Comments on economics, mystery fiction, drama, and art.

Thursday, July 24, 2014

Predictions that don't pan out.

A friend posted (on her Facebook page) two pages from Time magazine in 1953, because of the Kraft Credit Union ad.  On the facing page was an essay ("Asking for More Inflation") by Henry Hazlitt, an economist who has had quite a following.

I'm finding myself amused by the way the essay begins: "In the issue of March 2, I wrote here:  'We shall soon learn whether the American people really want to halt inflation, and whether they are willing to pay the price.' "


Being the kind of guy I am, I looked up the numbers. From March 1952 to March, 1953, the rate of inflation as measured by the Consumer Price Index was, um, 1.4%. For subsequent months:

4/52-4/53: 0..8%
5/53-5/53: 1.1%
6/52-6/53: 1.1%
7/52-7/53: 0.4%
8/52-8/53: 0.7%
9/52-9/53: 0.8%
10/52-10/53: 1.1%
11/52-11/53: 0.7%
12/52-12/53: 0.8%

It didn't get any faster (in fact it was slower) in 1954 and 1955...

Uh...there was an inflation problem?
(Hazlitt was a crank.)

Friday, July 04, 2014

Thinking about "Price Stability"

I read recently James Grant's essay in the Wall Street Journal, in praise of the gold standard (in what purported to be a book review).  Among other things, he argued that prices are more stable under the gold standard than under a fiat money standard.  And he used the sort of evidence often used when making the argument for price stability under the gold standard.  In the US, between 1774 and 1933, inflation as measured by the CPI (retrospectively estimated for years before 1913) averaged 0.3% per year.  But from 1933 to 2013, inflation averaged 3.3%.  (One gets similar results using the GDP deflator--from 1790 to 1033, inflation averaged 0.4%; from 1933-2013, it averaged 3.4%).  Clearly, then, prices were more stable when the US was on a gold standard.

Well, not so fast.  To measure stability of prices, we need to account both for price increases and for price decreases.  If the CPI doubles from year 1 to year 2, and then falls by 50% from year 2 to year 3, we will measure zero inflation between years 1 and 3.  But I don't think anyone would suggest that prices were stable.

So, I calculated the absolute value of the year-to-year changes n the CPI (1774-2013) and in the PCE Deflator (1790-2013), and then calculated the mean absolute percentage changes in these price indexes for the 1774-1933 (1790-1933) and 1933-2013 periods.  I also calculated the standard deviation of the absolute percentage changes.  The table below shows what I found.




 

CPI

PCE

Mean Annual Absolute % Change,
1790-1933 (1790-1933)
Standard Deviation, 1774-1933 (1790-1933)

Mean Annual Absolute % Change,
1933-2013
Standard Deviation, 1933-2013

4.8%

5.5%

 
3.8%

3.1%

4.4%

4.6%

 
3.5%

2.6%

What happened, obviously, in the earlier period is that price increases (inflation) were offset by price decreases (deflation), while in the later period, the economy has mostly experienced inflation.

But the year-to-year changes were, in general, larger before 1933 and smaller after 1933.  Furthermore, the variation in the year-to-year changes were also larger before 1933 and smaller after 1933. 

I would suggest that the price stability question probably ought to be scored as a "win" for the fiat money period.

(Data from the Economic History Association's web site.)

Friday, June 27, 2014

Why It's Important to Consider Volatility of Prices When Thinking About Measuring the Rate of Inflation--In One Graph

When we teach introductory macroeconomics, we frequently talk about the importance of trying to keep in mind that some prices are (much) more volatile than are others.  Indeed, the Bureau of Labor Statistics publishes what is often called the "core" CPI measure of the rate of inflation, based on the CPI excluding energy and food prices.  But it can be hard for people to "get" why excluding extremely volatile prices can be important.

Here's one graph that, I think, does it.  The black line is the 12-month percentage rate of change in the CPI (all goods, including gasoline) and the 12-month percentage rate of change in the price of gasoline.

 

(Click to enlarge.)

Keep in mind that the price of gasoline is included in the CPI, so the increases or decreases in gasoline prices are already a part of the rate of inflation as measured by the CPI.  Also, by using 12-month rates of change, rather than monthly rates of change, I have already damped somewhat the volatility of gasoline prices.  (The monthly percentage changes are something to behold...)

And it's true that during the 1967 to 2014 period gasoline prices increased at a faster annual average rate (5.3%) than did prices in general (4.3%), it's also true that the extraordinary volatility of gasoline price changes would give us, by themselves, a misleading idea of that is going on.

(Data from FRED.)

Monday, June 23, 2014

The "De-mall-ification" of America

This article (from Slate) and the accompanying photographs are extremely interesting.  The discussion of the decline of the stand-alone, enclosed shopping mall evokes a number of thoughts.

First, the initial "take" of many urban economists was that such malls were contributing to the decline of older urban shopping districts, either in central business districts or in neighborhoods. 

