Andrew Linford

posts by Andrew Linford

Last Insights into Global Economic Inequality

Calculations of economic development are usually separated from considerations of population and physical geography. The map above, which introduces the concept of GDP Density. This approach shows how much economic value is generated per unit of land. The map clearly displays not only which areas are the most economically productive, but it also shows that areas of extremely low population density produce relatively little, even if they are wealthy in per capita terms. Northern Canada and Alaska as well as the Australian Outback are thus mapped as having extremely low levels of GDP density. Although Africa overall is shown as having a low level of GDP density, several of its more densely populated regions are depicted as fairly productive. Other trends also stand out, such as the coastal focus of economic activity in South America. Finally, even though China and India are still relatively poor ”developing countries,” their huge populations help generate high levels of GDP density.

The map does, however, have a number of drawbacks and limitations. The data on which it is based are themselves problematic. GDP is calculated on a country-by-country basis, which leaves out regional inequalities within sovereign states. In India especially, huge economic disparities are ignored, which makes it appear as if Bihar were more economically productive than western Maharashtra just because it is more densely populated. Also, the level of resolution vastly varies across the map. For example, the United States is mapped largely at the county level, which provides a fairly detailed portrayal, whereas Russia is divided into much larger areas, mostly oblasts. As a result, Russia’s Arctic coastline is depicted as having much higher levels of economic activity than is justified. Finally, low-population areas that have highly productive mineral economies are left off the map. The mining zones of northwestern Australia, for example, produce a significant portion of the country’s wealth, yet one would never know this from the map.

This is my last post for Geocurrents, as my independent study on the geography of economic disparities has come to an end. While examining numerically calculated measurements of inequality, I concluded that although indexes can provide broad comparative insight about unequality among countries, it is very challenging to compare inequality between countries of different levels of economic development. In focusing on global inequality trends, I showed how the increasing prosperity of countries such as China is not evenly distributed; as a result, inequality within countries is becoming just as important as inequality between countries. However, as discussed by the Brookings Institution, although many countries are becoming richer and less egalitarian, failed states remain mired in relatively equally distributed poverty. Finally, I took a closer look at intra-nation inequality, specifically in Japan, South Africa, and the United States. All of these countries are characterized by different types of economic disparity, which interact with each other. In particular, I looked at three specific layers: regional disparities, the rural-urban divide, as well as the intricacies of wealth in urban areas. These layers exist everywhere, demonstrating the complexity and depth of economic inequality.

Although most of my Geocurrents entries delved into issues of economic inequality, I also took up two unrelated issues: the uprisings in the Middle East as foreseen by the Economist’s “Shoe-Thrower’s Index,” and national anthems and what they can tell us about their respective countries.

Finally, before signing off, I want to thank Professor Martin Lewis for his time and for being an enormous knowledge resource.

Andrew Linford, 2011

 

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Glimpses of Inequality in the United States

Inequality in the United States is a surprisingly complex issue. Although most Americans are aware at some level that major inequalities exist in their country, a substantial gap separates believed comprehension and the actual facts. This entry will explore inequality in the United States primarily through three lenses: regional differences, the rural-urban divide, and inequality within urban areas. Although these lenses are not all-inclusive, they do provide insight into the complexity of inequality in the United States.

Compared to other countries, the United States has relatively high—and growing—levels of inequality, as measured by the Gini coefficient. Although surpassed by the notoriously unequal developing countries of Brazil and South Africa (indexes of 56 and 65 respectively), America’s Gini index is significantly higher than that of most other countries at similar levels of economic development. For example, the Nordic countries have very low Gini indexes, with Sweden as low as 23, but the European Union as a whole is more reflective of developing countries with a coefficient of 31. Historically, the US has had high but fluctuating Gini measurements. In 1929 it was estimated to be 45, but it had declined to 38.6 in 1968, during the height of the Great Society. Since the 1980s it has again increased, reaching 46.8 in 2009.

On the map of GDP per capita by U.S. state, several overarching regional trends are noticeable. The greater Northeast, extending from New Hampshire to Virginia, makes up the wealthiest part of the country. In contrast, the Southeast tends to be much worse off, especially Arkansas and Mississippi. Most states in the Midwest rank near the middle, while the interior West tends to be slightly poorer. The Pacific coastal region, like the northeast, is economically above average. Such generalizations are, of course, very broad, and thus need to be qualified. A close examination of inequality in the United States reveals that the phenomenon is too complex to be considered merely on a state-by-state basis.

More specific patterns are visible in the map of household income by county, at the top of the page. Here Appalachia stands out clearly. The region from West Virginia through the eastern half of Kentucky into Tennessee is one of the poorest areas in the United States. And as can be clearly differentiated in the map to the right, eastern Kentucky clearly stands out as the poorest section of Appalachia.

