The many faces of inequality

This chapter provides some discussion and analyses on a few of the many dimensions of inequality that exist. The chapter begins by outlining some of the typical approaches to measuring inequality before discussing inequality from the specific context of the 2030 Agenda. Some analyses are then presented for global economic and income inequality. Further analyses are presented regarding gender inequalities in the field of international trade and access to banking. The chapter concludes by highlighting an emerging but very important dimension of inequality – access to data and information.

Reducing inequality

Inequality, and how it affects economies and societies, is a growing concern shared by politicians, economists and the global community. There is an emerging consensus that existing levels of inequality are not only morally unacceptable, but also economically and politically damaging and corrosive (Deaton, 2013; UNCTAD, 2013, 2014; Stiglitz, 2012).

Hence the growing interest in trying to assess whether globalization and the emergence of new technologies have exacerbated or improved the situation. Inequality has implications far beyond simple economic development, as it is recognized that it can be damaging to society, even threatening peace and security. Resentment over injustice, unequal access to public goods or social services, or political or social exclusion may all trigger unrest, hostility and violence (Brinkman et al., 2013). From a social justice perspective, discrimination of civil or political rights, of race, ethnicity, language, religion or of legal, political, social freedoms are all sources of inequality. The International Bill of Rights (United Nations and OHCHR, 2003), composed of the 1948 Universal Declaration on Human Rights; the 1966 International Covenant on Economic, Social and Cultural Rights and the 1966 International Covenant on Civil and Political Rights reaffirm the fundamental equality of all human beings. An issue that is also addressed in this chapter is unequal access to data – ‘Unequal access to knowledge and information leads to inequities in the uptake of social protection’ (Roelen et al., 2016, p. 235).

For this reason, the 2030 Agenda, and specifically SDG 10, sets out to reduce inequality within and among countries. In addition to Goal 10, the ambition to reduce inequality is also evident in several other goals. For example, some targets within SDG 4 (equal access to education) and all SDG 5 (gender equality) targets are essentially focusing on inequality. Furthermore, the Global SDG Indicator Framework requires that many of the SDG indicators are disaggregated by sex, age groups, urban/rural or persons with disabilities, thus implicitly targeting inequality.

Definitions of inequality typically refer to an absence of equal dignity, status, rank, privileges, rights or opportunities with others. They often also refer to lack of equal chance and rights to seek success in one’s chosen sphere regardless of social factors such as class, race, religion and sex. Inequality is often a complex amalgam of social, political and economic factors. Goal 10 reflects this broad spectrum, setting a series of targets promoting income growth, social and economic inclusion, equal opportunity, wage and social protection, improved financial regulation, safe migration of people and an improved representation for developing countries in decision-making and global international institutions. Furthermore, Target 10.21 explicitly demands equality irrespective of age, sex, disability, race, ethnicity, origin, religion or economic or other status.

Measuring inequality

Measuring inequality is typically done by placing specific values along a specific distribution in order to facilitate comparisons with different distributions. While most measures of inequality achieve this to some extent or other, all measures or indices have strengths and weaknesses. For a more detailed discussion, see Deaton (2004) and Milanović (2012). No single measure satisfies all desirable properties, and so the choice of one measure over another involves trade-offs as many of the measurement instruments are not without limitations, problems and biases.2 This section outlines some of the indices available and describes briefly their strengths and weaknesses. None can be considered superior, as all are useful given certain contexts. A well-balanced analysis of inequality should look at several measures (United Nations, 2015).

Inequality is often measured using indices. Both the Atkinson’s index3 and the Schultz (Hoover)4 index are popular measures. However, perhaps best known of the indices is the Gini index. Named after Italian statistician Corrado Gini, the Gini index or coefficient is a measure of statistical dispersion used to determine inequality among values of a frequency distribution. It can be used to measure the inequality of any distribution. A Gini index of 1 indicates perfect inequality, and 0 (zero) indicates perfect equality. It is a widely used indicator of income inequality or wealth concentration within an economy or society. It indicates how far the distribution of income among individuals (or households) deviates from a perfectly egalitarian distribution.5 The Gini index is not a perfect measure of inequality, however. It has been criticized for being more sensitive to movements or changes in the middle of the distribution, rather than the tails where the focus should be placed. It has also been criticized for being difficult to interpret as very different income distributions can have the same Gini index.

