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.

Scale and Scope

Note: A couple of friends have asked why I say “A commons doesn’t scale, it scopes”. This is a relatively quick note to explain some thinking on why. It’s a topic I’m deep into researching now and developing my thinking as it applies to higher education as a commons, so with the caveat that I may alter some stuff later, here’s my thinking right now. This is part one of a two part answer. Typo in paragraph about Facebook now corrected.

I’ve been saying for awhile now in discussions of the commons, OER, and higher education that a “commons doesn’t scale, it scopes”. Before I explain why I think a commons doesn’t scale very well, I probably need to briefly clarify what’s meant by scale and scope. Like many terms in economics, they’re both commonly used terms in both business and everyday life, but in economics they may carry a subtly different, more precise, or richer meaning. Both terms refer to the production of an increasing volume of output of some kind. Enthusiasts of particular good(s), be they an entrepreneur producing the a product they hope will make them rich or an open educator advocating for more open licensed textbooks because it will improve education, generally want to see their ideas scale. And by scale, they generally mean “be produced in larger and larger volumes”. Larger volume of output, of course, brings a larger volume of benefits to more users. More output –> more users –> more benefits. But it’s the behavior of costs that really intrigues us when we think of “scaling” as a way to increase output. More benefits is nice, but if more benefits also means an equal increase in costs, then it’s not so attractive.

The era of mass production has brought a popular expectation that increased output should bring an increased total cost, yes, but with decreasing average costs. In other words, as you produce more it, the product (or service, or activity) becomes cheaper. This is what we call economies of scale and it’s why scale seems to be such an attractive idea for things we want more of. The idea of economies of scale goes back to Adam Smith.

But since at least the work of Panzar and Willig (see Wikipedia footnotes 3, 4 for links and full citation) around 40 years ago, economists have added a richer explanation. We (well not all economists, but IO and institutional types do) now distinguish between economies of scale and economies of scope.

Scale is to produce to the same thing in larger and larger volumes. It’s doing the same thing over and over again. A lot. There’s little variety, just volume. Scope on the other hand is a way to get to large volume by adding variety to the mix. Scope means doing a lot of things that are different by share some apects. The more aspects shared, either in final form or in production process, the closer you get to scale. The more variety you have, the more scope you have.

For some simple examples, think Ford Motor Company’s Model T. That’s scale in action. Enormous volumes of the same car – even down to the same color. Mass production generally involves scale. Standardization is a virtue in scale. Standardized inputs, processes, and outputs, all enable the great of economies or efficiencies we associate with scale. Massive scale can be managed within a hierarchical structure. The hierarchy adds costs, but it more than makes up for it by through an ability to control and standardize inputs, processes, and outputs. Hierarchical management achieves enough economies of scale to more than offset its added overhead costs.

Scope can bring economies, too. This was part of the Panzar and Willig contribution. Economies of scope are more difficult and complex than economies of scale. They’re less automatic and less obvious. Variety, whether it’s variety of location, product, inputs, processes, or outputs complicates things greatly. However, economies of scope are possible through shared services or other aspects. There are lots of examples of scope economies in the business world, although not so many in real life as business people imagine (I speak from experience). When you hear an executive make the case for merging two different businesses and say they’ll achieve cost savings through “synergies”, that’s economies of scope they’re chasing (and likely not getting, but the investors won’t know that until management has fled the scene). When a school district operates a multiple types of schools (pre-K, elementary, middle, high school, specialty) in multiple locations but insists on centralized purchasing and accounting, that’s an attempt at economies of scope.

When businesses, industries, or products first start to grow, they usually scale. But eventually there are limits to scale. When firms hit the limits of scale in growth, they begin to scope. They usually start with product differentiation and geographic expansion. Then comes segmentation of the market and multiple brands. Variety and variation bite back. Remember Henry Ford’s famous quote about “the customer can get it in any color they want as long as it’s black”? Economies of scale talking there. Unfortunately for Henry, his quote came just as Sloan and Durant at General Motors were pioneering ways of adding product differentiation and segmentation – variety.

