Asymptosis

Tim Kane was nice enough to include my question in this year’s Hudson Survey of Leading Economics Bloggers (PDF). Here’s the question and the results:

Judging based on post-war economic data, how do prosperous, high-GDP/capita countries compare with one another? Countries with larger government sectors have _____ growth rates compared to countries with smaller government sectors.

As a group, they did very well on this question:

Almost 50% got the right answer, and those who got it wrong were evenly split.

Cross-posted at Angry Bear.

There’s great discussion out there on this topic, see Steve Randy Waldman’s links list here.

Karl Smith gives us this graph and asks:

I mean, honestly, would you look at the graph above and conclude that during the 1970s the economy dangerously overheated.

I’d like to offer a perhaps more useful (though more complicated) look. Here’s NGDP/capita, RGDP/capita, and the Civilian Unemployment Rate (the NGDP/RGDP gap is accounted for by inflation):

Here’s a story to match that:

In the late 70s, Arthur Burns managed to drive down unemployment even in the face of a historically monumental labor-force surge, while maintaining healthy RGDP growth — but at the cost of high inflation. (The famous unemployment surge arose after two years of NGDP/RGDP declines ’78-’80, and briefly held steady during the ’80/’81 GDP surge.)

The “cost” of that inflation? A massive transfer of real buying power share from creditors/holders of financial assets to debtors/holders of real assets (assets such as…the skills and ability to work). Think: Workers’ relative claims to a share of the future production pie. Don’t even start to talk to me about the trivial effects of “menu costs” and such relative to this huge and inexorably arithmetic “textbook” effect.

Monetarists dismissing Steve’s labor-surge effect seem to be claiming that the Phillips curve had shifted, and that tighter Fed policy (given the understandings and tools at the time) would not have increased the unemployment rate. Is that realistic?

Or: higher unemployment would have been acceptable to maintain and protect the relative buying-power of creditors/holders of financial assets.

Cross-posted at Angry Bear.

 

Steve Randy Waldman delivers another Aha! post (and a followup reply to Scott Sumner) pointing out a huge driver of the 1970s Great Inflation — the rise in the labor force:

Between the mid 60s and the mid 70s, the labor force grew by 30%.* 

Steve, emphasis mine:

The root cause of the high-misery-index 1970s was demographics, plain and simple. The deep capital stock of the economy — including fixed capital, organizational capital, and what Arnold Kling describes as “patterns of sustainable specialization and trade” — was simply unprepared for the firehose of new workers. The nation faced a simple choice: employ them, and accept a lower rate of production per worker, or insist on continued productivity growth and tolerate high unemployment.

Capitalists didn’t have the capacity to justify employing all those workers at the prevailing wage. The only way to employ them was to lower real wages (always sticky in real terms) via inflation. The Fed accommodated that, favoring employment over wage control. The alternative would have been massive unemployment of all those eager up-and-coming boomers. Would that have been A Good Thing?

I’ve been worrying at the various Great-Inflation explanations for a decade, and I’ve never found them satisfying. Some seeming errors and contortions of economic logic aside, the imputed causes (OPEC?) have just never seemed big enough to bring about the massive economic effects we encountered.

This definitely does. Steve has added a (maybe the) crucial piece that allows me to understand this period.

Do monetarists really want to argue that the “stag” part of stagflation (unemployment) would have been the same if the Fed had gone Volcker in 1975? They were facing a tradeoff driven by a massive demographic shift. Incorporate that shift in your thinking, and you can ask the important question: would excluding those new workers (“financial repression!”) have been worth it, in return for lower inflation? (Inflation that eviscerated the real buying power share of financial-capital holders and creditors.)

You can argue about the answer, but this is sure: the rise in the labor force that Steve points out is crucial to thinking about The Great Inflation, and it’s been completely absent from the mainstream-storyline Great-Inflation explanations that I’ve wrestled with over the years. Viz two epitomes of that mainstream here and here. Neither one even mentions the labor force.

* This also of course involving what is perhaps the single largest macroeconomic shift from the 60s to the turn of the millenium: women entering the work force:

Over the 60 years following WWII, women’s labor-force participation jumped from 35% to 75%. In the 70s alone it jumped from 50% to 65%.

Cross-posted at Angry Bear.

Scott Sumner makes a very good point (though my interest here is somewhat peripheral to the main thrust of his post):

Government price indices don’t measure the prices that are of macroeconomic interest.  For instance in the 6 years after the housing bubble peaked the US, BLS data shows housing prices rising by about 10%, while Case-Shiller showed a 35% decline.  Housing is 39% of the core CPI.  That’s a big deal.

Here’s what that looks like:

Yow. (The important CPI sub-components of housing, i.e. owner equivalent rent, look similar.)

Contra the sky-is-falling inflationistas at ShadowStats, this suggests that CPI has greatly overstated inflation since the (Shiller) housing peak in April 2006. Just for illumination, here’s a rough-and-ready shot at replacing the 40% of housing movement in the CPI with the movement we see in Case-Shiller:

If this has any merit, we’re looking back at three to six years of deflation. It also suggest that inflation has been shooting up in the last year or so. Do with that what you will.

Paul Krugman often defends CPI against ShadowStats-style attacks by pointing to the Billion Price Index, which tracks closely with CPI over time. But the BPI is an index of retail prices. It doesn’t include housing, health care, education, and many other components that make up more than 50% of the Consumer Price Index. (Which makes you wonder why CPI and BPI track so closely…)

I notice that this Sumner item caught Karl Smith’s eye as well, and he points out rightly that constructing indexes is always a problematic venture:

basically anyone with MS Excel and a rudimentary knowledge of the subject matter in question can create a workable index

But, still, based on this quick look, at least since the housing peak in 2006, Scott’s right that CPI has been looking like an especially dicey measure.

