Manufacturing myths

The Washington Times ^ | August 31, 2003 | Alan Reynolds

[Dead Dog]

Actually, I've been pretty succesful at finding US produced goods, usually at a superior value over the imports. Examples would be:

$28.00 Ralph Lauren cotton mattress cover(at Costco), great product, great price, and Made in the USA.

Appliances (Amana washer/dryer and Maytag Refer) are US made..some aren't, but it's not hard to find.

All of my power tools; saber saw, Air Comp, cordless drill, ext. All made in the USA.

Walmart-Faided Glory blue jeans $12.00, Made in USA (some are Mexico)

The one exclusion, I paid more for a set of US tie down straps, when the Chinese version were half the price but of what appeared to be equal quality.

Danner boots, some nice synthetic ones are Chinese. However, the no nonsense, non-envirowheenie, leather are US made.

[boingo_temp]

" In one striking example they cite from 1995, South Korean producer Hyundai obtained the engineering and technical specifications required to build wings for a Boeing/McDonnell jet.

Within two years, Hyundai had purchased state-of-the-art equipment and had successfully built wings for the Boeing 717. Meanwhile, the authors point out, Boeing's own equipment for building the same parts was around 30 years old.

"The competitors have the leverage to turn these deals very much to their advantage," said Pritchard. "

This is more a “striking example” of the futility of attempting to micro-manage trade to our benefit. Pritchard’s says that South Korea invested heavily to retool 6 years ago, very much to their advantage”, and now produces 717 wings. But the article in which it appears is claiming that those wings will soon no longer be produced. Who really outfoxed who here? Perhaps the technology can be reapplied, can’t tell.

I vaguely remember a principle expressed in a mass communication class 17 years ago that went something like, “It’s a physical impossibility for a closed dynamic system to regulate itself. The management requirements of regulation will always exceed the complexity of the system that it’s intended to regulate”. I suppose people not recognizing that principle would read this Boeing wing example and think, “We’ve gott’a stop this from happening again.”

[Starwind]

They (Asia) also have an abundance of capital to invest if/when they see fit. That has tremendous advantages over existing manufacturing infrastructure.

See Pricing Power Ain't What It Used to Be - Post #5 for an elaboration.

I'm sure much of Hyundai's plant is re-toolable at costs far below what a US plant would to spend to match it.

[boingo_temp]

"So electronics contributes a distorted 'health' to the latter decade of manufacturing, whereas manufacturing stood on its heavy industry strength in previous decades."

I think that I read that in your earlier and got distracted before bringing it up. I don’t understand your rationale for calling it “distorted”. The same could be said during the introduction of any new product such at automobiles or airplanes. Our society simply refocused on what to build.

BTW, Can you show me a reference to aerospace being taken out of these numbers? Now I agree that would seem to be a “distortion”.

[boingo_temp]

" Also declining prices due to global competiton are not shown - remember the purpose of chained dollar series is to remove price fluctuations as an effect - and prices have generally declined in manufacturing whereas by contrast they've generally increased in services (think tuition, lawyers, banking/mortgage fees, etc.). It is also true that productivity has increased; robotics and automation have helped to produce more with fewer workers. So even though the manufacturing base we do have may have produced more widgets (according to real GDP chained dollar data) , that does not mean that widget prices were profitable, or that widget-making workers kept their jobs, or that we've not lost widget companies.

Actually, if prices were kept low due to decreased profit margins and increased worker productivity, wouldn’t the chained data extracted from an earlier sample period under report todays industrial production (absent other effects that make it inaccurate)?

[Starwind]

The distortion is to the manufacturing statistic - what it would look like without Electronics sector or with aerospace included in the manufacturing statistics.

Go look at the tables I've linked. You can see for yourself there is no aircraft/aerospace sector, whereas for example there is a motor vehicle sector. Go look at the Factory Orders (for example U.S. July factory orders rose 1.6 pct) reports and you'll see defense and non-defense aircraft reported.

A chained data series is calculated for each time period in the series independent of other time periods. Each time period has it's own price adjustments (reflecting sector/product pricing during that period) applied to the actual sector/product output of that period. I don't know much more about the detailed mechanics of how each element in the series is actually computed or what the specific parameters are.

wouldn't the chained data extracted from an earlier sample period under report todays industrial production (absent other effects that make it inaccurate)?

