An interactive visualization of the Demic Atlas is now available on the website of the Stanford Spatial History Project, thanks to the unceasing efforts of Anne Fredell and Jake Coolidge. By clicking on the grey boxes on the page, one can toggle back and forth between demic and state-based world maps of per capita GDP and HDI. A link to the geospatial database used to construct these maps can be found at the same site. Click here to go directly to the database, located at geocommons.
The Stanford Spatial History website contains a number of interesting articles, maps, and visualizations, and I would urge readers to explore the site. Stanford University is deeply committed to the “digital humanities,” developing technologically mediated methods of exploring the traditional concerns of history and the arts. I would especially recommend the projects developed by the faculty leaders of the Spatial History Lab, Richard White and Zephyr Frank. Professor White’s venture, “Shaping the West,” focuses largely on railroads in the western United States, but delves into a number of related issues. Professor Frank’s work on Rio de Janeiro, “The Terrain of History,” includes a number of particularly intriguing and innovative visualizations.
As has been argued previously on GeoCurrents, the commonplace notion that the world is starkly divided between a prosperous and powerful “global north” and an impoverished and underdeveloped “global south” (with Australia and New Zealand forming southern outposts of the north) receives little support from world maps of socio-economic development. As can be seen in the state-based map of per capita GDP (PPP) posted below, the so-called global south is a diverse zone, containing relatively wealthy as well as extremely poor countries. As a result, the south fails to cohere as a region; in terms of virtually all development indicators, a well-off southern country such Chile is much closer to such northern regions as southeastern Europe than it is to central Africa. Most socio-economic maps constructed in the demic framework are even less supportive of the notion of a fundamental latitudinal divide. As the demic map of per capita GDP posted below indicates, the real zone of poverty is focused in tropical Africa and the northern part of southern Asia, rather than in a hypothetical globe-spanning south.
Such maps, however, do not tell the whole story. Statistical information can be mapped in many different ways depending on how the data are categorized. By playing with different modes of division, one can generate a diverse set of maps, some of which might uncover patterns that otherwise remain hidden. As it turns out, the database used in the Demic Atlas can indeed produce maps replicating the north-south global divide. Such a pattern is not pronounced, as it takes careful manipulation of the underlying information to expose it. Yet its presence is notable, and hence deserves consideration.
Most of the maps posted in GeoCurrents last week were based on the division of the data into quantiles. Quantiles break up a dataset to yield subsets of equal size; as the same number of units is slotted into each category on quantile maps, relatively well-balanced depictions generally result. In the Demic Atlas, seven such divisions were employed for most maps, as experimental evidence suggests that most people cannot readily differentiate more than seven values in a color spectrum. Although the seven-fold quantile design seems to be the most useful general vehicle for mapping global development, it is not without its problems. Most significantly, it under-represents the discrepancy between the poor and wealthy parts of the world. In absolute terms, the per capita GDP figures of areas placed in the middle categories on the quantile maps are closer to those of the countries on the low end of the spectrum than they are to those of the countries on the high end.
One alternative method of dividing data is that based on “equal intervals.” In this system, the value ranges of each category are of equal numerical scope, set at the same regular intervals regardless of how many units fall into each category. If, for example, the lowest figure in a five-category dataset is 1 and the highest is 100, the first category will cover the numerical range between 1 and 20, the second between 21 and 40, and so on. If most elements of the dataset are clustered in one part of the range, the resulting map may appear quite unbalanced, with several of its categories forming null sets, with no members.
Due to such data clumping, depicting global per capita GDP in equal intervals in a state-based framework yields an uninstructive map. As can be seen in the image to the left, almost the entire world appears impoverished in a seven-fold equal-interval scheme, with most countries falling into the lowest category. Although unsuitable for general purposes, this map does illustrate one important aspect of the global distribution of wealth among polities: a small handful of small countries and dependencies are vastly wealthier than the rest, at least in terms of per capita GDP. Qatar in particular, with its small population and vast reserves of oil and natural gas, towers above most of the world’s richest countries, with a per capita GDP that the CIA regards as almost four times greater than that of the United States. As the detailed map of Europe and the greater Middle East shows, Qatar, Luxembourg, and Lichtenstein monopolize the high-end categories in such a portrayal.
As the demic framework eliminates small countries by merging them with their neighbors, it allows much more effective equal-interval mapping. In the demic equal-interval depiction of per capita GDP (PPP) placed at the top of the post, large expanses of land fall into the upper categories, but even larger areas end up in the lowest grouping. The middle categories, in contrast, are sparsely occupied.
As a quick comparison with the small inset map indicates, the resulting categorization scheme conforms well with the conventional north/south global division. Whereas almost all regions to the south of the heavy black line fall into the bottom two categories (the only exceptions being Regions 19 [Arabian Peninsula] and 47 [Jiangsu and Shanghai]), none of the regions to the north of the line do. The line itself, moreover, falls almost exactly along the north/south division as it is conventionally mapped. The only differences here are the demic map’s placement of southeastern Europe and Israel in the global south and of Kazakhstan and Mongolia in the global north.
Although a north/south global split can be derived from the demic database, it would be unwise to read more into this division than is warranted. As explained above, the global south remains invisible in almost all other methods of mapping the data, whether in the state-based or the demic framework. More important, most of the north is currently exhibiting slow rates of economic growth, and has been for several years, whereas many southern countries, particularly China and India, are moving rapidly forward. If current trends continue, the north/south divide will vanish even on the equal-interval demic map within the coming decade.
The Demic Atlas project will conclude at the end of this week; next week’s posts will return to the standard GeoCurrents model, examining local issues of geographical significance. Today’s map merely shows which island groups are associated with which regions in the demic framework. As the map is self-explanatory, no further comment is provided. Tomorrow I will respond to the comments that have accumulated over the past few days—my apologies for not having done so already.
As originally envisaged, the Demic Atlas would have contained a number of maps showing the spatial patterning of a wide variety of development indicators. Depictions of literacy, average age of schooling, fertility, mortality, sex ratio and so on would have been included. As we have discovered, however, such information is simply not available in comparable form for most of the sub-national units used to construct the demic framework. Even for the indicators that were mapped (GDP and HDI), problems of data comparability compromised the project in several ways. Yet such difficulties were instructive in their own right, reinforcing the central thesis underwriting the entire project: sovereign states (and their dependencies) so dominate the realm of global data collection and collation that they systematically distort our view of the world. One can gather relatively solid information at the provincial level for most large countries, but only if one does so in isolation from the rest of the world, thwarting the comparison of units of like size. Although the states of India and the provinces and other first-order divisions of China are country-sized units, they cannot readily be contrasted with each other in a single framework. Outside of Europe, choropleth maps integrating data for a number of countries at the sub-state level are, to say the least, challenging to produce.
