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A Response To A Question About Post-Keynesian Interest Rate Theories (…And A Rant)

I got a question about references for post-Keynesian theories of interest rates. My answer to this has a lot of levels, and eventually turns into a rant about modern academia. Since I do not want a good rant to go to waste, I will spell it out here. Lo…

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I got a question about references for post-Keynesian theories of interest rates. My answer to this has a lot of levels, and eventually turns into a rant about modern academia. Since I do not want a good rant to go to waste, I will spell it out here. Long-time readers may have seen portions of this rant before, but my excuse is that I have a lot of new readers.

(I guess I can put a plug in for my book Interest Rate Cycles: An Introduction which covers a variety of topics around interest rates.)

References (Books)

For reasons that will become apparent later, I do not have particular favourites among post-Keynesian theories of interest rates. For finding references, I would give the following three textbooks as starting points. Note that as academic books, they are pricey. However, starting with textbooks is more efficient from a time perspective than trawling around on the internet looking for free articles.

  1. Mitchell, William, L. Randall Wray, and Martin Watts. Macroeconomics. Bloomsbury Publishing, 2019. Notes: This is an undergraduate-level textbook, and references are very limited. For the person who originally asked the question, this is perhaps not the best fit.

  2. Lavoie, Marc. Post-Keynesian economics: new foundations. Edward Elgar Publishing, 2022. This is an introductory graduate level textbook, and is a survey of PK theory. Chuck full o’ citations.

  3. Godley, Wynne, and Marc Lavoie. Monetary economics: an integrated approach to credit, money, income, production and wealth. Springer, 2006. This book is an introduction to stock-flow consistent models, and is probably the best starting point for post-Keynesian economics for someone with mathematical training.

Why Books?

If you have access to a research library and you have at least some knowledge of the field one can do a literature survey by going nuts chasing down citations from other papers. I did my doctorate in the Stone Age, and I had a stack of several hundred photocopied papers related to my thesis sprawled across on my desk1, and I periodically spent the afternoon camped out in the journal section of the engineering library reading and photocopying the interesting ones.

If you do not have access to such a library, it is painful getting articles. And if you do not know the field, you are making a grave mistake to rely on articles to get a survey of the field.

  1. In order to get published, authors have no choice but to hype up their own work. Ground-breaking papers have cushion to be modest, but more than 90% of academic papers ought never have to been published, so they have no choice but to self-hype.

  2. Academic politics skews what articles are cited. Also, alternative approaches need to be minimised in order handing ammunition to reviewers looking to reject the paper.

  3. The adversarial review process makes it safer to avoid anything remotely controversial in areas of the text that are not part of what allegedly makes it novel.

My points here might sound cynical, but they just are what happens if you apply any minimal intellectual standards to modern academic output. Academics have to churn out papers; the implication is that the papers cannot all be winners.

The advantage of an “introductory post-graduate” textbook is that the author is doing the survey of a field, and it is exceedingly unlikely that they produced all the research being surveyed. This allows them to take a “big picture” view of the field, without worries about originality. Academic textbooks are not cheap, but if you value your time (or have an employer to pick up the tab), they are the best starting point.

My Background

One of my ongoing jobs back when I was an employee was doing donkey work for economists. To the extent that I had any skills, I was a “model builder.” And so I was told to go off and build models according to various specifications.

Over the years, I developed hundreds (possibly thousands) of “reduced order” models that related economic variables to interest rates, based on conventional and somewhat heterodox thinking. (“Reduced order” has a technical definition, I am using the term somewhat loosely. “Relatively simple models with a limited number of inputs and outputs” is what I mean.) Of course, I also mucked around with the data and models on my own initiative.

To summarise my thinking, I am not greatly impressed with the ability of reduced order models to tell us about the effect of interest rates. And it is not hard to prove me wrong — just come up with such a reduced form model. The general lack of agreement on such a model is a rather telling point.

“Conventional” Thinking On Interest Rates

The conventional thinking on interest rates is that if inflation is too high, the central bank hikes the policy rate to slow it. The logic is that if interest rates go up, growth or inflation (or both) falls. This conventional thinking is widespread, and even many post-Keynesians agree with it.

Although there is widespread consensus about that story, the problem shows up as soon as you want to put numbers to it. An interest rate is a number. How high does it have to be to slow inflation?