The county-based map reveals other patterns hidden by the state-level map. Although the Northeast corridor stands out for its prosperity, not all of its counties share equally in the wealth. Marked differences within states are clearly visible, especially the distinction between eastern and western Pennsylvania and upstate versus downstate New York. Also notable is the fact that many areas in the generally poor South, such as Atlanta, do quite well.

Several additional patterns are evident in the map indicating counties of “persistent poverty” (defined as having over 20% of the population under the poverty line for the last four censuses). In particular, as can be explored with these interactive New York Times maps, all of these regions have clear racial correlations. Eastern Kentucky is noteworthy for being the main zone of persistent poverty with a White majority. More striking is the Southern “Black Belt,” which stretches from the Mississippi River to South Carolina. Not only is it the largest area of entrenched poverty, but it is also the only large area in the United States with an African-American majority. Similarly, Latinos heavily populate the persistent poverty areas of southern Texas. Finally, most of the other counties of persistent poverty contain Native American reservations. The largest of these areas is the Four Corners region, especially northwest New Mexico, but scattered reservations in North and South Dakota as well as Alaska also stand out.

The rural-urban divide is also clearly seen on the county-by-county map. Metropolitan regions such as Chicago, Houston, and Denver are substantially richer than their rural peripheries. In general, cities and their suburbs have many more economic opportunities than rural areas. Although not all cities are equally prosperous, and not all stand out clearly from their environs, the rural-urban divide in the United States is plainly evident.

Even though metropolitan areas tend to be more affluent than surrounding areas, cities themselves contain high levels of inequality. Such complexities can be appreciated by examining more localized household income maps of metropolitan Los Angeles, San Francisco, and Oakland:

In all three cities, significant income differences are found at the neighborhood level. Downtown areas generally have significantly lower income levels than peripheral neighborhoods, which is the general pattern in American cities. These maps also undermine several stereotypes, such as the notion that San Francisco is wealthy and Oakland poor. Although the average San Franciscan is indeed better off than the average resident of Oakland, tremendous spatial variation is found in both cities. San Francisco contains a number of poor areas, including much of the central business district. The so-called Tenderloin in particular is infamous for its poverty and homelessness. In contrast,  the eastern Oakland hills are roughly as affluent as the wealthiest neighborhoods of San Francisco. As all three maps demonstrate, geographic economic disparities are most extreme within cities. The broader and more generalized maps are, the more they tend to oversimplify.

The Theil index calculates how much various counties add to or subtract from the overall level of inequality in the United States. As can be seen on the map, the Theil index shows that urban areas tend to add the most inequality, whereas poor rural areas, especially those discussed previously, lower overall inequality. The general similarities between this map and the one at the top of the post are notable, especially as more prosperous counties tend to contribute much more to higher level of inequality than poorer countries.  Taken together, these maps indicate some of the challenges of measuring inequality.

In general, the three geographic lenses discussed – regional differences, the rural-urban divide, and inequality within urban areas –provide insight into the complexity of inequality in the United States, but they do not give the full picture. For example, issues of immigration and race, only briefly touched upon, also play an important role in determining levels of inequality.

Americans are generally aware of inequality in their country, but they tend to vastly underestimate it, as shown in the chart to the right. As the recent study of attitudes about inequalities demonstrates, most Americans significantly under-estimate the actual level of inequality found in their country.  Moreover, they would prefer to see levels of equality even greater than their generous over-estimate. Indeed, many pundits discuss the effects of an economically uneven society, but with looming budget deficits, there is little serious consideration of reversing the tide of increasing inequality.

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Inequality Trends in South Africa

GDP by province

GDP per capita by province, South Africa

According to the Gini coefficient, as well as other inequality measurements, South Africa ranks as one of the most unequal countries in the world. Of course, measuring inequality is multidimensional, which particularly applies to South Africa. In discussions of South Africa, severe economic disparities are often highlighted. Much of the country’s inequality stems from apartheid’s effect on different races, but other factors also play important roles.

Three basic forms of geographic inequalities outlined in a previous post also exist in South Africa, namely: regional disparities, a rural-urban divide, and an urban underclass. As can be seen from the map at the top, significant differences mark off South Africa’s regions. The poorer provinces tend to be along the southeastern coast and in the north, whereas the Johannesburg region and the southwest tend to be wealthier (this is also reflected in the map below). The most striking regional disparity is that between the relatively prosperous Western Cape province (which includes Cape Town) and the impoverished Eastern Cape province. This contrast encourages substantial internal migration towards the economic opportunity in Cape Town and away from the poverty of the Eastern Cape.