Further mapping of developmental indicators within the demic framework will therefore not be forthcoming. The remaining atlas posts will take up another issue, that of the variable methods one can use to construct maps out of a single set of data. Quantitative information can be broken down in many different ways, producing divergent images when translated into cartographic form. The maps posted last week, for example, divided GDP and HDI figures at regular intervals to yield subsets of equal size. Seven such quantiles were used, but the data can just as easily be broken down into more or fewer categories. The information set can also be divided in completely different ways; using “natural breaks,” for example, allows one to keep areas that deviate slightly from each other within the same categories. The remaining posts in this series will therefore focus on alternative methods of mapping the information in the Demic Atlas database. This endeavor will reveal otherwise invisible spatial patterns while also showing how the same set of information can yield strikingly different maps depending on how it is arrayed. Such maps will be constructed in both the demic and the state-based frameworks.
The Human Development Index (HDI) is the most widely used method of assessing the overall level of human wellbeing across the planet. Today’s post examines HDI rankings in the demic and state-based frameworks. As is the case in regard to GDP measurements, the demic map portrays broad regional patterns of development relatively well, while missing a number of local particularities captured by the conventional map. But the state-based framework also misses local distinctions, those found at the sub-national level.
The HDI is composite statistic that takes into account longevity, educational attainment, and standard of living. The calculations used to construct the index are involved, as several separate measurements and sub-indexes are employed.* Whether the results adequately reflect “human development” is an open issue; like all other social indexes, the HDI has notable limitations and omissions. It treats “standard of living,” for example, as reducible to per capita GDP (PPP), which is simply not the case. The HDI also ignores gender equity and environmental cleanliness, which many observers regard as significant aspects of wellbeing. But all told, the HDI is still the most comprehensive and widely used measurement of human development, and therefore deserves extended consideration.
In general, global HDI maps track GDP maps relatively closely, as can be seen in the pair of state-based maps to the left. Wealthy countries, not surprisingly, tend to have high HDI numbers, whereas poor countries generally post low numbers. Exception to this general rule can be instructive. On the quantile-based maps posted here, some oil-rich countries (Qatar, Equatorial Guinea) end up in significantly lower color categories in regard to HDI than they do in regard to GDP, reflecting the unbalanced nature of their development. A number of other countries, ranging from Kenya to Cambodia to Cuba to North Korea, on the other hand, fall into higher categories on the HDI map, reflecting in part successful educational programs. North Korea’s elevated HDI position, however, may point to statistical shortcomings and perhaps governmental manipulation of the data; certainly if a “malnutrition and food insecurity” component were added to the index, the position of North Korea would drop sharply.
As the demic HDI map posted above shows, exceptional areas also appear at the sub-national level. The most strikingly of such distinctions is found in southern South Asia. The region composed of Sri Lanka, the Maldives, and the Indian states of Tamil Nadu and Kerala ranks much higher in terms of human development than it does in regard to GDP. Sri Lanka and Kerala have long been noted for near universal literacy and impressive levels of public health, achievements realized despite meager economic resources. More recently, Tamil Nadu has made rapid progress in these same areas. Whereas in strictly economic terms, western India has the advantage, in terms of overall social development, southern India comes out ahead. North-central India, in contrast, lags well back in both domains.
In terms of human development, China appears much better off on the demic map than on the state-based map. As expected, coastal China scores higher than the rest of the country, but even the poorest interior regions come out relatively well. Note that the region composed of Shanghai and Jiangsu falls into the highest color category on this map. But the position of China on the two HDI maps also reveals a problem with the underlying data. All regions of China fall into higher color categories on the demic map than China as a whole does on the state-based map. As the two maps apply the same numerical ranges to the same color categories, such results are a statistically impossible.** What this seeming error indicates is that data collected at the state and the sub-state levels are not necessarily comparable.
In global terms, the most important take-home message from the demic HDI map is the low standing of tropical Africa. Whereas virtually all parts of tropical Africa fall into the bottom two color categories, only one region outside of Africa does the same, that composed of Nepal and the Indian state of Bihar. The state-based map also captures the low level of human development in tropical Africa, but not so clearly. In terms of health and education, much of tropical Africa posts figures lower than what might be expected on the basis of per capita GDP. The HIV epidemic plays a significant role here, as does under-investment in education.
Finally, the demic HDI map reveals an unexpected pattern in Latin America. In the GDP maps, the region composed of Colombia, Venezuela, Panama, Costa Rica and Ecuador comes out ahead of its neighbor to the south, the region composed largely of northern Brazil, Peru, Bolivia, and Paraguay. On the HDI map, the positions are reversed. As the state-based map shows, the relatively high position of Peru in human development is a significant factor here. Intriguingly, Peru’s new government is emphasizing human development, arguing that the previous administration put economic growth above broader developmental considerations.
* In 2010, the UN revised its method of calculating HDI, broadening the educational sub-index. The current post uses the earlier for of the index, as new figures for most sub-national entities are not yet available.
** If anything, the average HDI figures for the demic regions of China should be slightly lower than the figure for China as a whole, as the Chinese provincial data used to make the demic map are from 2008 (the most recent we could find), whereas the figure for China as a whole is from 2009. As China posts slight developmental improvement annually, the overall figure for 2009 should be slightly higher than that from 2008.
As mentioned in yesterday’s post, GDP values are increasingly given in terms of purchasing power parity (PPP) rather than in nominal terms based on currency equivalents, as PPP more accurately reflects conditions on the ground. When measured on the basis of purchasing power, GDP figures for poor countries tend to increase, as goods and services are typically inexpensive in such places. By the same token, GDP figures given in PPP for wealthy countries generally decrease relative to their standings in nominal terms. Today’s post will compare depictions of global GDP variation as assessed through both methods, contrasting as well the framing of the data in both state-based and demic maps. Note that the headline map, posted to the left, shows global GDP variation in PPP as arrayed in the demic framework; subsequent maps are all twinned to allow more ready comparison.
The first set of maps contrasts these two competing techniques of GDP evaluation within the standard geopolitical framework. The two individual maps, both of which divide the data by quantiles*, show relatively little variation. As is expected, some developing countries are slotted into higher categories on the PPP map, including China, Iran, and Botswana, whereas several highly developed countries end up in a lower category (France, Japan). More interesting are the states that deviate from this pattern. Burma (Myanmar), Angola, and Iraq, for example, fall into a lower color category on the PPP map. Although their actual PPP figures are still higher than their nominal figures, the discrepancies are less than what would be expected based on their overall economic situations. Such deviations can indicate irregularities in local currency valuation, best exemplified by Burma, where the kyat has long been grotesquely overvalued. Economic distortions resulting from oil-dependence can also result in lower than expected PPP figures. Angola, for example, remains a poor country outside of its narrow oil sector, yet its capital city, Luanda, is rated as the world’s most expensive city for professional-class residents.