Inequality can also be expressed in ratio form. The decile dispersion ratio (or inter-decile ratio)6 or the 20/20 ratio7 are ratios in common use. Perhaps the best known of the inequality ratios is the Palma ratio of inequality, proposed by Alex Cobham and Andy Sumner (2013). It is based on the proposition by Jose Gabriel Palma that changes in income inequality are almost exclusively due to changes in the share of the richest 10 per cent and poorest 40 per cent. It is the ratio of household incomes of the two tails of an income distribution and it compares the income inequality between the two groups. This index is defined as the ratio of average income per capita of the richest 10 per cent of households to that of the poorest 40 per cent.8 The Palma ratio too has its critics, who argue that an increase in the bottom share and an even greater increase at the top would raise the index, despite the poor being better off (Murawski, 2013).

Inequality can also be measured using a variety of statistical units. The various indices and ratios can be calculated at the individual, household, regional, national or even global level. Inequality can also be expressed as within or between comparators. For example, it is not unusual to examine inequality within countries but also between countries.

Measuring inequality in the SDGs

Setting a goal for inequality is conceptually complex as there are many types of inequality and there are many and varied perspectives on inequality as a social and economic problem. Furthermore, there is no consensus among economists as to what level of inequality is acceptable or tolerated (Fukuda‐Parr, 2019). This will most likely change from society to society, from culture to culture. Although there is an emerging consensus that inequality is damaging, there remains a counter perspective that ‘inequality is not always bad. Progress depends on it since society never moves in lockstep’ (Pilling, 2018, p. 117).

Although quite contested during the 2030 Agenda negotiations, inequality was finally recognised as being sufficiently important to deserve a full goal (10). Curiously however, none of the goal 10 targets actually addresses economic, income or wealth inequality directly. Target 10.39, for instance, looks at equality of opportunities by proposing a measure of personally felt discrimination, but there is no data yet. Hence, the IAEG-SDG adopted growth rates of household expenditure or income per capita among the bottom 40 per cent of the population and the proportion of people living below 50 per cent of national median income as the indicator for targets 10.1 and 10.2 respectively. Fukuda‐Parr (2019) and Adams and Judd (2019) have highlighted this issue, asking what are the policy questions being addressed by the targets in Goal 10.

Target 10.410, measured by the labour share of GDP11, provides information on the relative share of GDP accruing to workers relative to the share accruing to capital. In 2018, the simple average of labour share in GDP was 53.3 per cent for developed economies, down slightly from 54.3 per cent in 2000. In developing economies, the share fell from 49.8 per cent in 2000 to 33.9 per cent in 2014, when it resumed growth, increasing to 47 per cent in 2018 (United Nations, 2019). To analyse the underlying inequalities, more disaggregated data are required. A more detailed analysis by Schwellnus et al. (2018) found that technological change and greater global value chain participation have reduced labour shares, including by strengthening “winner-takes-most” dynamics among businesses. But this capital-labour substitution has been less pronounced for high-skilled workers, suggesting that policies that raise human capital through education and training will be crucial for better equality and broader sharing of productivity gains.

Global economic inequality

Trying to assess whether global inequality is increasing or decreasing is not a straightforward task. There are myriad forms of inequality. Measuring each type may yield a different answer. If the aim is to determine whether economic inequality is improving or not, then some internationally comparable measures must be agreed upon. These measures must facilitate both spatial and temporal comparison.

Figure 1 shows that over the last 10 years, the global distribution of GDP per capita has become more equal. For example, in 2007, the poorest economies, accounting for 80 per cent of the world’s population, contributed 22 per cent to world GDP. By 2017, their share of GDP rose to 32 per cent. Between 2012 and 2017, however, inequalities in GDP per capita reduced mainly among economies with moderately high income. The relative distance between the richest and poorest economies in the world remained almost unchanged.

Figure 1. Distribution of world GDP, 2007-2017
(Percentage)

Source: UNCTAD (2018a).
Note: Inequality within economies is not considered.

Figure 2.a presents a comparison of GDP per capita at constant (2010) prices over a longer time horizon. Here, the contribution of developing12 and developed economies is benchmarked against a baseline of average world per capita GDP. From this narrow perspective, inequality between developing and developed countries increased steadily between 1970 and 2001, when global economic inequality peaked. This inequality has been driven mainly by changes in GDP per capita of the developed countries. Since 2001 and until 2014, economic inequality between developed and developing countries fell back to levels experienced in the mid 1980’s.

Figure 2.a. Real GDP per capita relative to world average, by development status, 1970-2017
(Percentage of world average)

Source: UNCTAD calculations, based on UNCTAD (2019).
Note: Real GDP is measured in constant prices of 2010.

Figure 2.a also illustrates that the fall in economic inequality slowed very considerably around 2015. This pattern is more pronounced if mainland China is excluded. In fact, when China is excluded, the recent trend reverses and economic inequality after 2015 begins to increase again markedly and consistently.