In South Africa and its main cities
Darker green indicates higher levels of poverty

The maps to the left show the “headcount index,” which measures the proportion of the population living below the poverty line; darker shades of green signify higher levels of impoverishment. As can be clearly seen in the maps, urban centers have relatively few people below the welfare line, especially as compared with rural areas. The rural-urban divide is also detectable on the provincial map at the top, as the two most economically important cities, Johannesburg and Cape Town, are in the two wealthiest regions.

South Africa exemplifies economic differences within urban areas, as it contains some of the most unequal cities in the world. For example, in Cape Town, most migrants from the Eastern Cape live in segregated neighborhoods, separate from the professional areas in central Cape Town. All of these areas, however, are part of one city.

Levels of Inequality in South Africa and its cities
Darker red indicates higher levels of inequality

As can be seen in the maps of “generalized entropy” of inequality (where darker red indicates more unequal conditions), many neighborhoods in the centers of the cities are relatively equal. However, the neighborhoods are very segregated and tend to be equally wealthy or impoverished, but the map does not reflect average income levels

Inequality in South Africa is highly correlated with race. The system of apartheid, instituted from 1948-1994, determined economic possibilities and expectations based on race, contributing deeply to the unequal society that exists today. However, as the book Race, Class, and Inequality in South Africa* shows, apartheid did not have only the effect of increasing general inequality. For example, during early apartheid, the government was able to successfully decrease intra-race disparities. Furthermore, the overall levels of inequality remained fairly consistent, even through apartheid.

The National Party instituted apartheid in South Africa starting in 1948. It codified laws mandating racial categorization and exclusion. In particular, all non-whites were excluded not only from the political system, but also from most respectable jobs and good education. Specifically, apartheid aimed to physically separate the races, which was difficult to carry out in practice. Furthermore, the apartheid government concerned itself greatly with the welfare of the white population, hoping to improve the standing of poor whites As a result, poor people of European background greatly benefited from apartheid, as they were able to move into higher classes, bypassing well-educated non-whites.

Gini coefficients for the distribution of incomeAs can be seen in the table to the left (taken from Race, Class, and Inequality in South Africa), the Gini coefficient for the white population in 1975 stood at a relatively low .36, signifying the success of the apartheid government in bolstering the position of the poorer members of the white community. An essential component of this program was the reservation of well-paying jobs for the white population. Furthermore, when taking into account welfare expenditures and educational opportunities, the white population actually had greater equality than the Gini index shows.

Of course, this leveling of the economic differences within the white population was dependent on the widening gap between white South Africans and everyone else, especially the black Africans. Because the apartheid government restricted the professions that blacks could choose, blacks were basically limited to lower-level jobs.  In fact, this upper limit also facilitated an increase in equality within the black population, as even the well-educated blacks could not find well-paying jobs.

Intra-race inequality vs. inter-race inequalityAfter 1975, the tendency toward increasing intra-racial equality reversed. As can been seen in the table to the right (also from Race, Class, and Inequality in South Africa), recent decades have seen a dramatic increase in inequality within racial groups. In the mid 1970s, the apartheid regime began to loosen its restrictions, allowing some educated non-whites to take higher-level jobs. Also important was the shifting labor market. Jobs requiring little education, particularly in mining, began to diminish after 1975. Without jobs or a solid education to use in the evolving labor force, many non-whites were left without work. These factors, among others, allowed for the increase of inequality within races, even as overall disparities in South Africa remained similar to what they had been.

With the end of apartheid in 1994, the chances for mobility increased. As a result, levels of inequality within all racial groups are slowly beginning to reflect the national average. Indeed, the end of apartheid has given many educated non-whites the opportunity (through dismantling apartheid laws) and assistance (through affirmative action) to obtain professional jobs. However, the vast majority of the black population still suffers severely from lack of marketable skills. Such discrepancies will continue to be felt in the next generation, as levels of education vary tremendously among social classes.

Change of sources of income post-apatheidOne of the main post-apartheid changes to income distribution has come about through governmental redistribution of income through a progressive tax system, along with the creation of social programs to aid the poor.  As the graphs demonstrate, the South African government is continuing to expand its role as a primary income source for much of the country’s impoverished population. Indeed, when government transfers are taken into account, the Gini index of South Africa is significantly reduced. As Race, Class, and Inequality in South Africa states, the Gini coefficient drops to about 0.50 when taxes and cash transfers are taken into account, and are reduced even further, to about 0.44 (a figure similar to that of the United States), when public social spending is added.

