Section 2: An Empirical View of the Spaces of Mercantilism
2.1 Introduction
There seems to be at least three factors that are necessary
for mercantilist policies to develop in a world region:
1) There must be a high amount of trade
2) there must be a sufficient number of states to stimulate
competition among them
3) they must be sufficiently centralized, bureaucratically
effective states. Without (1) there would be little to gain
from mercantilist policies, without (2) there would be little
motivation to be competitive and no competitors from which
to gain, and without (3) there would be no way to effectively
implement competitive policies.
Although aspects of these relations have been discussed
many times, there is now more and better spatial data that
allows for these questions to be considered empirically
more fully, and in particular, the spatial distributions
of these social processes to be explained better than
in the past, taking out the circularity inherent in a spatial
discussions of development (Ballinger 2008e). ¨
Part II of this paper uses empirical data to better understand
the global distribution of mercantilist policies.
First we examine
modern data that shows that in the modern economy there is
a close connection between economic development and the types of
economies mercantilists believed to be conducive to economic growth.
9
We then consider the three factors listed above. Instead of anecdotal
stories, we search for data on that shows the relative potentials
of different parts of the world to have high levels of trade,
a large number of states, and the bureaucratically effective states.
Why world regions? First, mercantilism ony makes sense within
a group of polities. Second, disparities in economic development
are most pronounced at the world regional level, with greater
variation between world regions than within them. (Ballinger, 2011x).
2.2 Modern Economies and Mercantilism
Before exploring the historical relationship between increasing returns
industry and development, it is useful to measure the modern relationship
of the ratio of increasing returns production (manufacturing) and
decreasing returns production(primarily agriculture; see Reinert 1996)
and measures of development such as gross national income.
This can be done using the schema for classifying the development levels
of countries developed by Hoeschele (2002). Rather than judging
development on a one dimensional measure such as GDP, it is useful
to use several dimensions reflecting important aspects of an economy.
Measuring the balance between increasing returnssectors
and decreasing returns sectors is one method that gives a clearer
indication of types of economies, beyond just their wealth.
Using data on manufacturing sectors from the United Nations Department
of Economic and Social Affairs (the Standard International Trade
Classification or SITC) Hoeschele develops a measure of core
industrialized manufactures (CIM -textiles, metals manufactures,
chemicals, paper and paper products) and natural resource intensive
agricultural and mining products (NRI - basic metals, foods,
beverages tobacco, wood and wood products). Using data from
the Food and Agriculture Organization (FAO) Hoeschele develops
a measure of the non-agricultural population of countries.
Countries are plotted against each other using these measures,
and Hoeschele then divides them into six categories. (9)
9 Hoeschele’s original division is modified here to
approximate a lognormal distribution and equalize the number
of observations in each category. Interestingly, this division
shows the data to closely match the world regional divisions
discussed in Ballinger 2008g.
10
These are
IC (Industrialized, Core industrial manufactures exporting)
IN (Industrialized, N atural resource intensive exporting)
PIC (Partially Industrialized, Core industrial manufactures exporting)
PIN (Partially Industrialized, Natural resource intensive exporting)
LIC (Least Industrialized, Core industrial manufactures exporting)
LIN (Least I ndustrialized, N atural resource intensive exporting)
In Figure 1 the x-axis measures the percentage of non-agricultural population
in a country, while the y-axis measures the percentage of industrial exports.
(...)
15
Towards a Geography of Mercantilism
Critically, the relationship demonstrated in Table 1 seems
to hold historically as well. Economic growth is closely
associated with mercantilist policies in industrialized countries
over a period of centuries (Bairoch 1993; Reinert 1994, 1996;
see also Williamson 2002. Similar arguments are found in
Jacobs 1984 especially on the misunderstandings of the
rejection of import substitution; Masters 1988 shows the results
of European mercantilist policies historically against
an area of free trade in the Ottoman Empire; there are,
of course vast and closely related literatures on the usefulness
of import replacing and protectionism, globalization,
and free trade). Economic historian Paul Bairoch states
‘It is difficult to find another case where thefacts so contradict
a dominant theory than the one concerning the negative impact
of protectionism; at least as far as nineteenth-century world
economic history is concerned. In all cases protectionism
led to, or at least was concomitant with, industrialization
and economic development. Also, in the four examples of liberalism,
three had negative or very negative consequences.’ (Bairoch 1993, 54).
