Precarity and Artificial Intelligence: Review of Objective Functions, and A Contrarian Perspective

This post is a continuation of my series of posts on economic precarity.  As I mentioned in recent posts in this series, we have been exploring the subject of the educated precariat - that is, those people in the early 21st century who have obtained either bachelors or more advanced graduate degrees from a college or university, yet who cannot find stable work in their chosen profession.  However, the most recent previous post in this series began to explore the impact of machine artificial intelligence (AI) on the future of all work, whether that work requires advanced education or not.  

In my my most recent post in this series, I wrote that the greatest potential for the disruption of the future of work through machine AI lies in the development of machine AI tools that can tackle the increasingly complex  tasks that are normally associated with human (or at least highly developed animal) intelligence.  Such tasks include machine vision (including recognizing a human face or an animal), natural language processing, construction of buildings, navigating physically complex unstructured and random environments (such as forests), and optimization of problems with multiple objectives requiring multiple objective functions to model.  Today I'd like to amend that statement by saying that there is another potentially massive disruptive impact of machine AI on the future of work, namely, the ways in which the wide deployment of machine AI in a society might condition and change the humans in that society.  I'll have more to say on that subject in another post.  What I'd like to do right now is to take another look at mathematical objective functions and their place in the implementation of machine AI.  But even before that, let's review the two main types of applications we are talking about when we talk about "machine AI".  Also, let me warn you that today's post will move rather deep into geek territory.  I'll try to have some mercy.

As I mentioned in the most recent post in this series, AI applications can be broken down into two main categories.  The first category consists of the automation of repetitive or mundane tasks or processes in order to ensure that these processes take place at the proper rate and speed and thus yield the appropriate steady-state or final outcome.  This sort of AI has been around for a very long time and first came into existence in entirely mechanical systems such as steam engines with mechanical governors that regulated the speed of the engines.  An example of a mid-20th century electromechanical control system is the autopilot with mechanical gyroscopes and accelerometers and simple electronic computers which was invented to guide airliners and early guided missiles during long-distance flights.  Other early electronic examples include the programmable logic controllers (PLC's) which were developed in the 1960's to regulate assembly line processes in industrial plants.  In his 2022 paper titled "The two kinds of artificial intelligence, or how not to confuse objects and subjects," Cambridge Professor Alan F. Blackwell characterizes these systems as servomechanisms, which Merriam-Webster defines as "an automatic device for controlling large amounts of power by means of very small amounts of power and automatically correcting the performance of a mechanism" and which Blackwell himself defines as "...any kind of device that 'observes' the world in some way, 'acts' on the world, and can 'decide' to 'behave' in different ways as determined by what it observes."  

The construction (and hence performance) of servomechanisms can be increasingly complex as the number and type of processes regulated by the servomechanisms increases, but that does not mean that the servomechanisms possess any real native intelligence.  Consider, for instance, a very simple servomechanism such as the thermostat from a heating system in a house built during the 1950's.  Such a thermostat would regulate the timing and duration of the burning of a fuel in a heating furnace, and would most likely consist of a simple switch with a movable switch contact and a stationary contact with the movable contact attached to a bimetallic strip.  Because the shape of the bimetallic strip is regulated by the temperature of the air, when the air temperature drops, the movable switch contact eventually touches the stationary contact, closing the switch and turning on the flow of fuel to the furnace.  We can say that the thermostat "decides" when the furnace turns on or off, but that's all this thermostat can "decide" to do.  You certainly wouldn't want to rely on the thermostat to help you decide what movie to watch with your spouse on a weekend!  Servomechanisms are what Blackwell calls "objective AI" which "measures the world, acts according to some mathematical principles, and may indeed be very complex, even unpredictable, but there is no point at which it needs to be considered a subjective intelligent agent."  In other words, all a servomechanism can do is to mechanically or electronically regulate a physical process on the basis of process measurements provided to the controller by means of physical sensors that sense a process variable.  It can't think like humans do.

The second type of AI is designed to make value judgments about the world in order to predict how the world (or some small subset of the world) will evolve.  In the most optimistic cases, this AI uses these value judgments to generate the most appropriate response to the world which is supposedly evolving according to prediction.  But is this really a native intelligence created by humans, yet now embodied in a machine and existing independently of humans?  A possible answer to that question can be found in another paper written by Blackwell and published in 2019, titled, "Objective functions: (In)humanity and inequity in artificial intelligence."

The value judgments and predictions made by the second type of AI are made by means of objective functions. These objective functions are mathematical abstractions consisting of functions of several independent variables.  Each of the independent variables represents an independently controllable parameter of the problem.  If the purpose of the objective function is to predict the numerical value of an outcome based on historical values of independent input variables, then optimizing the function means making sure that for a given set of historical inputs, the objective function yields an output value that is as close as possible to the historical outcome associated with the particular historical inputs. This ensures that for any set of future possible inputs, the objective function will accurately predict the value of the output.  Two levels of objective functions are needed: the first level, which makes a guess of the value of an output based on certain values of inputs, then a second supervisory level which evaluates how close each guess is to a set of historical output values based on corresponding sets of input values.  The output of this second supervisory objective function is used to adjust the weights (in the case of a polynomial function, the coefficients) of the primary objective function in order to produce better guesses of the output value. 

Objective functions are mathematical expressions; hence, the second type of AI is a primarily mathematical problem which just happens to be solved by means of digital computers.  This also includes the implementation of multi-objective optimization, which is really just another mathematical problem even though it is implemented by machines.  Thus, the second type of AI is really just another expression of human intelligence.  This is seen not only in the development of the objective functions themselves, but also in the training of the supervisory objective function to recognize how close the output of the primary objective function is to the a value that actually reflects reality.  This training takes place by several means, including supervised learning (in which humans label all the training data), and partially-supervised and unsupervised learning (in which the training data is out there, but instead of it being labeled, a human still has to create the algorithms by which the machine processes the training data).  

An example that illustrates what we have been considering is the development of large language models (LLM's) such as ChatGPT which predict text strings based on inputs by a human being.  A very, very, very simple model for these AI implementations is that they consist of objective functions that guess the probability of the next word, phrase, sentence, or paragraph in a string on the basis of what a human has typed into an interface.  These AI implementations must be trained using data input by human beings so that they can calibrate their objective functions to reduce the likelihood of wrong guesses.  Cases like these lead scientists such as Alan Blackwell to conclude that the second type of AI is not really a separate "intelligence" per se, but rather the embodiment and disguising of what is actually human intelligence, reflected back to humans through the intermediary of machines.  The calibration of the objective functions of these AI deployments (or, if you will, the training of this AI) is performed by you every time you type a text message on your smartphone.  For instance, you start by typing "Hello Jo [the phone suggests "Jo", "John", "Joe", but the person you're texting is actually named "Joshiro", so as you type, your phone keeps making wrong guesses like "Josh", "Joshua", and "Josh's" but you keep typing until you've finished "Joshiro"]. You continue with "I'm at the gym right now, but I forgot my judo white belt [the phone guesses almost everything even though you misspelled "at" as "st" and the phone auto-corrected.  However, the phone chokes when you start typing "judo" so you have to manually type that yourself].  You finish with "Can you grab it out of my closet?"  The next time you text anyone whose first name starts with the letters "Jo", your phone will be "trained" to think you are texting Joshiro about something related to judo - or more accurately, the generative LLM in your phone will have determined that there is a statistically higher likelihood that your message will contain the words "Joshiro" and "judo".  Your phone's LLM is thus "trained" every time you correct one of its wrong guesses when you type a text.

