century, became even more disproportionately represented by women at the undergraduate level. Similarly, even as the numbers of women in many of the STEM fields that started out with a disproportionate representation of men have been increasing, the number of men earning bachelor’s degrees in several of these fields grew faster than the number of women — this trend was most pronounced in computer science and mathematics, while engineering was an exception (see chart).

While women earn close to half of all STEM bachelor’s degrees, there is wide variation in women’s representation across fields. In 2017, 49% of all bachelor’s degrees in science and engineering were awarded to women. This number masks substantial gender disparities across fields, however. Women earned two-thirds of bachelor’s degrees in life sciences, psychology and social science fields — 62% in biology, 78% in psychology, and 61% in social science. In contrast, women were only slightly over one quarter of degree recipients in the combined math-intensive fields of geoscience (39%), engineering (22%), economics (32%), mathematics (42%), computer science (19%), and the physical sciences (40%).

Prathi Seneviratne, "Are Women Reaching Parity with Men in STEM?"Econofact, April 15, 2022, https://econofact.org/are-women-reaching-parity-with-men-in-stem

Are Women Reaching Parity with Men in STEM?

Women have made tremendous gains in education and now make up 44% of college-educated workers in science, technology, engineering, and mathematics (STEM) occupations. However, a closer look at the underlying data reveals divergent patterns of gender (in)equality within STEM. Men continue to make up the vast majority of degree recipients in STEM fields that are spatially and mathematically intensive — geoscience, engineering, economics, mathematics, computer science, and physical science — while women are overrepresented in life sciences, psychology, and social sciences. If anything, these gender disparities appear to be widening slightly over time at the undergraduate level, with potential implications for gender differences in employment, pay, academia, and beyond.

The Facts:

Women have made dramatic inroads in education since the 1990s. From making up less than a third of the college-educated workforce in the early 1990s, women have come to surpass men — making up 52% of the workforce with at least a college degree in 2019. The representation of college-educated women in STEM occupations has also been trending upwards since the early 1990s.

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.