Figure 2.b illustrates the same analysis from a slightly different perspective. GDP per capita in developed economies was between 12 and 13 times higher than that of developing economies in the 1970’s and 1980’s. This ratio rose to 14 times, peaking around the turn of the century, before falling since. In this analysis, the rate of convergence between developed and developing economies has slowed quite considerably since 2014 but has not been arrested. Developments in China had a significant impact on these developments. Excluding China, the ratio between per capita GDP for developed and developing economies was about 10:1 in the early 1970’s. Economic inequality reduced to roughly 9:1 in the mid 1970’s but then began to increase quite steadily until 1999. Thereafter, per capita GDP converged between developed and developing economies excluding China until 2015. Since then economic inequality between the developed and developing economies excluding China has begun to very slowly rise again.

Figure 2.b. Real GDP of developed relative to developing economies, 1970-2017
(Percentage)

Source: UNCTAD calculations, based on UNCTAD (2019).
Note: Real GDP is measured in constant prices of 2010.

Global income inequality

Intellectual and political debate about the distribution of wealth has long been based on an abundance of prejudice and a paucity of fact.Piketty (2014)

Considering the prominence of inequality in economic debate at the moment, it is surprising how difficult it was to source and assemble the data presented in figure 3. No globally comparable data exist. Figure 3 below has been assembled carefully from a variety of data sources, but only by making several assumptions, including for example, log normal distributions. Hence the continued importance of GDP per capita for historic international comparisons of inequality – it is one of the only comparable time series that exist (and it should be noted that global GDP estimates are not free of problems). Lakner and Milanovic (2016) have highlighted the need for a globally comparable income survey.

Figure 3 shows how the income distribution has shifted steadily to the right between 1950 and 2016, illustrating a general improvement in global income. But the evolution of the global income distribution over the past 66 years or so has not been linear. A striking feature of figure 3 is the disappearance of the distinctive camel shaped, two peaked, distribution of the 1970’s and 1980’s that begins to merge back into a single peaked distribution from 2000. The 1980 distribution had two peaks, one centred close to PPP US$1 per day and another close to PPP US$30 per day. By 2008, the second peak had begun to flatten, with the distribution centring around PPP US$4.6 per day, had broadened to include more of the population.

During this period a very large, richer minority emerges, reflecting incomes in the advanced economies of the world pulling further away from the rest. By the turn of the century this extreme divide is less evident, but nevertheless still persists. By 2016, the LHS of the distribution has flattened, pulling more of the population towards the centre of the distribution, and suggesting greater equity. Nevertheless, the steeper RHS still shows a large minority with relatively high incomes, signifying persistent global income inequality.

Figure 3. Global income distribution, 1950-2016
(Proportion of global population at given level of income, per day, in US$ at 2011 PPP)

Source: UNCTAD calculations based on van Zanden et al. (2014), Lakner and Milanovic (2016) and Gapminder (2015).

Today, the top percentile (top 1 per cent of the population, accounting for approximately 76 million people) live on an average income of US$172 per day. In contrast, the poorest 50 per cent of the population (approximately 3.8 billion people) live on an average income of only US$4 a day. Comparing 2016 with 1950, the average income of the bottom 50 per cent of the population, slightly more than doubled, from PPP US$1.75 to 3.85. For the top 1 percent, average income trebled, from PPP US$57 to 172.

In recent years, the idea of a living wage has gained some prominence. The Global Living Wage Coalition have published living wages for several developing countries.13 By taking an average of these living wages14, weighted by country population, an average living wage of US$8.1 per day for developing countries is calculated. While this is a crude measure, it provides a threshold with which we can divide the global income distribution (see figure 4).

Figure 4. Global income distribution, 2016
(Proportion of global population at given level of income, per day, in US$ at 2011 PPP)

Source: UNCTAD calculations based on van Zanden et al. (2014), Lakner and Milanovic (2016) and Gapminder (2015).
Notes: Living wage for developing countries derived by secretariat data sourced from Global Living Wage Coalition (2019).

Figure 4 shows that 3.9 billion people or 52 per cent of the world’s population lived below the average living wage for developing countries or US$8.1 a day in 2016.

Inequality in gender and international trade

While many countries, businesses and socio-economic groups have reaped gains from international trade, billions have been marginalized or excluded. Trade reforms may have contributed to reducing income inequality between countries, but at times these measures have also coincided with widening income inequality within countries. Context-specific factors influence the impact trade has on inequality. For instance, trade affects women and men differently depending on existing gender disparities in production and consumption, through labour market structures, and disparities in access to resources and opportunities (UNCTAD, 2014).

