Economics: Principles and Issues

A Parable of Political Economy

Imagine a simple society in which Jack and Jill own neighboring farms that are equally endowed in natural resources, tools, and equipment. Jack makes bread and Jill makes butter. Jack also could make butter and Jill also could make bread, but both of them have learned that they are better off if they specialize. Thus:

  • Jack can make 1 loaf of bread or 0.5 pound of butter a day. (The rate of transformation is linear; e.g. Jack could make 0.5 loaf of bread and 0.25 pound of butter daily.)
  • Jill can make 1 loaf of bread or 1 pound of butter a day. (Again, the rate of transformation is linear; Jill could make 0.5 loaf of bread and 0.5 pound of butter daily.)
  • If both Jack and Jill make bread and butter their total daily output might be 1 loaf and 0.75 pounds.
  • Alternatively, if Jack specializes in bread and Jill specializes in butter their total daily output could be 1 loaf and 1 pound.

Jill is more intelligent than Jack, and thus more innovative. That’s why she is able to reap as much wheat and make as much bread as Jack, even though he’s stronger. That’s also why she’s able to produce twice as much butter as Jack.

Jill has an absolute advantage over Jack, in that she can make as much bread as he can, and more butter than he can. But Jack has a comparative advantage in the production of bread; if he specializes in bread and Jill specializes in butter, he and Jill will be better off than if they both produce bread and butter for themselves.

Jack and Jill negotiate the exchange rate between bread and butter. Each ends up with 0.5 loaf of bread; but Jill gets 0.6 pound of butter to Jack’s 0.4 pound. Jill ends up with more butter than Jack because her greater productivity puts in her in superior bargaining position. In sum, she earns more because she produces more.

Jack and Jill have another neighbor, June, who makes clothing. Jack and Jill are more productive when they’re properly clothed during the colder months of the year. So they’re willing to trade some of their output to June, in return for heavy clothing.

Jerry, another neighbor, is a laborer who used to work for Jack and Jill, but has been unemployed for a long time because of Jill’s technological innovations. Jerry barely subsists on the fruit and game that he’s able to find and catch. Jack and Jill would hire Jerry but he insists on a wage that they can’t afford to pay unless they spends less to maintain their equipment, which would eventually result in a lower rate of output.

Along comes Juan, a wanderer from another region, who has nothing to offer but his labor. Juan is willing to work for a lower wage than Jerry, but has to be fed and clothed so that he becomes strong enough to deliver the requisite amount of labor to be worthy of hire.

Jack, Jill, and June meet to discuss Jerry and Juan. They are worried about Jerry because he’s a neighbor whom they’ve known for a long time. They also empathize with Juan’s plight, though they’re not attached to him because he’s a stranger and doesn’t speak their language well.

Jake — the gunslinger hired by Jack, Jill, and June to protect them from marauders — invites himself the meeting and brings Jerry with him. Jake likes to offset his stern image by feigning compassion. He tells Jack and Jill that they have a duty to pay Jerry the wage that he demands. He also requires Jack and Jill to feed and clothe Juan until he’s ready to work, and then they must hire him and pay him the same wage as Jerry. Jack and Jill demur because they can’t afford to do what Jake demands and make enough bread and butter to sustain their families and put something aside for retirement. June, who reacts with great sympathy to every misfortune around her — perceived and real — sides with Jake. Jerry argues that he should be helped, but Juan shouldn’t be helped because he’s just a stranger with a strange accent who’s looking for a handout.

Jake the gunslinger, disregarding Jerry’s reservation about Juan, announces that Jack and Jill must abide by his decision, inasmuch as there are 3 votes for it and only 2 votes against it — and he has the gun.

What happens next? Several things:

Jack and Jill quite properly accuse Jake of breach of contract. He has assumed a power that wasn’t given to him by Jack, Jill, and June when they hired him. Jake merely laughs at them.

Jack, Jill, and June (though she doesn’t understand it) have lost control of their businesses. They can no longer produce their goods efficiently. This means less output, that is less to trade with each other. Less output also means that they won’t be able to invest as much as before in the improvement and expansion of their operations.

June is happy, for the moment, because Jake sided with her. But she will be unhappy when Jake abuses his authority in a way that she disapproves, and when she finally understands what Jake has done to her business.

Jack and Jill have good reason to resent Juan and Jerry for using Jake to coerce them, and June for siding with Jerry and Juan. There is now a rift that will hinder cooperation for mutual benefit (e.g., willingness to help each other in times of illness).

Juan and Jerry have become dependent on Jake, thus undermining their ability to develop marketable skills and good work habits. Their dependency will keep them mired in near-poverty.

In a sane world, Jack and Jill would get rid of Jake, and the others would applaud them for doing it.

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Related posts:
The Sentinel: A Tragic Parable of Economic Reality
Liberty, General Welfare, and the State
Monopoly and the General Welfare
Gains from Trade
Trade
A Conversation with Uncle Sam

Cost Disease in the Quasi-Government Sector

What is cost disease? According to Wikipedia, it

is a phenomenon described by William J. Baumol and William G. Bowen in the 1960s. It involves a rise of salaries in jobs that have experienced no increase of labor productivity, in response to rising salaries in other jobs that have experienced the labor productivity growth. This pattern seemingly goes against the theory in classical economics for which real wage growth is closely tied to labor productivity changes.

The rise of wages in jobs without productivity gains is from the requirement to compete for employees with jobs that have experienced gains and so can naturally pay higher salaries, just as classical economics predicts. For instance, if the retail sector pays its managers 19th-century-style salaries, the managers may decide to quit to get a job at an automobile factory, where salaries are higher because of high labor productivity. Thus, managers’ salaries are increased not by labor productivity increases in the retail sector but by productivity and corresponding wage increases in other industries.

There’s a lot more to cost disease than the kind of salary bloat described by Baumol and Bowen. Tyler Cowen addresses it here. Scott Alexander picks up the ball and runs with it here and here. Arnold Kling, as usual, gets to the heart of the matter:

1. At any given time, you will have sectors where demand is growing faster than productivity (think of health care and education) and other sectors where productivity is growing faster than demand (think of manufacturing). In the sectors where demand is growing faster than productivity, you have rising relative prices, or “cost disease.”

2. In health care and education, you also have a lot of government intervention, and government intervention almost always takes the form of subsidizing demand while restricting supply. Of course, that is going to cause relative prices to be higher, thereby exacerbating “cost disease.”

3. I would argue that there are plenty of barriers to competition in the college market. Accreditation is one such barrier. But there are natural incumbent advantages as well. You may be able to enter the market for high school graduates who are in no way prepared for college. But trying to enter the market at the level of a top 100 college is nearly impossible.

4. There are plenty of barriers in health care, also. Clinics are a good innovation, but the real expenses in health care are in chronic illnesses, and clinics do not compete to treat diabetes, Alzheimer’s, and so on.

5. It is in the nature of organizations for middle managers to try to build empires, adding to cost without necessarily creating value. In for-profit businesses, the owners have an incentive to check this, because the owners want to maximize profits. In non-profits, the natural checks operate only when revenues are not rising to cover the cost of expansion. Non-profits only worry about the bottom line when it threatens to go negative.

In short, some “cost disease” is natural. At any given time, some industries will have demand growing faster than productivity. However, much of it is artificial, as government subsidizes demand and restricts supply. Finally, some of it results from the fact that non-profits are less efficient than for-profit firms.

As a former officer of a tax-funded, non-profit, professional services corporation (TNPSC), I know about cost disease in the quasi-government sector of the economy.

First of all Baumol and Bowen’s definition of cost disease as salary bloat, though incomplete, is correct. Because my company — call it XYZ Corp. — derived all of its funds from government sources, our salary policies required the approval of government contracting officers. How did we gain that approval? Every few years XYZ hired a consulting firm that had access to salary data for private-sector companies. The consulting firm would then undertake a “study” to compare private-sector salaries with those of XYZ. Lo and behold, by selecting the right set of private-sector companies and the right set of jobs in those companies, the consulting firm found that XYZ’s salaries lagged, and should be boosted by more than the usual annual rate to keep pace with XYZ’s private-sector “competitors.” XYZ’s above-market benefits package (approved by contracting officers) and below-market turnover rate were conveniently ignored.

A related trick was to set executive salaries so that they kept pace with the salaries of executives at other TNPSCs. And how did the larger TNPSCs justify the high salaries to which XYZ aspired? With “studies” showing that their executive salaries lagged those of their private-sector “competitors.”

It’s a joke to compare salaries paid by relatively stable TNPSCs — organizations that have cozy, long-term relationships with their government sponsors — and salaries paid by private-sector companies. In addition to cushy benefits packages, employees of TNPSCs are well-insulated from competition, unlike their private-sector counterparts. Thus employees of TNPSCs are compensated not only with handsome salaries and benefits, but they also enjoy a high degree of job security. Which is why turnover rates at TNPSCs are low relative to private-sector companies.

How does Kling’s list of reasons for cost disease apply to TNPSCs?

Demand vs. productivity. I’m unfamiliar with the current state of “demand” for (i.e., government spending on) TNPSCs. But over the long haul, since the inception of TNPSCs during World War II, government spending on them has risen by orders of magnitude. It’s probably safe to say that the productivity of TNPSCs has risen little. Advances in computation and data storage have enabled such firms to collect and analyze data pertaining to a broader range of subjects, and to do it more rapidly. But there’s been little real innovation in the tools of analysis, most of which were devised during World War II and the decades immediately following the war. And the basic approach to “solving” the problems of government agencies remains the same as it was in World War II: Define the problem, collect the relevant data, analyze the data to find a preferred solution to the problem, and report the results to the government client. It was and still is a labor-intensive process.

Government intervention and barriers to competition. TNPSCs are formally designated by the government. There are relatively few of them, and most of them have pedigrees that date back to the 1940s, 1950s, and 1960s.

The cozy relationships between TNPSCs and their various government sponsors changed somewhat in the 1990s when profit-seeking professional-services firms declared war on TNPSCs. Some TNPSCs suffered funding cuts as a result, but the cuts were far from fatal and TNPSCs compensated by finding a broader range of government sponsors to maintain them in the style to which they had become accustomed. Some of them spun off for-profit counterparts, with the aid of fees earned on government contracts. More stringent contracting procedures imposed as a result of the war on TNPSCs also forced them to emulate the task-by-task funding of for-profits. But that’s just a cosmetic change; it adds to the cost of running TNPSCs, which the government defrays, of course.

Empire-building. Kling’s analysis is spot-on. Here’s some personal testimony: From the mid-1980s to the mid-1990s, the component of XYZ that I managed grew significantly. Staffing probably doubled, and costs rose accordingly. It wasn’t until 1995, when XYZ suffered funding cuts resulting from the war on TNPSCs, that my empire shrank. I ran the support side of XYZ, which encompassed contracting, accounting, information services, publication services, facility operations, security, computer operations, computer programming, and personnel (called “human resources,” of course). I handed off the computer programming function to another manager, who could sell its services to new clients, and cut the staffing of the other functions by about 20 percent. I did it so that the managers of the research divisions — the ones that do the work for which clients pay — could take much smaller staffing reductions. Did the 20-percent cut in support services hinder the work of the research divisions? Not that I noticed.

Why, then, did I grow the support division? Because I could. That’s empire-building, and I was far from the only empire-builder in XYZ or other TNPSCs. Ambition abounds, and it leads to empire-building for as long as the money is there to support it.

Kling is right. Cost disease prevails where government subsidizes demand and restricts supply. TNPSCs are small potatoes compared with the health-care industry, which is the largest component of the quasi-government sector of the economy. The industry is government subsidized (e.g., through Medicare, Medicaid, and research funding) and sheltered from serious competition by a vast web of laws and regulations. Those laws and regulations also impose heavy cost burdens on health-care providers, their suppliers, drug companies, and insurance companies. But the burdens are defrayed to a large extent by government funding. It’s a vicious cycle that’s largely responsible for the high cost of health care in the United States.

It’s even worse in the official government sector, which includes the vast federal apparatus, all manner of State and local agencies, public schools and universities (and heavily endowed private ones) — and myriad contractors to all of the foregoing. Massive cost overruns, dismal performance, administrative bloat, pension-fund raids on the public treasury (i.e., taxpayers), open-ended “entitlement” programs, etc., etc., etc.  It’s the non-accountability swamp. And the only way to drain it is to say “no” — period, full stop, end of discussion.

Prosperity Isn’t Everything

There is no denying that per-capita income rises with specialization and trade; for example:

  • A is a farmer with land that’s good for growing fruit trees; B is a farmer with land that’s good for raising cattle.
  • The total output of both apples and butter will be greater if A specializes in growing apples and B specializes in making butter than if both A and B grew apples and made butter.
  • A and B can then trade apples for butter so that of them is better off than he would have been in the absence of specialization and trade.

Sometimes A and B live in different cities, different States, and different countries. If the raison d’etre of specialization and trade is the maximization of income, it would be foolish to exclude international trade while allowing inter-State and inter-city trade. (Note that the preceding sentence begins with if.)

The combination of specialization, trade, invention, innovation, and entrepreneurship has wrought much good. Here’s Megan McArdle’s testimony:

By the standards of today, my grandparents were living in wrenching poverty. Some of this, of course, involves technologies that didn’t exist—as a young couple in the 1930s my grandparents had less access to health care than the most  neglected homeless person in modern America, simply because most of the treatments we now have had not yet been invented. That is not the whole story, however. Many of the things we now have already existed; my grandparents simply couldn’t afford them.  With some exceptions, such as microwave ovens and computers, most of the modern miracles that transformed 20th century domestic life already existed in some form by 1939. But they were out of the financial reach of most people.

If America today discovered a young couple where the husband had to drop out of high school to help his father clean tons of unsold, rotted produce out of their farm’s silos, and now worked a low-wage, low-skilled job, was living in a single room with no central heating and a single bathroom to share for two families, who had no refrigerator and scrubbed their clothes by hand in a washtub, who had serious conversations in low voices over whether they should replace or mend torn clothes, who had to share a single elderly vehicle or make the eight-mile walk to town  … that family would be the subject of a three-part Pulitzer prizewinning series on Poverty in America.

But in their time and place, my grandparents were a boring bourgeois couple, struggling to make ends meet as everyone did, but never missing a meal or a Sunday at church. They were excited about the indoor plumbing and electricity which had just been installed on his parents’ farm, and they were not too young to marvel at their amazing good fortune in owning an automobile. In some sense they were incredibly deprived, but there are millions of people in America today who are incomparably better off materially, and yet whose lives strike us (and them) as somehow objectively more difficult.

Much of that is true of my parents, who were of the same generation as McArdle’s grandparents. More of it is true of my maternal grandmother, who was born in 1880, wed in 1903, bore and raised ten children, and was widowed at the age of 60. I remember well the years before she reached the age of 70; until then she cooked on a wood-fired range, pumped water from a well in her backyard, and went to the outhouse for calls of nature. And yet, the following things, and much more, came to pass in her lifetime: alternating-current electricity, a telephone in most homes (though my grandmother lacked one until she was in her 70s), automobiles (though she never learned to drive), airplanes (she first flew at the age of 93), movies, radio, movies with sound, television (she never owned one), radar, penicillin, vaccinations against various debilitating diseases, electric typewriters, and early transistorized computers.

Because my dominant memories of my grandmother and her way of life in a small village are boyhood memories, it’s tempting to characterize them as nostalgic and somewhat romanticized. But I know that she was more or less typical of the residents of her village. Though she was far from rich, she wasn’t poor by the standards of the village. She certainly didn’t feel impoverished or resentful about her lack of material goods.

Today, however, relatively poor people in America have far, far more in the way of material goods than my grandmother ever dreamt of owning, yet they are anxious and even miserable, because… Here’s McArdle’s view:

[Not] everything has gotten better in every way, all the time. There are areas in which things have gotten broadly worse….

  • … Substance abuse, and the police response to it, has devastated both urban and rural communities.
  • Divorce broke up millions of families, and while the college educated class seems to have found a new equilibrium of stable and happy later marriages, marriage is collapsing among the majority who do not have a college degree, leaving millions of children in unstable family situations where fathers are often absent from the home, and their attention and financial resources are divided between multiple children with multiple women.
  • Communities are much less cohesive than they used to be, and while the educated elite may have found substitutes online, the rest of the country is “bowling alone” more and more often—which is not merely lonely, but also means they have fewer social supports when they find themselves in trouble.
  • A weekly wage packet may buy more than it did sixty years ago, but the stability of manufacturing jobs is increasingly being replaced by contingent and unreliable shift work that is made doubly and triply difficult by the instability of the families that tend to do these jobs. The inability to plan your life or work in turn makes it hard to form a family, and stressful to keep one together….
  • Widespread credit has democratized large purchases like furniture and cars. It has also enabled many people, particularly financially marginal people, to get into serious trouble.  Debt magnifies your life experience: when things are going relatively well, it gives you more options, but when things are going badly, it can turn a setback into a catastrophe—as many, many families found out in 2008….

This list illustrates why public policy seems to be struggling to come up with a plan of attack against our current insecurities. The welfare state is relatively good at giving people money: you collect the taxes, write a check, and now people have money. The welfare state has proven very bad at giving people stable jobs and stable families, a vibrant community life, promising career tracks, or a cure for their drug addiction. No wonder so many hopes now seem to be pinned on early childhood education, far in excess of the evidence to support them: it is the only thing we have not already tried and failed at.

But I think this list illustrates the poverty of trying to measure living standards by staring at median wages. Many of the changes of the last century show up in that statistic, but others, like the time no longer spent plucking chickens, or the joys of banishing lye from the pantry, appear nowhere.  Nor do the changes in job and family structure that have made the lives of people who are indisputably vastly materially richer than my young grandparents were, nonetheless feel much more precarious.

Where did it all go wrong? And I do believe that it went wrong. I say that as a man who has lived more than his three-score and ten years, remains in good health, lives comfortably, has a loving wife of 52 years, has two fine children and twelve joyous grandchildren, and is by nature an optimistic achiever who isn’t easily thrown off course by a setback.

It didn’t go wrong because of globalization, though globalization may have hastened the rot. It didn’t go wrong because of prosperity per se, though it was helped by the fevered pursuit of prosperity. It went wrong because of the fraying of the social ties that bound much of America for so long — even with the Civil War and its decades-long residue of bitterness.

Why did those ties fray? And why are they now weaker than than have been since the eve of the Civil War?

Let’s begin with social norms, which are the basis of social ties. If you and I observe the same social norms, we’re likely to feel bound in some way, even if we’re not friends or relatives. This, of course, is tribalism, which is verboten among those who view all of mankind as brothers, sisters, and whatevers under the skin — all mankind except smarty-pants Americans of East Asian descent, Israeli Jews and American Jews who support Israel, Southerners (remember the Civil War!), and everyone else who is a straight, non-Hispanic white male of European descent. To such people, the only legitimate tribe is the tribe of anti-tribalism.

You may by now understand that I blame leftists for the breakdown of social norms and social ties. But how can that be if, as McArdle says, “the college educated class seems to have found a new equilibrium of stable and happy later marriages”? The college-educated class resides mostly on the left, and affluent leftists do seem to have avoided the rot.

Yes, but they caused it. You could think of it as a non-suicidal act of terror. But it would be kinder and more accurate to call it an act of involuntary manslaughter.  Leftists meant to make the changes that caused the rot; they just didn’t foresee or intend the rot. Nor is it obvious that they care about it, except as an excuse to “solve” social problems from on high by throwing money and behavioral prescriptions at them — which is why there’s social rot in the first place.