As much as the current South African government has been active in promoting income redistribution, the success of the earlier apartheid regime in promoting prosperity and equality among whites has intertwined socioeconomic status and race. In particular, the apartheid government successfully provided an excellent education for the white population, which enabled many whites to retain professional jobs even after the end of mandated segregation. The white population, as well as the newly wealthy non-white population, utilized their economic position to send their children to high-quality schools. As a result, highly unequal levels of education will continue to be reflected by an extremely varied pattern of income distribution.

* Race, Class, and Inequality in South Africa, 2005, Yale University Press, by Jeremy Seekings and Nicoli Nattrass

 

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Japan: An Egalitarian Society?

Income of Japan's Prefectures

Income of Japan's Prefectures
Income of Japan’s Prefectures

Japan is commonly perceived as an egalitarian society. It is a well-developed country commonly thought to have limited poverty; and as such, Japan is often grouped with the egalitarian Nordic countries. For example, in The Spirit Level: Why Equality Makes Societies Stronger, Kate Pickett and Richard Wilkinson* argue that equal societies are better for all citizens, using Japan as an important example. In actuality, inequality in Japan runs deep. Japan may be more egalitarian than the United Statues, but it is still beset by many layers of inequality.

Proportion of Population Living on Welfare

My previous blog entry explored three distinct layers of geographic inequality, focused on China, which all apply to Japan: regional disparities, the rural/urban divide, and the existence of an urban underclass. The map posted here shows the percentage of the population defined as living on welfare. The prefecture with the greatest proportion of welfare households is Osaka, with 4.35 of every 100 people in this category (colored red in the map).  However, throughout Japan, more families live under the poverty line than live off welfare, as nearly one in six lives on less than $1,830 a month for a four-person family. The map highlights significant regional inequalities across Japan. In general, the north and the south (including the island of Okinawa) are poorer, whereas the center of Japan is better off. In particular, the area between Tokyo and Osaka has the lowest rates of households living on welfare.

Like most other countries, Japan also has a significant rural/urban divide. Cities have a much higher levels of development and economic vitality. This economic divide manifests itself in several forms, particularly education. The cities tend to have more student funding and are able to provide better educational opportunities, especially in regard to English language instruction. Although cities are generally better off than rural areas, there is a significant poor urban population across Japan, even in the wealthiest cities such as Tokyo. As seen in the map of households receiving welfare, the highest rates tend to be in large metropolitan areas.

Another form of inequality significant for Japan is the gender disparity. Among well-developed countries, Japan’s gender inequality is pronounced, as measured by several different indices. Although Japan is often compared to the Nordic countries, it has comparatively much higher levels of gender inequality. Opportunities for Japanese women may be better than those found in less-developed countries, however,  Japan’s gender disparity is unique for its level of development.

In many regards, Japanese culture tends to value humble and reserved behavior. This tendency directly relates to perceptions of economic disparity across Japan. Although many people live below the poverty line, such poverty is often hidden. As poorer people are often ashamed about their socio-economic status, they commonly work hard to “keep face” by seeming to be better off than they actually are. Such behavior makes economic inequality in Japan particularly easy to overlook. Furthermore, reserved attitudes make it difficult for the poorer population, as Japanese society as a whole is against inserting themselves in other’s lives, and hence often refrain from helping others economically. In contrast to many other countries, Japan tends to keep poverty out of sight and mind (the victims of the recent tsunami are an exception here.) Japanese culture is conducive to maintaining an illusion of greater equality than what actually exists.

Another major difference between Japan and most other countries is that the Japanese tend to not discuss or identify with a particular “social class.” Although people often know who is “binbo” (poor) and who is “okane-mochi” (money-holding, rich), politics are generally not based around such distinctions. As a result, the government’s ability to pursue class-based policies is limited, leaving poorer citizens’ interests neglected.

Percentage of Children in Poverty

A 2006 OECD (Organization for Economic Co-operation and Development) report on inequality in Japan provides insight on inequality in Japan. It shows that inequality has been increasing recently, linked to the stagnation of the Japanese economy. The report demonstrates that in some ways, Japan may actually have a less equal distribution of wealth than the OECD average. Although income disparities in Japan are lower than in most OECD countries, taxes and transfers do not always benefit those in need. In particular, the system of financial reallocation has been slightly regressive; as a result, the percentage of children living in poverty in Japan has increased since the 1980s if one takes into account taxes and transfers. In fact, Japan now clearly is above the OECD average in terms of percentage of children living in poverty. As this demonstrates, Japan is characterized by many significant hidden elements of economic inequality.

Note: Maps are taken from this map database. Also, special thanks to Tyler Mantaring for his insight.

