International statistics on inequality: political and spatial constructions

Statistiques internationales relatives aux inégalités : constructions politiques et spatiales
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Article Index
Comparison of indicators of inequality in South…
Multidimensional Poverty in Indian States
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This article aims to situate, in chronological terms and in terms of ideas, the emergence of international statistics on inequality. We highlight the limitations of the data that are most commonly used to quantify inequalities, such as national income per capita and the Gini coefficient, and present some alternatives that have been launched over the last 20 years. The contribution of geography to an evolving statistical corpus is demonstrated.

The limits of national accounting for understanding internal inequalities

International organizations are the main producers of economic data enabling comparisons between countries. These databases are supplied by official State bodies, such as National Statistical Institutes (NSI), ministries, commissions and governmental agencies. Economic statistics such as Gross National Income (GNI) and Gross Domestic Product (GDP), which record the total market added value for all goods and services sold in a year (on the basis of nationality for the former, and territory for the latter), are often used to compare inequalities between countries; and also to provide a picture of inequality in terms of economic power. The GNI, through a simple division, is commonly converted to a per capita figure and thus used to compare the average income per person. The "unit of account" is transformed from the nation to the individual, but this ratio completely masks the gaps within societies.

The GNI and GDP were not in fact designed for the quantification of inequalities. At the heart of what is called national accounts, these indicators were developed in the early 1940s, originally in the United States and later in Europe by researchers as tools of management and short-term forecasting of economic flows (the increase in production value and, in the context of the 1930s and 1940s, its possible contraction) as outlined by Thomas Piketty in this volume. These tools were originally part of a Keynesian approach, where the State affirms itself as the driver of the economy (Vanoli, 2002). Today, the GNI refers to a set of robust and standardized statistics, calculated with quarterly regularity in most countries. It remains valuable for longitudinal comparisons and/or retrospective estimates enabling the reconstruction of series that cover several centuries (Maddison, 2006).

In addition to the GNI, the Gini index on income reveals information about inequality in terms of a society's internal income distribution, within a particular country. Created in the 1920s and released at the international level by the World Bank and the OECD, this index is the only one to benefit from having the noun "inequalities" mentioned in the databases of both institutions, thus highlighting the emphasis that international organizations have placed on the strictly monetary aspects of inequality. The number of countries for which there are no recent Gini indicators (and sometimes no Gini indicators at all) is also very important.

On the World Bank's data website (http://data.worldbank.org/indicator/SI.POV.GINI), data under 5 years exist for only 34% of countries, 46% of which concern the period 1991-2007 and no Gini index is reported for 20% of States.

The financial and human effort entailed ​​in the determination of the GDP or GNI remains much higher than that deployed for the exhaustive quantification of domestic inequalities. Without doubt, the first priority of the World Bank is to establish accurate assessments of the macroeconomic situations of its debtors (Cling and Roubaud, 2008), while understanding the evolution of their internal inequalities does not take precedence, because such data does not directly provide information on the ability of countries to repay their debts. In response to the quantifications from the national accounts, which are primarily tools for States, alternative indicators of inequality are organized in two directions: not strictly monetary development and environment.

Challenges and alternatives to the quantification of monetary inequalities

From the report of the Club of Rome (Meadows, Meadows, Randers and Behrens III, 1972) to that of the Stiglitz commission (Stiglitz, Sen and Fitoussi, 2009), most of the critical debates on inequality indicators focus on what GNI/GDP actually quantify and on the overemphasis they place on economic growth. The limitations of GNI/GDP, which have been well known for a long time, include: only taking market activities into account (the entire informal sector is excluded) and ignoring their many negative externalities, for example on the environment (pollution of air and soil, greenhouse gas emissions, etc.) (Gadrey and Jany-Catrice, 2005). While knowledge of these limitations originally emerged in academic research environments, NGOs and a number of international institutions, such as the UNDP, now convey these criticisms.