Of course, the longer the predicted phrase or sentence or paragraph the AI is supposed to return, the more training data is required.  The boast of the developers of large language models and other similar AI implementations is that given enough training data and a sufficiently complex statistical objective function, they can develop AI that can accurately return the correct response to any human input.  This unfortunately leads to an unavoidable conclusion of the second type of AI: the assumption that the universe, reality, and life itself are all deterministic (thus there is no free will in the universe).  Why? Because the kind of intelligence that can accurately predict how the universe and everything in it will evolve and thus generate the most appropriate local response to the moment-by-moment evolution of your particular corner of the universe can always be modeled by an appropriately elaborate statistical objective function trained on an appropriately huge set of training data.  In other words, given enough data, a statistical objective function can be derived which accurately predicts that your spouse's sneeze at the dinner table tonight will provoke an argument which ends with you sleeping on the couch tomorrow night, and that this will lead to the invention of a new technology later in the week that causes the stock market of a certain country to crash, with the result that a baby will cry on a dark and stormy night five days from now, and that this cry will enable a computer to predict all the words that will be in the novel you sit down to write a week from today...  This is my rather facetious illustration of the "generative AI" of chatGPT and similar inventions.  The fallacy of determinism is that life, the Universe, and reality itself are full of phenomena and problems that can't easily be modeled by mathematics.  Scientists call them "wicked problems."  Thus the claims made about the second kind of AI may be overblown - especially as long as the implementation of this second kind of AI remains primarily dependent on the construction and optimization of appropriately complex statistical objective functions.

Yet it can't be denied that the second type of AI is causing some profound changes to the world in which we live, and even the first type of AI - the implementation of servomechanisms, or the science of cybernetics - has had a profound effect.  The effects of both types of AI to date - especially on the world of work - will be the subject of the next post in this series.

P.S. Although I am a technical professional with a baccalaureate and a master's degree in a STEM discipline, I am most definitely NOT an AI expert.  Feel free to take what I have written with a grain of salt - YMMV. 

P.P.S. For more commentary on ChatGPT and other LLM's, feel free to check out a 2021 paper titled, "On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?" by Emily Bender and others.  The authors of this paper were Google engineers who were fired by Google for saying things that their bosses didn't want to hear regarding LLM's.   Oh, the potential dangers of writing things that give people in power a case of heartburn...

 

Precarity and Artificial Intelligence: The Foundations of Modern AI

This post is a continuation of my series of posts on economic precarity.  As I mentioned in recent posts in this series, we have been exploring the subject of the educated precariat - that is, those people in the early 21st century who have obtained either bachelors or more advanced graduate degrees from a college or university, yet who cannot find stable work in their chosen profession.  Today's post, however, will begin to explore a particular emerging impact to employment for everyone, whether formally educated or not.  That impact is the impact of machine artificial intelligence on the future of work.

As I mentioned in a previous post, 

Labor casualization has been part of a larger tactical aim to reduce labor costs by reducing the number of laborers...This reduction of the total number of laborers can be achieved by replacing employees with machines.  That replacement has been occurring from the beginning of the Industrial Revolution onward, but in the last two or three decades it has accelerated greatly due to advances in artificial intelligence (AI).  A long-standing motive behind the recent massive investments in research in artificial intelligence is the desire by many of the world's richest people to eliminate the costs of relying on humans by replacing human laborers with automation.

So it is natural to ask what sort of world is emerging as the result of the use of increasingly sophisticated AI in our present economy.  Here we need to be careful, due to the number of shrill voices shouting either wildly positive or frighteningly negative predictions about the likely impacts of AI.  I think we need to ask the following questions:

• First, what exactly is artificial machine intelligence?  What is the theoretical basis of AI?  How does it work? ...

Today we'll start trying to answer the questions stated above.   And at the outset, I must state clearly that I am not an AI expert, although my technical education has exposed me in a rudimentary way to many of the concepts that will be mentioned in our discussion of AI.

First, let's paint a picture.  One of the original motives for trying to invent intelligent machines was the desire for machines that would reliably do the kind of mundane tasks that humans find to be distasteful or unpleasantly difficult.  This desire actually has a very long history, but was popularized in the science fiction of the mid-20th century.  Think of a kid from the 1950's or 1960's who wished he had a robot that could vacuum the living room carpet or take out the garbage or do homework or shovel snow out of the driveway so that the kid could play without being bothered by parents demanding that the kid himself do the tasks listed above.  What kind of "brain" would the robot need in order to know what tasks needed to be performed and when would that brain know the tasks had been performed to acceptable standards?

The question of the kind of "brain" required was solved by the invention of the first programmable all-electronic digital computer during World War Two.  This computer was itself an evolution of principles implemented in previous mechanical and electromechanical computers.  Once engineers developed digital computers with onboard memory storage, these computers became capable of automation of tasks that had been formerly automated by more crude mechanical and electromechanical means such as relays.  Thus the 1950's saw the emergence of digital control systems for automation of chemical processes at refineries; the 1950's and 1960's saw the emergence of computer-assisted or computer-based navigation for ships, aircraft, missiles, and spacecraft; and the late 1960's saw the emergence of programmable logic controllers (PLC's) for automation of factory processes at industrial assembly plants.

The digital electronic automation systems that have been developed from the 1950's onward have thus formed a key component of the development of modern machine AI.  But another key component consists of the principles by which these systems achieve their particular objectives.  These principles are the principles of mathematical optimization, and they also have a rather long history.

Mathematical optimization is the collection of techniques and methods for finding the maximum or minimum value of a mathematical function of one or more independent variables.  Some of the earliest methods of mathematical optimization were based on calculus.  More complex methods include such things as numerical methods for solving nonlinear differential equations.  These methods were only possible to implement easily once electronic digital computers became available.

The first step in optimizing real-world problems consists of turning a real-world problem into a mathematical abstraction consisting of a function of several independent variables.  Each of the independent variables represents an independently controllable parameter of the problem.  Then optimization techniques are used to find the desired maximum or minimum value of the function.  To put it another way,

"Optimization is the act of obtaining the best result under given circumstances.  In design, construction, and maintenance of any engineering system, engineers have to take (sic) many...decisions.  The ultimate goal of all such decisions is either to minimize the effort required or to maximize the desired benefit.  Since the effort required or the benefit desired in any practical situation can be expressed as a function of certain decision variables, optimization can be defined as the process of finding the conditions that give the maximum or minimum value of a function."  - Engineering Optimization: Theory and Practice, Rao, John Wiley and Sons, Inc., 2009

The function to be optimized is called the objective function.  When we optimize the objective function, we are also interested in finding those values of the independent variables which produce the desired function maximum or minimum value.  These values represent the amount of various inputs required to get the desired optimum output from a situation represented by the objective function.  

A simple case of optimization would be figuring out how to catch up with a separate moving object in the shortest amount of time if you started from an arbitrary starting position.  To use optimization techniques, you'd turn this problem into an objective function and then use calculus to find the minimum value of the objective function.  Since the velocity and acceleration are the independent variables of interest, you'd want to know the precise values of these (in both magnitude and direction) which would minimize the value of the objective function.  Note that for simple trajectories or paths of only two dimensions, adult humans tend to be able to do this automatically and intuitively - but young kids, not so much.  Try playing tag with a five or six-year-old kid, and you will see what I mean.  The kid won't be able to grasp your acceleration from observing you, so he will run to where he sees you are at the moment he sees you instead of anticipating where you'll end up.  Of course, once kids get to the age of ten or so, they're more than likely to catch you in any game of tag if you yourself are very much older than 30!

The easiest AI problems are those that can most easily be turned into mathematically precise objective functions with only one output variable.  Examples of such problems include the following: reliably hitting a target with a missile, winning a board game in the smallest number of moves, traveling reliably between planets, simple linear regression, regulating the speed or rate of industrial or chemical processes, and control of HVAC and power systems in buildings in order to optimize interior climate, lighting, and comfort.

