What’s the LMS Worth?

Herein, against my better judgement, I wade into the Great Instructure social media wars of 2019.  Last week, Instructure Inc., the publicly traded (NYSE: INST) company  announced it had agreed to go private and sell itself to private equity firm Thoma Bravo.  For people who teach in higher education this is big news. Instructure, is the current name for the company founded in 2008 that created and sells the Canvas LMS. Canvas in the last decade has toppled the previous king-of-the-LMS’s, Blackboard. Canvas is now widely reported to have largest market share of higher ed LMS market at least in North America. Moodle, the open source system, appears to dominate outside North America.

The announcement triggered a great deal of, let’s call it discussion, on social media, particularly Twitter. A lot of has gotten nasty and heated.  On the surface, the discussion seems to be about questions regarding what Instructure (or Canvas, or the data Instructure has collected) is “worth”.  Specifically, is it worth the $2billion Thoma Bravo has valued it at and why would TB pay that?

Underlying the valuation question though, is the real concern.  Can we discern the plans and future for Canvas (and thereby schools, instructors, students, the higher ed system, pedagogy, etc) from this transaction?  There’s roughly two camps. Both camps seem to think $2 billion is a big number.  I don’t but I’ll explain that later. One camp seems to be arguing that the $2 billion is perfectly justified as a valuation for Canvas as it is now and as an ongoing successful business and therefore there’s nothing to be concerned about here, nothing to see, just move along.  The other camp is seems to see $2 billion as a very big number and a clear indicator that Instructure’s new/future overlords will be monetizing the (relatively) massive database of user/student interactions (Instructure’s own claim as to it’s massiveness) and therefore putting students/faculty at risk from nefarious surveillance and profiling via AI (artificial intelligence and algorithms).

What I want to do is clarify some mistaken ideas/concepts that I see a lot of my education friends (and not so friends) arguing.  What’s been argued, by both camps at times, is not good economics or well informed finance. I’m not going to name folks here nor call out any one in particular. That’s not my intent. I’m hoping to clarify some thinking.

What’s a company worth?

Both camps seem to be arguing the “worth” (in precise economic/finance technical terms it is the “valuation”) of the company using the wrong theory or models of how valuation/worth is established.  The implicit model being used by all is familiar in economic/finance theory. It’s the idea that the current value of an investment (i.e. the purchase price of the company) should somehow be justified as expected present value of the future cash flows of the company from doing business.   That’s understandable. It’s a decent way to start evaluation of investment decisions – particularly inside companies when they decide to invest in something like a new machine or an expansion. It’s not the only consideration. There’s strategic considerations too.

So as an example  we’ve heard arguments that Instructure has been growing, generates cash, and has margins of 70%, so the value is just reasonable and therefore there’s nothing for the education community to worry about.

On the other hand, some have essentially argued that the only reason private  equity would pay this and/or the only pay they can recoup their money is if they monetize the data and that is presumed to lead to nefarious outcomes.

Let me clarify. The company was purchased, not the software and not an asset. The company. There is only one real-world way that valuations of companies are established: Will somebody pay a higher price later for this same company?  Let’s be very clear. This is a private equity deal. PE funds do not run companies. They do not sell things. They buy and sell companies. Period. That is all they do.  The only customers they have are the other PE firms or corporations or banks that they sell their  companies to.  Period. Thoma Bravo is not in the education or edtech business. They are in the buying-and-selling software companies business. That’s it. And no matter what they say about “being in it for the long run”, they aren’t. PE firms generally look to recoup and sell the business inside of 5 years, preferrably a lot sooner.

Conclusion #1:  No matter what any manager at Instructure or TB tells you, the needs of higher education are no longer the driving force.  The driving force is putting together a nice story supported by anecdotal financial data that leads to some future firm paying TB way more than $2b in a couple of years.

So is Instructure worth $2b?  We’ll find out if and when TB sells it. My guess is yes, TB will definitely flip this in a few years for substantial profit, assuming the bottom doesn’t totally drop out of the LMS market. (a small but real possibility).