When Facebook burst on the scene and seemingly everybody in America (and elsewhere) started signing up, that was scale. But when FB added What’s App and Instagram and Messenger to the corporate portfolio in order to keep the growth going, that was scope.

How does scale and scope apply in education? Scale seems to me to be the impossible dream. We’ve achieved very tiny little scale efforts. When a large flagship university (itself a shining example of wide scope) runs 600 seat lecture classes in principles of economics supplemented with smaller discussion/lab sessions taught by TA’s, that’s a scale effort. It’s tiny though. 600 is only 20x the size of the principles class I teach at the community college. In contrast, business world scale usually means thousands-times larger. We’ve tried to scale by producing textbooks and that has had some positive effect in that it enabled hiring more instructors (adjuncts in particular) at lower costs. But it’s limited too.

Society has for much of the past century been trying to “scale”. Society needs more college-educated people, yet, for many reasons, it is reluctant to pay more them. The idea of scaling education is tempting. If only we could scale up education like we did cars, or clothing, or beer, or music, then we could have more college educated folk and not have to pay the full costs. It hasn’t really happened.

I’d argue it can’t. Scale economies require standardization from inputs to process to outputs. That’s not education. Every learner is different – that’s variety and scope there. What works for one doesn’t work for another. Processes are different. Despite all our efforts in recent decades to define “learning outcomes”, they still defy definition let alone control and standardization. Education requires scope.

There’s more to why a commons won’t scale, but that’s in part two.

Response to Mike Caulfield Question

Mike Caulfield on Twitter asks a question today:

There’s more to it. It’s a whole thread.  Rather than respond in what would inevitably be a  long thread myself, I’ll just post my reactions & poorly formed thoughts here. Disclaimer: I haven’t read Simons in decade(s) and all economic “facts” I mention here are really stylized facts or trends.  Enter at your own risk.

Mike asks for example:

No. I don’t think so.  The idea of  industrial production –> scarcity of capital & scarcity of markets doesn’t fit.  Rather, I’d characterize the broad swath as surplus of savings amongst elite –> supply of finance for capital –> capital investment –> industrial production –> greater surplus of savings amongst rich elite –> rinse and repeat.  If anything, we suffer in recent decades from a surplus, not scarcity of capital. Indeed there’s been a fair literature about that in recent times.  Somewhere in that cycle, the supply of finance for capital creates a demand for markets (both capital & final production). I don’t see much evidence that there’s been a shortage of markets, though.  Indeed, the supply of markets seems to be rather elastic and responsive to finance capital’s demand for markets.

I agree with Simons observation but I think it helps to understand the mechanism. Scarcity issues are often driven by either physical constraints (real scarcity) or changes in opportunity costs (relative scarcity).  In the information – attention context he’s talking about it’s both real and relative scarcity.  There’s a real, fixed, unchangeable constraint on attention. Attention necessarily requires time (also other inputs such as cognition, etc). Each human is at maximum only capable of 24 hrs of attention per day. Information, all information, requires some degree of time to process (i.e. “pay attention”), ergo, more information bumps up against fixed constraint. Result: increasing real scarcity.

We can also consider the opportunity cost of paying attention to a piece of information.  Notice we use the term “pay attention” – we’re implicitly doing the trade-off.  As more information exists, the value of our attention rises. When I pay 10 minutes of attention to a particular chunk of info in order to gain the benefit of knowing that info, the opportunity cost is the not-knowing-other-stuff.  When there’s more info, that means there’s a lot more other-stuff–to-not-know.  It gets expensive opportunity cost-wise to learn something in particular.

I’m not sure where your’e going with this, Mike, but one econ phenomenon that might be relevant is the entry of married (middle+upper class) women into the workforce in the mid60’s to mid-80’s. In that period, the rise of feminism and feminist attitudes led to a cultural and values change in the middle and upper classes (in U.S.).  Workforce participation among married women rose from 1 in 4 married women working outside the house for pay to 3 of 4.  That was a big shift. It was a huge increase in supply of married women to labor markets.

