Problems with Clarke's Student Debt Post, Part I: Lifetime Consumption Issues

There are serious and very important problems with Conor Clarke's post, "Let College Students Get Into Debt". In part 1 of this series I discuss his statement, "…if the the point of credit-based consumption is to bring lifetime consumption more in line with lifetime income – as I believe it is – then college students more than anyone else should be getting into debt."

How in line does Clarke mean? In the real world, unlike in the simple classical lifetime consumption models, it is not optimizing to have consumption (spending) exactly equal throughout life. It's far from it. Did Clarke mean that it was optimal to perfectly even-out spending? I don't know, but a reader could interpret it this way. This is a misconception that comes from the all too common problem in economics of taking models with grossly simplifying assumptions literally, or overly literally.

First, spending usually just gets a lot more valuable with age – and non-spending gets a lot more costly and consequential. As you get older and your health, energy, and durability decrease you need a lot more spending on medical care and conveniences. At age 19 you can sleep like a baby on a beat up old discount futon or mattress. At age 45 or 60 or 75, this may lead to serious chronic back pain, and sleep/rest deprivation. And obviously at age 19 you need to spend far less on medical and dental care to avoid severe, or even deadly, consequences than you do at age 45, 60, or 75.

You can have the vacation of your life at age 19 sleeping at dirt cheap youth hostels, down and dirty motels, and campgrounds. It's going to probably be a lot less fun, and a lot more painful and risky, at age 50 or 70 (and this includes the fact that prestige externalities – that pink elephant of economics – get much greater with age). There's just a whole wealth of cheap or free activities that are great fun at 19, but are typically less fun, and less do-able, at 50 or 70. Examples include soccer, surfing, and even, how shall I put this, intimate activities.

And let's talk food. At 19, you can have a great time stuffing yourself with cheap and delicious Entenmann's and Whoppers, with relatively little or no weight gain. At 50 or 60, with a far lower metabolism, eating like that may preclude walking very well, or at all.

Now, even at 19, or 3 for that matter, there are, in fact, serious long-term risks to eating these things. The risks of cancer and heart disease drop much more, to extremely low levels, if a healthy, 80%+ little or unprocessed plant food diet is started in childhood (for the strong scientific case, see the books "Eat to Live" and "Disease-Proof Your Child", by Joel Fuhrman, M.D.)

But even eating healthy at age 19, you can still stuff yourself with delicious and fattening, but otherwise healthy and cheap foods like nuts, juices, 100% whole grain pasta, fresh backed whole grain breads smothered in zero trans-fat all vegetable margarine, 100% whole grain waffles swimming in 100% maple syrup, etc. At age 40 or 70, if you want to get as much taste pleasure in your life, or anything close, without ballooning up, you're going to have to spend a lot more for expensive ingredients and restaurants that can give you the same taste with far less calories.

And do you really think it's optimal to spend the same amount of money as a single 19 or 25 year old as when you're a 35 or 45 year old with three kids?

Of course eventually it can go in the other direction. By the time you're 100, you may not be in a condition to get much pleasure out of your money, at least with current medical and rejuvenation/restoration technology. But even there, it may be very important to you to help your children and grandchildren, or to leave a legacy with your wealth.

On top of this, as I've mentioned, positional/context/prestige externalities are far greater at 50 than at 19. If you're like the vast majority of people, you're going to get a lot less pleasure out of driving around in a beat-up 20 year old Honda Civic (with a great stereo), and being seen in it, (and trying to attract the opposite sex), when you're 19 than when your 50. Positional/context/prestige externalities are an extremely important factor in individual and societal utility maximization. The costs to society of academic economics treating them like a pink elephant are monumental.

I could go on; to put it in economics terms, the marginal utility function of money, despite the common simplifying implied assumption, is far from constant throughout life.

There are also some crucial behavioral factors that are ignored by simple lifetime consumption models. First, I've talked about positional/context/prestige externalities, where the utility you get from consumption depends on the level (and type) of consumption of others. It is also the case that the utility that you get out of current consumption is dependent on your own past levels of consumption; in fact it's highly dependent.

