In their chapter for the IFS Deaton Review of Inequalities, Bourquin, Brewer and Wernham (2022) paint a comprehensive portrait of changing inequalities in the United Kingdom, in most cases since the 1960s or 1970s. This rich synthesis reports levels and trends with respect to multiple facets of economic inequality. Bourquin et al. present diverse indicators spanning income, consumption and wealth inequalities, and then focus on trends in labour market outcomes, including employment rates, hours worked and wages. Throughout the chapter, outcomes are extensively disaggregated – by gender, age, birth cohort, household type, education, geographic region, and more. Towards the end of the chapter, they assess the impact of the COVID-19 crisis on economic inequality.
The exhaustive synthesis presented in the chapter is almost exclusively focused on levels and trends in the UK. Bourquin et al. complement their rich UK-based results with four cross-national exhibits: two on income inequality and two on wealth inequality.[1] The goal of this brief commentary is to expand selected analyses presented by Bourquin et al., by extending the cross-national component – with a focus on income inequality and relative income poverty, both across selected countries and over time.
The results presented in this commentary are based on microdata available from LIS,[2] the cross-national data centre in Luxembourg. LIS is home to two large multi-country databases: the larger, longstanding Luxembourg Income Study (LIS) Database, focused on income, and the newer and smaller Luxembourg Wealth Study (LWS) Database, focused on wealth. (This commentary focuses exclusively on income.) The figures in this commentary compare UK outcomes[3] to those in five countries that Bourquin et al. include in one or more of their cross-national presentations. These comparators include: one Nordic country, Norway; one continental European country, Germany; and three other Anglophone countries, Australia, Canada and the US.[4]
This commentary is organised as follows.
This commentary closes with a brief discussion of policy lessons, drawing on the large and ever-growing research literature based on the LIS data. The value of cross-national comparisons, especially among similar countries, is largely self-evident. Still, it is worth underscoring that looking outside one’s home country brings into relief that socio-economic outcomes seen in any one country – levels or
trends – are not inevitable. While variation across similar countries may exist against a backdrop of commonality, variation is nonetheless the norm. Assessing that variation, especially when multiple indicators are available, enables analyses of the causes and consequences of socio-economic outcomes – analyses that are difficult (and sometimes impossible) to carry out within any one country. Those analyses, in turn, lay the groundwork for policy/institutional analyses that could, and often do, lead to within-country efforts aimed at policy reform and institutional redesign.
[1] See their figure 2 (post-tax/post-transfer income inequality, 37 countries, 2019 or latest year), figure 3 (post-tax/post-transfer income inequality, six countries, over time), figure 6 (top 1% share in net wealth, 26 countries, 2016 or latest year), and figure 7 (top 1% share in net wealth, six countries, over time).
[2] The LIS team gathers microdata sets from a large number of high- and middle-income countries, and harmonises them into a common template, so that they may be used for comparative research across countries and over time. For detailed information about LIS, including names and overviews of the original data sets, extensive documentation, and instructions on how to access both the harmonised microdata and the aggregates constructed by LIS, see https://www.lisdatacenter.org/.
[3] For the UK, the LIS Database includes data from the Family Expenditure Survey (FES) before 1994, and from the Family Resources Survey (FRS) in 1994 and later.
[4] These countries were selected because they are all high-income OECD countries; the LIS data sets include income both pre and post taxes and transfers; and the LIS Database includes data sets from these countries going back to 1980 (which corresponds to ‘wave I’ in the LIS Database).
[5] In the following two sections, we include only non-elderly households. Elderly households rely heavily (in many households, exclusively) on transfers; thus, they experience much more redistribution than do their non-elderly counterparts. To avoid mixing two different scenarios, here we include only non-elderly households.
Cite this as:
Gornick, J. C. (2022), ‘Income inequality and income poverty in a cross-national perspective’, IFS Deaton Review of Inequalities, https://ifs.org.uk/inequality/income-inequality-and-income-poverty-in-a-cross-national-perspective
Graduate Center, City University of New York (CUNY)