Harder AI problems include machine vision (including recognizing a human face or an animal), natural language processing, construction of buildings, navigating physically complex unstructured and random environments (such as forests), and optimization of problems with multiple objectives requiring multiple objective functions to model.  The machine vision and natural language processing problems are harder because they require the use of logistical regression functions as objective functions, and in order to accurately assign the appropriate "weights" to each of the variables of these objective functions, the AI which implements these functions needs massive amounts of training data.  However, these and other harder problems are now being solved with increasing effectiveness through technologies such as deep learning and other advanced techniques of machine learning.  It is in the tackling of these harder problems that AI has the greatest potential to disrupt the future of work, especially of cognitively demanding work that formerly only humans could do.  In order to assess the potential magnitude and likelihood of this disruption, we will need to examine the following factors:

• The current state of the art of machine learning
• The current state of the art of designing objective functions
• And the current state of the art of multi-objective mathematical optimization.

I'll try tackling these questions in the next post in this series.

Introducing the Main Street Alliance

I'd like to take this opportunity to introduce readers to the Main Street Alliance, an organization which seeks to foster the creation and growth of small businesses in the United States.  As I resume my series of posts on the problem of economic precarity, I will also discuss solutions.  As I mentioned in a previous post, I believe that the eradication of the monopoly power of the rich and the fostering of small business among the poor are two strategic efforts which can reduce or eliminate economic precarity in the United States.  This is what the Main Street Alliance is working to achieve.

Those who read about the activities of the Main Street Alliance will also learn about how the rich and the powerful in the United States are trying to destroy small businesses, especially those run by minorities, and how these bad actors are using Republican-appointed Federal court justices in their attacks against small business.  This should be of great concern to those of you who are small entrepreneurs.  The latest attack against small business consists of judicial challenges to the Federal tax code.  Readers of this blog can learn from the Main Street Alliance website how they can join in the fight to foster and protect small business.

Precarity, Late Capitalism, And Artificial Intelligence: Pinocchio's Mischief

This post is a continuation of my series of posts on economic precarity.  As I mentioned in recent posts in this series, we have been exploring the subject of the educated precariat - that is, those people in the early 21st century who have obtained either bachelors or more advanced graduate degrees from a college or university, yet who cannot find stable work in their chosen profession.  The two most recent previous posts in this series discussed the fact that there are now more college graduates being produced in our society than there are jobs into which to plug those graduates.  The most recent post discussed why this is the case.  As I wrote last week, 

"...the decline in opportunities for college graduates (along with everyone else) is correlated with the rise in the concentration of economic power in the hands of an ever-shrinking elite.  In fact, I will go even farther and assert that the decline in stable employment for college graduates (even those with technical professional degrees) is a direct outcome of the concentration of economic power at the top of society.

Consider the fact that as of 2015, "America's 20 wealthiest people - a group that could fit comfortably in one single Gulfstream G650 luxury jet - now own more wealth than the bottom half of the American population combined..."  These people therefore have an enormous amount of economic and political clout.  And they have used (and continue to use) that clout in order to turn the American economy into a machine whose sole function is to make them as rich as possible.  The increase in precarity, the casualization of increasing types of employment, and the increasing use of task automation and artificial intelligence are typical of the strategies which these wealthy and powerful people have deployed in order to maximize the wealth they can extract from the American economy while minimizing the amount of wealth they give to the rest of us.  The aggressive expansion of the "gig" economy is another such strategy..."

A basic strategic aim in capitalism is that business owners should maximize profit.  A basic tactic for the achievement of this aim is to maximize profit per unit of goods sold by lowering the cost of production for each unit of goods sold.  Lowering costs can be achieved by attacking the cost of materials, capital machinery, energy, and labor.  In the limit, at the extreme of optimization, this leads to extremely flimsy goods sold for extremely high prices, goods that are produced by extremely poor laborers.

The labor part of this tactic is what we have been discussing in our consideration of precarity.  By making employment casual and temporary, with no fixed covenant between businesses and laborers and no benefits (other than a wage) granted to laborers, businesses have succeeded in driving down the cost of labor.  As mentioned in last week's post, that pressure on labor costs has reached even technical professions requiring a baccalaureate degree or above.  This is leading to an increasingly unsustainable situation in which, for instance, you might spend more than $40,000 to earn a four-year engineering degree - only to find yourself working for an engineering temp agency after graduation!

Labor casualization has been part of a larger tactical aim to reduce labor costs by reducing the number of laborers.  If you're the CEO of a large company, the progression of this tactic can be sketched as follows: First, destroy any expectation of stable employment or decent wages among your labor pool.  Then, reduce the actual number of laborers you use.  This reduction of the total number of laborers can occur by a number of means (including working employees to death by giving each employee the amount of work that should normally be handled by two or three such employees).  It can of course also be achieved by replacing employees with machines.  That replacement has been occurring from the beginning of the Industrial Revolution onward, but in the last two or three decades it has accelerated greatly due to advances in artificial intelligence (AI).  A long-standing motive behind the recent massive investments in research in artificial intelligence is the desire by many of the world's richest people to eliminate the costs of relying on humans by replacing human laborers with automation.

So it is natural to ask what sort of world is emerging as the result of the use of increasingly sophisticated AI in our present economy.  Here we need to be careful, due to the number of shrill voices shouting either wildly positive or frighteningly negative predictions about the likely impacts of AI.  I think we need to ask the following questions:

• First, what exactly is artificial machine intelligence?  What is the theoretical basis of AI?  How does it work?
• What can AI do and not do?
• What countries are at the forefront of AI deployment in their societies?
• How will AI capabilities likely evolve over the next few decades?
• What effects might AI have on human life and human societies over the next few decades?
• How will AI affect the world of work over the next few decades?

The next few posts in this series will attempt to tackle these questions.  I must warn you that what you'll get in those posts are merely my guesses at an answer.  However, because I want the guesses to be educated guesses, I'm going to need to do some research.  So these guesses might be slow in coming.

The Educated Precariat: Why The Mismatch?

This post is a continuation of my series of posts on economic precarity.  As I mentioned in recent posts in this series, we have been exploring the subject of the educated precariat - that is, those people in the early 21st century who have obtained either bachelors or more advanced graduate degrees from a college or university, yet who cannot find stable work in their chosen profession.  The most recent previous post in this series discussed the university system as a machine that produces graduates for use within the larger machinery of modern late-stage capitalism, and what is happening to those graduates because of the fact that there are more graduates being produced than there are jobs into which to plug those graduates.

That previous post highlighted the fact that from at least the 1990's onward (and possibly starting from the 1970's onward), there has been a growing number of college graduates who have found themselves underemployed after graduation.  Moreover, as time has passed, the number of college graduates who have entered long-term underemployment after graduation has increased as a percentage of total college graduates.  Note that to be underemployed as a college graduate means to hold a job that does not require the knowledge, skills, and abilities that a person would acquire as part of a college education.  As a hypothetical example, think of a gas station cashier with a recent baccalaureate degree in organizational psychology.  Moreover, the sources cited in that post listed the types of college major most likely to lead to underemployment and precarious work.  From those sources it would seem that baccalaureate degrees in STEM (science, technology, engineering, and mathematics) offer the greatest likelihood of full employment and decent wages.  However, note that a 2018 Canadian study titled, "No Safe Harbour: Precarious Work and Economic Insecurity Among Skilled Professionals in Canada" cited the fact that a technical professional degree is no longer an ironclad guarantee against precarious employment.  

Why then is there such a huge mismatch between the number of people obtaining degrees and the number of available jobs which would utilize the skills implied by these degrees while paying the degree holders a decent living wage?  That is the question which today's post will try to answer.  