Cross-posted at Angry Bear.

You might well ask: “Whaddaya mean by ‘his,’ buster?”

Nick does a full-faith effort here (including the comments) to characterize Steve Keen’s position (aggregate demand = GDP + change in debt), using Nick’s preferred language and mental modeling. It’s a darned good effort, but I think it’s crippled (as is Steve’s construct) by a conceptual failing about the nature(s) of “demand.”

The problem is perhaps best revealed here:

Aggregate actual nominal income equals aggregate expected nominal income plus amount of new money created by the banking system minus increase in the stock of money demanded.

Nothing in the above violates any national income accounting identity.

The last statement is neither right nor wrong, because “demand” is not an accounting measure. You’ll never find “demand” anywhere in the national accounts, in balance sheets, income statements, or flows of funds. Demand is a (potentially) useful economic concept and construct.

In its general form, demand is conceived as a curve, not an amount. It describes what people, at a given moment, would spend over an ensuing period, at various price points. (You can’t include a curve in an accounting identity.)

But if you assume a price point — say, the price point that exists at that given moment — you can specify demand at that moment as a number, an amount, a point on the curve: how much people would spend over the ensuing period at that price point, if nothing changed and supply was unconstrained. This works, for instance, if you assume that that moment’s price point will pertain over the ensuing period — not crazy for short periods. You can say “this is how much people, at this moment and this price point, want to spend over the ensuing period.”

That numerical amount — demand at that moment — could say something useful about the state of the economy at that moment (especially in the context of other measures).

Demand in the textbook understanding is always demand at a moment. It’s an “instantaneous flow.” (Google that term to to see how flow-over-time measures for water, electricity, etc. that encompass or surround a moment can be used to estimate/derive such an instantaneous measure, and what formulas can be used to do so.)

Aside: Nick says in the comments that demand (or at least “”money demanded”) is a stock, and change in demand is a flow. I think he’s conceptualizing it wrong — the stock of demand?? — but he’s intuiting what I’m thinking: “demand” is like a stock measure because it describes a moment.

So here’s the question: “What was demand (for widgets, or aggregate demand) on July 31, 2011?” Gimme a number. How has that number changed over time? Graph it for me.

Have you ever seen a fever chart of aggregate demand over the decades? Would be darned interesting, no?

So I’m kind of amazed that economists aren’t, haven’t been, all over the problem of defining a formula to specify a measure of aggregate demand for an economy at a given moment. Steve Keen’s trying to do that. So is Ed Lambert.

You need a formula that draws on now-available, post-hoc accounting measures to derive an estimate of this economic measure for that moment. What accounting measures, and what formula combining those measures, deliver the most useful (accurate?) estimate of that moment’s “demand”? (The measure’s usefulness will ultimately depend on the larger model(s) in which it is employed, but we can begin by thinking in more general terms.)

Simple accounting measures don’t work. GDP over the ensuing period doesn’t do it, for instance. That’s “quantity actually supplied/bought/sold” during the period, not quantity demanded over the period, or demand at the beginning of the period. Supply constraints, price changes, etc. could (almost certainly do) mean that those numbers are quite different.

So what could work? There are an infinite number of possible formulas to estimate this measure, employing an infinite number of accounting measures. The most useful measures might be stock measures (describing the “demand moment” we’re examining), or flow measures (describing a period or periods preceding, succeeding, and/or encompassing that moment), or some combination of the two. It would be great if we could come up with a formula that relies on measures antecedent to the “demand moment” we’re estimating, because then we could estimate current “demand” in semi-real-time (subject to delays in measurement and reporting).

So what about Steve’s formula — GDP plus change in debt? I find it problematic because he seems to be specifying demand for a period, not a moment. Saying “demand for (the period) 2011 was GDP plus change in debt in 2011” is not very useful; it simply restates existing accounting measures for a period using a different word (“demand”). I want to know: what was demand on January 1, 2011? (If we’re using ensuing-period — say, 12-month — accounting measures to make the estimate, we may need to be precise in describing our measure: something like “ensuing-12-month-derived demand on January 1 was…”)

Also — assuming in my construct that Steve is deriving today’s “demand” from ensuing-twelve-month GDP and change in debt (I don’t think he’s actually doing that) — we don’t know what GDP and change in debt will be over the next twelve months. So the measure gives us no idea of what demand is today.

(It seems quite possibly or even likely to me, though, that  useful measures of “instantaneous demand”  will incorporate some debt/lending measures. Intuitively: when people borrow more they spend more, increasing the demand that producers face.)

This is all why I’m rather taken with Ed Lambert’s work. He’s given us a formula, based on most-recent accounting measures (Real GDP, Labor Share of Income, Capacity Utilization, and the Unemployment Rate), to calculate a measure he calls “effective” demand, at a given moment, i.e. now or any point in the reported past. And he graphs that measure over time relative to other measures.

Is it, will it, be a useful measure — allowing prediction or at least coherent understanding? That remains to be seen. But I’d sure like to see other economists developing competing measures of demand-at-a-given-moment, and accompanying models that make predictions based on those measures. It could result in some healthy Darwinian natural selection in the field.

Cross-posted at Angry Bear.