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Distribution, Structural Change and Economic
Performance in Settler Societies, 1870-2000
Henry Willebald
Economic and Social History Programme, Faculty of Social Sciences, Universidad de la
República, Uruguay ([email protected])
Luis Bértola
Economic and Social History Programme, Faculty of Social Sciences, Universidad de la
República, Uruguay ([email protected])
Paper to be presented at Session 97
“Settler Economies in World History”
XIV International Economic History Congress
Helsinki-Finland 21-25 August 2006
1
1. Introduction
This paper is written by people from the South of the Americas. The underlying problem is
the relative failure of South American Settler Societies to keep pace with the growth
performance of other settler societies, and their steady decline in the international per capita
GDP ranking. Even if, Chile has been recently considered as a case of partial success, in the
long-run all the Latin American countries, which by the mid-1800 century could be
considered as settler societies, have been failing to grow at steady high rates and fairly
improve the quality of life of the population.
The paper adopts a comparative perspective in order to find relevant differences in the
pattern of growth of different settler societies, and to explain the varied outcomes.
The attempt to link economic growth to the distribution of income has many
antecedents. While the Kuznetsian tradition focused mainly on the causality from growth and
structural change on income distribution, the inverse causality has been the core of recent
research. This paper will mainly explore this second line of causality.
With respect to structural change, an intensive debate has taken place. The Latin
American debate has been intensive and has recently increased hand in hand with the
development of new ideas in the front of development economics, inspired by PostKeynesian, Schumpeterian and Evolutionary economics. While structural change seems to be
considered as a natural outcome of economic growth by mainstream economics, the other
referred theories seem to point towards the need for structural change in order to make
growth possible. This latter line of research also inspires the present paper.
However, the relation between structural change and income distribution in the process
of economic growth has not been an important topic. This is the third line of research, which
we will try to follow. Our hypothesis bolds down to the idea that an uneven income and
wealth distribution poses limits to structural change in different ways. One of them, the
pattern of demand growth, will be especially considered here.
Finally, settler societies have some particularities of their own. Resource-abundant,
labour-scarce regions constitute specific scenarios on which the interplay between growth,
income distribution and specialization takes place.
The following second section tackles the theoretical debate and presents a theoretical
model. The third section presents some stylized facts about settler societies. Section 4
presents the results of a simple model, which considers productive specialization, as the
result of original income distribution. Section 5 makes a brief consideration on the relative
development of industry in settler societies during the first decades of the 20th Century.
2
Section 6 presents a more complex model to tackle the relation between inequality,
specialization, trade and performance. We conclude with a few remarks.
2. Theory and antecedents
Income distribution and growth
Until about one decade ago, the dominant approach to income distribution was the
Kuznetsian one (Kuznets, 1955), which stylized the well-known inverted “U”-curve. His
sectoral approach had some similarities with the way in which Lewis approached the specific
case of developing economies with unlimited supplies of labour: they were expected to grow
with increasing inequality during the traditional phase of growth, until the labour reservoir
was absorbed and wages started to grow in the commercial phase (Lewis, 1954).
After considerable research, the attempt to transform Kuznets’ stylized facts into an
“economic law” seems to have failed: according to Deininger and Squire (1998), on a
country by country basis, the existence of the curve is rejected in 90% of the cases. This does
not mean that the Kuznetsian approach (consisting in the study of how structural change and
a wide range of social, institutional, demographic and other factors -not considered by
economists as the proper field of economics- impacts on income distribution) is oldfashioned or fruitless (Bértola, 2005).
Galor & Zeira (1993), Persson & Tabellini (1994) and Alesina & Rodrik (1994) were
among the first contributors, who stressed the negative impact of income inequality on
economic growth. Some kind of consensus seems to exist around the idea that while high
income inequality at the starting point of a growth process not necessarily leads to a poor
economic performance, high inequality in the distribution of wealth does (Deininger and
Squire, 1998).