First, let's consider the answer offered by people like Peter Turchin, the well-fed Russian emigre to the United States whom I mentioned in another post in this series on precarity.  Turchin asserts that the supposed "excess" of college graduates, the supposed "mismatch" between the number of college graduates and the number of appropriate jobs for these graduates, is the result of an imbalance between the higher education sector and the rest of the economy.  He also asserts that the "excess" of college graduates is increasing the likelihood of instability in society caused by the radicalization of these "excess" graduates.  To put it in the language of Wikipedia, 

"Elite overproduction is a concept developed by Peter Turchin, which describes the condition of a society which is producing too many potential elite members relative to its ability to absorb them into the power structure. This, he hypothesizes, is a cause for social instability, as those left out of power feel aggrieved by their relatively low socioeconomic status." [Emphasis added.]

Note the first sentence and its mention of the capacity of a society to absorb newly educated citizens into an existing power structure.  I will return to the notion of existing power structures later in this post.  Note also that Turchin's "solution" to this problem of "overproduction" is to limit access to higher education.  This "solution" is remarkably similar to the "solution" proposed by Richard Vedder, Christopher Denhart, and Jonathan Robe in their 2013 report titled, "Why Are Recent College Graduates Underemployed? University Enrollments and Labor-Market Realities" which I cited in the previous post in this series.  To quote their report,

"The mismatch between the educational requirements for various occupations and the amount of education obtained by workers is large and growing significantly over time. The problem can be viewed two ways. In one sense, we have an “underemployment” problem; College graduates are underemployed, performing jobs which require vastly less educational tools than they possess. The flip side of that, though, is that we have an 'overinvestment' problem: We are churning out far more college graduates than required by labor-market imperatives. The supply of jobs requiring college degrees is growing more

slowly than the supply of those holding such degrees. Hence, more and more college graduates are crowding out high-school graduates in such blue-collar, low-skilled jobs as taxi driver, firefighter, and retail sales clerks..."

In evaluating whether these assertions are valid, it is helpful to consider the present-day structure of the American economy as a representative of the typical economies of the Global North.  It is also helpful to consider the background of the people who have made these assertions in order to glimpse something of their possible motives.  As I mentioned previously, Peter Turchin is an academic who is already both tenured and well-established (thus well-fed, with multiple income streams), and his assertions of the need to limit access to higher education are not likely to hurt him in any way.  As for Vedder, Denhart, and Robe, Vedder is an adjunct member of the American Enterprise Institute (AEI).  Denhart is one of Vedder's former students.  I don't know how much of Vedder's ideology was passed on to Denhart and Robe, but I do know that Vedder is a strong supporter of big business even when it pays exploitative wages to workers, as seen in his support of Wal-Mart and of the 2008 taxpayer bailout of American businesses deemed to be "too big to fail".  (Note that that 2008 taxpayer-funded bailout is one of the biggest reasons why the richest Americans are now so rich!) Moreover, the AEI itself has the policy goal of supporting big business at the expense of small businesses, going as far as advocating that the role of the American government should be to help big businesses grow bigger.  The AEI wants further to eliminate all government support for small business, especially small business incubation, as I pointed out in a previous post.

From such observations, it is possible to move to a consideration of the structural reasons for the mismatch between jobs requiring a college education and the supposed "excess" of college graduates.  I will once again state my belief that high-quality, advanced education should be made available to as many people as want it - regardless of race, creed, national origin, or economic status.  Moreover, I once again assert that education is one of the great equalizing factors in a society, as it is a key component in the struggle of historically oppressed peoples to liberate themselves from historical and ongoing oppression.  This, for instance, was the motivation for the Polish underground "flying universities" which were organized in the 1800's when Poland had been partitioned by Germany, Austria, and Russia, and these nations had forbidden Poles from having access to higher education.  This was also the motivation for the underground "freedom schools" which sprang up in the American South during the antebellum days when white Southern power made it illegal to teach Black people (my people) to read.

But education alone is rather impotent without an opportunity to use it.  And the opportunities for the use of education are constrained by the structure of the society in which that education must operate.  Too often, the structure of a society is dictated and constrained by the dominant power-holders in that society.  I will therefore suggest that the decline in opportunities for college graduates (along with everyone else) is correlated with the rise in the concentration of economic power in the hands of an ever-shrinking elite.  In fact, I will go even farther and assert that the decline in stable employment for college graduates (even those with technical professional degrees) is a direct outcome of the concentration of economic power at the top of society.

Consider the fact that as of 2015, "America's 20 wealthiest people - a group that could fit comfortably in one single Gulfstream G650 luxury jet - now own more wealth than the bottom half of the American population combined..."  These people therefore have an enormous amount of economic and political clout.  And they have used (and continue to use) that clout in order to turn the American economy into a machine whose sole function is to make them as rich as possible.  The increase in precarity, the casualization of increasing types of employment, and the increasing use of task automation and artificial intelligence are typical of the strategies which these wealthy and powerful people have deployed in order to maximize the wealth they can extract from the American economy while minimizing the amount of wealth they give to the rest of us.  The aggressive expansion of the "gig" economy is another such strategy, as is the crafting of laws and regulations (especially by Republicans) which disadvantage small businesses (and all the rest of us, especially those of us who are not of their "tribe") while giving breaks to big business.  

What would a society look like if it provided citizens with the maximum optimal education and the maximum optimal opportunity to use that education in the pursuit of meaningful work?  I'd like to suggest that first, such a society would have a mechanism in place to prevent any one person or entity from concentrating more than a very small fraction of economic output into one set of hands.  Second, I suggest that such a society would be composed largely of artisans, artists, and small businesses owners who exercised their knowledge, education, and creativity to a maximal extent.  In other words, this society would be largely composed of "yeoman entrepreneurs" similar to the "yeoman farmers" idealized by Thomas Jefferson.   Some might say that such a society would be impossible in the 21st century, but I'd like to suggest that some positive aspects of what such a society might look like can be found in the depiction of the fictional Mars City in Hao Jingfang's novel Vagabonds.  I will mention that novel again in a future post. (Note also that although there was much to like about Mars City, it was not exactly a perfect utopia - there were indeed a few flies in that ointment, so to speak.)

Lastly, I suggest that such a society would be resilient - much more so than a more stratified, unequal society would be.  This is because such a society would have a much higher degree of decentralized group intelligence than would exist in a society of stratification and inequality.  This would make the more egalitarian society much more able to respond to emergent threats and opportunities than the more stratified society.  Consider the late 19th century and early-to-middle 20th-century history of Britain as a stratified society of the Global North.  Consider how its rigid class hierarchy and caste system prevented some of its principal actors from seeing the big picture and acting appropriately in the face of challenges.  Cases in point include the failure of Robert Scott's Antarctic expedition in comparison to the successful expedition of Roald Amundsen, as well as failures in World Wars 1 and 2 that resulted from a hidebound British system of honor, privilege and caste which blindsided British leadership.  The strident attempt by the Republican Party and other right-wing elements in the United States to re-establish an American system of caste and privilege constitutes the real threat to the "existing power structures" cited by Turchin, because it is leading to the "fragilization" of these structures.

The Educated Precariat: Mandarin Spoilage

This post is a continuation of my series of posts on economic precarity.  As I mentioned in recent posts in this series, we have begun to delve the subject of the educated precariat - that is, those people in the early 21st century who have obtained either bachelors or more advanced graduate degrees from a college or university, yet who cannot find stable work in their chosen profession.  The most recent previous post in this series discussed the origins and evolution of the modern university as a European institution and its spread as a model of higher education throughout the world.  That post also discussed the late-stage signs of dysfunction which have begun to appear in the modern university system during the last 40 years, particularly in English-speaking countries such as the United States and Australia.