If a sector (aircraft) were to be included in manufacturing, data for 1987 would not be added back in 1987 and then computed forward with some yearly adjustment. The aircraft production in each year would be adjusted for price (relative to prices in 1996) in each year and then would become a new series of yearly figures.

Your question implies an inherent or embedded interrelationship among each years elements in a series which I don't believe exists. Each years value is purely the result of that years production and pricing, regardless of what happended in previous or subsequent years. But I'm not sure I understand your question.

[ohmage]

Lone Ranger bump

[boingo_temp]

"Go look at the tables I've linked. You can see for yourself there is no aircraft/aerospace sector, whereas for example there is a motor vehicle sector. "

I think you are mistaken. The BEA Gross domestic product by industry Table that you linked to breaks out "Other transportation equipment", listed as about 15% smaller than automobile production. I don’t think that’s just bicycles, boats and trains.

The only place that I can see aerospace excluded is in one of the narrow 3 month period tablesLocated Here (PDF)

Also, I can’t imagine where aerospace would be listed if it were broken out of manufacturing Here
:

[Starwind]

I think you are mistaken.

I know you do. I'm not. "other transport" is buses, trucks, rolling stock, containers, ferries, etc but not oil tankers, cargo ships, aircraft, etc. The differentiator being cost and complexity of product.

Also, I can?t imagine where aerospace would be listed if it were broken out of manufacturing

I've already cited one of several tables where aircraft is reported to demonstrate there are figures, but the BEA has yet to include aircraft in the Real GDP by industry tables. Until 1996 that was also true of Electronics. It remains true of aircraft. Get over it.

I couldn't care less (at this point) how NAM wants to position themselves. NAM also wants to include software in their manufacturing stats, which is lame.

[Starwind]

Correction:

I know you do. I'm not. "other transport" is buses, trucks, rolling stock, containers, ferries ore barges, etc but not oil tankers, cargo ships, ferries, aircraft, etc. The differentiator being cost and complexity of product.

[boingo_temp]

<small><blockquote><i>" While Mr. Uchitelle first began whining about manufacturing being "downsized," it actually grew by 5.3 percent a year from 1992 through 2000. Manufacturing then fell 4.1 percent in 2001 (the bottom of his "trend")…."</i> -Reynolds</blockquote> <small>

<i>"Unfortunately Mr. Reynolds does not cite where he gets his index data so it is not possible to demonstrate what is wrong with it, other than it does not reflect what the Dept of Commerce BEA data shows, which is that manufacturing has declined some 27% by dollar volume over the last 15 years. "</i><p>

Going through some of what you've posted to me, I found two sets of BEA data that doesn’t' "exactly" match Reynold's claim above but the second is close. I see your source for a 27% drop, but you're reporting a drop in manufacturing percent of GDP, and Reynolds referred to growth without a GDP reference.

Contributions to Percent Change in Real Gross Domestic Product by Industry Group
	http://www.bea.gov/bea/dn2/contrib.htm
1991	1992	1993	1994	1995	1996	1997	1998	1999	2000	1992-2000 AVE	2001
	0.3	0.6	1.3	0.11	0.4	0.9	0.7	0.8	0.8	0.66	-0.9
Real Gross Domestic Product by Industry in Chained (1996) Dollars
	http://www.bea.doc.gov/bea/dn2/gpox.htm#1994-2001
1,066.30	1,085.00	1,122.90	1,206.00	1,284.70	1,316.00	1,387.20	1,444.30	1,513.90	1,585.40		1,490.30
Less prev year	18.70	37.90	83.10	78.70	31.30	71.20	57.10	69.60	71.50		-95.10
% change	1.8%	3.5%	7.4%	6.5%	2.4%	5.4%	4.1%	4.8%	4.7%	-6.0%	-6.0%

[boingo_temp]

"I know you do. I'm not. "other transport" is buses, trucks, rolling stock, containers, ferries ore barges, etc but not oil tankers, cargo ships, ferries, aircraft, etc. The differentiator being cost and complexity of product."

I couldn’t find that on the BEA site or in a Google search. Can you post a source for that?

[Starwind]

I found two sets of BEA data that doesn?t' "exactly" match Reynold's claim above but the second is close.

Yes, without him being more exact, I assumed the second was his source as the numbers he quotes are the same as Bartlett's, which numbers are computed invalidly from chained data, the 2nd source you noted.