The next set of maps depicts global GDP variation within the demic framework, contrasting nominal and PPP evaluations. The differences found between the two maps are minor yet instructive. In purchasing power terms, the petroleum-rich zone in west central Africa slips down one color category, reflecting the unbalanced nature of local oil-based economies. Southwestern Europe (Iberia, southwestern France, and southern Italy) also slips a notch, reflecting the high cost of living that is often associated with the Euro. In contrast, the position of India is elevated on the PPP map, with several of its demic regions moving up one color category.** China also sees several of its demic regions reclassified at a higher level, with both the region encompassing Guangdong and Hong Kong and that composed of Jiangsu and Shanghai bumped up to the second highest color category. In contrast, the mineral-rich Chinese demic region constructed out of Inner Mongolia, Heilongjiang, and Shanxi ends up in a lower category. In all such cases, the PPP map seems to reflect local conditions more accurately than the map of nominal GDP.
The final set of maps contrasts the depiction of global GDP (PPP) disparities in the demic and state-based frameworks. The differences encountered here are similar to those analyzed in the previous post, which examined nominal GDP in the two schemes. As a result, they are not subjected to extended analysis here. But I would again note that the demic map captures broad regional variation relatively well, but misses a number of significant local variations. As a result, it is perhaps most useful to examine the two maps in tandem.
* Quantiles divide the data set at regular intervals to yield subsets of equal size.
** If we had had access to up-to-date data at the district level for India, it is possible that both the region encompassing Gujarat and western Maharashtra and that encompassing eastern Maharashtra and northwestern Andhra Pradesh would have had more elevated positions on this map.
The Demic Atlas provides an alternative to standard state-based maps of global development, designed for broad comparative purposes. As several GeoCurrents commentators have noted, the demic framework is not suitable for all forms of comparison. Studies aimed at determining the relationship between governmental action and economic development, for example, must rely on the state-based framework, as it is sovereign states and their subdivisions that generate and implement official policies. The current project aims instead for a generalized overview that can reveal expansive global patterns obscured in standard maps. Based on the principle of demographic egalitarianism, the demic maps portray heavily populated countries at a much higher level of resolution than do standard depictions, while representing lightly populated countries at a lower level of resolution. The goal is to render the world as a whole with broad but relatively even brush strokes. To see whether the demic framework successfully serves such a purpose, the current post contrasts a world map of per capita Gross Domestic Product (GDP) constructed on its terms with a conventional, country-based map of the same indicator.
Before comparing the two maps, it is important to note the limitations encountered when assessing economic development through GDP. GDP measurements, which ostensibly tally the total value of goods and services produced in a given year within a particular territory, are not as comprehensive as they might seem. Nominal GDP, calculated on the basis of official exchange rates, fails to take into account the different quantities of goods and services that can be purchased with the same amount of money in different parts of the world. As a result, GDP is increasingly measured in purchasing power parity (PPP) terms. Consequently, a separate GeoCurrents post will compare GDP as calculated in PPP. More troublesome is the fact that GDP figures tell us nothing about the distribution of wealth, and hence cannot always indicate whether a given region is, in general terms, wealthy or impoverished. Equatorial Guinea, for example, appears to be a fairly rich little country, with a nominal per capita GDP assessed at US $24,400 by the CIA in 2010 (although the World Bank comes up a figure of only 11,033 for the same year, a sure sign of data irregularities). Yet Equatorial Guinea remains a poor and underdeveloped country, as the vast bulk of its wealth flows into very few hands. Finally, GDP measurements ignore goods and services produced on a subsistence basis, or that are exchanged through barter outside of the formal economy. As a result, an utterly impoverished, desperately malnourished area dominated by market exchange might appear more prosperous than a subsistence-oriented area in which most people are relatively well fed and well housed.
Despite such limitations, per capita GDP remains the most common metric of economic development, and it is therefore employed on our first set of maps. For purposes of immediate comparison, the two maps use the same color scheme and divide the data into the same number of categories, based in both cases on quantiles (dividing the data set at regular intervals to yield subsets of equal size). Note that the numbers associated with the color categories on the two maps do not match. This is partly a matter of the greater level of aggregation found in the demic map, which employs fewer spatial units and therefore reduces the level of economic differentiation across the world. The demic map also skews toward lower numerical values for the same color categories. Note how the second highest category in the state-based maps extends down to $14,239 and the third down to $6,636, whereas on the demic map the comparable figures are $8,395 and $4,466. This disparity reflects the fact that many wealthy countries, especially those of Europe, have small populations, inflating the number of units in the high-end group. Another consequence is to push large areas of the world into higher color categories on the demic map; compare, for example, the coloration of southwestern Africa on the two maps. (Both data sets can yield strikingly different maps depending on how many categories are used and where the breaks between them are placed; such issues will be explored in subsequent posts.)
The most conspicuous differences between the two maps are found in their depictions of South and East Asia. In the conventional state-based map, China is uniformly shown as a moderately poor country; in the demic map, China as a whole appears much more economically productive, while the substantial gaps between its prosperous coastal zone and its lagging periphery are revealed. Such changes, we believe, more accurately reflect actual conditions in the region. South Asia is also much more finely differentiated on the demic map. The standard view shows India and Pakistan as evenly falling into the second lowest economic category, while placing the smaller countries of the region in either the lowest slot (Nepal, Bangladesh) or the next highest category (Sri Lanka, Bhutan). In the demic map, a wide swath of north-central India falls in the bottommost grouping, whereas much of western India is placed significantly higher. Put differently, the standard map locates the poorest parts of Asia outside of India, whereas the demic map largely places them within that country. The actual differentials found within India, moreover, are greater than even the demic map indicates. The yellowish area of elevated economic standing in the western part of the country is composed of rapidly growing Gujarat and western Maharashtra, the latter area encompassing Mumbai, India’s economic core. Although western Maharashtra has a substantially higher per capita GDP than the eastern part of the state, such differences are not reflected in the data, as we were not able to locate up-to-date information at the district level for India; as a result, we had to treat each India state as a uniform entity. (Such expedients, we hope, can be avoided in later iterations of the atlas.)