UNCTAD (2018b) has proposed a conceptual framework to measure the gender and trade nexus within official statistics. Subsequently, in 2019, Statistics Finland carried out the first ever study on gender and trade by linking statistical micro-data from various business and social surveys and registers. This study suggests that the benefits from international trade are not distributed equally between women and men in Finland (Lindroos et al., 2019). In 2016, only 18 per cent of entrepreneurs in exporting firms were women, and women accounted for 27 per cent of the labour input of exporting firms on an FTE basis.15

While trading enterprises are, on average, more productive and generally pay higher salaries compared with other firms in Finland, they employ less women and have a higher gender wage gap. The results show that female business owners hire more women and more highly skilled women than male business owners. Productivity is higher in male-owned exporting firms but wages are higher in female-owned exporting firms (see figure 5). These preliminary results suggest that lower female participation in international trade may also exacerbate differences in capital and salary incomes between women and men.

The 2030 Agenda includes several targets relating to inclusive trade, such as aid for trade (target 8.a), special treatment for developing countries (target 10.a), open and non-discriminatory trade (target 17.10) in addition to targets relating to empowering women (targets 5.b, 5.c and 10.2) etc. More data and statistics are needed to monitor the transformation towards inclusive trade that provides greater and more equitable access to the benefits of global markets.

Figure 5. Labour productivity and salaries in exporter firms by the gender of the owner, 2016
(in constant €)

Source: Statistics Finland.
Notes: Labour productivity is measured in terms of value added per FTE. The Finnish tax administration requests limited liability enterprises to provide a list of owners who have at least 10 per cent of the shares. Here, female or male-owned limited liability enterprises include those where either females or males own more than 60 per cent of the shares. Balanced ownership (8 per cent of limited liability enterprises) refers to cases where females and males own from 40 to 60 per cent of shares. Enterprises with highly distributed ownership, often large enterprises, have been classified to an unknown person owner group (21 per cent of firms). The figure focuses on differences between female and male-owned enterprises only. In total, less than 5 per cent of these male-owned firms are exporters and about 2 per cent of female-owned firms.

The 2030 Agenda also addresses financial inequality through Target 8.1016, which aims to improve access to formal banking and financial services. Although indicator 8.10.217 has been classified by the IAEG-SDG as Tier I, for many countries, statistics are only available for 2017 and in many cases with no sex disaggregation. However, other statistics on individuals’ and businesses’ access to finance are available.

According to the Global Findex database (World Bank, 2016), in 2017 about 1.7 billion adults remained ‘unbanked’, i.e. without an account at a financial institution or through a mobile money provider. In 2014, that number was 2 billion. Regardless of the increasing share of adults who have an account, inequalities persist as women are still less likely to have an account. In developed economies, 92 per cent of women and 93 per cent of men have an account (see figure 6). The gender gap is also relatively small in transition economies (about 3 percentage points), and largest in Africa where 47 per cent of men, but only 36 per cent of women, have an account. In the developing countries of America and Asia, the gender gap is about 8 percentage points. While the overall proportion of account owners increased between 2011 and 2017, the gender gap in account ownership also increased in all regions, other than developing Asia.

Recently, the spread of mobile money accounts has created new opportunities to close gender gaps especially in African countries were these accounts are becoming common. Mobile accounts appear to be more accessible for women and men alike and for the poorest that may otherwise be excluded from formal financial systems.

Figure 6. The gender gap in account ownership across regions, 2011 and 2017
(Proportion of account owners by gender)

Source: UNCTAD calculations based on based on Global Findex database (World Bank, 2016)

Financial inequalities can also be assessed by looking at businesses’ access to and use of funding, e.g. by firm size, industry or gender of the owner. In OECD countries, women-owned or managed businesses used bank loans as a source of financing at significantly lower rates than men in 2018 – 14.5 and 19.5 per cent respectively. This may reflect both gender bias in lending but also the different types of business activities in which women and men engage. Some countries do not have a notable gender gap in businesses’ use of bank loans. This is the case for the Russian Federation, South Africa, Spain, Sweden and Turkey, while a large gap was observed in Germany, Greece and Israel (OECD, 2018).

New dimensions of inequality

Major gaps are already opening up between the data haves and have-nots. Without action, a whole new inequality frontier will open up, splitting the world between those who know, and those who do not. Many people are excluded from the new world of data and information by language, poverty, lack of education, lack of technology infrastructure, remoteness or prejudice and discrimination.United Nations Secretary-General’s Independent Expert Advisory Group on a Data Revolution (2014)

In a data driven world, access to data and information is essential. Barriers to access are creating a new dimension of inequality. With the data revolution, a new cold war has begun – a war between individuals, corporations and States for control of our personal data (Wired, 2018). At stake in this war is individual privacy; sovereignty of data ownership; weaponization of data; and the use of algorithms.

