Rise and Fall: East-West Synchronicity and Indic Exceptionalism Reexamined

Karakorum - the Mongol capital of Eurasia

Christopher Chase-Dunn, Sociology, Johns Hopkins University, chriscd@jhu.edu

Thomas D. Hall,
Sociology, Depauw University thall@depauw.edu

Susan Manning, Sociology, Johns Hopkins University

Abstract: All world-systems with at least a chiefdom level of political organization exhibit a pattern of the rise and fall of large polities. Among chiefdoms this pattern has been referred to as "cycling". In state-based systems it is known as the rise and fall of empires. And in the modern system it is called the "power cycle" or the "hegemonic sequence." This paper reexamines the question of synchronicities of rise and fall in systems linked only by very long distance prestige goods trade. Earlier research found that increases and decreases in the territorial sizes of empires and the population sizes of cities were highly correlated in East Asia and West Asia/Mediterranean regions from about 600 BCE to 1500 CE. Though data were somewhat scarce for South Asia, it appeared that Indic civilization did not rise and fall in tandem with the East and the West. In this paper we report an improved test of the synchronicity of empire sizes and the different pattern found in India.

Presented at the annual meetings of the International Studies Association, Minneapolis, Minn. March 17-21, 1998. Session on "Recent approaches to analyzing world historical systems." DRAFT v. 3/16/98

The world-systems perspective developed as a theoretical approach for modeling and interpreting the expansion and deepening of the capitalist regional system as it emerged in Europe and incorporated the whole globe over the past 500 years (Wallerstein 1974; Chase-Dunn 1989; Arrighi 1994). The idea of a core/periphery hierarchy composed of "advanced" economically developed and powerful states dominating and exploiting "less developed" peripheral regions has been a central concept in the world-systems perspective. In the last decade the world-systems approach has been extended to the analysis of earlier and smaller intersocietal systems. Andre Gunder Frank and Barry Gills (1993) have argued that the contemporary global political economy is simply a continuation of a 5000-year old world system that emerged with the first states in Mesopotamia. Chase-Dunn and Hall (1997) have modified the basic world-systems concepts to make them useful for a comparative study of very different kinds of systems. They include very small intergroup networks composed of sedentary foragers, as well as larger systems containing chiefdoms, early states, agrarian empires and the contemporary global system in their scope of comparison.

World-system Cycles: Rise-and-Fall and Pulsations

Comparative study reveals that all world-systems exhibit cyclical processes of change. Chase-Dunn and Hall (1997) focus on two major cyclical phenomena: the rise and fall of large polities, and pulsations in the spatial extent of interaction networks. What they call "rise and fall" corresponds to changes in the centralization of political/military power in a set of polities. They note that all world-systems in which there are hierarchical polities experience a cycle in which relatively larger polities grow in power and size and then decline. This applies to interchiefdom systems as well as interstate systems, to systems composed of empires, and to the modern rise and fall of hegemonic core powers (e.g. the Netherlands, Britain and the United States). Very egalitarian and small scale systems such as the sedentary foragers of Northern California (Chase-Dunn and Mann 1998) do not display this kind of cycle, however.

Chase-Dunn and Hall also note that all systems, including even very small and egalitarian ones, exhibit cyclical expansions and contractions in the spatial extent of interaction networks. They develop a schema for spatially bounding regional world-systems in which smaller bulk goods networks (BGNs) are contained within larger political-military networds (PMNs) , prestige goods networks (PGNs) and information networks (INs). They posit the hypothetical pulsation of BGNs, PMNs, PGNs and INs. By this they mean that the spatial scale of interaction increases and then decreases at each of these network levels. Interaction densities increase because there is more exchange and events at any single point have consequences over a greater distance. Thus both the amount of interaction and the range of interaction increase and then contract (Chase-Dunn and Hall 1997: Figure 10.2)

This paper is not about pulsation. But pulsation may be systematically related to the phenomenon under study here - the rise and fall of political/military centralization within systems of polities. The main unit of analysis in this paper is PMNs - political military networks in which states are fighting and allying with one another. The main phenomenon we are investigating is the discovery that widely separated PMNs exhibit a curious synchronicity in the rise and fall of large empires. The PMNs under study here - the West Asian/Mediterranean (Central) region, South Asia, and East Asia, were parts of a larger prestige goods network (PGN), but they did not, with a few exceptions, make war on one another directly until recent centuries.