Latin America is also mapped quite differently in the two schemes. As the region as a whole is relatively lightly populated, the conventional map depicts most of it with a higher degree of resolution than does the demic map. But Brazil and Mexico, Latin America’s two most populous countries, are exceptions, and as such are portrayed more precisely on the demic map. As that map indicates, southeastern Brazil is much more economically productive than the north, whereas southern Mexico is much less productive than the country’s northern and central areas. As a result, Latin America takes on a zonal aspect in the demic map, characterized by distinct latitudinal belts. Here one sees relatively high levels of economic development in the far north and south, with moderately low levels in the middle belt interrupted by a mid-income grouping in northern South America and southern Central America. Which of the two maps more accurately depicts the region is an open question. The demic map, in our opinion, best captures broad differences, whereas the standard map better illustrates a number of important local distinctions (the particularly low rankings of Haiti and Nicaragua, for example). Yet the standard map can also be misleading one this score; not how it exaggerates the economic standing of French Guiana, which, as an integral part of the country, is mapped at the same level as metropolitan France.
Africa appears in similar form in both maps. To be sure, the demic version reduces the standings of wealthy but lightly populated Gabon, Libya, and especially Equatorial Guinea, while elevating the position of the larger oil-rich region around the Bight of Benin and the Bight of Bonny in west-central Africa. As was the case in India, the economic differential in this area would have been accentuated if we had had access to comparable economic data for all the states of Nigeria. Lacking such information, we treated all Nigerian states the same, even though the southern part of the country, owing largely to its oil resources, is vastly more economically productive than the north. Elsewhere in Africa, the lower level of resolution found in the demic map causes it to miss some significant distinctions, such as the elevated standings of Kenya and Zambia vis-à-vis their neighbors.
Owing to the small size of most of its constituent units, Europe is depicted much more precisely in the conventional map. Note how the demic depiction slots all of Western Europe into the highest GDP category, whereas in the state-based map its southern reaches (Spain, Portugal, and Italy) are ranked slightly lower, reflecting that map’s finer level of differentiation at the upper end of the scale. In southeastern Europe, the state-based map again better captures local differences, such as the low standing of Moldova. Yet in the broadest comparative terms, one could argue that the small population of Moldova (3.5 million) renders it undeserving of such focused attention.
The demic framework functions most poorly in the Middle East (Southwest Asia), a region characterized by profound economic diversity. Here one finds both the world’s richest country according to most measurements (Qatar), as well as one of the poorest states outside sub-Saharan Africa (Yemen). As a result, some countries in the region are unduly elevated on the demic map. This flaw is especially notable in regard to Iraq, which is classified with Saudi Arabia and the Gulf states, and Syria, which is placed in the same region as much wealthier Turkey, Cyprus, and Greece. The position of Yemen, on the other hand, is unduly demoted on the demic map, due to its classification with Ethiopia, Somalia, and Eritrea.
The final map shows the degree of difference found between the two maps, as reflected in all of the constituent units of the demic framework. As is immediately apparent, some of the greatest changes are found in the poorer parts of large countries that are abstracted from their usual national positions (north-central India, interior China, northern Brazil). Perhaps most interesting is this map’s depiction of North America. Whereas Canada and the United States appear in identical form in the first two maps, this map shows that the actual numbers associated with different parts of both countries have changed considerably. Canada comes out with a higher figure on the demic map, due its classifications with western and northeastern United States. Southeastern US, on the other hand, ends up with a significantly lower figure, due to its separation from the more economically productive parts of the country.
The Demic Atlas rests on the proposition that socio-economic comparisons work best when based on comparable units, framed at approximately the same scale of analysis. The obscure term demic—“pertaining to populations of people”—highlights the demographic egalitarianism central to the project. Ideally, regions of equal population should be compared against each other; otherwise, the individual inhabitants of some parts of the world are weighed more heavily than those of other areas. Conventional comparisons based on sovereign states necessarily violate this principle, effectively giving the residents of small countries far more attention than their counterparts in big, densely populated states. The premise of the Demic Atlas is that deploying roughly comparable categories will yield a more illuminating picture of global development.
The first step in this project has been to create an alternative base-map: one in which all units have similar numbers of inhabitants. After much experimentation, we have settled on 67 regions of roughly 100 million persons each. This is admittedly a rough grid; only eleven sovereign states have more than 100 million inhabitants. A smaller target figure of fifty or even twenty-five million might have been desirable, but such an option was precluded by data limitations (as explored below) as well as design difficulties. (Across East and South Asia, doubling the number of units would have cluttered the map and made it difficult to read without magnification). As it stands, we are persuaded that the 100-million norm, crude though it may be, is an improvement over customary global maps. A signal advantage is the ability to highlight internal diversity within the world’s demographic giants (India and China), and contrast these with zonal patterns in the Americas or Africa in a single global snapshot.
The data difficulties that stand in the way of creating smaller demic regions stem from the need to rely on conventional categories even while trying to transcend them. Literally as well as figuratively, sovereign states are the units that count; these are the bodies that conduct censuses and gather most data. It is no coincidence that the term “statistics” derives from the Latin for “of the state.” When international agencies such as the World Bank and the International Monetary Fund (IMF) tabulate country data, they steadfastly ignore sub-national divisions, no matter how large or important internal regions may be. Nor is this always a bad strategy. For while most countries collect information on their own subdivisions, they do so in diverse ways. For instance, GDP figures are available for the states of India, the provinces of China, the prefectures of Japan, and so on, but such information is usually gathered in different years by different countries, and is seldom fully comparable. For a number of the poorest countries, usable socio-economic information at the provincial level is simply unavailable.
A non-state-based appraisal of global socio-economic development must therefore use states and their major subdivisions as the building blocks of an alternative scheme. Middling, small, and tiny countries have to be grouped together to form units of a more appropriate size. By the same token, large countries need to be broken down into their provinces or prefectures, which can then be selectively re-aggregated to form units approaching the target population. These two methods alone, however, do not always yield regions of approximately 100 million inhabitants. Consider the situation in North America. The United States, with a little more than 300 million people, could be easily split into three demic regions in the target range. But Canada, with 34 million inhabitants, is far too small to constitute such a region on its own, yet it has no neighbors with which it can be joined other than the United States. Unless one were to create an ocean-spanning region linking Canada to northwestern Europe, Canada has to be combined—whether as a whole or in parts—with some cluster of U.S. states. Similar challenges arise in other parts of the world as well, where the shape and distribution of landmasses and archipelagos is such that the only way to create units in the target range is by splitting and merging countries in highly unorthodox ways. Such an exercise demands tedious data manipulations, but we are convinced that it proves useful for depicting places in which developmental gradients are deeply out of sync with the geopolitical framework. It also helps to unsettle the notion that countries form natural units of observation, one of the overriding goals of the larger project.