Any argument you make about the deal based on business fundamentals is nonsense and fantasy. It’s part of popular econo-myths. Before you try to argue with me on that, do this one test: can your implied model of valuation explain why Uber went public at a valuation of ~$100 billion when Uber has never made money, is cash negative, and has no prospects of making money?  Can your model explain WeWork?  If you still don’t believe me, I suggest researching a little with Professor Scott Galloway (@profgalloway) about how valuations and funding happens real world these days.

What’s next?

What can we expect? Will the data be monetized? Will it be sold off piece-by-piece? Will Instructure/TB now invest heavily in all kinds of accelerated innovation? (Ok, I just threw that last question in for laughs. Of course they won’t. Real innovation costs money, time, and work). Really, we don’t know but there are some high probabilities based on the new capital structure and owners.

First off, there’s the possibility of some good old fashioned battle of the funds. We know very little about the specifics of the Instructure-TB deal. That’s how private equity works. It’s private. It’s not transparent. However, it seems that Instructure has 35 days (counting holidays) to find a better deal. Some other funds, hedge funds in this case, have taken positions in Instructure and they don’t think $2 is enough.  Typically the only people who come out ahead in these situations are lawyers, banks, and partners at the biggest funds. Little shareholders and the rest of the human race, not so much.

Once the deal closes, the priority at Instructure will be clear and it has two parts. First priority is get the money (cash) back to TB. I’ve heard it said on the Twitters that TB is putting out $2b of it’s money to buy Instructure. Again, we don’t know details for sure, but that’s almost certainly false. PE deals don’t work that way -especially with a company like Instructure that generates a healthy positive cash flow, is profitable, and has little debt (AFAIK).  Typically the playbook is that the PE firm buys the company largely with the target company’s own money.  In this scenario, the PE fund (TB in this case) puts up a relatively small amount of their own cash up front. They take a very short-term bridge loan from a friendly bank to get the total $2b in cash needed to buy out the shareholders. Once the deal closes, Instructure Inc. then is directed by their new owners, TB, to get a loan from a bank secured by the company’s assets. The proceeds of that loan are then paid as some kind of “special dividend” to the new owners to retire their loan. The PE fund has a small at-risk stake at that point. Management fees or sell-off of some assets in the first year can often pay back that cash. By maybe the end of the first year, the PE fund has gotten all it’s cash back and is playing with house money at that point. The target firm (Instructure in this case) is likely a lot more debt-laden than before with a lot less free cash flow.

At that point, we consider the other priority (don’t worry, these folks can multi-task so you’l hear this one right away). Namely, the big priority is to develop a story that leads to another big pocket putting out well more than $2 in a few years. Tell the story and tell it hard. Once they’re private, that becomes a bit easier. Less real data has to disclosed since they’re no longer public, so it’s easier to be selective with the data and put your own spin on it without fear of those pesky shareholder suits and the SEC (is anyone actually still afraid of the SEC?).

PE firms, like Venture Capitalists or hedge funds, aren’t looking for nice safe returns on their money. You and I would be ecstatic to get annual returns of 10-20% on our retirement funds. These funds look for more. They want multiples of the initial investment. So they’re looking for deep pocket buyers that can and will spend not $2b, but maybe $4b or $6b or more in just a couple years.  The PE fund wants a big exit and once the deal closes the only thought is the exit. Running the business is only important to the degree it helps tell a story that helps them exit.

Why would anyone pay that in a couple years from now?  Go back up to the section on “What’s it Worth?”.  There aren’t that many routes for exit for a PE firm:

  • do an IPO (initial public offering) -not likely here since they just took it private – obviously the public market wouldn’t value it high enough
  • find a bigger sucker PE fund – the story of why there are untold, untapped riches becomes critical
  • find a really big, deep pockets corporation that wants to add to it’s portfolio of businesses thinking this will add that magical “synergy” to its other businesses.  This is a possibility for Instructure, but the likely candidates are:
    • Google, FB, MSFT, Amazon, or Apple – the people trying to collect everybody’s data about everything in the hope of controlling/monetizing everything.  A story of the value of the data and the ability to predict the future lives of students could lead them to write a big check.
    • Textbook publishers – OK, there are only two left, Pearson and Cengage-McGraw Hill.  They could fall in love with a story of becoming the single source books-homework-courseware-LMS provider. In fact, they’ve tried the LMS before, but couldn’t do it themselves. They might choose to buy in. I’m not sure their pockets are deep enough though.
    • When all else fails, merge. Instructure could merged with Bb or Brightspace using some other PE fund’s money.