The pioneering approaches to how inequality affects growth, are: i) political economy
(Alesina & Rodrik, 1994; Bertola 1993; Persson & Tabellini, 1994; Alesina & Perotti, 1996);
ii) imperfect capital markets (Galor & Zeira; 1993; Banerjee & Newman, 1993; Ferreira,
1995; Galor y Zeira, 1993; Aghion & Bolton, 1997; Banerjee & Newman, 1991); iii) social
conflicts (Alesina & Perotti, 1996; Rodrik, 1997; Fajnzylber, Lederman & Loayza, 1998;
Bourguignon, 1998), and iv) endogenous fecundity (Dahan y Tsiddon, 1998; Galor y Zang,
1997; Morand, 1999; Galor y Weil, 1996).
More recently, some authors, as Lundberg & Squire (2001) and Fielding & Torres
(2005), argued that inequality and growth were two parts of the same process with reciprocal
3
causalities. “Our main result is to show that the determinants of growth and inequality are not
mutually exclusive. The consequences of policy choices are then much broader than
previously assumed” (Lundberg & Squire: 2001:15).
From the field of economic history, neo-institutionalist thinking has revived old
development theories with institutionalist and structuralist flavour. The concept of pathdependence points to deep historical roots explaining patterns of institutional building and
distribution of wealth, which lead to economic institutions with structures of incentives more
oriented towards the extraction of rents than to innovation (Engerman & Sokoloff, 2000;
Acemouglu, Johnson & Robinson 2001, 2002, 2005; World Bank, 2004).
Specialization and growth
The idea that specialisation is not neutral for growth has deep roots in the history of
economic thought. Since Ricardo, al least, the idea that manufacturing gives more
opportunities for technological change has been present in one way or another in the work of
many scholars and in policy making.
This idea has been at the centre of Latin American thinking on development.
Industrialization was, in the work of Prebisch, the central goal, as technical progress
advanced more rapidly in the industrial sector and because demand tended to shift from
primary production to more sophisticated manufactures and services (ECLA, 1949).
ECLAC’s works, since the 1990s, have been strongly influenced by Fernando Fajnzylber’s
fruitful production (1983 and 1987). Fajnzylber shifted to an approach based on “systemic
competitiveness”, in the sense that a diversified productive structure and export sector should
not reproduce traditional dualist structures, but needed a rather homogeneous productive
sector with significant domestic innovative cores.
ECLAC’s production since the 1990s has been clearly influenced by Post-Keynesian
thinking á la Kaldor-Thirlwall, by neo-institutionalist ideas, as well as by Neo-Schumpeterian
and Evolutionary theories, as those of Nelson & Winter, Freeman, Soete, Pérez, Vespagen,
Cimoli, etc. Along this line of research, hard criticisms have been directed towards the
structural reforms introduced in Latin America since the 1980s. Liberal reforms led to a
degradation of the structure of production and exports of Latin America towards items with
lower value-added and technological content, leading to high volatility and a continued
relative retardation (ECLAC, 2004; Cimoli et al., 2005). A long-run study of the Southern
Cone countries along these lines is presented in Bértola & Porcile (2006).
4
Specialization and income distribution
The link between specialization and income distribution has been rarely tackled, but has been
the object of recent developments.
The distribution of income has an important impact on demand patterns, as different
income groups have different structures of demand. In Figure 1 we present a hypothetic
structure of demand derived from the distribution of personal income.
Following Engel’s Law and Kindelberger (1989), who means that Engel’s Law may be
considered a law on consumption and not only on alimentation, low-income groups spend
most part of their incomes in the satisfaction of basic needs, as foodstuffs, clothes and
housing. On the contrary, high-income groups are supposed to save and invest, and their
pattern of consumption is more intensive in luxury goods, services and high-tech goods.
Thus, the structure of aggregate demand of all income groups (some kind of “average”
demand) will be shaped by the weight of each income group. The usual assumption on
unitary income elasticity of demand for all goods gives thus place to non-homothetic
preferences. This approach is highly sensible to historical contexts, as the income elasticity of
demand for different goods may shift considerably through time, due to changes in average
income, technology, relative prices, etc. (Rowthorn & Wells, 1987).