* The Spirit Level: Why Equality Makes Societies Stronger, Bloomsbury Press, April 2010, by Kate Pickett and Richard Wilkinson

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Global Inequality: Where is it Found?

Poverty and inequality are contentious topics whose geography is often oversimplified. When many people think of extreme poverty and aid, they often focus on Sub-Saharan Africa, but global inequality and poverty are much more complex issues. Overall, it is increasingly apparent that a country-based framework that generalizes levels of income over entire national territories is inadequate, as inequality exists in at a variety of spatial scales. In many parts of the world, inequality is increasingly experienced at local levels.

In The New Geography of Global Income Inequality, Glenn Firebaugh makes several key claims with regards to global trends in income disparity. He focuses on two components of income inequality: between-country inequality, and within-country inequality. I previously explored the challenges of measuring income disparity, but Firebaugh uses different statistical methods to successfully demonstrate that inter-country inequality has declined recently, whereas intra-country inequality has dramatically increased.

Currently, inter-country income inequality accounts for approximately two-thirds of total global inequality. However, this figure is decreasing as many poorer countries are experiencing more rapid economic growth than wealthier countries. This trend leads to gradual convergence and hence less disproportion of wealth between countries. As can be seen from the PPP per capita GDP map above, between-country inequality exhibits distinct world regional patterns, but disparity within regions is also notable.

As The New Geography of Global Income Inequality acknowledges, much of both the decline in inter-country inequality and the increase in intra-country inequality stem from China’s recent economic development and parallel growth in internal wealth variation. With around one sixth of the world’s population, China significantly affects global levels of income inequality. During and immediately after the Cultural Revolution (1966-1976), China had relatively high levels of income equality: in 1978 its Gini index was even around 0.3. Although China was extremely poor at this time its population was fairly equally impoverished. Due both to China’s large population and its extreme deprivation, its standing at this time significantly heightened inter-nation disparities. With industrialization and the movement to market-based economics, China grew much wealthier, but its newfound riches have not been equally distributed, greatly increasing inequality within the country. China is thus a major factor in, and a great example of, the shift from inter-country inequality to intra-country inequality.

The general trends of inequality in China are similar to those found in many other countries. China has regional divides, rural and urban splits, as well as a significant urban underclass in the cities as well. Geocurrents has previously explored the regional differences within China itself, noting that the coastal provinces are significantly better off than provinces inland. However, it is important to note (as Geocurrents has), that even within the coastal provinces large economic distinction are found between different provinces. The rural/urban divide adds a separate layer of inequality in both coastal and inland provinces. The urban areas, on average, are much wealthier than rural areas, across all provinces. China’s hukou system of laws, moreover, seeks to prevent rural people from moving to cities unless they have official documentation. This system not only contributes to the inequality between urban and rural areas, but it also results in large migrant populations of undocumented rural workers in Chinese cities, generating rapidly increasing inequality and a large urban underclass in cities across China. Even though major Chinese cities have experienced breathtaking growth and are generally much wealthier than rural areas, a large portion of their population remains in severe poverty. The development of China has thus been linked with increasing inequality across the country, and has become a cause for concern within China.

China, as the world’s most populous country, exhibits high levels of economic differentiation, as is explored by this selection of interactive maps from the Economist. China is a good example of how increasing development is leading to greater intra-country disparities across the world. As China continues to economically expand, more areas of the country will become similar to the well-developed regions of Western Europe and the United States, symbolic of decreasing differences between countries. However, there will likely continue to be an increase of inequality within China, visible from multiple viewpoints, from the macro perspective across regions to the micro scale of individual cities and their neighborhoods.

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Difficulties Calculating Inequality and the Gini Coefficient

Gini Index for Countries around the World in 2009

Gini Index for Countries around the World in 2009

Global and local inequality has been a major topic of debate, leading to many attempts to quantify income disparity. The Gini Coefficient is the best-known measure of inequality, but it has its flaws, as do all inequality measurements.

A popular measurement of economic inequality focuses on variations in income among people in a state. Since no country has perfect equality, the question becomes one of calculating how unequal a particular society is. Various formulas have been devised to measure the difference between the average income and the distribution of earnings. At the end, a single number is used to represent a nation’s level of inequality. A good index should take into account several basic rules. For example, the size of the country should not affect the level of inequality. Moreover, the absolute value of income should not change the calculated level of disparity. Finally, if income is transferred from the rich to the poor, the level of inequality should fall.

Numerous challenges are encountered with all proposed formulas. Perhaps the greatest is that of data collection. Average income may be fairly easy to measure, but for any disparity index a detailed data set on income distribution is also required. Such data is often unavailable, especially for poorer countries in which “off the record” transactions are common. As can be noted in the above map, the Gini coefficients of many countries have not been calculated due to the lack of data. Much of sub-Saharan Africa in particular thus remains blank.

