Archive for March 2013


Zero fiscal multiplier, Ed Balls edition

Here’s Britmouse:

. . . here’s a quote from a Bloomberg interview with Ed Balls from yesterday, hat tip to Richard Williamson:

Guy Johnson: Ed Balls, you do have your economics GCSE, we all know that.  Do you think the change in the [BoE] mandate will lead to higher inflation?

Balls: No, because I think the Bank of England will do its job, and it’s got a very clear remit to meet a symmetric target of 2% and that’s not changed.

My emphasis.  Yes, I omitted transcribing the previous question in the interview where Balls talked about a liquidity trap and how monetary policy was “pushing on a string”… so shoot me.

And Dan S left this comment in my previous post:

Along the same lines, a character named “Economist Hulk” has popped up on Twitter in the past couple days. My favorite so far: “HULK CONFUSED BY @EDBALLSMP. WANTS FISCAL STIMULUS BUT TO KEEP 2% INFLATION TARGET. HULK NOT AWARE OF MACRO MODEL WHERE THIS MAKES SENSE.”

Perhaps supply-side models.  The world of Keynesian economics continues to entertain and amuse.

BTW, Britmouse also produced this excellent graph, illustrating the “musical chairs” problem in Britain:

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What would a zero fiscal multiplier look like?

Something like this:

A government report due on April 5 is expected to show employers added 197,000 workers to their payrolls in March. That would be slower than during the prior month but still suggestive of a labor market recovery that is gaining traction.

Despite recent a recent acceleration in hiring, the Federal Reserve has appeared worried that budget tightening by the government could dampen progress made in the labor market, and policymakers last week pledged to keep buying bonds at a monthly pace of $85 billion until the labor market outlook improved substantially.

PS.  Don’t believe the headline 1.4% rise in Q4 NGDP.  The actual rate of increase was 3.6%, or 2.6% in real terms.  You want to always use the NGDI estimate of NGDP, which is more accurate than the NGDP estimate of NGDP.   Of course the job growth in Q4 was consistent with a 2.6% RGDP number, and wildly inconsistent with 0.4% RGDP growth.

PPS. Karl Smith discusses an interesting James Hamilton post on the housing bubble.  Both support the “mistakes were made” hypothesis.  I agree, and would add that in my view the “mistakes were made” hypothesis is the only one consistent with the EMH.  If the market (and government and academic) consensus knew it was a bubble then prices never would have risen so high in the first place.

They both rely on a truly ingenious research paper by Ing-Haw Cheng, Sahil Raina, and Wei Xiong.

PPPS.  This excellent Ryan Avent post might have been entitled “the real problem is nominal.”

Bubbles are caused by asset prices crashes, just as mountains are caused by valleys

Start with a flat plain.  How could you get a bunch of steep mountains?  One way would be to pile rocks up in some areas, but not others.  Another approach would be to cut deep valleys, such as those created by the famous four parallel rivers slicing through the East Tibetan plateau.  (Well they’re not famous, but they ought to be.)

Pretend the DJIA rose smoothly and steadily for 100 years.  Assume the level is theoretically “appropriate.”  How could you get bubbles?  One approach would be to have stocks occasionally rise far above their theoretically appropriate level. That’s probably how most people envision bubbles. Another would be for stocks to occasionally plunge far below fair market value. Then the “normal” looks like a bubble. I believe that if the EMH is not true, the latter is actually the most likely cause of bubbles.  Risk adjusted stock returns have been way too high for the past 100 years, according to standard finance models.  This implies stocks are only valued fairly at peaks like 1929 and 2000, and are otherwise greatly under-priced.

So we have have stock “bubbles” for the same reason the Tibet/Sichuan border area has lots of mountains—deep valleys have been cut in the appropriate stock price path.

TravisV recently asked me an interesting question:

At the AEI event with Avent and Pethokoukis, you suggested as an aside that there is an association between volatile NGDP and asset price bubbles. Could you please clarify that reasoning? I don’t see the connection, given that NGDP was very volatile during the 1970″²s and there weren’t any major asset price bubbles.