Back in the 1990s, there were a lot of people who argued that the magic cut off point for the effect of the real interest rate was the “potential trend growth rate” of the economy (working age population growth plus productivity growth). The beauty of that theory is that it gave a testable prediction. The minor oopsie was that it did not in fact work.

So we were stuck with what used to be called the “neutral interest rate” — now denoted r* — which moves around (a lot). The level of r* is estimated with a suitably complex statistical process. The more complex the estimation the better — since you need something to distract from the issue of non-falsifiability. If you use a model to calibrate r* on historical data, by definition your model will “predict” historical data when you compare the actual real policy rate versus your r* estimate. The problem is that if r* keeps moving around — it does not tell you much about the future. For example, is a real interest rate of 2% too low to cause inflation to drop? Well, just set it at 2% and see whether it drops! The problem is that you only find out later.

Post-Keynesian Interest Rate Models

If you read the Marc Lavoie “New Foundations” text, you will see that what defines “post-Keynesian” economics is difficult. There are multiple schools of thought (possibly with some “schools” being one individual) — one of which he labels “Post-Keynesian” (capital P) that does not consider anybody else to be “post-Keynesian.” I take what Lavoie labels as the “broad tent” view, and accept that PK economics is a mix of schools of thought that have some similarities — but still have theoretical disagreements.

Interest rate theories is one of big areas of disagreement. This is especially true after the rise of Modern Monetary Theory (MMT).

MMT is a relatively recent offshoot of the other PK schools of thought. What distinguished MMT is that the founders wanted to come up with an internally consistent story that could be used to convince outsiders to act more sensibly. The effect of interest rates on the economy was one place where MMTers have a major disagreement with others in the broad PK camp, and even the MMTers have somewhat varied positions.

The best way to summarise the consensus MMT position is that the effect of interest rates on the economy are ambiguous, and weaker than conventional beliefs. A key point is that interest payments to the private sector are a form of income, and thus a weak stimulus. (The mainstream awkwardly tries to incorporate this by bringing up “fiscal dominance” — but fiscal dominance is just decried as a bad thing, and is not integrated into other models.) Since this stimulus runs counter to the conventional belief that interest rates slow the economy, one can argue that conventional thinking is backwards. (Warren Mosler emphases this, other MMT proponents lean more towards “ambiguous.”)

For what it is worth, “ambiguous” fits my experience of churning out interest rate models, so that is good enough for me. I think that if we want to dig, we need to look at the interest rate sensitivity of sectors, like housing. The key is that we are not looking at an aggregated model of the economy — once you have multiple sectors, we can get ambiguous effects.

What about the Non-MMT Post-Keynesians?

The fratricidal fights between MMT economists and selected other post-Keynesians (not all, but some influential ones) was mainly fought over the role of floating exchange rates and fiscal policy, but interest rates showed up. Roughly speaking, they all agreed that neoclassicals are wrong about interest rates. Nevertheless, there are divergences on how effective interest rates are.

I would divide the post-Keynesian interest rate literature into a few segments.

  • Empirical analysis of the effects of interest rates, mainly on fixed investment.

  • Fairly basic toy models that supposedly tell us about the effect of interest rates. (“Liquidity preference” models that allegedly tell us about bond yields is one example. I think that such models are a waste of time — rate expectations factor into real world fixed income trading.)

  • Analysis of why conventional thinking about interest rates is incorrect because it does not take into account some factors.

  • Analysis of who said what about interest rates (particularly Keynes).

From an outsiders standpoint, the problem is that even if the above topics are interesting, the standard mode of exposition is to jumble these areas of interest into a long literary piece. My experience is that unless I am already somewhat familiar with the topics discussed, I could not follow the logical structure of arguments. 

The conclusions drawn varied by economist. Some older ones seem indistinguishable from 1960s era Keynesian economists in their reliance on toy models with a couple of supply/demand curves.

I found the strongest part of this literature was the essentially empirical question: do interest rates affect behaviour in the ways suggested by classical/neoclassical models? The rest of the literature is not structured in a way that I am used to, and so I have a harder time discussing it.