Figure 1: Distribution and demand: an illustration
PERSONAL INCOME DISTRIBUTION
POOR (x%)
MIDDLE (y%)
Housing
Services
---
High
Tech
High
Tech
Foods
Housing
RICH (z%)
Services
Foods
Services
Foods
HighTech
Housing
---
---
DEMAND COMPOSITION (“AVERAGE”)
FUNTIONAL INCOME DISTRIBUTION ASSOCIATED TO
DERIVED FACTORIAL DEMAND
Phisical
Capital
Financial
Capital
Skilled
Labour
Land
NEW PERSONAL INCOME
DISTRIBUTION
HighSkilled
Labour
Unskilled
Labour
5
Following this idea, Mani & Hwang (2001) argue that countries facing high inequality
have to meet a wide variety of products (the simplest and the most sophisticated ones) for
which demand is relatively small. As international competitiveness is assumed to develop as
an extension of production for the home-market in a process of cumulative learning, the
potential for a learning process in the production of high-tech goods is rather limited. This
turns into a restriction on the development of skills, returns to skill, economic growth and
increased per capita income. The transition to a stage featured by high per capita income is
thus endogenously limited (Mani 2000).
Zweimüller (2000) argues in a similar way: when income is highly concentrated the
potential market for a new product is rather small and it takes a long time until massconsumption is possible. The expected returns to innovations are low, which delays technical
progress and productivity growth.
When wealth inequality is considered, landownership for instance, the path of
development may shift from a situation in which high income-inequality fuels growth, to
another in which it refrains growth. This is, according to Thorp (1998), the case of the Latin
American countries, as the patterns of unequal wealth-distribution were consolidated in the
growth process. The process of industrialization is hindered by institutions oriented towards
the extraction of natural rents, rather than those oriented to physical and human capital
accumulation (Galor, Moav y Vollrath, 2004). The reversal of fortune is highlighted by
Engerman & Sokolof (2000) and Acemoglu, Johnson & Robinson (2002).
It is also possible to argue, that the impact of the structure of demand on growth occurs
not only at the national level, but also internationally. Income distribution affects the way in
which countries specialize and in which technical progress and productivity can advance.
Even more, an unequal world economy may show lower growth rates than an equal one. A
link between the presented approach and the idea of club convergence developed by Quah
(1993 a y b, 1996) seems attractive. Figure 2 illustrates the referred relations.
6
Figure 2: Distribution, demand and geographical and institutional factors
Learning
D
I
S
T
R
I
B
U
T
I
O
N
Innovation and
technical
progress
DEMAND
Human
Capital
E
C
O
N
O
M
I
C
G
R
O
W
T
H
INSTITUTIONAL
AND
GEOGRAPHICAL
FACTORS
3. Settler economies 1870-2000: some stylized facts
By the year 1900, Australia, New Zealand and the USA were the world top-ranked nations in
terms of per capita income. Argentina, Canada, Chile and Uruguay had also a high position in
the world ranking, and looked like countries with a promissory future. South Africa was still
at low levels of per capita income.
As shown in Figures 3 and 4, this group of countries experienced some kind of
convergence until the 1930s, with a decreasing standard deviation of per capita incomes
within the group, due to the declining Australasian position and the good performance of
Argentina and Canada. Afterwards both Australasian countries, Canada and the USA
increased their growth rates in relation to the others and the gap widened. By the 1970s the
dispersion of per capita incomes had surpassed the levels of 100 years ago and continued to
increase. As well-known, the USA strengthened their leadership, being Canada the only
country that kept a similar growth rate, without converging in absolute terms. The Southern
Cone countries did it relatively bad, and their position in the world ranking worsened
significantly. South Africa did it even worse.
7
Figure 3
INCOME IN SETTLER ECONOMIES
Per capita GDP in relation to USA (ln)
5.5
Australia
New Zeland
5.0
USA
Figure 4
INCOME IN SETTLER ECONOMIES
Standard desviation in per capita GDP in relation to USA
Canada
0.70
0.60
4.5
0.50
4.0
0.40
3.5
0.30
3.0
South Africa
Chile
2.5
0.20
Uruguay
Argentina
Minimum value in
1934
0.10
2.0
1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000
Source: Maddison (2001).
0.00
1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000
Source: Maddison (2001).