The good intentions embedded in governmental acts and decrees have stealthily expanded and centralized government’s power, and in the process have sundered civil society. Walter Williams puts it this way in “Culture and Social Pathology” (creators.com, June 16, 2015):

A civilized society’s first line of defense is not the law, police and courts but customs, traditions, rules of etiquette and moral values. These behavioral norms — mostly transmitted by example, word of mouth and religious teachings — represent a body of wisdom distilled over the ages through experience and trial and error. They include important thou-shalt-nots, such as thou shalt not murder, thou shalt not steal and thou shalt not cheat. They also include all those courtesies that have traditionally been associated with ladylike and gentlemanly conduct.

The failure to fully transmit these values and traditions to subsequent generations represents one of the failings of what journalist Tom Brokaw called “The Greatest Generation.” People in this so-called great generation, who lived during the trauma of the Great Depression and fought World War II, not only failed to transmit the moral values of their parents but also are responsible for government programs that will deliver economic chaos….

For nearly three-quarters of a century, the nation’s liberals have waged war on traditional values, customs and morality. Our youths have been counseled that there are no moral absolutes. Instead, what’s moral or immoral is a matter of personal opinion. During the 1960s, the education establishment began to challenge and undermine lessons children learned from their parents and Sunday school with fads such as “values clarification.” So-called sex education classes are simply indoctrination that undermines family and church strictures against premarital sex. Lessons of abstinence were considered passe and replaced with lessons about condoms, birth control pills and abortions. Further undermining of parental authority came with legal and extralegal measures to assist teenage abortions with neither parental knowledge nor parental consent….

If it were only the economic decline threatening our future, there might be hope. It’s the moral decline that spells our doom.

The undoing of traditional mores began in earnest in the 1960s, with a frontal assault on traditional morality and the misguided expansion of the regulatory-welfare state. The unraveling continues to this day. Traditional morality is notable in its neglect; social cohesion is almost non-existent, except where the bonds of religion and ethnicity remain strong. The social fabric that once bound vast swaths of America has rotted — and is almost certainly beyond repair.

The social fabric has frayed precisely because government has pushed social institutions aside and made dependents of hundreds of millions of Americans. As Ronald Reagan said in his first inaugural address, “In this present crisis, government is not the solution to our problem, government is the problem.”

Now for an ironic twist. Were the central government less profligate and intrusive, Americans would become much more prosperous.

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Related posts:
Social Norms and Liberty
Whiners — Left and Libertarian
The Adolescent Rebellion Syndrome
“Intellectuals and Society”: A Review
Government vs. Community
The Left’s Agenda
The Left and Its Delusions
The Destruction of Society in the Name of “Society”
The Spoiled Children of Capitalism
Politics, Sophistry, and the Academy
Society and the State
Are You in the Bubble?
The Culture War
Ruminations on the Left in America
God-Like Minds
Non-Judgmentalism as Leftist Condescension
An Addendum to (Asymmetrical) Ideological Warfare
Democracy, Human Nature, and the Future of America
1963: The Year Zero
Society
How Democracy Works
“Cheerful” Thoughts
How Government Subverts Social Norms
Turning Points
The Twilight’s Last Gleaming?
How America Has Changed

Economics, the Dismal Quasi-Science: 3. What Is Scientific about Economics?

This is the third entry in what I hope will become a book-length series of posts. That result, if it comes to pass, will amount to an unorthodox economics textbook. Here are the chapters that have been posted to date:

1. What Is Economics?
2. Pitfalls
3. What Is Scientific about Economics?

Perhaps the biggest pitfall that awaits an economist, student of economics, or reader of economic literature is the belief that economics is a science because of its mathematical and statistical content. David S. D’Amato takes a clear-headed view in “Is Economics a Hard Science?” (The American Spectator, January 4, 2017):

[E]conomists and social scientists are gathering and analyzing statistical data constantly. [But] those data are limited by the density of the causal atmosphere of the environment from which they emerge, a rich and variable sea of causes and effects. Isolating one or even a few factors becomes impossible.

As Jim Manzi explains in his book Uncontrolled, “[W]e can never be sure that any experiment actually has controlled for every possible alternative cause of an outcome.” And while this is, of course, true in every field of inquiry, the problem is especially acute within the social sciences, so-called. That’s because, as Manzi observes, “human social organizations have a causal density that dwarfs anything astrophysics considers.”…

For any given observable phenomenon, the scientist must attempt to parse a convoluted web of actual and potential causes. Unable to control the experiment, its environmental inputs, groups, etc., the social scientist is unable to know whether the hypothesis being tested has been confirmed. This causal density means economic data must always be the subject of several competing explanations, informed by ideology and extra-economic social theory…

…The great classical liberal political economist Jean-Baptiste Say foresaw the complacency of today’s economists, their tendency to oversell the power of data and mathematics. Anticipating the praxeology of Ludwig von Mises, Say held the proper foundations for economics are “the rigorous deductions of undeniable general facts,” not “new particular fact[s]” (i.e., statistics), but basic laws of human action….

If empirical data are often too messy, too causally intricate, without the help of a philosophical or interpretative framework, then mathematical models are in a sense too neat to tell us very much about reality; they reduce enormously complex concepts and arguments about economic behavior to sterile formulae. Sometimes this is useful, as in the case of an economic model that explains the relationship between supply and demand. But as economists address their model-building processes to more difficult questions, the serviceability of the models diminishes. And if we are to believe the critics of “mathiness,” whom we can find all over the spectrum of ideas, the preoccupation with practically useless mathematical models has all but completely overtaken the economics profession.

Mathematical models, agglomerations of equations using multivariable calculus, are, it turns out, not a language suited to the task of describing something as dynamic as human behavior. Among the axioms of modern economics is the idea that economic value is something assigned to good and services subjectively by individual buyers and sellers. As Austrian School economists frequently point out, there is an irreducible subjectivity at the heart of all economic action. This explanation of value in terms of subjective preference and marginal utility replaced classical theories that made value a function of the quantities of labor expended during a good’s production. If value subjectivism holds, then, for example, one’s partiality for Chicago-style pizza as opposed to New York-style pizza is simply not the kind of preference that can be quantified. There is, as the saying goes, no accounting for taste.

It’s a simple example, but it points to a much more general and far-reaching truth: Formal logic and mathematics are not a stable foundation for the economist. This has been borne out by the inability of computer models to anticipate the movements of actual markets. For all their complex mathematics and pretensions to rigorousness, these models rely on crude oversimplifications. As New York University economist Mario J. Rizzo notes, “Ceteris paribus prediction is prediction of ‘stylized facts,’” whose connection to the real world is tenuous at best.

Yet, as Arnold Kling explains in “An Important Emerging Economic Paradigm” (TCS Daily, March 2, 2005), many (perhaps most) economists have lost sight of the axioms of economics in their misplaced zeal to emulate the methods of the physical sciences:

The most distinctive trend in economic research over the past hundred years has been the increased use of mathematics. In the wake of Paul Samuelson’s (Nobel 1970) Ph.D dissertation, published in 1948, calculus became a requirement for anyone wishing to obtain an economics degree. By 1980, every serious graduate student was expected to be able to understand the work of Kenneth Arrow (Nobel 1972) and Gerard Debreu (Nobel 1983), which required mathematics several semesters beyond first-year calculus.

Today, the “theory sequence” at most top-tier graduate schools in economics is controlled by math bigots. As a result, it is impossible to survive as an economics graduate student with a math background that is less than that of an undergraduate math major. In fact, I have heard that at this year’s American Economic Association meetings, at a seminar on graduate education one professor quite proudly said that he ignored prospective students’ grades in economics courses, because their math proficiency was the key predictor of their ability to pass the coursework required to obtain an advanced degree.

The raising of the mathematical bar in graduate schools over the past several decades has driven many intelligent men and women (perhaps women especially) to pursue other fields. The graduate training process filters out students who might contribute from a perspective of anthropology, biology, psychology, history, or even intense curiosity about economic issues. Instead, the top graduate schools behave as if their goal were to produce a sort of idiot-savant, capable of appreciating and adding to the mathematical contributions of other idiot-savants, but not necessarily possessed of any interest in or ability to comprehend the world to which an economist ought to pay attention.

. . . The basic question of What Causes Prosperity? is not a question of how trading opportunities play out among a given array of goods. Instead, it is a question of how innovation takes place or does not take place in the context of institutional factors that are still poorly understood.

Economic models usually are clothed in the language of mathematics and statistics. But those aren’t scientific disciplines in themselves; they are tools of science. Expressing a theory in mathematical terms may lend the theory a scientific aura, but a theory couched in mathematical terms is not a scientific one unless (a) it can be tested against facts yet to be ascertained and events yet to occur, and (b) it is found to accord with those facts and events consistently, by rigorous statistical tests. In sum, modeling is not science.

Economics is a science only to the extent that it yields empirically valid insights about  specific economic phenomena (e.g., the effects of laws and regulations on the prices and outputs of specific goods and services). The Keynesian multiplier, about which I’ll say more in a later chapter, is not a scientific theory. It is a hypothesis that rests on a simplistic, hydraulic view of the economic system. (Other examples of pseudo-scientific economic theories are the labor theory of value and historical determinism.)

A scientific theory is a hypothesis that has thus far been confirmed by observation, and which has not yet been refuted (falsified) by observation.* (The Keynesian multiplier has been falsified.) Every scientific theory rests eventually on axioms: self-evident principles that are accepted as true without proof. Economics, as D’Amato notes, is no exception. It rests on these self-evident axioms:

1. Each person strives to maximize his or her sense of satisfaction, which may also be called well-being, happiness, or utility (an ugly word favored by economists). Striving isn’t the same as achieving, of course, because of lack of information, emotional decision-making, buyer’s remorse, etc

2. Happiness can and often does include an empathic or expedient concern for the well-being of others; that is, one’s happiness may be served by what is usually labelled altruism or self-sacrifice.

3. Happiness can be and often is served by the attainment of non-material ends. Not all persons (perhaps not even most of them) are interested in the maximization of wealth, that is, claims on the output of goods and services. In sum, not everyone is a wealth maximizer. (But see axiom number 12.)

4. The feeling of satisfaction that an individual derives from a particular product or service is situational — unique to the individual and to the time and place in which the individual undertakes to acquire or enjoy the product or service. Generally, however, there is a (situationally unique) point at which the acquisition or enjoyment of additional units of a particular product or service during a given period of time tends to offer less satisfaction than would the acquisition or enjoyment of units of other products or services that could be obtained at the same cost.

5. The value that a person places on a product or service is subjective. Products and services don’t have intrinsic values that apply to all persons at a given time or period of time.

6. The ability of a person to acquire products and services, and to accumulate wealth, depends (in the absence of third-party interventions) on the valuation of the products and services that are produced in part or whole by the person’s labor (mental or physical), or by the assets that he owns (e.g., a factory building, a software patent). That valuation is partly subjective (e.g., consumers’ valuation of the products and services, an employer’s qualitative evaluation of the person’s contributions to output) and partly objective (e.g., an employer’s knowledge of the price commanded by a product or service, an employer’s measurement of an employees’ contribution to the quantity of output).

7. The persons and firms from which products and services flow are motivated by the acquisition of income, with which they can acquire other products and services, and accumulate wealth for personal purposes (e.g., to pass to heirs) or business purposes (e.g., to expand the business and earn more income). So-called profit maximization (seeking to maximize the difference between the cost of production and revenue from sales) is a key determinant of business decisions but far from the only one. Others include, but aren’t limited to, being a “good neighbor,” providing employment opportunities for local residents, and underwriting philanthropic efforts.

8. The cost of production necessarily influences the price at which a good or and service will be offered for sale, but doesn’t solely determine the price at which it will be sold. Selling price depends on the subjective valuation of the products or service, prospective buyers’ incomes, and the prices of other products and services, including those that are direct or close substitutes and those to which users may switch, depending on relative prices.

9. The feeling of satisfaction that a person derives from the acquisition and enjoyment of the “basket” of products and services that he is able to buy, given his income, etc., doesn’t necessarily diminish, as long as the person has access to a great variety of products and services. (This axiom and axiom 12 put paid to the myth of diminishing marginal utility of income.)

10. Work may be a source of satisfaction in itself or it may simply be a means of acquiring and enjoying products and services, or acquiring claims to them by accumulating wealth. Even when work is satisfying in itself, it is subject to the “law” of diminishing marginal satisfaction.

11. Work, for many (but not all) persons, is no longer be worth the effort if they become able to subsist comfortably enough by virtue of the wealth that they have accumulated, the availability of redistributive schemes (e.g., Social Security and Medicare), or both. In such cases the accumulation of wealth often ceases and reverses course, as it is “cashed in” to defray the cost of subsistence (which may be far more than minimal).

12. However, there are not a few persons whose “work” is such a great source of satisfaction that they continue doing it until they are no longer capable of doing so. And there are some persons whose “work” is the accumulation of wealth, without limit. Such persons may want to accumulate wealth in order to “do good” or to leave their heirs well off or simply for the satisfaction of running up the score. The justification matters not. There is no theoretical limit to the satisfaction that a particular person may derive from the accumulation of wealth. Moreover, many of the persons (discussed in axiom 11) who aren’t able to accumulate wealth endlessly would do so if they had the ability and the means to take the required risks.

13. Individual degrees of satisfaction (happiness, etc.) are ephemeral, nonquantifiable, and incommensurable. There is no such thing as a social welfare function that a third party (e.g., government) can maximize by taking from A to give to B. If there were such a thing, its value would increase if, for example, A were to punch B in the nose and derive a degree of pleasure that somehow more than offsets the degree of pain incurred by B. (The absurdity of a social-welfare function that allows As to punch Bs in their noses ought to be enough shame inveterate social engineers into quietude — but it won’t. They derive great satisfaction from meddling.) Moreover, one of the primary excuses for meddling is that income (and thus wealth) has a  diminishing marginal utility, so it makes sense to redistribute from those with higher incomes (or more wealth) to those who have less of either. Marginal utility is, however, unknowable (see axioms 4 and 5), and may not always be negative (see axioms 9 and 12).

14. Whenever a third party (government, do-gooders, etc.) intervene in the affairs of others, that third party is merely imposing its preferences on those others. The third party sometimes claims to know what’s best for “society as a whole,” etc., but no third party can know such a thing. (See axiom 13.)

15. It follows from axiom 13 that the welfare of “society as a whole” can’t be aggregated or measured. An estimate of the monetary value of the economic output of a nation’s economy (Gross Domestic Product) is by no means an estimate of the welfare of “society as a whole.”

That may seem like a lot of axioms, which might give you pause about my claim that some aspects of economics are scientific. But economics is inescapably grounded in axioms such as the ones that I propound, just as much of modern physics is inescapably grounded in the principle of uniformity.**

It is important to distinguish between axioms, which are self-evidently true, and biases that stem from normative views of what ought to be. Behavioral economists, for example, see the world through the lens of wealth-and-utility-maximization. Their great crusade is to force everyone to make rational decisions (by their lights), through “nudging.” It almost goes without saying that government should be the nudger-in-chief. (See “The Perpetual Nudger” and the many posts linked to therein.)

Other economists — though not as many as in the past — are obsessed by monopoly and oligopoly (the domination of a market by one or a few sellers). They’re heirs to the trust-busting of the late 1800s and early 1900s, a movement led by non-economists who sought to blame the woes of working-class Americans on the “plutocrats” (Rockefeller, Carnegie, Ford, etc.) who had merely made life better and more affordable for Americans, while also creating jobs for millions of them and reaping rewards for the great financial risks that they took. (See “Monopoly and the General Welfare” and “Monopoly: Private Is Better than Public.”) As it turns out, the biggest and most destructive monopoly of all is the federal government, so beloved and trusted by trust-busters — and too many others. (See “The Rahn Curve Revisited.”)

Nowadays, a lot of economists are preoccupied by income inequality, as if it were something evil and not mainly an artifact of differences in intelligence, ambition, and education, etc. And inequality — the prospect of earning rather grand sums of money — is what drives a lot of economic endeavor, to the benefit of workers and consumers. (See “Mass (Economic) Hysteria: Income Inequality and Related Themes” and the many posts linked to therein.) Remove inequality and what do you get? The Soviet Union and Communist China, in which everyone is equal except party operatives and their families, friends, and favorites. As George Orwell put it in Animal Farm, “all [people] are equal, but some [people] are more equal than others.”

When the inequality-preoccupied economists are confronted by the facts of life, they usually turn their attention from inequality as a general problem to the (inescapable) fact that an income distribution has a top one-percent and top one-tenth of one-percent — as if there were something especially loathsome about people in those categories. (Paul Krugman shifted his focus to the top one-tenth of one percent when he realized that he’s in the top one percent, so perhaps he knows that’s he’s loathsome and wishes to deny it — to himself, at least.)

Crony capitalism is trotted out as a major cause of very high incomes. But that’s hardly a universal cause, given that a lot of very high incomes are earned by athletes and film stars beside whom most investment bankers and CEOs earn slave wages. Moreover, as I’ve said on several occasions, crony capitalists are bright and driven enough to be in the stratosphere of any income distribution. Further, the breeding ground of crony capitalism is the regulatory power of government that makes it possible.

Many economists became such, it would seem, in order to promote big government and its supposed good works — income redistribution being one of them. Joseph Stiglitz and Paul Krugman are two leading exemplars of what I call the New Deal school of economic thought, which amounts to throwing government and taxpayers’ money at every perceived problem, that is, every economic outcome that is deemed unacceptable by accountants of the soul. (See “Accountants of the Soul.”)

Stiglitz and Krugman — both Nobel laureates in economics — are typical “public intellectuals” whose intelligence breeds in them a kind of arrogance. (See “Intellectuals and Society: A Review.”) It’s the kind of arrogance that reveals itself in a penchant for deciding what’s best for others, even beyond the arrogance of behavioral “nudgers.”

New Deal economists like Stiglitz and Krugman carry it a few steps further. They ascribe to government an impeccable character, an intelligence to match their own, and a monolithic will. They then assume that this infallible and wise automaton can and will do precisely what they would do: Create the best of all possible worlds. (See the preceding chapter, in which I discuss the nirvana fallacy.)

I hold economists of the New Deal stripe partly responsible for the swamp of stagnation into which the nation’s economy has descended. (See “Economic Growth Since World War II.”) Largely responsible, of course, are opportunistic if not economically illiterate politicians who pander to rent-seeking, economically illiterate constituencies. (Yes, I’m thinking of pensioners and the many “disadvantaged” groups with which “compassionate” politicians have struck up an alliance of convenience.)

Enough said, for now. Some economics is science. Too much of it is nothing more than special pleading cloaked in the jargon of economics, and pseudo-scientific theorizing overlaid with a veneer of mathematics or statistics.

Caveat lector.
__________
* This is from Karl Popper‘s classic statement of the scientific method. Richard Feynman, a physicist (and real scientist), had a different view. I see Feynman’s view as complementary to Popper’s, not at odds with it. What is “constructive skepticism” (Feynman’s term) but a gentler way of saying that a hypothesis or theory might be falsified and that the act of falsification may point to a better hypothesis or theory?