Appendix: Academic Dysfunction

I will close with a rant about the state of modern academia, which also explains why I am not particularly happy with wading through relatively recent (post-1970!) journal articles trying to do a literature search. I am repeating old rant contents here, but I am tacking on new material at the end (which is exactly the behaviour I complain about!).

The structural problem with academia was the rise of the “publish or perish” culture. Using quantitative performance metrics — publications, citation counts — was an antidote to the dangers of mediocrity created by “old boy networks” handing out academic posts. The problem is that the quantitative metric changes behaviour (Goodhart’s Law). Everybody wants their faculty to be above average — and sets their target publication counts accordingly.

This turns academic publishing into a game. It was possible to produce good research, but the trick was to get as many articles out of it as possible. Some researchers are able to produce a deluge of good papers — partly because they have done a good job of developing students and colleagues to act as co-authors. Most are only able to get a few ideas out. Rather than forcing people to produce and referee papers that nobody actually wants to read, everybody should just lower expected publication count standards. However, that sounds bad, so here we are. I left academia because spending the rest of my life churning out articles that I know nobody should read was not attractive.

These problems affect all of academia. (One of the advantages of the college system of my grad school is that it drove you to socialise with grad students in other fields, and not just your own group.) The effects are least bad in fields where there are very high publication standards, or the field is vibrant and new, and there is a lot of space for new useful research.

Pure mathematics is (was?) an example of a field with high publication standards. By definition, there are limited applications of the work, so it attracts less funding. There are less positions, and so the members of the field could be very selective. They policed their journals with their wacky notions of “mathematical elegance,” and this was enough to keep people like me out.

In applied fields (engineering, applied mathematics), publications cannot be policed based on “elegance.” Instead, they are supposed to be “useful.” The way the field decided to measure usefulness was to see whether it could attract industrial partners funding the research. Although one might decry the “corporate influence” on academia, it helps keep applied research on track.

The problem is in areas with no recent theoretical successes. There is no longer an objective way to measure a “good” paper. What happens instead is that papers are published on the basis that they continue the framework of what is seen as a “good” paper in the past.

My academic field of expertise was in such an area. I realised that I had to either get into a new research area that had useful applications — or get out (which I did). I have no interest in the rest of economics, but it is safe to say that macroeconomics (across all fields of thought) has had very few theoretical successes for a very long time. Hence, there is little to stop the degeneration of the academic literature.

Neoclassical macroeconomics has the most people publishing in it, and gets the lion share of funding. Their strange attachment to 1960s era optimal control theory means that the publication standards are closest to what I was used to. The beauty of the mathematical approach to publications is that originality is theoretically easy to assess: has anyone proved this theorem before? You can ignore the textual blah-blah-blah and jump straight to the mathematical meat.

Unfortunately, if the models are useless in practice, this methodology just leaves you open to the problem that there is an infinite number of theorems about different models to be proven. Add in a large accepted gap between the textual representations of what a theorem suggests and what the mathematics does, you end up with a field that has an infinite capacity to produce useless models with irrelevant differences between them. And unlike engineering firms that either go bankrupt or dump failed technology research, researchers at central banks tend to fail upwards. Ask yourself this: if the entire corpus of post-1970 macroeconomic research had been ritually burned, would it have really mattered to how the Fed reacted to the COVID pandemic? (“Oh no, crisis! Cut rates! Oops, inflation! Hike rates!”)

More specifically, neoclassicals have an array of “good” simple macro models that they teach and use as “acceptable” models for future papers. However, those “good” models can easily contradict each other. E.g., use an OLG model to “prove” that debt is burden on future generations, use other models to “prove” that “MMT says nothing new.” Until a model is developed that is actually useful, they are doomed to keep adding epicycles to failed models.

Well, if the neoclassical research agenda is borked, that means that the post-Keynesians aren’t, right? Not so fast. The post-Keynesians traded one set of dysfunctions for other ones — they are also trapped in the same publish-or-perish environment. Without mathematical models offering good quantitative predictions about the macroeconomy, it is hard to distinguish “good” literary analysis from “bad” analysis. They are not caught in the trap of generating epicycle papers, but the writing has evolved to be aimed at other post-Keynesians in order to get through the refereeing process. The texts are filled with so many digressions regarding who said what that trying to find a logical flow is difficult.