While there are a wide variety of elements that contribute to ‘data inequality’, such as language or literacy, this section concentrates on two: (1) lack of access to technology to access data; and (2) lack of access to data itself.

Lack of access to technology

In 2018, the International Telecommunication Union estimated that global Internet penetration was only 51 per cent, although it was as high as 81 per cent in the developed world. Notwithstanding that global coverage is improving rapidly, it still means that in 2018 almost half of the world’s population did not use the web or web-based services. Digital divides exist because a wide range of access barriers exist, such as: gender; social; educational; geographic; or economic. As discussed in section “The potential benefits and risks from ICT“, the offline population is disproportionately comprised of women, elderly, less educated, people with lower income and those living in rural areas. In developing economies, the proportion of women using the Internet is five per cent lower than for men. A gap almost three times larger is observed between individuals living in urban and rural areas.

While SDG indicators 17.6.218 and 17.8.119 measure the availability of fixed Internet broadband subscriptions and the use of Internet, more information is needed to understand how people use Internet and what kind of skills they need to maximise the benefits of using it. These data would help policymakers address the related socio-economic inequalities and provide education addressing the skills gaps to avoid exclusion.

The digital divide – limited or no access and connectivity to the web or mobile phones – is creating a data divide. To quote William Gibson: ‘The future is already here – it’s just not very evenly distributed’ (The Economist, 2001). In an increasingly digitized world, anyone who is not connected to the web or using a mobile phone will not only find it increasingly difficult to access data and information, but furthermore, as they will not create a digital footprint, they may also find themselves effectively excluded from many new statistical indicators which increasingly rely on digital data as their source.

Lack of access to and use of data

One of the biggest contributing factors to data inequality is the lack of access to it. Many data are proprietary – typically commercially or privately-owned data are unavailable to the public. For example, data generated from the use of credit cards, search engines, social media, mobile phones and store loyalty cards are all proprietary and are not publicly accessible. While there are sound commercial and privacy reasons for this, the growth in proprietary data is exacerbating the split between ‘the data haves and have-nots’.

This poses some challenging questions for the open data movement as the asymmetry in openness expected of private and public sector data may be inadvertently contributing to the growth in inequality. Many ‘open data’ initiatives are in fact drives to open government data.20 This of course makes sense, in that tax payers should own the data they have paid for with their taxes, and so those data should be public, within sensible limits. But arguably people also ‘own’ much of the data being held by search engines, payments systems and telecommunication providers too. After all, these data were created through their labour and activities, and so they can legitimately lay some claim to their ownership. Hence the exclusive focus on public or government data is somewhat problematic. The philosophy of open data should be more evenly applied to avoid creating asymmetrical conditions.

This is important as the availability and use of data and statistics contribute to societies with more empowered people, better policies, more effective and accountable decision-making, greater participation and stronger democratic mechanisms (UNECE, 2018).

Data, statistics and information have a central role in 2030 Agenda (United Nations, 2015). The importance of having access to information is clearly recognized. For example, Targets 12.822 and 16.1023 set out the aspiration that people should have access to information to help live a sustainable life and to protect fundamental freedoms. Arguably, data are even more central to 2030 Agenda than is immediately obvious. For example, Target 1.424 aims to give men and women equal rights to economic resources and access to basic services. In a data driven world, data must be considered both an economic resource and a basic service. Furthermore, data should be considered as an integral part of a State’s infrastructure. SDG Target 9.125, which deals with reliable and sustainable infrastructure, although presumably not drafted with data in mind, nevertheless summarises perfectly the requirements of a global statistical system. It should consist of quality, reliable, sustainable and resilient infrastructure, including regional and trans-border infrastructure. In other words, a global statistical system not only contains high quality data and statistics, but well designed and robust codes, identifiers, classifications and mechanisms for transmitting and disseminating those data (MacFeely and Barnat, 2017). Equitable and affordable access to data and statistics is fundamental to supporting economic development and human well-being.

To achieve the ambitions of 2030 Agenda, Governments need access to more statistics, and the capacity to use them, to inform policy formulation and evaluation. Avendaño et al. (2018) use text mining to evaluate the use of statistics in national development plans and poverty reduction strategies by identifying 572 keywords. Their assessment covers 102 developing countries and 199 documents from across Africa, America, Asia, Europe and Oceania, spanning the years 2000 to 2017. Figure 6 shows the increased use of data and statistics between two waves of national development planning to advance progress towards the 2030 Agenda.

