IID. United States
International Terms of Trade. Delfim Netto (1959) partly reprinted in Pelaez (1973)
conducted two classical nonparametric tests (Mann 1945, Wallis and Moore 1941;
see Kendall and Stuart 1968) with coffee-price data in the period of free
markets from 1857 to 1906 with the following conclusions (Pelaez, 1976a, 280):
“First, the null hypothesis of no trend was accepted
with high confidence; secondly, the null hypothesis of no oscillation was
rejected also with high confidence. Consequently, in the nineteenth century
international prices of coffee fluctuated but without long-run trend. This
statistical fact refutes the extreme argument of structural weakness of the
coffee trade.”
In his
classic work on the theory of international trade, Jacob Viner (1937, 563)
analyzed the “index of total gains from trade,” or “amount of gain per unit of
trade,” denoted as T:
T= (∆Pe/∆Pi)∆Q
Where ∆Pe
is the change in export prices, ∆Pi
is the change in import prices and ∆Q
is the change in export volume. Dorrance (1948, 52) restates “Viner’s index of
total gain from trade” as:
“What should be done is to
calculate an index of the value (quantity multiplied by price) of exports and
the price of imports for any country whose foreign accounts are to be analysed.
Then the export value index should be divided by the import price index. The
result would be an index which would reflect, for the country concerned,
changes in the volume of imports obtainable from its export income (i.e.
changes in its "real" export income, measured in import terms). The
present writer would suggest that this index be referred to as the ‘income
terms of trade’ index to differentiate it from the other indexes at present
used by economists.”
What really matters for an export activity especially
during modernization is the purchasing value of goods that it exports in terms
of prices of imports. For a primary producing country, the purchasing power of
exports in acquiring new technology from the country providing imports is the
critical measurement. The barter terms of trade of Brazil improved from 1857 to
1906 because international coffee prices oscillated without trend (Delfim Netto
1959) while import prices from the United Kingdom declined at the rate of 0.5
percent per year (Imlah 1958). The accurate measurement of the opportunity
afforded by the coffee exporting economy was incomparably greater when
considering the purchasing power in British prices of the value of coffee
exports, or Dorrance’s (1948) income terms of trade.
The
conventional theory that the terms
of trade of Brazil deteriorated over the long term is without reality (Pelaez 1976a, 280-281):
“Moreover,
physical exports of coffee by Brazil increased at the high average rate of 3.5
per cent per year. Brazil's exchange receipts from coffee-exporting in sterling
increased at the average rate of 3.5 per cent per year and receipts in domestic
currency at 4.5 per cent per year. Great Britain supplied nearly all the
imports of the coffee economy. In the period of the free coffee market, British
export prices declined at the rate of 0.5 per cent per year. Thus, the income
terms of trade of the coffee economy improved at the relatively satisfactory
average rate of 4.0 per cent per year. This is only a lower bound of the rate
of improvement of the terms of trade. While the quality of coffee remained
relatively constant, the quality of manufactured products improved
significantly during the fifty-year period considered. The trade data and the
non-parametric tests refute conclusively the long-run hypothesis. The valid
historical fact is that the tropical export economy of Brazil experienced an
opportunity of absorbing rapidly increasing quantities of manufactures from the
"workshop" countries. Therefore, the coffee trade constituted a
golden opportunity for modernization in nineteenth-century Brazil.”
Imlah (1958) provides decline of British export prices
at 0.5 percent in the nineteenth century and there were no lost decades,
depressions or unconventional monetary policies in the highly dynamic economy
of England that drove the world’s growth impulse. Inflation in the United
Kingdom between 1857 and 1906 is measured by the composite price index of
O’Donoghue and Goulding (2004) at minus 7.0 percent or average rate of decline
of 0.2 percent per year.