The MMT literature (that I am familiar with) was cleaner — because it took a '“back to first principles” approach, and focussed on some practical problems. The target audience was fairly explicitly non-MMTers. They were either responding to critiques (mainly post-Keynesians, since neoclassicals have a marked inability to deal with articles not published in their own journals), or outsiders interested in macro issues. It is very easy to predict when things go downhill — as soon as papers are published solely aimed at other MMT proponents.

Is there a way forward? My view is that there is — but nobody wants it to happen. Impossibility theorems could tell us what macroeconomic analysis cannot do. For example, when is it impossible to stabilise an economy with something like a Taylor Rule? In other words, my ideal graduate level macroeconomics theory textbook is a textbook explaining why you cannot do macroeconomic theory (as it is currently conceived).

1

Driving my German office mate crazy.


Email subscription: Go to https://bondeconomics.substack.com/ 

(c) Brian Romanchuk 2024

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Default: San Francisco Four Seasons Hotel Investors $3 Million Late On Loan As Foreclosure Looms

Default: San Francisco Four Seasons Hotel Investors $3 Million Late On Loan As Foreclosure Looms

Westbrook Partners, which acquired the San…

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Default: San Francisco Four Seasons Hotel Investors $3 Million Late On Loan As Foreclosure Looms

Westbrook Partners, which acquired the San Francisco Four Seasons luxury hotel building, has been served a notice of default, as the developer has failed to make its monthly loan payment since December, and is currently behind by more than $3 million, the San Francisco Business Times reports.

Westbrook, which acquired the property at 345 California Center in 2019, has 90 days to bring their account current with its lender or face foreclosure.

Related

As SF Gate notes, downtown San Francisco hotel investors have had a terrible few years - with interest rates higher than their pre-pandemic levels, and local tourism continuing to suffer thanks to the city's legendary mismanagement that has resulted in overlapping drug, crime, and homelessness crises (which SF Gate characterizes as "a negative media narrative).

Last summer, the owner of San Francisco’s Hilton Union Square and Parc 55 hotels abandoned its loan in the first major default. Industry insiders speculate that loan defaults like this may become more common given the difficult period for investors.

At a visitor impact summit in August, a senior director of hospitality analytics for the CoStar Group reported that there are 22 active commercial mortgage-backed securities loans for hotels in San Francisco maturing in the next two years. Of these hotel loans, 17 are on CoStar’s “watchlist,” as they are at a higher risk of default, the analyst said. -SF Gate

The 155-room Four Seasons San Francisco at Embarcadero currenly occupies the top 11 floors of the iconic skyscrper. After slow renovations, the hotel officially reopened in the summer of 2021.

"Regarding the landscape of the hotel community in San Francisco, the short term is a challenging situation due to high interest rates, fewer guests compared to pre-pandemic and the relatively high costs attached with doing business here," Alex Bastian, President and CEO of the Hotel Council of San Francisco, told SFGATE.

Heightened Risks

In January, the owner of the Hilton Financial District at 750 Kearny St. - Portsmouth Square's affiliate Justice Operating Company - defaulted on the property, which had a $97 million loan on the 544-room hotel taken out in 2013. The company says it proposed a loan modification agreement which was under review by the servicer, LNR Partners.

Meanwhile last year Park Hotels & Resorts gave up ownership of two properties, Parc 55 and Hilton Union Square - which were transferred to a receiver that assumed management.

In the third quarter of 2023, the most recent data available, the Hilton Financial District reported $11.1 million in revenue, down from $12.3 million from the third quarter of 2022. The hotel had a net operating loss of $1.56 million in the most recent third quarter.

Occupancy fell to 88% with an average daily rate of $218 in the third quarter compared with 94% and $230 in the same period of 2022. -SF Chronicle

According to the Chronicle, San Francisco's 2024 convention calendar is lighter than it was last year - in part due to key events leaving the city for cheaper, less crime-ridden places like Las Vegas. 

Tyler Durden Sun, 03/17/2024 - 18:05

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Correcting the Washington Post’s 11 Charts That Are Supposed to Tell Us How the Economy Changed Since Covid

The Washington Post made some serious errors or omissions in its 11 charts that are supposed to tell us how Covid changed the economy. Wages Starting with…

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The Washington Post made some serious errors or omissions in its 11 charts that are supposed to tell us how Covid changed the economy.