As shown in Table 1, average growth rates were clearly higher in 1870-1913. In 19131929 Argentina and Australia were the only countries showing negative growth rates (New
Zealand was stagnating). In the 1930s, Australia and New Zealand were the only countries
showing positive growth-rates. Even more, their growth rates were the highest of the whole
period 1870-1940. Without any doubt, the imperial preferences established by the Agreement
of Ottawa in 1932 benefited the Commonwealth countries as much as they damaged the
countries of outside it.
Table 1
PER CAPITA GDP IN SETTLERS ECONOMIES
Argentina
PPP dollars 1995
1870
1,311
1938
4,069
Australia
Canada
Chile
USA
New Zealand
South Africa Uruguay Average
3,946
5,886
1,710
4,545
1,151
3,134
2,454
6,126
3,100
6,463
858
2,184
2,225
3,723
2,094
4,516
Index, average=100
1870
63
1938
88
190
127
82
98
55
68
118
132
149
140
41
47
107
80
101
98
Annual growth rates
1870/1938
1.7
1870/1913
2.9
1913/1929
-0.3
1929/1938
-0.4
0.6
0.9
-0.5
1.2
1.4
2.2
0.8
-1.2
1.5
2.0
1.6
-0.9
1.4
1.8
1.7
-1.3
1.1
1.2
0.1
2.3
1.4
1.5
1.2
1.2
0.8
1.0
0.9
-0.6
1.1
1.6
0.6
0.0
Source: Maddison (1995, 2001).
As shown in Table 2, by the 1870s, all countries had a structure of exports completely
dominated by primary production and their exports amounted to more than 15% of GDP. The
USA already showed some different features: a somewhat lower export-coefficient and a
more diversified structure of exports. By 1900, average export-coefficients had increased,
being the USA the only exception. Both the USA and Canada further diversified their
exports, but Australasian and the Latin-American Southern Cone do not break their trends.
8
Table 2
SHARE OF PRIMARY PRODUCTS IN TOTAL EXPORTS AND SHARE OF TOTAL EXPORTS IN GDP
Percentage
1870-1889
Primary
Exports
products
% GDP
% total
exports
Argentina
Australia
Chile
Canada
USA
New Zealand
Uruguay
100
97
99
95
86
99
100
15
15
22
12
6
16
22
1890-1909
Primary
Exports
products
% GDP
% total
exports
99
97
99
91
80
96
100
19
20
19
15
6
23
19
1920-1939
Primary
Exports
products
% GDP
% total
exports
99
96
100
74
57
99
100
14
15
12
19
5
25
18
Primary products: "live animals", "foods and drinks", "raw materials or simply-prepared products"
Source: Blattman, Hwang & Williamson (2004), Williamson (2004).
4. Distribution and specialization: a simple model
In order to explain why some countries were able to keep high growth rates in the second part
of the 20th Century, we will test the hypothesis that a more unequal distribution of income
limited the process of structural change, thus keeping those countries dependent on traditional
primary exports.
Following O’Rourke, Taylor & Williamson (1996), O’Rourke & Williamson (1994),
O’Rourke & Williamson (1999 a and b), Williamson (1997) and Williamson (2000), Bértola
y Porcile (2002); Bértola y Williamson (2003); Greasley & Oxley (2001 a and b) we will use
the wage/rental ratio as a proxy for income distribution.
Specialization is proxied by the share of agrarian export to GDP each fifth year since
WWII.
The following models are tested:
AXt = β0 + β1 [WRt-WR0] + ut
APSt = β0 + β1 [WRt-WR0] + ut
Were:
AXt: share of agrarian exports on total exports in period t, when t=1940, 1945, ..., 1995.
APSt: share of agrarian value added in GDP in period t; when t=1940, 1945, ..., 1995
(represents the agrarian productive structure of the economy).
WRt: the rental/wage ratio t, con t=1940, 1945, ..., 1995.
WR0: the original (0) rental/wage ratio; when 0 is the first available information since 1870.
When WRt-WR0 >0, income distribution improves.