The question I will seek to address empirically is:
If economic growth is associated with mercantilist policies
historically, and the concentration within a state’s borders
of increasing returns industries both historically and
in modern data, what has caused the spatial distribution
of mercantilist policies? This is important because it seems
that the historical spatial distribution of mercantilist
policies underlies the modern distribution of development.
2.3 Condition One: Number of States
As noted in the introduction to Part Two, the three conditions
for a mercantilist system to develop are a large number of states
in close interaction, and sufficiently centralized, a large
amount of trade, and bureaucratically effective states.
Without the first condition there is no one from which
to gain anything. Without the second there is not enough
to be gained. And without he third there is no way to implement
mercantilist polices.
There are of course many factors that can conceivably
influence the number of states in a region.
Some regions are dominated by single large empires for long
16
periods of time, others by many smaller competing polities,
and others are essentially stateless for long periods and over
large areas. Different world regions differ radically on
the number of states with perhaps two of the strongest factors
influencing this number being the antiquity of state development
and the degree to which large empires formed. We will consider
the historical-political considerations below. However, besides
the real world historical considerations, ceteris paribus,
another important factor is simply size the larger the area
the greater the possibility for more nations. This is especially
true given the fact that there were strong limitations on effective
state-sizes due to transport and communication limits
in pre-industrial times. Ignoring for the moment other historically
contingent factors, which world regions were the largest?
(...)
19
2.4 Transport Costs and Regional Trade Potential
One of the key factors shaping the historical distribution of world-regional
trade is transport costs. Mellinger, Sachs, and Gallup (1999) and Rappaport
and Sachs (2001) collect data that shows the close historical and modern
association of low transport costs with high levels of trade and industrial
agglomeration. Some of their data is easily visualized and shows the close
relationship between transport costs and spatial patterns of high trade
and economic development. For instance, Mellinger, Sachs, and Gallup map data
on the transport capacity of world rivers. Taking coasts and ocean navigable
rivers and highlighting the areas within easy land-transport range
(100 kilometers) of these provides a good picture of the relative transport
cost potentials of world regions.
[
these maps are
unfortunately of very low quality: notably, the Nile river is partly missing,
and the Indus river is missing completely.]
This transport-cost data can then be usefully compared to other
extensive data, such as global GDP density.
In recent decades detailed and accurate maps of the density of global
economic production have been developed (a reflection of both the population
density and
20
economic productivity of a region). Comparing transport-cost data
and GDP density measurements reveals the long-term influence of transport-cost
potentials on cumulative levels of population and economic activity
(spurious correlation or reverse causation are ruled out by the many
detailed historical accounts of the mechanisms linking transport costs
with both population growth and economic and industrial location).
In Figure 2 the upper section highlights in black 100 kilometers inland
of ice free coast and on both sides of ocean navigable rivers.
Directly below it is a modern detailed map of global GDP density.
Note the close spatial correlations between the factors.
Figure 2 Global Water Transport Potential and GDP Density
Sources: Mellinger, Sachs, and Gallup 1999
21
The statistical methods of measuring GDP density used in Figure 2
may be inaccurate for a number of reasons, with two especially
important limitations being 1) the estimated two billion people,
especially in rural areas and developing countries, which remain
outside of the formal economy and 2) that areas with high levels
of economic activity such as commercial centers, warehouse districts,
industrial zones, and airports have low resident population densities.