A major problem with any index is that of oversimplification. To begin with, absolute and relative levels of inequality are difficult to measure. For example, in most inequality indexes, a hypothetical country with three people with yearly incomes of $750, $1,000, and $20,000 would be counted as having the same level of inequality as a three-person country with incomes of  $75,000, $100,000, and $2 million. In the first case, however, the individuals with incomes of $750 or $1,000 would have little to spend on anything but basic necessities. In contrast, the proportion of income spent on basic necessities in the second country would be relatively low across the board. Although the income variation is encapsulated by the inequality index, effective variation in spending power can be drastically different.

Other aspects of inequality can be hidden via demographics or government policies. The population distribution by age is not factored into any of the available indexes.  Yet a country with many impecunious college students would show relatively high levels of inequality even though the students can be expected to graduate into higher income brackets. Furthermore, government transfers, such as food stamps and other welfare programs, are usually not taken into account, even though they often have important effects on overall inequality. These are just some examples of the problems with reducing a complex situation to a single number: intricacies are lost and such numbers can be misleading.

Despite such intrinsic problems, there is still a great interest in assessing inequality through the use of a simple index. The global standard for calculating income disparity has become the Gini coefficient. As defined by the CIA Factbook, the Gini coefficient is:

“This index measures the degree of inequality in the distribution of family income in a country. The index is calculated from the Lorenz curve, in which cumulative family income is plotted against the number of families arranged from the poorest to the richest. The index is the ratio of (a) the area between a country’s Lorenz curve and the 45 degree helping line to (b) the entire triangular area under the 45 degree line. The more nearly equal a country’s income distribution, the closer its Lorenz curve to the 45 degree line and the lower its Gini index, e.g., a Scandinavian country with an index of 25. The more unequal a country’s income distribution, the farther its Lorenz curve from the 45 degree line and the higher its Gini index, e.g., a Sub-Saharan country with an index of 50. If income were distributed with perfect equality, the Lorenz curve would coincide with the 45 degree line and the index would be zero; if income were distributed with perfect inequality, the Lorenz curve would coincide with the horizontal axis and the right vertical axis and the index would be 100.”

Despite the flaws of the Gini coefficient, it does provide useful insight into the geography of inequality. For example, Latin America has some of the highest Gini levels of any region, which stems from the large disparities present in many countries, especially those with poorer indigenous populations. This contrasts with much of Europe, which has much lower levels of inequality.  In general, countries that have higher levels of economic development have a lower Gini value, but the United States is an exception to this, signifying the relatively high levels of inequality in the US compared to Europe.

However, comparing countries across disparate regions of the world leads to a realization that the Gini index does not tell the full story. For example, New Zealand and India have similar Gini figures, 36.2 and 36.8 respectively (according to the CIA Factbook), but their circumstances are very different. New Zealand, despite having a significant gap between the indigenous Maoris and the population of European descent, has still managed to create an economy with little pronounced poverty and without the levels of extreme wealth found in the United States. India, with its vast population of malnourished people working in servitude, coupled with its sizable population of servant-dependent elites, certainly appears to vastly higher levels of inequality than New Zealand. One would also expect India’s GINI coefficient to have increased markedly in recent years, given its impressive economic growth rate as well as its persistently high levels of absolute poverty. Yet India has seen a surprisingly steady Gini score, potentially casting doubt either on its economic data or on the index itself. Other countries in the same general Gini range, such as Israel and Ghana, are also marked by different kinds of inequality.  Although the Gini Coefficient –like other inequality measurements—can be used broadly to show varying levels of inequality, it is best used in conjunction with other sources of information to gain a better picture of actual disparities.

Finally, the Gini index is merely one of several indices that measure income inequality. The Theil index provides an extra feature in that it is “decomposable,” meaning the inequality score can be broken down into smaller sections, allowing one to discern regional contributions to the measured inequality. As can be seen on the Theil index map of the United States, counties in red contribute drastically to the level of inequality, whereas counties colored black lower the level of inequality. Although the Theil index will always be positive (a value between 0 and 1), certain areas can contribute negatively to the index, signifying such regions lower the overall level of inequality. Despite the benefit of decomposability, the Theil index is little used, as the Gini index remains the standard.

 

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National Anthems: Forced National Identity?

 

When countries adopted their current national anthems

Every country and many non-sovereign states have national anthems. They are an indispensable representation of nations, played from official receptions to sporting events. As described by Wikipedia:

“A national anthem is a generally patriotic musical composition that evokes and eulogizes the history, traditions and struggles of its people, recognized either by a nation’s government as the official national song, or by convention through use by the people.”