9
Figures 5 and 6 show a negative correlation between the variables, i.e., when income
distribution worsened, a higher share of primary exports, alternatively a higher share of the
agrarian sector, is found. In other words, it may be concluded that economies with better
distribution of income are expected to go through a deeper structural transformation. The
statistical significance is weak so the exercise is far from being conclusive.
Figure 5
TRADE SPECIALIZATION AND INEQUALITY
Figure 6
PRODUCTIVE SPECIALIZATION AND INEQUALITY
0.30
Agricultural Value-Added % GDP
Agricultural exports % total exports
1.0
0.9
0.8
0.7
0.6
0.5
0.4
-1200
-1000
-800
-600
-400
-200
0
200
0.25
0.20
0.15
0.10
0.05
0.00
-1200
400
-1000
-800
-600
-400
-200
0
200
400
Variation of the wage/rental ratio
Variation of the wage/rental ratio
Sources: see Appendix.
Sources: see Appendix.
5. Income distribution and industrial growth
In the previous section, we saw how income inequality at the starting point, may hinder
diversification and growth. The study of the features of the manufacturing industry in
different settler societies may give some idea about the relation between income inequality
and industrial growth. Besides, even if most settler economies still were primary exporters
during the first decades of the 20th Century, the features of the manufacturing sector may give
an idea of the potential for diversification of exports.
Tables 3 and 4 contain information about different variables related with manufacturing
performance: share production by principal branches, power installed and employment. For
all variables, the differences between the English-speaking countries, on one side, and
Argentina and Uruguay, on the other, are very clear. In terms of horse-power and number of
workers per establishment, the first group of countries is far more developed than the other
one. If we look at the structure of manufacturing, we can see that the weight of those sectors
that Pavitt (1984) once considered as carriers and diffusers of technology are clearly more
developed in the first group of countries than in the South American group. These differences
increased during the first decades of the 20th Century.
10
Table 3
STRUCTURE OF MANUFACTURING PRODUCTION
Share of Gross Production Value 1/
1910s
Argentina
(1918)
Australia
(1913)
Canada
(1911)
Chile
(1913)
USA
(1914)
New Zealand
(1915-16)
Uruguay
(1919)
43.0
27.3
2.3
27.4
23.8
13.7
24.7
37.8
31.0
16.7
24.1
28.2
46.5
23.5
6.7
23.4
29.2
29.1
4.1
37.6
59.8
18.8
7.4
14.0
69.1
7.6
0.0
23.3
Argentina
(1938)
Australia
(1938-39)
Canada
(1938)
Chile
USA
(1937)
New Zeland
(1937-38)
Uruguay
(1936)
41.8
19.0
15.5
23.8
21.7
14.3
32.5
31.5
29.8
12.9
29.4
27.9
nd
nd
nd
nd
15.2
14.8
39.7
30.3
57.4
9.8
12.6
20.2
57.0
19.2
8.7
15.1
Foods, drinks and tobacco
Textile, clothing and leather
Machinery and metalic products
Other
1930s
Foods, drinks and tobacco
Textile, clothing and leather
Machinery and metalic products
Other
1/ For USA is reported Manufacturing Added-Value.
Sources: Vitelli (1999); Thomas (1988);Boehm (1972);Leacy (1983); Bureau of the Census (1975);Prichard (1970); Statistics New Zealand; Bértola (1991);
Maubrigades (2001); Dirección General de Estadística y Censos (1908), Carmagnani (1971), Cariola y Sunkel (1982).
Table 4
MANUFACTURING: HORSEPOWER AND EMPLOYMENT
1913
1939
000s HP
286
1,187
Argentina
HP/establ. Employees Emp./establ.
7.4
362,312
9.4
24.5
630,800
13.0
1917
1938
000s HP
1,659
4,970
Canada
HP/establ. Employees Emp./establ.
75.9
606,523
27.8
197.4
640,300
25.4
1910
1939
000s HP
100
903
New Zealand
HP/establ. Employees Emp./establ.
28.4
45,965
13.1
142.4
108,722
17.1
1911
1938
000s HP
-1,458
Australia
HP/establ. Employees Emp./establ.
-311,710
21.6
55.4
552,500
21.0
1914
1939
000s HP
22,288
49,891.0
USA
HP/establ. Employees Emp./establ.