A typical person will get far more utility from $100,000 per year in consumption if his past consumption was $50,000 per year or less, than if it was $500,000 per year or more. This makes it so that a forced large decrease in consumption is especially utility decreasing. It can be devastating to be forced to experience a large and lasting drop in consumption (including its associated prestige – very much including this).

As a result, it's usually well worth it for people to play it relatively safe with their consumption, to try to decrease the odds of having to lower their spending greatly from what they've grown accustomed to, to try to insure against this. Taking out debt goes in the exact opposite direction, it increases the odds that you will grow accustomed to a certain consumption (and prestige) level and then have to go substantially below that due to something going wrong, or not as you had hoped.

The simple lifetime consumption models that conclude you should borrow extensively if necessary in an attempt to keep your consumption exactly the same throughout life don't take this into account. They assume a world with no risk.

In addition, people really enjoy constantly improving, or improving their circumstances. This is an important part of human nature. Thus, one might expect that a typical person would prefer, all other things equal, to have constantly increasing consumption throughout life over always having exactly the same consumption throughout life, if the total amount of consumption is equivalent. In fact, Andrew E. Clark, writes in his 1999 Journal of Economic Behavior & Organization article:

Recent years have seen the admission of a new member to the battery of explanations of increasing wage profiles. Commonly known as the Forced Saving Hypothesis, it states that workers prefer wages that rise over time. It has two components:

1. Workers make inter-temporal comparisons over their level of consumption. An increasing consumption stream is preferred to a flat consumption profile with the same present discounted value.

2. It is possible to create increasing consumption profiles from a flat or downward-sloping wage profile by saving appropriately. Agents, due to a lack of self-control, cannot do so. Increasing wage profiles are one way of giving some external actor the power to ensure that the agent’s consumption rises over time.

The basic classical lifetime consumption model, with its grossly simple utility function, is good for teaching the lesson that the phenomena of diminishing marginal returns for money makes it desirable to smooth consumption, all other things equal. This is a valuable lesson, but it's not the whole story. It's not all of the phenomena, or factors.

Like all models, the basic classical lifetime consumption model is only as good as its interpretation. It's not reality. It teaches some lessons, but not all. There are many additional important factors in lifetime consumption decisions than just what's best, all other things equal, when the marginal utility of money is decreasing.

And it's not always decreasing. The Law of Diminishing Returns does not say that marginal return is always decreasing. It just says that eventually you reach a point where the marginal return is decreasing. That point can be extremely far out (if it even exists. This is not a physics law. What's called, or what has been called, a "law" in economics can have many exceptions), and you may never get near it. This is why we often see the opposite of diminishing returns – economies of scale.

Upcoming:

Part 2: um, the interest rate does matter

Part 3: Very Asymmetric Information, Very Imperfect Decision Making

Part 4: Costs to Productive Risk Taking, Innovation, and Flexibility

Part 5: Consumption Smoothing through Taxes, Transfers, and Social Insurance

McHongKong?

From Michael Perelman:

Paul Romer [one of the world's foremost growth economists] proposes that developing countries could invite instant Hong Kongs---new cities in new locations run by experienced governments such as Canada or Finland. They would enrich the country where they are built as special economic zones while also rewarding the distant government that makes the investment of building the new city state and installing a set of fair and productive rules.

This reminds me a lot of franchising in business, where a small entrepreneur gets often badly needed aid in the form of a well established and successful system and ingredients, training, and a vast and sophisticated support network, but the motivated and energetic entrepreneur provides the bulk of the work on the ground. The entrepreneur is also restricted to following many of the rules of the franchiser that are for the purpose of ensuring quality control, efficiency, and success.

This has been an extremely beneficial relationship for a great number of entrepreneurs. It's a powerfully synergystic and successful concept, or model.

Given the great logic and success of franchising, Romer's idea certainly appears to have potential, but you need to be very smart and careful in structuring the details so as not to cause strong opposition due to sovereignty and nationalism issues.

Rodrik's post on governance has substantial potential to mislead

It's important to be careful in discussing the link between good governance and economic growth because it's easy to mislead. First, there's what you define as "good governance", and second there's the issue of whether you're talking about direct effects, indirect effects, or total effects.

For example, Harvard economist Dani Rodrik talk's about how a good industrial policy can be very helpful. The government makes the decision whether to implement a good industrial policy, so you could say that how good you call governance is based in part on this.

Likewise, the policies and ingredients for moving out of poverty that Rodrik describes in his book, "One Economics, Many Recipes", are highly dependent on government actions. Certainly a government which pursues those things aggressively and intelligently (and you could certainly think this is an important part of what's called "good governance") greatly increases the odds of the country quickly moving out of poverty, especially as opposed a government which does the opposite – very harmful things to development.

Rodrik might say that he defines good governance not this way, but as democratic, transparent, non-corrupt, etc., but it looks like you could make a good case that even with these things, usually, or often, depending on other particulars, the more you have, the better the policies and environment will be for moving out of poverty.

I especially think Rodrik's apparent claim that the U.S. in the 2000s is an example of how a country can have terrible economic results with good governance, and so good governance can be a non strongly positive factor, is dead wrong (or at least has great potential to mislead about very important things).

The example actually shows the exact opposite. It shows the great impact good governance (as I think most people would interpret the term) can have on economic growth and wealth.

The quality of governance plummeted astoundingly with the take over of Bush and the Republicans from Clinton and a more balanced mix of power in the rest of government – and the drop in wealth, efficiency, and growth that resulted from this was amazing over a period as short as eight years. They brought us from an overall sound economy and record surpluses to record deficits and the brink of a depression in a mere eight years, and if we had just a few more years of their governance, we probably would have had a depression.

The Bush years were just the opposite of what Rodrik apparently claimed; they were an amazing example of the how much bad governance can hurt economic growth.

And the Clinton and Obama periods are an amazing example of how much good governance can help economic growth, especially if you don't want all of the growth (and more) going to the rich.

And even if you call good governance just democraticness, lack of corruption, transparancy, etc., it was not having enough of those things that allowed Bush and the Republicans to seize power in the 2000 election; it was not having enough of those things that greatly aided them in doing so much harm. And I'm not talking about just the corrupt Supreme Court decision; I'm talking about the Electoral College; I'm talking about the fact that there are no run-off elections so that a Nader can cause the more popular of the two top candidates to lose; I'm talking about the fact that Wyoming has the same voting power in the Senate as California, and the District of Columbia, which has more people than Wyoming, gets zero voting power in the senate; I'm talking about how corporations can donate enormous sums to help politicians, and more.

But, it was having enough democraticness, lack of corruption, transparency, etc. which allowed us to make the amazingly positive change in 2008, in economics and so many other areas, of going from Republican control to Democratic.

Now, that was a roaring place to end, but my primary purpose of writing this blog is to teach, and discuss for important understanding. This takes a priority far higher than "good writing style". So I will continue with two statements by Dani that I think it's important to respond to:

"Johnson argues that U.S. economic policies have been captured by a (financial) oligarchy, in much the same way that business elites corrupt policy-making in much poorer countries such as Russia. The U.S., it turns out, is not that different."

Even in the darkest depths of Republican control, the U.S. was still far better than Russia in this regard. Plus, look at how quickly the U.S. Democratically pulled out of corrupt and incompetent control by electing the Democrats. It's far harder and takes far longer for Russians to identify and expel corrupt and incompetent administrations than Americans precisely because Americans have much better democraticness, lack of corruption, transparency, etc.

"After all, no-one can deny that the United States, for all its financial follies, is a rich country. It turns out that it is possible to be corrupt in a fundamental way and still be rich."

Not if you continue that way. Not if the U.S. had stayed that way. If the U.S. had kept Republican control, there would have been a depression lasting many years, and after that a long-term descent towards banana republic status. After a few generations, other first world countries would have left us far behind. But again, that's where good governance, as defined by democraticness, lack of corruption, transparency, etc., can be so valuable. It can allow a nation to kick out a really harmful, corrupt, and incompetent administration relatively quickly and easily.

Scientific does not mean simple-minded

William Easterly writes today:

Airline passengers recently ejected an innocent Muslim family from an airplane because they were afraid the family were terrorists. Similar reasoning explains why Dani Rodrik favors industrial policy as a key to success...

All of us are making the amazingly common mistake of REVERSING CONDITIONAL PROBABILITIES. The airline passengers perceived from media coverage that the probability that IF you are a terrorist, THEN you are a Muslim is high. Unfortunately for the poor family, the passengers confused this with the relevant probability, which is the chance that IF you are a Muslim, THEN you are a terrorist (which is extremely low even if the first probability really is high, because terrorists are very rare).

So here is Dani Rodrik on success and industrial policy: “the countries that have produced steady, long-term growth during the last six decades are those that relied on a different strategy: promoting diversification into manufactured … goods” (cited in Economist’s View).

So Dani concludes, “What matters [for growth in developing countries] is their output of modern industrial goods” and that developing countries will have to get busy with “real industrial policies.” Finally, “external policy actors (for example, the World Trade Organization) will have to be more tolerant of these policies.”

Unfortunately, Dani is also REVERSING CONDITIONAL PROBABILITIES. Dani’s evidence is based on what he believes is the high probability that IF you have had steady growth for six decades, THEN you had industrial policy. This is interesting, but this is not the right probability in deciding whether to choose industrial policy, which is “IF you have industrial policy, THEN what is your chance of steady growth for six decades?”

This second, correct, probability would seem to be pretty low, since many other countries -- especially African and Latin American -- extensively tried industrial policies over the past six decades with low and erratic growth as a result...

I am really going through a MAJOR Mlodinow slash Kahneman phase about how economists (present company included) misinterpret data.

Here is what I think is the biggest overall way that economists misinterpret data: They think scientific means simple-minded, like you aren't scientific unless you assume away (and really take these assumptions literally, or substantially overly literally, act as though they are actually true in making conclusions for the real world) all of the complexity, all of the non-formal evidence and information, and assume everything relevant all fits into an extremely unrealistically simple formula, turn off your high-level, high-dimensional flexible thinking (which can still be rock solidly logical, with each link in the logic chains completely solid, even if it's not written in fancy looking, but really grossly over-simplified mathematics), and ignore any a priori information that's not formal and published in an academic journal in your field, no matter how important and compelling it is.

Harvard Economist Dani Rodrik is clearly an extremely proficient and regular user of high level intelligence. He is not saying that any industrial policy is a sufficient condition for strong movement out of poverty, he is just saying that the formal simple statistical data suggests that it can substantially increase the odds, and if you read his books, you see that he constructs very strong logic chains using less mathematical and formal, but also less simple, evidence to add a great deal of support to the simple statistical data.

Yes, there are a lot of countries that used at least some form (not necessarily a good one) and some amount (not necessarily relatively large and sustained) of industrial policy and failed, but that doesn't mean that industrial policy (good industrial policy) often cannot substantially increase the odds and amount of success, because there is a lot more to success than just having any industrial policy.

Every ATP tour tennis pro plays and practices an average of more than 10 hours per week, but only a fraction of 1% of people who play and practice more than 10 hours per week achieve ATP tour status. So are you going to make the simple-minded argument that it doesn't matter if you play and practice more or less than 10 hours per week, that it doesn't matter how much you play and practice per week in your probability of becoming an ATP tour pro? Or that "it's impossible to tell" (This is one of the lines I find most irritating. It's usually highly inaccurate and misleading. Often the evidence, all of the evidence at hand, although it won't tell you for sure, it will tell you that one possibility is much more likely than another, but people tend to feel so smart and level-headed saying "It's impossible to tell".)

First, clearly, if playing and practicing heavy hours was unrelated to success it would be very unlikely that it would just purely by chance end up existing in every single success story, but be relatively rare in the non-success stories. Now, there is data mining, but to see whether this is a random artifact of data mining, you should look at more than only the simple statistical data. You should use your high level intelligence and look at all of the evidence, even if it doesn't fit into a simple mathematical formula. You look at human biology, physiology, neurology; you look at evidence from sports in general, etc., etc. From this you can construct extremely compelling, rock solid logic chains, anchored to just very reasonable and mild assumptions (as opposed to the kind typically used by Chicago-style economists), showing that how much you play and practice matters greatly in your probability of achieving ATP pro status, even if those logic chains don't consist of solely highly simplifying mathematical symbols.

And, by the way, I do think advanced mathematics can be very useful, and use it myself, but only if it's used and interpreted intelligently.

You can come to some pretty ridiculous conclusions if you ignore the vast majority of the evidence because it does not fit some snobbish and simple-minded definition of what's scientific. Here's my definition of what's scientific, or at least the most important part of it: It's logical. The conclusions to the real world that these snobbish pseudo-scientific types make often don't meet this definition (see Chicago School).

Induction, deduction, and a model is only as good as its interpretation

View, understand, and then predict the behavior of the macro environment, rather than attempting to go from assumptions about micro to predictions about macro. – Robert Haugen, Emeritus Professor of Finance, University of California, Irvine. From his 2004 book, "The New Finance", 3rd Edition, page 123.

...as we travel from left to right, we go from order to complexity and finally into chaos.

At the extreme left, where there is order, mathematical models predict and explain well [as in much of physics].

As we move to the right, induction and statistical estimation dominate deduction and mathematical modeling in their ability to explain and predict... –"The New Finance", page 131.

Financial economists, both rational and behavioral, dazzle themselves with sophisticated mathematics. They gain much comfort in the intellectual rigor of their methodologies.

It makes no difference if their assumptions are completely unrealistic, so long as they parallel those made by their peers.

To them elegance [advanced, complete, and impressive mathematics] is all that matters. They look with disdain on studies of psychologists, sociologists, and anthropologists because their work seems so mushy in comparison to their own. They dismiss as unimportant forces that may actually be crucial but impossible to treat with mathematical rigor. –"The New Finance", page 132.

Reading London School economist John Kay's recent Financial Times article, "How Economics Lost Sight of the Real World", reminded me of Robert Haugen. Haugen is a maverick (let's not let John McCain and Sarah Palin ruin an important word), who fought very hard, and caustically, for decades against grossly unrealistic literal, or relatively literal, interpretation of models that show tremendous efficiency by making assumptions like, for example:

– Everyone in the world has perfect, or near perfect, rationality.

– Everyone in the world has advanced and specialized expertise in finance, economics, law, government, science, etc., that takes years or even decades of education and training to acquire. Or, they are able to perfectly know who has that expertise and can also be trusted to give it honestly, and at relatively little or no cost.

– Gathering information relevant to financial asset valuation (information, not just data) takes no time and is costless – even massive information gathering.

– Analysis of information relevant to financial asset valuation – even massive amounts of very complicated and difficult to interpret information – takes no time and is costless.

– Unlimited liquidity for all assets, and all buyers and short sellers.

– All assets can be sold short, and this short selling can be done instantly and at zero transactions cost.

– Even a relatively tiny number of savvy investors will have enough wealth, or access to enough wealth, that they can always buy assets up to their efficient price (Note: Even if they actually did have enough wealth to do this if they wanted to, a big problem that I pointed out in a 2006 letter in the Economist's Voice, which I have not seen elsewhere in the literature at least explicitly, is that they would be constrained by how undiversified their portfolio could become. As I wrote:

...suppose IBM is currently selling for $100, but its efficient, or rational informed, price is $110. It must be remembered that the rational informed price is what the stock is worth to the investor when added in the appropriate proportion to his properly diversified portfolio of other assets. Such a savvy investor will purchase more IBM as it only costs $100, but as soon as he purchases more IBM, IBM becomes worth less to him per share, because it becomes increasingly risky to put so much of his money in the IBM basket. By the time this investor has purchased enough IBM that it constitutes 20 percent of his portfolio, the stock may have become so risky that it’s worth less than $100 to him for an additional share. At that point he may have only purchased enough IBM stock to push the price to $100.02, far short of its efficient market price of $110. Thus, if the rational and informed investors do not hold or control enough—a large enough proportion of the wealth invested in the market—they may not be able to come close to pushing prices to the efficient level.

– The global equilibrium of the model is reached before any of the exogenous factors change, rather than those exogenous factors regularly changing before the economy can get anywhere close to that equilibrium (oh, and heaven forbid that a model should ever not have an equilibrium, that some key things should just always move, on average, in one direction. That never happens in reality over any time period of important length – except for trivial things like GDP growth, accumulation of knowledge, and advancement of technology)

– All asset returns have a normal data generating process (DGP), or some other DGP which is simple enough to write mathematically on a single line, or maybe a few, (The real DGP, depending on the level of precision you desire, can take thousands of pages to describe, or more), and has at least relatively thin, well behaved tails.

– Quick simple local numerical optimization techniques will find the global optimum even in highly complicated, high dimensional problems (and any cherry picking the starting point to get a more publishable result is fine, especially since you're almost never asked to provide the computer programs and a large class of important assumptions and details you used by the academic finance journals, including by and large the top ones)

– Accuracy and stability of numerical algorithms is never a problem, so you can use whatever techniques you know, whichever are the easiest, or give you the most publishable results. (And, you learn how to do numerical accuracy and stability well and then spend the time to do it well at your own peril. Because it's given little if any consideration at the academic finance journals, and it takes a lot of time, which will substantially lower your publication production and therefore your advancement.)

Of course, not all models in economics and finance that conclude great efficiency make all of these assumptions, but they all make assumptions like these, that as a group are extremely unrealistic. Does that mean that you can't still learn some valuable lessons from models like these? No. Often you can. But you have to interpret the model intelligently, using high level, not mechanical, intelligence. You certainly don't unthinkingly automatically interpret these models literally, as if the real world behaves exactly, or even qualitatively exactly, like the model. And the same goes for econometric models and techniques utilizing empirical data.

Now let's get back to Haugen. In his 2004 book, "The New Finance", 3rd edition (the 4th is being released May 2nd), he makes many of the same points Kay does, as well as important related ones. I think this book can convey some very important insights that are seldom or never heard in the academic finance literature, but like models, and like most writing today, you should be careful about interpreting it too literally. There's hyperbolic or very hyperbolic writing throughout the book, and many of the statements are literally exaggerated, or falsely absolute.

Does Haugen understand that these statements are exaggerated and falsely absolute? I've read a lot of his work, and he's extremely intelligent. I think he does understand this, at least in most cases. In part, though, like almost everyone, he succumbs at least to a substantial extent to the great pressure to write with what's considered "good style", and that means smooth and simple, (as well as, depending on the venue, profound-sounding, "professional", entertaining, etc.) even if it results in a false simplicity, saying things that are literally false and/or likely to mislead a substantial percentage of the readers in important ways (for more on this, see my very first blog post).

In large part, though, Haugen is just shouting because he's angry, and because it's so hard to get through with the grip the unrealistic efficient market people have (and especially had) on academic finance, with their great control of the journals and departments, and therefore advancement, prestige and money.

That said, let's get to some of the statements in Haugen's book that are similar to, or related to, statements in Kay's article. I think they can add valuable insight; there's a lot of truth to them. But again, I recommend that you be careful not to take Haugen's exaggerated and absolutist statements completely literally:

KAY: Since the 1970s economists have been engaged in a grand project. The project’s objective is that macroeconomics should have microeconomic foundations...

Most economists would claim that the project has been a success. But the criteria are the self-referential criteria of modern academic life. The greatest compliment you can now pay an economic argument is to say it is rigorous. Today’s macroeconomic models are certainly that...

But policymakers and the public at large are, rightly, not interested in whether models are rigorous. They are interested in whether the models are useful and illuminating – and these rigorous models do not score well here...There is not, and never will be, an economic theory of everything. Physics may, or may not, be different. But the knowledge we can hope to have in economics is piecemeal and provisional, and different theories will illuminate different but particular situations. We should observe empirical regularities and – as in other applied subjects such as medicine and engineering – we will often find pragmatic solutions that work even though our understanding of why they work is incomplete.

HAUGEN: Chaos aficionados sometimes use the example of smoke from a cigarette rising from an ashtray. The smoke rises in an orderly and predictable fashion in the first few inches. Then the individual particles, each unique, begin to interact. The interactions become important. Order turns to complexity. Complexity turns to chaotic turbulence...(page 122)

How then to understand and predict the behavior of an interactive system of traders and their agents?

Not by taking a micro approach, where you focus on the behaviors of individual agents, assume uniformity in their behaviors, and mathematically calculate the collective outcome of these behaviors.

Aggregation will take you nowhere.

Instead take a macro approach. Observe the outcomes of the interaction – market-pricing behaviors. Search for tendencies after the dynamics of the interactions play themselves out.

View, understand, and then predict the behavior of the macro environment, rather than attempting to go from assumptions about micro to predictions about macro...(page 123)

...as we travel from left to right [in figure 10-5 above], we go from order to complexity and finally into chaos.

At the extreme left, where there is order, mathematical models predict and explain well [as in much of physics].

As we move to the right, induction and statistical estimation dominate deduction and mathematical modeling in their ability to explain and predict...

Induction dominates deduction in its predictive power... (page 131)

Financial economists, both rational and behavioral, dazzle themselves with sophisticated mathematics. They gain much comfort in the intellectual rigor of their methodologies.

It makes no difference if their assumptions are completely unrealistic, so long as they parallel those made by their peers.

To them elegance [advanced, complete, and impressive looking mathematics] is all that matters. They look with disdain on studies of psychologists, sociologists, and anthropologists because their work seems so mushy in comparison to their own. They dismiss, as unimportant forces that may actually be crucial but impossible to treat with mathematical rigor. (page 132)

I largely agree with Haugen, but a key point of disagreement, at least with what he often writes literally, is that deduction is useless, or near useless, in highly complex situations. Deduction can still be extremely valuable.

Although in such situations deduction alone is not very good at very precise forecasts:

a) It can still give you very valuable qualitative understanding and ideas, like if you do X, or follow X policy, you will become much wealthier, at least 50% wealthier. You don't know the amount very precisely, but you do know that it's in a big range, and so the policy or idea is well worth doing. For example, the Capital Asset Pricing Model (CAPM) may not be very good (used alone) at precise forecasts of stock prices, but it does make clear that I can get a far lower risk level, not just a little lower, but far lower, for a given mean return, if I buy stocks in a large, highly diversified portfolio, than if I buy them singly, if I'm a layperson with no special information or analysis.

b) The understanding we get from deduction can help us improve our inductive research and models. It can give us a much better idea of where to look and what to look at in the inductive process of studying end results and situations. But we only get good and valuable understanding from deductive models if we interpret them intelligently, not automatically literally, or automatically qualitatively literally. It's worth repeating: A model is only as good as its interpretation.

c) Deductive understanding can be combined with inductive models and econometrics to substantially – often greatly – improve the forecasts. It can tell us when the inductive model's forecast based on the past will be much too low, or much too high, because of important recent changes from the past.

The problem isn't that deduction, and deductive models, are useless, or near-useless, in highly complex situations. It's that especially fresh water economists have been making ridiculously overly-literal interpretations and claims from deductive models. This is partly due to a focus on mathematics rather than economic intuition and other high level thinking. And it's partly due to the fact that many of these economists are extremely Libertarian, and are very willing to intentionally mislead in making conclusions from these models to support Libertarian economic policies.