Such maneuvers, however, still prove inadequate to the task of generating units of an appropriate scale across the world. On the one hand, several Indian states and one Chinese province in themselves exceed the 100-million guideline. Most are close enough to the target number that they could be mapped as demic regions in their own right. But Uttar Pradesh—the world’s largest “statoid” (as first-order subdivisions of sovereign states are sometimes called)—has nearly 200 million inhabitants. By the logic of our project, a unit of this size needs to be divided. Likewise, to create a grid of geographically contiguous blocks of roughly 100 million inhabitants across India, two other Indian states were split and re-aggregated at the district level. (In our model, Western Maharashtra has been paired with Gujarat, eastern Maharashtra joined with northwestern Andhra Pradesh [Telangana], and the rest of Andhra Pradesh connected with Karnataka.) This time-consuming procedure, however, proved in the end to be of marginal statistical utility, as comparable socio-economic data for Indian districts was not obtained.
Other issues, too, complicated the drive to delineate areas of 100 million inhabitants. One was the design desideratum for our regions to be spatially compact. Although it would have been easier in some areas of the world to reach the target population by devising irregularly shaped regions, such a procedure would have resulted in a fair amount of gerrymandering. Even in the best of circumstances, the underlying geopolitical substrate frustrates the attempt to craft truly compact regions. Many countries have aberrant shapes; the odd outline of Cameroon, for example, contributes to an oddly shaped Region 14 in the demic base-map. Exclaves can be even more problematic, since outliers that fragment the territorial cohesion of individual countries can do the same for the regions to which those countries are assigned. Ideally, exclaves are placed within the spatially appropriate demic regions; Russia’s Baltic exclave of Kaliningrad, for example, is classified in Region 61, rather than in western-Russia-focused Region 59. By the same token, Angola’s exclave of Cabinda should have been placed in Region 14, rather than with the rest of Angola in Region 10. Doing so, however, would have required breaking Angola down into its constituent provinces, a procedure too time-consuming for the current iteration of the Demic Atlas.
A third divisional principle was that, in addition to being spatially compact, demic regions should be characterized by roughly similar levels of socio-economic development. Average figures for a region split into between a wealthy, highly educated area and an impoverished, poorly educated area would tell us little about the region as a whole. Clumping countries and their subdivisions into reasonably coherent developmental regions is possible, as levels of socio-economic development across the world tend to be highly geographically structured. But perfect aggregation of this sort is again impossible. In some parts of the world, areas of extremely high and extremely low developmental standing are spatially interspersed. The Caribbean is particularly diverse on this score, containing both very wealthy areas (Cayman Islands) and very poor ones (Haiti). Since the prosperous parts of the Caribbean are demographically overshadowed by the region’s poorer zones, the region as a whole shows relatively low levels of development.
Archipelagic environments like the Caribbean pose yet another challenge to the regionalization scheme. The guideline of spatially compactness would seemingly rule out maritime-centered regions linking the opposing shores of intervening water-bodies. But the world’s only islands populous enough to stand on their own are Indonesia’s Java and Japan’s Honshu; all others must be grouped with other islands or, more often, with nearby peninsulas. As a result, several sea-focused regions do appear on the map, such as Region 65. The criterion of socio-economic similarity generates further compromises along these lines, as certain islands are in developmental terms best grouped not with their closest mainland neighbors but rather with more distant islands and shores. Region 43, composed of Taiwan, South Korea, and Japan’s Kyushu, Shikoku, and Ryukyu Archipelago, is particularly problematic in this manner. By the principle of spatial compactness, South Korea would have been much better grouped with North Korea and a segment of northeastern China, while Taiwan would have fit better with Fujian in mainland China. Such a maneuver, hoverer, was rejected, as it would have required uniting highly divergent economies. Hong Kong and Macao, however, were grouped with the Chinese province of Guangdong, even though socio-economic considerations would have called for them to be put in the same category as Taiwan and South Korea. In this case, the spatial irregularity that would have resulted was deemed excessive.
One part of the world that stubbornly resisted our regionalization guidelines to the last was Australia and environs. By the principle of compactness, Australia can only be joined to eastern Indonesia; any other scheme would require spanning vast stretches of sea-space. But the Austral lands resist regionalization with Java and the Lesser Sunda Islands, as the developmental gap between them is too large. In the end, since Australia and New Zealand lack sizable neighbors with similar socio-economic conditions, they have been granted the status of a region in their own right, along with most of the rest of Oceania. But considering its meager population, Region 66 is best considered a quarter-region. As is often the case, Australia is revealed to be a most distinctive land.
The demic base-map is thus a product of many agonizing trade-offs, in which the criteria of population, shape, and socio-economic standing had to be constantly weighed against each other. The map consequently went through a number of changes over the course of its construction. Region 10, for example, was originally much larger, including Zimbabwe and Mozambique, as the population of the six countries currently constituting the region was judged inadequate. But linking Zimbabwe and Mozambique, two of the world’s least developed countries, with sub-Saharan Africa’s otherwise most highly developed region seemed unfair. In the end, an additional region was carved out of eastern Africa, resulting in the out-of-order numbering scheme currently found on the map. In Brazil, the state of Mato Grosso was originally slotted on socio-economic grounds with Region 2, while Bahia was placed in Region 3 on the same grounds; the resulting Region 3, however, was deemed too irregular, while the population of Region 2 was considered too small.
The resulting division of the world is thus not merely idiosyncratic, but is replete with vexing compromises. Criticisms and suggestions are welcome; the map remains open to change. The GIS files by which it was constructed will eventually be posted online, allowing others to build their own alternatives to the state-based global framework. If our future plans come to fruition, other sizable countries will also be broken down into their first-order subdivisions, which would allow more complex regionalization schemes.
Finally, let us stress that the demic regions outlined here are strictly intended as a framework for socio-economic comparison. Having no cultural, political, or historical significance, they are completely useless for (and could cause grave mischief in) many geographical questions. It is our hope that the construction of culturally and historically based alternatives to the standard geopolitical framework will also someday be advanced, but that is not the goal of the Demic Atlas.
The next few posts will consider the sixty-seven (or sixty-six and a quarter) demic regions in more detail. Next week, socio-economic maps using the scheme will begin to appear on GeoCurrents.