Today's post will consider the university system as a machine that produces components for use within the larger machinery of modern late-stage capitalism, and what is happening to those components because of the fact that there are more components being produced than there are slots into which to plug those components.  The "components" in this case are recent college graduates.  Historically, an American guy or gal who managed to earn a mortarboard perched on his or her head, an academic robe on his or her body, and a sheepskin in his or her hand could expect to pursue one of two possible vocational paths after graduation:

• He or she could become a career scholar, otherwise known as an academic.  This academic career could be focused on teaching or on research, or on a mixture of both.
• He or she could become a member of the professional class, the "managers, officials, and professionals" described by Gary Roth in his book The Educated Underclass.

The next three paragraphs will cite extensively from Gary Roth's book.  

The prospects for those college graduates pursuing either path were very bright from the late 1800's until around 1970.  This was true because the rapid expansion of the American economy and the growth of urban populations produced a need for professionals with the requisite training to serve the resulting societal needs.  The pre-existing system of private higher education was inadequate to produce these professionals, as noted by Roth: "Tuition-driven institutions have never been a viable model at any level of the income spectrum ..."  Thus the government (at both the Federal and State level) intervened to fund public universities that could fill the demand for degreed professionals and managers.  These universities became important research centers which boosted commercial development as they published their research findings, particularly in agricultural science.  These public universities also helped to rapidly expand education in law, medicine, and engineering.

Although the absolute number of degreed professionals thus steadily increased, the number of these professionals as a percentage of the total American population remained small until World War Two.  On the eve of the war, less than 5 percent had a four-year college or university degree.  However, the war drastically increased the need for degreed professionals, and the G.I. Bill of 1944 stimulated the supply of these professionals and the expansion of the American public university system.  This stimulation was amplified by other non veteran-related Federal and State funding for higher education.

The demand for the graduates of this expanded higher education system was fueled by the drastic expansion of the managerial class of the American business sector.  For instance, between 1950 and 1970, the number of American white-collar workers grew by 75 percent.  Many of these workers could be considered to be "private-sector mandarins" involved in management and the administration of big business bureaucracy.  The growth in the numbers of these private-sector mandarins was paralleled by the growth in public-sector mandarins as Federal and State governments expanded.  Indeed, the number of State and local government employees increased much more drastically than the number of Federal employees.  The demand for graduates was also fueled by the growth of the public university system itself, which saw the addition of 200,000 faculty positions between 1950 and 1970.  Thus in 1970, higher education had come to be seen as a guaranteed means of upward social mobility.  By 1970, 32.1 percent of all Americans between 18 and 24 years of age were enrolled in some sort of college.  

But 1970 was the beginning of a tangible slowdown in American fortunes, a tangible curbing of American power and prestige.  Some of the causes were obvious, including the rejection of American values by many nations of the Third World, and the loss of prestige of the American military in Vietnam.  One of the causes was hidden to most observers, namely, the peak in American conventional oil production which occurred in 1970 and the beginning of the outsourcing of American manufacturing to other countries with cheaper labor.  These changes wrought changes in the American economy which began to curtail the opportunities open to people holding college degrees.  Although the conventional wisdom held that a college education remained a key to upward mobility, reality began to look different.  A growing number of college graduates began to experience the phenomenon of underemployment, that is, working in jobs which require less education than the job-holder possesses, or, working in jobs which offer less than stable full-time employment even though the job-holder would like to be fully employed.  Let's close this post with a discussion of both types of underemployment.

First, although underemployment has gained recent attention as part of the phenomenon of precarity, there are sources who indicate that underemployment has existed for the last several decades.  For instance, a 1963 U.S. Government publication titled, Two Years After The College Degree states that "Two years post-graduation, 18 percent of the class of 1958 reported that a four-year degree was not necessary for the jobs they held." (Roth, Chapter 4.)  However, Gary Roth points out that those graduates were living in an environment in which there was a surplus of available job positions and a relative shortage of workers with college degrees.  

That has not been the case for at least the last two decades (and perhaps longer).  For instance, in the 2013 paper "Why Are Recent College Graduates Underemployed?" by Vedder, Denhart and Robe, Figure 10 shows the number of degree holders who occupy certain occupations which do not require a college degree, and shows how the percentage of these jobs occupied by degree-holders has increased between 1970 and 2010.  Note, for instance, the steep increase in the number of college-educated taxi drivers, salespersons, and retail clerks.  Also, in the 2014 New York Fed paper "Are Recent College Graduates Finding Good Jobs?" by Abel, Deitz, and Su, we can see that already by 1990, the underemployment rate for recent college graduates was over 40 percent.  Younger college graduates had underemployment rates that were nearly 50 percent.  Recent college graduates who were working part-time after graduation were also above 15 percent in 1990.  Those recent college graduates who occupied low-wage jobs was around 15 percent in 1990.  These numbers did not show any consistent long-term improvement from 1990 to 2014.

According to Vedder, Denhart and Robe, the number of Americans with a bachelors degree or higher was expected to grow by 31 percent between 2010 and 2020, whereas the number of actual jobs requiring such degrees was expected to grow by only 14.3 percent.  This would translate to 19 million additional Americans with bachelors degrees or higher compared to only 7 million additional jobs requiring such degrees.  This would also mean that the number of underemployed graduates would increase to 30 million.  

What's more, those who start their post-graduation careers underemployed are at great risk for remaining underemployed five and ten years after graduation, as noted in "The Permanent Detour: Underemployment’s Long-Term Effects on the Careers of College Grads," a 2018 paper by the Strada Institute for the Future of Work and Burning Glass International, Inc.  According to this paper, 43 percent of college graduates are now starting their post-graduate careers underemployed.  Of these, 29 percent will continue to be underemployed after five years and 23 percent will be underemployed after ten years.  The figures are worse for women: 47 percent will start out underemployed and 31 percent will be underemployed after five years.

Why is there such a mismatch between present-day numbers of college graduates and the present-day number of education-appropriate job positions for these graduates?  What coping mechanisms are the college educated precariat using to cope with underemployment?  And how are these coping mechanisms affecting those members of the precariat who do not have a college education?  We'll start tackling those questions in the next post in this series.

The Educated Precariat: The Modern University - Birth, Growth, Late-Stage Diseases

This post is a continuation of my series of posts on economic precarity.  As I mentioned in recent posts in this series, we have begun to delve the subject of the educated precariat - that is, those people in the early 21st century who have obtained either bachelors or more advanced graduate degrees from a college or university, yet who cannot find stable work in their chosen profession.  The most recent previous post in this series discussed the origins and evolution of formal education and of the creation of higher education systems in ancient societies.  Here we discovered that these societies left records of the creation and operation of institutions of higher education, and that these institutions served the following purposes:

• The creation of cadres of people who could either participate in politics and governance as ruling practitioners of statecraft, or as people who could serve as competent administrators/bureaucrats under these ruling elites.
• The teaching and research of basic scientific knowledge and skills in such arenas as medicine, mathematics, and astronomy.

This previous post also discussed the widespread distribution of these institutions throughout the world, in the ancient societies that existed on the African continent, in Iran, and in China, as well as the ancient Greek and Byzantine schools.  This point is important, as our present society tends to hold up Europe as the sole source and origin of lasting intellectual inquiry.  This point of view is clearly not valid if one examines the history of other societies.  (See "Ancient Centers of Higher Learning: A Bias In The Comparative History of the University?", Michael A. Peters, January 2019.  Peters also points out the existence of ancient centers of higher learning in India that existed thousands of years before any such institutions in the West.)

Nonetheless, most institutions called "universities" in the 21st century can trace their structure back to the medieval European university as it came into being from the 11th century onward.  So today's post will briefly sketch the origins and motivation for the medieval university.  We will then examine the functions of the medieval university, and how those functions evolved over time to produce the modern research university.  We will close with an examination of how the growth of certain ancillary functions within the university have distorted the mission and focus of the university system.