That in turn led to much larger numbers of employed women. The opportunity costs of time changed a lot. Their time was now worth a lot more since it could be traded for substantial $ in labor market and previously social/cultural constraints prevented that.  At the time, social/cultural constaints on married men cooking meals for their households hadn’t changed yet (that’s been pretty laggy), so the “responsibility” for meal production in households still largely resided with the married women.  A home cooked, largely from scratch dinner now became very, very expensive opportunity cost wise.  Goodbye home-cooked from scratch meatloaf or fried chicken, and hello McDonalds, KFC, or microwaved factory-prepared food.  Ultimately, this translates into a what appears to be a relative scarcity of home-cooked food from fresh ingredients.

Don’t know if I helped. I fear I only muddied things. But then, that’s what I do. I’m an economist.

 

An Economics of Polarization

This post is a response to yesterday’s discussion in Davidson Now’s pop-up MOOC,  “Engagement in a Time of Polarization”.   The key provocation for the discussion was Chris Gilliard’s great essay Power, Polarization, and TechThe video of the hangout discussion is embedded at the end of this post for you.


In his discussion of social media rules and platforms, Chris poses an interesting hypothetical:

If we had social media and rules for operating on platforms made by black women instead of bros, what might these platforms look like? What would the rules be for free speech and who gets protected? How would we experience online “community” differently than we do now? Would polarization be a bug instead of a feature? The historical disenfranchisement of black and brown women and men is compounded by these same folks still being walled off and locked out of tech institutions through hiring policy, toxic masculinity at the companies, and lack of access to venture capital. “Black women are the most educated and entrepreneurial group in the U.S., yet they receive less than 1% of VC (Venture Capital) funding.”

I’m going to argue that if Facebook or Twitter or one of the other monster social media platforms had been staffed and created by black women (or just about any other historically disenfranchised group) the results would likely have been the same.  I’m not arguing an “all people are corrupt” position. Rather, I want to highlight the institutional conditions and economics by which these firms come about.  The institutional framework in the US, combined with some straight forward economics pretty much sets the path. Any group of entrepreneurs would likely end up in the same place, behaving the same way, and producing the same polarizing products/services.

I say this not as a voice of gloom, but rather to highlight that if we want to avoid or dismantle the damaging polarization and surveillance capabilities of these social media mega-platforms, we need to make institutional and legal changes.  And those legal and institutional changes may be in areas you don’t suspect such as antitrust law. First, I want to bring to light two different aspects of the institutional economics of these firms. The first is price discrimination and the second is corporate capital funding structures, especially for start-ups.

The bros that started, coded, and grew these social media platforms such as FB, Twitter, Google, and even Amazon, didn’t set out to polarize the population. Each had an interesting concept to provide people such as search (Google), interpersonal social connection (FB), or quick broadcast chat (Twitter).  But those services required large user bases and people were unlikely to pay for the privilege. So a monetization model was needed. Advertising and/or data sold to advertisers. Most folks know that these platforms with their data enable advertisers to “target” specific higher-probability buyers for their products.  But just increasing the likelihood that a specific ad will result in a sale isn’t the gold.

The gold is in price discrimination. Always has been.  I don’t have time now to fully explain price discrimination, but there’s a Wikipedia entry on it and an Economics Help site entry for it. An individual’s real demand curve for a product is very difficult to ascertain. It’s a hypothetical. It’s how many would you buy at all the possible prices? Looked at from a seller’s viewpoint, it’s what’s the maximum price I can charge and still sell as many as I want?  If the seller knows, he/she can charge prices that capture all the consumer surplus value for themselves instead of sharing the joint benefits of the transaction. 