Whatever route leads to the exit, that’s the priority now at Instructure. In my opinion, all those avenues are fraught with very good reasons why colleges, professors, and students should be concerned.

Where will the money come from?

Another thing I read on the Twitter was the suggestion that Instructure is somehow impervious to the all-too-common private equity strategy of carve-it-up and sell off the parts.  Nonsense. That tweet came from somebody who purports to know and advocate for private equity but apparently, judging by their tweet, thinks Hollywood movies about whores are primers about finance.  I won’t deal with that aspect of the tweet other than to say that misogynistic tweet was all the evidence to convince me the dude has spent too much time in either tech or finance culture. Unfortunately, he’s not very skilled at the private equity portion. It takes little imagination to see how Instructure could be carved up and pieces sold off. I’m not saying they will. I’m just saying it’s a piece of cake. They’ve made 2-3 acquisitions in recent years. Reverse those and sell. They’ve already told everyone they’re positioning for a possible split-off. They’ve stated they’re separating the codebase for Bridge from Canvas.  Add to that, any business with multiple services, even when sold to the same segment, can be carved up. It doesn’t even take much imagination to do it. All it takes is a willing buyer. And all that takes is a plausible story about the riches at the end the rainbow.

Education is not THE Story Anymore

We in higher education have a tendency to think we’re important as a market. We’re not. For a long time, edtech companies and Silicon Valley have fed that fantasy. We think in terms of the edtech “market” and think it’s attractive. In truth, it’s largely failed to meet to meet SV expectations.  The LMS market is mature. Very mature. Most LMS’s are really based on 1990’s architectures ported to the Web. Canvas was an innovation in 2008 by being cloud based. But product wise, all of them are still largely the same conception of the product as 20+ yrs ago. Everybody who needs an LMS has one.

Yes, Instructure has had decent growth numbers (not sterling by SV standards, but good) in recent years. But finance is all about how are you going to top that going forward. Finance doesn’t look back. Truth is, Instructure or any of the LMS’s are going to have a hard time finding big new sources of revenue. There just isn’t much left in the higher ed budget for their stuff. Even the data analytics for learning part has failed to take off revenue wise. That’s why data mining for AI/Algorithms, monetizing the data to non-education folks, is so tempting.

Yes, any of these LMS firms, or publishers for that matter, could have had decent solid, satble, modestly profitable businesses that were mature. But that’s not how finance capitalism works.  Instructure isn’t an education tech company anymore. It’s just a software company and data processing service that happens to get its data from college and university students.  It will likely be managed that way.

FUD for thought?

I should put a word in about FUD.  Not sure if I introduced it into the conversations on Twitter or somebody else did. I didn’t realize the term was new to so many.  It’s an acronym that stands for Fear, Uncertainty, and Doubt.  The original usage that I’m familiar with dates back to software deals and business deals in the 90’s. FUD was something some firms tried to create in the market about their competitors. For example, back in those days, Microsoft was often accused of putting out PR releases and statements trying to create FUD about whether Linux or open source software was any good.  A more recent example in edtech world would be a few years ago when for-profit publishers would spread stories casting doubt (FUD) about whether OER was any good. They helped perpetuate doubts about the quality of OER in order to justify their high priced books. Nowadays, those publishers have tried to enclose (“embrace and extinquish” – another old Microsoft strategy) OER instead of spreading the FUD.

The thing about FUD is that it usually isn’t specific or justified.  It’s an attempt to cause people to feel uncomfortable about things.

The ironic part now is that I don’t think the concerns expressed on Twitter about the Intructure deal are FUD.  What the concerns have shown is there’s reason to be uncertain – the details aren’t disclosed and won’t be. There’s good reason to be doubtful: private equity deals very often do end up butchering or hampering the core business.