A useful method for correcting these problemsis to directly
measure the spatial distribution of economic activity using
measures of night time light emissions, which serve as an
especially accurate spatial indicator of both the distribution
and intensity of economic activity (Doll et al. 2000; Sutton
and Costanza 2002). Below (Figure 3) is a photograph of global
light emissions, again compared with a map showing
the 100 kilometer zone of coasts and ocean navigable rivers
(this time using a negative of the image for easier comparison
with the light emissions image).
22
Figure 3 Global Light Emissions Compared with Land Within
100 Km. of Ocean Navigable Water
Source: NASA (top) and Mellinger, Sachs, and Gallup (1999, 28)
Again the close correlation between the exceptionally
low transport costs associated with water transport
and the density of economic activity are evident, suggesting
the effects of long-term and cumulative causation
of transport costs on population and industrial
agglomeration.
Just as there is now much more extensive,
accurate, and detailed data available to consider older
arguments on relationships between transport costs,
trade and development (the first factor mentioned as
necessary conditions for mercantilist policies to develop)
there is likewise data that might be used to revisit
arguments concerning the
number and strength of states in world regions (the second
and third factors mentioned as necessary for mercantilist
policies to develop). Much of this datais also usefully
visualized with descriptive statistics and maps.
&&&&&&&&&&&&&&&&&&&&&
Recent global yet detailed data, such as the Global Agro-Ecological Zones(GAEZ) data
12
has been developed that shows distributions of agricultural potential.
GAEZ measures numerous important factors with thousands of observations at 1
23
degree by 1 degree scales or smaller including critical data on daily
precipitation, seasonal variations in precipitation, evapotranspiration
rates, frost days, terrain slopes, soil depth, soil fertility, soil chemistry,
soil drainage, and soil texture. These factors have been combined
to precisely delineate the spatial patterns of agriculturally productive
land for many of the world’s most important crops and crop types
(grains, root crops, oil crops etc.). Taken together, these measurements
allow for a far more detailed yet at the same time spatially extensive
consideration of variations in world regional agricultural potentials.
Although developed primarily to estimate potential world food supplies
and mitigate famine, this data can also be used to show that there
are indeed dramatic differences and clear patterns to world regional
potentials for food production. This is important because some, such
as Blaut (1993, 2000, who in turn is frequently cited, e.g. Robbins 2003)
reject development arguments that are based on differences in agricultural
productivity. Blaut selectively uses data to refute earlier arguments
of this type; more modern, detailed and spatially extensive data clearly
shows that Blaut’sarguments are either incorrect or misleading
(i.e., there are, as Blaut claims, areas of productive tropical soils;
this does not mitigate the fact that overall there are serious problems
with and drawbacks to tropical agriculture). Many parts of the world
clearly do suffer from important limitations and temporal instabilities
(Davis 2002) of food production potential, while others have unusually
high and stable capacities for food production.
13
Increasingly, work such as Sachs 1997, Gallup et. al. 1999, Masters
and McMillan 2000, Masters and Wiebe 2000, and Masters and Sachs 2001
show mechanisms whereby food production stability and potential
(and other geographic factors) directly and indirectly impact social
and economic development outcomes,
13 Based on modern soil, terrain and climate data the GAEZ
researchers note ‘that more than three-quarters of the global
land surface (excluding Antarctica)… suffer rather severe
constraints for rain-fed cultivation. Some 13 percent is too
cold, 27 percent is too dry, 12 percent is too steep, and about
65 percent are constrained by unfavorable soil conditions
(percentages do not sum up to 100, because different constraints
coincide in some locations). The analysis concludes
that only 3.5 percent of the land surface can be regarded
to be entirely free of constraining factors. Only for some
sub-regions in Europe did the share of essentially
constraint-free conditions reach 20 percent and more.
’Similarly, Davis (2002) finds that Europe is virtually
the only part of the world not seriously negatively affected
by the ENSO oscillation variations in weather so strongly
detrimental to stable agricultural production, especially
in Africa and Asia.