I see your source for a 27% drop, but you're reporting a drop in manufacturing percent of GDP

Yes. A percentage connot be computed correctly from the real GDP chained dollar data. I relied upon the 1st source, as did Roberts in his rebutal of Bartlett.

and Reynolds referred to growth without a GDP reference

Yes, part of the problem to which I alluded that he had not explained his source or computations. As I mentioned above, based on the numbers he cited and having seen those citations before I assumed he was doing his own math on the chained dollar data (or borrowing others) but the math itself remains invalid for chained dollar data.

[Starwind]

I couldn't find that on the BEA site or in a Google search. Can you post a source for that?

Yeah, they don't make it easy. I've given the BEA website a cursory look and it isn't promising. It's been a couple years, I'll look further.

[boingo_temp]

"Yes, without him being more exact, I assumed the second was his source as the numbers he quotes are the same as Bartlett's, which numbers are computed invalidly from chained data, the 2nd source you noted. "

Bad assumption. Reynolds use of chained weighting’s not invalid in these circumstances according to the BEA

"Chained (1996) dollar values are calculated as the product of the chain-type quantity index (divided by 100) and the 1996 current-dollar value. Because the formula for the chain-type quantity indexes uses weights of more than one period, the corresponding chained-dollar estimates are usually not additive. The "Not allocated by industry" line is the difference between GDP and the statistical discrepancy plus the sum of the detailed industries. For current periods, this amount is usually small. However, it tends to become larger as one moves further from the base period. In such cases, the table of contributions of industries to the change in real GDP provides a better basis for determining the composition of real GDP growth. "

These numbers of Reynold’s did span only periods close to the base. Additionally, he wasn’t adding or doing ratios vertically, and that appears to be the major problem according to Using Chain-Weighted NIPA Data (by the Fed of SF)

You’ll need to source your claim that Reynolds methods here are invalid or else your criticism in this reguard’s “invalid and devoid of substance” ;^)

[Starwind]

Bad assumption.

In his email to you he discussed Manufacturing's share of the U.S. economy, as measured by real Gross Domestic Product (GDP*)

That I'm aware of, there is only the table I've cited that reports Real GDP for manufacturing as a component of total GDP. That narrows the tables he can rely on for discussing manufacturing's contributions to GDP to the two tables I've already cited (and you noted).

These numbers of Reynold's did span only periods close to the base.

No they don't or there wouldn't be differences of 27% decline since 1988 versus 5% growth since 1992. And even using numbers close to the base is still invalid - the error is just smaller.

He disputes that "Manufacturing's share of real gross domestic product... has dropped to between 16 and 17 percent and disputes the validity of a 27% decline since 1988 (alluded to in his reference to Similar statistical exercises recently led to an interesting debate between my old friends Bruce Bartlett and Paul Craig Roberts.).

To support his view, Reynolds would have to go back to 1988 as Bartlett and Roberts did. Further, he (or whomever) had to go back 1992 to support a claim of it actually grew by 5.3 percent a year from 1992 through 2000. Unless, of course, he was just dismissing someone elses math without checking it against his own.

Additionally, he wasn't adding or doing ratios vertically,

He (or whomever) is relying on the implicit assumption that the column components vertically total to 100% at the top - else computing a lower line item's percentage of the total at the column top has no meaning.

However, without Reynolds showing his math, your guess is no better than mine as to what he (or whomever) did. We only have his allusions to Manufacturing's share of Real GDP. Regardless, computing 1988, 1992, or 2000 percentages (to either affirm or refute a viewpoint) from Manufacturing's Real GDP chained dollar data as did Bartlett (whom Reynolds references and holds similar views) is invalid.

[boingo_temp]

"No they don't or there wouldn't be differences of 27% decline since 1988 versus 5% growth since 1992."

I’ve finally got a handle on this. Current Dollar (nominal) and Chained weighted (“real”) GDP estimates are measuring two distinctly different things. Current Dollar is measuring the economy’s components over time by their value/price, and Chained is measuring them by their units of serviced or units constructed (loosely speaking)

The wild variance over such a short time that you site between the two (-27% verses 5%) isn’t a product of inaccuracy in the chained weighted method as you say. (It would be utterly worthless if so.) The inaccuracy in chained weighting is very small over that 8 year time period, something like 1%. It’s broken out by the BEA in the GDP table we’ve been discussing under “Not Allocated by Industry”

	1992	1993	1994	1995	1996	1997	1998	1999	2000
GDP	6880.0	7062.6	7347.7	7543.8	7813.2	8159.5	8508.9	8859.0	9191.4
Not Allocated by Industry	-59.3	-28.3	-2.2	-2.2	0.0	-33.3	-48.9	-97.1	-159.1
	-0.9%	-0.4%	0.0%	0.0%	0.0%	-0.4%	-0.6%	-1.1%	-1.7%

The real variance due to how rapidly the two above listed aspects of our economy diverge.