** The principle of uniformity is a fundamental axiom of modern physics, most notably of Einstein’s special and general theories of relativity. According to the principle of uniformity, for example, if observer B is moving away from observer A at a certain speed, observer A will perceive that he is moving away from observer B at that speed. This statement holds only if A and B can’t see another object. But suppose, for example, there’s an object C that’s visible to A, and which A perceives as stationary. If A sees that B is moving away from C as well as from A, then A will perceive that B is in motion while A is at rest (relative to C, at least). That aside, A still doesn’t have an absolute velocity or direction of travel. Velocity and direction are always relative to an arbitrary reference point.

Economics, the Dismal Quasi-Science: 2. Pitfalls

This is the second entry in what I hope will become a book-length series of posts. That result, if it comes to pass, will amount to an unorthodox economics textbook. Here are the chapters that have been posted to date:

1. What Is Economics?
2. Pitfalls

A person who wants to learn about economics should be forewarned about pernicious tendencies and beliefs — often used unthinkingly and expressed subtly — that lurk in writings and speeches about economics and economic issues. This chapter treats seven such tendencies and beliefs:

  • misuse of probability
  • reductionism
  • nirvana fallacy
  • social welfare
  • romanticizing the state
  • paternalism
  • judging motives instead of results

MISUSE OF PROBABILITY

Probability is seldom invoked in non-technical economics. But when it is, beware of it. A statement about the probability of an event is either (a) a subjective evaluation (“educated” guess) about what is likely to happen or (b) a strict, mathematical statement about the observed frequency of the occurrence of a well-defined random event. I will bet you even money that the first meaning applies in at least six of the next ten times that you read or hear a statement about probability or its cognate “chance,” as in 50-percent chance of rain. And my subjective evaluation is that I have a 90-percent probability of winning the bet.

Let’s take the chance of rain (or snow or sleet, etc.). You may rely heavily on a weather forecaster’s statement about the probability that it will rain today. If the stated probability is high, you may postpone an outing of some kind, or take an umbrella when you leave the house, or wear a water-repellent coat instead of a cloth one, and so on. That’s prudent behavior on your part, even though the weather forecaster’s statement isn’t really probabilistic.

What the weather forecaster is telling you (or relaying to you from the National Weather Service) is a subjective evaluation of the “chance” that it will rain in a given geographic area, based on known conditions (e.g., wind direction, presence of a nearby front, water-vapor imagery). The “chance” may be computed mathematically, but its computation rests on judgments about the occurrence of rain-producing events, such as the speed of a front’s movement and the direction of water-vapor flow. In the end, however, you’re left with only a weather forecaster’s judgment, and it’s up to you to evaluate it and act accordingly.

What about something that involves “harder” numbers, such as the likelihood of winning a lottery (where there’s good information about the number of tickets sold) or casting the deciding vote in an election (where there’s good information about the number of votes that will be cast)? I will continue with the case of voting, which is discussed in chapter 1 as an example of the extent to which economics has spread beyond its former preoccupations with buyers, sellers, and the aggregation of their activities.

An economist named Bryan Caplan has written a lot about voting. For example, he says the following in “Why I Don’t Vote: The Honest Truth” (EconLog, September 13, 2016):

Aren’t we [economists] always advising people to choose their best option, even when their best option is bleak?  Sure, but abstention [from voting] is totally an option.  And while politicians have a clear incentive to ignore we abstainers, only remaining aloof from our polity gives me inner peace.

You could respond, “Inner peace at what price?”  It is only at this point that I invoke the miniscule probability of voter decisiveness.  If I had a 5% chance of tipping an electoral outcome, I might hold my nose, scrupulously compare the leading candidates, and vote for the Lesser Evil.  Indeed, if, like von Stauffenberg, I had a 50/50 shot of saving millions of innocent lives by putting my own in grave danger, I’d consider it.  But I refuse to traumatize myself for a one-in-a-million chance of moderately improving the quality of American governance.  And one-in-a-million is grossly optimistic.

Caplan links to a portion of his lecture notes for a course in the logic of collective action. The notes include this mathematical argument:

III. Calculating the Probability of Decisiveness, I: Mathematics

A. When does a vote matter? At least in most systems, it only matters if it “flips” the outcome of the election.

B. This can only happen if the winner wins by a single vote. In that case, each voter is “decisive”; if one person decided differently, the outcome would change.

C. In all other cases, the voter is not decisive; the outcome would not change if one person decided differently.

D. It is obvious that the probability of casting the decisive vote in a large electorate is extremely small….

H. Now suppose that everyone but yourself votes “for” with probability p – and “against” with probability (1-p).

I. Then from probability theory: caplan-on-voting-probability-of-tie

J. From this formula, we can see that the probability of a tie falls when the number of voters goes up….

K. Intuitively, the more people there are, the less likely one person makes a difference….

IV. Calculating the Probability of Decisiveness, II: Examples

A. What is neat about the above formula is that it allows us to say not just how the probability of decisiveness changes, but how much….

I. Upshot: For virtually any real-world election, the probability of casting the decisive vote is not just small; it is normally infinitesimal. The extreme observation that “You will not affect the outcome of an election by voting” is true for all practical purposes.

J. Even if you were to play around with the formula to increase your estimate a thousand-fold, your estimated answer would remain vanishingly small.

What Caplan and other economists who write in the same vein ignore is the influence of their point of view. It’s self-defeating because it appeals to extremely rationalistic people like Caplan. One aspect of their rationalism is a cold-eyed view of government, namely, that it almost always does more harm than good. That’s a position with which I agree, but it’s a reason to vote rather than abstain. If rationalists like Caplan abstain from voting in large numbers, their abstention may well cause some elections to be won by candidates who favor more government rather than less.

Moreover, Caplan’s argument against voting is really a way of rationalizing his disdain for voting. This is from “Why I Don’t Vote: The Honest Truth”:

My honest answer begins with extreme disgust.  When I look at voters, I see human beings at their hysterical, innumerate worst.  When I look at politicians, I see mendacious, callous bullies.  Yes, some hysterical, innumerate people are more hysterical and innumerate than others.  Yes, some mendacious, callous bullies are more mendacious, callous, and bully-like than others.  But even a bare hint of any of these traits appalls me.  When someone gloats, “Politifact says Trump is pants-on-fire lying 18% of the time, versus just 2% for Hillary,” I don’t want to cheer Hillary.  I want to retreat into my Bubble, where people dutifully speak the truth or stay silent.

Thus demonstrating the confirmation bias in Caplan’s mathematical “proof” of the futility of voting.

Nor is his “proof” really probabilistic. A single event — be it an election, a lottery drawing, of the toss of a fair coin — doesn’t have a probability.  What does it mean to say, for example, that there’s a probability of 0.5 (50 percent) that a tossed coin will come up heads (H), and a probability of 0.5 that it will come up tails (T)? Does such a statement have any bearing on the outcome of a single toss of a coin? No, it doesn’t. The statement is only a shorthand way of saying that in a sufficiently large number of tosses, approximately half will come up H and half will come up T. The result of each toss, however, is a random event — it has no probability. You may have an opinion (or a hunch or a guess) about the outcome of a single coin toss, but it’s only your opinion (hunch, guess). In the end, you have to bet on a discrete outcome.

An election that hasn’t taken place can’t have a probability. There will be opinion polls — a lot of them in the case of a presidential election — but choosing to vote (or not) because of opinion polls can be self-defeating. Take the recent presidential election. Almost all of the polls, including those that forecast the electoral vote as well as the popular vote, had Mrs. Clinton winning over Mr. Trump.

But despite the high “probability” of a victory by Mrs. Clinton, she lost. Why? Because the “ignorant” voters in several swing States turned out in large numbers, while too many pro-Clinton voters evidently didn’t bother to vote. It’s possible that she lost some crucial States because of the abstention of voters who believed the high “probability” that she would win.

The election of 2016 — like every other election — isn’t even close to being something as simple as the toss of a fair coin. And, despite its mathematical precision, a statement about the probability of the next toss of a fair coin is meaningless. It will come up H or it will come up T, but it will not come up 0.5 H or T.

REDUCTIONISM

This subject is more important than probability, so I will say far less about it.

Reductionism is the adoption of a theory or method which holds that a complex idea or system can be completely understood in terms of its simpler components. Most reductionists will defend their theory or method by agreeing that it is simple, if not simplistic. But they will nevertheless adhere to that theory or method because it’s “the best we have.” That claim should remind you of the hoary joke about the drunk who searched for his keys under a street light because he could see the ground there, even though he had dropped the keys half a block away.

Caplan’s adherence to the simplistic, mathematical analysis of voting is a good example of reductionism. Why? Because it omits the crucial influence of group behavior. It also omits other reasons for voting (or not). It certainly omits Caplan’s real reason, which is his “extreme disgust” for voters and the candidates from whom they must choose. Finally, it omits the psychic value of voting — its “feel good” effect.

Economists also are guilty of reductionism when they suggest that persons act rationally only when they pursue the maximization of income or wealth. I’ll say more about that when I get to paternalism.

NIRVANA FALLACY

The nirvana fallacy is the logical error of comparing actual things with unrealistic, idealized alternatives. The actual things usually are the “somethings” about which government is supposed to “do something.” The unrealistic, idealized alternatives are the outcomes sought by the proponents of a particular course of government action.

There is also a pervasive nirvana fallacy about government itself. Government — which is a mere collection of fallible, squabbling, power-lusting humans — is too often thought and spoken of as if it were a kind of omniscient, single-minded, benevolent being that can overcome the forces of nature and human nature which give rise, in the first place, to the “something” about which “something must be done.”

Specific examples of the nirvana fallacy will arise in later chapters of this book.

SOCIAL WELFARE

Wouldn’t you like to arrange the world so that everyone is better off? If you would — and I suspect that most people would — you’d have to define “better off.” Happier, healthier, and wealthier make a good starting point. Of course, you’d have to arrange it so that everyone would be happier and healthier and wealthier in the future as well as in the present. That is, for example, you couldn’t arrange greater happiness at the cost of greater wealth, or at the cost of the greater happiness or wealth of those living today or their descendants.

It’s a tall order isn’t it? In fact, it’s an impossibility. (You might even call it a state of nirvana.) In the real world of limited resources, the best that can happen is that a change of some kind (e.g., the invention of an anti-polio vaccine, hybridization to produce healthier and more abundant crops) makes it possible for many people to be better off — but at a price. There is no free lunch. Someone must bear the costs of devising and implementing beneficial changes. In market economies, those costs are borne by the people who reap the benefits because they (the beneficiaries) voluntarily pay for whatever it is that makes their lives better.

Enter government, whose agents decide such things what lines of medical research to fund, and how much to spend on each line of research. A breakthrough in a line of research might be a boon to millions of Americans. But other millions of Americans — many more millions, in fact — won’t benefit from the breakthrough, though a large fraction of them will have funded the underlying research through taxes extracted from them by force. I say by force because tax collections would decline sharply if it weren’t for the credible threat of heavy fines and imprisonment tax collections.

A voluntary exchange results when each of the parties to the exchange believes that he will be better off as a result of the exchange. An honest voluntary exchange — one in which there is no deception or material lack of information — therefore improves the well-being (welfare) of all parties. An involuntary exchange, as in the case of tax-funded medical research, cannot result make all parties better off. No government agent — or economist, pundit, or politician — can look into the minds of millions of people and say that each of them would willingly donate a certain amount of money to fund this or that government program. And yet, that is the presumption which lies behind government spending.

That presumption is the fallacious foundation of cost-benefit analysis undertaken to evaluate government programs. If the “social benefit” of a program is said to equal or exceed its cost, the program is presumably justified because the undertaking of it would cause “social welfare” to increase. But a “social benefit” — like a breakthrough in medical research — is a always a benefit to some persons, while the taxes paid to elicit the benefit are nothing but a burden to other persons, who have their own problems and priorities.

Why doesn’t the good outweigh the bad? Think of it this way: If a bully punches you in the nose, thus deriving much pleasure at your expense, who is to say that the bully’s pleasure outweighs your pain? Do you believe that there’s a third party who is entitled to say that the result of your transaction with the bully is a heightened state of social welfare? Evidently, there are a lot of voters, economists, pundits, and politician who act as if they believe it.

ROMANTICIZING THE STATE

This section is a corollary to the preceding one.

It is a logical and factual error to apply the collective “we” to Americans, except when referring generally to the citizens of the United States. Other instances of “we” (e.g., “we” won World War II, “we” elected Barack Obama) are fatuous and presumptuous. In the first instance, only a small fraction of Americans still living had a hand in the winning of World War II. In the second instance, Barack Obama was elected by amassing the votes of fewer than 25 percent of the number of Americans living in 2008 and 2012. “We the People” — that stirring phrase from the Constitution’s preamble — was never more hollow than it is today.

Further, the logical and factual error supports the unwarranted view that the growth of government somehow reflects a “national will” or consensus of Americans. Thus, appearances to the contrary (e.g., the adoption and expansion of national “social insurance” schemes, the proliferation of cabinet departments, the growth of the administrative state) a sizable fraction of Americans (perhaps a majority) did not want government to grow to its present size and degree of intrusiveness. And a sizable fraction (perhaps a majority) would still prefer that it shrink in both dimensions. In fact, The growth of government is an artifact of formal and informal arrangements that, in effect, flout the wishes of many (most?) Americans. The growth of government was not and is not the will of “we Americans,” “Americans on the whole,” “Americans in the aggregate,” or any other mythical consensus.

PATERNALISM

Paternalism arises from the same source as “social welfare”; that is, it reflects a presumption that there are some persons who are competent to decide what’s best for other persons. That may be true of parents, but it is most assuredly not true of so-called libertarian paternalists.

Consider an example that’s used to explain libertarian paternalism. Some workers choose “irrationally” — according to libertarian paternalists — when they decline to sign up for an employer’s 401(k) plan. The paternalists characterize the “do not join” option as the default option. In my experience, there is no default option: An employee must make a deliberate choice between joining a 401(k) or not joining it. And if the employee chooses not to join it, he or she must sign a form certifying that choice. That’s not a default, it’s a clear-cut and deliberate choice which reflects the employee’s best judgment, at that time, as to the best way to allocate his or her income. Nor is it an irrevocable choice; it can be revisited annually (or more often under certain circumstances).

But to help employees make the “right” choice, libertarian paternalists would find a way to herd employees into 401(k) plans (perhaps by law). In one variant of this bit of paternalism, an employee is automatically enrolled in a 401(k) and isn’t allowed to opt out for some months, by which time he or she has become used to the idea of being enrolled and declines to opt out.

The underlying notion is that people don’t always choose what’s “best” for themselves. Best according to whom? According to libertarian paternalists, of course, who tend to equate “best” with wealth maximization. They simply disregard or dismiss the truly rational preferences of those who must live with the consequences of their decisions.

Libertarian paternalism incorporates two fallacies. One is what I call the rationality fallacy (a kind of reductionism), the other is the fallacy of central planning.

As for the rationality fallacy, there is simply a lot more to maximizing satisfaction than maximizing wealth. That’s why some couples choose to have a lot of children, when doing so obviously reduces the amount of wealth that they can accumulate. That’s why some persons choose to retire early rather than stay in stressful jobs. Rationality and wealth maximization are two very different things, but a lot of laypersons and too many economists are guilty of equating them.

Nevertheless, many economists do equate rationality and wealth maximization, which leads them to propose schemes for forcing us to act more “rationally.” Such schemes, of course, are nothing more than central planning, dreamt up by self-anointed wise men who seek to impose their preferences on the rest of us. They are, in other words, schemes to maximize that which can’t be maximized: social welfare.

JUDGING MOTIVES INSTEAD OF RESULTS

If a person commits what seems to be an altruistic act, that person may seem to sacrifice something (e.g., a life, a fortune) but the “sacrifice” was that person’s choice. An altruistic act serves an end: the satisfaction of one’s personal values — nothing more, nothing less. There is nothing inherent in a supposedly altruistic act that makes it morally superior to profit-seeking, which is usually thought of as the opposite of altruism.

To illustrate my point I resort to the following bits of caricature:

1. Suppose Mother Teresa’s acts of “sacrifice” were born of rebellion against parents who wanted her to take over their business empire. That is, suppose Mother Teresa derived great satisfaction in defying her parents, and it is that which drove her to impoverish herself and suffer many hardships. The more she “suffered” the more her parents suffered and the happier she became.

2. Suppose Bill Gates really wanted to become a male version of Mother Teresa but his grandmother, on her deathbed, said “Billy, I want you to make the world safe from the Apple computer.” So, Billy went out and did that, for his grandmother’s sake, even though he really wanted to be the male Mother Teresa. Then he wound up being immensely wealthy, much to his regret. But Billy obviously put his affection for or fear of his grandmother above his desire to become a male version of Mother Teresa. He satisfied his personal values. And in doing so, he make life better for millions of people, many millions more than were served by Mother Teresa’s efforts. It’s just that Billy’s efforts weren’t heart-rending, and were seemingly motivated by profit-seeking.

Now, tell me, who is the altruist, my fictional Mother Teresa or my fictional Bill Gates? You might now say Bill Gates. I would say neither; each acted in accordance with her and his personal values. One might call the real Mother Teresa altruistic because her actions seem altruistic, in the common meaning of the word. But one can’t say (for sure) why she took those actions. Suppose that the real Mother Teresa acted as she did not only because she wanted to help the poor but also because she sought spiritual satisfaction or salvation. Would that negate her acts? No, her acts would still be her acts, regardless of their motivation. The same goes for the real Bill Gates.

Results matter more than motivations. (“The road to hell,” and all that.) It is arguable that profit-seekers like the real Bill Gates — and the real John D. Rockefeller, Andrew Carnegie, Henry Ford, and their ilk — brought more happiness to humankind than did Mother Teresa and others of her ilk.

That insight is at least 240 years old. Adam Smith put it this way in The Wealth of Nations (1776):

By pursuing his own interest [a person] frequently promotes that of the society more effectually than when he really intends to promote it. I have never known much good done by those who affected to trade for the public good.

A person who makes a profit makes it by doing something of value for others.

Economics, the Dismal Quasi-Science: 1. What Is Economics?

This is the first entry in what I hope will become a book-length series of posts. That result, if it comes to pass, will amount to an unorthodox economics textbook. This first chapter gives a hint of things to come. Here are the chapters that have been posted to date:

1. What is Economics?
2. Pitfalls

A book about economics should begin by explaining what the author means by the word. Many economists have given many definitions of economics. You can look them up.

Regardless of where it started, economics seems to have become the study of how human beings make choices and how those choices affect them directly (e.g., the demand for and supply of new automobiles, enrollment in an employer’s retirement plan) and indirectly (e.g., the effects of government actions on the income available for the purchase of new automobiles or on the benefits paid out by retirement plans). The parenthetical examples are about choices that usually come with dollar signs attached. And most non-economists probably think of economics as having something (or everything) to do with money – earning it, spending it, making a profit (or not) by making and selling things, adding up the dollar value of items bought and sold to arrive at an estimate of aggregate economic activity, and understanding why the aggregate grows and shrinks, for example.

But there are many economists nowadays who have taken the study of choice into areas that would seem strange to economists of yore. Here’s just one example: voting, as in whether or not to vote and how much time (if any) to spend in the pursuit of information about the candidates or issues on the ballot. Some economists tackle voting as they would any other aspect of economics: by arguing (pro or con) that voting is rational (or irrational) given the amount of time involved (time that could spent on other pursuits, such as making money), the vanishingly small chance that an individual vote will tip the balance in an election (at least in an election where there are more than a few hundred voters), and the effect of the election results on the individual voter’s well-being (usually in terms of money).