24
through affecting average lifespan (and thus also investment in
education), the accumulation through early agricultural productivity
of the minimal capital necessary for improvements in infrastructure,
education, and health initiatives, the lack of agricultural surpluses
for trade, which has knock-on consequences on the development of
policy choices and institutional development (such as banks and
legal institutions) and so on. In addition to these arguments, recent
extensive yet detailed agricultural data may be relevant to state
competition and the second factor – a large number of states –
necessary for mercantilist policies.
Section 3: Centralization of States
The last statement above alludes not just to the
number of states,
but to the
strength and centralization of states. But can this
factor also be better considered empirically now than in the past?
The third factor important for a geography of mercantilism is the
distribution of strong, centralized, bureaucratically effective states.
To some degree the third condition is a result of the first two;
there are arguments that trade stimulated the rise of and improved
the quality or efficiency of institutions (Knack and Keefer 1995;
Acemoglu et. al. 2005; this connection is often thought to occur
in close conjunction with urbanization, e.g., Fox 1971, 1989, 1991;
Jacobs 1969, 1984). There are also arguments that many, closely
interacting states and the resulting interstate competition
increased bureaucratic centralization and effectiveness, especially
via military competition increasing bureaucratization (e.g., taxes
levied to support the military,centralization of powers to enforce
taxation, censuses in order to gauge war-making capacity - see especially
Tilly 1990, as well as Spruyt 1994 and the long list of examples
from de Vries 2002 above). These interrelated aspects of regional
state andinstitutional development then become self-reinforcing
in Myrdal-type cumulative causation: States with strong or effective
institutions in turn were able to trade more and compete more
effectively, further improving their institutions and so on.
It was shown in Figure 4 that there are a number of world regions
with extensive areas of productive agriculture, and noted that these
areas are associated with early
25
state development and with varying subsequent degrees of political
fragmentation. Were these systems not just of many, but of strong
competing states? It may be possible to use a measure of the
long-term historical strength of states developed to test
the relationship between state antiquity and economic growth
in Bockstette, Chanda, and Putterman (2002) (14).
Bockstette, Chanda and Putterman (2002) divide the period from 1 to 1950 C.E.
into 39 half centuries. They rank each half century based on three questions:
1. Is there a government above the tribal level? (1 point if yes, 0 points if no)
2. Is this government foreign or locally based? (1 point if locally based, 0.5 points
if foreign (i.e., the country is a colony), 0.75 if in between
(a local government with substantial foreign oversight)
3. How much of the territory of the modern country was ruled by this government?
(1 point if over 50%, 0.75 points if between 25% and 50%, 0.5 points if
between 10% and 25%, 0.3 points if less than 10%).
COMBINING THE FACTORS TOTAL PROPENSITY FOR MERCANTILISM BY WORLD REGIONS
As a combined indicator representing both state strength and the potential
for state competition (number of states interacting in a region)
the 108 measures for historical state strength for Afro-Eurasian countries
can be divided into the eight Afro-Eurasian world regions of Lewis
and Wigen gives the following, which can be viewed as an objective empirical
estimate of state competition potential.
Western Europe
|
13.9
|
W Asia/N Africa
|
9.0
|
Sub Saharan Africa
|
8.6
|
Southeast Asia
|
5.4
|
14 ‘Answers were extracted from the historical accounts
on each of 119 countries in the Encyclopedia Britannica.
The scores on the three questions were multiplied by one
another and by 50, so that for a given fifty year period,
what is today a country has a score of 50 if it was an
autonomous nation, 0 if it had no government above
the tribal level, 25 if the entire territory was ruled
by another country, and soon. We then combined the data
for the 39 periods, experimenting with different ways
of “discounting”to reduce the weight of periods in the more
remote past.’ (Bockstette et. al. 2002, 346).
26
Eastern Europe
|
5.4
|
East Asia
|
3.9
|
Central Asia
|
3.8
|
South Asia
|
3.6
|
However, at different time periods there were greater
and lesser degrees of interaction between world regions.