I see his point, but I think people tend to focus too much on the big rise in prices during a “bubble” and forget that the big fall is equally important.  If prices rise and stay high, as with San Francisco or Manhattan housing, then people eventually stop thinking of it as a bubble, and start thinking that it’s “normal” that housing would be expensive in highly desirable areas like San Francisco or Manhattan.

The 1920s and the Great Moderation both saw relatively stable NGDP growth. So why the big bubbles?  Because NGDP growth crashed in 1929-30 and 2008-09. In 2008-09 NGDP growth slowed by 9% relative to trend, nothing like that happened in the 1970s.  It was the crash that (partly) created the bubble.  Without the crashes, we wouldn’t even be talking about the great stock bubble of 1929, or the great housing bubble of 2006.  I don’t hear people talking about the Australian housing bubble of 2006.

Keep in mind I am not claiming any sort of deterministic relationship between NGDP instability and bubbles.  Merely that asset prices are more likely to be unstable when NGDP is highly unstable.  And people see bubbles when asset prices are highly unstable.  Certainly there are other factors at work during the 1970s—such as the fact that rising trend rates of inflation can depress real equity prices.  This is one reason the 1970s inflation showed up in gold prices rather than stock prices.

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Name names, please


Economists predicted catastrophe: As workers started taking home paychecks in January that were 2 percent lighter – thanks to the expiration of the payroll tax cut – they thought consumer confidence and spending would tank.

So far, it hasn’t.

Retail sales rose more than analysts expected last month, and consumer confidence rebounded.

It would be interesting to see the “economists predicting catastrophe” list—I doubt it would include any market monetarists.

Off Topic, here’s an excellent Paul Krugman post on monetary policy and low interest rates, and another good one on Poland’s mind-bogglingly foolish decision to join the euro.

PS.  Say it ain’t so.  Is Obama really trying to pressure Japan to adopt the sort of monetary policy that only Ron Paul could love?

According to Ron Napier, head of research company Napier Investment Advisors, one reason for the pause in the rhetoric on the yen could be “an agreement between Abe and [U.S. President Barack] Obama that the Japanese will not undervalue the yen [any further].

Can anyone confirm?  That would be worth a post, or fifty.

Money and Inflation, Pt. 4 (Answers, plus the role of expectations)

[Read my previous post first, if you have not already done so.  Also note that Marcus Nunes has some excellent graphs illustrating these points.]

I didn’t get as many answers as I expected, but there were a few good ones.  Phil got the ball rolling but fell a bit short on the last two questions.  George wins the gold star for getting there first, with honorable mention to Don and Steve who also nailed it.  Others came close.

Here’s how I analyze the Barro data set (which I’ll reproduce on the bottom of this post.)

1.  The eyeball test suggests the Quantity Theory of Money (QTM) works dramatically better for high inflation countries than low inflation countries.  (Also true of PPP and the Fisher effect, for much the same reason.)  In fact, money growth affects prices in all countries, but other factors are relatively more important when inflation is low.  Let’s suppose that the gap between money growth and inflation does not vary with the average rate of money growth.  For instance, suppose the gap is 3% on average, when examining very long run data.  In that cases the gap will seem almost trivial when money growth rates become very large, say more than 30%/year.

If you see the QTM as claiming money growth and inflation rates are highly correlated, then the theory works better for high inflation countries.  If you view it as it claiming that a given one time increase in the money supply will cause a proportionate increase in prices in the long run, then it should work equally well in low and high inflation countries.  Indeed even at the zero interest rate bound.

2.  In the vast majority of countries the money growth rate exceeded the inflation rate.  That means the money demand curve tends to shift to the right over time, at least when you describe money demand as a function of the value of money (1/P).  Many people prefer to visualize this pattern with the Equation of Exchange:  M*V = P*Y.  If V is fairly stable, and Y increases over time, then inflation will be less than money growth.  RGDP growth is deflationary!!  (Something our textbooks ignore—and check out Singapore below.)  In Barro’s sample of 83 countries, only one experienced falling RGDP on average over the entire 30 year period.

