Synchronous
East-West
Urban and
Empire Upsweeps?
Christopher
Chase-Dunn, Hiroko Inoue, Alexis Alvarez, Kirk Lawrence and James Love
Institute for
Research on World-Systems
Frederic Teggart’s
Earlier studies found a curious interregional synchrony in the growth
and decline of large cities and empires. From about 500 BCE until about 1500 CE
cities and empires in East Asia and the West Asian/Mediterranean region appeared
to be growing and declining in the same periods, whereas intervening
To
be presented at the 2009 conference of the Social
Science History Association, Long Beach, CA Sunday, November 15: 10:15
AM-12:15 PM. Session
on Synchrony in History. V. DRAFT:
v. 11/17/09 xxxx words
This paper is available at https://irows.ucr.edu/papers/irows53/irows53.htm
New graphs of east/west cities and empires together.
Results of new search for east/west city
and empire synchrony. Also south
Upward sweeps stuff.
Diff between cycles and upsweeps.
Population stuff.
Partial correlations.
Adrefs turchin, upsweeps pap. Strogatz, lagged synch?
The growth and decline patterns and upward sweeps in the sizes of the
world’s largest cities and empires and their changing locations over the past
three millennia provide an important window on world history and sociocultural
evolution. Earlier research has shown what appears to be a fascinating
synchrony in the growth/decline phases of the largest cities and empires in
East Asia and in the West Asian/Mediterranean region (e.g. Chase-Dunn and Willard 1993; Chase-Dunn,
Manning and Hall 2000; Chase-Dunn and Manning 2002).
Using
data on the population sizes of largest cities and the territorial sizes of
largest empires it has seemed that medium-term growth/decline phases in
The population and areal sizes of
human settlements have increased since the emergence of sedentism around 12,000
years ago, and so have the sizes of the largest polities. But these general
long-term trends have been complicated by sequential middle-term declines in
the sizes of the largest cities and empires in all regions where urban and
polity sizes have been studied quantitatively. The population size estimates of
both modern and ancient cities are subject to large errors, and existing
compilations (Chandler 1987; Modelski 2003) badly need to be improved using
better methods of estimation (e.g. Pasciuti and Chase-Dunn 2002). The same can
be said for existing compilations of estimates of the territorial sizes of the
world’s largest empires (Taagepera 1978a, 1978b, 1979, 1997). When these
upgraded estimates become available, the East/West synchrony findings discussed
here will need to be reexamined with the improved data.
This phenomenon of East/West urban
and empire synchrony in middle-run growth/decline phases has been subjected to
several different methods of analysis. Both changes in the size of the largest
cities and changes in the steepness of the city-size distributions have been
used. And earlier studies have used two different kinds of spatial units of
analysis: constant regions and expanding political-military networks
(interaction networks of fighting and allying states). The East/West synchrony
has been found with both.
Detrending
is important because the long-term trend for city and empires sizes to increase
and so this alone would produce a positive correlation across distant regions.
Two different methods of detrending have been used: partial correlation
controlling for year and decadal change scores in which the earlier year is
subtracted from the later year. In the studies of empire sizes, empires that
touch adjacent macro-regions such as the Mongol Empire of the thirteenth
century CE have been removed from the analysis because they build in a degree
of synchrony by appearing in both regions at the same time. The synchrony
finding is strong even when this case has been removed from the calculations.
Frederick
Teggart’s (1939) path-breaking world historical study of temporal
correlations between events on the edges of the Roman and Han Empires argued
the thesis that incursions by Central Asian steppe nomads were the key to
East/West synchrony. An early study of city-size distributions in Afroeurasia
(Chase-Dunn and Willard 1993; see also Chase-Dunn and Hall 1997: 222-223) found
an apparent synchrony between changes in city size distributions and the growth
of largest cities in
Figure 1: Sizes of Largest cities in
Comparable
other instances of distant systems that came into weak contact with one another
can be found. Within the Old World, the Mesopotamian and Egyptian core
regions were interacting with one another by means of prestige goods exchange
from about 3000 BCE until their political-military networks (state systems)
merged in 1500 BCE. Chase-Dunn, Pasciuti, Alvarez and Hall (2006) have already
examined this case for synchrony and have not found it, though the data on
Bronze Age city and empire sizes are very crude with regard to temporality and
accuracy. It is also possible to study the temporality of rise and fall and
oscillations among distant regions in the
Chase-Dunn,
Alvarez and Pasciuti (2005) also report detrended correlations between constant
regions for total population estimates taken from McEvedy and Jones
(1975). These total population estimates at 100-year intervals show rather high
growth/decline synchronies for several regions, also noted and discussed by
McEvedy and Jones (1975: 343-48).
The
East/West growth/decline synchrony seems to be rather robust, though better
estimates and finer temporal resolution of empire and city sizes might
challenge it. Interregional synchrony can be caused when two cyclical processes
get simultaneously reset, either by the same cause or by different causes. This
could be a one-shot occurrence. Or a process that is similarly cyclical can
cause synchrony. Candidates for the East/West synchrony are: climate change,
epidemic diseases, trade interruptions, or attacks by
Climate
change might affect regions by causing growth and decline of agricultural productivity
that in turn affects cities and empires. Perhaps because
But
climate change could also be involved in somewhat more complicated ways.