The major historical divisions Lewis and Wigenmake are
first between the ‘new worlds’ and Eurasia, and within
the vast Afro-Eurasian area, between Sub-Saharan Africa,
East Asia, and the rest of Eurasia. There are then
increasingly less important divisions between South Asia,
Europe, and East and West Europe. With time the trade
and cultural connections between East, Southeast,
and South Asia would increase while at the same time
the connections between East and West Europe,
the Mediterranean and North Africa (at times)
would increase. If we take the varying degrees of
interaction between regions into account (again using
Lewis and Wigen, the major divisions in sociopolitical
interaction were between East Asia, South Asia,
and west-Asia/Mediterranean), we get something like
the following:
Sub-Saharan Africa
|
8.6
|
East/South/Southeast Asia
|
12.9
|
Europe (East and West)
|
19.3
|
Europe/+Mediterranean
|
24.1
|
27
On this measurement, which combines levels of trade and historical
interaction between regions, western Eurasia had by far
the greatest potential for state competition and competitive
policies to develop.
15 Africa may seem to be especially problematic. It has a vast
agricultural ecumene comparable to that of western Eurasia
(even when taking into account large areas of dominantly
forest cover that significantly reduce agricultural land
in the Americas and Sub-Saharan Africa). However, here is
a good example of where rejections of environmental influence
such as those of Blaut (1993), Sluyter (2003) or Robbins (2003)
are problematic: They often reject single factors in a piecemeal
fashion. Yet in complex systems, of which society is
a supreme example, it is widely emphasized andaccepted
that it is the interplay between factors that are often
of primary importance.
In Africa, the difficulty of the environment is reflected
in the unusually low population densities of the continent
(which preexist and are largely spatially unrelated
to the slave trade see Bairoch 1993 and Maddison 1995
and 2001; the low population density of Africa is often
unnoticed because many map projections obscure the vast size
of Africa relative to other world regions, increasing
the likelihood of failing to notice how relatively small
African populations are given the continent’s vast size).
Also, as evident in Figure 8.2, the water transport potential
in Africa is exceptionally low; none of the great African
rivers are easily navigable into the interior, many dropping
off the great African plateaus, a fact that combines
with the low degree of coastal indentation and
few natural ports to make water transport very difficult
in Africa (Sowell 1996, 1998; Mellinger, Sachs and Gallup 1999).
Although this is frequently remarked upon, it is the
interaction of multiple factors such as transport costs,
agricultural productivity, demographic factors, the
biogeography of disease, and state and institutional development
and other factors that is important, a subtle and
complex interrelationship that cannot be understood
by looking at any one of the relevant factors on their own.
[continued next page]
28
These measures are of course rough estimates. But they do allow
for an objective and empirical measure, as opposed to many
of the vague statements and assertions one finds in the historical,
economic, political and development literature, of the degree
of state competition within regions. I plan to further refine
and quantify these empirical measurements. Overall, it seems
that there was a much greater potential for state competition
in the vast western Eurasian agricultural ecumene than in any
other region in the world. As has been argued by so many
social scientists from different
Of particular relevance here, the number of historically
strong states in Africa is much smaller than the grid
of modern artificially imposed European state structure suggests.
Furthermore, given their wide spatial separation
(Stock 1995, below) and the under appreciated vastness
of the Sub-Saharan region, they likely had significantly
less interaction than states in many other Eurasian regions,
and the Bockstette et. al. measure likely overestimates
the overall level of state strength and interaction in
Sub-Saharan Africa
Major Precolonial African States and Empires
Source: Stock 1995, p. 62
29
political and disciplinary perspectives, this seems
to have been a crucial factor in European development.
It was an impetus towards trading post and colonial
expansion, increasing the likelihood that it would be
a Western Eurasian power that would first stumble upon
the then unknown extra-Eurasian lands. This, as
[
devrait
être ajouté: Cosandey 1997&2007],
Blaut 1993, Frank 1998, Pomeranz 2000, Acemoglu et. al. 2003,
2005 and others have argued, had profound knock-on
effects on European political, social, demographic,
technological, and institutional development with
effects that are still felt today, and are especially
apparent in global spatial patterns of development today.
30
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