Simon Kuznets (1971) analyzes modern economic growth in his Lecture in Memory of Alfred Nobel:
“The major breakthroughs in the advance of
human knowledge, those that constituted dominant sources of sustained growth
over long periods and spread to a substantial part of the world, may be termed
epochal innovations. And the changing course of economic history can perhaps be
subdivided into economic epochs, each identified by the epochal innovation with
the distinctive characteristics of growth that it generated. Without
considering the feasibility of identifying and dating such economic epochs, we
may proceed on the working assumption that modern economic growth represents
such a distinct epoch - growth dating back to the late eighteenth century and
limited (except in significant partial effects) to
economically developed countries. These countries, so classified because they
have managed to take adequate advantage of the potential of modern technology,
include most of Europe, the overseas offshoots of Western Europe, and
Japan—barely one quarter of world population.”
Cameron
(1961) analyzes the mechanism by which the Industrial Revolution in Great
Britain spread throughout Europe and Cameron (1967) analyzes the financing by
banks of the Industrial Revolution in Great Britain. O’Donoghue and Goulding
(2004) provide consumer price inflation in England since 1750 and MacFarlane
and Mortimer-Lee (1994) analyze inflation in England over 300 years. Lucas
(2004) estimates world population and production since the year 1000 with
sustained growth of per capita incomes beginning to accelerate for the first
time in English-speaking countries and in particular in the Industrial
Revolution in Great Britain. The conventional theory is unequal distribution of
the gains from trade and technical progress between the industrialized
countries and developing economies (Singer 1950, 478):
“Dismissing, then, changes in productivity as a
governing factor in changing terms of trade, the following explanation presents
itself: the fruits of technical progress may be distributed either to producers
(in the form of rising incomes) or to consumers (in the form of lower prices).
In the case of manufactured commodities produced in more developed countries,
the former method, i.e., distribution to producers through higher incomes, was
much more important relatively to the second method, while the second method
prevailed more in the case of food and raw material production in the
underdeveloped countries. Generalizing, we may say -that technical progress in
manufacturing industries showed in a rise in incomes while technical progress
in the production of food and raw materials in underdeveloped countries showed
in a fall in prices”
Temin (1997, 79) uses a Ricardian trade
model to discriminate between two views on the Industrial Revolution with an
older view arguing broad-based increases in productivity and a new view
concentration of productivity gains in cotton manufactures and iron:
“Productivity advances in British
manufacturing should have lowered their prices relative to imports. They did.
Albert Imlah [1958] correctly recognized this ‘severe deterioration’ in the net
barter terms of trade as a signal of British success, not distress. It is no
surprise that the price of cotton manufactures fell rapidly in response to
productivity growth. But even the price of woolen manufactures, which were
declining as a share of British exports, fell almost as rapidly as the price of
exports as a whole. It follows, therefore, that the traditional ‘old-hat’ view
of the Industrial Revolution is more accurate than the new, restricted image.
Other British manufactures were not inefficient and stagnant, or at least, they
were not all so backward. The spirit that motivated cotton manufactures
extended also to activities as varied as hardware and haberdashery, arms, and
apparel.”
Phyllis
Deane (1968, 96) estimates growth of United Kingdom gross national product
(GNP) at around 2 percent per year for several decades in the nineteenth
century. The facts that the terms of trade of Great Britain deteriorated during
the period of epochal innovation and high rates of economic growth while the
income terms of trade of the coffee economy of nineteenth-century Brazil
improved at the average yearly rate of 4.0 percent from 1857 to 1906 disprove
the hypothesis of weakness of trade as an explanation of relatively lower
income and wealth. As Temin (1997) concludes, Britain did pass on lower prices
and higher quality the benefits of technical innovation. Explanation of late
modernization must focus on laborious historical research on institutions and
economic regimes together with economic theory, data gathering and measurement
instead of grand generalizations of weakness of trade and alleged neocolonial
dependence (Stein and Stein 1970, 134-5):
“Great Britain, technologically and
industrially advanced, became as important to the Latin American economy as to
the cotton-exporting southern United States. [After Independence in the
nineteenth century] Latin America fell back upon traditional export activities,
utilizing the cheapest available factor of production, the land, and the
dependent labor force.”
Summerhill (2015) contributes momentous
solid facts and analysis with an ideal method combining economic theory,
econometrics, international comparisons, data reconstruction and exhaustive
archival research. Summerhill (2015) finds that Brazil committed to service of
sovereign foreign and internal debt. Contrary to conventional wisdom, Brazil
generated primary fiscal surpluses during most of the Empire until 1889
(Summerhill 2015, 37-8, Figure 2.1). Econometric tests by Summerhill (2015,
19-44) show that Brazil’s sovereign debt was sustainable. Sovereign credibility
in the North-Weingast (1989) sense spread to financial development that
provided the capital for modernization in England and parts of Europe (see
Cameron 1961, 1967). Summerhill (2015, 3, 194-6, Figure 7.1) finds that
“Brazil’s annual cost of capital in London fell from a peak of 13.9 percent in
1829 to only 5.12 percent in 1889. Average rates on secured loans in the
private sector in Rio, however, remained well above 12 percent through 1850.”
Financial development would have financed diversification of economic
activities, increasing productivity and wages and ensuring economic growth.
Brazil restricted creation of limited liability enterprises (Summerhill 2015,
151-82) that prevented raising capital with issue of stocks and corporate bonds.
Cameron (1961) analyzed how the industrial revolution in England spread to
France and then to the rest of Europe. The Société
Générale de Crédit Mobilier of Émile and Isaac Péreire provided the
“mobilization of credit” for the new economic activities (Cameron 1961).
Summerhill (2015, 151-9) provides facts and analysis demonstrating that
regulation prevented the creation of a similar vehicle for financing
modernization by Irineu Evangelista de
Souza, the legendary Visconde de Mauá.
Regulation also prevented the use of negotiable bearing notes of the Caisse Générale of Jacques Lafitte
(Cameron 1961, 118-9). The government also restricted establishment and
independent operation of banks (Summerhill 2015, 183-214). Summerhill (2015,
198-9) measures concentration in banking that provided economic rents or a
social loss. The facts and analysis of Summerhill (2015) provide convincing
evidence in support of the economic theory of regulation, which postulates that
regulated entities capture the process of regulation to promote their
self-interest. There appears to be a case that excessively centralized
government can result in regulation favoring private instead of public
interests with adverse effects on economic activity. The contribution of
Summerhill (2015) explains why Brazil did not benefit from trade as an engine
of growth—as did regions of recent
settlement in the vision of nineteenth-century trade and development of
Ragnar Nurkse (1959)—partly because of restrictions on financing and
incorporation. Professor Rondo E. Cameron, in his memorable A Concise Economic History of the World
(Cameron 1989, 307-8), finds that “from a broad spectrum of possible forms of
interaction between the financial sector and other sectors of the economy that
requires its services, one can isolate three type-cases: (1) that in which the
financial sector plays a positive, growth-inducing role; (2) that in which the
financial sector is essentially neutral or merely permissive; and (3) that in
which inadequate finance restricts or hinders industrial and commercial
development.” Summerhill (2015) proves exhaustively that Brazil failed to
modernize earlier because of the restrictions of an inadequate institutional
financial arrangement plagued by regulatory capture for self-interest.
There is
analysis of the origins of current tensions in the world economy (Pelaez and
Pelaez, Financial Regulation after the Global Recession (2009a), Regulation
of Banks and Finance (2009b), International Financial Architecture
(2005), The Global Recession Risk (2007), Globalization and the State Vol. I (2008a), Globalization and the State Vol. II (2008b), Government
Intervention in Globalization (2008c)).
The
US Bureau of Economic Analysis (BEA) measures the terms of trade index of the
United States quarterly since 1947 and annually since 1929. Chart IID-1
provides the terms of trade of the US quarterly since 1947 with significant
long-term deterioration from 150.474 in IQ1947 to 108.040 in IIQ2020.
Significant part of the deterioration occurred from the 1960s to the 1980s
followed by some recovery and then stability.
Chart IID-1, United States Terms of Trade
Quarterly Index 1947-2020
Source: Bureau of Economic Analysis
Chart
IID-1A provides the annual US terms of trade from 1929 to 2019. The index fell
from 142.590 in 1929 to 109.740 in 2019. There is decline from 1971 to a much
lower plateau.
Chart IID-1A, United States Terms of Trade
Annual Index 1929-2019, Annual
Source: Bureau of Economic Analysis
Chart IID-1B provides the US terms of trade
index, index of terms of trade of nonpetroleum goods and index of terms of
trade of goods. The terms of trade of nonpetroleum goods dropped sharply from
the mid-1980s to 1995, recovering significantly until 2014, dropping and then
recovering again into 2019. There is relative stability in the terms of trade
of nonpetroleum goods from 1967 to 2019 but sharp deterioration in the overall
index and the index of goods.
Chart IID-1B, United States Terms of Trade
Annual Indexes 1967-2019, Annual
Source: Bureau of Economic Analysis
The US Bureau of
Labor Statistics (BLS) provides measurements of US international terms of
trade. The measurement by the BLS is as follows (https://www.bls.gov/mxp/terms-of-trade.htm):
“BLS terms of trade indexes measure the change in the U.S. terms
of trade with a specific country, region, or grouping over time. BLS terms of
trade indexes cover the goods sector only.
To calculate the U.S. terms of trade index, take the U.S.
all-export price index for a country, region, or grouping, divide by the
corresponding all-import price index and then multiply the quotient by 100.
Both locality indexes are based in U.S. dollars and are rounded to the tenth
decimal place for calculation. The locality indexes are normalized to 100.0 at
the same starting point.
TTt=(LODt/LOOt)*100,
where
TTt=Terms of Trade Index at time t
LODt=Locality of Destination Price Index at time t
LOOt=Locality of Origin Price Index at time t
The terms of trade index measures whether the U.S. terms of trade are improving or deteriorating over time compared to the country whose price indexes are the basis of the comparison. When the index rises, the terms of trade are said to improve; when the index falls, the terms of trade are said to deteriorate. The level of the index at any point in time provides a long-term comparison; when the index is above 100, the terms of trade have improved compared to the base period, and when the index is below 100, the terms of trade have deteriorated compared to the base period.”
TTt=(LODt/LOOt)*100,
where
TTt=Terms of Trade Index at time t
LODt=Locality of Destination Price Index at time t
LOOt=Locality of Origin Price Index at time t
The terms of trade index measures whether the U.S. terms of trade are improving or deteriorating over time compared to the country whose price indexes are the basis of the comparison. When the index rises, the terms of trade are said to improve; when the index falls, the terms of trade are said to deteriorate. The level of the index at any point in time provides a long-term comparison; when the index is above 100, the terms of trade have improved compared to the base period, and when the index is below 100, the terms of trade have deteriorated compared to the base period.”
Chart IID-3 provides the BLS terms
of trade of the US with Canada. The index increases from 100.0 in Dec 2017 to
117.8 in Dec 2018 and decreases to 104.0 in Feb 2020. The index increases to
116.3 in Jun 2020.
Chart IID-3, US Terms of Trade, Monthly, All Goods, Canada,
NSA, Dec 2017=100
Chart IID-3,
US Terms of Trade, Monthly, All Goods, Canada, NSA, Dec 2017=100
Chart IID-4 provides
the BLS terms of trade of the US with the European Union. There is improvement
from 100.0 in Dec 2017 to 102.8 in Jan 2020 followed by decrease to 99.5 in Jun
2020.
Chart IID-4,
US Terms of Trade, Monthly, All Goods, European Union, NSA, Dec 2017=100
Chart
IID-4 provides the BLS terms of trade of the US with Mexico. There is
deterioration from 100.0 in Dec 2017 to 96.8 in Jun 2020.
Chart IID-5,
US Terms of Trade, Monthly, All Goods, Mexico, NSA, Dec 2017=100
Chart IID-4
provides the BLS terms of trade of the US with China. There is deterioration
from 100.0 in Dec 2017 to 98.0 in Sep 2018, improvement to 100.6 in Apr 2019
with deterioration to 97.6 in Jun 2020.
Chart IID-6,
US Terms of Trade, Monthly, All Goods, China, NSA, Dec 2017=100
Chart IID-4 provides the BLS terms of trade
of the US with Japan. There is deterioration from 100.0 in Dec 2017 to 99.2 in
Dec 2019 and deterioration to 95.9 in Jun 2020.
Chart IID-7,
US Terms of Trade, Monthly, All Goods, Japan, NSA, Dec 2017=100
Manufacturing is
underperforming in the lost cycle of the global recession. Manufacturing
(NAICS) in Jun 2020 is lower by 13.0 percent relative to the peak in Jun 2007,
as shown in Chart V-3A. Manufacturing (SIC) in Jun 2020 at 95.0970 is lower by
15.3 percent relative to the peak at 112.3113 in Jun 2007. There is cyclical
uncommonly slow growth in the US instead of allegations of secular stagnation. There is similar
behavior in manufacturing. There is classic research on analyzing deviations of
output from trend (see for example Schumpeter 1939, Hicks 1950, Lucas 1975,
Sargent and Sims 1977). The long-term trend is growth of manufacturing at
average 2.9 percent per year from Jun 1919 to Jun 2020. Growth at 2.9 percent
per year would raise the NSA index of manufacturing output (SIC, Standard
Industrial Classification) from 108.2987 in Dec 2007 to 154.8159 in Jun 2020.
The actual index NSA in Jun 2020 is 95.097 which is 38.6 percent below trend.
The underperformance of manufacturing in Jun 2020 originates partly in the
earlier global recession augmented by the current global recession with output in
the US reaching a high in Feb 2020 (https://www.nber.org/cycles.html), in the lockdown of economic activity in the COVID-19.
Manufacturing grew at the average annual rate of 3.3 percent between Dec 1986
and Dec 2006. Growth at 3.3 percent per year would raise the NSA index of
manufacturing output (SIC, Standard Industrial Classification) from 108.2987 in
Dec 2007 to 162.5089 in Jun 2020. The actual index NSA in Jun 2020 is 95.0970,
which is 41.5 percent below trend. Manufacturing output grew at average 1.6
percent between Dec 1986 and Jun 2020. Using trend growth of 1.6 percent per
year, the index would increase to 132.0671 in Jun 2020. The output of
manufacturing at 95.0970 in Jun 2020 is 28.0 percent below trend under this
alternative calculation. Using the NAICS (North American Industry Classification
System), manufacturing output fell from the high of 110.5147 in Jun 2007 to the
low of 86.3800 in Apr 2009 or 21.8 percent. The NAICS manufacturing index increased
from 86.3800 in Apr 2009 to 96.1857 in Jun 2020 or 11.4 percent. The NAICS
manufacturing index increased at the annual equivalent rate of 3.5 percent from
Dec 1986 to Dec 2006. Growth at 3.5 percent would increase the NAICS
manufacturing output index from 106.6777 in Dec 2007 to 163.9940 in Jun 2020.
The NAICS index at 96.1857 in Jun 2020 is 41.3 below trend. The NAICS
manufacturing output index grew at 1.7 percent annual equivalent from Dec 1999
to Dec 2006. Growth at 1.7 percent would raise the NAICS manufacturing output
index from 106.6777 in Dec 2007 to 131.6999 in Jun 2020. The NAICS index at
96.1857 in Jun 2020 is 27.0 percent below trend under this alternative
calculation.
Chart V-3A,
United States Manufacturing NSA, Dec 2007 to Jun 2020
Board of
Governors of the Federal Reserve System
Chart V-3A,
United States Manufacturing (NAICS) NSA, Jun 2007 to Jun 2020
Board of
Governors of the Federal Reserve System
Chart V-3B
provides the civilian noninstitutional population of the United States, or
those available for work. The civilian noninstitutional population increased
from 231.713 million in Jun 2007 to 260.204 million in Jun 2020 or 28.491
million.
Chart V-3B,
United States, Civilian Noninstitutional Population, Million, NSA, Jan 2007 to
Jun 2020
Source: US
Bureau of Labor Statistics
Chart V-3C
provides nonfarm payroll manufacturing jobs in the United States from Jan 2007
to Jun 2020. Nonfarm payroll manufacturing jobs fell from 13.987 million in Jun
2007 to 12.169 million in Jun 2020, or 1.818 million.
Chart V-3C,
United States, Payroll Manufacturing Jobs, NSA, Jun 2007 to Jun 2020, Thousands
Source: US
Bureau of Labor Statistics
Chart V-3D provides the index of US manufacturing (NAICS) from Jan 1972
to Jun 2020. The index continued increasing during the decline of manufacturing
jobs after the early 1980s. There are likely effects of changes in the
composition of manufacturing with also changes in productivity and trade. There
is sharp decline in the
global recession, with output in the US reaching a high in Feb 2020 (https://www.nber.org/cycles.html), in the lockdown of economic activity in the COVID-19
event. There is initial recovery in May-Jun 2020.
Chart V-3D,
United States Manufacturing (NAICS) NSA, Jan 1972 to Jun 2020
Source:
Board of Governors of the Federal Reserve System
Chart V-3E provides
the US noninstitutional civilian population, or those in condition of working,
from Jan 1948, when first available, to May 2020. The noninstitutional civilian
population increased from 170.042 million in Jun 1981 to 260.204 million in Jun
2020, or 90.162 million.
Chart V-3E,
United States, Civilian Noninstitutional Population, Million, NSA, Jan 1948 to
Jun 2020
Source: US
Bureau of Labor Statistics
Chart V-3F provides
manufacturing jobs in the United States from Jan 1939 to May 2020. Nonfarm
payroll manufacturing jobs decreased from a peak of 18.890 million in Jun 1981
to 12.169 million in Jun 2020.
Chart V-3F, United States, Payroll Manufacturing Jobs, NSA, Jan 1939 to Jun 2020, Thousands
Source: US
Bureau of Labor Statistics
Table
I-13A provides national income without capital consumption by industry with
estimates based on the Standard Industrial Classification (SIC). The share of
agriculture declines from 8.7 percent in 1948 to 1.7 percent in 1987 while the
share of manufacturing declines from 30.2 percent in 1948 to 19.4 percent in
1987. Colin Clark (1957) pioneered the analysis of these trends over long
periods.
Table I-13A,
US, National Income without Capital Consumption Adjustment by Industry, Annual
Rates, Billions of Dollars, % of Total
|
1948
|
% Total
|
1987
|
% Total
|
|
|
National
Income WCCA
|
249.1
|
100.0
|
4,029.9
|
100.0
|
|
Domestic
Industries
|
247.7
|
99.4
|
4,012.4
|
99.6
|
|
Private
Industries
|
225.3
|
90.4
|
3,478.8
|
86.3
|
|
Agriculture
|
21.7
|
8.7
|
66.5
|
1.7
|
|
Mining
|
5.8
|
2.3
|
42.5
|
1.1
|
|
Construction
|
11.1
|
4.5
|
201.0
|
5.0
|
|
Manufacturing
|
75.2
|
30.2
|
780.2
|
19.4
|
|
Durable Goods
|
37.5
|
15.1
|
458.4
|
11.4
|
|
Nondurable
Goods
|
37.7
|
15.1
|
321.8
|
8.0
|
|
Transportation
PUT
|
21.3
|
8.5
|
317.7
|
7.9
|
|
Transportation
|
13.8
|
5.5
|
127.2
|
3.2
|
|
Communications
|
3.8
|
1.5
|
96.7
|
2.4
|
|
Electric,
Gas, SAN
|
3.7
|
1.5
|
93.8
|
2.3
|
|
Wholesale
Trade
|
17.1
|
6.9
|
283.1
|
7.0
|
|
Retail Trade
|
28.8
|
11.6
|
400.4
|
9.9
|
|
Finance, INS,
RE
|
22.9
|
9.2
|
651.7
|
16.2
|
|
Services
|
21.4
|
8.6
|
735.7
|
18.3
|
|
Government
|
22.4
|
9.0
|
533.6
|
13.2
|
|
Rest of World
|
1.5
|
0.6
|
17.5
|
0.4
|
|
|
2003.9
|
11.6
|
2016.3
|
11.5
|
|
|
252.6
|
1.5
|
257.9
|
1.5
|
Notes: Using
1972 Standard Industrial Classification (SIC). Percentages Calculates from
Unrounded Data; WCCA: Without Capital Consumption Adjustment by Industry; RE:
Real Estate; PUT: Public Utilities; SAN: Sanitation
Source: US
Bureau of Economic Analysis
Table
I-13B provides national income without capital consumption estimated based on
the 2012 North American Industry Classification (NAICS). The share of
manufacturing fell from 14.9 percent in 1998 to 9.5 percent in 2018.
Table I-13B,
US, National Income without Capital Consumption Adjustment by Industry,
Seasonally Adjusted Annual Rates, Billions of Dollars, % of Total
|
1998
|
% Total
|
2018
|
% Total
|
|
|
National
Income WCCA
|
7,744.4
|
100.0
|
17,136.5
|
100.0
|
|
Domestic
Industries
|
7,727.0
|
99.8
|
16,868.6
|
98.4
|
|
Private
Industries
|
6,793.3
|
87.7
|
14,889.6
|
86.9
|
|
Agriculture
|
72.7
|
0.9
|
119.7
|
0.7
|
|
Mining
|
74.2
|
1.0
|
202.7
|
1.2
|
|
Utilities
|
134.4
|
1.7
|
157.7
|
0.9
|
|
Construction
|
379.2
|
4.9
|
902.5
|
5.3
|
|
Manufacturing
|
1156.4
|
14.9
|
1635.3
|
9.5
|
|
Durable Goods
|
714.9
|
9.2
|
964.9
|
5.6
|
|
Nondurable
Goods
|
441.5
|
5.7
|
670.4
|
3.9
|
|
Wholesale
Trade
|
512.8
|
6.6
|
958.2
|
5.6
|
|
Retail Trade
|
610.0
|
7.9
|
1124.1
|
6.6
|
|
Transportation
& WH
|
246.1
|
3.2
|
554.4
|
3.2
|
|
Information
|
294.3
|
3.8
|
629.7
|
3.7
|
|
Finance,
Insurance, RE
|
1280.9
|
16.5
|
3058.8
|
17.8
|
|
Professional
& Business Services
|
889.8
|
11.5
|
2522.6
|
14.7
|
|
Education,
Health Care
|
607.1
|
7.8
|
1764.8
|
10.3
|
|
Arts, Entertainment
|
290.5
|
3.8
|
756.6
|
4.4
|
|
Other
Services
|
244.9
|
3.3
|
502.5
|
2.9
|
|
Government
|
933.7
|
12.1
|
1979.0
|
11.5
|
|
Rest of the
World
|
17.4
|
0.2
|
267.9
|
1.6
|
Notes:
Estimates based on 2012 North American Industry Classification System (NAICS).
Percentages Calculates from Unrounded Data; WCCA: Without Capital Consumption
Adjustment by Industry; WH: Warehousing; RE, includes rental and leasing: Real
Estate; Art, Entertainment includes recreation, accommodation and food
services; BS: business services
Source: US
Bureau of Economic Analysis
http://www.bea.gov/iTable/index_nipa.cfm© Carlos M. Pelaez, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020.
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