Central Asian steppe nomads (discussed below) were very susceptible to climate
change because their pastoral economy was greatly affected by changes in
temperature and rainfall. It is possible that climate change in
The
above hypotheses all conceive of climate change as an exogenous variable. But
it is also possible that city and empire growth change the climate. We know
that population growth and the development of complex civilizations changes the
environment by means of deforestation, soil erosion and the construction of
large irrigation systems (Diamond 2005). These changes may have affects on
climate. Modern studies show that the construction of large cities creates an
“urban heat island” that changes the environment in the immediate vicinity and
downwind of cities. Cities ingest and egest water, air and energy, and while
industrial cities do this on a much larger scale, earlier large cities also did
it to some extent. So large-scale agriculture and city-building may be causes of
climate change. Thus climate change may also be an endogenous variable.
We
know that Central Asian steppe nomads who raised horses and sheep periodically
formed large confederacies and attacked the agrarian empires of the East and
the West (Barfield 1989). Famous examples are the Huns and the Mongols. Perhaps
there was a cycle of Central Asian incursions that impacted upon the agrarian
civilizations of the East and the West and that accounts for the synchrony.
We
know that epidemic diseases spread across
We
also know that the Roman and Han empires were linked by long distance trade
routes across the Silk Roads and by sea. Perhaps interruptions to trade, or
periods of greater and easier trade flows, affected the Eastern and Western
civilizations simultaneously.
It
is also possible that two systems that are cycling independently can become
synchronized if they are both reset by a simultaneous accidental shock. This is
the so-called “Moran Effect” known in population ecology. We have discussed
this possibility in Chase-Dunn, Alvarez and Pasciuti (2006).
Figure
2 is a propositional inventory that includes most of the possible causes of
East/West synchrony.
Figure 2: Possible Causes of East/West Synchrony
More
research is required to find out which of these possible causes was responsible
for the East/West synchrony. We have found some evidence that temperature
changes in
Cycles, Upward Sweeps, Collapses and Ceilings
We empirically identify “upward sweeps,” when the scale of cities and states dramatically increased. We review and synthesize explanations of chiefdom-formation, state-formation, empire-formation and the rise and fall of modern hegemonic core states in order to produce formal explanatory models. And we study the emergent characteristics that distinguish these different scales in order to comprehend how the processes have qualitatively evolved, and in order to consider what kinds of qualitative transformation might occur in the future. Our approach avoids the unscientific pitfalls of progressivist, functionalist, inevitabalist and teleological presumptions that have plagued many earlier approaches to socio-cultural evolution. We do not identify complexity and hierarchy with progress, but neither do we assume that they are the opposites of progress.
Our project compares relative small regional systems with larger continental and global systems, thus we must abstract from scale in order to examine changes in the structural patterns of small, medium and large human interaction networks. That said, we are also interested in medium term change in the scale of polities and settlements. We are not considering very long-term trends in this discussion. When an interacting set of polities or settlements is the unit of analysis nearly all systems oscillate in what we may term a normal cycle of rise and fall – the largest city or polity reaches a peak and then declines and then this or another city or polity returns to the peak again. We call this a normal cycle of rise and fall. It roughly approximates a sine wave, although few cycles that involve the behavior of groups of humans actually display the perfect regularity of amplitude and period found in the pure sine wave. In Figure 3 the cycle of rise and fall is half way down the figure and is labeled “normal rise and fall.” At the top of Figure 3 is a depiction of an upward sweep in which the size of the largest entity (state or city) increases by a factor of 2. Such a sweep may be relatively rapid or may be slow, and Rein Taagepera (1978a) contends the speed of the rise is often related to the sustainability of the upsweep, at least in the case of empires. Taagepera notices that empires that rise more slowly tend to last longer than those that rise abruptly. When an upward sweep is sustained and a new level of scale becomes the norm we call this an upward sweep. When it is temporary and returns to the old lower norm we call it a “surge” (see the 2nd line from the top in Figure 3). We also distinguish between three types of decline, a “normal” decline which is part of the normal rise and fall cycle, a short-term collapse in which a decline goes significantly below what had been established as the normal trough, and a sustained collapse in which the new lower scale becomes the norm for some extended period of time. Jared Diamond (2005) has examined the complex causes of a large collection of collapses, though he does not rely on quantitative indicators of collapse and he often focuses on particular societies or settlements that collapsed while ignoring neighboring societies or settlements that rose. If intersocietal interaction networks (world-systems) had been Diamond’s unit of analysis instead of single societies most of the cases of “collapse” that he studied would have been instances of normal rise and fall cycles rather than instances of system-wide collapse. A genuine collapse is when all the societies in a region go down and stay down for a long period. [2]
Figure 3: Types of medium-term scale change in the largest settlement or polity in an interacting region
Replication of the Earlier Synchrony Findings:
Cities
The
published studies that found synchrony between Eastern and Western
growth/decline phases of largest cities in each region were based on Tertius
Chandler’s (1987) compendium of estimates of the sizes of large cities. George
Modelski (2003) has produced a new and improved compendium of city size
estimates. For this paper we have used Modelski’s estimates to study upward
sweeps of city sizes and here we use them to once again examine the question of
East/West urban synchrony. The regions we are comparing are East Asia,
including
Figure 4 below
shows the results of the East/West comparision of largest cities and should be
compared with Figure 1 above that shows the same results using
Figure 4: East/West Largest Cities
Figure 5: East-West Largest City Annual Growth Rates
Correlations |
|||
|
|
W annual growth rate |
E annual growth rate |
W annual growth rate |
Pearson Correlation |
1 |
.181 |
Sig. (2-tailed) |
|
.215 |
|
N |
49 |
49 |
|
E annual growth rate |
Pearson Correlation |
.181 |
1 |
Sig. (2-tailed) |
.215 |
|
|
N |
49 |
49 |
Table 1: East-West Largest City Growth Rate Correlation
The Pearson’s r bivariate correlation coefficient for the East and West
largest annual growth rates is over the whole period from 1700 BCE to 1900 CE
is .18 (n=49) and this is not a significant correlation. We also have examined
the subperiod from 500 BCE to 1500CE during which an East-West city synchrony
was found based on
Replication of the Earlier Synchrony Findings:
Empires
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West |
|
|
East |
|
|
Year |
Largest City Population |
City Name |
Year |
Largest City Population |
City Name |
-1700 |
60 |
|
1700 |
40 |
Erlitou |
-1650 |
67 |
|
-1650 |
24 |
Erlitou |
-1600 |
75 |
Avaris |
-1600 |
24 |
BO (Yanshi) |
-1500 |
60 |
|
-1500 |
74 |
|
-1484 |
64 |
|
-1484 |
100 |
|
-1400 |
80 |
|
-1400 |
104 |
|
-1300 |
120 |
|
-1300 |
120 |
Yin |
-1200 |
160 |
Pi-Ramses |
-1200 |
120 |
Yin |
-1173 |
150 |
|
-1173 |
120 |
Anyang/Yinxu |
-1100 |
120 |
Pi-Ramses |
-1100 |
112 |
|
-1000 |
120 |
|
-1000 |
100 |
Haoqing |
-900 |
100 |
Memphis/Thebes/Babylon |
-900 |
125 |
Haoqing |
-800 |
100 |
Memphis/Thebes/Babylon |
-800 |
125 |
Haoqing |
-700 |
100 |
Memphis/Thebes/Babylon/Nineveh |
-700 |
100 |
|
-600 |
200 |
|
-600 |
200 |
Louyang |
-500 |
200 |
|
-500 |
200 |
|
-400 |
200 |
Carthage/Babylon |
-400 |
320 |
Xiatu |
-300 |
500 |
|
-300 |
350 |
Linzi |
-200 |
600 |
|
-200 |
200 |
|
-100 |
1000 |
|
-100 |
400 |
Changan |
1 |
800 |
|
1 |
420 |
Changan |
100 |
1000 |
|
100 |
420 |
|
200 |
1200 |
|
200 |
100 |
|
300 |
1000 |
|
300 |
250 |
|
400 |
800 |
|
400 |
300 |
|
500 |
500 |
|
500 |
500 |
Luoyang/Nanjing |
600 |
600 |
|
600 |
500 |
|
700 |
400 |
|
700 |
1000 |
Changan |
800 |
700 |
|
800 |
800 |
Changan |
900 |
900 |
|
900 |
200 |
Loyang/Kyoto |
1000 |
1200 |
|
1000 |
400 |
|
1100 |
1200 |
|
1100 |
1000 |
|
1150 |
1100 |
|
1150 |
1000 |
|
1200 |
1000 |
|
1200 |
1000 |
Kaifeng/Hangzhou |
1250 |
300 |
|
1250 |
1250 |
|
1300 |
400 |
|
1300 |
1500 |
|
1350 |
350 |
|
1350 |
1250 |
|
1400 |
360 |
|
1400 |
1000 |
|
1450 |
380 |
|
1450 |
1000 |
|
1500 |
400 |
|
1500 |
1000 |
|
1550 |
660 |
|
1550 |
1000 |
|
1600 |
700 |
|
1600 |
1000 |
|
1650 |
700 |
|
1650 |
844 |
|
1700 |
700 |
|
1700 |
688 |
|
1750 |
676 |
|
1750 |
900 |
|
1800 |
1117 |
|
1800 |
1100 |
|
1825 |
2100 |
|
1825 |
1350 |
|
1850 |
3750 |
|
1850 |
1648 |
|
1875 |
4850 |
|
1875 |
900 |
|
1900 |
6500 |
|
1900 |
1497 |
|
Table 2: Largest Cities in the East and the
Central System (numbers in blue are interpolations)
[1] This was first noticed in 1992 by Chase-Dunn and
Willard (1993) who were studying changes in the city-size distributions of
several different regional world-systems. A striking similarity between the
East Asian and West Asian city-size distribution trajectories was first
confirmed by overlaying the graphs and holding them up to the bright light of a
window.
[2] These are rare, but the long inter-regnum in