Map 1. Change in scores for the use of statistics in policy making for plans published in 2000-2008 compared with 2009-2017
Economies

While it is difficult to define the monetary value of having reliable statistics, some attempts have been made. Bakker et al. (2014) estimated that each New Zealand dollar invested in the census in New Zealand generated a net benefit of five dollars in the economy. Benefits of a similar magnitude were estimated for the 2011 population census in the United Kingdom. Further, a study comparing developments in Wales and England, after Wales ceased publishing school performance statistics, in 2001, while England continued, estimated a return of US$17 for every dollar invested in school statistics (UNECE, 2018). Manyika et al. (2013) have estimated the potential global economic benefits of open government data and shared private data to be about three trillion dollars annually.

A concluding caution

There are many faces to inequality. Such a complex issue can be difficult to understand. Depending on the variable selected, or the time horizon analysed, global inequality may be said to be falling or rising. For example, looking at global economic inequality over a ten year horizon, inequality can be said to be falling. From a shorter time horizon, say the last three years, the picture is less clear. Furthermore, global economic inequality may be falling while simultaneously economic inequality within countries may be increasing. There are also many faces to data quality. Caution should be exercised when discussing economic inequality in general as the quality of GDP estimates vary enormously, particularly in developing countries with large informal economies (Jerven, 2015; Pilling, 2018). It is quite possible that current estimates of GDP in many developing countries are underestimated, thus overstating global economic inequality. In other countries, particularly those where MNEs dominate the domestic economy, GDP may be a particularly uninformative indicator of economic development and may serve to only distort international comparisons. For global income inequality the converse may be true, as often the highest incomes prove very difficult to measure using traditional survey instruments, thus introducing the risk of understating global income inequality. The same is true of every other measure – caution should always be exercised. As Muller (2018) wisely counsels “measurement is not an alternative to judgement: measurement demands judgement” (p. 176). Rosling et al. (2018) have also highlighted the need to be skeptical about conclusions derived purely from number crunching.

Caution should also be exercised regarding the ownership of, and access to data. The concentration of power in this field, must surely be a cause of growing concern (Reich, 2015; Lagarde in Reuters, 2019).

Notes

  1. Target 10.2 – By 2030, empower and promote the social, economic and political inclusion of all, irrespective of age, sex, disability, race, ethnicity, origin, religion or economic or other status.
  2. For example, household surveys may exclude households of linguistic minorities, those without telephones or fixed addresses, nomadic households, and households in distant or difficult-to-reach locations. They typically exclude the homeless or those without a fixed address.
  3. A popular welfare-based measure of inequality. It presents the percentage of total income that a society would have to forego in order to have more equal shares of income between its citizens.
  4. It shows the proportion of all income which would have to be redistributed to achieve a state of perfect equality. In other words, the value of the index approximates the share of total income that has to be transferred from households above the mean to those below the mean to achieve equality in the distribution of incomes.
  5. This is typically done using the Lorenz curve. Developed by American economist Max Lorenz, the Lorenz curve is a graphical representation of the distribution of income or wealth. It shows the proportion of overall income or wealth held by the bottom x per cent of households. Many economists consider it to be a good measure of social inequality.
  6. It is the ratio of the average income of the richest decile of the population to the average income of the poorest decile.
  7. It compares the ratio of the average income of the richest 20 per cent of the population to the average income of the poorest 20 per cent of the population. Used by the United Nations Development Programme Human Development Report (called “income quintile ratio”).
  8. Palma index (per capita) = [(Income share held by the highest 10 per cent)/10] / [(Income share held by lowest 40 per cent)/40].
  9. Indicator 10.3.1 – Percentage of the population reporting having personally felt discriminated against or harassed within the last 12 months on the basis of a ground of discrimination prohibited under international human rights law.
  10. Target 10.4 – Adopt policies, especially fiscal, wage and social protection policies, and progressively achieve greater equality.
  11. Indicator 10.4.1 – Labour share of GDP, comprising wages and social protection transfers.
  12. Including UNCTAD transition economies.
  13. Bangladesh, Brazil, China, Colombia, Dominican Republic, Ghana, Guatemala, India, Kenya, Malawi, Nicaragua, Pakistan, South Africa and Vietnam.
  14. In countries where one or more living wages are published per country, the lowest living wage was taken.
  15. These figures are based on the FOLK data on occupational status that also include detailed data on employees’ earnings and their formation as well as background information on the employer.
  16. Strengthen the capacity of domestic financial institutions to encourage and expand access to banking, insurance and financial services for all.
  17. Indicator 8.10.2 – Proportion of adults (15 years and older) with an account at a bank or other financial institution or with a mobile-money-service provider.
  18. Indicator 17.6.2 – Fixed Internet broadband subscriptions, by speed.
  19. Indicator 17.8.1 – Proportion of individual using the internet.
  20. For example: the OECD Open Government Data (OECD, 2019) is a philosophy, and increasingly a set of policies, that promotes transparency, accountability and value creation by making government data available to all. In the United States, Data.gov (2019) aims to make government more open and accountable. Opening government data increases citizen participation in government, creates opportunities for economic development, and informs decision making in both the private and public sectors. In the European Union, there is a legal framework promoting the re-use of public sector information (EU, 2013).
  21. The HDI includes three components: income (gross national income per capita), education (years of schooling) and health (life expectancy at birth).
  22. Target 12.8: By 2030, ensure that people everywhere have the relevant information and awareness for sustainable development and lifestyles in harmony with nature.
  23. Target 16.10: Ensure public access to information and protect fundamental freedoms, in accordance with national legislation and international agreements.
  24. Target 1.4: By 2030, ensure that all men and women, in particular the poor and the vulnerable, have equal rights to economic resources, as well as access to basic services, ownership and control over land and other forms of.
  25. Target 9.1: Develop quality, reliable, sustainable and resilient infrastructure, including regional and trans-border infrastructure, to support economic development and human well-being, with a focus on affordable and equitable access for all.