Second, suburban shopping malls began to die, at least piecemeal, a long time ago.  In Indianapolis, where I live, the first suburban mall--Eastgate, about 7.5 miles east of the city center (it opened in 1957)--was eclipsed by Washington Square (opening in 1974, about 2.5 miles further east) and shuttered by the late 1980s.  Lafayette Square Mall, about 9 miles northwest of the city center, opened in 1968, and is now essentially empty (and has been for more than a decade), a sea of concrete surrounding the few remaining stores.  Glendale, a near-suburban mall, about 8 miles north-northeast of the center, had undergone a number of transformations, from open-air to enclosed to essentially re-built with a free-standing Target, a Macy's, and maybe a half-dozen other stores.  Near its end (in the early 2000s), there were about a dozen remaining businesses.

Third, central cities have been restructured (I'm not sure I want so say revived) by the construction of "vertical" malls (such as Water Tower Place in Chicago) and adaptations of old downtown stores into malls (such as Circle City Center, in Indianapolis).

-----------------------------------------------------------------
References:
--- "Birth, Death, and Shopping:  The Rise and Fall of the Shopping Mall," The Economist, December 2007.
 
Amie Dickinson and Murray D. Rice, "Retail Development and Downtown Change:  Shopping Mall Impacts on Port Huron, Michigan," Applied Research in Economic Development, V. 7, N. 2010, pp. 2-13.
 
Kenneth T. Jackson, "All the World's a Mall: Reflections on the Social and Economic Consequences of the American Shopping Center," The American Historical Review, Vol. 101, No. 4 (Oct., 1996), pp. 1111-1121.
 
Michael Fix, "Addressing the Issue of the Economic Impact of Regional Malls in Legal Proceedings," Journal of Urban and Contemporary Lay:  Urban Law Annual, V. 20, 1980, pp. 101-133.
 
Kent A. Robertson, "Downtown Development Strategies in the United States:  An End-of-the-Century Assessment," Journal of the American Planning Association, V. 61, N. 4, 1995. pp. 429-437.
 

Thursday, June 19, 2014

I'm a Profit Center!

I'm continuing to teach a little bit in my retirement, and the third iteration of an intro econ class for MBA students starts June 24.  I have 20 students, who each paid something like $4K each in tuition for the course.  I'm being paid $3K to teach it.  So, I am, in fact, a profit center!  (Maybe I should give my lecture on the theory of the optimal bribe and see whether I can extract any additional consumer surplus from them.)  (It'd be better to extract some surplus from the university, but I can't quite figure out how to do that.)

Wednesday, May 28, 2014

Broadband Download Speeds and Costs

Much has been written lately about the speed and cost of broadband in the US relative to other countries.  An article in the New York Times allows us to look at a snapshot of these two factors for a group of 21 mostly higher-income countries.  Figure 1 shows the relative speeds and costs, by country.  To get this, I divided each country's speed (cost) by the (unweighted) average of the download speed (download cost).


(Click to enlarge.)

Compared to this group of countries, the US has a relatively slow download speed (16.9 thousand megabits per second, compared to a group average of 27.4, and a relatively high download cost of $0.53 per megabit per second (group average, $0.46).  The US cost is not that far from the group average (15% higher), but the download speed is substantially (38%) slower.

If we look at the relationship between download speed and download cost, we get this:


(Click to enlarge.)

On average, download costs fall as download speeds rise (the correlation coefficient is -0.397, significantly different from zero at the 1% level).  Interestingly, the US lies almost perfectly on the regression line--on average, the U.S. has a download cost that we would "predict" from its download speed.  [I should note that the relationship is strongly affected by four outliers--two extremely high-cost, low-speed (Greece and Turkey) and two very low cost, high-speed (The Netherlands and South Korea) countries.  Excluding those four countries, the correlation coefficient falls to -0.288, still significant, and the US has a higher cost than "predicted."]

I have no big point here, just some interesting data.

Sunday, May 25, 2014

Volatility of the Components of Consumption Spending

Every now and then I create a chart that shows so incredibly clearly what we expect to see that I go back and make sure I didn't do something wrong.  This is one of those times.

I decided to look at the volatility of the three main components of consumption spending (real spending on consumer durable goods, real spending on consumer non-durable goods, and real spending on consumer services).  My measure of volatility is the absolute value of the month-to-month percentage changes in spending.  The data (from FRED) are for January 1959 to April 2014.  And this is the chart:

(Click to enlarge.)

Red is consumer durable goods, yellow is consumer non-durable goods, and green is consumer services.  And I found the results almost too good to be true, so I checked them several times.  Yep.

And the descriptive statistics go right along with it.  Durable goods spending is roughly three times as volatile as non-durable goods spending, and almost six times as volatile as spending on services.  Not only that, but the variation in changes in durable goods spending (as measured by the coefficient of variation) is also larger.






Spending Component


Mean of Monthly
Percentage
Changes

Standard Deviation of Monthly Percentage Changes

Coefficient of Variation of Monthly Percentage Changes

Real Consumer Durable Goods

Real Consumer Non-Durable Goods

Real Consumer Services

1.98%


0.60%


0.34%

1.85%


0.50%


0.23%

0.95


0.84


0.70

 Another interesting bit of data is that the price index for consumer durable goods follow a remarkably different time path than do the price indexes for non-durable or services, as the following chart reveals.
(Click to enlarge.)