And there’s reason to be fearful:  that giant database of student data has value to big players in the surveillance capitalism industry. There’s the big obvious ones: Google, MSFT, Apple, Amazon, and FB. But there’s a host of other hidden players – data brokers, Palantir, banks, and many others, the lords of the algorithm cults. They often have deep pockets or they’re backed by funds with deep pockets. All Instructure/TB needs to do is convince them of a story about how Instructure’s data can add value to their existing trough.

A Final Lesson

I’ve argued extensively that higher education (perhaps all education, but I’m not expert in K-12) is best organized as a commons. The boundary between commons and the market-oriented capitalist economy is tricky. Capitalists and market-thinkers inevitably seek to enclose the commons, privatizing benefits and externalizing costs onto society.

This boundary is particularly tricky in the edtech world. If there’s one lesson I hope to impart to people in education, it’s the need to do your due diligence on your vendors and “partners”.  Current product offerings aren’t enough. Product roadmaps matter. Plans matter.

But most of all, capital structure matters. No matter how nice the people at the vendor, no matter how good the values of the hired managers are at that edtech “partner”, ultimately it’s capital that calls the tune.  That’s why it’s called capitalism.

Innumeracy and Generosity – Don’t be deceived by big numbers

Just a quick note here.  Lots of people today, especially the media, are making a big deal out of Jeff Bezos and his wife’s donation of $33 million for a scholarship fund for DACA Dreamers. For example there’s this CNN article.  Lots of tweets. It’s a nice gesture. It’s definitely a worthy cause – although worthy causes are legion.

My problem is with the intimation that this is somehow a noble sacrifice. The problem here is common in economics data. We get lost in big numbers and get fooled.  $33 million sounds like a lot. To over 99.9% of Americans, it’s a number we can’t really fathom. It sounds like so much money.  Let’s take a closer look. Bezos household net worth – the value of his personally owned assets minus their debt – is estimated at $105 billon (Bloomberg) or $104 billion (Forbes) (source: Google on Jan 13, 2018 ).  That’s billion with a B. Bezos is 54 years old.

The median household net worth for Americans in his age bracket was $100,404 according to the most recent data for 2013/2014 from Census Survey of Income.  The median means there are as many households with more assets as there are with less assets. It’s the middle observation. It’s typical.

So Bezos has pretty close to a million-times larger net worth than the typical household for somebody of his age. He and his wife sacrificed $33 million of their assets to make this donation. On a strict linear scale, that’s the equivalent of the typical household for his age bracket donating $33.  Yep, that’s all. $33.

Bezos’ sacrifice is the equivalent of an ordinary, typical 54-year old giving $33. Actually, it’s less of a sacrifice. Economics teaches us about diminishing marginal utility of income or money. Basically, when you’re rich each additional dollar of income or asset is much less valuable to you than if you’re poor. To a poor person, the $33 means eating or healthcare. When you’re really rich, it’s just another digit you’ll round-off on your financial statement.

I laud the Bezos family for making a donation. It’s a good thing to do. But let’s not make it out to be more noble than it is.  The bottom 20% of households in that age bracket have zero or negative net worth. The single mother with no assets that stuffs a twenty in the Salvation Army bucket at Christmas makes a lot bigger personal sacrifice.

Critical Analytics: It’s Stories All the Way Down

I’ve been hearing much lately about stories, narratives, analytics, data, and “big data”.  I have no need to call out exactly who or which pieces of writing. You know who you are. My aim here is not to criticize, oppose, or take sides. It’s to take a brief critical look at what’s being discussed.

Much of the discussion strikes me as one tribe (I’ll call them non-quants) pleading that stories and narratives are important too!  All of which is an understandable reaction to how the other tribe (I’ll call them quants) have seemingly gained a favored position and perceived superiority at divining the “truth” because they are evidence based!  Because data! I’m actually a member of both tribes and find the posturing of stories and narratives as alternative to quantitative analysis disheartening.