On the other hand (as economists are supposedly fond of saying), there are economists who recognize that casting a ballot is a “feel good” act, and that voting is therefore rational if it makes one happier. But that’s only a local, short-run effect. Some economists understand that voting leads to the enactment of policies that harm voters (or many of them), regardless of why they choose to vote. This points to two conclusions: (1) Voting should be discouraged, and (2) the power of government should be curbed so that voters can feel good without causing harm (or as much of it as they do now).

So, which is it? Is voting a waste of time or is it a good use of time if it makes the voter feel good? And is it worse than a waste of time if it leads to harm? This conundrum illustrates a key point about economics (and analysis in general): It leads to conclusions that are built into the assumptions (usually implicit) that guide the economist who studies an issue. If the economist cares about liberty, he is likely to tackle the issue of voting as it affects persons other than the voter. If the economist isn’t interested in liberty – or if he sees it only as a peripheral issue — he is likely to tackle the issue of voting as it affects the voter.

Unfortunately, too many economists take the view that if government can do something to promote economic well-being, it ought to be empowered to do so. But economic well-being is in the eye of the beholder. And in this era of massive redistribution, one person’s benefit is another person’s cost. Who, other than an arrogant economist, presumes to weigh one person’s benefit against another person’s cost? My list begins with the greedy voter who believes that he can get something for nothing; the smug pundit; and the power-hungry, vote-buying politician.

There is much more to be said about the wayward paths taken by economists, and the essays in this book say a lot of it. But more than that, this book is a defense of liberty against economists who – wittingly or not – undermine it. And, ironically, the diminution of liberty results in the diminution of prosperity, which economists claim to love.

In sum economics is fraught with dangerous error. This book is meant as a warning and antidote.

Economics from the Bottom Up

This is the sixth entry in a series of loosely connected posts on economics. Previous entries are here, here, here, here, and here.

NEEDS AND WANTS

What human beings want and what they need are popularly thought to be distinguishable things. Needs are said to be those things that sustain life: food (the minimum daily requirement, with no frills), clothing (just enough to protect us from the elements), and shelter (ditto). Wants are generally thought to be everything else.

But individuals vary in their perceptions of what they need; one person’s need is another person’s luxury. If a rich playboy “needs” a $300,000 Lamborghini, that’s for him to say. Accountants of the soul — moralists who believe that they know how the world should be ordered — will assert that the rich playboy can make do with a $10,000 Kia. But if that’s true, everyone can make do with a $10,000 Kia. And if that’s true, why not a bus token? (Only the older folks will remember those.)

Once the accountants of the soul are loosed, the people are allowed to need (or want) whatever the accountants of the soul approve. Except for those people who are in the good graces of the accountants of the soul. If you suspect that I’m alluding to places like the the former Soviet Union, the former German Democratic (sic) Republic, and the present Cuba and Venezuela, you’re right.

Given that “need” is a loaded word, I use the less-loaded “want.” Thus the rich playboy wants a $300,000 Lamborghini. Whether he needs it is not for economists or accountants of the soul to determine. It’s for the rich playboy to determine as he allocates his available income and wealth to competing products, services, and investment vehicles.

Wants are limitless. I may want a $300,000 Lamborghini (though I am not a rich playboy), a villa on the Mediterranean, and a collection of Rembrandts. Those wants lie far beyond my reach, and the reach of most human beings.

The main attribute of wants, other than their inherent limitlessness, is their vast variety and changeableness. Wants and their ordering can change from minute to minute, depending on what a person was doing a minute ago, what he happens to be doing now, where he happens to be doing it, the conditions in which he is doing it, who or what happens to impinge on his consciousness, how he is feeling (emotionally and physically), and so on. It takes free markets — not command economies run by soul-accountants — to respond to the vast variety and changeableness of wants.

TASTES AND PREFERENCES

A particular want is sometimes referred to as a taste. My taste for ice cream has persisted for more than seven decades. But I may, at various times, want different kinds of ice cream. And I may, at various times, prefer a certain ice cream to, say, chocolate cake, or vice versa.

To take another example, a middle-aged man may have only a residual taste for action movies; that is, he rarely wants to view one, given the alternative ways in which in can use his time and given his recently acquired taste for technical non-fiction books. Yet, twenty years earlier, when he had a strong taste for action movies, the now middle-aged man had no interest in technical non-fiction; it didn’t enter into his thoughts when he pondered whether to watch an action film or do something else.

If tastes represent the kinds of things wanted by a person, preferences are the order in which a person wishes to satisfy those tastes. Preferences, like tastes, change with time (often rapidly), and are also situation-dependent. For example, when I’ve finished eating a large bowl of chocolate ice cream, I’m likely to prefer a glass of cold root beer to another bowl of chocolate ice cream.

The preference for a glass of cold root beer rather than another bowl of ice cream doesn’t necessarily mean that the second bowl of ice cream would give me less satisfaction than the first bowl. It might or might not. But, in the circumstances, I would enjoy a glass of cold root beer more than another bowl of ice cream. (The concept of diminishing marginal utility may apply to particular things at particular times, but it is neither generally true nor a valid reason for redistributing income by force.)

UTILITY AND DEMAND

There are two classic microeconomic constructs that reduce wants, tastes, and preferences to discrete quantities: indifference curves and demand curves.

An indifference curve is said to depict the rate at which a consumer is willing to exchange units of product X for units of other products, while holding constant his level of satisfaction (utility) and his preferences (ordering of wants). Given the rapidity with which preferences can change, I see little utility in indifference curves — except as a pedagogic device.

A demand curve for X can be derived from indifference curves by showing how the amount of X preferred by the consumer varies with the price of X, where (at each price) the consumer chooses the mix of X and other economic goods that maximize his utility. (I use economic goods to stand for products — material items — and services, which require the use of material items but which aren’t material (e.g., a haircut, the use of a credit card to make a purchase).

But an individual’s wants, tastes, and preferences are fuzzy at any given time. So, an individual’s demand for X at any given time is fuzzy — and then it changes, in fuzzy ways.

The summation of all consumers’ demand curves for X yields, in theory, an aggregate or market demand for X at an instant in time — holding constant wants, preferences, income, and the prices of other goods. In other words, the demand for a given economic good is very fuzzy. It may be possible to estimate approximately the demand for a particular economic good for a brief period of time, though the approximation will necessarily come with a range of uncertainty.

SUPPLY: THE SATISFACTION OF WANTS

If demand is one blade of a scissor, supply is the other blade. By supply I mean the ways in which human beings contrive to satisfy at least some of their wants some of the time. Supply comes in three basic forms: individual action, cooperative behavior, and voluntary exchange.

Individual action is just that: what each of us does to satisfy his wants without the help of others, and without recourse to the exchange of one’s resources for the resources of others.  Needless to say, individual action is limited mainly to Robinson Crusoe cases: situations in which a person must fend for himself, to the best of his ability and given the resources at hand.

Cooperative behavior is more relevant to the satisfaction of wants. It is the kind of behavior that wasn’t uncommon in the rural America of decades past, when each farm family operated as an economic unit. The combined efforts of a family — joined at times by neighbors — yielded shelter, food, and (sometimes) clothing, all of which were shared within the family.

To the extent that the family’s efforts failed to yield all of the kinds of food and clothing wanted by the family, it would then turn to voluntary exchange. It would trade some of its products (or labor) in order to acquire things that it could not produce or — this is a key point — could not produce as efficiently as another family or business. Voluntary exchange is of course today’s main mechanism for satisfying wants.

Voluntary exchange in a complex economy is a roundabout process, through which persons with marketable skills (e.g., real accountants) trade their services for monetary income, which enables them to choose from myriad products, services, and investment vehicles.

SUPPLY CREATES DEMAND (BUT NOT VICE VERSA)

Say’s law — popularly rendered as “supply creates its own demand” — is explained by Steven Horwitz in “Understanding Say’s Law of Markets“:

In the passage where he gets at the insight behind the notion that supply creates its own demand, Say writes: “it is production which opens a demand for products. . . . Thus the mere circumstance of the creation of one product immediately opens a vent for other products.” Put another way, Say was making the claim that production is the source of demand. One’s ability to demand goods and services from others derives from the income produced by one’s own acts of production.

Can demand exist without supply? Only if the person who wants something but lacks the wherewithal to pay for it is able to finance his purchase in one of three lawful ways:

  • He may receive a gift of money. But that gift reduces the purchasing power of the giver, either directly as a subtraction from his income or indirectly as a subtraction from his wealth. So the net effect on the demand for all goods may be zero.
  • He may receive a subsidy from government. But the subsidy reduces the purchasing power of the persons who are compelled to finance it through taxes, or the purchasing power persons or companies who are able to borrow less because government borrowing (to finance the subsidy) displaces their borrowing.
  • He may receive credit, either from the seller or a third party. Credit usually will be extended on the basis of the borrower’s prospective future earnings.

The third case is the only one that clearly results in an additional demand for goods. And it is the one in which demand is financed by supply. Demand creates supply only when demand is financed by a claim on the demander’s future supply of economic goods. The ability of a creditor to finance demand rests ultimately on the creditor’s previous production (and sale) of economic goods.

The Keynesian proposition that demand can create supply of thin air, simply by throwing money at unemployed resources, is a fantasy perpetuated by mathematical trickery.

MEANINGLESS AGGREGATION

Are you better off, as a consumer, than you were 5, 10, or 15 years ago? That’s a question which only you can answer. And the answer won’t necessarily depend on your rate of spending today as compared with your rate of spending 5, 10, or 15 years ago. It will depend on how you — and only you — feel about the enjoyment that you derive from your expenditures.

Like you, A and B will derive different kinds and amounts of enjoyment the goods that they buy. And those different kinds and amounts of enjoyment cannot be summed because they are unique to A and to B, just as they are unique to you. If meaningful aggregation is impossible for A and B, how can it be possible for an economy that consists of millions of economic actors and an untold variety of goods and services? And how is it possible when technological change yields results such as this?

GDP, in other words, is nothing more than what it seems to be on the surface: an estimate of the dollar value of economic output. It is not a measure of “social welfare” because there is no such thing.

Not-So Random Thoughts (XIX)

ITEM ADDED 12/18/16

Manhattan Contrarian takes on the partisan analysis of economic growth offered by Alan Blinder and Mark Watson, and endorsed (predictably) by Paul Krugman. Eight years ago, I took on an earlier analysis along the same lines by Dani Rodrik, which Krugman (predictably) endorsed. In fact, bigger government, which is the growth mantra of economists like Blinder, Watson, Rodrik, and (predictably) Krugman, is anti-growth. The combination of spending, which robs the private sector of resources, and regulations, which rob the private sector of options and initiative, is killing economic growth. You can read about it here.

*     *     *

Rania Gihleb and Kevin Lang say that assortative mating hasn’t increased. But even if it had, so what?

Is there a potential social problem that will  have to be dealt with by government because it poses a severe threat to the nation’s political stability or economic well-being? Or is it just a step in the voluntary social evolution of the United States — perhaps even a beneficial one?

In fact,

The best way to help the people … of Charles Murray’s Fishtown [of Coming Apart] — is to ignore the smart-educated-professional-affluent class. It’s a non-problem…. The best way to help the forgotten people of America is to unleash the latent economic power of the United States by removing the dead hand of government from the economy.

*     *     *

Anthropogenic global warming (AGW) is a zombie-like creature of pseudo-science. I’ve rung its death knell, as have many actual scientists. But it keeps coming back. Perhaps President Trump will drive a stake through its heart — or whatever is done to extinguish zombies. In the meantime, here’s more evidence that AGW is a pseudo-scientific hoax:

In conclusion, this synthesis of empirical data reveals that increases in the CO2 concentration has not caused temperature change over the past 38 years across the Tropics-Land area of the Globe. However, the rate of change in CO2 concentration may have been influenced to a statistically significant degree by the temperature level.

And still more:

[B]ased on [Patrick[ Frank’s work, when considering the errors in clouds and CO2 levels only, the error bars around that prediction are ±15˚C. this does not mean—thankfully— that it could be 19˚ warmer in 2100. rather, it means the models are looking for a signal of a few degrees when they can’t differentiate within 15˚ in either direction; their internal errors and uncertainties are too large. this means that the models are unable to validate even the existence of a CO2 fingerprint because of their poor resolution, just as you wouldn’t claim to see DnA with a household magnifying glass.

And more yet:

[P]oliticians using global warming as a policy tool to solve a perceived problem is indeed a hoax. The energy needs of humanity are so large that Bjorn Lomborg has estimated that in the coming decades it is unlikely that more than about 20% of those needs can be met with renewable energy sources.

Whether you like it or not, we are stuck with fossil fuels as our primary energy source for decades to come. Deal with it. And to the extent that we eventually need more renewables, let the private sector figure it out. Energy companies are in the business of providing energy, and they really do not care where that energy comes from….

Scientists need to stop mischaracterizing global warming as settled science.

I like to say that global warming research isn’t rocket science — it is actually much more difficult. At best it is dodgy science, because there are so many uncertainties that you can get just about any answer you want out of climate models just by using those uncertianties as a tuning knob.

*     *     *

Well, that didn’t take long. lawprof Geoffrey Stone said something reasonable a few months ago. Now he’s back to his old, whiny, “liberal” self. Because the Senate failed to take up the nomination of Merrick Garland to fill Antonin Scalia’s seat on the Supreme Court — which is the Senate’s constitutional prerogative, Stone is characterizing the action (or lack of it) as a “constitutional coup d’etat” and claiming that the eventual Trump nominee will be an “illegitimate interloper.” Ed Whelan explains why Stone is wrong here, and adds a few cents worth here.

*     *     *

BHO stereotypes Muslims by asserting that

Trump’s proposal to bar immigration by Muslims would make Americans less safe. How? Because more Muslims would become radicalized and acts of terrorism would therefore become more prevalent. Why would there be more radicalized Muslims? Because the Islamic State (IS) would claim that America has declared war on Islam, and this would not only anger otherwise peaceful Muslims but draw them to IS. Therefore, there shouldn’t be any talk of barring immigration by Muslims, nor any action in that direction….

Because Obama is a semi-black leftist — and “therefore” not a racist — he can stereotype Muslims with impunity. To put it another way, Obama can speak the truth about Muslims without being accused of racism (though he’d never admit to the truth about blacks and violence).

It turns out, unsurprisingly, that there’s a lot of truth in stereotypes:

A stereotype is a preliminary insight. A stereotype can be true, the first step in noticing differences. For conceptual economy, stereotypes encapsulate the characteristics most people have noticed. Not all heuristics are false.

Here is a relevant paper from Denmark.

Emil O. W. Kirkegaard and Julius Daugbjerg Bjerrekær. Country of origin and use of social benefits: A large, preregistered study of stereotype accuracy in Denmark. Open Differential Psychology….

The high accuracy of aggregate stereotypes is confirmed. If anything, the stereotypes held by Danish people about immigrants underestimates those immigrants’ reliance on Danish benefits.

Regarding stereotypes about the criminality of immigrants:

Here is a relevant paper from the United Kingdom.

Noah Carl. NET OPPOSITION TO IMMIGRANTS OF DIFFERENT NATIONALITIES CORRELATES STRONGLY WITH THEIR ARREST RATES IN THE UK. Open Quantitative Sociology and Political Science. 10th November, 2016….

Public beliefs about immigrants and immigration are widely regarded as erroneous. Yet popular stereotypes about the respective characteristics of different groups are generally found to be quite accurate. The present study has shown that, in the UK, net opposition to immigrants of different nationalities correlates strongly with the log of immigrant arrests rates and the log of their arrest rates for violent crime.

The immigrants in question, in both papers, are Muslims — for what it’s worth.

* * *

ADDED 12/18/16:

I explained the phoniness of the Keynesian multiplier here, derived a true (strongly negative) multiplier here, and added some thoughts about the multiplier here. Economist Scott Sumner draws on the Japanese experience to throw more cold water on Keynesianism.

The IQ of Nations

In a twelve-year-old post, “The Main Causes of Prosperity,” I drew on statistics (sourced and described in the post) to find a statistically significant relationship between a nation’s real, per-capita GDP and three variables:

Y =  – 23,518 + 2,316L – 259T  + 253I

Where,
Y = GDP in 1998 dollars (U.S.)
L = Index for rule of law
T = Index for mean tariff rate
I = Verbal IQ

The r-squared of the regression equation is 0.89 and the p-values for the intercept and independent variables are 8.52E-07, 4.70E-10, 1.72E-04, and 3.96E-05.

The effect of IQ, by itself, is strong enough to merit a place of honor:

per-capita-gdp-and-average-verbal-iq

Another relationship struck me when I revisited the IQ numbers. There seems to be a strong correlation between IQ and distance from the equator. That correlation, however, may be an artifact of the strong (negative) correlation between blackness and IQ: The countries whose citizens are predominantly black are generally closer to the equator than the countries whose citizens are predominantly of other races.

Because of the strong (negative) correlation between blackness and IQ, and the geographic grouping of predominantly black countries, it’s not possible to find a statistically significant regression equation that accounts for national IQ as a function of the distance of nations from the equator and their dominant racial composition.

The most significant regression equation omits distance from the equator and admits race:

I = 84.0 – 13.2B + 12.4W + 20.7EA

Where,
I = national average IQ
B = predominantly black
W = predominantly white (i.e., residents are European or of European origin)
EA = East Asian (China, Hong Kong, Japan, Mongolia, South Korea, Taiwan, and Singapore, which is largely populated by persons of Chinese descent)

The r-squared of the equation is 0.78 and the p-values of the intercept and coefficients are all less than 1E-17. The F-value of the equation is 8.24E-51. The standard error of the estimate is 5.6, which means that the 95-percent confidence interval is plus or minus 11 — a smaller number than any of the coefficients.

The intercept applies to all “other” countries that aren’t predominantly black, white, or East Asian in their racial composition. There are 66 such countries in the sample, which comprises 159 countries. The 66 “other” countries span the Middle East; North Africa; South Asia; Southeast Asia; island-states in Indian, Pacific, and Atlantic Oceans; and most of the nations of Central and South America and the Caribbean. Despite the range of racial and ethnic mixtures in those 66 countries, their average IQs cluster fairly tightly around 84. By the same token, there’s a definite clustering of the black countries around 71 (84.0 – 13.2), of the white countries around 96 (84.0 + 12.4), and of the East Asian countries around 105 (84.0 + 20.7).

Thus this graph, where each “row” (from bottom to top) corresponds to black, “other,” white, and East Asian:

estimated-vs-actual-iq

The dotted line represents a perfect correlation. The regression yields a less-than-perfect relationship between race and IQ, but a strong one. That strong relationship is also seen in the following graph:

iq-vs-distance-from-the-equator

There’s a definite pattern — if a somewhat loose one — that goes from low-IQ black countries near the equator to higher IQ white countries farther from the equator. The position  of East Asian countries, which is toward the middle latitudes rather than the highest ones, points to something special in the relationship between East Asian genetic heritage and IQ.