Origins of the Medieval University

(Sources: "State-Building and the Origin of Universities in Europe, 800-1800", Hollenbach and Pierskalla, Copenhagen Business School, Denmark, October 2022; and Wisdom's Workshop: The Rise of the Modern University, Chapter 1, James Axtell, Princeton University Press, 2016.)

The medieval university arose from the growth experienced in European societies from the eleventh century onward.  This growth included the growth of European populations (with a resulting increase in the number of new towns and cities), and a corresponding growth of trade.  This was accompanied by a growing need in the new parishes of the Roman Catholic Church for advanced training for its new priests and administrators, along with a growing need for trained secular administrators in the newly forming towns, villages, and cities.  The training and education of an administrative class had been formerly performed by monasteries, but these monasteries were unable to provide the increasingly complex and advanced training needed by secular and ecclesiastical administrators from the 12th century onward.

The Church responded to this need by establishing "cathedral schools" for advanced training of its clerics.  From these schools came academics who desired a freer rein in teaching and scholarship than the Church was willing to grant them.  One of these scholars, Peter Abelard, founded his own schools for advanced learning in the twelfth century.  In addition, some of the secular scholars that studied at cathedral schools also went on to found their own schools.  These schools eventually organized themselves into self-governing "guilds of masters and scholars", or studia generale which received and taught aspiring scholars from any locale.  In order to free themselves from the kinds of obligations and interference that both Church and secular authorities imposed on ordinary people, these guilds petitioned both the Pope and the kings of their respective nations for the granting of formal legal autonomy and freedom of operation.  Such formally sanctioned guilds thus became the first medieval universities.  Note that the Catholic Church competed with these universities sometimes by co-opting some of them into its own power structure, and sometimes by founding universities of its own.

The charters granted by either State or Church or both, combined with the organization of these universities as scholastic guilds, produced a unique internal structure and operating environment for the medieval university.  Let's examine that structure and operating environment more closely.

Functions And Structure of the Medieval University

(Sources: ""Ancient Centers of Higher Learning: A Bias In The Comparative History of the University?", Michael A. Peters, Taylor and Francis Group, January 2019; "The Medieval University", J.E. Healey, CCHA, Report, 17 (1950); Wisdom's Workshop: The Rise of the Modern University, Chapter 1, James Axtell, Princeton University Press, 2016.)

The medieval university had the following characteristics:

1. It received students from everywhere and not just its own local region.
2. It engaged in higher learning, going beyond "the Seven Liberal Arts of antiquity and the early Middle Ages" to include the re-discovered teachings and writings of Greek philosophers such as Aristotle as well as Arabic learning.
3. "A significant part of the teaching was done by Masters (teachers with a higher degree)." (Peters, cited above; Healey, cited above.)
4. It was a self-governing, autonomous institution (a corporation run like the craft guilds) with a high degree of control over its budget and expenditures, and complete academic freedom over what degrees were awarded, and to whom.  Indeed, those universities which depended entirely on student tuition had complete control over their own budgets and expenditures. (Axtell, cited above.)  This self-governance was usually exercised entirely by the university faculty, that is, the collection of masters who taught university courses.  However, sometimes, this self-governance was exercised by students, who could choose which masters to hire or fire in addition to their other administrative powers.  (See the University of Bologna, for instance.)  Note that there were no early cases of universities being run by "administrators" who were not directly involved in teaching or learning.  This point will become important later.
5. Its main function was to produce the European equivalent of mandarins and other "professionals to maintain and lead the established social order, secular as well as religious."  (Axtell, cited above.)  Research was not a major function of the medieval university, although the influence of Aristotelian thought on the university curriculum did produce a spirit of inquiry.
6. The individual universities eventually became part of a European university system in which a degree issued by any one university was recognized as valid by any other university and anyone who achieved the degree of master was to be recognized as such by any university and to be allowed to teach at any university without having to undergo further examination.

This medieval system was adequate for times in which the technologies available to European societies evolved relatively slowly.  This is also why although inquiry was encouraged through Aristotelian thinking, research was not a primary university function.  However, the strains in European society produced by the Industrial Revolution forced a reform and transformation of the university into an institution whose main mission is research.  This transformation began in Germany in the 1800's.  Let's examine this in more detail.

The Birth of the Modern Research University

(Sources: The Challenge for Research in Higher Education: Harmonizing Excellence and Utility, Alan W. Lindsay and Ruth T. Neumann, ASHE-ERIC Higher Education Reports 1988; "The Rise of Academic Laboratory Science: Chemistry and the ‘German Model’ in the Nineteenth Century", History of Universities: Volume XXXIV/1: A Global History of Research Education: Disciplines, Institutions, and Nations, 1840-1950, Chang and Rocke, Oxford University Press, July 2021.)

Although the medieval university system did not deliberately focus on research, the fact is that a large number of scholars who were products (either graduates or professors) of medieval universities went on to do the work that laid the foundation for the Industrial Revolution.  These included such figures as Isaac Newton, Leonhard Euler, and Gottfried Wilhelm Leibniz.  It can be argued that the contributions of such intellectuals were greatly amplified and expanded by the transformation of the German university system, even though the stated motivations for that transformation were not initially the pursuit of practical scientific knowledge.  According to Lindsay and Neumann (cited above), the reforms of German universities triggered in the 18th and 19th centuries were "based on an acceptance of the view that the purpose of higher education was to advance as well as to preserve and transmit knowledge."  However, another significant motivation for these reforms was the fact that Prussia had been badly humiliated by France during the wars of the early 19th century, and so the German university system was reformed in order to catch up with and pass up the French.

The main reformer was Wilhelm von Humboldt, who stated that 

"Universities should view knowledge as incomplete and so subject to discovery, although full or final knowledge could never be attained.  Further, knowledge was pure and was to

be found deep within the self. It could not be gained merely by the extensive collection of facts. Only knowledge that came from, and could be developed within, the self formed one's character; and it was character and the manner of behaving that was important for the state and for humanity, not merely knowledge and eloquence ..."

In other words, von Humboldt helped to create a system in which universities engaged in the pursuit of new knowledge simply for its own sake, and not merely for any utilitarian ends.  However, it is undeniable that this focus on research for its own sake produced great advances in German science, including chemistry, and that these advances had a number of immediate practical applications.  Those nations whose universities adopted the German model of fostering pure research also began to reap the pragmatic benefits of the discoveries which that research achieved.  This has been the basis of the astonishing technological prowess achieved by the United States in the early and middle decades of the 20th century.  However, the changes in broad American attitudes toward the public good and the maintenance of the public commons have undercut American investments in basic science from the 1970's onward.  This pressure was felt and articulated as far back as 1988, when Lindsay and Neumann wrote that

"Over the last decade, university research has gradually changed its character under the influence of cost pressures, ambivalent public attitudes, and increasingly narrow notions of "utility." The natural sciences have received higher priority, and research has been increasingly concentrated in large teams and centers. The proportion of applied research has increased and closer links with industry developed. These trends have contributed to a weakening of the teaching-research nexus. Relationships with government have been marked by increasing bureaucratization and control. The business community and the government both stress the contribution of university research to national economic and social renewal, but the pattern of postwar development in higher education has brought utility into conflict with excellence, the traditional criterion for funding research. The challenge is to incorporate utility into research policy and funding without compromising the pursuit of excellence."

In other words, American funding and administration of American universities (both public and private) has fallen victim to the same "free-market" conservative ideology that has begun to destroy many other institutions that once served the public good.  The purpose of this destruction has been to continue to concentrate the majority of our societal wealth in the hands of a few capitalist parasites at the top of our collective food chain.  Thus American universities have become cash cows which have unfortunately fallen into a lake full of piranhas.  Let's close with a picture of the feeding frenzy and how universities have tried to cope. 