Wages

Starting with its second chart, the article gives us an index of average weekly wages since 2019. The index shows a big jump in 2020, which then falls off in 2021 and 2022, before rising again in 2023.

It tells readers:

“Many Americans got large pay increases after the pandemic, when employers were having to one-up each other to find and keep workers. For a while, those wage gains were wiped out by decade-high inflation: Workers were getting larger paychecks, but it wasn’t enough to keep up with rising prices.”

That actually is not what its chart shows. The big rise in average weekly wages at the start of the pandemic was not the result of workers getting pay increases, it was the result of low-paid workers in sectors like hotels and restaurants losing their jobs.

The number of people employed in the low-paying leisure and hospitality sector fell by more than 8 million at the start of the pandemic. Even at the start of 2021 it was still down by over 4 million.

Laying off low-paid workers raises average wages in the same way that getting the short people to leave raises the average height of the people in the room. The Washington Post might try to tell us that the remaining people grew taller, but that is not what happened.

The other problem with this chart is that it is giving us weekly wages. The length of the average workweek jumped at the start of the pandemic as employers decided to work the workers they had longer hours rather than hire more workers. In January of 2021 the average workweek was 34.9 hours, compared to 34.4 hours in 2019 and 34.3 hours in February.

This increase in hours, by itself, would raise weekly pay by 2.0 percent. As hours returned to normal in 2022, this measure would misleadingly imply that wages were falling.

It is also worth noting that the fastest wage gains since the pandemic have been at the bottom end of the wage distribution and the Black/white wage gap has fallen to its lowest level on record.

Saving Rates

The third chart shows the saving rate since 2019. It shows a big spike at the start of the pandemic, as people stopped spending on things like restaurants and travel and they got pandemic checks from the government. It then falls sharply in 2022 and is lower in the most recent quarters than in 2019.

The piece tells readers:

“But as the world reopened — and people resumed spending on dining out, travel, concerts and other things that were previously off-limits — savings rates have leveled off. Americans are also increasingly dip into rainy-day funds to pay more for necessities, including groceries, housing, education and health care. In fact, Americans are now generally saving less of their incomes than they were before the pandemic.

This is an incomplete picture due to a somewhat technical issue. As I explained in a blogpost a few months ago, there is an unusually large gap between GDP as measured on the output side and GDP measured on the income side. In principle, these two numbers should be the same, but they never come out exactly equal.

In recent quarters, the gap has been 2.5 percent of GDP. This is extraordinarily large, but it also is unusual in that the output side is higher than the income side, the opposite of the standard pattern over the last quarter century.

It is standard for economists to assume that the true number for GDP is somewhere between the two measures. If we make that assumption about the data for 2023, it would imply that income is somewhat higher than the data now show and consumption somewhat lower.

In that story, as I showed in the blogpost, the saving rate for 2023 would be 6.8 percent of disposable income, roughly the same as the average for the three years before the pandemic. This would mean that people are not dipping into their rainy-day funds as the Post tells us. They are spending pretty much as they did before the pandemic.

 

Credit Card Debt

The next graph shows that credit card debt is rising again, after sinking in the pandemic. The piece tells readers:

“But now, debt loads are swinging higher again as families try to keep up with rising prices. Total household debt reached a record $17.5 trillion at the end of 2023, according to the Federal Reserve Bank of New York. And, in a worrisome sign for the economy, delinquency rates on mortgages, car loans and credit cards are all rising, too.”

There are several points worth noting here. Credit card debt is rising, but measured relative to income it is still below where it was before the pandemic. It was 6.7 percent of disposable income at the end of 2019, compared to 6.5 percent at the end of last year.

The second point is that a major reason for the recent surge in credit card debt is that people are no longer refinancing mortgages. There was a massive surge in mortgage refinancing with the low interest rates in 2020-2021.

Many of the people who refinanced took additional money out, taking advantage of the increased equity in their home. This channel of credit was cut off when mortgage rates jumped in 2022 and virtually ended mortgage refinancing. This means that to a large extent the surge in credit card borrowing is simply a shift from mortgage debt to credit card debt.

The point about total household debt hitting a record can be said in most months. Except in the period immediately following the collapse of the housing bubble, total debt is almost always rising.