*     *     *

Related posts:
Race and Reason: The Victims of Affirmative Action
Race and Reason: The Achievement Gap — Causes and Implications
“Conversing” about Race
Evolution and Race
“Wading” into Race, Culture, and IQ
Evolution, Culture, and “Diversity”
The Harmful Myth of Inherent Equality
Let’s Have That “Conversation” about Race

Today’s Lesson in Economics: How to Think about War

David Henderson writes at EconLog about “Noah Smith on the Islamic Civil War“:

Noah Smith has a beautifully numerate discussion of wars being fought by radical Muslims. He does it in the context of analyzing Trump advisor Steve Bannon, and that analysis is not bad.

But what really struck me was his response to this claim of Bannon:

[I]t’s a very unpleasant topic, but we are in an outright war against jihadist Islamic fascism. And this war is, I think, metastasizing far quicker than governments can handle it…
. . .I believe you should take a very, very, very aggressive stance against radical Islam…If you look back at the long history of the Judeo-Christian West struggle against Islam, I believe that our forefathers kept their stance, and I think they did the right thing. I think they kept it out of the world, whether it was at Vienna, or Tours, or other places… It bequeathed to use [sic] the great institution that is the church of the West.

Smith then reports on the numbers on deaths from some Islamic groups fighting others. H writes:

Let’s look at the main wars currently being fought by radical Islamic forces. These are:Syrian Civil War (~470,000 dead)
2nd Iraqi Civil War (~56,000 dead)
Boko Haram Insurgency (~28,000 dead)
War in Afghanistan (126,000 dead)
Somali Civil War (~500,000 dead)
War in Northwest Pakistan (~60,000 dead)
Libyan Civil War (~14,000 dead)
Yemeni Civil War (~11,000 dead)
Sinai Insurgency (~4,500 dead)

Smith adds:

This is a lot of dead people – maybe about 2 million in all, counting all the smaller conflicts I didn’t list. But almost all of these dead people are Muslims – either radical Islamists, or their moderate Muslim opponents. Compare these death tolls to the radical Islamist terror attacks in the West. 9/11 killed about 3,000. The ISIS attack in Paris killed 130. The death tolls in the West from radical Islam have been three orders of magnitude smaller than the deaths in the Muslim world.Three orders of magnitude is an almost inconceivable difference in size. What it means is that only a tiny, tiny part of the wars of radical Islam is bleeding over into the West. What we’re seeing is not a clash of civilizations, it’s a global Islamic civil war. The enemy isn’t at the gates of Vienna – it’s at the gates of Mosul, Raqqa, and Kabul.

And radical Islam is losing the global Islamic civil war. In Syria and Iraq, ISIS is losing. In Nigeria, Boko Haram is losing. In all of these wars except for possibly Afghanistan, radical Islamic forces have been defeated by moderate Islamic forces.

Sometimes that’s because of Western aid to the moderates. But much of it is just because a medievalist regime holds very, very little appeal for the average Muslim in any country. Practically no one wants to live under the sadist, totalitarian control of groups like ISIS. These groups are fierce, but their manpower is small and their popular support is not very large anywhere.

How tragic it would be if Steve Bannon’s innumeracy helped cause the U.S. government to embroil itself in the Middle East even more than Bush and Obama did.

Henderson’s counsel to avoid “embroilment” overlooks Iran.

There sometimes comes a point at which it makes sense to become embroiled in a distant war. Take World War II, for example. FDR’s economic policies were disastrous for the U.S. — of that there’s never been any doubt in my mind. But I give FDR credit for his ability to see that if Germany and Japan gained dominance over Europe and the Pacific, the U.S. would eventually be squeezed into submission, economically and militarily. My point is that not all “embroilments” are necessarily bad.

Which brings me to the Middle East. If the U.S. allows Iran to develop nuclear weapons — which seems to be certain given Obama’s supine attitude toward Iran — disaster will follow. Iran will be able to control the region through nuclear blackmail, and given its reserves of oil and the willingness of its leaders to accept economic isolation, it (meaning its leaders) will be able to disrupt life in the West because of its ability to shut off the supply of oil to the West.

To paraphrase Andy Granatelli, the U.S. can stop Iran now, before it has done what Obama is allowing it to do, or the U.S. can stop it later, after it has done great economic damage, which the U.S. won’t escape inasmuch as the market for oil is unitary. Nor will the U.S. escape human damage if the U.S. doesn’t act until after Iran becomes capable of attacking the U.S.

It doesn’t matter who did what to cause Iran’s leaders to view the U.S. as “the great Satan.” (Sunk costs are sunk.) There’s no longer an option to butt out of Iran’s affairs. Given the fanatical enmity of Iran’s leaders toward the U.S. (which isn’t dispelled by superficial cordiality), it’s beyond belief that Iran isn’t steadily striving to acquire the ability to strike the U.S. with weapons of mass destruction — nuclear missiles, perhaps delivered from off-shore vessels instead of by ICBMs; “suitcase” bombs; coordinated strikes on the power grid, oil-production facilities, and water supplies; and much more that the U.S. intelligence apparatus should but may not anticipate, and which the U.S. government’s leaders may in any event fail to prepare for.

I may be wrong about all of this, but it’s the kind of thinking that should be done — even by economists — instead of latching onto Noah Smith’s superficial numeracy.

Economic Modeling: A Case of Unrewarded Complexity

This is the fifth entry in a series of loosely connected posts on economics. Previous entries are here, here, here, and here.

I wrote “About Economic Forecasting” twelve years ago. Here are some highlights:

In the the previous post I disparaged the ability of economists to estimate the employment effects of the minimum wage. I’m skeptical because economists are notoriously bad at constructing models that adequately predict near-term changes in GDP. That task should be easier than sorting out the microeconomic complexities of the labor market.

Take Professor Ray Fair, for example. Prof. Fair teaches macroeconomic theory, econometrics, and macroeconometric models at Yale University. He has been plying his trade since 1968, first at Princeton, then at M.I.T., and (since 1974) at Yale. Those are big-name schools, so I assume that Prof. Fair is a big name in his field.

Well, since 1983, Prof. Fair has been forecasting changes in real GDP over the next four quarters. He has made 80 such forecasts based on a model that he has undoubtedly tweaked over the years. The current model is here. His forecasting track record is here. How has he done? Here’s how:

1. The median absolute error of his forecasts is 30 percent.

2. The mean absolute error of his forecasts is 70 percent.

3. His forecasts are rather systematically biased: too high when real, four-quarter GDP growth is less than 4 percent; too low when real, four-quarter GDP growth is greater than 4 percent.

4. His forecasts have grown generally worse — not better — with time.

Prof. Fair is still at it. And his forecasts continue to grow worse with time:

FIGURE 1
fair-model-forecasting-errors-vs-time
This and later graphs pertaining to Prof. Fair’s forecasts were derived from The Forecasting Record of the U.S. Model, Table 4: Predicted and Actual Values for Four-Quarter Real Growth, at Prof. Fair’s website. The vertical axis of this graph is truncated for ease of viewing; 8 percent of the errors exceed 200 percent.

You might think that Fair’s record reflects the persistent use of a model that’s too simple to capture the dynamics of a multi-trillion-dollar economy. But you’d be wrong. The model changes quarterly. This page lists changes only since late 2009; there are links to archives of earlier versions, but those are password-protected.

As for simplicity, the model is anything but simple. For example, go to Appendix A: The U.S. Model: July 29, 2016, and you’ll find a six-sector model comprising 188 equations and hundreds of variables.

And what does that get you? A weak predictive model:

FIGURE 2
fair-model-estimated-vs-actual-growth-rate

It fails the most important test; that is, it doesn’t reflect the downward trend in economic growth:

FIGURE 3
fair-model-year-over-year-growth-estimated-and-actual

Could I do better? Well, I’ve done better — without knowing it until now — with the simple model that I devised to estimate the Rahn Curve. It’s described in “The Rahn Curve Revisited.” The following quotations and discussion draw on the October 20, 2016, version of that post:

The theory behind the Rahn Curve is simple — but not simplistic. A relatively small government with powers limited mainly to the protection of citizens and their property is worth more than its cost to taxpayers because it fosters productive economic activity (not to mention liberty). But additional government spending hinders productive activity in many ways, which are discussed in Daniel Mitchell’s paper, “The Impact of Government Spending on Economic Growth.” (I would add to Mitchell’s list the burden of regulatory activity, which grows even when government does not.)

Rahn curve (2)

. . . .

In an earlier post, I ventured an estimate of the Rahn curve that spanned most of the history of the United States. I came up with this relationship (terms modified for simplicity:

G = 0.054 -0.066F

To be precise, it’s the annualized rate of growth over the most recent 10-year span (G), as a function of F (fraction of GDP spent by governments at all levels) in the preceding 10 years. The relationship is lagged because it takes time for government spending (and related regulatory activities) to wreak their counterproductive effects on economic activity. Also, I include transfer payments (e.g., Social Security) in my measure of F because there’s no essential difference between transfer payments and many other kinds of government spending. They all take money from those who produce and give it to those who don’t (e.g., government employees engaged in paper-shuffling, unproductive social-engineering schemes, and counterproductive regulatory activities).

When F is greater than the amount needed for national defense and domestic justice — no more than 0.1 (10 percent of GDP) — it discourages productive, growth-producing, job-creating activity. And because government spending weighs most heavily on taxpayers with above-average incomes, higher rates of F also discourage saving, which finances growth-producing investments in new businesses, business expansion, and capital (i.e., new and more productive business assets, both physical and intellectual).

I’ve taken a closer look at the post-World War II numbers because of the marked decline in the rate of growth since the end of the war:

Real GDP 1947q1-2016q2

Here’s the revised result (with cosmetic changes in terminology):

G = 0.0275 -0.347F + 0.0769A – 0.000327R – 0.135P

Where,

G = real rate of GDP growth in a 10-year span (annualized)

F = fraction of GDP spent by governments at all levels during the preceding 10 years

A = the constant-dollar value of private nonresidential assets (business assets) as a fraction of GDP, averaged over the preceding 10 years

R = average number of Federal Register pages, in thousands, for the preceding 10-year period

P = growth in the CPI-U during the preceding 10 years (annualized).

The r-squared of the equation is 0.73 and the F-value is 2.00E-12. The p-values of the intercept and coefficients are 0.099, 1.75E-07, 1.96E-08, 8.24E-05, and 0.0096. The standard error of the estimate is 0.0051, that is, about half a percentage point. (Except for the p-value on the coefficient, the other statistics are improved from the previous version, which omitted CPI).

Here’s how the equations with and without P stack up against actual changes in 10-year rates of real GDP growth:

rahn-curve-model-actual-vs-estimates-with-and-without-p

The equation with P captures the “bump” in 2000, and is generally (though not always) closer to the mark than the equation without P.

What does the new equation portend for the next 10 years? Based on the values of F, A, R, and P for the most recent 10-year period (2006-2015), the real rate of growth for the next 10 years will be about 1.9 percent. (It was 1.4 percent for the version of the equation without P.) The earlier equation (discussed above) yields an estimate of 2.9 percent. The new equation wins the reality test, as you can tell by the blue line in the second graph above.

In fact the year-over-year rates of real growth for the past four quarters (2015Q3 through 2016Q2) are 2.2 percent, 1.9 percent, 1.6 percent, and 1.3 percent. So an estimate of 1.9 percent for the next 10 years may be optimistic.

I took the data set that I used to estimate the new equation and made a series of out-of-sample estimates of growth over the next 10 years. I began with the data for 1946-1964 to estimate the growth for 1965-1974. I continued by taking the data for 1946-1965 to estimate the growth for 1966-1975, and so on, until I had estimated the growth for every 10-year period from 1965-1974 through 2006-2015. In other words, like Prof. Fair I updated my model to reflect new data, and I estimated the rate of economic growth in the future. How did I do? Here’s a first look:

FIGURE 4
rahn-curve-model-estimation-errors-vs-actual-values

The errors get larger with time, but they are far smaller than the errors in Fair’s model (see figure 1).

Not only that, but there’s a much better fit. Compare the following graph with figure 2:

FIGURE 5
rahn-curve-model-10-year-real-rates-of-growth-actual-and-estimated

Why do the errors in Fair’s model and mine increase with time? Probably of the erratic downward trend in economic growth, which Fair doesn’t capture in his estimates (see figure 3), but which is matched more closely by my estimates:

FIGURE 6
rahn-curve-model-estimated-vs-actual

The moral of the story: It’s futile to build complex models of the economy. They can’t begin to capture the economy’s real complexity, and they’re likely to obscure the important variables — the ones that will determine the future course of economic growth.

A final note: In earlier posts I’ve disparaged economic aggregates, of which GDP is the apotheosis. And yet I’ve built this post around estimates of GDP. Am I contradicting myself?

Not really. There’s a rough consistency in measures of GDP across time, and I’m not pretending that GDP represents anything but an estimate of the monetary value of those products and services to which monetary values can be ascribed.

As a practical matter, then, if you’re a person who wants to know the likely future direction and value of GDP, stick with simple estimation techniques like the one I’ve demonstrated here. Don’t get bogged down in the inconclusive minutiae of a model like Prof. Fair’s.

Mathematical Economics

This is the fourth entry in a series of loosely connected posts on economics. Previous entries are here, here, and here.

Economics is a study of human behavior, not an exercise in mathematical modeling or statistical analysis, though both endeavors may augment an understanding of human behavior. Economics is about four things:

  • wants, as they are perceived by the persons who have those wants
  • how people try to satisfy their wants through mutually beneficial, cooperative action, which includes but is far from limited to market-based exchanges
  • how exogenous forces, including government interventions, enable or thwart the satisfaction of wants
  • the relationships between private action, government interventions, and changes in the composition, rate, and direction of economic activity

In sum, economics is about the behavior of human beings, which is why it’s called a social science. Well, economics used to be called a social science, but it’s been a long time (perhaps fifty years) since I’ve heard or read an economist refer to it as a social science. The term is too reminiscent of “soft and fuzzy” disciplines such as history, social psychology, sociology, political science, and civics or social studies (names for the amalgam of sociology and government that was taught in high schools way back when). No “soft and fuzzy” stuff for physics-envying economists.

However, the behavior of human beings — their thoughts and emotions, how those things affect their actions, and how they interact — is fuzzy, to say the least. Which explains why mathematical economics is largely an exercise in mental masturbation.

In my disdain for mathematical economics, I am in league with Arnold Kling, who is the most insightful economist I have yet encountered in more than fifty years of studying and reading about economics. I especially recommend Kling’s Specialization and Trade: A Reintroduction to Economics. It’s a short book, but chock-full of wisdom and straight thinking about what makes the economy tick. Here’s the blurb from Amazon.com:

Since the end of the second World War, economics professors and classroom textbooks have been telling us that the economy is one big machine that can be effectively regulated by economic experts and tuned by government agencies like the Federal Reserve Board. It turns out they were wrong. Their equations do not hold up. Their policies have not produced the promised results. Their interpretations of economic events — as reported by the media — are often of-the-mark, and unconvincing.

A key alternative to the one big machine mindset is to recognize how the economy is instead an evolutionary system, with constantly-changing patterns of specialization and trade. This book introduces you to this powerful approach for understanding economic performance. By putting specialization at the center of economic analysis, Arnold Kling provides you with new ways to think about issues like sustainability, financial instability, job creation, and inflation. In short, he removes stiff, narrow perspectives and instead provides a full, multi-dimensional perspective on a continually evolving system.

And he does, without using a single graph. He uses only a few simple equations to illustrate the bankruptcy of macroeconomic theory.

Those economists who rely heavily on mathematics like to say (and perhaps even believe) that mathematical expression is more precise than mere words. But, as Kling points out in “An Important Emerging Economic Paradigm,” mathematical economics is a language of “faux precision,” which is useful only when applied to well defined, narrow problems. It can’t address the big issues — such as economic growth — which depend on variables such as the rule of law and social norms which defy mathematical expression and quantification.

I would go a step further and argue that mathematical economics borders on obscurantism. It’s a cult whose followers speak an arcane language not only to communicate among themselves but to obscure the essentially bankrupt nature of their craft from others. Mathematical expression actually hides the assumptions that underlie it. It’s far easier to identify and challenge the assumptions of “literary” economics than it is to identify and challenge the assumptions of mathematical economics.

I daresay that this is true even for persons who are conversant in mathematics. They may be able to manipulate easily the equations of mathematical economics, but they are able to do so without grasping the deeper meanings — the assumptions and complexities — hidden by those equations. In fact, the ease of manipulating the equations gives them a false sense of mastery of the underlying, real concepts.

Much of the economics profession is nevertheless dedicated to the protection and preservation of the essential incompetence of mathematical economists. This is from “An Important Emerging Economic Paradigm”:

One of the best incumbent-protection rackets going today is for mathematical theorists in economics departments. The top departments will not certify someone as being qualified to have an advanced degree without first subjecting the student to the most rigorous mathematical economic theory. The rationale for this is reminiscent of fraternity hazing. “We went through it, so should they.”

Mathematical hazing persists even though there are signs that the prestige of math is on the decline within the profession. The important Clark Medal, awarded to the most accomplished American economist under the age of 40, has not gone to a mathematical theorist since 1989.

These hazing rituals can have real consequences. In medicine, the controversial tradition of long work hours for medical residents has come under scrutiny over the last few years. In economics, mathematical hazing is not causing immediate harm to medical patients. But it probably is working to the long-term detriment of the profession.

The hazing ritual in economics has as least two real and damaging consequences. First, it discourages entry into the economics profession by persons who aren’t high-IQ freaks, and who, like Kling, can discuss economic behavior without resorting to the sterile language of mathematics. Second, it leads to economics that’s irrelevant to the real world — and dead wrong.

Reaching back into my archives, I found a good example of irrelevance and wrongness in Thomas Schelling‘s game-theoretic analysis of segregation. Eleven years ago, Tyler Cowen (Marginal Revolution), who was mentored by Schelling at Harvard, praised Schelling’s Nobel prize by noting, among other things, Schelling’s analysis of the economics of segregation:

Tom showed how communities can end up segregated even when no single individual cares to live in a segregated neighborhood. Under the right conditions, it only need be the case that the person does not want to live as a minority in the neighborhood, and will move to a neighborhood where the family can be in the majority. Try playing this game with white and black chess pieces, I bet you will get to segregation pretty quickly.

Like many game-theoretic tricks, Schelling’s segregation gambit omits much important detail. It’s artificial to treat segregation as a game in which all whites are willing to live with black neighbors as long as they (the whites) aren’t in the minority. Most whites (including most liberals) do not want to live anywhere near any “black rednecks” if they can help it. Living in relatively safe, quiet, and attractive surroundings comes far ahead of whatever value there might be in “diversity.”

“Diversity” for its own sake is nevertheless a “good thing” in the liberal lexicon. The Houston Chronicle noted Schelling’s Nobel by saying that Schelling’s work

helps explain why housing segregation continues to be a problem, even in areas where residents say they have no extreme prejudice to another group.

Segregation isn’t a “problem,” it’s the solution to a potential problem. Segregation today is mainly a social phenomenon, not a legal one. It reflects a rational aversion on the part of whites to having neighbors whose culture breeds crime and other types of undesirable behavior.

As for what people say about their racial attitudes: Believe what they do, not what they say. Most well-to-do liberals — including black one like the Obamas — choose to segregate themselves and their children from black rednecks. That kind of voluntary segregation, aside from demonstrating liberal hypocrisy about black redneck culture, also demonstrates the rationality of choosing to live in safer and more decorous surroundings.