The Present Day: Administrative Takeover and the University as Cash Cow

The shift in viewpoint of the American university toward a perspective of the university as a business is not entirely new.  In his 1918 book titled The Higher Learning in America: A Memorandum on the Conduct of Universities by Business Men, Thorstein Veblen wrote that American universities are 

"... corporations of learning [which] set their affairs in order after the pattern of a well-conducted business concern. In this view the university is conceived as a business house dealing in merchantable knowledge, placed under the governing hand of a captain of erudition, whose office it is to turn the means in hand to account in the largest feasible output. It is a corporation with large funds, and for men biased by their workday training in business affairs it comes as a matter of course to rate the university in terms of investment and turnover. Hence the insistence on business capacity in the executive heads of the

universities, and hence also the extensive range of businesslike duties and powers that

devolve on them."

In other words, even as far back as 1918, American universities were viewed by their administrators as businesses.  (For a look at this process in an Australian context, see "How we got here: The transformation of Australian public universities into for-profit corporations", James Guthrie and Adam Lucas, 2022.)  (BTW, lemme break one thing down for ya: when Veblen uses the term "captain of erudition," what he means is "business executive as college administrator.")

What's more, even as far back as 1918, the function of governing these universities was being moved away from faculty and students, and was being transferred to administrators who had no direct role in either teaching or learning.  Veblen was ruthless in his evaluation of these administrators: "They are needless..."  (That's "needless" as in, "useless"!)  Yet the ranks of college administrators have grown steadily over the decades, at first slowly, then meteorically during the period from the 1970's onward.  I don't have time to write the statistics here (it's late in the day - gotta clean the bathroom and kitchen, and water the vegetables!), but I will leave a list of articles that interested readers can check out themselves if they are curious.  Suffice it to say that the administrative function of modern universities has begun to displace all other functions, hogging resources like a cancerous tumor even as faculty tenure is eliminated, faculty input into university policy is marginalized, faculty pay stagnates or declines, the percentage of adjunct faculty relative to full-time faculty increases, and student tuition (along with student debt) skyrockets.  

It may well be that the growth of the administrative and non-teaching professional sector of university staff has begun to threaten the long-term economic viability of American universities, both public and private.  This would explain two phenomena which I have noticed over the last decade or so and which I identify as possible coping mechanisms: the increasing promotion of university athletic programs (particularly football) in universities which never used to care much about athletics, and the expansion of a bewildering offering of professional graduate degrees and certificates.  I suggest that these professional graduate certificates and degrees are producing a glut of mandarins of the Global North at a time in which the job market for these mandarins is becoming saturated.

What is to be done about these new mandarins and their dwindling job prospects?  One suggestion comes from Peter Turchin, a corpulent Russian academic who has proposed that elites should limit access to higher education lest their less fortunate yet educated underlings become a source of the kind of upheaval and social transformation that destroys the power of these elites.  I can't say that I agree with his moral viewpoint.  I argue that education should be made as widely available as possible precisely because of the power of educated people to transform situations of inequality dominated by entrenched elites.  But for this to occur, ordinary people must regain a sense of the purpose of education in order that they might produce and revive grassroots expressions of that purpose.  More on that in another post.

Additional Sources:

• "Globalization, Neoliberalism, and International Student Enrolments in Higher Education: Expanding Global Interconnectedness and Academic Commodification", pages 32-42
• "Degrees of Change: How New Kinds of Professional Doctorates are Changing Higher Education Institutions"
• Article by Michael Delucchi, Richard Dadzie, Eric Dean, and Xuan Pham in Review of Social Economy, June 2021 (Language warning!)
• "A Bad Bargain: Administrative Bloat and Low Faculty Pay at UDC"
• "How Much is Too Much? Controlling Administrative Costs Through Effective Oversight"
• "Report: IU Athletics generated 13th most revenue nationally in 2022"

The Educated Precariat: The Seedlings Of Early Trees

This post is a continuation of my series of posts on economic precarity.  As I mentioned in recent posts in this series, we are now starting to delve the subject of the educated precariat - that is, those people who have obtained either bachelors or more advanced graduate degrees from a college or university, yet who cannot find stable work in their chosen profession.  I suggest that the troubled lives of the educated precariat are a symptom of the troubled state of higher education generally - especially in the First World (also known as the Global North).  Two troubled groups come immediately to mind, namely, academics (college professors or salaried researchers) and college or university graduates.  We will explore the plight of new college professors and researchers later.  But suffice it to say that the guaranteed career of a tenured professor is increasingly out of reach for this group.  (See also, "Tenure Track for Professors In States Like Texas May Disappear," USA Today, 13 April 2023.)  A third group that may not know it's in trouble consists of new and continuing college and university students whose necks will one day be broken by the mousetrap of student loan debt.  A fourth group consists of the administrators and employees of the system itself.  Their trouble arises from the fact that they are running out of a key resource, namely, new students!  This is due to a number of factors, such as declining birth rates, as well as a sober realization on the part of young men and women that college education itself has begun to yield sharply diminished returns even as it has become unbearably expensive.

In considering the historical role of higher education in the development of global civilizations, it is natural to ask how things got to this state in which American higher education has begun to crumble. Where exactly did we come from that we have arrived at this destination?  To answer that question, we need to look at where we started from - in other words, it's time to look at the historical origins of education in general and of higher education in particular.

The first thing we notice is that there are records on almost every continent from almost every civilization describing the origins and evolution of formal education and of the creation of higher education systems. Ancient places of higher learning can be found in places such as these (this is a very partial list, by the way):

• Mali, African Continent (University of Timbuktu containing the Sankore Madrasah)
• Tunisia, African Continent (University of Ez-Zitouna)
• China 
• Academy of the White Deer Cavern (白鹿洞書院); 
• Songyang Academy (嵩陽書院) (This was a private rather than public institution.)
• Taixue (太學) ("National University" established by Emperor Wu)
• Greece, European Continent
• The school of Isocrates
• The school of Plato (Also known as the "Academy", which was the first historical use of this term in the West.)  (See also Higher Education In Greece, Kyriazis and Asderaki, CEPES, 2008)
• The school of Pythagoras
• The Byzantine Empire
• The Pandidakterion (known today as the University of Constantinople)

Note that although some of these institutions are called "universities," the actual entity known as the modern university did not come to being until the Middle Ages in Europe.

The entire educational process including both primary and higher education has been documented for the Greco-Roman and Chinese cases, and so it is useful to examine these cases in more detail.  First, let's consider the Greco-Roman case.  And in the case of Greece, we must consider the distinction between education in the Athenian city-state and education in Sparta.  According to Wikipedia, formal education in Athens was reserved for boys who were free-born.  The education of slaves was forbidden.  Formal education was conducted by either public schools or by private tutors.  I was not able to find out how much access to public schooling depended on family wealth, but the sources I have found do indicate that the extent of this formal education did depend on how much a family could afford to pay.  Access to higher education was strictly on the basis of a student's ability to pay, and it appears that the system of higher education was largely created and run by private individuals with sufficient means for leisure.  Thus figures such as Aristotle and Plato could be considered a kind of educational entrepreneur.  As for Sparta, while both free men and free women could participate, the purpose of Spartan education was solely to train the nation for war-fighting.

A funny thing happened to educated Athenian Greeks who had enjoyed the status of free-born intellectuals: when the Greek city-states were conquered by and absorbed into the Roman Empire, these free-born intellectuals became slaves themselves.  However, these educated slaves were able to lighten the burden of their slavery by becoming tutors and founding their own private schools (often with very slim profit margins).  This system of private education began to assume the role which Roman fathers as heads of households had traditionally held as the educators of their children.  In the Roman empire, there was no state-funded public education, either at the primary or the secondary level.  Yet those who wanted to participate in Roman politics were required to obtain a formal higher education.  This limited participation in Roman politics to the wealthy.  Also, whereas in Greece, higher education was seen as an activity of leisure which should not be tainted by any practical application (From Formal to Non-Formal: Education, Learning and Knowledge, pages 8 and 9), in the Roman empire the situation was different.  For Romans insisted that all education should have some practical purpose.  