And the rise in delinquencies simply reflects the fact that they had been at very low levels in 2021 and 2022. For the most part, delinquency rates are just getting back to their pre-pandemic levels, which were historically low.  

 

Grocery Prices and Gas Prices

The next two charts show the patterns in grocery prices and gas prices since the pandemic. It would have been worth mentioning that every major economy in the world saw similar run-ups in prices in these two areas. In other words, there was nothing specific to U.S. policy that led to a surge in inflation here.

 

The Missing Charts

There are several areas where it would have been interesting to see charts which the Post did not include. It would have been useful to have a chart on job quitters, the number of people who voluntarily quit their jobs during the pandemic. In the tight labor markets of 2021 and 2022 the number of workers who left jobs they didn’t like soared to record levels, as shown below.

 

The vast majority of these workers took other jobs that they liked better. This likely explains another item that could appear as a graph, the record level of job satisfaction.

In a similar vein there has been an explosion in the number of people who work from home at least part-time. This has increased by more than 17 million during the pandemic. These workers are saving themselves thousands of dollars a year on commuting costs and related expenses, as well as hundreds of hours spent commuting.

Finally, there has been an explosion in the use of telemedicine since the pandemic. At the peak, nearly one in four visits with a health care professional was a remote consultation. This saved many people with serious health issues the time and inconvenience associated with a trip to a hospital or doctor’s office. The increased use of telemedicine is likely to be a lasting gain from the pandemic.

 

The World Has Changed

The pandemic will likely have a lasting impact on the economy and society. The Washington Post’s charts captured part of this story, but in some cases misrepr

The post Correcting the Washington Post’s 11 Charts That Are Supposed to Tell Us How the Economy Changed Since Covid appeared first on Center for Economic and Policy Research.

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Women’s basketball is gaining ground, but is March Madness ready to rival the men’s game?

The hype around Caitlin Clark, NCAA Women’s Basketball is unprecedented — but can its March Madness finally rival the Men’s?

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In March 2021, the world was struggling to find its legs amid the ongoing Covid-19 pandemic. Sports leagues were trying their best to keep going.

It started with the NBA creating a bubble in Orlando in late 2020, playing a full postseason in the confines of Disney World in arenas that were converted into gyms devoid of fans. Other leagues eventually allowed for limited capacity seating in stadiums, including the NCAA for its Men’s and Women’s Basketball tournaments.

The two tournaments were confined to two cities that year — instead of games normally played in different regions around the country: Indianapolis for the men and San Antonio for the women.

But a glaring difference between the men’s and women’s facilities was exposed by Oregon’s Sedona Prince on social media. The workout and practice area for the men was significantly larger than the women, whose weight room was just a single stack of dumbbells.

The video drew significant attention to the equity gaps between the Men’s and Women’s divisions, leading to a 114-page report by a civil rights law firm that detailed the inequities between the two and suggested ways to improve the NCAA’s efforts for the Women’s side. One of these suggestions was simply to give the Women’s Tournament the same March Madness moniker as the men, which it finally got in 2022.

But underneath the surface of these institutional changes, women’s basketball’s single-biggest success driver was already emerging out of the shadows.

During the same COVID-marred season, a rookie from Iowa led the league in scoring with 26.6 points per game.

Her name: Caitlin Clark.

Caitlin Clark has scored the most points and made the most threes in college basketball.

Matthew Holst/Getty Images

As it stands today, Clark is the leading scorer in the history of college basketball — Men’s or Women’s. Her jaw-dropping shooting ability has fueled record viewership and ticket sales for Women’s collegiate games, carrying momentum to the March Madness tournament that has NBA legends like Kevin Garnett and Paul Pierce more excited for the Women’s March Madness than the Men’s this year.

Related: Ticket prices for Caitlin Clark's final college home game are insanely high

But as the NCAA tries to bridge the opportunities given to the two sides, can the hype around Clark be enough for the Women’s March Madness to bring in the same fandom as the Men for the 2024 tournaments?

TheStreet spoke with Jon Lewis of Sports Media Watch, who has been following sports viewership trends for the last two decades; Melissa Isaacson, a veteran sports journalist and longtime advocate of women’s basketball; and Pete Giorgio, Deloitte’s leader for Global and US Sports to dissect the rise Caitlin Clark and women’s collegiate hoops ahead of March Madness.