Dave Patterson of the defunct Order from Chaos put it this way:

[G]ame theory has one major flaw inherent in it: The arbitrary assignment of expected outcomes and the assumption that the values of both parties are equally reflected in these external outcomes. By this I mean a matrix is filled out by [a conductor, and] it is up to that conductor’s discretion to assign outcome values to that grid. This means that there is an inherent bias towards the expected outcomes of conductor.

Or: Garbage in, garbage out.

Game theory points to the essential flaw in mathematical economics, which is reductionism: “An attempt or tendency to explain a complex set of facts, entities, phenomena, or structures by another, simpler set.”

Reductionism is invaluable in many settings. To take an example from everyday life, children are warned — in appropriate stern language — not to touch a hot stove or poke a metal object into an electrical outlet. The reasons given are simple ones: “You’ll burn yourself” and “You’ll get a shock and it will hurt you.” It would be futile (in almost all cases) to try to explain to a small child the physical and physiological bases for the warnings. The child wouldn’t understand the explanations, and the barrage of words might cause him to forget the warnings.

The details matter in economics. It’s easy enough to say, for example, that a market equilibrium exists where the relevant supply and demand curves cross (in a graphical representation) or where the supply and demand functions yield equal values of price and quantity (in a mathematical representation). But those are gross abstractions from reality, as any economist knows — or should know. Expressing economic relationships in mathematical terms lends them an unwarranted air of precision.

Further, all mathematical expressions, no matter how complex, can be expressed in plain language, though it may be hard to do so when the words become too many and their relationships too convoluted. But until one tries to do so, one is at the mercy of the mathematical economist whose equation has no counterpart in the real world of economic activity. In other words, an equation represents nothing more than the manipulation of mathematical relationships until it’s brought to earth by plain language and empirical testing. Short of that, it’s as meaningful as Urdu is to a Cockney.

Finally, mathematical economics lends aid and comfort to proponents of economic control. Whether or not they understand the mathematics or the economics, the expression of congenial ideas in mathematical form lends unearned — and dangerous — credibility to the controller’s agenda. The relatively simple multiplier is a case in point. As I explain in “The Keynesian Multiplier: Phony Math,”

the Keynesian investment/government-spending multiplier simply tells us that if ∆Y = $5 trillion, and if b = 0.8, then it is a matter of mathematical necessity that ∆C = $4 trillion and ∆I + ∆G = $1 trillion. In other words, a rise in I + G of $1 trillion doesn’t cause a rise in Y of $5 trillion; rather, Y must rise by $5 trillion for C to rise by $4 trillion and I + G to rise by $1 trillion. If there’s a causal relationship between ∆G and ∆Y, the multiplier doesn’t portray it.

I followed that post with “The True Multiplier“:

Math trickery aside, there is evidence that the Keynesian multiplier is less than 1. Robert J. Barro of Harvard University opens an article in The Wall Street Journal with the statement that “economists have not come up with explanations … for multipliers above one.”

Barro continues:

A much more plausible starting point is a multiplier of zero. In this case, the GDP is given, and a rise in government purchases requires an equal fall in the total of other parts of GDP — consumption, investment and net export. . . .

What do the data show about multipliers? Because it is not easy to separate movements in government purchases from overall business fluctuations, the best evidence comes from large changes in military purchases that are driven by shifts in war and peace. A particularly good experiment is the massive expansion of U.S. defense expenditures during World War II. The usual Keynesian view is that the World War II fiscal expansion provided the stimulus that finally got us out of the Great Depression. Thus, I think that most macroeconomists would regard this case as a fair one for seeing whether a large multiplier ever exists.

I have estimated that World War II raised U.S. defense expenditures by $540 billion (1996 dollars) per year at the peak in 1943-44, amounting to 44% of real GDP. I also estimated that the war raised real GDP by $430 billion per year in 1943-44. Thus, the multiplier was 0.8 (430/540). The other way to put this is that the war lowered components of GDP aside from military purchases. The main declines were in private investment, nonmilitary parts of government purchases, and net exports — personal consumer expenditure changed little. Wartime production siphoned off resources from other economic uses — there was a dampener, rather than a multiplier. . . .

There are reasons to believe that the war-based multiplier of 0.8 substantially overstates the multiplier that applies to peacetime government purchases. For one thing, people would expect the added wartime outlays to be partly temporary (so that consumer demand would not fall a lot). Second, the use of the military draft in wartime has a direct, coercive effect on total employment. Finally, the U.S. economy was already growing rapidly after 1933 (aside from the 1938 recession), and it is probably unfair to ascribe all of the rapid GDP growth from 1941 to 1945 to the added military outlays. [“Government Spending Is No Free Lunch,” The Wall Street Journal (online.WSJ.com), January 22, 2009]

This is from Valerie A. Ramsey of  the University of California-San Diego and the National Bureau of Economic Research:

. . . [I]t appears that a rise in government spending does not stimulate private spending; most estimates suggest that it significantly lowers private spending. These results imply that the government spending multiplier is below unity. Adjusting the implied multiplier for increases in tax rates has only a small effect. The results imply a multiplier on total GDP of around 0.5. [“Government Spending and Private Activity,” January 2012]

In fact,

for the period 1947-2012 I estimated the year-over-year percentage change in GDP (denoted as Y%) as a function of G/GDP (denoted as G/Y):

Y% = 0.09 – 0.17(G/Y)

Solving for Y% = 0 yields G/Y = 0.53; that is, Y% will drop to zero if G/Y rises to 0.53 (or thereabouts). At the present level of G/Y (about 0.4), Y% will hover just above 2 percent, as it has done in recent years. (See the graph immediately above.)

If G/Y had remained at 0.234, its value in 1947:

  • Real growth would have been about 5 percent a year, instead of 3.2 percent (the actual value for 1947-2012).
  • The total value of Y for 1947-2012 would have been higher by $500 trillion (98 percent).
  • The total value of G would have been lower by $61 trillion (34 percent).

The last two points, taken together, imply a cumulative government-spending multiplier (K) for 1947-2012 of about -8. That is, aggregate output in 1947-2012 declined by 8 dollars for every dollar of government spending above the amount represented by G/Y = 0.234.

But -8 is only an average value for 1947-2012. It gets worse. The reduction in Y is cumulative; that is, every extra dollar of G reduces the amount of Y that is available for growth-producing investment, which leads to a further reduction in Y, which leads to a further reduction in growth-producing investment, and on and on. (Think of the phenomenon as negative compounding; take a dollar from your savings account today, and the value of the savings account years from now will be lower than it would have been by a multiple of that dollar: [1 + interest rate] raised to nth power, where n = number of years.) Because of this cumulative effect, the effective value of K in 2012 was about -14.

The multiplier is a seductive and easy-to-grasp mathematical construct. But in the hands of politicians and their economist-enablers, it has been an instrument of economic destruction.

Perhaps “higher” mathematical economics is potentially less destructive because it’s inside game played by economists for the benefit of economists. I devoutly hope that’s true.

Economists As Scientists

This is the third entry in a series of loosely connected posts on economics. The first entry is here and the second entry is here. (Related posts by me are noted parenthetically throughout this one.)

Science is something that some people “do” some of the time. There are full-time human beings and part-time scientists. And the part-timers are truly scientists only when they think and act in accordance with the scientific method.*

Acting in accordance with the scientific method is a matter of attitude and application. The proper attitude is one of indifference about the correctness of a hypothesis or theory. The proper application rejects a hypothesis if it can’t be tested, and rejects a theory if it’s refuted (falsified) by relevant and reliable observations.

Regarding attitude, I turn to the most famous person who was sometimes a scientist: Albert Einstein. This is from the Wikipedia article about the Bohr-Einstein debate:

The quantum revolution of the mid-1920s occurred under the direction of both Einstein and [Niels] Bohr, and their post-revolutionary debates were about making sense of the change. The shocks for Einstein began in 1925 when Werner Heisenberg introduced matrix equations that removed the Newtonian elements of space and time from any underlying reality. The next shock came in 1926 when Max Born proposed that mechanics were to be understood as a probability without any causal explanation.

Einstein rejected this interpretation. In a 1926 letter to Max Born, Einstein wrote: “I, at any rate, am convinced that He [God] does not throw dice.” [Apparently, Einstein also used the line in Bohr’s presence, and Bohr replied, “Einstein, stop telling God what to do.” — TEA]

At the Fifth Solvay Conference held in October 1927 Heisenberg and Born concluded that the revolution was over and nothing further was needed. It was at that last stage that Einstein’s skepticism turned to dismay. He believed that much had been accomplished, but the reasons for the mechanics still needed to be understood.

Einstein’s refusal to accept the revolution as complete reflected his desire to see developed a model for the underlying causes from which these apparent random statistical methods resulted. He did not reject the idea that positions in space-time could never be completely known but did not want to allow the uncertainty principle to necessitate a seemingly random, non-deterministic mechanism by which the laws of physics operated.

It’s true that quantum mechanics was inchoate in the mid-1920s, and that it took a couple of decades to mature into quantum field theory. But there’s more than a trace of “attitude” in Einstein’s refusal to accept quantum mechanics, to stay abreast of developments in the theory, and to search quixotically for his own theory of everything, which he hoped would obviate the need for a non-deterministic explanation of quantum phenomena.

Improper application of the scientific method is rife. See, for example the Wikipedia article about the replication crisis, John Ioannidis’s article, “Why Most Published Research Findings Are False.” (See also “Ty Cobb and the State of Science” and “Is Science Self-Correcting?“) For a thorough analysis of the roots of the crisis, read Michael Hart’s book, Hubris: The Troubling Science, Economics, and Politics of Climate Change.

A bad attitude and improper application are both found among the so-called scientists who declare that the “science” of global warming is “settled,” and that human-generated CO2 emissions are the primary cause of the apparent rise in global temperatures during the last quarter of the 20th century. The bad attitude is the declaration of “settled science.” In “The Science Is Never Settled” I give many prominent examples of the folly of declaring it to be “settled.”

The improper application of the scientific method with respect to global warming began with the hypothesis that the “culprit” is CO2 emissions generated by the activities of human beings — thus anthropogenic global warming (AGW). There’s no end of evidence to the contrary, some of which is summarized in these posts and many of the links found therein. There’s enough evidence, in my view, to have rejected the CO2 hypothesis many times over. But there’s a great deal of money and peer-approval at stake, so the rush to judgment became a stampede. And attitude rears its ugly head when pro-AGW “scientists” shun the real scientists who are properly skeptical about the CO2 hypothesis, or at least about the degree to which CO2 supposedly influences temperatures. (For a depressingly thorough account of the AGW scam, read Michael Hart’s Hubris: The Troubling Science, Economics, and Politics of Climate Change.)

I turn now to economists, as I have come to know them in more than fifty years of being taught by them, working with them, and reading their works. Scratch an economist and you’re likely to find a moralist or reformer just beneath a thin veneer of rationality. Economists like to believe that they’re objective. But they aren’t; no one is. Everyone brings to the table a large serving of biases that are incubated in temperament, upbringing, education, and culture.

Economists bring to the table a heaping helping of tunnel vision. “Hard scientists” do, too, but their tunnel vision is generally a good thing, because it’s actually aimed at a deeper understanding of the inanimate and subhuman world rather than the advancement of a social or economic agenda. (I make a large exception for “hard scientists” who contribute to global-warming hysteria, as discussed above.)

Some economists, especially behavioralists, view the world through the lens of wealth-and-utility-maximization. Their great crusade is to force everyone to make rational decisions (by their lights), through “nudging.” It almost goes without saying that government should be the nudger-in-chief. (See “The Perpetual Nudger” and the many posts linked to therein.)

Other economists — though far fewer than in the past — have a thing about monopoly and oligopoly (the domination of a market by one or a few sellers). They’re heirs to the trust-busting of the late 1800s and early 1900s, a movement led by non-economists who sought to blame the woes of working-class Americans on the “plutocrats” (Rockefeller, Carnegie, Ford, etc.) who had merely made life better and more affordable for Americans, while also creating jobs for millions of them and reaping rewards for the great financial risks that they took. (See “Monopoly and the General Welfare” and “Monopoly: Private Is Better than Public.”) As it turns out, the biggest and most destructive monopoly of all is the federal government, so beloved and trusted by trust-busters — and too many others. (See “The Rahn Curve Revisited.”)

Nowadays, a lot of economists are preoccupied by income inequality, as if it were something evil and not mainly an artifact of differences in intelligence, ambition, and education, etc. And inequality — the prospect of earning rather grand sums of money — is what drives a lot of economic endeavor, to good of workers and consumers. (See “Mass (Economic) Hysteria: Income Inequality and Related Themes” and the many posts linked to therein.) Remove inequality and what do you get? The Soviet Union and Communist China, in which everyone is equal except party operatives and their families, friends, and favorites.

When the inequality-preoccupied economists are confronted by the facts of life, they usually turn their attention from inequality as a general problem to the (inescapable) fact that an income distribution has a top one-percent and top one-tenth of one-percent — as if there were something especially loathsome about people in those categories. (Paul Krugman shifted his focus to the top one-tenth of one percent when he realized that he’s in the top one percent, so perhaps he knows that’s he’s loathsome and wishes to deny it, to himself.)

Crony capitalism is trotted out as a major cause of very high incomes. But that’s hardly a universal cause, given that a lot of very high incomes are earned by athletes and film stars beside whom most investment bankers and CEOs are making peanuts. Moreover, as I’ve said on several occasions, crony capitalists are bright and driven enough to be in the stratosphere of any income distribution. Further, the fertile soil of crony capitalism is the regulatory power of government that makes it possible.

Many economists became such, it would seem, in order to promote big government and its supposed good works — income redistribution being one of them. Joseph Stiglitz and Paul Krugman are two leading exemplars of what I call the New Deal school of economic thought, which amounts to throwing government and taxpayers’ money at every perceived problem, that is, every economic outcome that is deemed unacceptable by accountants of the soul. (See “Accountants of the Soul.”)

Stiglitz and Krugman — both Nobel laureates in economics — are typical “public intellectuals” whose intelligence breeds in them a kind of arrogance. (See “Intellectuals and Society: A Review.”) It’s the kind of arrogance that I mentioned in the preceding post in this series: a penchant for deciding what’s best for others.

New Deal economists like Stiglitz and Krugman carry it a few steps further. They ascribe to government an impeccable character, an intelligence to match their own, and a monolithic will. They then assume that this infallible and wise automaton can and will do precisely what they would do: Create the best of all possible worlds. (See the many posts in which I discuss the nirvana fallacy.)

New Deal economists, in other words, live their intellectual lives  in a dream-world populated by the likes of Jiminy Cricket (“When You Wish Upon a Star”), Dorothy (“Somewhere Over the Rainbow”), and Mary Jane of a long-forgotten comic book (“First I shut my eyes real tight, then I wish with all my might! Magic words of poof, poof, piffles, make me just as small as [my mouse] Sniffles!”).

I could go on, but you should by now have grasped the point: What too many economists want to do is change human nature, channel it in directions deemed “good” (by the economist), or simply impose their view of “good” on everyone. To do such things, they must rely on government.

It’s true that government can order people about, but it can’t change human nature, which has an uncanny knack for thwarting Utopian schemes. (Obamacare, whose chief architect was economist Jonathan Gruber, is exhibit A this year.) And government (inconveniently for Utopians) really consists of fallible, often unwise, contentious human beings. So government is likely to march off in a direction unsought by Utopian economists.

Nevertheless, it’s hard to thwart the tax collector. The regulator can and does make things so hard for business that if one gets off the ground it can’t create as much prosperity and as many jobs as it would in the absence of regulation. And the redistributor only makes things worse by penalizing success. Tax, regulate, and redistribute should have been the mantra of the New Deal and most presidential “deals” since.

I hold economists of the New Deal stripe partly responsible for the swamp of stagnation into which the nation’s economy has descended. (See “Economic Growth Since World War II.”) Largely responsible, of course, are opportunistic if not economically illiterate politicians who pander to rent-seeking, economically illiterate constituencies. (Yes, I’m thinking of old folks and the various “disadvantaged” groups with which they have struck up an alliance of convenience.)

The distinction between normative economics and positive economics is of no particular use in sorting economists between advocates and scientists. A lot of normative economics masquerades as positive economics. The work of Thomas Piketty and his comrades-in-arms comes to mind, for example. (See “McCloskey on Piketty.”) Almost everything done to quantify and defend the Keynesian multiplier counts as normative economics, inasmuch as the work is intended (wittingly or not) to defend an intellectual scam of 80 years’ standing. (See “The Keynesian Multiplier: Phony Math,” “The True Multiplier,” and “Further Thoughts about the Keynesian Multiplier.”)

Enough said. If you want to see scientific economics in action, read Regulation. Not every article in it exemplifies scientific inquiry, but a good many of them do. It’s replete with articles about microeconomics, in which the authors uses real-world statistics to validate and quantify the many axioms of economics.

A final thought is sparked by Arnold Kling’s post, “Ed Glaeser on Science and Economics.” Kling writes:

I think that the public has a sort of binary classification. If it’s “science,” then an expert knows more than the average Joe. If it’s not a science, then anyone’s opinion is as good as anyone else’s. I strongly favor an in-between category, called a discipline. Think of economics as a discipline, where it is possible for avid students to know more than ordinary individuals, but without the full use of the scientific method.

On this rare occasion I disagree with Kling. The accumulation of knowledge about economic variables, or pseudo-knowledge such as estimates of GDP (see “Macroeconomics and Microeconomics“), either leads to well-tested, verified, and reproducible theories of economic behavior or it leads to conjectures, of which there are so many opposing ones that it’s “take your pick.” If that’s what makes a discipline, give me the binary choice between science and story-telling. Most of economics seems to be story-telling. “Discipline” is just a fancy word for it.

Collecting baseball cards and memorizing the statistics printed on them is a discipline. Most of economics is less useful than collecting baseball cards — and a lot more destructive.

Here’s my hypothesis about economists: There are proportionally as many of them who act like scientists as there are baseball players who have career batting averages of at least .300.
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* Richard Feynman, a physicist and real scientist, had a different view of the scientific method than Karl Popper’s standard taxonomy. I see Feynman’s view as complementary to Popper’s, not at odds with it. What is “constructive skepticism” (Feynman’s term) but a gentler way of saying that a hypothesis or theory might be falsified and that the act of falsification may point to a better hypothesis or theory?

Economics and Science

This is the second entry in what I expect to be a series of loosely connected posts on economics. The first entry is here.

Science is unnecessarily daunting to the uninitiated, which is to say, the vast majority of the populace. Because scientific illiteracy is rampant, advocates of policy positions — scientists and non-scientists alike — are able to invoke “science” wantonly, thus lending unwarranted authority to their positions.

Here I will dissect science, then turn to economics and begin a discussion of its scientific and non-scientific aspects. It has both, though at least one non-scientific aspect (the Keynesian multiplier) draws an inordinate amount of attention, and has many true believers within the profession.

Science is knowledge, but not all knowledge is science. A scientific body of knowledge is systematic; that is, the granular facts or phenomena which comprise the body of knowledge must be connected in patterned ways. The purported facts or phenomena of a science must represent reality, things that can be observed and measured in some way. Scientists may hypothesize the existence of an unobserved thing (e.g., the ether, dark matter), in an effort to explain observed phenomena. But the unobserved thing stands outside scientific knowledge until its existence is confirmed by observation, or because it remains standing as the only plausible explanation of observable phenomena. Hypothesized things may remain outside the realm of scientific knowledge for a very long time, if not forever. The Higgs boson, for example, was hypothesized in 1964 and has been tentatively (but not conclusively) confirmed since its “discovery” in 2011.