The period of time we focus on here is the last 2500 years, but we end our data set in 1800 CE This is because the separate PMNs became merged into a larger nearly-global PMN in recent centuries, and because the rise of the modern colonial empires creates a temporal trend in all regions that we wish to avoid in analyzing rise and fall and synchronicity. The synchronicity of the modern system is not at issue. We are interested in ups and downs. The long term trend toward larger polities is important, but it is not our main focus here.

Qualitative Differences in Rise and Fall

Close examination reveals that rise and fall has different characteristics and different causes in distinct types of world-systems. The rise and fall of chiefdoms is analytically similar to the rise and fall of empires and the rise and fall of hegemonic core powers. All these are related to the stability of institutions for extracting resources from distant regions. But there are also important differences, in addition to the obvious difference of scale. David G. Anderson's (1994) study of the rise and fall of Mississippian chiefdoms in the Savannah River valley provides an excellent and comprehensive review of the anthropological and sociological literature about "cycling," the processes by which a chiefly polity extends its control over adjacent chiefdoms and erects a two-tiered hierarchy of administration over the tops of local communities. At a later point these regionally centralized chiefly polities distintegrate back toward a system of smaller and less hierarchical polities.

Chiefdoms rely more completely on hierarchical kinship relations, control of ritual hierarchies, and control of prestige goods imports than do the rulers of true states. States have specialized organizations for extracting resources that chiefdoms lack. And states and empires in the tributary world-systems were more dependent on the projection of armed force over great distances than the modern hegemonic core states have been. The development of commodity production and mechanisms of financial control as well as further development of bureaucratic "techniques of power" have allowed modern hegemons to extract resources from far-away places with much less overhead cost.

The development of techniques of power have made core/periphery relations ever more important in competition among core powers and have altered the way in which the rise-and-fall process works in other respects. One big difference between the rise and fall of empires and the rise and fall of modern hegemons is in the degree of centralization achieved within the core. Tributary systems alternate back and forth between a structure of multiple and competing core states on the one hand, and core-wide (or nearly core-wide) empires on the other. The modern interstate system experiences the rise and fall of hegemons, but these never take over the other core states to form a core-wide empire. This is the case because modern hegemons are pursuing a capitalist, rather than a territorialist, form of accumulation.

Previous Findings

While examining the relationships within PMNs of urban growth and decline Chase-Dunn and Willard (1993) accidentally discovered that city growth and changes in city size distributions seemed to occur synchronously in the Central (West Asian and Meditteranean) and the East Asian PMNs.

Chase-Dunn and Hall further confirmed this interesting synchronicity by studying growth and decline periods of empires in East Asia and the West Asian region between 600 B.C.E. and 1800 C.E. (Chase-Dunn and Hall 1997: Figures 10.7-9). Chase-Dunn and Hall used the data on the territorial sizes of empires coded by Rein Taagepera (1978a,1978b,1979, 1986,1997). Taagepera used historical atlases and histories to estimate the territorial sizes of empires from 3000 BC to the present. Chase-Dunn and Hall noted that the correlation between the Central and East Asian empire sizes is somewhat inflated by the fact that both were temporarily united by the Mongol Empire in the thirteenth century. This correlation is also more positive because there is a general upward trend in the sizes of empires over time. When the time trend was statistically controlled (by computing a partial correlation that controls for year) and when the Mongol Empire was removed from the calculation, the correlation of Central and East Asian empire sizes remained quite positive (Pearson's r =.61) and statistically significant (Chase-Dunn and Hall 1997:218).

These findings suggest the possibility of systemness in the Afroeurasian system far earlier than most historians would imagine. This is a hypothesis strongly argued by Frank and Gills (1994). But the 400 year cycles of growth and decline posited by Frank (1993) are not well-supported by the data on city and empire growth (Chase-Dunn and Hall 1997: Figures 10.5 and 10.6). It was also found that the intermediate Indic PMN did not experience a similar sequence of growth and decline phases in city populations (Chase-Dunn and Hall 1997: Figure 10.11). The causality of these synchronous cycles in distant PMNs is not well understood. We also examined changes in city and empire sizes for the Mesopotamian and Egyptian PMNs in order to see if these revealed synchronicity. Like East and West Asia these were separate PMNs linked by a larger PGN. But we found no synchronicity between Egypt and Mesopotamia (Chase-Dunn and Hall 1996). Both PMNs experienced cycles of rise-and-fall, but these were not in phase with one another despite having been linked into a larger PGN.

We will discuss possible explanations of the indicated synchronicity in the last part of this paper. But the main task here is to improve upon the analysis of the Taagepera data in order to confirm or disconfirm the indicated syncronicity and to further investigate the Indic exceptionalism. To do this we reworked and fine-tuned our analysis of the Taaegepera data specifically in order to test for synchronicity. In the original analysis presented in Chase-Dunn and Hall (1997) we used the information contained in Taagepera's published and unpublished papers to create a data set with empire sizes at 50-year intervals. This involved interpolating from the years given in Taagepera to the closest 50 year time points, a possible source of error. It is possible that this kind of error might have produced an apparent syncronicity because any two systems that are oscillating might appear to be in sync with one another if your measurement of time is too crude.

To gain a more exact estimation of the timing of events that changed empire sizes we created a new date set with 10-year intervals. This allowed us to code the Taagepera years with much less error. We still have a great deal of interpolation, but the real numbers are much closer to the actual years in which historical events occured. This provides a much stronger test of the hypothesis of synchronicity.

We also made another change to improve our ability to test the synchronicity hypothesis. We note that the Afroeurasian world-system came together in a series of spasms, rather than in an abrupt expansion or a long slow expansion (Chase-Dunn and Hall 1997: Chapter 8 and Figure 10.2). The most well-known spasm was the Mongol Empire that briefly united the East Asian and Central PMNs from CE 1210 to CE 1300. When we include empires that encompass more than one of our PMNs we are building in a correlation of empire sizes because the same empire is included in more than one PMN. In order to eliminate this possible source of correlation we created variables that removed the empires that extended into more than one of the PMNs under study, substituting the largest non-shared empire in each region for the shared empire.

The earlier results were based on examining the sizes of the largest empires in each PMN. In this reanalysis we also coded the second largest empire in each PMN in order to be able see if these also exhibit synchronicity, and to calculate a two-empire size distribution measure. This latter is the ratio of the size of the largest empire to the sum of the largest and second largest. This is supposed to indicate the relative centrality of a local system using empire sizes as an indicator. It is analogous to a city-size distribution. It would have been desirable to have also third and fourth largest empires to calculate an empire size distribution, but these data are available for only a few time periods. We settled for a 2-empire measure of the empire size distribution. A sample from our data set looks like this:

year cemp1c cemp2c

-1500.00 .65 egypt .24 mitanni

-1490.00 .72 egypt .25 mitanni

-1480.00 .79 egypt .26 mitanni

-1470.00 .86 egypt .28 mitanni

-1460.00 .93 egypt .29 mitanni

-1450.00 1.00 egypt .30 mitanni

-1440.00 .98 egypt .30 mitanni

-1430.00 .96 egypt .30 mitanni

-1420.00 .94 egypt .30 mitanni

-1410.00 .92 egypt .30 mitanni

-1400.00 .90 egypt .30 mitanni

-1390.00 .88 egypt .30 mitanni

-1380.00 .86 egypt .30 mitanni

-1370.00 .84 egypt .18 mitanni

-1360.00 .82 egypt .22 hittites

This shows the first fifteen time points and the empire sizes for the largest and second largest empires in the Central PMN.

In addition to improving our data, we also decided to use stronger tests of synchronicity and better methods for detrending. As mentioned above, the long-term trend toward larger empires builds in a degree of correlation over time. The method used by Chase-Dunn and Hall to remove the trend was to calculate a partial correlation controlling for year. In the present analysis we use two additional techniques for detrending. We calculate first differences change scores by subtracting the value in the year previous from the current year. This reduces autocorrelation. And we also detrend by regressing our empire size variables on year and then computing the correlations of the residuals.

In order to have a closer look at the Indic PMN we used David Wilkinson's (1993) coding of the power configurations of South Asian states that he derived from Schwartzberg (1992). Wilkinson produced a coding using decennial time points of the relative configuration of the Indic states system. His categories (and our codes) are as follows:

6: universal state (one superpower, no great powers, no more than two local


5 : hegemony (either one superpower, no great powers, three or more

local powers; or no superpowers, one great power, no more than one local


4: unipolar (all other configurations with one superpower)

3: bipolar (two great powers)

2 : tripolar (three great powers)

1: multipolar (more than three great powers)

0 : nonpolar (no great powers)

Because the empire size data for South Asia are missing for some periods we hoped to use Wilkinson's coding as an alternative source for studying the empire size distribution and for comparing the Indic PMN with the Central and East Asian PMNs.

Results of the Reanalysis

First we will discuss the three PMNs separately. Figure 1 shows the rise and fall of the largest empires in the interstate system that was located in Western Asia and the Mediterranean. We follow Wilkinson in designating this as the Central system because it developed states first and it eventually enveloped all the other systems. Figure 1 labels only a few of the largest empires but all of the largest empires were plotted in this graph. Both the long term upward trend in empire size and the sequential rise and fall can be seen. As with many social "cycles" the rise and fall phenomenon does not approximate a sine wave, but is rather an irregular oscillation with a varying period and magnitude. The size and duration aspects of empires are analyzed and discussed in Taagepera's articles (see references).

Generally empires that rise fast also decline fast and do not last long. It is notable that all the largest empires and the ones that establish new size records are dynasties that came from semiperipheral regions. This is the phenomenon of semiperipheral marcher states discussed by Chase-Dunn and Hall (1997:Chapter 5). Wilkinson (19xx) notices another interesting pattern - that of the fore-runner. Often a semiperipheral conquering state will make a grand attempt that fails, while an immediately subsequent attempt by a different empire-builder succeeds.

Figure 2 shows the territorial sizes of the two largest empires in the Central PMN. It appears to the eye that the two largest empires rise and fall together and indeed this is supported by the correlation of .62 between these. Rather than empire sizes being a zero-sum game, it would appear that empires get larger and smaller together. The temporal relationships between the largest and the second largest empires in the East Asian and the Indic PMNs are not as positive (Pearson's r= .26 and .35), but neither are they negative.

The East Asian PMN shows a similar pattern of long-term upward trend and shorter rises and falls (Figure 3). Note that the Mongol empire appears in both Figures 1 and 3 because the Mongols brought East and West Asia temporarily into the same PMN.

The Turks that appear in Figure 3 are the Uighurs, another Central Asian group that became famous in China long before their entrance into the Central PMN as the Ottoman Empire.

The Indic PMN looks visually quite different from the Central and East Asian PMNs (Figure 4). The early emergence of states in the Indus River valley is not included because there is no reliable way to estimate the territorial sizes of those states. The emergence of states in the valley of the Ganges in the seventh century BCE is what appears first in Figure 4.

The Mauryan empire was large even by the standards of the other PMNs. In addition to the question of synchronicity (below) there are two striking differences between the Indic system and the Central and East Asian PMNs.. The first is that there is no long-term upward trend of empire sizes in the Indic system. The Mauryan empire of the fourth century BCE was nearly as large as the Mogul empire of eighteeenth century CE. Indeed the mean size of states in the Indic system was only 1.5 square megameters, while for the East Asian PMN the mean was 3.4, and for the Central PMN it was 4.0. We may suppose that this is a function of the size and isolation of South Asian sub-continent. But while Indic empires never expanded outside of the subcontinent, empires from both the East and the West did occasionally expand into India. The second difference is that there were several long periods in which the largest states in the Indic system were very small. These conclusions will probably not be changed by improving the data on empire sizes in South Asia. But we should note that Taagepera's data often provides estimates for only one state in this region. In order to compute our two-empire measure of empire size we simply assumed that the second state was small and estimated its size as .1 square megameter. This procedure can certainly be improved.

Figure 5 graphs Wilkinson's power configuration coding over the same period covered by Figure 4. Wilkinson used the Historical Atlas of South Asia (Schwartzberg 1992) to code information about the size and power of states in the Indian subcontinent. Wilkinson's coding was constructed using categories based on the comparative study of geopolitics in states systems. It was our thought that this information might provide a somewhat independent measure of the power of the largest states and the relative distribution of power among states that we could use to supplement Taagepera's data on empire sizes.

The coding of the scores on the left margin of Figure 5 is provided above. It may be that the power configuration conceptualization constructed by Wilkinson is not a straightforward indicator of the power of the largest state or the relative distribution of power among a set of states. Of course many would argue that the territorial size of empires is not an ideal indicator of the power and importance of states. Some empires claim to control vast territories, but these are worthless vacant tracts, while others may hold smaller but richer lands. Be that as it may we supposed that there would be a positive relationship between the territorial size of the largest empire and the Wilkinson codes, or at least between power configuration and our two-empire measure of the empire size distribution. In fact these are positively correlated but the Pearsons' r coeficients are small (.20 and .12). We do not know where the problem is here. It may be with a poor fit between Wilkinson's categories and the empire size data. This problem could be further investigated by using the Schwartzberg volume, which was not available to Taagepera, to estimate the territorial size of Indic empires.


How does our improved method for investigating the finding synchronous empire growth and decline reinforce or undermine previous conclusions? The first thing we will discuss is the periodization of synchronicity between the Central and East Asian systems. Recall that we have no estimates of empire sizes in South Asia before the seventh century BCE.

Figure 6 focuses in on the period from 1500 BCE to 250 BCE. During this period we can see that there is little Easr-West synchronicity. The Pearson's r correlation coefficient for the sizes of the largest empires in the Central and the East Asian PMNs is -.20 for this period. The period of the huge Achaemenid (Persian) empire occured during the end of the warring states period in China in which several small states competed and allied with one another in an interstate system. The synchronicity between East and West began with the rise of the Han and Roman empires. The simultaneities of Roman and Chinese events documented by Teggart (1939) support the notion that systemic forces had emerged that were causing East and West to march to the same drummer.

It can also be seen in Figure 6 that the growth of Chinese and Gangetic states track closely from the seventh to the third centuries BCE. Indeed, the correlation coefficient for these 35 time points is .80, but this is a relationship that does not extend to the larger time period that we shall examine next.

Now let us consider the entire span of time from 1500 BCE to 1800 CE. Rather than exclude the years considered above we shall keep them in despite the negative relationship shown above between Eastern and Western empire sizes. We want to provide the strongest test possible of the synchronicity hypothesis. Figure 7 graphs the sizes of the largest empires in the Central, Indic an East Asian PMNs.

The correlation coefficients among the three regions are as follows:

PMN pair Pearson's r N p<
East Asian/Central .82 331 .0001
Indic/Central .24 227 .0001
East Asian/Indic .17 227 .012

There is a large and statistically significant correlation between the East Asian and Central empire sizes. This is a replication of the finding reported in Chase-Dunn and Hall (1997:Chapter 10). The shift from 50-year to 10-year intervals did not obliterate the finding of East-West synchronicity. The correlation of .24 between the Indic and Central systems is shown to be statistically significant, but we think that autocorrelation and extensive interpolation could be inflating the level of statistical significance. In order to reduce this source of error we need to examine first differences. The small positive relationship may indeed exist, but could be do to chance alone in two systems that are oscillating.

The next step is to test the relationship between East and West after the shared empires (such as the Mongol) have been removed from the data. Figure 8 graphs the largest empires in each PMN after the shared empires have been removed. When an empire is removed it is replaced by the second largest empire in a region.

This results in somewhat different patterns than those produced in Figure 7. The correlation coefficients among the variables also change to some extent:

PMN pair Pearson's r N p<
East Asian/Central .68 331 .0001
Indic/Central .35 223 .0001
East Asian/Indic .31 223 .0001

The correlation between the East Asian and Central empires is reduced from .82 to .68 because of removing the Mongol empire. This is still a high correlation and demonstrates that the East-West synchronicity was not entirely a function of the Mongol conquest. The correlation of the Indic empire sizes with the other PMNs increases when shared empires are removed. This would seem to be logically impossible, but here is the explanation. Though the removal of shared empires does reduce the Indic correlation with the other PMNs, the removal of the Mongol empire, which did not conquer India, increases the correlation because its gigantic relative size was a major source of the difference between India and the other regions. The net effect is to increase the correlation between the Indic system and the others when shared empires are removed. Should we now conclude that Indic exceptionalism should be rejected? We think not, because the positive relationship is still much smaller than than between the Eastern and Western PMNs.

We also investigated that possibility that synchronicity might be revealed in the correlation of changes in the sizes of the second largest empires in the different regions. The resulting correlation coefficients were very small ( from .05 to -.09) and none were statistically significant.

To further test the East-West syncronicity we examined the correlations across PMNs of our 2-empire measures of empire size distributions. The hypothesis here is that inequalities in the sizes of empires within regions might rise and fall simultaneously across regions. Recall that we did find an East-West correspondence in changes in city size distributions (Chase-Dunn and Hall 1997: Table 10.7). The following table shows the resulting correlations when the examined variables are two-empire measures of size distribution:

PMN pair Pearson's r N p<
East Asian/Central -.01 331 .902
Indic/Central .30 227 .0001
East Asian/Indic .22 227 .001

There is no East-West synchronicity in empire size distributions, despite the large positive relationship when we study changes in the size of the largest empires alone. But the Indic empire size distributions have small but significant positive correlations with both the Central and the East Asian PMNs. This indicates some small level of correspondence in processes of rise and fall between the Indic and the other regions, but the lack of any East-West relationship simply adds another conundrum to the complex mystery of Afroeurasian patterns of social change.

As was done by Chase-Dunn and Hall (1997) we can examine the extent to which the apparent synchronicity between East and West is due to the long-run upward trend in empire sizes. Chase-Dunn and Hall calculated a partial correlation controlling for time. When we replicate this using our refined data set on the largest empires in each PMN we produce the following results:

PMN pair Pearson's r N p<
East Asian/Central .74 224 .0001
Indic/Central .24 224 .0001
East Asian/Indic .16 224 .015

This implies that the East-West synchronicity is not solely due to the long-term upward trend in empire sizes. The correlation between East and West remains quite high. And the correlation between Indic and Central empire sizes is still statistically significant, though it is not large.

A stronger test of the East-West synchronicity of largest empire sizes is provided by the study of first differences. First differences are change scores that are computed by subtracting the value of a variable a Time 0 from its value at Time 1. These change scores have the advantage that they have a great deal less autocorrelation and so simultaneity without this bias is estimable. The results of correlating first differences on the complete variables (including shared empires such as the Mongols) is as follows:

PMN pair Pearson's r N p<
East Asian/Central .83 330 .0001
Indic/Central -.01 225 .904
East Asian/Indic -.04 225 .547

Interestingly, the East-West correlation for the complete (shared) set of largest empires is slightly larger with first differences than it is straight up. The other correlations are smaller. In fact they are so near zero that one might infer that there are no real synchronicities between Indic empire sizes and the other regions.

When we compute the first difference correlations for the variables that do not include shared empires we obtain the following results:

PMN pair Pearson's r N p<
East Asian/Central .41 330 .0001
Indic/Central .12 221 .075
East Asian/Indic .21 221 .001

Without autocorrelation and without the Mongols the East-West relationship is reduced, but it is still present. The relationship between Indic and East Asia is still statistically significant.

Another strong test for ruling out the effects of the long-term trend is accomplished by regressing year on the empire size measures and then correlating the unstandardized residuals to see if the relationship holds up independent of the long-term upward trend. When we perform this operation on the complete variables (those containing shared empires) the results are thus:

PMN pair Pearson's r N p<
East Asian/Central .70 331 .0001
Indic/Central .24 227 .0001
East Asian/Indic .16 227 .015

Here we see that the East-West synchronicity holds up well and the correlation between the Indic and Central empire sizes, though much smaller, is still significant. But what about the time residuals when we eliminate the shared empires:

PMN pair Pearson's r N p<
East Asian/Central .47 331 .0001
Indic/Central .38 223 .0001
East Asian/Indic .35 223 .0001

The news here is that the East-West relationship is somewhat reduced but is still substantial. And the Indic relationship with the others is the highest that we have found in all our tests. This implies that the Indic system does indeed experience a substantial degree of synchronicity with the Central and East Asian PMNs, though it is less than the East-West relationship.

Causes of Synchronic Rise and Fall

The question remains as to what caused the synchronous growth of cities and empires in the Central and East Asian PMNs. The hypothesis of simultaneous expansions and contractions across a wide region should specify the causal mechanisms that are thought to cause these synchronicities. It is possible that climate changes explain the similar timing of growth and decline in Western Asia and China. India, at a more equatorial latitude, probably experienced a very different climatic sequence. Climate change can affect urban growth and empire-formation through its affects on agricultural productivity (Nix 1985). Periods of flooding may disrupt irrigation systems, and periods of drought also may negatively affect agriculture. Recently acquired evidence indicates that the collapse of Mayan states may have been caused by an extended period of drought. Weiss et.al. (1993) contend that both the expansion and collapse of the Akkadian empire were spurred by climate changes.

If we should find significant relationships between indicators of climate change and the urban and empire growth/decline sequences we will want to examine the important issue of the direction of causality. Does climate change cause urban change or does the expansion of agriculture associated with urban growth cause climate change? It is possible that expanded agricultural activity, and/or deforestation due to human exploitation of forest resources, may have effects on local and regional rainfall patterns and ground water levels. Thus intense agriculture and forest exploitation related to population density (and thus urbanization) may affect climate change. There is a huge developing literature on the anthropogenic causes of climate change. It is well-known that the intensification of productive activities cause environmental degradation and that this has been a major process affecting the development of human societies from the very beginning. If urban or empire growth episodes precede climate change or changes in water levels then causality in the direction of human effects on climate will be supported.

It may be that climate changes are related to growth and decline phases but that these do not explain the synchronicities we observe between the Central and East Asian PMNs. Another possible explanation involves the flows of microparasites and their affects on human populations. As trade increases in density and volume formerly isolated disease pools come into contact, unleashing epidemics -- what Alfred Crosby (1972,1986) and others call virgin soil epidemics. These can produce massive disruptions and, following Goldstone's (1991) argument, can unleash all sorts of social, economic and political changes. As pathogens and hosts adapt to each other these diseases become less lethal and populations recover. Trade then resumes and the cycle can repeat as other, formerly isolated disease pools come into contact or as new diseases spread along expanding trade networks.

A more interesting explanation of Central/East Asian synchronicity from the world-systems perspective is Frank's (1992) hypothesis of the "centrality of Central Asia" as a peripheral region linking both ends of the Eurasian continent. As we have already mentioned, the Mongol Empire briefly linked the Western Asia and China into a single polity in the thirteenth century CE. Owen Lattimore (1940) was the first to observe the tight core/periphery interaction between the horse pastoralists of Central Asia and the agrarian Chinese empires. Thomas Barfield (1989) traces the long-term linkage of the rise and fall of steppe empires with the rise and fall of agrarian empires in China. Frank (1992) contends that processes of peripheral migration and steppe-empire formation and their effects on the long distance trade carried along the Silk Roads of Central Asia are the explanation of the event simultaneities found by Teggart (1939) and also account for Frank's hypothesized 200-year phases of growth and decline. While our research indicates only mixed support for the timing of expansion and contraction phases as hypothesized by Frank (1993), we do find that the sequences of growth and decline of Western Asian and East Asian PMNs track quite closely.

Perhaps it is Frank's Central Asian linkage that accounts for this. In order to find additional support for this hypothesis we would need to rule out the climatic hypothesis by gathering data on climate change for the relevant regions, and to compare closely the data on long-distance trade, warfare, migrations and steppe-empire formation to sort out the causes of the synchronicities. We also need to understand why India was not affected in the same way by these processes.

Frank's Central Asian hypothesis is quite plausible. The spread of the Bubonic plague from the Central Asian steppes to both Europe and China by the Mongols is well known (McNeill 1976). But if this is the explanation, why did South Asia (the Indian subcontinent) follow a different sequence? One explanation might be that the tropical and semi-tropical climates of South Asia followed a different disease regime. It is also likely, given the Himalayan barrier, that climatic cycles in South Asia differed significantly from northern Eurasia. The monsoons certainly follow a different rhythm than northern regions.

A much more plausible explanation can be found in the ways in which South Asia was connected into the Afroeurasian PGN. India had multiple connections into the Afroeurasian trading networks: overland, either via the Hindu Kush passes to the Silk Roads or through Yunnan and Assam, and by sea. The sea routes are quite old, going back at least two millennia. At first they involved coastal routes, but later sailors mastered the monsoons and were able to sail across the Arabian Sea and the Bay of Bengal (see maps in Chaudhuri 1985 and Abu-Lughod 1989: 172-3, 202,252). Thus at any given time South Asian tributary states had multiple routes of access to the larger PGN. Disruption of any one route -- for whatever reason -- could be compensated for by means of alternate routes.

South Asia also had an alternative source of trade in its extensive links with Southeast Asia. At those times when the straits of Malacca or Sunda were controlled by pirates that made sea trade very risky, portages across the Malay Peninsula or overland through northern Southeast Asia (what is today northern Myanmar, Thailand, Laos and Vietnam) were used. Thus, while a large state (e.g. Funan, the Khmer Empires, Srivijaya, or later Siam) could block one or more routes, no single state could control all the paths from India to China.

Indian connections to, and trade with, various Southeast Asian states buffered India from blockages that occurred on other routes. Southeast Asia supplied aromatic woods, spices and especially gold. When access to northern sources of gold were severed by Bactria and when the Romans sought to curb the export of gold to the east in the first centuries of the current era India turned to Southeast Asia to fill the gap. As Coedes (1968:19ff) notes, this was not the kind of Gold Rush that occurred in California in 1849, but the region became known as the "land of gold." Further more, Southeast Asia was an outlet for surplus elites, especially Brahmans and Kshatriyas, who served in various Southeast Asian states. Since the connection to Southeast Asia was maritime, Indian states did not get bogged down in expensive land wars as China did from time to time.

Finally, the combination of streams of immigration into Southeast Asia by the Tai peoples from the northeast and by the Burmo-Tibetans from the northwest , along with impacts of China and Indian states, prevented the formation of a single region-wide power in Southeast Asia. Funan, located approximately where modern Cambodia is, came closest in the early centuries of the current era. But even Funan could not control all the routes.

Here we note that Frank's thesis is bolstered by the consideration of what happened in Southeast Asia. Central Asians played a role, albeit an indirect one, in Indianization of Southeast Asia. The Mongols, especially in the Yuan Dynasty under Kublai Khan, triggered major changes in the political organization of Southeast Asia. To avoid prolonged wars and insure tribute Kublai supported small states and blocked the formation of large ones. Mongol attacks helped finish the Burmese empire centered on Pagan, already in decline. Their support of smaller spin-off states in the north among Shans and Thais started the marcher state process that eventuated in the rise of Siam. Finally, China's withdrawal from oceanic trade in the Ming Dynasty, in part due to fear of another Mongol invasion, left the southern oceans open to Arab, then European traders (Fitzpatrick 1992).

In short, the states in Southeast Asia played an important role in the connections between China and India, and through India to West Asia and the Mediterranean. However, unlike the Central Asian steppe federations, which rose and fell with Chinese empires (Barfield 1989,1991), the states of Southeast Asia waxed and waned countercyclically with Chinese and Indian Empires.

Warfare is an interaction variable that is known to affect both urban growth and the territorial size of empires. The hypothesis about processes of steppe empire formation and the migration of pastoral nomads out of Central Asia being the key to the synchronous rise and fall of agrarian empires at both ends of the Eurasian land mass could be supported if we find simultaneous increases and decreases in warfare between steppe nomads and agrarian states in both West Asia and East Asia. Thomas Barfield's (1989) Perilous Frontier provides the information for the East Asian region. For West Asia, the approach to data on warfare utilized by the LORANOW project (Cioffi-Revilla 1991;1994) could be used to examine this hypothesis.

Summary and Conclusions

Formal comparative research on premodern world-system cycles has only begun and firm conclusions would be unwarranted. Earlier research found only weak support for synchronous changes within PMNs between the political rise-and-fall cycle and economic expansion and contraction as measured by urban growth. Perhaps this should not be surprising. The long business cycles of the modern world-system correspond only roughly with the rise and fall of hegemonic core powers. Modelski and Thompson's (1996) "twin peaks" model postulates that there are two Kondratieff waves per "power cycle." Other researchers (e.g. Goldstein 1988; Arrighi 1994) contend that hegemonic cycles are only roughly related to Kondratieff waves. More research is needed on both the modern and premodern world-systems before certain conclusions can be supported.

In this paper we have improved the analysis of Taagepera's data on empire growth/decline sequences for the purpose of testing for synchronicities across regions.

We have found rather strong additional evidence of synchronicity of empire rise and fall between the Central (Mediterranean-West Asian) and the East Asian PMNs. India did not rise and fall as synchronously with the more distant ends of the Eurasian PGN but the relationship was more positive than reported earlier. Indeed the stronger tests of synchronicity increased the size of the relationship with India. While India was not marching to the same drummer, the beat was heard and there were consequences even in the subcontinent. An earlier investigation of Mesopotamian and Egyptian urban and empire growth/decline phases from 3000 BCE to 1500 BCE failed to find synchronicity (Chase-Dunn and Hall 1996).

More research is need to further test for synchronicity and to examine possible causes. New information on city population sizes needs to be added to Chandler's (1987) monumental work to further examine the possible synchronicities of urban growth and decline. Taagepera's (1978-1986) empire sizes also need extension to cover the smaller South Asian states more thoroughly. Testing different explanations for the observed Central/East Asian synchronicity would require the assembly of data on climate change, warfare, and trade (see Chase-Dunn 1995).

A Proposal

We propose that scholars who are interested in these matters should work together to produce a World History GIS (geographical information system) that will contain comparable measures over time for the relevant locations and time periods. A joint effort could focus on a dozen main measures and divide the labor among a group of scholars who would coordinate their efforts. The first step is to organize an interdisciplinary group of researchers to plan and carry out this project. We propose that the World Historical Systems working group of the International Political Economy section of the International Studies Association be the organizational mechanism for facilitating communications and presentations of the results of this collaborative effort to construct a World History GIS for the purpose of studying the long-term patterns of changing world systems.
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