The map shown above groups countries by when they adopted their national anthems (all dates are taken from this website, a detailed database of all national anthems). In some instances, the data had to simplified in order to create a single chronology, especially in regards to de facto and de jure anthem recognition. Also, if a country returned at some point to an older national anthem, the date of re-adoption was used. Furthermore, the map charts the adoption of anthem music and not necessarily lyrics, which in some instances came later.

Several regional trends appear on the map, mostly related to dates of independence from colonial powers. In particular, Latin America stands out due to the longevity of its anthems. No other region of the world has quite as many long-standing national songs. Most Latin American countries gained independence in the early 1800s, and many of the anthems date from the middle of the century. In fact, Latin American anthems are so distinctive that they can be grouped together as an “anthem family”:

“Latin American epic anthems: Possibly the easiest to identify, these are found in Latin American (Spanish-speaking Central and South America) countries and tend to be rather long, have an epic quality in the music, often containing both a quick, patriotic section of music, and a slower, stately part, and consists of many verses, usually chronicling the history of the country. Many are also composed by Italians (or other Europeans) … Examples include Argentina, Ecuador, El Salvador, Honduras, and Uruguay.”

Most Latin American anthems require four to five minutes to be played in full. Such lengthiness represents a significant problem for events such as the Olympics, which allows only eighty seconds per anthem. As a result, only a short section of many Latin American national songs are actually played. It should also be noted, however, that a few Latin American anthems, such as Venezuela’s, are much shorter.

The trend of new national anthems appearing after independence is visible in other regions of the world including South Asia, Southeast Asia, East Asia, Africa, and Eastern Europe. In eastern and southern Asia, most countries gained sovereignty shortly after World War II, and many adopted existing anthems as part of their new identity.  Before their official adoption, these musical pieces served different purposes, but most had been associated with nationalist movement.  The current Chinese anthem was originally written in 1935 and used as a Japanese resistance song, but once the Communists took power in 1949, the communists adopted it and elevated it to national standing. Similarly, Indonesia’s anthem was originally the song of an anti-colonial party, and was adopted by the new government after independence. In Africa, most countries selected new anthems as they gained independence between 1958 and 1975. Eastern European countries that gained independence from the Soviet Union in 1991 also opted for new anthems at the time, as did several of the other new states of the region.

Countries that have retained independence for long periods of time often have long-standing national anthems. Japan’s anthem dates back to 1883 during the Meiji period, as a national anthem was perceived as a requirement of a modern nation. Thailand and Tonga, which largely escaped European colonialism, have also used the same national anthems for a long period of time.

In regard to issues of national identity, the Commonwealth countries of Canada, Australia, and New Zealand are particularly intriguing. As part of the British Empire, they long used “God Save the King/Queen” as their only official anthems. Although local songs began to be used in unofficial circumstances, “God Save the King/Queen” continued to be employed long after independence had been gained. Canada did not switch to “Oh Canada” until 1980. Update: Although “Oh Canada” was not the de jure anthem until 1980, by 1939, it was considered the de facto national anthem. In Australia, the change came only in 1984. In New Zealand, “God Save the King/Queen” is still a co-anthem (although rarely used), and was only demoted to ‘co’ status in 1977. For these countries, switching away from the British anthem reflected increasing nationalist sentiments. At the same time, the importance of their British heritage is demonstrated by their reluctance to finally abandon “God Save the King/Queen.”

Several countries have changed their national anthems since 2000, often signaling intent by the government in power to shift the national image. Although Russia adopted a new anthem in 1990, in 2000, Putin returned to the official Soviet music, albeit with new lyrics, advertising a shift from post-Soviet instability. Under the Taliban, Afghanistan was the only country without a national anthem, as its rulers rejected virtually all forms of music as un-Islamic. Afghanistan’s old national anthem was restored in 2002 after the outing of the Taliban, only to be replaced again by the Karzai government in 2006 as a sign of a new Afghanistan. In 2001, Rwanda changed to a new anthem, adopting it as well as other official symbols, signifying the new post-genocide Rwandan order.

The selection of a national anthem is not only a reminder of differences of national differences, but can also demonstrate similarities. With the independence from the Soviet Union in 1991, Estonia re-adopted its pre-Soviet national anthem, which is the exact same piece of music used by nearby Finland. This unusual shared national anthem is symbolic of the very close cultural and linguistic relations between Finns and Estonians.