Science has other key characteristics. Facts and patterns must be capable of validation and replication by persons other than those who claim to have found them initially. Patterns should have predictive power; thus, for example, if the sun fails to rise in the east, the model of Earth’s movements which says that it will rise in the east is presumably invalid and must be rejected or modified so that it correctly predicts future sunrises or the lack thereof. Creating a model or tweaking an existing model just to account for a past event (e.g., the failure of the Sun to rise, the apparent increase in global temperatures from the 1970s to the 1990s) proves nothing other than an ability to “predict” the past with accuracy.

Models are usually clothed in the language of mathematics and statistics. But those aren’t scientific disciplines in themselves; they are tools of science. Expressing a theory in mathematical terms may lend the theory a scientific aura, but a theory couched in mathematical terms is not a scientific one unless (a) it can be tested against facts yet to be ascertained and events yet to occur, and (b) it is found to accord with those facts and events consistently, by rigorous statistical tests.

A science may be descriptive rather than mathematical. In a descriptive science (e.g., plant taxonomy), particular phenomena sometimes are described numerically (e.g., the number of leaves on the stem of a species), but the relations among various phenomena are not reducible to mathematics. Nevertheless, a predominantly descriptive discipline will be scientific if the phenomena within its compass are connected in patterned ways, can be validated, and are applicable to newly discovered entities.

Non-scientific disciplines can be useful, whereas some purportedly scientific disciplines verge on charlatanism. Thus, for example:

  • History, by my reckoning, is not a science because its account of events and their relationships is inescapably subjective and incomplete. But a knowledge of history is valuable, nevertheless, for the insights it offers into the influence of human nature on the outcomes of economic and political processes.
  • Physics is a science in most of its sub-disciplines, but there are some (e.g., cosmology) where it descends into the realm of speculation. It is informed, fascinating speculation to be sure, but speculation all the same. The idea of multiverses, for example, can’t be tested, inasmuch as human beings and their tools are bound to the known universe.
  • Economics is a science only to the extent that it yields empirically valid insights about  specific economic phenomena (e.g., the effects of laws and regulations on the prices and outputs of specific goods and services). Then there are concepts like the Keynesian multiplier, about which I’ll say more in this series. It’s a hypothesis that rests on a simplistic, hydraulic view of the economic system. (Other examples of pseudo-scientific economic theories are the labor theory of value and historical determinism.)

In sum, there is no such thing as “science,” writ large; that is, no one may appeal, legitimately, to “science” in the abstract. A particular discipline may be a science, but it is a science only to the extent that it comprises a factual and replicable body of patterned knowledge. Patterned knowledge includes theories with predictive power.

A scientific theory is a hypothesis that has thus far been confirmed by observation. Every scientific theory rests eventually on axioms: self-evident principles that are accepted as true without proof. The principle of uniformity (which can be traced to Galileo) is an example of such an axiom:

Uniformitarianism is the assumption that the same natural laws and processes that operate in the universe now have always operated in the universe in the past and apply everywhere in the universe. It refers to invariance in the metaphysical principles underpinning science, such as the constancy of causal structure throughout space-time, but has also been used to describe spatiotemporal invariance of physical laws. Though an unprovable postulate that cannot be verified using the scientific method, uniformitarianism has been a key first principle of virtually all fields of science

Thus, for example, if observer B is moving away from observer A at a certain speed, observer A will perceive that he is moving away from observer B at that speed. It follows that an observer cannot determine either his absolute velocity or direction of travel in space. The principle of uniformity is a fundamental axiom of modern physics, most notably of Einstein’s special and general theories of relativity.

There’s a fine line between an axiom and a theory. Was the idea of a geocentric universe an axiom or a theory? If it was taken as axiomatic — as it surely was by many scientists for about 2,000 years — then it’s fair to say that an axiom can give way under the pressure of observational evidence. (Such an event is what Thomas Kuhn calls a paradigm shift.) But no matter how far scientists push the boundaries of knowledge, they must at some point rely on untestable axioms, such as the principle of uniformity. There are simply deep and (probably) unsolvable mysteries that science is unlikely to fathom.

This brings me to economics, which — in my view — rests on these self-evident axioms:

1. Each person strives to maximize his or her sense of satisfaction, which may also be called well-being, happiness, or utility (an ugly word favored by economists). Striving isn’t the same as achieving, of course, because of lack of information, emotional decision-making, buyer’s remorse, etc

2. Happiness can and often does include an empathic concern for the well-being of others; that is, one’s happiness may be served by what is usually labelled altruism or self-sacrifice.

3. Happiness can be and often is served by the attainment of non-material ends. Not all persons (perhaps not even most of them) are interested in the maximization of wealth, that is, claims on the output of goods and services. In sum, not everyone is a wealth maximizer. (But see axiom number 12.)

4. The feeling of satisfaction that an individual derives from a particular product or service is situational — unique to the individual and to the time and place in which the individual undertakes to acquire or enjoy the product or service. Generally, however, there is a (situationally unique) point at which the acquisition or enjoyment of additional units of a particular product or service during a given period of time tends to offer less satisfaction than would the acquisition or enjoyment of units of other products or services that could be obtained at the same cost.

5. The value that a person places on a product or service is subjective. Products and services don’t have intrinsic values that apply to all persons at a given time or period of time.

6. The ability of a person to acquire products and services, and to accumulate wealth, depends (in the absence of third-party interventions) on the valuation of the products and services that are produced in part or whole by the person’s labor (mental or physical), or by the assets that he owns (e.g., a factory building, a software patent). That valuation is partly subjective (e.g., consumers’ valuation of the products and services, an employer’s qualitative evaluation of the person’s contributions to output) and partly objective (e.g., an employer’s knowledge of the price commanded by a product or service, an employer’s measurement of an employees’ contribution to the quantity of output).

7. The persons and firms from which products and services flow are motivated by the acquisition of income, with which they can acquire other products and services, and accumulate wealth for personal purposes (e.g., to pass to heirs) or business purposes (e.g., to expand the business and earn more income). So-called profit maximization (seeking to maximize the difference between the cost of production and revenue from sales) is a key determinant of business decisions but far from the only one. Others include, but aren’t limited to, being a “good neighbor,” providing employment opportunities for local residents, and underwriting philanthropic efforts.

8. The cost of production necessarily influences the price at which a good or and service will be offered for sale, but doesn’t solely determine the price at which it will be sold. Selling price depends on the subjective valuation of the products or service, prospective buyers’ incomes, and the prices of other products and services, including those that are direct or close substitutes and those to which users may switch, depending on relative prices.

9. The feeling of satisfaction that a person derives from the acquisition and enjoyment of the “basket” of products and services that he is able to buy, given his income, etc., doesn’t necessarily diminish, as long as the person has access to a great variety of products and services. (This axiom and axiom 12 put paid to the myth of diminishing marginal utility of income.)

10. Work may be a source of satisfaction in itself or it may simply be a means of acquiring and enjoying products and services, or acquiring claims to them by accumulating wealth. Even when work is satisfying in itself, it is subject to the “law” of diminishing marginal satisfaction.

11. Work, for many (but not all) persons, is no longer be worth the effort if they become able to subsist comfortably enough by virtue of the wealth that they have accumulated, the availability of redistributive schemes (e.g., Social Security and Medicare), or both. In such cases the accumulation of wealth often ceases and reverses course, as it is “cashed in” to defray the cost of subsistence (which may be far more than minimal).

12. However, there are not a few persons whose “work” is such a great source of satisfaction that they continue doing it until they are no longer capable of doing so. And there are some persons whose “work” is the accumulation of wealth, without limit. Such persons may want to accumulate wealth in order to “do good” or to leave their heirs well off or simply for the satisfaction of running up the score. The justification matters not. There is no theoretical limit to the satisfaction that a particular person may derive from the accumulation of wealth. Moreover, many of the persons (discussed in axiom 11) who aren’t able to accumulate wealth endlessly would do so if they had the ability and the means to take the required risks.

13. Individual degrees of satisfaction (happiness, etc.) are ephemeral, nonquantifiable, and incommensurable. There is no such thing as a social welfare function that a third party (e.g., government) can maximize by taking from A to give to B. If there were such a thing, its value would increase if, for example, A were to punch B in the nose and derive a degree of pleasure that somehow more than offsets the degree of pain incurred by B. (The absurdity of a social-welfare function that allows As to punch Bs in their noses ought to be enough shame inveterate social engineers into quietude — but it won’t. They derive great satisfaction from meddling.) Moreover, one of the primary excuses for meddling is that income (and thus wealth) has a  diminishing marginal utility, so it makes sense to redistribute from those with higher incomes (or more wealth) to those who have less of either. Marginal utility is, however, unknowable (see axioms 4 and 5), and may not always be negative (see axioms 9 and 12).

14. Whenever a third party (government, do-gooders, etc.) intervene in the affairs of others, that third party is merely imposing its preferences on those others. The third party sometimes claims to know what’s best for “society as a whole,” etc., but no third party can know such a thing. (See axiom 13.)

15. It follows from axiom 13 that the welfare of “society as a whole” can’t be aggregated or measured. An estimate of the monetary value of the economic output of a nation’s economy (Gross Domestic Product) is by no means an estimate of the welfare of “society as a whole.” (Again, see axiom 13.)

That may seem like a lot of axioms, which might give you pause about my claim that some aspects of economics are scientific. But economics is inescapably grounded in axioms such as the ones that I propound. This aligns me (mainly) with the Austrian economists, whose leading light was Ludwig von Mises. Gene Callahan writes about him at the website of the Ludwig von Mises Institute:

As I understand [Mises], by categorizing the fundamental principles of economics as a priori truths and not contingent facts open to empirical discovery or refutation, Mises was not claiming that economic law is revealed to us by divine action, like the ten commandments were to Moses. Nor was he proposing that economic principles are hard-wired into our brains by evolution, nor even that we could articulate or comprehend them prior to gaining familiarity with economic behavior through participating in and observing it in our own lives. In fact, it is quite possible for someone to have had a good deal of real experience with economic activity and yet never to have wondered about what basic principles, if any, it exhibits.

Nevertheless, Mises was justified in describing those principles as a priori, because they are logically prior to any empirical study of economic phenomena. Without them it is impossible even to recognize that there is a distinct class of events amenable to economic explanation. It is only by pre-supposing that concepts like intention, purpose, means, ends, satisfaction, and dissatisfaction are characteristic of a certain kind of happening in the world that we can conceive of a subject matter for economics to investigate. Those concepts are the logical prerequisites for distinguishing a domain of economic events from all of the non-economic aspects of our experience, such as the weather, the course of a planet across the night sky, the growth of plants, the breaking of waves on the shore, animal digestion, volcanoes, earthquakes, and so on.

Unless we first postulate that people deliberately undertake previously planned activities with the goal of making their situations, as they subjectively see them, better than they otherwise would be, there would be no grounds for differentiating the exchange that takes place in human society from the exchange of molecules that occurs between two liquids separated by a permeable membrane. And the features which characterize the members of the class of phenomena singled out as the subject matter of a special science must have an axiomatic status for practitioners of that science, for if they reject them then they also reject the rationale for that science’s existence.

Economics is not unique in requiring the adoption of certain assumptions as a pre-condition for using the mode of understanding it offers. Every science is founded on propositions that form the basis rather than the outcome of its investigations. For example, physics takes for granted the reality of the physical world it examines. Any piece of physical evidence it might offer has weight only if it is already assumed that the physical world is real. Nor can physicists demonstrate their assumption that the members of a sequence of similar physical measurements will bear some meaningful and consistent relationship to each other. Any test of a particular type of measurement must pre-suppose the validity of some other way of measuring against which the form under examination is to be judged.

Why do we accept that when we place a yardstick alongside one object, finding that the object stretches across half the length of the yardstick, and then place it alongside another object, which only stretches to a quarter its length, that this means the first object is longer than the second? Certainly not by empirical testing, for any such tests would be meaningless unless we already grant the principle in question. In mathematics we don’t come to know that 2 + 2 always equals 4 by repeatedly grouping two items with two others and counting the resulting collection. That would only show that our answer was correct in the instances we examined — given the assumption that counting works! — but we believe it is universally true. [And it is universally true by the conventions of mathematics. If what we call “5” were instead called “4,” 2 + 2 would always equal 5. — TEA] Biology pre-supposes that there is a significant difference between living things and inert matter, and if it denied that difference it would also be denying its own validity as a special science. . . .

The great fecundity from such analysis in economics is due to the fact that, as acting humans ourselves, we have a direct understanding of human action, something we lack in pondering the behavior of electrons or stars. The contemplative mode of theorizing is made even more important in economics because the creative nature of human choice inherently fails to exhibit the quantitative, empirical regularities, the discovery of which characterizes the modern, physical sciences. (Biology presents us with an interesting intermediate case, as many of its findings are qualitative.) . . .

[A] person can be presented with scores of experiments supporting a particular scientific theory is sound, but no possible experiment ever can demonstrate to him that experimentation is a reasonable means by which to evaluate a scientific theory. Only his intuitive grasp of its plausibility can bring him to accept that proposition. (Unless, of course, he simply adopts it on the authority of others.) He can be led through hundreds of rigorous proofs for various mathematical theorems and be taught the criteria by which they are judged to be sound, but there can be no such proof for the validity of the method itself. (Kurt Gödel famously demonstrated that a formal system of mathematical deduction that is complex enough to model even so basic a topic as arithmetic might avoid either incompleteness or inconsistency, but always must suffer at least one of those flaws.) . . .

This ultimate, inescapable reliance on judgment is illustrated by Lewis Carroll in Alice Through the Looking Glass. He has Alice tell Humpty Dumpty that 365 minus one is 364. Humpty is skeptical, and asks to see the problem done on paper. Alice dutifully writes down:

365 – 1 = 364

Humpty Dumpty studies her work for a moment before declaring that it seems to be right. The serious moral of Carroll’s comic vignette is that formal tools of thinking are useless in convincing someone of their conclusions if he hasn’t already intuitively grasped the basic principles on which they are built.

All of our knowledge ultimately is grounded on our intuitive recognition of the truth when we see it. There is nothing magical or mysterious about the a priori foundations of economics, or at least nothing any more magical or mysterious than there is about our ability to comprehend any other aspect of reality.

(Callahan has more to say here. For a technical discussion of the science of human action, or praxeology, read this. Some glosses on Gödel’s incompleteness theorem are here.)

I omitted an important passage from the preceding quotation, in order to single it out. Callahan says also that

Mises’s protégé F.A. Hayek, while agreeing with his mentor on the a priori nature of the “logic of action” and its foundational status in economics, still came to regard investigating the empirical issues that the logic of action leaves open as a more important undertaking than further examination of that logic itself.

I agree with Hayek. It’s one thing to know axiomatically that the speed of light is constant; it is quite another (and useful) thing to know experimentally that the speed of light (in empty space) is about 671 million miles an hour. Similarly, it is one thing to deduce from the axioms of economics that demand curves generally slope downward; it is quite another (and useful) thing to estimate specific demand functions.

But one must always be mindful of the limitations of quantitative methods in economics. As James Sheehan writes at the website of the Mises Institute,

economists are prone to error when they ascribe excessive precision to advanced statistical techniques. They assume, falsely, that a voluminous amount of historical observations (sample data) can help them to make inferences about the future. They presume that probability distributions follow a bell-shaped pattern. They make no provision for the possibility that past correlations between economic variables and data were coincidences.

Nor do they account for the possibility, as economist Robert Lucas demonstrated, that people will incorporate predictable patterns into their expectations, thus canceling out the predictive value of such patterns. . . .

As [Nassim Nicholas] Taleb points out [in Fooled by Randomness], the popular Monte Carlo simulation “is more a way of thinking than a computational method.” Employing this way of thinking can enhance one’s understanding only if its weaknesses are properly understood and accounted for. . . .

Taleb’s critique of econometrics is quite compatible with Austrian economics, which holds that dynamic human actions are too subjective and variegated to be accurately modeled and predicted.

In some parts of Fooled by Randomness, Taleb almost sounds Austrian in his criticisms of economists who worship “the efficient market religion.” Such economists are misguided, he argues, because they begin with the flawed hypothesis that human beings act rationally and do what is mathematically “optimal.” . . .

As opposed to a Utopian Vision, in which human beings are rational and perfectible (by state action), Taleb adopts what he calls a Tragic Vision: “We are faulty and there is no need to bother trying to correct our flaws.” It is refreshing to see a highly successful practitioner of statistics and finance adopt a contrarian viewpoint towards economics.

Yet, as Arnold Kling explains, many (perhaps most) economists have lost sight of the axioms of economics in their misplaced zeal to emulate the methods of the physical sciences:

The most distinctive trend in economic research over the past hundred years has been the increased use of mathematics. In the wake of Paul Samuelson’s (Nobel 1970) Ph.D dissertation, published in 1948, calculus became a requirement for anyone wishing to obtain an economics degree. By 1980, every serious graduate student was expected to be able to understand the work of Kenneth Arrow (Nobel 1972) and Gerard Debreu (Nobel 1983), which required mathematics several semesters beyond first-year calculus.

Today, the “theory sequence” at most top-tier graduate schools in economics is controlled by math bigots. As a result, it is impossible to survive as an economics graduate student with a math background that is less than that of an undergraduate math major. In fact, I have heard that at this year’s American Economic Association meetings, at a seminar on graduate education one professor quite proudly said that he ignored prospective students’ grades in economics courses, because their math proficiency was the key predictor of their ability to pass the coursework required to obtain an advanced degree.

The raising of the mathematical bar in graduate schools over the past several decades has driven many intelligent men and women (perhaps women especially) to pursue other fields. The graduate training process filters out students who might contribute from a perspective of anthropology, biology, psychology, history, or even intense curiosity about economic issues. Instead, the top graduate schools behave as if their goal were to produce a sort of idiot-savant, capable of appreciating and adding to the mathematical contributions of other idiot-savants, but not necessarily possessed of any interest in or ability to comprehend the world to which an economist ought to pay attention.

. . . The basic question of What Causes Prosperity? is not a question of how trading opportunities play out among a given array of goods. Instead, it is a question of how innovation takes place or does not take place in the context of institutional factors that are still poorly understood.

Mathematics, as I have said, is a tool of science, it’s not science in itself. Dressing hypothetical relationships in the garb of mathematics doesn’t validate them.

Where, then, is the science in economics? And where is the nonsense? I’ve given you some hints (and more than hints). There’s more to come.

The Essence of Economics

This is the first entry in what I expect to be a series of loosely connected posts on economics.

Market-based voluntary exchange is an important if not dominant method of satisfying wants. To grasp that point, think of your day: You sleep and awaken in a house or apartment that you didn’t build yourself, but which is “yours” by dint of payments that you make from income you earn by doing things of value to other persons.* During your days at home, in a workplace, or in a vacation spot you spend many hours using products and services that you buy from others — everything from toilet paper, soap, and shampoo to clothing, food, transportation, entertainment, internet access, etc.

It is not that the things that you do for yourself and in direct cooperation with others are unimportant or valueless. Economists acknowledge the psychic value of self-sufficiency and the economic value of non-market cooperation, but they can’t measure the value of those things. Economists typically focus on market-based exchange because it involves transactions with measurable monetary values.