The most encouraging blog piece I’ve read recently comes from Michael Feldstein.  In his lengthy (and excellent) post called Analytics Literacy is a Major Limiter of Edtech Growth.  Please do read it.   He argues for the dissolving this false juxtaposition between “stories” and “data”.

…some of these arguments position analytics in opposition to narratives. That part is not right. Analytics are narratives. They are stories that we tell, or that machines tell, in order to make meaning out of data points. The problem is that most of us aren’t especially literate in this kind of narrative and don’t know how to critique it well.

I wholeheartedly agree.  Feldstein is (correctly) arguing that data points are nothing without stories.  The meaning we take from the data is itself nothing but a story we weave using the data points as we might use punctuation or particular words.  In essence, quantitative analysis is itself a story.

This really isn’t news or at least it shouldn’t be.  I remember how powerful McCloskey’s Rhetoric of Economics was for me when I read it decades ago.  McCloskey powerfully made the point that no matter how much we wrapped an idea in data, mathematical formalism, or econometric analysis, everything we said in economics was just a metaphor or a story we imposed on the data. Alan Grossman long ago pointed out that even that high temple of data-driven evidence, Science(tm), it’s still just rhetoric and it’s still just stories.

Yes, the meaning we attach to a set of data is itself a story.  So stories are not alternatives to data. Data is a story.  But it’s not just the obvious story we tell with the data. There’s a story unstated underneath the data the we use. Our choice of particular data variables constitutes a story itself. We (or at least the data collector) have in mind a story and narrative of what’s important before they collect the data.  They don’t collect data about the context that they don’t see as important or relevant (or easy enough to collect), so they assume a story about that uncollected contextual data holds no meaning.  There’s a story underneath the story we told with the data.

But it keeps getting deeper. Much like the philosophical turtles, it’s stories all the way down. That measure of the data you’re using. The one you think is just basic stats or math, something like the average (properly called arithmetic mean), or the variance, or correlation, or whatever.  It has a story too.  Let’s take that arithmetic mean (average) and each observation’s difference from the average. We think of that average as “the norm” – but that’s just a story invented by a couple of different statisticians in the 19th century.

I can’t really do justice here to the story of how that story of what the average or norm is.  I strongly urge you to read The End of Average by Todd Rose.  It’s fully accessible to members of both tribes, quants and non-quants.  You’ll never use your quantitative data the same way again. Todd Quinn writing in the Elearning magazine of the ACM had the same kind of dramatic reaction as I had.

I’ve finished reading Todd Rose’s The End of Average, and I have to say it was transformative in ways that few books are. I read a fair bit, and sometimes what I read adds some nuance to my thinking, and other times I think the books could stand to extend their own nuances. Few books fundamentally make me “think different,” but The End of Average was one that did, and I believe it has important implications for learning and business.

Rose’s point is pretty simple: All our efforts to try to categorize people on a dimension like GPA or SAT or IQ are, essentially, nonsensical.

But going another level down, as Rose explains in End of Average, there are assumptions beneath the calculation and use of ordinary stats like the average or the variance.  Let’s face it, “assumptions” is another way of staying “believed a story to be so true that it didn’t need to be stated”.  In the case of the average and the calculation of differences from “the norm”, that assumed story has to do with the ergodic properties of what’s being examined.  So what’s “ergodic  properties”? Well here’s Wikipedia’s attempt to explain ergodicity. It’s not very accessible to non-quants (or even most quants!).  Again, I would refer you  to Rose’s book for a beginning glimpse of what ergodicity means. I can’t explain it here, but the essence is that mathematically, statistically the vast majority of the stories being told with quantitative analytics are complete nonsense. Garbage. Invalid. Wishful alchemy.

It’s stories all the way down.  At first this might seem discouraging. But it’s not. I’m calling for not just analytics literacy but a critical analytics.  We need to investigate and become aware of not only the stories we tell using data, but also the assumed stories we slide under the table by choosing particular measures and statistical techniques without thinking about them. We wouldn’t let the semantics of narratives escape critical examination. Why should we let analytics?

 

Data and Visualization Resources for Incomes and Inequality

Posting links to two incredibly useful resources for students and people doing research on incomes, income distribution, and income inequality. These resources are useful for both historical data and visualizations as well as cross-country comparisons.