“Nobody is moving the needle like Caitlin Clark,” Lewis told TheStreet. “Nobody else in sports, period, right now, is fueling record numbers on all these different networks, driving viewership beyond what the norm has been for 20 years."

The Caitlin Clark Effect is real — but there are other reasons for the success of women's basketball

The game in which Clark broke the all-time college scoring record against Ohio State on Sunday, Mar. 3 was seen by an average of 3.4 million viewers on Fox, marking the first time a women’s game broke the two million viewership barrier since 2010. Viewership for that game came in just behind the men’s game between Michigan State vs Arizona game on Thanksgiving, which Lewis said was driven by NFL viewership on the same day.

A week later, Iowa’s Big Ten Championship win over Nebraska breached the three million viewers mark as well, and the team has also seen viewership numbers crack over 1.5 million viewers multiple times throughout the regular season.

The success on television has also translated to higher ticket prices, as tickets to watch Clark at home and on the road have breached hundreds of dollars and drawn long lines outside stadiums. Isaacson, who is a professor at Northwestern, said she went to the game between the Hawkeyes and Northwestern Wildcats — which was the first sellout in school history for the team — and witnessed the effect of Clark in person.

“Standing in line interviewing people at the Northwestern game, seeing men who've never been to a women's game with their little girls watching and so excited, and seeing Caitlin and her engaging with little girls, it’s just been really fun,” Isaacson said.

But while Clark is certainly the biggest success driver, her game isn’t the only thing pulling up the women’s side. The three-point revolution, which started in the NBA with the introduction of deeper analytics as well as the rise of stars like Steph Curry, has been a positive for the Women’s game.

“They backed up to the three-point line and it’s opening up the game,” Isaacson said.

One of the major criticisms from a lot of women’s hoops detractors has been how the game does not compare in terms of quality to the men. However, shooting has become a great equalizer, displayed recently during the 2024 NBA All-Star Weekend last month when the WNBA’s Sabrina Ionescu nearly defeated Curry — who is widely considered the greatest shooter ever — in a three-point contest.

Clark has become the embodiment of the three-point revolution for the women. Her shooting displays have demanded the respect of anyone who has doubted women’s basketball in the past because being a man simply doesn’t grant someone the ability to shoot long-distance bombs the way she can.

Basketball pundit Bill Simmons admitted on a Feb. 28 episode of “The Bill Simmons Podcast” that he used to not want to watch women’s basketball because he didn’t enjoy watching the product, but finds himself following the women’s game this year more than the men’s side in large part due to Clark.

“I think she has the chance to be the most fun basketball player, male or female, when she gets to the pros,” Simmons said. “If she’s going to make the same 30-footers, routinely. It’s basically all the same Curry stuff just with a female … I would like watching her play in any format.”

But while Clark is driving up the numbers at the top, she’s not the only one carrying the greatness of the product. Lewis, Isaacson, Giorgio — and even Simmons, on his podcast — agreed that there are several other names and collegiate programs pulling in fans.

“It’s not just Iowa, it’s not just Caitlin Clark, it’s all of these teams,” Giorgio said. “Part of it is Angel Reese … coaches like Dawn Staley in South Carolina … You’ve got great stories left and right.”

LSU's Angel Reese (right) and her head coach Kim Mulkey are two of the biggest names in Women's college hoops. 

Eakin Howard/Getty Images

The viewership showed that as well because the SEC Championship game between the LSU Tigers and University of South Carolina Gamecocks on Sunday, Mar. 10 averaged two million viewers.

Bridging the gap between the Men’s and Women’s March Madness viewership

The first reason women are catching up to the men is really star power. While the Women’s division has names like Clark and Reese, there just aren’t any names on the Men’s side this year that carry the same weight.

Garnett said on his show that he can’t name any men’s college basketball players, while on the women’s side, he could easily throw out the likes of Clark, Reese, UConn’s Paige Bueckers, and USC’s JuJu Watkins. Lewis felt the same.

“The stars in the men's game, with one and done, I genuinely couldn't give you a single name of a single men’s player,” Lewis said.