Another thing that economists can’t deal with, because it’s beyond the ken of economics, is the essence of life itself: one’s total sense of well-being, especially as it is influenced by the things done for oneself, solitary joys (reading, listening to music), and the happiness (or sadness) shared with friends and loved ones.

In sum, despite the pervasiveness of voluntary exchange, economics really treats only the marginalia of life — the rate at which a person would exchange a unit of X for a unit of Y, not how X or Y stacks up in the grand scheme of living.

That is the essence of economics, as a discipline. There is much more to it than that, of course; for example, how supply meets demand, how exogenous factors affect economic behavior, how activity at the level of the person or firm sends ripples across the economy, and why those ripples can’t be aggregated meaningfully.

More to come.
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* Obviously, a lot of people derive their income from transfer payments (Social Security, food stamps, etc.), which I’ll address in future posts.

Not-So-Random Thoughts (XVIII)

Links to the other posts in this occasional series may be found at “Favorite Posts,” just below the list of topics.

Charles Murray opines about “America Against Itself“:

With the publication in 2012 of Coming Apart: The State of White America, 1960-2010, political scientist Charles Murray – celebrated and denigrated in equal measure for his earlier works, Losing Ground (1984) and The Bell Curve (1994) – produced a searing, searching analysis of a nation cleaving along the lines of class, a nation, as he put it, ‘coming apart at the seams’. On the one side of this conflicted society, as Murray sees it, there is the intellectual or ‘cognitive’ elite, graduates of America’s leading universities, bound together through marriage and work, and clustered together in the same exclusive zipcodes, places such as Beverly Hills, Santa Monica and Boston. In these communities of the likeminded, which Murray gives the fictional title of ‘Belmont’, the inhabitants share the same values, the same moral outlook, the same distinct sense of themselves as superior. And on the other side, there is the ‘new lower class’, the white Americans who left education with no more than a high-school diploma, who increasingly divorce among themselves, endure unemployment together, and are gathered in neighbourhoods that Murray gives the title of ‘Fishtown’ – inspired by an actual white, blue-collar neighbourhood of the same name in Philadelphia.

It is in Fishtown that the trends Murray identifies as the most damaging over the past 50 years – family breakdown, loss of employment, crime and a loss of social capital – are felt and experienced. Its inhabitants have a set of values (albeit threadbare ones), an outlook and a way of life that are entirely at odds with those from Belmont. And it is between these two almost entirely distinct moral communities, that the new Culture Wars now appear to be being fought….

Collins: I was thinking about how, in Coming Apart, you explore how the elites seek to distance themselves from the working class. They eat so-called healthier foods, they have different child-rearing practices, and so on. Then, from afar, they preach their preferred ways to the working class, as if they know better. The elites may no longer preach traditional civic virtues, as you note in Coming Apart, but they are still preaching, in a way. Only now they’re preaching about health, parenting and other things.

Murray: They are preaching. They are legislating. They are creating policies. The elites (on both the right and the left) do not get excited about low-skill immigration. Let’s face it, if you are members of the elite, immigration provides you with cheap nannies, cheap lawn care, and so on. There are a variety of ways in which it is a case of ‘hey, it’s no skin off my back’ to have all of these new workers. The elites are promulgating policies for which they do not pay the price. That’s true of immigration, that’s true of education. When they support the teachers’ unions in all sorts of practices that are terrible for kids, they don’t pay that price. Either they send their kids to private schools, or they send their kids to schools in affluent suburbs in which they, the parents, really do have a lot of de facto influence over how the school is run.

So they don’t pay the price for policy after policy. Perhaps the most irritating to me – and here we are talking about preaching – is how they are constantly criticising the working class for being racist, for seeking to live in neighbourhoods in which whites are the majority. The elites live in zipcodes that are overwhelmingly white, with very few blacks and Latinos. The only significant minorities in elite zipcodes are East and South Asians. And, as the American sociologist Andrew Hacker has said, Asians are ‘honorary whites’. The integration that you have in elite neighbourhoods is only for the model minority, not for other minorities. That’s a kind of hypocrisy, to call working-class whites ‘racist’ for doing exactly the same thing that the elites do. It’s terrible.

The elites live in a bubble, which Murray explains in Coming Apart, and which I discuss in “Are You in the Bubble?” — I’m not — and “Bubbling Along.”

*     *     *

Meanwhile, in the climate war, there’s an interesting piece about scientists who got it right, but whose article was pulled because they used pseudonyms. In “Scientists Published Climate Research Under Fake Names. Then They Were Caught” we learn that

they had constructed a model, a mathematical argument, for calculating the average surface temperature of a rocky planet. Using just two factors — electromagnetic radiation beamed by the sun into the atmosphere and the atmospheric pressure at a planet’s surface — the scientists could predict a planet’s temperature. The physical principle, they said, was similar to the way that high-pressure air ignites fuel in a diesel engine.

If proved to be the case on Earth, the model would have dramatic implications: Our planet is warming, but the solar radiation and our atmosphere would be to blame, not us.

It seems to me that their real sin was contradicting the “settled science” of climatology.

Well, Francis Menton — author of “The ‘Science’ Underlying Climate Alarmism Turns Up Missing” — has something to say about that “settled science”:

In the list of President Obama’s favorite things to do, using government power to save the world from human-caused “climate change” has to rank at the top.  From the time of his nomination acceptance speech in June 2008 (“this was the moment when the rise of the oceans began to slow and our planet began to heal . . .”), through all of his State of the Union addresses, and right up to the present, he has never missed an opportunity to lecture us on how atmospheric warming from our sinful “greenhouse gas” emissions is the greatest crisis facing humanity….

But is there actually any scientific basis for this?  Supposedly, it’s to be found in a document uttered by EPA back in December 2009, known as the “Endangerment Finding.”  In said document, the geniuses at EPA purport to find that the emissions of “greenhouse gases” into the atmosphere are causing a danger to human health and welfare through the greenhouse warming mechanism.  But, you ask, is there any actual proof of that?  EPA’s answer (found in the Endangerment Finding) is the “Three Lines of Evidence”….

The news is that a major new work of research, from a large group of top scientists and mathematicians, asserts that EPA’s “lines of evidence,” and thus its Endangerment Finding, have been scientifically invalidated….

So the authors of this Report, operating without government or industry funding, compiled the best available atmospheric temperature time series from 13 independent sources (satellites, balloons, buoys, and surface records), and then backed out only ENSO (i.e., El Nino/La Nina) effects.  And with that data and that sole adjustment they found: no evidence of the so-called Tropical Hot Spot that is the key to EPA’s claimed “basic physical understanding” of the claimed atmospheric greenhouse warming model, plus no statistically significant atmospheric warming at all to be explained.

What an amazing non-coincidence. That’s exactly what I found when I looked at the temperature record for Austin, Texas, since the late 1960s, when AGW was supposedly making life miserable for the planet. See “AGW in Austin? (II)” and the list of related readings and posts at the bottom. See also “Is Science Self Correcting?” (answer: no).

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REVISED 11/18/16

Ten years ago, I posted “An Immigration Roundup,” a collection of 13 posts dated March 29 through September 22, 2006. The bottom line: to encourage and allow rampant illegal immigration borders on social and economic suicide. I remain a hardliner because of the higher crime rate among Hispanics (“Immigration and Crime“), and because of Steven Camarota’s “So What Is the Fiscal and Economic Impact of Immigration?“:

The National Academies of Sciences, Engineering, and Medicine have just released what can fairly be described as the most comprehensive look at the economic and fiscal impact of immigration on the United States. It represents an update of sorts of a similar NAS study released in 1997, in the middle of an earlier immigration debate. Overall the report is quite balanced, with a lot of interesting findings….
The most straightforward part of the study is its assemblage of estimates of the current fiscal impact of immigrants. The study shows that immigrants (legal and illegal) do not come close to paying enough in taxes to cover their consumption of public services at the present time. The NAS present eight different scenarios based on different assumptions about the current fiscal impact of immigrants and their dependent children — and every scenario is negative. No matter what assumption the NAS makes, immigrants use more in public services than they pay in taxes. The largest net drain they report is $299 billion a year. It should be pointed out that native-born American are also shown to be a net fiscal drain, mainly because of the federal budget deficit — Washington gives out a lot more than it takes in. But the fiscal drain created by immigrants is disproportionately large relative to the size of their population. Equally important, a fiscal drain caused by natives may be unavoidable. Adding more immigrants who create a fiscal drain, on the other hand, can be avoided with a different immigration policy….
With regard to economics — jobs and wages — the results in the NAS study, based on the standard economic model, show that immigration does make the U.S economy larger by adding workers and population. But a larger economy is not necessarily a benefit to natives. The report estimates that the actual benefit to the native-born could be $54.2 billion a year — referred to as the “immigrant surplus.” This is the benefit that accrues to American businesses because immigration increases the supply of workers and reduces American wages. Several points need to be made about this estimate. First, to generate this surplus, immigration has to create a very large redistribution of income from workers to owners of capital. The model works this way: Immigration reduces the wages of natives in competition with immigrant workers by $493.9 billion annually, but it increases the income of businesses by $548.1 billion, for a net gain of $54.2 billion. Unfortunately, the NAS does not report this large income redistribution, though it provides all the information necessary to calculate it. A second key point about this economic gain is that, relative to the income of natives, the benefit is very small, representing a “0.31 percent overall increase in income” for native-born Americans.
Third, the report also summarizes empirical studies that have tried to measure directly the impact of immigration on the wages of natives (the analysis above being based on economic theory rather than direct measurement). The size of the wage impact in those empirical studies is similar to that shown above. The NAS report cites over a dozen studies indicating that immigration does reduce wages primarily for the least-educated and poorest Americans. It must be pointed out, however, that there remains some debate among economists about immigration’s wage impact. The fourth and perhaps most important point about the “immigrant surplus” is that it is eaten up by the drain on the public fisc. For example, the average of all eight fiscal scenarios is a net drain (taxes minus services) of $83 billion a year at the present time, a good deal larger than the $54.2 billion immigrant surplus.

There’s much more, but that’s enough for me. Build that wall!

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It’s also time to revisit the question of crime. Heather Mac Donald says “Yes, the Ferguson Effect Is Real,” and Paul Mirengoff shows that “Violent Crime Jumped in 2015.” I got to the root of the problem in “Crime Revisited,” to which I’ve added “Amen to That” and “Double Amen.”

What’s the root of the problem? A certain, violence-prone racial minority, of course, and also under-incarceration. Follow all of the links in the preceding paragraph, and read and weep.

The Wages of Simplistic Economics

If this Wikipedia article accurately reflects what passes for microeconomics these days, the field hasn’t advanced since I took my first micro course almost 60 years ago. And my first micro course was based on Alfred Marshall’s Principles of Economics, first published in 1890.

What’s wrong with micro as it’s taught today, and as it has been taught for the better part of 126 years? It’s not the principles themselves, which are eminently sensible and empirically valid: Supply curves slope upward, demand curves slope downward, competition yields lower prices, etc. What’s wrong is the heavy reliance on two-dimensional graphical representations of the key variables and their interactions; for example, how utility functions (which are gross abstractions) generate demand curves, and how cost functions generate supply curves.

The cautionary words of Marshall and his many successors about the transitory nature of such variables is no match for the vivid, and static, images imprinted in the memories of the millions of students who took introductory microeconomics as undergraduates. Most of them took no additional courses in micro, and probably just an introductory course in macroeconomics — equally misleading.

Micro, as it is taught now, seems to purvey the same fallacy as it did when Marshall’s text was au courant. The fallacy, which is embedded in the easy-to-understand-and remember graphs of supply and demand under various competitive conditions, is the apparent rigidity of those conditions. Professional economists (or some of them, at least) understand that economic conditions are fluid, especially in the absence of government regulation. But the typical student will remember the graph that depicts the dire results of a monopolistic market and take it as a writ for government intervention; for example:

Power that controls the economy should be in the hands of elected representatives of the people, not in the hands of an industrial oligarchy.

William O. Douglas
(dissent in U.S. v. Columbia Steel Co.)

Quite the opposite is true, as I argue at length in this post. Douglas, unfortunately, served on the Supreme Court from 1939 to 1975. He majored in English and economics, and presumably had more than one course in economics. But he was an undergraduate in the waning days of the anti-business, pro-regulation Progressive Era. So he probably never got past the simplistic idea of “monopoly bad, trust-busting good.”

If only the Supreme Court (and government generally) had been blessed with men like Maxwell Anderson, who wrote this:

When a gov­ernment takes over a people’s eco­nomic life, it becomes absolute, and when it has become absolute, it destroys the arts, the minds, the liberties, and the meaning of the people it governs. It is not an ac­cident that Germany, the first paternalistic state of modern Eu­rope, was seized by an uncontrol­lable dictator who brought on the second world war; not an accident that Russia, adopting a centrally administered economy for human­itarian reasons, has arrived at a tyranny bloodier and more abso­lute than that of the Czars. And if England does not turn back soon, she will go this same way. Men who are fed by their govern­ment will soon be driven down to the status of slaves or cattle.

The Guaranteed Life” (preface to
Knickerbocker Holiday, 1938, revised 1950)

And it’s happening here, too.

The Rahn Curve Revisited

REVISED 10/18/16, to report a new estimate of the Rahn curve after correcting a slight error in the previous estimate.

REVISED 10/20/16, to add a fourth explanatory variable, which improves the fit of the equation.

The theory behind the Rahn Curve is simple — but not simplistic. A relatively small government with powers limited mainly to the protection of citizens and their property is worth more than its cost to taxpayers because it fosters productive economic activity (not to mention liberty). But additional government spending hinders productive activity in many ways, which are discussed in Daniel Mitchell’s paper, “The Impact of Government Spending on Economic Growth.” (I would add to Mitchell’s list the burden of regulatory activity, which grows even when government does not.)

What does the Rahn Curve look like? Mitchell estimates this relationship between government spending and economic growth:

Rahn curve (2)

The curve is dashed rather than solid at low values of government spending because it has been decades since the governments of developed nations have spent as little as 20 percent of GDP. But as Mitchell and others note, the combined spending of governments in the U.S. was 10 percent (and less) until the eve of the Great Depression. And it was in the low-spending, laissez-faire era from the end of the Civil War to the early 1900s that the U.S. enjoyed its highest sustained rate of economic growth.

In an earlier post, I ventured an estimate of the Rahn curve that spanned most of the history of the United States. I came up with this relationship (terms modified for simplicity:

G = 0.054 -0.066F

To be precise, it’s the annualized rate of growth over the most recent 10-year span (G), as a function of F (fraction of GDP spent by governments at all levels) in the preceding 10 years. The relationship is lagged because it takes time for government spending (and related regulatory activities) to wreak their counterproductive effects on economic activity. Also, I include transfer payments (e.g., Social Security) in my measure of F because there’s no essential difference between transfer payments and many other kinds of government spending. They all take money from those who produce and give it to those who don’t (e.g., government employees engaged in paper-shuffling, unproductive social-engineering schemes, and counterproductive regulatory activities).

When F is greater than the amount needed for national defense and domestic justice — no more than 0.1 (10 percent of GDP) — it discourages productive, growth-producing, job-creating activity. And because government spending weighs most heavily on taxpayers with above-average incomes, higher rates of F also discourage saving, which finances growth-producing investments in new businesses, business expansion, and capital (i.e., new and more productive business assets, both physical and intellectual).

I’ve taken a closer look at the post-World War II numbers because of the marked decline in the rate of growth since the end of the war:

Real GDP 1947q1-2016q2

Here’s the revised result (with cosmetic changes in terminology):

G = 0.0275 -0.347F + 0.0769A – 0.000327R – 0.135P

Where,

G = real rate of GDP growth in a 10-year span (annualized)

F = fraction of GDP spent by governments at all levels during the preceding 10 years

A = the constant-dollar value of private nonresidential assets (business assets) as a fraction of GDP, averaged over the preceding 10 years

R = average number of Federal Register pages, in thousands, for the preceding 10-year period

P = growth in the CPI-U during the preceding 10 years (annualized).

The r-squared of the equation is 0.73 and the F-value is 2.00E-12. The p-values of the intercept and coefficients are 0.099, 1.75E-07, 1.96E-08, 8.24E-05, and 0.0096. The standard error of the estimate is 0.0051, that is, about half a percentage point. (Except for the p-value on the coefficient, the other statistics are improved from the previous version, which omitted CPI).

Here’s how the equations with and without P stack up against actual changes in 10-year rates of real GDP growth:

rahn-curve-model-actual-vs-estimates-with-and-without-p

The equation with P captures the “bump” in 2000, and is generally (though not always) closer to the mark than the equation without P.

What does the new equation portend for the next 10 years? Based on the values of F, A, R, and P for the most recent 10-year period (2006-2015), the real rate of growth for the next 10 years will be about 1.9 percent. (It was 1.4 percent for the version of the equation without P.) The earlier equation (discussed above) yields an estimate of 2.9 percent. The new equation wins the reality test, as you can tell by the blue line in the second graph above.

In fact the year-over-year rates of real growth for the past four quarters (2015Q3 through 2016Q2) are 2.2 percent, 1.9 percent, 1.6 percent, and 1.3 percent. So an estimate of 1.9 percent for the next 10 years may be optimistic.

And it probably is. If F were to rise from 0.382 (the average for 2006-2015) to 0.438, the rate of real growth would fall to zero, even if A, R, and P were to remain at their 2006-2015 levels. (And R is much more likely to rise than to fall.) It’s easy to imagine F hitting 0.438 with a Democrat president and Democrat-controlled Congress mandating “free” college educations, universal “free” health care, and who knows what else.

Unintended Consequences

Now comes this unsurprising revelation from The Economist:

Forcing job applicants to declare they have a criminal record—whether or not it is relevant to the post—allows employers to filter out ex-convicts, it is argued, and prevents them finding the sort of work that would help them stay out of prison. So activists across the world have called for “ban-the-box” laws, which prohibit employers from inquiring about criminal histories prior to job interviews or offers.

Some 24 states and many municipalities in America have now introduced laws along those lines….

A paper by Jennifer Doleac of the University of Virginia and Benjamin Hansen of the University of Oregon, published on August 1st, looked at the impact of introducing ban-the-box policies on labour-market data from America’s population census. It found that withholding criminal-record data from employers encouraged them to treat certain minority groups as if they were more likely to have criminal pasts. In areas where ban-the-box laws have taken effect, the study found, the probability of being employed has fallen by 5.1% for young, low-skilled African-American men, and by 2.9% for young, low-skilled Hispanic men….

Other research backs up this conclusion. Amanda Agan of Princeton University and Sonja Starr of the University of Michigan sent 15,000 fictitious job applications to employers in New York and New Jersey. Before ban-the-box was introduced in these states, white applicants received around 7% more callbacks than similar black applicants. But when the policy took effect the gap increased to 45%.

How do you think a lot of employers cope with racial hiring quotas affirmative action? They use names and other clues to identify those applicants for employment who are black. They then weed out all but those black candidates who seem exceptionally well-qualified, and obviously better-qualified than the white or Asian candidates — which is often none. Why? Because once a black person shows up for an interview, he or she becomes a potential liability — a prospective employee who, if not hired, can file a racial discrimination claim. And it costs a lot of money to defend racial discrimination claims.

Result: Racial hiring quotas affirmative action means that fewer blacks are hired than would otherwise be the case.

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Related posts:

Guilty Until Proven Innocent

Race and Reason: The Victims of Affirmative Action