81.8
7,022,322
25.8
287.1
9,672,000
55.6
1908 1/
1936
000s HP
23
115
Uruguay
HP/establ. Employees Emp./establ.
16.8
22,224
9.2
10.9
65,962
6.3
1/ HP only for Montevideo.
Fuente:Vitelli (1999), Boehm (1972), Thomas (1988), Beaulieu & Riddell (2003), Leacy Ed. (1983), Bureau of the Census (1975), Prichard (1970), Statistics New
Zealand, Bértola (1993), Maubrigades (2001), Dirección General de Estadística y Censos (1908).
6. A more complex model: settler societies 1940-2000
In this section, we will work with a wider sample of countries, which also includes non-settler
societies, that can be considered, during most part of the period 1870-2000, the main
importers of settler countries exports.
We will adapt the Mani & Hwang (2001) model, to cover factors considered relevant
for this research. The variables included in the model are as follows:
•
AX: ratio of agrarian exports to total exports.
•
APS: share of agrarian value added in GDP.
•
GIL: Gini-coefficient of land ownership distribution.
•
GII: Gini-coefficient of income distribution.
•
PL: stock of pasture and permanently cultivated land per capita.
11
•
HK: stock of human capital.
•
PK: stock of physical capital.
•
OP: index of openness.
The relation tested with panel data is as follows:
AXi
t+j
EPAi
= β0 + β1.GILi
t+j
t+j
= β0 + β1.GILi
+β2.GIIi
t+j
t+j
+β2.GIIi
+ β3 PCLi
t+j
t+j
+ β3 PCLi
+β4 HK i
t+j
t+j
+β4 HK i
+ β5 FKi
t+j
t+j
+ β5 FKi
+ β6 OP i
t+j
t+j
+ β6 OP i
t+j
Where i represents each of the countries in the sample (the “relevant world”), t is the
initial year of each sub-period (1940, 1945, 1950, 1955, 1960, 1965, 1970, 1975, 1980, 1985,
1990 and 1995) and t+j the final year, with j=4.
The evidence suggests that high levels of inequality are positively related to the
persistence over time of high agrarian export-shares and agriculture to GDP shares: the
estimated coefficients for income and wealth inequality are in most cases positive and highly
significant (see Table 5).
Specialisation
agrarian
Table 5
RESULTS OF ESTIMATION
in
production
is
Dependent
Variable
Agricultural
Exports
(AX)
Agricultural
Exports
(AX)
Agricultural
Product
(APS)
Agricultural
Product
(APS)
Models
I
II
III
IV
resources (Models I, II, III
GIL
0.56
(1,69)
and IV) and negatively
GII
correlated to human capital
PL
and
FK
positively correlated with a
high
level
of
economic
natural
openness
(Models II and IV). This is
0.87
(9,88)
0.82
(2,70)
0.83
(4,98)
1.01
(6,57)
0.75
(5,92)
0.71
(4,42)
1.61
(16,78)
0.79
(6,82)
0.43
(2,09)
-0.31
(-3,06)
1.12
(5,0)
FK(-1)
-0.53
(-2,86)
valid as long as we consider
HK
-0.37
(-2,80)
-0.82
(-8,25)
OP
-0.18
(-2,54)
-0.10
(-1,73)
income as a proxy for
inequality, but not when we
2
consider landownership.
Physical
capital
Ra
N
F
0.85
250
63.24
0.90
198
64.06
0.86
204
52.49
0.93
134
64.85
Variables in logarithms. Estimation with fixed effects.
accumulation is significant
All coefficients are significative at 5% except GIL in Model I and OP in Modelo IV, which are
significative at 10%.
to
explain
specialization
(Models II, III and IV), but the sign is not always negative, as theoretically expected, when a
lag is not introduced. In the case of agrarian trade-specialization, the sign is positive (Model
II); in the case of agrarian production the relation is negative (Model III). This latter result
12
allows us to argue that export activities need physical capital accumulation for their success
(railways, bridges, ports and infrastructure in general). The situation is different in the case of
the agrarian activity, where the intensity of capital is lower than in other sectors (as
manufacturing or construction). In agriculture, lagged physical capital is significant. It points
to the fact that, once the economy reaches high levels of physical capital-accumulation,
agricultural production tends to decrease in the future.
