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Créer des emplois en baissant les salaires ? : Une histoire de chiffres Broché – 10 septembre 2015
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One problem with claiming causation in macroeconomics is that there is always a lot of stuff going on at any one time in an economy — it’s pretty much impossible to isolate causes, or to construct reproducible experiments. Attempts to infer causation rely on the very technical apparatus of econometrics and statistical inference. MH wonders why “these mathematized exercises almost always lead to conservative results,” and speculates that it isn’t necessarily because researchers are ideologues, but because they’re « savants fous [mad scientists] » (@36). After reading MH’s analyses, though, this seems like a rather generous verdict: the methods are sometimes so arbitrary that tossing eye of newt and toe of frog into a bubbling cauldron might give equally good results. Among the problems MH uncovers are:
a) Fallacious computations of elasticities of employment vs. wages (i.e., how much employment changes for a unit change in wages — this is a key parameter for any model of job loss or creation). An especially frequent fallacy is to use microeconomic elasticities to model the macroeconomy — frequent, since it’s pretty much impossible to measure the macroeconomic elasticities directly, even though that’s really what the “mad scientists” would need to do to sustain their claims. But MH also cites even worse abuses, such as building the elasticity value the researchers wound up with into the assumptions of their econometric model; complaining that the values found in other papers are flawed, and then using an average of those values anyway; and attempting to measure a value, and then rejecting it in favor of a number that “feels” better, chosen from near the high end of the range of what various other reputable authors had published previously. Observing that some of these papers invoke the mathematical theorem called the « loi des grands nombres » (law of large numbers), MH points out tartly that others rely more on the « loi des grands noms » (“law of Big Names,” @39).
b) Interpretations that ignore the symmetry in most models between wages and productivity. On the one hand, mandating an increase in wages could lead firms to substitute capital for labor, which could lead to a higher productivity of labor, and therefore lower employment (**for a given level of production**). But on the other, the causality could also be reversed, with wage increases resulting from sharing the benefits of productivity improvements. As MH explains, economists typically favor one interpretation over the other when they write papers, even though the models can’t resolve this.
c) Explanations that rely on the relative cost of capital and labor. One problem is that this relative cost is very difficult to observe (since the cost of capital itself is hard to define and very difficult to observe). Another is that such models assume that firms can instantly substitute the most efficient means of production for labor in any proportion they desire. To some extent, says MH, this may be true in industries that can pick up their tents from one country and move to a cheaper one — but in these cases the basis for their decision is usually the absolute labor costs, rather than the relative costs of capital and labor. And as for other companies, the models ignore that they will have an installed base of older means of production, and that the greater efficiency will apply only to newer purchases.
d) The assumption that workers are paid at precisely the marginal productivity of labor (in accordance with neoclassical equilibrium theory), even though this is pretty much never true in real life.
One of the most engaging sections of the book is also one of the most technical, when MH analyzes the problem from the point of view of standard macroeconomic models (@87ff). The standard model would have productivity increase linearly with time —- but that doesn’t conform to observations, which show productivity growth in France (and many other developed economies) slowing down. Since salary growth shows a similar declining trend, the productivity growth slow-down is typically explained by models that add a term for wages, hours worked, and/or unemployment into the equation for productivity. In « un exercise iconoclaste », MH shows how time series data for productivity can be fit as well — or better — by a model that simply adds a term quadratic in time (t^2) to the linear one. That is, wages, etc. aren’t necessary to explain slowing down of productivity growth at all; and in fact no one really knows how to explain productivity's waxing and waning.
MH does provide ultra-brief explanations of econometrics, elasticity and other key concepts, but you’ll be much better off if you’ve encountered these concepts previously and perhaps often. To calibrate: though far shorter, the book assumes a good dose more technical background than Thomas Piketty's "Capital in the 21st Century." It also helps to have a Wonk Quotient high enough that you can find math-based polemics entertaining. The style is straightforward, with only occasional flourishes of irony (quite restrained, for a French academic), though obviously the title question is meant to be asked with an intonation somewhere on the spectrum between incredulity and sarcasm.
This work provides a concise, multi-dimensional critique of econometrics practice, via a case-study of a topic that's perennially in the headlines. For a longish technical essay to have been published as a relatively inexpensive stand-alone book is a testament to the glories of French publishing culture (for the time being, anyway). Even though something like this is really needed for economists across the Channel and across the Atlantic, it may take years for a translation to appear: a university press would most likely want to package it with enough other work by MH or other “critical economists” (as he calls himself) to produce a proper book for which it could charge an arm and a leg. Decades ago, one of my college profs said to me with a smile, “Well, everyone needs to learn to read French sooner or later, don’t they?” That seemed like a really pompous remark at the time; but unless I'm proved wrong in my expectations of British and North American publishing, this useful book is one among many hundreds, if not thousands, of reasons why he was right.