The first is the World Top Incomes Database from the Paris School of Economics. Many thanks to the Paris School and researchers Facundo Alvaredo, Tony Atkinson, Thomas Piketty and Emmanuel Saez. It’s a a tremendous resource.

The second is a tremendous resource also. It’s Our World In Data. It’s a work in progress project by Max Roser,   but it’s already jam packed with great data and visualizations on incomes, health, war and violence, poverty, and food and hunger. And best of all, it’s all CC-BY-SA licensed.  I love it when collaboration and the commons come together to support learning.

China, Growth, and the Weakness of Real GDP

Sara Hsu asks if All Growth is Good? The Case of China Of course, not all growth is good. It makes little difference, whether it’s economic or human tissue growth. Edward Abbey famously wrote that “growth for the sake of growth is the ideology of the cancer cell”. Obesity is another form of high-growth, yet it hardly improves well-being or health.

Unfortunately, we economists have not (yet?) developed measures that help us or policy-makers distinguish between healthy growth and malignant growth.  The only real comprehensive measure of growth we have is growth of real GDP. We do know better, as Sara notes:

Since the seventies, with the assertion by Gunnar Myrdal that economic development should prioritize equality, economists have increasingly come to believe that not all types of growth are wholly “good.” Growth that ignores human well-being and equality are viewed as problematic.  Certainly growth that results in severe environmental destruction, as in the case of China over the past twenty years, cannot be classified as good, either, despite the country’s much-lauded successes during this period. Real-world views of growth depicted in the mainstream media do not fall in line, however, with the economic development literature. The focus on China’s growth in the news has distracted from a more balanced view of the looming inequality problems or polluting production methods in the world’s most populous nation.  As China’s growth has slowed, headlines have read, “China’s Economic Growth at Stake,” “China’s Economic Growth Slows,” and “China’s Second Quarter Growth Slows.” –

Yes, China’s real GDP growth rate has been spectacular for several decades now. That growth has lifted literally hundreds of millions of people into better lives. Yet, in strange case of the metaphor becoming real, that economic growth has literally brought cancer with it. Specifically, many “cancer villages” along the Huaihe River.

China’s economy illustrates the problem of growth measured in numbers versus measured in real economic change. The surge in fixed asset investment carried out post-global crisis resulted in an inflation of growth figures, despite the creation of uninhabited apartment buildings, or even entire cities. This is socially unproductive growth, wasteful production, “bad” or false growth. Although the distinction between “good” and “bad” growth exists only in theory, it is essential to clarify the difference to the public in order to move along the path of long-term development.

Admittedly, it may be overambitious to request that a more comprehensive view of growth penetrate the media. However, it would benefit our understanding of China’s economic performance; reconceiving growth would increase competition to generate “good” growth and discourage the race to build businesses that produce “bad” growth.

Yes, I agree. It is indeed an ambitious project, the idea that we could create more comprehensive measures of growth that help us to separate healthy improvement in well-being from cancerous, destructive economic growth. But it seems to me no more an ambitious goal than the vision less than 100 years ago to create the national accounts systems and begin collecting the data (from whence we get GDP measures).

Not Performing Up To Potential

When I was kid there was a comment I dreaded but got too often on too many grade reports to my parents: Not performing up to potential. I hated that. I must say, though, that there times when it did motivate me to do better.

The same comment, not performing up to potential, can easily be applied to the U.S. economy for at least the last 6 years.  I really wish it would motivate our economic policymakers to do better, but alas, they seem to be indifferent to the challenges.

For the details of just how much we are under-performing, I give you the Center for Budget Policy Priorities summation of the latest Congressional Budget Agency report on the economy (below the fold): Continue reading

Busting the Medicare Myths – Presentation

I gave a presentation today to the Michigan Intergenerational Network at Madonna University on the economic prospects of Medicare (U.S.). Thanks to the Madonna Univ. Gerontology Department for support and assistance.

For a downloadable and viewable copy of the presentation, see:  https://jimluke.com/course-resources/presentations/busting-myths-about-medicare/.