If an advertiser/seller can gain enough information about a potential buyer’s real demand curve, it’s the route to profit nirvana. But historically it’s been difficult to do price discrimination. For products, there’s that pesky Robinson-Patman antitrust law. Often it’s been done via proxy indicators of group preferences – think Ladies’ Night at the bar or higher prices for business travellers on airlines. Getting the knowledge has been tough.  Big data from social media solves that problem.  That’s why social media data is so valuable and profitable and why FB/Google/Amazon/Twitter chose that route to monetization instead of subscriptions or memberships.

This price discrimination behavior is nothing new and neither are the abuses. It’s what made John D. Rockefeller’s Standard Oil so profitable and so socially destructive 120 years ago.  The urge to find ways to price discriminate is inherent in corporate market behavior.  The only limits legal.  We used to pass and enforce antitrust laws against such behavior, but that’s been considered bad form ever since the Reagan administration listened to the Chicago boys back in the early ’80’s.

To enable price discrimination practices, the social media monsters had to find more and more data about each and every user.  There’s a direct line between individualized data and monetization.  Now the marketers don’t call it discrimination. They call it differentiation.  They want to know exactly how every person is different from everybody else and find little homogenous groups to put them in.

The purpose was economic & marketing discrimination/differentiation. But once the differences are revealed. Polarization, a side effect, is all about finding differences, not commonalities. Finding commonalities doesn’t make money for marketers.

I don’t think any of the bros that did this at these platforms intended or planned to polarize the nation. It was just an unintended, unconsidered consequence.  Don’t get me wrong. I’m not absolving them of responsibility.  Sometimes unintended consequences could and should have been foreseen. It’s kind of like drunk driving. Very few, if any, people set out to drink and the drive with intent of killing somebody.  It happens because they didn’t think and didn’t foresee the consequences of their actions.

Given the incentives and demands of capital structure, I think any group would likely have gone for the price discrimination-data collection jackpot, especially since there are no legal guard rails against it and they likely would have to as a startup.

Now that gets us to another question. Why did FB/Twitter/Google, et al, find the need to maximize the monetization?  Well, here we can fault them. The reason was greed but again it was unintended, unforeseen consequences.  Their choice of capital structure forced it. They went for too much cash at the IPO’s.

Chris is right. Black women as a group are highly entrepreneurial. But there are maybe 4 motivations for entrepreneurship. Some do small businesses because there’s no other option – that’s a lot of present black women entrepreneurship. Some start businesses just to be left alone (like me 20 yrs ago). Some just want to get stinking rich and leave (Peter Theil, Paul Allen). And some want to get stinking rich, build a huge legacy corporation, and rule the world (Zuckerberg, Bezos).  FB/Google/Twitter et al chose to go the IPO route to become stinking rich.  Google, IIRC, did it twice.  The cash they gathered from those IPO’s did more than fund operations and some growth. It was in excess of their real cash needs. The consequence was they needed continuous high growth rate in both users and profits.  That’s what Wall Street style financial capitalism both rewards and requires. With the high, continuous growth, there’s no stock premium No stock premium = low stock price = founder isn’t really that rich.

My argument is that some other group, black women or POC or whoever, might have done things differently, but only if they had set different goals of not getting rich. Unfortunately, the US corporate funding and legal systems don’t really allow for enterprises that in-between. It’s either struggle for funds as a non-profit or go for continuous profit maximizing high growth.

There’s not really an institutional option for funding “just adequate to provide a utility-like service”.  To get the funding to start, any group effectively commits to the profit max, high growth route.  And that commitment drives the monetization strategy of data collection to seize the gold of price discrimination.

Is it all gloom and doom? No. I don’t think so.  But arguments that simply ask for firms and developers to be more “ethical” or even just more diverse aren’t likely to work in my opinion.  We need to change a lot of the rules of the game.

I do have suggestions for those changes, but this more than enough for tonight.

 

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.

