Inaugural Lecture
Universitat Pompeu Fabra
October 8 2008
Globalization and the Great Divergence
Jeffrey G. Williamson
Harvard University and the University of Wisconsin
Motivation
In David Landes’ (1998) words, why is the Third
World periphery in the South so poor, and the
industrial OECD core in the North so rich?
The competing explanations or fundamentals:
Culture: Polyani 1944; Landes 1998; Clark 2007
Geography: Diamond 1997; Sachs 2000, 2001;
Easterly & Levine 2003
Institutions: North & Weingast 1989; AJR 2001,
2002, 2005
Problems
Fundamentals don’t change very much over time.
So, what explains the timing of the great
divergence between Core and Periphery? Why
did the gap open so fast 1800-1913?
One possible explanation: the world was -Closed and anti-global pre-1800
Open and pro-global 1800-1913
Closed and anti-global 1913-1950
Open and pro-global 1950-2008
Four Big Facts
Fact 1: Rise in the Core-Periphery Income Per
Capita Gap
The rise of the North-South gap
Rise in the Core-Periphery Income Per Capita Gap 1820-1998
14
12
10
Western
Europe/Africa
Western Europe/Asia
8
Western Europe/Latin
America
Parity
6
4
Source: Maddison (2001,
Table B-21)
2
0
1820
1870
1913
1950
1973
1998
… and extending backwards with
real wages
Table 1. The Great Divergence: Income Per Capita Gaps 1775-1913
1775
1820
1870
1913
Western Europe
100
100
100
100
Southern Europe
Eastern Europe
Latin America
Asia
Africa
75.2
70.0
75.2
56.4
46.1
62.4
58.1
55.3
42.6
34.8
52.7
48.8
37.9
27.5
22.7
47.3
42.0
40.9
20.0
15.5
Poor Periphery Average
64.6
50.6
37.9
33.1
Four Big Facts
Fact 1: Rise in the Core-Periphery Income Per
Capita Gap
Fact 2: De-Industrialization in the Poor Periphery
Do Industrial Countries Get Richer?
Current GDP per capita 1820-1950 and Industrialization 50 or 70 Years Before
Per Capita Levels of Industrialization 1750-1953
1750
1800
1860
1913
1953
European Core
8
8
17
45
90
Asian and Latin American Periphery
7
6
4
2
5
1.1
1.3
4.3
22.5
18
Ratio Core/Periphery
Source: Bairoch (1982, Table 4, p. 281). The European core contains: Austria-Hungary, Belgium,
France, Germany, Italy, Russia, Spain, Sweden, Switzerland, United Kingdom. The Asian and Latin
American periphery contains: China, India (plus Pakistan in 1953), Brazil and Mexico.
More de-industrialization figures
India 1833
India 1887
Ottoman 1820s
Ottoman 1870s
Mexico 1800s
Mexico 1879
Textiles
Percent of Home Market Supplied by
Imports
Domestic Industry
5
95
58-65
35-42
3
62-89
25
40
97
11-38
75
60
Four possible causes of de-industrialization
in the Poor Periphery
● World market integration (e.g. globalization)
induces greater specialization (e.g. a new economic
order); implies tot improvement for periphery
● Rapid industrial productivity growth in Europe:
implies tot improvement for periphery
● Deterioration in industrial productivity and
competitiveness in periphery; implies no tot
improvement for periphery
● Improved productivity in primary product export
sector in periphery; implies no tot improvement for
periphery
Four Big Facts
Fact 1: Rise in the Core-Periphery Income Per
Capita Gap
Fact 2: De-Industrialization in the Poor
Periphery
Fact 3: Secular Terms of Trade Boom and Bust
in the Periphery
The 18th c calm before the storm …
The
th
19
c storm …
Some more than others
Figure 4. The Poor Periphery: Net Barter Terms of Trade 1796-1913
250
Terms of Trade
200
150
100
Middle East
50
Latin America
Southeast Asia
European Periphery
South Asia
0
1796 1802 1808 1814 1820 1826 1832 1838 1844 1850 1856 1862 1868 1874 1880 1886 1892 1898 1904 1910
And the terms of trade bust, as seen
from Latin America 1811-1939
Figure 1
Latin American Terms of Trade 1811-1939
160
140
120
Px/Pm
100
80
60
40
Average LA TOT Unadjusted
Average LA TOT Adjusted
20
0
1939
1935
1931
1927
1923
1919
1915
1911
1907
1903
1899
1895
1891
1887
1883
1879
1875
1871
1867
1863
1859
1855
1851
1847
1843
1839
1835
1831
1827
1823
1819
1815
1811
Year
Source: Unadjusted--Clingingsmith and Williamson (2004), Figure 9, based on data in Coatsworth and Williamson (2004a); Adjusted--see Appendix
1.
What caused the 120-year secular boombust in terms of trade for primaryproduct producers?
First
World market integration generated by a
world-wide transport revolution caused CPC,
lowered Pm and raised Px. Very fast initially,
then a slow-down to steady state.
The 19th Century Transport Revolution on Sea Lanes
And then a slow approach to steady state …
84
94
-1
9
89
-1
9
90
19
79
-1
9
85
19
74
-1
9
80
19
69
-1
9
75
19
64
-1
9
70
19
59
-1
9
65
19
54
-1
9
60
19
49
-1
9
55
19
44
-1
9
50
19
39
-1
9
45
19
34
-1
9
40
19
29
-1
9
35
19
24
-1
9
30
19
19
-1
9
25
19
14
-1
9
20
19
09
-1
9
15
19
04
-1
9
10
19
99
-1
9
05
19
94
-1
8
00
19
89
-1
8
95
18
18
84
-1
8
90
18
85
18
84
-1
8
79
-1
8
80
18
74
-1
8
75
18
70
18
Figure 2.2: Real Global Freight Rate Index(1869-1997) (1884=1.00)
1.40
1.20
1.00
0.80
0.60
0.40
0.20
0.00
Second
Diffusion of the industrial revolution in core raised
GDP growth rates there, and thus in the derived
demand for luxury foodstuffs.
Growth rates of manufacturing were even greater
in core – since its share in GDP was rising, and
thus so too was derived demand for primary
product intermediates.
Manufacturing growth slowed down in core as
industrial transition was completed there, and
thus so too did the derived demand for primary
product intermediates.
Third
Manufacturing searched for new technologies
and synthetic products to save on or even
replace the increasingly expensive primary
products. It finally found them adding
further to the demand-led terms of trade
bust.
Four Big Facts
Fact 1: Rise in the Core-Periphery Income Per
Capita Gap
Fact 2: De-Industrialization in the Poor
Periphery
Fact 3: Secular Terms of Trade Boom and Bust
in the Periphery
Fact 4: Terms of Trade Volatility Much Bigger
in the Periphery
Table 3. Terms of Trade Volatility 1782-1913
Core vs Poor Periphery
Region
Before 1820 1820-1870
United Kingdom
Average Periphery
1870-1913
11.985
6.460
2.910
9.176
2.006
7.089
European Periphery
Italy
Russia
Spain
4.036
0.922
3.226
7.959
10.720
19.003
10.722
6.472
7.058
11.214
6.104
6.023
Latin America
Argentina
Brazil
Mexico
3.728
4.409
N/A
1.658
6.429
6.961
2.174
5.531
8.140
8.303
10.283
5.379
Middle East
Egypt
Ottoman Turkey
2.902
2.982
2.821
13.611
17.861
6.549
7.316
11.760
3.289
11.876
17.860
5.891
9.628
7.590
11.666
5.364
7.532
3.196
7.788
7.992
7.583
6.977
9.778
7.951
7.303
6.603
6.732
15.554
15.554
N/A
10.527
19.752
1.302
4.952
4.311
5.592
South Asia
Ceylon
India
Southeast Asia
Philippines
Siam
East Asia
China
Japan
Four Big Facts
Fact 1: Rise in the Core-Periphery Income Per
Capita Gap
Fact 2: De-Industrialization in the Poor
Periphery
Fact 3: Secular Terms of Trade Boom and Bust
in the Periphery
Fact 4: Terms of Trade Volatility Much Bigger
in the Periphery
One Big Question
Are the correlations spurious
or are they causal?
So, what about the theory,
and what about the magnitudes?
What’s the Impact of a Secular Improvement in the
Terms of Trade for a Primary Product Exporter?
Short Run: unambiguous income
increase
Medium Run: unambiguous income increase
via resource allocation and specialization
response, e.g. de-industrialization
Long Run: ambiguous impact on growth due to
de-industrialization and the belief that industry
is a carrier of modern economic growth
Net Impact: theory ambiguous, history must
resolve the issue
What’s the Impact of a Secular Improvement in the
Terms of Trade for an Exporter of Manufacturers?
Short Run: unambiguous income increase
Medium Run: unambiguous income increase via
resource allocation and specialization response, e.g.
more industrialization
Long Run: unambiguous impact on growth due to
industrialization and the belief that industry is a
carrier of modern economic growth
Net Impact: theory unambiguous
So …
What Should We Find in History?
Asymmetric impact
of secular terms of trade improvement
Core versus Periphery!
What’s the Impact of Terms of Trade
Volatility on the Exporter of Manufactures in
the Rich Core?
Exporters of manufactures in the rich core
can
insure against price volatility cheaply since:
● they face well developed capital markets;
● governments have varied revenue sources;
● rich families can consumption smooth;
● they export many products, spreading risk;
● their export prices are less volatile.
What’s the Impact of Terms of Trade
Volatility on the Primary Product Exporter in
the Poor Periphery?
Poor primary product exporters cannot insure
against price volatility cheaply since:
● they face undeveloped capital markets;
● governments rely very heavily on import
duties and export taxes;
● poor families cannot consumption smooth;
● they export few products, so more vulnerable to
price shocks;
● their export prices are more volatile.
And risk-aversion begats lower accumulation!
So ….
What Should We Find In History?
Asymmetric impact
of terms of trade volatility
Core versus Periphery!
Identification Assumptions: Two Concerns
First
Was the terms of trade exogenous everywhere in
the periphery? Was every poor country a price
taker? No, but results are robust to exclusion of
suspected price-makers e.g.
● remove any with 33% of world exports of any
commodity: Australia, Brazil, Chile, China,
India, Philippines, Russia; same result
● plus, remove any with 25% of world exports of
any commodity: Argentina, Canada, Japan; same
result.
Second
Did some fundamental – institutions, geography or
culture -- drive both the choice of export product and
growth? Maybe, but so what?
● captured by country fixed effects, since export
“choice” was made long before 1870 and persisted
until 1939
● anyway, no correlation between price volatility and
institutional quality
A new historical database, annual, 35
countries, 1870-1939
6 Core industrial leaders: AH, Fr, Ger, It, UK, USA
8 European Periphery: Den, Grc, Nor, Port, Serb, Sp, Swe, Rus
8 Latin American Periphery: Arg, Brz, Col, Ch, Cuba, Mex, Per, Ur
10 Asia-MidEast: Bur, Cey, Egy, Ind, Indo, Jap, Phil, Siam, Turk
3 English-speaking European Offshoots: Aus, Can, NZ
Covers more than 85% of world population
and more than 95% of world GDP in 1914.
Results are insensitive to alternative Core
versus Periphery allocations.
Growth and the Terms of Trade 1870-1939
(Dependent variable: Decadal average GDP per capita growth)
Periphery
Core
0.05
0.63
[0.119]
[0.251]**
-0.08
0.02
[0.033]**
[0.058]
Observations
167
32
R-squared
0.35
0.74
Decade Dummies
Yes
Yes
Country Dummies
Yes
Yes
Controls
Yes
Yes
GDP Growth
1.05
[1.66]
1.59
[1.28]
TOT Growth
-0.28
0.3
[1.46]
[1.02]
8.8
6.82
[5.17]
[4.86]
TOT Growth
TOT Volatility
Summary Statistics:
TOT Volatility
Impact on Growth:
TOT Growth
0.07
0.64
TOT Volatility
-0.39
0.11
Robust standard errors in
brackets
** significant at 5%
Growth and the Terms of Trade 1870-1939
(Dependent variable: Decadal average GDP per capita growth)
Periphery
Core
0.05
0.63
[0.119]
[0.251]**
-0.08
0.02
[0.033]**
[0.058]
Observations
167
32
R-squared
0.35
0.74
Decade Dummies
Yes
Yes
Country Dummies
Yes
Yes
Controls
Yes
Yes
GDP Growth
1.05
[1.66]
1.59
[1.28]
TOT Growth
-0.28
0.3
[1.46]
[1.02]
8.8
6.82
[5.17]
[4.86]
TOT Growth
TOT Volatility
Summary Statistics:
TOT Volatility
Impact on Growth:
TOT Growth
0.07
0.64
TOT Volatility
-0.39
0.11
Robust standard errors in
brackets
** significant at 5%
Growth and the Terms of Trade 1870-1939
(Dependent variable: Decadal average GDP per capita growth)
Periphery
Core
0.05
0.63
[0.119]
[0.251]**
-0.08
0.02
[0.033]**
[0.058]
Observations
167
32
R-squared
0.35
0.74
Decade Dummies
Yes
Yes
Country Dummies
Yes
Yes
Controls
Yes
Yes
GDP Growth
1.05
[1.66]
1.59
[1.28]
TOT Growth
-0.28
0.3
[1.46]
[1.02]
8.8
6.82
[5.17]
[4.86]
TOT Growth
TOT Volatility
Summary Statistics:
TOT Volatility
Impact on Growth:
TOT Growth
0.07
0.64
TOT Volatility
-0.39
0.11
Robust standard errors in
brackets
** significant at 5%
Growth and the Terms of Trade 1870-1939
(Dependent variable: Decadal average GDP per capita growth)
Periphery
Core
0.05
0.63
[0.119]
[0.251]**
-0.08
0.02
[0.033]**
[0.058]
Observations
167
32
R-squared
0.35
0.74
Decade Dummies
Yes
Yes
Country Dummies
Yes
Yes
Controls
Yes
Yes
GDP Growth
1.05
[1.66]
1.59
[1.28]
TOT Growth
-0.28
0.3
[1.46]
[1.02]
8.8
6.82
[5.17]
[4.86]
TOT Growth
TOT Volatility
Summary Statistics:
TOT Volatility
Impact on Growth:
TOT Growth
0.07
0.64
TOT Volatility
-0.39
0.11
Robust standard errors in
brackets
** significant at 5%
Growth and the Terms of Trade 1870-1939
(Dependent variable: Decadal average GDP per capita growth)
Periphery
Core
0.05
0.63
[0.119]
[0.251]**
-0.08
0.02
[0.033]**
[0.058]
Observations
167
32
R-squared
0.35
0.74
Decade Dummies
Yes
Yes
Country Dummies
Yes
Yes
Controls
Yes
Yes
GDP Growth
1.05
[1.66]
1.59
[1.28]
TOT Growth
-0.28
0.3
[1.46]
[1.02]
8.8
6.82
[5.17]
[4.86]
TOT Growth
0.07
0.64
TOT Volatility
-0.39
0.11
TOT Growth
TOT Volatility
Summary Statistics:
Note:
Percentage point
impact of 1 st. dev.
change
TOT Volatility
Impact on Growth:
Robust standard errors
in brackets
** significant at 5%
What About pre-1870 History?
The data aren’t sufficient to estimate impact
as we did for 1870-1938.
But terms of trade volatility was even bigger
pre-1870 than post-1870, so bigger negative
impact on growth if the post-1870 impact
conditions also held for the pre-1870 period.
Table 3. Terms of Trade Volatility 1782-1913
Core vs Poor Periphery
Region
Before 1820 1820-1870
United Kingdom
Average Periphery
1870-1913
11.985
6.460
2.910
9.176
2.006
7.089
European Periphery
Italy
Russia
Spain
4.036
0.922
3.226
7.959
10.720
19.003
10.722
6.472
7.058
11.214
6.104
6.023
Latin America
Argentina
Brazil
Mexico
3.728
4.409
N/A
1.658
6.429
6.961
2.174
5.531
8.140
8.303
10.283
5.379
Middle East
Egypt
Ottoman Turkey
2.902
2.982
2.821
13.611
17.861
6.549
7.316
11.760
3.289
11.876
17.860
5.891
9.628
7.590
11.666
5.364
7.532
3.196
7.788
7.992
7.583
6.977
9.778
7.951
7.303
6.603
6.732
15.554
15.554
N/A
10.527
19.752
1.302
4.952
4.311
5.592
South Asia
Ceylon
India
Southeast Asia
Philippines
Siam
East Asia
China
Japan
What About pre-1870 History?
The data aren’t sufficient to estimate impact as we
did for 1870-1938.
But terms of trade volatility was even bigger pre-1870
than post-1870, so bigger negative impact on
growth if the post-1870 impact conditions also
held for the pre-1870 period.
In addition, the de-industrialization conditions were
much greater pre-1870 during terms of trade boom
then during post-1870 terms of trade bust,
implying even greater negative impact on growth
before 1870 than after.
Reminder: Terms of trade boom
versus bust (in Latin America)
Figure 1
Latin American Terms of Trade 1811-1939
160
140
120
Px/Pm
100
80
60
40
Average LA TOT Unadjusted
Average LA TOT Adjusted
20
0
1939
1935
1931
1927
1923
1919
1915
1911
1907
1903
1899
1895
1891
1887
1883
1879
1875
1871
1867
1863
1859
1855
1851
1847
1843
1839
1835
1831
1827
1823
1819
1815
1811
Year
Source: Unadjusted--Clingingsmith and Williamson (2004), Figure 9, based on data in Coatsworth and Williamson (2004a); Adjusted--see Appendix
1.
Bottom Lines
● Did globalization experience contribute to the Great
Divergence before 1940? Absolutely!
● How much of the gap in growth rates between core
and periphery 1870-1940 was explained by different tot
growth and volatility impact? Big: a third to a half.
● Would we expect the same tot impact pre-1870?
Bigger: secular tot boom, not bust, and tot volatility at
least as big.
Lessons of History?
Would we expect the same today after five
decades (1950-2008) in to the second global
century?
No! The effect has almost certainly vanished
today since the old economic order has also
vanished everywhere in the poor periphery
except Africa, where it is vanishing.
Many thanks!