Although a number of countries around the world are still changing their national anthems, Latin America’s stability in this regard is noteworthy. This constancy demonstrates how anthems can become more important to national identity than regimes themselves, as new governments in Latin America do not change the anthems.  However, for much of the rest of the world, new national songs will likely emerge as political instability continues and as new regimes try to influence national identity.

National Anthems: Forced National Identity? Read More »

The Economist’s “Shoe-Thrower’s Index”: A Success?

As revolution in the Arab World spread from Tunisia, The Economist magazine developed a “Shoe-Throwers Index” (STI). The STI combines available data for most of the Arab League to gain insight into what countries are at the greatest risk for revolution. Originally published on February 9th, the STI came out two days before the departure of Egypt’s Hosni Mubarak. Since then, several countries high on the list have also experienced massive unrest.

The STI is formatted through several indicators, which are all weighted separately (as shown in this Economist video.) By far the most heavily weighted factor of the index is the percentage of population under 25 (the greater the percentage, the higher the score), which accounts for 35% of the total STI score. Other factors include the number of years a ruler has been in power, democracy rankings, and corruption rankings, each of which individually account for 15% of total score. (For a more in-depth analysis of the variables, go here.) Overall, the STI compiles a unique set of information, often relying on other rankings to complete the index. However, The Economist was careful not to include every piece of relevant data.  For example, the data on unemployment was considered “too spotty” to be used. More recently, The Economist has provided an interactive index, which allows users to change factor weighing, and thus generate unique “Shoe Thrower” indices.

Several relatively straightforward geographical patterns are apparent with the STI, as shown in the map above. The clearest pattern is found among the rich Gulf States, especially Qatar, Bahrain, and the U.A.E. Here, oil and gas wealth in combination with higher levels of social development has generated low STIs; these countries have some of the lowest percentages of population under the age of 25.  The Maghreb (Morocco, Algeria, and Tunisia) also scores relatively low in the STI, as in all three countries less than half of the population is under 25. The countries that top the STI list –Yemen, Libya, Syria, Iraq, and Egypt — do not share much geographically; beyond the fact Egypt/Libya and Iraq/Syria have common borders.

Has the STI been successful in forecasting which countries in the Arab world are at the great risk for further unrest? In many cases, the STI has been on target, especially in regard to countries at the top and bottom of the rankings. Yemen, the highest scorer, was relatively calm when the index came out, but since that time it has undergone intense turmoil, with prominent government officials resigning. The country with the second highest score, Libya, has experienced not just protests but an actual revolution, eliciting a major international response. In third-ranking Egypt, protests were already in progress when the index was created, which eventually dislodged Mubarak. The fourth country on the list, Syria, has also experienced recent protests and crackdowns. On the other end of STI, Qatar, Kuwait, and U.A.E. have not been substantially impacted by unrest, and Qatar and U.A.E. have even provided military help to the international mission in Libya.

In some countries, however, the STI fails to explain recent events. Most notably, Bahrain has the fifth lowest score, but has still seen intense protests. Iraq, fifth on the list, has experienced some anti-government demonstrations, but they seem unrelated to the democratic wave moving across the Arab World. Furthermore, the country that set-off the wave of unrest, Tunisia, ranks as relatively stable. Although the index does highlight zones of potential unrest, it does not do so perfectly.

Analyzing the STI indicates some problems that arise when one attempts to create a single index for a continually changing dynamic of contemporary events. One of the first issues faced by The Economist’s researchers was to decide which countries to include. They initially selected the Arab League, but “took out the Comoros and Djibouti, which do not have a great deal in common with the rest of the group, and removed the Palestinian territories, Sudan and Somalia for lack of data.” Although this maneuver may seem reasonable, the STI left out Iran, which, although not in the Arab League, has also experienced significant protests. On the BBC website, an informational map of the unrest diverges from The Economist selection. The BBC includes Iran, but leaves off Iraq Qatar, Kuwait, and U.A.E. Although excluding the Gulf States for a map of unrest is understandable, the BBC map contains a significant difference in viewpoint from that of The Economist.

Another problem with the STI is that of signaling a country’s chance for uprising with a single number calculated through a standardized system. Although Libya and Bahrain may be grouped together into the same region, they are separated by vast social, political, and economic differences. Libya, for example, is noted for its harsh autocratic rule and its tribal divisions, whereas the major challenge facing Bahrain is its religious schism between the ruling Sunnis and the majority Shi’ites. Libya and Bahrain demonstrate that while the STI does provide some insight into some of the factors that cause unrest, it cannot predict where unrest will occur or what form it will take.

Create Your Own STI With Custom Factor Weighting

 

The Economist’s “Shoe-Thrower’s Index”: A Success? Read More »