Although the normative power of the UNDP is definitely lower than that of international donors such as the World Bank, in 1990, through its Human Development Report Office (HDRO), it launched the Human Development Index (HDI). The HDI's originators intended it to be an alternative to GDP, moving the basis of the quantification of human well-being onto individuals, rather than on macroeconomic income growth. The HDI has evolved over time, evidenced by its abandonment of UNESCO's official statistics on Education, to use international surveys instead (Barro and Lee, 2010), and then the use of GNI instead of GDP to take into account income transfers between residents and non-residents.

In response to continued criticism of the HDI as merely an average value that masks inequalities within societies, in 2010 the HDRO introduced an adjusted version of the index, taking inequalities into account (Box 1). The popularity of the HDI has led to a global spread of its use at subnational scales, by provinces or even by counties (such as in Brazil, where the HDI is updated annually for the 5560 municípios).

Taking the environmental dimension into account

A significant part of the criticisms aimed at indicators of monetary inequalities derives from the fact that they neglect or only barely consider environmental aspects. In the early 1990s, before the Earth Summit (Rio, 1992), two researchers from the University of British Columbia (Vancouver, Canada), William Rees and Mathis Wackernagel, defined and produced the first quantifications of the Ecological Footprint (EF). The innovation of this indicator is that it is expressed in global hectares and obtained by subtracting the total amount of resources consumed by humanity from the Earth's regenerative capacity. A negative value indicates a depletion of the Earth's reserves, while a positive value indicates the sustainable use of resources. The design of the indicator allows it to be used to calculate the EF for different territorial levels (States, cities, regions, etc.) and for various actors (companies, governments, individuals, etc.). Supported by the World Wide Fund for Nature in the early 2000s and then highly publicized, the EF became the symbol of the alternative environmental indicator: experimental and "unofficial" in the beginning, and now commonplace in institutional reports and government rhetoric. Today the EF is a trademarked concept, organized in a global network that brings NGOs and academic centres together.

Faced with these initiatives, international organizations in turn integrated environmental factors into their own indicators, even if they remained largely monetary-based. Thus, in 2002 the World Bank launched the concept of "Genuine Savings". Expressed as a share of GNI, it is calculated by subtracting the damage caused to economic and natural capital from gross savings (from the national accounts), followed by the addition of investment in education. In addition, very recently at the 2012 Rio +20 Summit the United Nations University (IHDP) and UNEP seized the opportunity to present its tests involving several countries on the measurement of an "Inclusive Wealth Index" that is intended to provide an alternative to per capita GNI and HDI.

Figure 1 shows a comparison between the results of a number of inequality indicators that were applied for South America. It is important to note that the levels and rankings of States change significantly according to the choice of indicator. A general dynamic is difficult to emerge: some countries tend to retain their rank (intermediate for Brazil, top ranking for Argentina), while others, such as Chile or Bolivia, show very different positions depending on whether one focuses on human development or environmental sustainability.

Comparison of indicators of inequality in South America, 2011

There are many possible indicators of inequality. All of these indicators, however, or nearly all, rank the performance of states differently - in this case for Latin America. It is difficult to see the emergence of a general dynamic: some countries tend to stay at the same level (intermediate for Brazil, the highest for Argentina), while others, such as Chile or Bolivia, show very different standings depending on whether one focuses on economic performance, human development or environmental sustainability. In addition to these indicators, slum population estimates reveal that countries that rank highly in this regard, particularly Brazil, may also have numerous billionaires (including as a proportion of the population).
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The contribution of geography