Micro, Macro, and the Minimum Wage

Meme showing man in office with coffee mug looking skeptical saying "Yeah, I'm going to have to go ahead and disagree with you there."Economists disagree. It’s so common that there are jokes about.  For example,

If all of the economists in the world were laid end-to-end they would scarcely reach a conclusion.

and

Economics is the only field in which two people can get a Nobel Prize for saying the opposite thing.

Why?  I can’t explain all of economists’ disagreements here (I don’t have enough pixels!), but I can explain some of the disagreement over questions of raising the minimum wage.  There are numerous calls for Congress to raise the minimum wage, yet Congress has remained bitterly divided on the issue. Their disagree to a large extend reflects the disagreement among economists. To non-economists, the disagreement seems to either indicate that there really isn’t any science in economics and it’s all opinion, or that some economists must be lying or deliberating obfuscating.  In truth, though, there’s another reason for the apparent disagreement: the difference between micro and macro level economic analyses.

First, let’s establish some historical perspective on the debate. I want to clarify the difference between “normative” and “positive”.  Positive arguments are statements or conclusions about what the predicted effects of a proposal will be without taking a stand on whether those effects are desirable or tolerable. (note that the word “positive” here denotes “factual or likely”, not “good”) Normative arguments are when someone argues whether a proposal should be done. Normative arguments typically are based upon a combination of predicted outcomes and a value judgement as to whether those outcomes are desirable or tolerable compared to the alternative.  For a long time in economics, economists were actually largely in agreement on the positive science, or the predicted effects, of a rise in the minimum wage.  It was generally agreed that raising the minimum wage would give larger incomes to those who continued to work at minimum wage (i.e. low skilled) jobs but that the rise would decrease the numbers of those jobs and thus raise unemployment rates among those seeking low-skilled jobs. Historically the disagreement over minimum wage hikes was over the normative aspects: was the rise in unemployment and loss of jobs worth the increased incomes to others.

The agreement over the predicted positive effects wasn’t always unanimous. There have always been some dissenters. But in the early 1990’s Card and Krueger studied a “natural experiment” by comparing fast food restaurants on two sides of a state line when one state raised the minimum wage and the other didn’t. Their results started a fierce debate that still rages over the predicted effects of rises in minimum wages.  On one side there are now many economists who side with Card & Krueger in saying that raising the minimum wage, even if raised very significantly such as to $15 per hour from less than $8, will not decrease employment and will have a very large increase incomes. On the other side, maintaining the older stance are those such as Don Boudreaux who doggedly argue that any rise in minimum wage must increase unemployment significantly.  Like most topics in economics the practicality of measuring and analyzing the empirical data is somewhat equivocal.  Although there have been numerous studies since Card and Krueger that have buttressed their results, the empirical data along always leaves enough room for some argument.  So what does the theory say?

Don Boudreaux and others of the “increases in minimum wage MUST increase unemployment” camp, would have use believe that theory is unequivocal. The essence of their argument is that low skilled labor is a commodity sold in a market. It has a demand (firms want to buy it) and a supply (low skill workers want to sell it and get paid).  The wage that gets paid is the price of this labor commodity. The most basic supply-and-demand analysis tells us that if the government forces the price up somewhat artificially by setting a price floor (i.e. a minimum wage) below which transactions cannot occur, then there will a smaller quantity of hours of labor demanded. In other words, firms will hire and pay fewer workers. There’s often an appeal to the concept that if the price (cost) of an input or resource goes up, then the firm’s profits will go down and the firm will be less inclined to produce that good or service and therefore will buy less (hire fewer workers).

How can good theory-toting economists dispute this?  Isn’t it supply-and-demand, the most basic micro economic concept as taught in the first few chapters of any principles of econ text?  It’s easy actually. The key is that this supply-and-demand theory as argued against a rise in minimum wage has three major flaws. Two flaws are the result of the theory as applied being too simple (there’s more chapters in the micro text!) and the other  flaw reflects the difference between micro and macro in economics.