In China, primary education began as an informal, communal process.  According to Dr. Ulrich Theobald, "The oldest word for "school" is xiang 庠, which actually means a building for livestock with two facing walls, where elderly people reared sheep, pigs or cattle and at the same time were entrusted with the duty to watch children and instruct them."  Primary education in China eventually evolved into a system of both private and public schools.  The public schools came into being during the Tang and Ming periods.  These schools, along with private primary schools and tutors, prepared students to enter the Chinese academy system, which then prepared promising students for posts in the Chinese civil service.  A couple of noteworthy facts regarding these academies is that there were times when private academies were either outlawed, disbanded, or taken over by the state as exemplified by the emperor.  Also, there were periods in which the state created or funded public academies in the academy system.  Lastly, some of the academies of the 18th and 19th centuries assumed research duties in addition to teaching.  The Taixue 太學 "National University" had already assumed a research role during the Southern Dynasties period from 420 to 589 AD.  

From the Chinese and Greco-Roman cases we can see that a key function of ancient higher education was to produce an elite class - that is, people who could either participate in politics and governance as ruling practitioners of statecraft, or as people who could serve as competent administrators/bureaucrats under these ruling elites.  Therefore the function of many ancient institutions of higher learning was not primarily research, although, as noted above, exceptions to this did exist in both ancient Greece and in China.  Stronger examples of a focus on both research and applied knowledge can be found in the Academy of Gondishapur in what is now modern Iran.  This academy was a center for the learning of medicine and science, among other subjects, and the modern hospital system owes much of its inspiration and foundational philosophy to this academy.  The Sankore Madrasah on the African continent also evolved a research function, although its main original purpose was Islamic education.  We don't have time today to explore the beginnings of the modern European university, but suffice it to say that the modern university system seems from the outset to have had the dual purposes of research and teaching.  Thus the early modern universities took over the function of producing the clerics of the Roman Catholic Church (the Western form of the mandarin administrator) in addition to producing research.

What is interesting to note is how systems of higher education fare in societies undergoing decline.  The Byzantine system of higher education is a key example.  The vicissitudes of the Byzantine empire in the 7th and 8th centuries and in the 13th century dramatically decreased the central government's ability to fund higher education and led to the privatization of higher education.  It is certain that this influenced the supply of competent practitioners of statecraft as well as competent administrators.  It is also true that declining Byzantine imperial power also produced declines in the number of jobs available to would-be mandarins who graduated from any Byzantine program of higher education.  This has significant implications for the American system of higher education, as the process of accelerating inequality continues in the United States, and as the rich parasites at the top of the food chain continue to suck nutrients from the rest of society.  More on that in another post.

A Short Spring Break, and a Few More Observations on Precarity

I have a ton of things that need to get done in realspace, so I will need to take a break from blogging for a couple of weeks at least.  However, rest assured that there is yet much to say on the subject of precarity.  Note also that rampant inequality has led to a kind of precarity among large corporations.  In the first months of 2023, a number of high profile businesses have suffered bankruptcy, including Bed Bath & Beyond.  This year may be rather brutal for large retail chains.  

One odd thing I have noticed is that the rollout of electric cars by the major automakers has largely been aimed at the luxury market.  Most EV's are therefore priced over $30,000 - in some cases well over $30,000 - which puts them out of reach of large numbers of ordinary people.  Indeed, one such automaker's electric vehicles have a starting price of $73,000.  Most electric vehicle manufacturers (including traditional automakers who have started their own electric vehicle product lines) have thus become similar to other aspirational manufacturers and retailers who are aiming to make large profits from the market of potential luxury buyers.  However, this seems to me to be a sketchy strategy due to the fact that the pool of such buyers is shrinking, while the much larger pool of people who can't afford luxury is rapidly expanding.  The Ford Motor Company has already gotten its fingers burned by pursuing this strategy.  What we are seeing here is therefore yet another symptom of the insanity of trying to pursue infinite profit growth in a finite economy.  It will be interesting to see how things develop as time passes.

The Educated Precariat - A Preview

It is now nearly time to consider a particular subset of the precariat, namely, those people who have college degrees yet who have been forced into precarious employment - especially, those who are in low-wage jobs.  In this consideration we will move beyond the United States to look at the surplus of college graduates and the lack of appropriate employment for these graduates as a global phenomenon.  We will find that among the ranks of these are coffee shop baristas with graduate training in fields such as psychology as well as technical professionals hired by temp agencies and the legions of adjunct professors at public and private universities throughout the United States.  We will consider the underemployed college graduate in both American, European and Chinese contexts, and compare these to the ranks of underemployed graduates in the developing world.  We will also try to examine the phenomenon of college graduate precarity as it exists in Russia.  However, examining the Russian case may prove difficult if one wants a recent and accurate snapshot, due to the fact that the regime of Vladimir Putin has been trying as hard as possible over the last few years to patch up the fig leaf dress which Russia has sewn to cover up its putrid nakedness.  (In fact, it has become much easier to obtain an accurate picture of daily life for ordinary people in China than in Russia.  China is actually more open and honest!)

Today's post will ask some preliminary questions.  First, how did we get to this present place in which a four-year or advanced college degree is no longer a guarantee of stable, middle-class employment?  To answer this question, we will need to answer the following questions:

• What was the original purpose of college?  Note that the word "college" comes from the Latin word collegium, defined by Wiktionary as "colleagueship (connection of associates, colleagues, etc.", guild, corporation, company, ... (persons united by the same office or calling or living by some common set of rules), college (several senses), school ..."
• What did the world's first colleges look like?  You may not know this, but one of the world's oldest continuously operating universities is the University of Ez-Zitouna, which was founded in Tunisia on the African Continent.  What was the mission of the world's first and earliest universities, and how was that mission funded and carried out?  How did the roles of education and research interact?
• What was the origin of the system of public universities in the United States?  (For instance, what was the role of the presidency of Abraham Lincoln in the birth of American public universities?)
• What are the origins of the for-profit college or university, and how did these institutions cause the purpose of college to mutate over time?
• How has the decline in public and private funding for basic research affected the employment landscape for academics?  (You may not know this, but the United States no longer has any major corporately-funded laboratories dedicated to pure research.  Bell Labs, which was responsible for the discovery of radio astronomy and many other scientific breakthroughs, is now a wholly-owned subsidiary of Nokia, a Finnish corporation.)
• What is the impact of declining numbers of youth and declining college enrollment on universities and colleges?
• How will the defunding of public colleges and universities affect the future of those nations such as the United States which pursue rabidly conservative "free-market" principles?  See, for instance, "Modeling research universities: Predicting probable futures of public vs. private and large vs. small research universities", 2018.
• What can college-educated members of the precariat (especially those college-educated who have been historically marginalized, such as people of color) do both individually and collectively to create a better situation for themselves?  For the present-day contraction of opportunities for the college-educated is being orchestrated by the present masters of our society in an attempt to maintain and amplify existing inequality.  What steps can we therefore take to create our own alternative spaces of collective self-reliance?

I hope to answer these questions (maybe with a little help from some friends) during the next few posts in this series.  I'd like to end with something that's somewhat related to this series of posts and to other posts which I've written for this blog over the last four or five years, namely, another link to a short fiction story which I recently enjoyed.  The name of the story is "Tempus Fugit" and the author is Ketty Steward.