Indicators and sources for exploring tax privileges of the super-rich SDG indicator 10.4.2 also measures the distributional ef­fects of taxes and transfers with respect to the changing GINI index on income inequality. That is a great idea and at least theoretically gets closest to what we want to measure, but it has two major pitfalls: First, it is so far only available for 52 countries. But, more importantly, it usually does not make any correction for high income, excludes tax on capi­tal income and only looks at direct transfers as well as spending on health and education. SOURCE 1: GLOBAL COMPARATIVE STATISTICS Several international organisations as well as academics and NGOs seek to compare the distribution of wealth and in­come as well as the effects of taxation and social policy on the distribution of after-tax income and personal welfare. For example, official statistics compare labour share, income distribution and the structure of tax revenue at a global lev­el. In addition, the World Inequality Lab as well as wealth managers such as Credit Suisse collect data on the distribu­tion of wealth and income. Official and academic statistics provide an overview of the sources of state revenue across most countries. In addition, various attempts such as the Commitment to Reducing Inequality Index of Oxfam try to measure the effects of taxation on inequality. But because data on the distribution of wealth and capital income are very unreliable and the taxation of capital and capital in­come is very country-specific, these global analyses can on­ly serve as a rough starting point in identifying excessive tax privileges for the rich. DISTRIBUTION OF WEALTH Because only very few countries have comprehensive wealth taxes and the super-rich are usually not sufficiently covered by surveys, reliable data on the distribution of wealth is very hard to come by. Many estimates rely on extrapolations us­ing the distribution of income or rich-lists produced by jour­nalists. For others, the point of departure is macroeconom­ic data on wealth and how it is then distributed through the population based on existing data and assumptions regard­ing distribution of the share that remains unallocated. The World Inequality Database(WID) contains estimates on wealth distribution in 179 countries and also assesses the quality of these estimates. At nearly 55 per cent, the richest one per cent in South Af­rica has the biggest share of wealth, followed by Swaziland. At 49 per cent, Chile and Brazil come next. In the most equal(European) countries, this share falls below 20 per cent. The share of the bottom 50 per cent is largely a mirror of the top end. At – 2 per cent, South Africa comes close to the bottom, exceeded only by Ireland with – 3 per cent. Again, the Netherlands and Belgium can be found close to the top, at around 8 per cent, but at 6 per cent China also comes close. LABOUR SHARE AND THE DISTRIBUTION OF INCOME SDG 10(Reduce inequality within and among countries), Target 4(Adopt policies, especially fiscal, wage and social protection policies, and progressively achieve greater equal­ity), Indicator 1(Labour share of GDP) The most direct way to achieve more equality within coun­tries is often through jobs and better salaries for people who can or do work, but still belong to the poorer strata of society, combined with lower rents and profits for those who own rental housing and companies and nearly exclu­sively belong to the richest part of society. That is why SDG 10.4.1 measures the share of domestic product that com­pensates work rather than capital. Based on the standard­ised system of national accounts that are available for most countries worldwide and survey data that are available in standardised format for 95 countries, the International La­bour Organisation(ILO) produced a baseline study in 2019 (ILO, 2019) and now provides regular worldwide data 3 on the share of labour income as well as the distribution of la­bour income. Apart from the usual problems involved in measuring eco­nomic output in national accounts(i. e. accounting for the in­formal economy and non-monetarised values like the envi­ronment or unpaid care work), this data faces one main chal­lenge: about 50 per cent of the global workforce is self-­employed. And while the share of employed workers comes close to 90 per cent in high-income countries, it can be less than one-fifth of the workforce in low-income countries. The ILO data bridges this gap and even produces statistics on the distribution of labour income using survey data that at least in Belgium offered results largely comparable with official statis­tics. Measuring inequality this way has the big advantage that it is relatively“easy” and widely accessible. And it provides in­teresting results. The data shows that the share of income earned by workers declined from 53.7 per cent in 2004 to 51.4 per cent in 2017. But there are big differences between countries. While in Tajikistan or Gabon labour receives less than 30 per cent of total income, in 14 countries, including Nigeria, Chile or Honduras, this share exceeded 60 per cent. Northern Africa and Central America were the regions with the lowest share, and across all regions lower-income coun­tries have comparatively lower labour shares. And the data al­so shows that distribution matters. Increasing incomes at the very top comes at the expense of everyone except the richest 10 per cent, while increases for median-income workers ben­efit everyone except the richest 10 per cent. And once again here, there are very big differences between countries. Some countries like Germany, the UK and the US see large increas­es at the very top, partly at the expense of the poor, while the share of labour income for the top 10 per cent varies between 19.4 per cent in Jordan and 88.1 per cent in Niger. 3 The dataset uses imputations and extrapolations for many countries that according to ILO can make the results highly uncertain and not comparable. More information is available at: https://ilostat.ilo.org/ topics/labour-income/. 13