References

  • Adams B and Judd K (2019). Who influences whom in the policy arena? Statisticians seek greater voice. Global Policy Watch Briefing 29. March. Available at https://www.globalpolicywatch.org/blog/2019/03/25/policy-arena-statisticians (accessed 12 June 2019).
  • Avendaño R, Baredes BY and Tian Y (2018). Proposing a use of statistics indicator for national development plans. Paper prepared for the 16th Conference of IAOS. Paris. 20 September. PARIS21. Available at https://www.oecd.org/iaos2018/ (accessed 12 June 2019).
  • Bakker C, Taylor Duignan Barry, New Zealand and Statistics New Zealand (2014). Valuing the Census. Statistics New Zealand. Wellington.
  • Brinkman H-J, Attree L and Hezir S (2013). Addressing horizontal inequalities as drivers of conflict in the post-2015 development agenda. Saferworld. February. Available at https://www.saferworld.org.uk/resources/publications/725-addressing-horizontal-inequalities-as-drivers-of-conflict-in-the-post-2015-development-agenda (accessed 30 May 2019).
  • Cobham A and Sumner A (2013). Is it all about the tails? The Palma measure of income inequality. Working Paper No. 343. Center for Global Development.
  • Data.gov (2019). Open government. See https://www.data.gov/open-gov/ (accessed 13 June 2019).
  • Deaton A (2004). Measuring poverty in a growing world (or measuring growth in a poor world). NBER Working Paper No. 9822. National Bureau of Economic Research.
  • Deaton A (2013). The Great Escape: Health, Wealth, and the Origins of Inequality. Princeton University Press. Princeton, New Jersey.
  • EU (2013). Directive 2013/37/EU of the European Parliament and of the Council of 26 June 2013 amending Directive 2003/98/EC on the re-use of public sector information June. Available at https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32013L0037&from=FR (accessed 13 June 2019).
  • Fukuda‐Parr S (2019). Keeping out extreme inequality from the SDG Agenda: The politics of indicators. Global Policy. 10(S1):61–69.
  • Gapminder (2015). Data sources used in Don’t Panic — End Poverty. September. Available at https://www.gapminder.org/news/data-sources-dont-panic-end-poverty/ (accessed 13 June 2019).
  • Global Living Wage Coalition (2019). See https://www.globallivingwage.org/ (accessed 13 June 2019).
  • Jerven M (2015). Africa: Why Economists Get It Wrong. African arguments. Zed Books. London.
  • Lakner C and Milanovic B (2016). Global income distribution: From the fall of the Berlin Wall to the Great Recession. The World Bank Economic Review. 30(2):203–232.
  • Lindroos P, Luomaranta H and Nurmi S (2019). The role of gender in trade – evidence from linking Finnish microdata on employees and entrepreneurs. Presented at the Work Session on Gender Statistics. Neuchâtel, Switzerland. 15 May. Available at http://www.unece.org/index.php?id=50364 (accessed 13 June 2019).
  • MacFeely S and Barnat N (2017). Statistical capacity building for sustainable development: Developing the fundamental pillars necessary for modern national statistical systems. Statistical Journal of the IAOS. 33(26):1–15.
  • Manyika J et al. (2013). Open data: Unlocking innovation and performance with liquid information. Available at https://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/open-data-unlocking-innovation-and-performance-with-liquid-information (accessed 13 June 2019).
  • Milanović B (2012). The Haves and the Have-Nots: A Brief and Idiosyncratic History of Global Inequality. Basic Books. New York.
  • Muller JZ (2018). The Tyranny of Metrics. Princeton University Press. Princeton, New Jersey.
  • Murawski S (2013). Gini, Palma and the median inequality indicator: Sub-debate on measuring inequality. December. Available at http://www.thebrokeronline.eu/Articles/Gini-Palma-and-the-median-inequality-indicator (accessed 30 May 2019).
