The General Educational Development test is a seven-hour exam that allows high school dropouts to show they are equivalent to high school graduates … In a 2011 study, the GED Testing Service found that within six years of earning a GED, about 40 percent of GED recipients enroll in college — but most drop out within a year. Only about 1 percent earns a bachelor’s degree.
So this year they are launching a new, more difficult test …
The GED is a good measure of scholastic ability, but it misses a completely different set of skills that matter in high school and in life. As measured by scores on other achievement tests, GED recipients are just as smart as those who graduate but do not go on to college. But why do GED recipients drop out of high school? The GED test — and achievement tests in general — miss skills like motivation, persistence, self-esteem, time management and self-control. A growing body of evidence has shown that these types of skills can be measured and that they rival raw intelligence in determining success in the labor market and school …
Making the GED harder will not address the real problem — it still will not capture many of the skills that matter in high school and in life. Most GED preparation programs focus on test preparation, with the average student studying only 30 hours before taking the exam. It is life skills that matter, not certificates.
If a picture is worth a thousand words, music like this is worth a thousand pictures
Esteemed colleagues Paul Krugman and Brad DeLong were not exactly überjoyed over my post Minsky on the IS-LM obfuscation, where I was quoting Hyman Minsky’s critique of the Hicksian IS-LM interpretation of Keynes.
I have to confess that I’m equally unimpressed by the Krugman-DeLong retort. I still share Minsky’s doubts on IS-LM being an adequate reflection of the width and depth of Keynes’s insights on the workings of modern market economies:
1 Almost nothing in the post-General Theory writings of Keynes suggests him considering Hicks’s IS-LM anywhere near a faithful rendering of his thought. In Keynes’s canonical statement of the essence of his theory — in the famous 1937 Quarterly Journal of Economics article — there is nothing to even suggest that Keynes would have thought the existence of a Keynes-Hicks-IS-LM-theory anything but pure nonsense. John Hicks, the man who invented IS-LM in his 1937 Econometrica review of Keynes’ General Theory — “Mr. Keynes and the ‘Classics’. A Suggested Interpretation” — returned to it in an article in 1980 — “IS-LM: an explanation” — in Journal of Post Keynesian Economics. Self-critically he wrote that ”the only way in which IS-LM analysis usefully survives — as anything more than a classroom gadget, to be superseded, later on, by something better — is in application to a particular kind of causal analysis, where the use of equilibrium methods, even a drastic use of equilibrium methods, is not inappropriate.” What Hicks acknowledges in 1980 is basically that his original IS-LM model ignored significant parts of Keynes’ theory. IS-LM is inherently a temporary general equilibrium model. However — much of the discussions we have in macroeconomics is about timing and the speed of relative adjustments of quantities, commodity prices and wages — on which IS-LM doesn’t have much to say.
2 IS-LM forces to a large extent the analysis into a static comparative equilibrium setting that doesn’t in any substantial way reflect the processual nature of what takes place in historical time. To me Keynes’s analysis is in fact inherently dynamic — at least in the sense that it was based on real historic time and not the logical-ergodic-non-entropic time concept used in most neoclassical model building. And as Niels Bohr used to say — thinking is not the same as just being logical …
3 IS-LM reduces interaction between real and nominal entities to a rather constrained interest mechanism which is far too simplistic for analyzing complex financialised modern market economies.
4 IS-LM gives no place for real money, but rather trivializes the role that money and finance play in modern market economies. As Hicks, commenting on his IS-LM construct, had it in 1980 — “one did not have to bother about the market for loanable funds.” From the perspective of modern monetary theory, it’s obvious that IS-LM to a large extent ignores the fact that money in modern market economies is created in the process of financing — and not as IS-LM depicts it, something that central banks determine.