Current Dollar simply measures the output price of the components/industries of the GDP each year (adjusting for inflation). What each industry actually produced is irrelevant. Manufacturing could have doubled the number of widgets produced last year, but if they generated the same income as the previous year no change is reported.

Chained Weighted is a kind of cross between Current Dollar and an earlier method called “Fixed Weighted”. Fixed weighted simply measured the output of things produced or performed by each industry for a time period and multiplied it by their price to get the total. Subsequent periods simply re-measured what was produced and applied the same fixed price to get a new period total. Chained weighting does the same, but makes a price adjustment for inflation across all units each period.

The inflation adjustment in Chained Weighting yields discrepancies that grow larger over time, but that’s not the primary source of divergence between Chain Weighted and Current Dollar GDP figures. Again, the difference is what’s being measured. Simply speaking, it’s the difference between measuring stuff done over time and the value of stuff done over time.

[Starwind]

Well, you're certainly a lot closer now. Please allow me to refine your understanding a little further.

The wild variance over such a short time that you site between the two (-27% verses 5%) isn't a product of inaccuracy in the chained weighted method as you say.

I never said the Real GDP chained dollar data was inaccurate. I said the math used by Bartlett and Reynolds to derive a Manufacturing percentage of total GDP was invalid because the aggregates in a chained dollar series are not additive, which means that the components don't add up to the total GDP, which means that each component is not represented in its proper proportion or share of the total GDP, which ultimately means one can not compute manufacturing's percent of 2001 GDP, for example, or any year except the base year 1996 (see my posts #20 and #39).

Note below that the 2001 GDP components in lines 2-84 (excluding the subtotals in lines 2 & 77) don't sum to the 2001 Total GDP in line 1. They aren't additive. So any attempt to derive a percentage for Mfg as

16.17% = 1,490.3 / 9,214.5 x 100

is invalid (with lesser or greater error depending on interval to the base year, and such error is not linearly predictive - i.e. you can't compensate for it).

  Real Gross Domestic Product by Industry in Chained (1996) Dollars, 1994-2001
                    [Billions of chained (1996) dollars]

Line                                                       2001
 1        Gross domestic product......................... 9,214.5
                                                             
 2   Private industries.................................. 8,189.4
 3      Agriculture, forestry, and fishing...............   163.9
 6      Mining...........................................   106.8
11      Construction.....................................   371.9
12      Manufacturing.................................... 1,490.3 
36      Transportation and public utilities..............   780.5
49      Wholesale trade..................................   748.7
50      Retail trade.....................................   951.2
51      Finance, insurance, and real estate.............. 1,843.5
61      Services......................................... 1,843.3 
76      Statistical discrepancy1.........................  -108.3
77   Government.......................................... 1,107.5
78      Federal..........................................   350.9
81      State and local..................................   756.1
84    Not Allocated by Industry2.........................  -204.4
  
  Now add up lines 2-84, skipping subtotal lines 2 & 77   _______
  This sum does not total 2001 GDP at top of column =>     9094.4

Within manufacturing the additive error is greater. Try summing the Mfg components (13-35) to get the line 12 subtotal.

Current Dollar simply measures the output price of the components/industries of the GDP each year (adjusting for inflation).

It reports the dollar value of the output product or service (units x price/unit) and adjusts for inflation.

Chained weighting does the same, but makes a price adjustment for inflation across all units each period.

The fact that it is termed 'Real GDP' means it has been adjusted for inflation. The chained dollars further adjusts for sellers' price changes relative to 1996 (see my 'university' example in post #39)

The inflation adjustment in Chained Weighting yields discrepancies that grow larger over time, but that's not the primary source of divergence between Chain Weighted and Current Dollar GDP figures. Again, the difference is what's being measured. Simply speaking, it's the difference between measuring stuff done over time and the value of stuff done over time.

Without too much quibbling on my part, both accurately reflect what they purport to reflect. The issue is chained dollar does not lend itself to computing percentages of the total because the data is not additive.