3.  Why does the money growth/inflation gap exceed 10% in only one of the 83 cases?  Partly because RGDP growth averages less than 10% in all 83 countries.  But it also requires velocity to be relatively stable.  Keep in mind that a 30% change in velocity over 30 years is fairly large, and yet is still less than 1% per year on average.  For money growth to exceed inflation by more than 10% you need the RGDP growth rate minus the change in velocity to exceed 10%, which occurred only in Libya.

4 and 5.  In order for the inflation rate to exceed the money supply growth rate you’d need the change in velocity to exceed the RGDP growth rate.  That would be a fairly large increase in velocity, and occurred in only 12 of the 83 countries.  The vast majority of these situations occurred in the high inflation countries.  (Seven of twelve in the 13 highest inflation countries.)  This is because velocity is positively correlated with nominal interest rates.  For velocity to rise sharply you normally need a large increase in interest rates, which usually implies a large increase in inflation (or more precisely NGDP growth) expectations. Unfortunately the table doesn’t show the change in inflation expectations. However it stands to reason that a very large increase in inflation expectations is more likely to occur in countries where the average rate of inflation is higher, and that’s what we observe.

To summarize, in equilibrium:

P = Ms/(Md/P)

Let’s assume real money demand is k*Y.  The parameter ‘k’ is the share of gross income that the public chooses to hold in the form of base money.  In that case:

P = M/(k*Y)  (or MV=PY, if you prefer.)

Now let’s assume that k is negatively relative to the opportunity cost of holding base money, and that base money doesn’t earn interest:

P = M/[k(i)*Y],

where i is positively related to both the level of NGDP relative to trend, and the expected rate of NGDP growth.

If you are more interested in NGDP than the price level (and if you aren’t you should be), then we have:

P*Y = M/k(i)

That’s my version of the QTM.  Until I see something better I’m not interested in alternative Keynesian, MMT or fiscal theories of inflation/NGDP.  Barro’s data set is the one I use to judge all competing theories of money and inflation.

However the fact that expectations play a role in the inflation process makes things far more complicated than the early QTM proponents assumed.  In the next post we’ll see how expectations shifts can lead to some results that look wildly inconsistent with the simple QTM.

PS.  Lars Christensen is forming a Global Monetary Policy Network.  I’ve already joined.

Country     MB growth    RGDP growth    Inflation   Time period

Brazil              77.4%             5.6%                 77.8%        1963-90

Argentina        72.8%             2.1%                 76.0%        1952-90

Bolivia             49.0%            3.3%                  48.0%        1950-89

Peru                49.7%             3.0%                 47.6%        1960-89

Uruguay          42.4%             1.5%                 43.1%         1960-89

Chile               47.3%             3.1%                 42.2%        1960-90

Yugoslavia       38.7%             8.7% (FWIW)     31.7%         1961-89

Zaire               29.8%             2.4%                  30.0%       1963-86

Israel               31.0%             6.7%                 29.4%        1950-90

Sierra Leone     20.7%            3.1%                  21.5%        1963-88

.  .  .

Canada            8.1%              4.2%                  4.6%         1950-90

Austria             7.1%             3.9%                   4.5%         1950-90

Cyprus            10.5%            5.2%                   4.5%         1960-90

Netherlands      6.4%             3.7%                   4.2%         1950-89

U.S.                 5.7%              3.1%                   4.2%        1950-90

Belgium           4.0%             3.3%                   4.1%         1950-89

Malta               9.6%             6.2%                    3.6%        1960-88

Singapore       10.8%            8.1%                     3.6%        1963-89

Switzerland       4.6%             3.1%                   3.2%        1950-90

W. Germany     7.0%             4.1%                    3.0%        1953-90