5 IS-LM is typically set in a current values numéraire framework that definitely downgrades the importance of expectations and uncertainty — and a fortiori gives too large a role for interests as ruling the roost when it comes to investments and liquidity preferences. In this regard it is actually as bad as all the modern microfounded Neo-Walrasian-New-Keynesian models where Keynesian genuine uncertainty and expectations aren’t really modelled. Especially the two-dimensionality of Keynesian uncertainty — both a question of probability and “confidence” — has been impossible to incorporate into this framework, which basically presupposes people following the dictates of expected utility theory (high probability may mean nothing if the agent has low “confidence” in it). Reducing uncertainty to risk — implicit in most analyses building on IS-LM models — is nothing but hand waving. According to Keynes we live in a world permeated by unmeasurable uncertainty — not quantifiable stochastic risk — which often forces us to make decisions based on anything but “rational expectations.” Keynes rather thinks that we base our expectations on the “confidence” or “weight” we put on different events and alternatives. To Keynes expectations are a question of weighing probabilities by “degrees of belief,” beliefs that often have preciously little to do with the kind of stochastic probabilistic calculations made by the rational agents as modeled by “modern” social sciences. And often we “simply do not know.”
6 IS-LM not only ignores genuine uncertainty, but also the essentially complex and cyclical character of economies and investment activities, speculation, endogenous money, labour market conditions, and the importance of income distribution. And as Axel Leijonhufvud so eloquently notes on IS-LM economics — “one doesn’t find many inklings of the adaptive dynamics behind the explicit statics.” Most of the insights on dynamic coordination problems that made Keynes write General Theory are lost in the translation into the IS-LM framework.
Given this, it’s difficult to see how and why Keynes in earnest should have “accepted” Hicks’s construct.
In an earlier post on his blog, self-proclaimed “proud neoclassicist” Paul Krugman has argued that “Keynesian” macroeconomics more than anything else “made economics the model-oriented field it has become.” In Krugman’s eyes, Keynes was a “pretty klutzy modeler,” and it was only thanks to Samuelson’s famous 45-degree diagram and Hicks’s IS-LM that things got into place. Although admitting that economists have a tendency to use ”excessive math” and “equate hard math with quality” he still vehemently defends — and always have — the mathematization of economics:
I’ve seen quite a lot of what economics without math and models looks like — and it’s not good.
Sure, “New Keynesian” economists like Krugman — and their forerunners, “Keynesian” economists like Paul Samuelson and (young) John Hicks — certainly have contributed to making economics more mathematical and “model-oriented.”
But if these math-is-the-message-modelers aren’t able to show that the mechanisms or causes that they isolate and handle in their mathematically formalized macromodels are stable in the sense that they do not change when we “export” them to our “target systems,” these mathematical models do only hold under ceteris paribus conditions and are consequently of limited value to our understandings, explanations or predictions of real economic systems.
Science should help us disclose the causal forces at work behind the apparent facts. But models — mathematical, econometric, or what have you — can never be more than a starting point in that endeavour. There is always the possibility that there are other (non-quantifiable) variables – of vital importance, and although perhaps unobservable and non-additive, not necessarily epistemologically inaccessible – that were not considered for the formalized mathematical model.
The kinds of laws and relations that “modern” economics has established, are laws and relations about mathematically formalized entities in models that presuppose causal mechanisms being atomistic and additive. When causal mechanisms operate in real world social target systems they only do it in ever-changing and unstable combinations where the whole is more than a mechanical sum of parts. If economic regularities obtain they do it (as a rule) only because we engineered them for that purpose. Outside man-made mathematical-statistical “nomological machines” they are rare, or even non-existant. Unfortunately that also makes most of contemporary mainstream neoclassical endeavours of mathematical economic modeling rather useless. And that also goes for Krugman and the rest of the “New Keynesian” family.
When it comes to modeling philosophy, Paul Krugman has in an earlier piece defended his position in the following words (my italics):
I don’t mean that setting up and working out microfounded models is a waste of time. On the contrary, trying to embed your ideas in a microfounded model can be a very useful exercise — not because the microfounded model is right, or even better than an ad hoc model, but because it forces you to think harder about your assumptions, and sometimes leads to clearer thinking. In fact, I’ve had that experience several times.
The argument is hardly convincing. If people put that enormous amount of time and energy that they do into constructing macroeconomic models, then they really have to be substantially contributing to our understanding and ability to explain and grasp real macroeconomic processes. If not, they should – after somehow perhaps being able to sharpen our thoughts – be thrown into the waste-paper-basket (something the father of macroeconomics, Keynes, used to do), and not as today, being allowed to overrun our economics journals and giving their authors celestial academic prestige.
The final court of appeal for macroeconomic models is the real world, and as long as no convincing justification is put forward for how the inferential bridging de facto is made, macroeconomic model building is little more than “hand waving” that give us rather little warrant for making inductive inferences from models to real world target systems. If substantive questions about the real world are being posed, it is the formalistic-mathematical representations utilized to analyze them that have to match reality, not the other way around. As Keynes has it:
Economics is a science of thinking in terms of models joined to the art of choosing models which are relevant to the contemporary world. It is compelled to be this, because, unlike the natural science, the material to which it is applied is, in too many respects, not homogeneous through time.
If macroeconomic models – no matter of what ilk – make assumptions, and we know that real people and markets cannot be expected to obey these assumptions, the warrants for supposing that conclusions or hypotheses of causally relevant mechanisms or regularities can be bridged, are obviously non-justifiable. Macroeconomic theorists – regardless of being New Monetarist, New Classical or ”New Keynesian” – ought to do some ontological reflection and heed Keynes’ warnings on using thought-models in economics:
The object of our analysis is, not to provide a machine, or method of blind manipulation, which will furnish an infallible answer, but to provide ourselves with an organized and orderly method of thinking out particular problems; and, after we have reached a provisional conclusion by isolating the complicating factors one by one, we then have to go back on ourselves and allow, as well as we can, for the probable interactions of the factors amongst themselves. This is the nature of economic thinking. Any other way of applying our formal principles of thought (without which, however, we shall be lost in the wood) will lead us into error.
A gadget is just a gadget — and brilliantly silly simple models — IS-LM included — do not help us working with the fundamental issues of modern economies any more than brilliantly silly complicated models — calibrated DSGE and RBC models included.
Added 17:00 GMT: Matias Vernengo’s comment on this debate is well worth reading.
We start with the model of the late Hyman Minsky, a man with a reputation among monetary theorists for being particularly pessimistic, even lugubrious, in his emphasis on the fragility of the monetary system and its propensity to disaster. Although Minsky was a monetary theorist rather than an economic historian, his model lends itself effectively to the interpretation of economic and financial history. Indeed, in its emphasis on the instability of the credit system, it is a lineal descendant of a model, set out with personal variations, by a host of classical economists including John Stuart Mill, Alfred Marshall, Knut Wicksell, and Irving Fisher. Like Fisher, Minsky attached great importance to the role of debt structures in causing financial difficulties, and especially debt contracted to leverage the acquisition of speculative assets for subsequent resale.
According to Minsky, events leading up to a crisis start with a “displacement,” some exogenous, outside shock to the macroeconomic system. The nature of this displacement varies from one speculative boom to another. It may be the outbreak or end of a war, a bumper harvest or crop failure, the widespread adoption of an invention with pervasive effects–canals, railroads, the automobile–some political event or surprising financial success, or debt conversion that precipitously lowers interest rates. An unanticipated change of monetary policy might constitute such a displacement and some economists who think markets have it right and governments wrong blame “policy-switching” for some financial instability.
But whatever the source of the displacement, if it is sufficiently large and pervasive, it will alter the economic outlook by changing profit opportunities in at least one important sector of the economy. Displacement brings opportunities for profit in some new or existing lines and closes out others. As a result, business firms and individuals with savings or credit seek to take advantage of the former and retreat from the latter. If the new opportunities dominate those that lose, investment and production pick up. A boom is under way.
In Minsky’s model, the boom is fed by an expansion of bank credit that enlarges the total money supply. Banks typically can expand money, whether by the issue of bank’s notes under earlier institutional arrangements or by lending in the form of addictions to bank deposits. Bank credit is, or at least has been, notoriously unstable, and the Minsky model rests squarely on that fact. This feature of the Minsky model is incorporated in what follows, but we go further. Before banks had evolved, and afterward, additional means of payment to fuel a speculative mania were available in the virtually infinitely expansible nature of personal credit. For a given banking system at a given time, monetary means of payment may be expanded not only within the existing system of banks but also by the formation of new banks, the development of new credit instruments, and the expansion of personal credit outside of banks. Crucial questions of policy turn on how to control all these avenues of monetary expansion. But even if the instability of old and potential new banks were corrected, instability of personal credit would remain to provide means of payment to finance the boom, given a sufficiently throughgoing stimulus.
Let us assume, then, that the urge to speculate is present and transmuted into effective demand for goods or financial assets. After a time, increased demand presses against the capacity to produce goods or the supply of existing financial assets. Prices increase, giving rise to new profit opportunities and attracting still further firms and investors. Positive feedback develops, as new investment leads to increases in income that stimulate further investment and further income increases. At this stage we may well get what Minsky called “euphoria.” Speculation for price increases is added to investment for production and sale. If this process builds up, the result is often, though not inevitably, what Adam Smith and his contemporaries called “overtrading.”
Now, “overtrading” is by no means a clear concept. It may involve pure speculation for a price rise, an overestimate of prospective returns, or excessive “gearing.” Pure speculation, of course involves buying for resale rather than use in the case of comodities or for resale rather than income in the case of financial assets. Overestimation of profits comes from euphoria, affects firms engaged in the production and distributive processes, and requires no explanation. Excessive gearing arises from cash requirements that are low relative both to the prevailing price of a good or asset and to possible changes in its price. It means buying on margin, or by installments, under circumstances in which one can sell the asset and transfer with it the obligation to make future payments.
As firms or households see others making profits from speculative purchases and resales, they tend to follow: “Monkey see, monkey do.” In my talks about financial crisis over the last decades, I have polished one line that always gets a nervous laugh:
There is nothing so disturbing to one’s well-being and judgment as to see a friend get rich.
When the number of firms and households indulging in these practices grows large, bringing in segments of the population that are normally aloof from such ventures, speculation for profit leads away from normal, rational behavior to what has been described as “manias” or “bubbles.” The word mania emphasizes the irrationality; bubble foreshadows the bursting. In the technical language of some economists, a bubble is any deviation from “fundamentals,” whether up or down, leading to the possibility and even the reality of negative bubbles, which rather gets away from the thrust of the metaphor. More often small price variations about fundamental values (as prices) are called “noise.” In this book, a bubble is an upward price movement over an extended range that then implodes. An extended negative bubble is a crash.
As we shall see in the next chapter the object of speculation may vary widely from one mania or bubble to the next. It may involve primary products, especially those imported from afar (where the exact conditions of supply and demand are not known in detail), or goods manufactured for export to distant markets, domestic and foreign securities of various kinds, contracts to buy or sell goods or securities, land in the country or city, houses, office buildings, shopping centers, condominiums, foreign exchange. At a late stage, speculation tends to detach itself from really valuable objects and turn to delusive ones. A larger and larger group of people seeks to become rich without a real understanding of the processes involved. Not surprisingly, swindlers and catchpenny schemes flourish.
Although Minsky’s model is limited to single country, overtrading has historically tended to spread from one country to another. The conduits are many. Internationally traded commodities and assets that go up in price in one market will rise in others through arbitrage. The foreign-trade multiplier communicates income changes in a given country to others through increased or decreased imports. Capital flows constitute a third link. Money flows of gold, silver (under gold standard or bimetallism), or foreign exchange are a fourth. And there are purely psychological connections, as when investor euphoria or pessimism in one country infects investors in others. The declines in prices on October 24 and 29, 1929, and October 19, 1987, were practically instantaneous in all financial markets (except Japan), far faster than can be accounted for by arbitrage, income changes, capital flows, or money movements.
Observe with respect the money movements that in an ideal world, a gain of specie for one country would be matched by a corresponding loss for another, and the resulting expansion in the first case would be offset by the contraction in the second. In the real world, however, while the boom in the first country may gain speed from the increase in the supply of reserves, or “high-powered money,” it may also rise in the second, despite the loss in monetary reserves, as investors respond to rising prices and profits abroad by joining in the speculative chase. In other words, the potential contraction from the shrinkage on the monetary side may be overwhelmed by the increase in speculative interest and the rise in demand. For the two countries together, in any event, the credit system is stretched tighter.
As the speculative boom continues, interest rates, velocity of circulation, and prices all continue to mount. At some stage, a few insiders decide to take their profits and sell out. At the top of the market there is hesitation, as new recruits to speculation are balanced by insiders who withdraw. Prices begin to level off. There may then ensue an uneasy period of “financial distress.” The term comes from corporate finance, where a firm is said to be in financial distress when it must contemplate the possibility, perhaps only a remote one, that it will not be able to meet its liabilities.
For an economy as a whole, the equivalent is the awareness on the part of a considerable segment of the speculating community that a rush for liquidity–to get out of other assets and into money–may develop, with disastrous consequences for the prices of goods and securities, and leaving some speculative borrowers unable to pay off their loans. As distress persists, speculators realize, gradually or suddenly, that the market cannot go higher. It is time to withdraw. The race out of real or long-term financial assets and into money may turn into a stampede.
[h/t Brad DeLong]
And for those of you who prefer to read Swedish … there is a translation of Kindleberger’s book — Manier, panik och krascher (Pontes Förlag, 1999) – in which yours truly has an introductory chapter, trying more explicitly to connect Kindleberger’s exposition with the theories of Keynes, Fisher and Minsky.
The conventional wisdom, codified in the theory of the non-accelerating-inflation rate of unemployment (NAIRU) … holds that in the longer run, an economy’s potential growth depends on – what Milton Friedman called – the “natural rate of unemployment”: the structural unemployment rate at which inflation is constant.
This NAIRU depends on the extent to which labor markets deviate from the benchmark competitive labor market model as a result of regulatory interventions in the form of minimum wages, employment protection legislation, unemployment benefits, and wage-bargaining institutions, many of which are designed to reduce inequalities in pay, provide security to workers, and reduce inter-firm competition. If the labor market is more regulated, the NAIRU must be higher and potential growth lower.
It follows that if one wants to reduce structural unemployment, the only way to achieve this is by abolishing regulatory interventions in the labor market; the price of a dynamic economy and low unemployment is heightened inequality and “traumatized workers.” A second implication of NAIRU economics is that neither central bank policy nor fiscal policy affects natural unemployment. Macro policy is presumably ineffective.
We argue in our book Macroeconomics Beyond the NAIRU that the NAIRU doctrine is wrong because it is a partial, not a general, theory. Specifically, wages are treated as mere costs to producers. In NAIRU, higher real-wage claims necessarily reduce firms’ profitability and hence, if firms want to protect profits (needed for investment and growth), higher wages must lead to higher prices and ultimately run-away inflation. The only way to stop this process is to have an increase in “natural unemployment”, which curbs workers’ wage claims.
What is missing from this NAIRU thinking is that wages provide macroeconomic benefits in terms of higher labor productivity growth and more rapid technological progress …
NAIRU wisdom holds that a rise in the (real) interest rate will only affect inflation, not structural unemployment. We argue instead that higher interest rates slow down technological progress – directly by depressing demand growth and indirectly by creating additional unemployment and depressing wage growth.
As a result, productivity growth will fall, and the NAIRU must increase. In other words, macroeconomic policy has permanent effects on structural unemployment and growth – the NAIRU as a constant “natural” rate of unemployment does not exist.
This means we cannot absolve central bankers from charges that their anti-inflation policies contribute to higher unemployment. They have already done so. Our estimates suggest that overly restrictive macro policies in the OECD countries have actually and unnecessarily thrown millions of workers into unemployment by a policy-induced decline in productivity and output growth. This self-inflicted damage must rest on the conscience of the economics profession.
Uppdrag Gransknings program Jobb-bluffen som sändes igår var ett stycke alldeles lysande ekonomijournalistik. Se det här!
A couple months ago in a discussion of differences between econometrics and statistics, I alluded to the well-known fact that everyday uncertainty aversion can’t be explained by a declining marginal utility of money.
What really bothers me — it’s been bothering me for decades now — is that this is a simple fact that “everybody knows” … but, even so, it remains standard practice within economics to use this declining-marginal-utility explanation …
Also, let me emphasize that the solution to the problem is not to say that people’s preferences are correct and so the utility model is wrong. Rather, in this example I find utility theory to be useful in demonstrating why the sort of everyday risk aversion exhibited by typical students (and survey respondents) does not make financial sense. Utility theory is an excellent normative model here.
Which is why it seems particularly silly to be defining these preferences in terms of a nonlinear utility curve that could never be.
It’s as if you went into a bathroom in a bar and saw a guy pissing on his shoes, and instead of thinking he has some problem with his aim, you suppose he has a positive utility for getting his shoes wet.
[h/t Dwayne Woods]
Mathematics can be beguilingly elegant. It can also be dangerous when people mistake its elegance for truth.
Albert Einstein’s theory of general relativity might be the best example of elegant math, capturing a wide range of subtle and surprising phenomena with remarkable simplicity. Step toward the practical, though, and physics moves quickly away from elegance to makeshift usefulness. There’s no pretty expression for the operation of a nuclear reactor, or for how air flows past the swept wings of an aircraft. Understanding demands ugly approximations, or brute-force simulation on a large computer …
In one very practical and consequential area, though, the allure of elegance has exercised a perverse and lasting influence. For several decades, economists have sought to express the way millions of people and companies interact in a handful of pretty equations.
The resulting mathematical structures, known as dynamic stochastic general equilibrium models, seek to reflect our messy reality without making too much actual contact with it. They assume that economic trends emerge from the decisions of only a few “representative” agents — one for households, one for firms, and so on. The agents are supposed to plan and act in a rational way, considering the probabilities of all possible futures and responding in an optimal way to unexpected shocks.
Surreal as such models might seem, they have played a significant role in informing policy at the world’s largest central banks. Unfortunately, they don’t work very well, and they proved spectacularly incapable of accommodating the way markets and the economy acted before, during and after the recent crisis.
Now, some economists are beginning to pursue a rather obvious, but uglier, alternative. Recognizing that an economy consists of the actions of millions of individuals and firms thinking, planning and perceiving things differently, they are trying to model all this messy behavior in considerable detail. Known as agent-based computational economics, the approach is showing promise …
Consider, for example, the assertion of some prominent economists, such as Stanford University’s John Taylor, that the low-interest-rate policies of the Federal Reserve were to blame for the housing bubble. Some dynamic stochastic general equilibrium models can be used to support this view. The agent- based model, however, suggests that interest rates weren’t the primary driver: If you keep rates at higher levels, the boom and bust do become smaller, but only marginally.
A much more important driver might have been leverage — that is, the amount of money a homebuyer could borrow for a given down payment. In the heady days of the housing boom, people were able to borrow as much as 100 percent of the value of a house — a form of easy credit that had a big effect on housing demand. In the model, freezing leverage at historically normal levels completely eliminates both the housing boom and the subsequent bust.
Does this mean leverage was the culprit behind the subprime debacle and the related global financial crisis? Not necessarily. The model is only a start and might turn out to be wrong in important ways. That said, it makes the most convincing case to date (see my blog for more detail), and it seems likely that any stronger case will have to be based on an even deeper plunge into the messy details of how people behaved. It will entail more data, more agents, more computation and less elegance.
If economists jettisoned elegance and got to work developing more realistic models, we might gain a better understanding of how crises happen, and learn how to anticipate similarly unstable episodes in the future. The theories won’t be pretty, and probably won’t show off any clever mathematics. But we ought to prefer ugly realism to beautiful fantasy.