The first flaw in the simple supply-and-demand model application to minimum wage type jobs is that there’s really very little evidence that labor markets behave like commodity markets or that they conform to the assumptions necessary to use a supply-and-demand model.  Most jobs, including minimum wage jobs, are more like long-lasting relationships. They aren’t commodity, transaction based like a market for selling widgets or apples or even theatre tickets.  There are dramatic transaction costs involved. Put another way, it’s expensive to hire people (and to fire them and then replace them). Minimum wage jobs aren’t homogenous (they aren’t all the same) the way the theory requires.  Further, the wage paid affects the productivity of the worker, which in turn affects the value of that worker’s output to the firm. When the wage is boosted, workers work harder, stay longer on the job, quit less often, and gradually acquire more productivity and skills. Firms often find that when forced to pay the higher wage, the firm’s total costs, including hiring costs, etc, stays level or even declines.  This is the essence of Arindajit Dube’s studies.

The second flaw is in focusing on the cost of the worker’s wages as if it were the sole consideration in the firm’s decision of how much to produce. The standard theory of the firm and production, which is covered in-depth just a few chapters later in the same economics textbooks after the supply-and-demand model makes it clear:  a firm will produce whatever quantity makes it the most profit. The primary constraints on the output are the demand for the end product, the pricing of the end product, and the core technology used. In other words, if the firm can still sell the output to consumers, it will produce it and the technology (means of production) will require it to hire the necessary labor. A rise in the price of a particular input does not necessarily mean a drop in the quantity produced.

The other two flaws in the arguments against minimum wage increases require shifting to a macro perspective. Micro economics is often described as studying individuals and individual products/markets.  That’s only partially true. Actually micro is a methodology. It’s more properly called “comparative statics using partial equilibrium analysis”.  Micro theories and models explicitly focus on only one particular shifting variable (the wage in this case) and it assumes that all other variables or influences are held constant or unchanged. (Economists call this the ceteris paribus assumption).  In contrast, macro theories are often described as focusing on large aggregate phenomena such gross national product or the inflation rate or the national unemployment rate.  But again, there’s actually a methodological difference. Macro theories require a general systems approach accounting for multiple effects and ripples of many variables that are interrelated.  Let’s look at minimum wage increases as an example of these differences in methodology between macro and micro.

In micro, there’s really only the price (i.e. the wage itself), the quantity of jobs offered, and the quantity of workers available, all of it in the low skill arena.  That’s it. So the micro analysis sees that when the minimum wage is boosted, the firm pays more per worker and each employed worker gets more. End of story.  The only micro question is how it all affects the quantities of jobs.

Macro, however, recognizes that nothing happens in isolation in the economy. There’s a circular flow. Workers are also simultaneously customers. So when the minimum wage goes up, yes, the workers get paid more and firm pays out more money. But what do those workers do with the additional money income? They buy things. Who do they buy them from? Firms that sell and produce products. So the firms not only pay out more money to workers, the firms also get to collect more money by selling more to the increased consumer demand.  But, you say, Acme’s newly enriched minimum wage workers don’t buy that much stuff from Acme. Doesn’t matter. The workers spend it somewhere. And that firm uses the additional money and additional demand to buy more inputs and pay more profits. And those firms and workers then experience income increases and so on and so on as the money circulates throughout the economy. Eventually even Acme sees an increase in sales and revenue collected which in turn helps pay for the wage boosts.  Macro looks at the whole system.

In recent years, many cities and some states have taken it on themselves to raise the minimum wage, often to a so-called “living wage”.  The empirical results have pretty clearly supported the macro analysis. Rises in minimum wages tend to not depress employment and actually tend to stimulate the local economy.  This is the macro analysis.

Sometimes economists just disagree and sometimes they let their ideological and political biases color their professional arguments. Some of that happens in the debates on minimum wage increases.  However, much of apparent disagreement arises from the choice of whether to view the issue through a micro lens or a macro lens.

To read more about the economic analysis of minimum wage increases see these earlier posts:

 

 

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