A major reason for this is that the Women’s side has the continuity that the Men’s side does not. The rules of the NBA allow for players to play just one year in college — or even play a year professionally elsewhere — before entering the draft, while the WNBA requires players to be 22-years-old during the year of the draft to be eligible.

“You know the stars in the women's game because they stay longer,” Lewis said. “[In the men’s game], the programs are the stars … In the women's game, it's a lot more like the NBA where the players are the stars.”

Parity is also a massive factor on both sides. The women’s game used to be dominated by a few schools like UConn and Notre Dame. Nowadays, between LSU, Iowa, University of South Carolina, Stanford, and UConn, there are a handful of schools that have a shot to win the entire tournament. While this is more exciting for fans, the talent in the women's game isn’t deep enough, so too many upsets are unlikely. Many of the biggest draws are still expected to make deep runs.

But on the men’s side, there is a bigger shot that the smaller programs make it to the end — which is what was seen last year. UConn eventually won the whole thing, but schools without as big of a national fanbase in San Diego State, Florida Atlantic University, and the University Miami rounded out the Final Four.

“People want to see one Cinderella,” Lewis said. “They don't want to see two and three, they want one team that isn't supposed to be there.”

Is Women's March Madness ready to overtake the Men?

Social media might feel like it’s giving more traction to the Women’s game, but experts don’t necessarily expect that to show up in the viewership numbers just yet.

“There’s certainly a lot more buzz than there used to be,” Giorgio said. “It’s been growing every year for not just the past few years but for 10 years, but it’s hard to compare it versus Men’s.”

But the gap continues to get smaller and smaller between the two sides, and this year's tournament could bridge that gap even further.

One indicator is ticket prices. For the NCAA Tournament Final Four in April, “get-in” ticket prices are currently more expensive for the Women’s game than the Men’s game, according to TickPick. The ticketing site also projects that the Women’s Final Four and Championship game ticket prices will smash any previous records for the Women’s side should Clark and the Hawkeyes make a run to the end.

NCAA "get-in" price comparison.

Getty Images/TheStreet

The caveat is that the Women’s Final Four is played in a stadium that has less than a third of the seating capacity of the Men’s Final Four. That’s why the average ticket prices are still more expensive for the men, although the gap is a lot smaller this year than in previous years.

The gap between the average ticket prices of the Final Four tournaments is getting smaller.

But that caveat pretty much sums up where the women’s game currently stands versus the men’s: There is still a significant gap between the distribution and availability of the former.

While Iowa’s regular season games have garnered millions of viewers, the majority of the most-viewed games are still Men’s contests.

To illustrate the gap between the men’s and women’s game — last year’s Women’s Championship game that saw the LSU Tigers defeat the Hawkeyes was a record-breaking one for the women, drawing an average of 9.9 million viewers, more than double the viewership from the previous year.

One of the main reasons for that increase, as Lewis pointed out, is that last year’s Championship game was on ABC, which was the first time since 1995 that the Women’s Championship game was on broadcast television. The 1995 contest between UConn and Tennessee drew 7.4 million viewers.

The Men’s Championship actually had a record low in viewership last year garnering only 14.7 million viewers, driven in-part due to a lack of hype surrounding the schools that made it to the Final Four and Championship game. Viewership for the Men’s title game has been trending down in recent years — partly due to the effect the pandemic had on collective sports viewership — but the Men’s side had been easily breaching 20 million viewers for the game as recently as 2017.

The 2023 Women's National Championship was the most-viewed game ever, while the Men's Championship was the division's least watched. 

Iowa's Big Ten Championship win on Sunday actually only averaged 6,000 fewer viewers than the iconic rivalry game between Duke and University of North Carolina Men’s Basketball the day prior. However, there is also the case that the Iowa game was played on broadcast TV (CBS) versus the Duke-UNC game airing on cable channel (ESPN).

So historical precedence makes it unlikely that we’ll see the women’s game match the men’s in terms of viewership as early as this year barring another massive viewership jump for the women and a lack of recovery for the Men’s side.

But ultimately, this shouldn’t be looked at as a down point for Women’s Basketball, according to Lewis. The Men’s side has built its viewership base for years, and the Women’s side is still growing. Even keeping pace with the Men’s viewership is already a great sign.

“The fact that these games have Caitlin Clark are even in the conversation with men's games, in terms of viewership is a huge deal,” Lewis said.

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