As the past several GeoCurrents posts have explained, sovereign states make poor units of socio-economic comparison due to their vast size disparities. But issues of scale are not the only reasons for considering an alternative scheme of division. In the standard model of global affairs, countries are the all-purpose and essential units of human organization. According to this view, independent states are neatly demarcated geo-bodies administered over their entire expanses by globally recognized, fully autonomous governments, their inhabitants bound together by common sentiments of national solidarity. Yet the nation-state ideal is seldom fully realized. The world is replete with nationless states, stateless nations, contested nationalities, vacuums of sovereignty, and so on. As Jim Wilson perceptively pointed out in a GeoCurrents comment, such geopolitical “anomalies” are too common to be considered anomalous. The political structure of the world, in short, is far too complex for the reigning model. As a result, the global representation found on country-based political maps is simplistic at best and misleading at worst. On a world map, Somalia has the full appearance of a sovereign state; in actuality it has virtually none of the substance.
Over-reliance on a flawed world model results in more than intellectual mischief. In the early years of this century, U.S. military and political planners had little doubt that Afghanistan and Iraq could be quickly and cheaply stabilized and democratized, as it was assumed that they were coherent nation-states, their people tied together by bonds of common affiliation, and hence willing to work together to achieve national aspirations. A decade on, trillions of dollars and many thousands of lives have not proven adequate in either case. As the United States lurches through its second economic crisis in three years, its financial resources stretched near the breaking point, those billions upon billions of dollars that have flowed into nation-building endeavors in Iraq and especially Afghanistan increasingly seem like poor investments. Had we been less beholden to a normative geopolitical model, perhaps such inordinately optimistic policies would not have been pursued.
The goal of the Demic Atlas is to denaturalize the state-based picture by viewing the world through an alternative lens. It is not to argue that states are passé or unimportant in any way, much less that the standard political map should be abandoned. The contention is rather that countries should not be the only units used for depicting the socio-economic differentiation of the human community. Different modes of division can show different patterns and, we believe, yield new insights.
The classification scheme employed in the Demic Atlas will seem odd to most viewers, and shocking to some. The standard world map and model are so ubiquitous, so taken-for-granted, that any alternative is bound to appear perverse. Many if not most educated people have also been so schooled in nationalism that their own countries at least seem inviolable, forming natural units of not just political power but also of social and economic organization. Quick previews of the demic world map—which will be posted here next Tuesday or Wednesday—provoked quick objections, as viewers found it wrong to see their own countries sundered and then re-aggregated with pieces of other states. But this is precisely the purpose of the exercise: to unsettle conventional notions of global geographical organization by challenging the essentialism of the sovereign state.
As instinctive as it has become for us to divide the world along geopolitical lines, it is not a particularly long-standing maneuver. Old maps reveal that before the 1800s, European cartographers typically deployed a hybrid system of terrestrial division. In the early modern period (1500-1800), they typically began by splitting the world into continents, and then carving each of the resulting landmasses into a handful of major divisions. Some of these sub-continental entities were geopolitically delimited kingdoms, empires and the like, such as France, Russia, Persia, and China. Others were former sovereign powers that had long lost that standing, such as Hungary and the German (or Holy Roman) Empire, while others were (at the time) mere regions with no political coherence (Italy, Arabia, and India, for example).* Such units were by no means of equal size, as those of Asia dwarfed those of Europe, reflecting the Eurocentrism of European cartographers. But within each continental frame, all the constituent “countries” were roughly comparable. It would have struck an 18th century cartographer as absurd to elevate the pocket states of northern Italy, let alone those of western Germany, to the same level as France or Spain on their basic maps. Early modern cartographers did occasionally depict the small polities of Italy and the Holy Roman Empire, but only when they were explicitly illustrating the geopolitical order. Maps devised for general purposes relied instead on a hybrid divisional scheme.
A somewhat similar situation obtained in early modern Japan. Before the Meiji Restoration of 1868, Japan was a semi-unified state; the Tokugawa Shogunate exercised hegemony over the main islands, but over 200 feudal lords maintained autonomy within their own domains, many of which were spatially dispersed. Tokugawa cartographers by and large ignored the complexly fractured political order that resulted, and instead mapped the archipelago in accordance with the provinces of classical Japan—units that had no political significance at the time. The continued use of a system of defunct subdivisions in basic maps has struck many observers as a quirky anachronism, but as Kären Wigen has shown in her recent book, A Malleable Map, the strategy had its own compelling logic. The provinces of Tokugawa Japan were observational rather than administrative units, providing spatial containers for place-specific information. Provinces served this purpose well because they were deeply rooted in historical memory, were no longer politically charged, and were spatially stable. Also, unlike the domainal territories of Tokugawa Japan, they were relatively compact and of roughly similar size.
Like the provinces of Tokugawa Japan, the spatial divisions in the Demic Atlas are designed for observational purposes: to serve as politically neutral containers for marshalling socio-economic data. Unlike the old Japanese provinces, of course, they are nakedly artificial constructs without historical precedent. Whether or not such manufactured units prove useful is for readers to decide.
*Intriguingly, mapmakers of the time sometimes claimed to base their divisions on geopolitical criteria, yet in practice they did not exactly do so. Consider Thomas Kitchin’s 1787 map, “Europe Divided into its Empires, Kingdoms, States, Republics, Etc.,” published in London by Robert Sayer. As actually mapped, the “principal parts” of Europe included such non-sovereign entities (at the time) as Italy, Germany, Ireland, and Hungary. The accompanying text notes that “Germany is full of sovereign princes and counts …, every one of which is more free and absolute than several crowned heads.” A 1742 version of Guillaume Delisle’s Atlas Nouveau (published in Amsterdam) likewise contains a map purporting to show Asia “accurately divided into Empires, Kingdoms, States, and Peoples,” yet it depicts all of mainland Southeast Asia as one entity, and all of central and northern Asia as another (“Tartary”). These kinds of maneuvers were typical of the time.
Enlightenment-era views on the geopolitical division of space are perhaps best represented in the Encyclopédie Méthodique par Ordre des Matières (“Methodical Encyclopedia by Order of Subject Matter”), a 200-plus-volume reference work published by Charles-Joseph Panckoucke, designed to follow the more famous encyclopedia of Denis Diderot. Here the Europe entry (vol. 2, page 574) details the division of the landmass into three empires (Russia, Germany, and Turkey-in-Europe), twelve kingdoms, one great ecclesiastical realm (the papal state), one archduchy, one grand duchy, four great republics, and four less powerful republics. Not all of Europe, however, was classifiable in this scheme, with northern Italy presenting particular challenges. In another entry, the encyclopedia describes Italy as “a great country of Europe,” noting that it has too many political divisions to report (vol. 1, page 94). The work also notes that the kingdoms and duchies of Europe are not all ruled by their own sovereigns. Although Hungary is listed as a kingdom (joined with that of Bohemia), the author allows that it is currently “under the House of Austria” (vol. 94, page 2).
Maps and text from the forthcoming non-state-based (or “demic”) atlas will begin appearing in GeoCurrents next week. This week, the blog is presenting the work’s preface.
As noted in the previous post, countries are incomparable units, due to their vast variation in scale. Yet in tables and charts, Nauru, with ten thousand inhabitants living on eight square miles (twenty-one square kilometers), counts the same as China, with 1.3 billion inhabitants living on 3.7 million square miles (9.6 million square kilometers). Cartographic depictions are inherently less distorting, as they are spatially scaled. On most maps, small countries are appropriately small, although only those using equal-area projections maintain strict size proportionality. Miniscule states such as Nauru and Monaco thus tend to vanish from view. Unfortunately, mapmakers seem to be increasingly depicting micro-states with large circles, again privileging diplomatic pretense over geographical reality. Curiously, the Wikipedia GDP map posted here balloons Europe’s feudal remnants by several orders of magnitude, yet does not provide any information about them.
Although spatial imbalances are minimized on most maps, demographic disparities remain concealed. True, tiny countries usually have tiny populations, just as large ones tend to have many inhabitants, but the correlation is not strong. Confined to 268 square miles (694 square kilometers), Singapore’s five million inhabitants have little presence on standard maps, whereas Mongolia’s 2.8 million residents figure large in their 603,909 square-mile homeland (1,564,115 square kilometers). Unless one has a good sense of the distribution of human settlement across the world, maps of socio-economic development tend to mislead. On a world map of per capita GDP, wealthy Australia and Canada far overshadow India, even though India’s population is twenty-five times greater than that of the other two countries combined. In Africa, areas of relatively high per capita GDP are not as significant as they appear. Botswana, Namibia, and Gabon are sizable countries that boast elevated economic figures, but together they contain five and a half million people, roughly half the population of the destitute metropolis of Kinshasa in the Democratic Republic of Congo.
Mapmakers have devised a number of innovative techniques for depicting data in a more proportional manner. In demographic cartograms, the areas of geographically defined entities are expanded or reduced according to their populations. GDP figures are also well suited to cartogramic treatment, as both total economic output and output per capita can be depicted by a combination of size and color. The resulting image posted below nicely illustrates the concentration of economic power in a few parts of the world, as well as its near absence over a large swath of central Africa.
But for all of their powers of presentation, global cartograms conceal as much as they reveal. Areas with low numerical values in regard to whatever feature is being mapped tend to shrink into invisibility. On the example posted here, the Democratic Republic of Congo, a massive country of more than seventy million, can hardly be discerned. More to the point, cartograms of global development are almost always structured around sovereign states, and thus treat massive and highly differentiated countries as uniform entities. Note that this cartogram portrays Taiwan as if it really were part of China, rather than as the de facto sovereign state that it is; as a result, Taiwan appears smaller than Sri Lanka, even though its economy is almost an order of magnitude larger. (Why the cartographer depicts uninhabited Antarctica as having an economy roughly the size of Canada’s at a per capita level similar to that of India is anyone’s guess.)
Another method of addressing disparities of scale when mapping economic production is encountered in the GDP density map, analyzed earlier in this blog by Andrew Lindford. Portraying “GDP per square kilometer” is an intriguing idea, but the effort fails, as the cartographer treats countries as undifferentiated wholes in regard to economic but not demographic data. The result is merely a map of settlement density in which the populations of wealthy countries are weighed more heavily than those of poor countries. Treating countries as economic uniformities again results in nonsense. Note how China’s Sichuan basin is shown as more economically productive per unit of area than its lower Yangtze region; observe how India’s impoverished lower Ganges Valley is depicted as more “economically dense” than its bustling greater Mumbai area.
The root problem is clear: basic patterns of social and economic development do not necessarily track the contours of political geography. To be sure, the immediate gap across national boundaries can be profound. When one moves from the U.S. state of New Mexico to the Mexican state of Chihuahua, per capita GDP drops three-fold, from $36,000 to $12,300. Yet the economic gaps within Mexico are more substantial than the chasm along its northern border. The southern Mexican state of Chiapas posts a per capita GDP figure (in PPP) of only $3,700, whereas Nuevo León in the north comes in at $16,300 and the central Federal District reaches $23,000, the latter figure not out of line with Mississippi’s $32,000. Similarly discrepant patterns are apparent across much the world. In socio-economic terms, southern Brazil is much more similar to Argentina than it is to northeastern Brazil, just as northern Italy is more akin to Switzerland than to southern Italy. A state-based system of comparison obscures all such internal differentiation.
If we are to devise more appropriate methods of portraying global social and economic disparities, we must move beyond the default framework of sovereign states and their dependent territories. This is not to claim that the governments and policies of independent countries are insignificant in determining which areas of the world are more prosperous or healthier than others. Nor is it to argue that conventional country-based maps of socio-economic development should be jettisoned, as they do serve a significant purpose. It is rather merely to insist that sovereign states are not the all-important, all-purpose units of global geography that they ubiquitously taken to be. As a result, the standard state-based map should be complemented by other modes of presentation. The current project is a first step in that direction.
GeoCurrents has taken a summer hiatus to create a new cartographic framework for analyzing socio-economic development. This project is a collaborative effort involving three team-members: Jake Coolidge, a geospatial historian at Stanford University’s Spatial History Lab; Anne Fredell, a Stanford University undergraduate; and myself. The Spatial History Lab at Stanford, which has provided extensive technical assistance, will eventually publish the maps as an on-line document. GeoCurrents will also post maps from the project, as well as commentary on the process. Beginning today, I will discuss both the intellectual rationale for such an atlas and the problems that we have encountered in creating it.
The Non-Comparability of Sovereign States
Global economic and social comparisons are almost always made within the framework of sovereign states. Countries are numerically ranked against each other on such measurements as per capita GDP, literacy, and longevity, much as students are tallied together on a class grade sheet. If one wants to know what part of the world is the richest, healthiest, or best educated—or the opposite—the answer will generally come in the form of a national name. Whether on maps, tables, or charts, the country is the category that counts.
Our atlas starts from the premise that, while sovereign states are certainly the essential units of the geopolitical order, they are not necessarily appropriate units of socio-economic comparison. In actuality, countries are ill suited for such purposes. For starters, they are simply not comparable entities, varying enormously in both area and population. We know this, but we rarely let it truly sink in. Consider the discrepancy between China, with 1.3 billion inhabitants, and Tuvalu, with ten thousand. Comparing these two independent states is like weighing a single person against a city of 130,000. To appreciate the absurdity of such an exercise, consider what it would mean to compare either with a hypothetical entity equally far removed in the opposite direction. A country as small relative to Tuvalu as Tuvalu is to China would be inhabited by one twelfth of a person, while a country as large relative to China as China is to Tuvalu would be a galactic polity of 160 trillion inhabitants. No serious study would ever make such a comparison, spanning more than five orders of magnitude. Yet when it comes to assessing the economic and social conditions of the world, making such gargantuan leaps in scale is the price we pay for using country-based data.
Relying on an inappropriate geopolitical framework for social and economic analysis can quickly leads one astray. Consider the CIA World Factbook’s list of countries by average longevity (a list that is replicated in Wikipedia). Surprisingly, one country stands well above all others: Monaco. Whereas twenty-four entries are crowded in the eighty- to eighty-four year life-expectancy range, miniscule Monaco reaches almost ninety (89.7). Intriguingly, the third and fourth places are also occupied by European microstates: San Marino and Andorra. As it turns out, most of the top positions on the CIA list are taken by small, tiny, and smaller-than-tiny polities located in Europe, eastern Asia, and the Caribbean. As a result, some of the seemingly healthiest and wealthiest major countries do not rank particularly high on the longevity index. Germany comes in 32nd out of 223, the United Kingdom is 36th, and the United States trails well back at 50th. A quick glance at the table might make it seem as if the U.S. were bested in life expectancy by almost a quarter of the world. In actuality, the total population of the forty-nine top entries is less than ten per cent of the global sum. That is not exactly a stellar showing for the U.S., especially considering the fact that it is bested by several much poorer countries, including Jordan and Bosnia. Still, the fiftieth-place position indicated by the Factbook is misleadingly low.
The preponderance of microstates in the upper reaches of the longevity list could easily lead to erroneous deductions about country size and public health. The correlation, after all, is striking: sixteen of the top fifty entries on the list have fewer than 100,000 people, while none of the bottom fifty do. One might reasonably conclude that small polities are somehow better able to meet the health needs of their citizens than their more populous neighbors. Could political devolution enhance longevity?
Any such conclusion would be nonsensical. The people of Andorra, a feudal remnant in the Pyrenees sandwiched between France and Spain, may live longer than the average residents of neighboring countries, but they do not out-live the inhabitants of adjacent French and Spanish districts. Put differently, if all Europe were divided into states the size of Monaco (population 36,000), Monaco’s sizable advantage would instantly vanish, as other tiny, wealthy enclaves located in salubrious environments would boast similar longevity figures.
In the end, the CIA rankings are compromised by comparing incommensurable entities. But it is not just the World Factbook that is at fault here. Virtually all numerical assessments of global development shoehorn socio-economic data into the same geopolitical categories, where size means nothing. In the world of international statecraft, to be sure, all sovereign countries are treated as theoretically equivalent individuals, regardless of their population or power. Such pretense may be necessary in the halls of diplomacy, but it does not help anyone grasp the complex patterns of social and economic disparity found across the surface of the earth.
While most global comparisons are made strictly within the framework of sovereign states, which number slightly fewer than 200, the CIA World Factbook employs an expanded list, noting 223 “countries” in its longevity chart. The additional entries are actually dependent territories, most of which boast impressive life-expectancy figures (Cayman Islands, Bermuda, Gibraltar, the Isle of Man, etc.). Such an inclusive approach is beginning to be followed by other major data sources as well, no doubt from a desire to be fair and comprehensive. Just because Greenland and Guernsey lack full independence is no reason to consign them to statistical oblivion. In the process, however, the problem of incomparability is compounded. While all of the world’s independent countries (barring the anomalous Vatican City) have at least 10,000 inhabitants, many dependencies are much smaller. Wikipedia’s inclusive “list of countries by population” bottoms out with 224th-place Pitcairn, which boasts all of fifty residents at last count. Although Pitcairn does not make the CIA’s longevity table, a number of other miniscule dependencies do. Adding these micro-units clutters the list while providing little information of value.
The biggest distortion that results from using states or quasi-states as all-encompassing spatial containers for socio-economic comparison is that lightly populated areas might receive precise scrutiny, while some of the world’s most populous places are subjected to extraordinarily crude aggregation. As a consequence, the residents of small countries literally count for more than do the residents of large ones. An equal appraisal of individual polities, in other words, results in an intrinsically unfair weighting of the individual persons within those polities. In the World Factbook’s tabulation, the average inhabitant of the British dependency of Saint Helena, Ascension and Tristan da Cunha (population 5,660) is inadvertently deemed twenty-six million times more attention-worthy than the average resident of China.
China and India, the world’s demographic giants, are particularly ill-served by being treated as singularities. Not only do these two countries have huge populations—more than a third of the global total between them—but both are characterized by vast regional disparities. As a result, numbers given for China and India as a whole are almost worthless. When overall per capita GDP is calculated in terms of purchasing power parity, China’s $7,500 figure ranks well below the global average of $11,100. But the commercial core areas of eastern China, increasingly vital drivers of the world economy, evince per capita GDP figures well above the world average, reaching $13,000 in Jiangsu, $18,500 in Shanghai, and $46,000 in Hong Kong. In contrast, Guizhou in China’s south-central interior produced only $3,400 worth of goods and services per person in 2010, a figure comparable to that of war-ravaged Iraq. In global comparative terms, China spans the gap between the rich and poor worlds. Grasping such regional differences is essential for understanding the economy of China, and hence that of the world. Yet in the standard method of tabulating and portraying global economic data, such disparities remain invisible.*
The depiction of the world as divided into supposedly comparable individual geopolitical entities reaches its extreme form in a number of almanacs and children’s atlases in which each country is accorded its own map and page or two of text. In such cases, China typically receives a bit more attention than Tuvalu—but not much. The genre is nicely parodied in Our Dumb World: The Onion’s Atlas of the Planet Earth. Its mocking caption for San Marino, whose 32,000 people inhabit twenty-four square miles, reads, “These A**holes Don’t Belong In An Atlas,” while the text focuses on the absurdity of elevating such an insignificant piece of territory to the same level as that of major countries. A sidebar, entitled “A Marino You Should Care About,” claims that “Miami Dolphins quarterback Dan Marino achieved more during his 17-year Hall of Fame career than the ‘nation’ of San Marino has managed to accomplish since A.D. 301.” In actuality, the history of the little state is rather more illustrious than that; in early modern Europe, San Marino was often highlighted by geographers because of the fact that it was a rare republic (officially, “the most serene republic”) during a period of monarchical dominance. But the humorists at The Onion have a point; putting San Marino at the same level as Italy, let alone India, is an exercise in absurdity.
How might such absurdity be avoided? This is a complex issue that will occupy the pages of GeoCurrents over the next several weeks.
* Hong Kong, a Special Administrative Region of China, with its own laws and currency, is usually tabulated separately from the rest of the country