In addition, there is a very strong incentive to make untrue big claims about an academic's models, or the models in an academic's area, so that they are more likely to be published in top journals, which is overwhelmingly what determines employment, promotion, power, prestige, and earnings. Academics especially feel freer to make these claims when they use advanced mathematics so little known that not only the general public can't read the papers to see how overblown and downright false they are, even the vast majority of fellow economists can't; even for them these paper are like written in Greek. They may even speak some Greek, but to decipher Greek this elaborate and advanced would take a lot more time than they have, especially since the vast majority of economists are not in this area and don't get paid to spend time in this area.

Eventually we reached a point where academics making grandiose, but ridiculous claims from highly mathematical models with efficient equalibria exerted great control at the top journals. And they used their gatekeeper ability to fight very hard to allow in papers that agreed with them, and to keep out papers that didn't. Sadly, they have been very successful at this, and they did it largely because if their research became a lot less prestigious and publishable, it would mean a huge decrease in personal prestige, positions, prizes, and earnings.

Likewise, their students, future economists, had a huge incentive to push these ridiculous claims and conclusions, because they had spent years learning these models and the associated mathematics, and if the claims of these models were exposed, their ability to publish in top journals, and from that to get high paying, high prestige jobs at top universities, would decrease dramatically. Since Economics Ph.D. students at schools like Chicago are largely type A, ultra-ambitious from childhood, work-a-holics, the thought of ending up making $80,000 per year as a professor at Penn State, rather than hundreds of thousands, or millions per year, including royalties, consulting, etc., at Chicago, is a huge incentive to support the party line.

So there are serious problems in economics and finance academia that stem pivotally from enormous asymmetric information, the fact that those predominantly paying for the research, the tax payers, have almost no ability to understand the highly mathematical and technical papers, and discern which are of great societal value, which are of little, and which have extremely unrealistic and harmful overly-literal conclusions from models to the real world.

We talk about market problems in finance justifying greater regulation, but the same may be true of finance academia, and economics academia. We really need to think about having a federal government department to monitor, study, and regulate, at least to some extent, how academia uses it's human and other resources, whether they are being spent in proportion to their risk-adjusted expected societal value over the short, medium, long, and extremely long run. The journals and departments right now, and for some time, award publications, grants, jobs, promotions, and prizes grossly out of line with the social NPV of the research work.

We really need to seriously study the idea, and specifics, of a large federal government department staffed by academics in economics, finance and other fields to study whether their academic fields, their departments and journals, are rewarding, and spending resources, in line with societal NPV (which does, of course, consider all benefits, including very long term, and unlikely but potentially huge), rather than largely in line with the enjoyment, prestige, and enrichment of those in control. And if things are way out of line, the government department has strong and wide ranging powers to do something about it.

Such a government department will, of course, need to develop a culture of loyalty to the public good, and not one's academic field. And there will need to be transparency and watchdogs, and in general the systems, techniques, and procedures which have greatly improved the efficiency and professionalness of civil service over the last century (especially when we have a party in power that tries to make government succeed, rather than one which tries to make it fail, and uses it extensively to enrich cronies).

Certainly there are problems with this idea; any implementation would require a lot of fine tuning, checks and balances and safeguards, with some portion of funds earmarked to be spent completely at academic department disgression, but too many economists forget a cornerstone of economics, that analyses should be cost-benefit, not cost alone. Yes, there are costs and problems with this idea, but the benefits could be enormous, and the costs of doing nothing could be far larger. If academic economics and finance had been tightly focused on honest research to maximize societal NPV over the last generation, we could easily have generated trillions of dollars more in wealth and total societal utility. A great place to start in increasing economics' societal NPV would be stopping the gatekeepers from ignoring the pink elephant of economics, positional/context/prestige externalities.