  • OECD (2018). Entrepreneurship at a Glance 2018 Highlights. October. Available at http://www.oecd.org/sdd/business-stats/ (accessed 13 June 2019).
  • OECD (2019). Open government data. See http://www.oecd.org/gov/digital-government/open-government-data.htm (accessed 13 June 2019).
  • Piketty T (2014). Capital in the Twenty-First Century. Belknap Press. Cambridge, Mass.
  • Pilling D (2018). The Growth Delusion: The Wealth and Well-Being of Nations. Bloomsbury. London.
  • Reich RB (2015). Saving Capitalism: For the Many, Not the Few. Alfred A. Knopf. New York.
  • Reuters (2019). IMF’s Lagarde highlights potential disruptive nature of fintech. 8 June.
  • Roelen K, Sabates-Wheele R and Devereux S (2016). Social protection, inequality and social justice. World Social Science Report 2016: Challenging Inequalities: Pathways to a Just World. UNESCO Publishing. Paris: 231–236.
  • Rosling H, Rosling O and Rosling-Rönnlund A (2018). Factfulness: Ten Reasons We’re Wrong about the World and Why Things Are Better than You Think. Flatiron Books. London.
  • Schwellnus C, Pak M, Pionnier P-A and Crivellaro E (2018). Labour share developments over the past two decades: The role of technological progress, globalisation and “winner-takes-most” dynamics. OECD Economics Department Working Papers No. 1503.
  • Stiglitz JE (2012). The Price of Inequality: How Today’s Divided Society Endangers Our Future. Norton. New York.
  • The Economist (2001). Broadband blues – Why has broadband internet access taken off in some countries but not in others? 21 June.
  • UNCTAD (2013). Growth and poverty eradication: Why addressing inequality matters. UNCTAD Post-2015 Policy Brief Series No. 2. UNCTAD/PRESS/PB/2013/4. Geneva.
  • UNCTAD (2014). Tackling inequality through trade and development in the post-2015 development agenda. TD/B/61/7. Geneva. 4 August.
  • UNCTAD (2018a). Handbook of Statistics 2018. United Nations publication. Sales No. E.19.II.D.1. Geneva.
  • UNCTAD (2018b). Better data and statistics for gender responsive trade policy. UNCTAD Policy Brief No. 70. UNCTAD/PRESS/PB/2018/7. Geneva.
  • UNCTAD (2019). UNCTADStat. See https://unctadstat.unctad.org/ (accessed 10 April 2019).
  • UNECE (2018). Recommendations for Promoting, Measuring and Communicating the Value of Official Statistics. United Nations. ECE/CES/STAT/2018/2. New York and Geneva.
  • United Nations (2015). Inequality measurement. Development Issues No. 2. Department of Economic and Social Affairs. Available at https://www.un.org/development/desa/dpad/document_gem/development-issues-series/.
  • United Nations (2019). Global SDG Indicators Database. See https://unstats.un.org/sdgs/indicators/database/ (accessed 12 June 2019).
  • United Nations and OHCHR (2003). Human Rights A Compilation of International Instruments – Universal Instruments. United Nations publication. Sales No. 02.XIV.4. New York.
  • United Nations Secretary-General’s Independent Expert Advisory Group on a Data Revolution (2014). A world that counts: Mobilising the data revolution for sustainable development. Available at http://www.undatarevolution.org/report (accessed 4 June 2019).
  • Wired (2018). The next cold war Is here, and it’s all about data. March.
  • World Bank (2016). The Global Findex Database. See https://globalfindex.worldbank.org/ (accessed 6 December 2016).
  • van Zanden JL, Baten J, Foldvari P and van Leeuwen B (2014). The changing shape of global inequality 1820-2000; Exploring a new dataset. Review of Income and Wealth. 60(2):279–297.

Click on a star to rate this page!

Average rating / 5. Vote count

We are sorry that this post was not useful for you!

Let us improve this post!

Share this page: