Abstract
This chapter reviews the current debate on inequality with a focus on the main global trends and their likely causes. Notwithstanding significant progress, data challenges still limit the degree of confidence one should have on the evidence concerning the evolution of inequality between and, especially, within countries. Finding common causes for the heterogeneous experiences across countries might be unfounded, however it is important to focus on the two main overarching explanations proposed in the literature for the recent evolution of inequality, technology and trade. These two elements are surely and everywhere important drivers of inequality, although their interaction with each country’s institutions and policies is an equally relevant factor.
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Hamburg Action Plan (link).
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A. Sen (1997), p. 198.
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This is not to say that focusing on these aspects is sufficient to describe, analyse and reduce inequality.
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The implicit assumption of perfect symmetry among world citizens implicit in the construction of most global inequality indicators can be relaxed allowing for the fact that “national borders matter and cannot be ignored in setting the principles of international distributive justice”(Brandolini and Carta 2016).
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For an historical sketch of household surveys see I. Visco (2015) and the literature cited therein.
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Kuznets (1955), p. 26. For the bulk of his analysis, he used data for the United States, the United Kingdom and two German states (Prussia and Saxony) from the end of the 19th century to 1950 (with differences across countries).
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Typically income or consumption per capita estimated in the national account are higher than the respective mean per capita measured derived from surveys; Deaton argues that the latter are to be preferred over the former for developing countries.
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For a more detailed assessment of available data sources for international comparison of income distributions see Forster and Toth (2015).
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In 2013, the OECD published non-binding guidelines on the measurement of household wealth at the micro level, followed by the Framework for Statistics on the Distribution of Household Income, Consumption and Wealth.
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EU-SILC was launched in 2003 on the basis of an agreement between Eurostat, six Member States (Austria, Belgium, Denmark, Greece, Ireland, Luxembourg) and Norway. It was later expanded to cover all of the EU Member States.
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The first wave included only 17 countries.
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Variables like disposable income or consumption are divided by the square root of the number of family members.
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Latin America whose poors’ headcount ratio fell by about 10% points (from more than 15% in 1990 to almost 5% in 2013) is included in the “RoW” (Rest of the World) aggregate
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As noted in Atkinson and Brandolini (2010) “people are interested in both world inequality and world poverty, but the two literatures are separate… with an uneasy relationship between them”.
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One cannot simply sum income differences by converting all incomes in a common currency, say, the U.S. dollar; to aggregate world citizens in a single global measure one must take into account the differences in what a dollar can buy in different countries. This gives rise to quite complicated measurement issues; the construction of a different numeraire can have strong effects on the relative position of citizens of some countries and hence on the global measure.
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Some lumped into “country groups”.
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This curve is “anonymous” as it does not tell what actually happened to people that were in a given decile of the income distribution in 1988 over the next 20 years since the regional composition of the different global income groups changed radically, because growth was uneven across regions.
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This dataset covers more than 90% of world GDP and 95% of world population.
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Expressed in dollar terms, at 2011 PPP.
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Western Europe, North America, Oceania and Japan.
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The concept of “class” here must be interpreted with some care, first of all because the “anonymity” of the clusters does not allow for a clear identification of who is in each income bracket in a given year. Furthermore, to identify in a more convincing fashion a “class”, one should examine other dimensions beyond income, like the role of property and of occupations (Atkinson and Brandolini 2011).
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Corlett (2016).
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Goldin and Margo (1992).
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Technological innovation in products like the iPhones or Boeing airplanes cannot be separated from the fact that their production process is fragmented internationally, thanks to globalization, which enables the exploitation of costs reduction opportunities and productivity gains from increased specialization.
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Also related phenomena, like immigration and weaker trade unions, were taken into consideration.
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These are mainly workers from China, India and former Soviet Union bloc, which up until the late 1980s were de facto excluded from international markets.
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Studies on the impact of globalization on inequality in developing economies find—in general—a stronger effect (Goldberg and Pavcnik 2007).
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About 560 thousand jobs.
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The importance in the overall decline of earnings of the job-loss related fall in income is roughly 1.5 times that due to the fall in wages.
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See Acemoglu et al. (2012).
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CPS is the most commonly used survey for this purpose in the U.S.
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“I was at the World Bank and a commission reviewed our work on inequality for the U.S. Congress or somebody, and the head of the commission said to us: ‘You are spending taxpayer money to study issues like inequality? Which goes directly against capitalism and growth.’ That was the perception, that it should not be studied” (Branko Milanovic interview at PBS, Jun 29, 2017).
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“A common reaction in the popular press, in political debate, and in academic discussions is to regard the increase in inequality as a problem that demands new redistributive policies. I disagree. I believe that inequality as such is not a problem and that it would be wrong to design policies to reduce it. What policy should address is not inequality but poverty.” (Feldstein 1999)
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An unconditional transfer paid to all citizens in a given country.
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“[…] for instance, to shift priorities from driverless cars, which will likely reduce jobs, to technology that helps the elderly stay in their homes, which would increase the demand for caregivers.” (Atkinson 2016 p. 30).
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Cristadoro, R. (2018). The Unintended Consequences of Globalization and Technological Progress. In: Ferrara, L., Hernando, I., Marconi, D. (eds) International Macroeconomics in the Wake of the Global Financial Crisis. Financial and Monetary Policy Studies, vol 46. Springer, Cham. https://doi.org/10.1007/978-3-319-79075-6_5
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