The fact that the distribution of land ownership has little explanatory power would
suggest, as a first approach, that it is the generation of income flows, acting jointly with the
incorporation of capital –in its various modalities–, that creates the dynamics of demand that
impacts on trade and productive specialisation. It is also possible, that even in countries
where competitiveness still is highly dependent on natural resources, forms of capital
ownership other than land may be more significant for the distribution of income (financial
assets, urban property, industries processing primary products, etc.).
Final remarks
Settler Economies of temperate climate producing primary products for the buoyant
demand of the industrialized world ranked among the wealthiest in the universe by 1900.
Turner (1903) considered the open frontier to be a guarantee for democracy, freedom and
equity. The prospects for these countries were optimistic.
By the 1930s the club of settler societies belonging to what we can call the Atlantic
Economy, started to disperse. The South American countries lagged behind.
In this paper, we tried to explore the idea, that income and wealth distribution could
have an impact on the pattern of productive and trade specialization. In turn, productive and
trade specialisation were considered to be important factors in order to explain technological
change, productivity growth and economic performance.
A simple model was tested for 6 settler economies, in which it was assumed that
inequality trends since the late 1870s, proxied by the wage-rental ratios, could explain trade
and productive specialization since the 1940s, assuming that agrarian societies and primary
exporters show lower levels of per capita GDP. Our results are not conclusive, but seem to
validate our point. A brief analysis of some features of the manufacturing sector in 19101940, also points to a close relation between inequality and productive diversification.
A second and more comprehensive model was tested for 1940-2000. In this case the
sample was extended to include more settler economies as well as other countries, which
were the main importers of settler countries exports. Income distribution was approached by
13
menas of the Gini-coefficients for income and land-ownership. Other variables were
included, which were considered as proxies for economic performance, as openness and
physical and human capital. The productive structure was also measured in terms of the ratio
of natural resources to population.
The evidence presented in this study shows that there is a positive relation between
inequality and productive-trade specialization in low added-value goods. These results are
partly understood, as the impact of an aggregate demand split in a wide variety of goods,
which restricts competitive learning and leads to low-yield behaviour in technological
innovation. This is also connected to the consolidation of institutions that tend to reinforce in
a path-dependent way patterns of distribution of income and wealth strongly linked to natural
resources. These once successful structures, turned to blockade structural change and
technical progress towards industrialization and the diversification of exports.
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Appendix: Sources and construction of variables
For the econometric model the sources and variables used are the following.
AX: the ratio of agrarian exports in total exports, includes live animals, foods and drinks,
tobacco, raw materials (except petroleum, coal and products), animal and vegetal fats and
oils. The data is for 5-year periods. UN Yearbooks (several issues) and UN website.
APS: share of agrarian value added in GDP. The share of agrarian (agriculture and livestock)
value added in total GDP at current prices. Mitchell (1993 a, b, c) and National Accounts for
each country.
GIL: the Gini-coefficient of land ownership. FIDA, FAO and Agrarian Census of Uruguay.
GII: the Gini-coefficient. Wider (1999), Bértola (2005) y Prados de la Escosura (2005).
PL: stock of pasture and permanently cultivated land per capita. 1960-2000: FAO; 19401959: own estimations on the basis of national sources and population data (UN).
17
HK: stock of human capital. This indicator is constructed as a weighted average of gross
enrolment rates at primary, secondary and tertiary education (the coefficients are 1, 1.4 and 2,
respectively). For 1971-2000, with data from Word Bank-UNESCO website. For 1940-1969,
own estimates based on Mitchell (1993 a, b, c).
PK: stock of physical capital. 10-year moving average of the investment rate. Data: PWT 6.1,
Mitchell (1993 a, b, c) and National Accounts of each country.
OP: index of openness: a binary indicator of openness (1) and closeness (0): 5-year averages.
Data from Sachs &. Warner (1995) and CID at Harvard, Sachs &. Warner, Trade Openness
Indicators.
18