International statistics that use the nation as the "base unit" cover very heterogeneous situations. Demographic giants with over a billion people (such as India for example) are contrasted with islands of a few hundred thousand people (e.g. some of the Pacific Islands). In terms of densities, continent-States with low population densities, such as Russia, differ considerably from totally urbanized city-states like Singapore. The so-called "anamorphic" maps (or cartograms) where each country is swollen or shrunk according to the size of its population, partly enable these differences to be highlighted (Levy, 2008). In addition to the quantification of inequalities within societies, it seems necessary to "zoom in" to finer spatial scales - what geographers would consider as "large scales" - such as provinces or municipalities. Figure 2 shows that national (federal in the case of India) averages can mask strong internal geographical inequalities and that, according to this multidimensional poverty index, the Indian States experiencing the worst situations are comparable to some sub-Saharan African countries. The necessary fine scale microdata for this purpose do not fall within the open data movement that exist for some indicators, and are therefore often difficult to obtain or require payment.

In addition, cities and urban areas carry a greater weight in both demographic and economic terms. Today, more than half of the world's population inhabit urban areas, which are emerging as essential sites and nodes for globalization processes (Sassen, 2007). It is a damning indictment of international organizations that they do not provide a level of analysis sufficient to take urban areas into account, given that such data is critical for understanding inequality. Cities concentrate the rich elites and also the poor, with the contrast being particularly pronounced in developing countries (Rio de Janeiro, Johannesburg, Bombay, Mexico, etc.) where gated communities coexist alongside slums. The statistical initiatives of international organizations such as UN Habitat attempt to go beyond the State as the account unit (which does not allow the consideration of urban areas spanning over several countries) by providing quantitative comparisons between cities in different countries. The GaWC university network (Globalization and World Cities Research Network), which initially focused on the connectivity between global cities, also provides many relevant indicators (infrastructure, living standards, population density, services, companies, etc.) to enable the study of inequalities between cities (Taylor, 2004).

Finally, the current "Westphalian" statistical system (which has the nation-State as its unit) appears obsolete and inadequate for the capture of the deep dynamics of inequalities that depend both on global logics (affecting the entire globe) and transnational logics (which escape, even partially, the control and actions of States) (Durand et al., 2013). Thus, the recent cases of transboundary pollution caused by oil spills (in the Gulf of Mexico for example) or from nuclear disaster (Fukushima) on the one hand, and the flows (migration or financial, legal or otherwise) on the other, show that States, confined to their national territories, cannot provide adequate statistical tools to quantify these phenomena.

We see a familiar theme emerging in the production of new data that is better able to provide valuable information on contemporary challenges, compared to the existing statistical system: as was the case for the GDP, or more recently for the Top Income Database, it is often academics who initiate the criticism of the existing systems and then launch new indicators that are better adapted to measuring changes in inequality. International organizations have fully understood the advantages of such a dynamic and develop multiple partnerships with academic research centres. In this hybrid collaboration, the latter benefit from the visibility and institutional authority of the former, in return, they bring scientific credentials.

Quantification of inequalities by Human Development Index (HDI)

There are three main methods used for the quantification of inequalities: 1. A version of the HDI adjusted to inequality - the IHDI - has been in use since 2010, which goes down when internal inequalities are high. 2. The Multidimensional Poverty Index (MPI), which considers the multiple deprivations faced by the poorest. This initiative, which was launched by the University of Oxford, has been adopted and supported by the HDRO. 3. Gender disparities are quantified through the gender inequality index (GII). It is worth noting that the latter two indicators do not take the monetary criterion of income into account. The independent status of the HDRO, vis-à-vis the UNDP and its Member States, gives it a freedom to create such indicators. The HDRO functions as a de facto research centre and has the ability to make relatively unrestricted decisions about the statistical sources that it considers relevant.

Multidimensional Poverty in Indian States

The MPI overcomes the purely economic approach (income) to analysing living conditions. When applied at the sub-state level, it enables the comparison of Indian states with other countries in the world, both in terms of proportion of the population affected and of the number of individuals involved. Thus, in terms of ratio, the lowest levels (in Delhi) are close to those of China, while the highest (Bihar) are comparable to those of Sierra Leone. As for numbers, the poor are more numerous in Uttar Pradesh than in Nigeria.

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