that Denmark is more effective in equalizing opportunities than the United States. These kinds of findings can presumably be expanded in future work.

The book makes its most important contributions on its central terrain of measurement. Even if no researcher disputes that studying different concepts of inequality clearly requires reliable data on inequality, the measurement of inequality and socio-economic mobility is inherently challenging – so much so that the ideal data is rarely available, which makes it hard to piece together reliable trends and patterns in inequality and mobility across time, space, and groups. MDMIW documents impressive improvements in the measurement of income inequality, wealth inequality (which is even harder to measure) and socio-economic mobility that make creative use of new (and big) data sources. Even so, it is clear that tracking changes over time remains an enormous challenge, especially when trying to discern trends across the full spectrum of country experience. In the World Bank’s most recent Poverty and Shared Prosperity Report (World Bank 2022), for example, data across two time periods on ‘shared prosperity’ – the rate of growth of the bottom 40% of a country’s income distribution – is only available for a total of 91 economies,<sup>6</sup> and of these, only four are low-income and 21 are lower-middle-income; put most starkly, though approximately 650 million people live in the world’s poorest economies, there is change-in-inequality data (itself of variable quality) on only 13 million of them (see World Bank 2020: 64-65). In short, despite ‘promoting shared prosperity’ being one of the World Bank’s “twin goals” (the other being the eradication of extreme poverty), at the global level progress has been slow in terms of collecting and curating widespread data on changes in inequality (and for the moment is likely to remain so, since at least two datasets, separated by a time span of 5-10 years, are required to determine any such change). In this sense, the rising global interest in and policy stature of inequality, especially as it pertains to the world’s poorest people, _precedes_ the recent academic data revolution that has transpired for inequality research in high-income economies.

The world’s richest countries offer the best possible data for the study of income and wealth distributions and the intergenerational transmission of income and wealth. Making good use of these data, MDMIW offers a multitude of empirical insights. Allow us to highlight some (not being featured here is by no means a judgement of the study). First, returning to the observation highlighted in our opening paragraph, “there appears to be a broad trend across many countries toward an increase in wealth inequality” (Chetty et al., 2022; page 5). More specifically, the collective empirical findings point to recent increases in income inequality and a decline in intergenerational mobility in the high-income world, consistent with the Great Gatsby Curve (Corak, 2013). Chapter 3 (by McKinney, Abowd, and Sabelhaus) observes a recent increase in inequality and decline in income mobility in large metropolitan areas of the United States (over the period 1998-2017). Fisher and Johnson (in chapter 14) similarly observe an increase in inequality in the United States. Using Canadian census and tax returns data linked across

> 6 When discussing matters pertaining to economic life and comparing global trends, the World Bank uses the designation of ‘economies’ (rather than the more familiar term ‘countries’) in deference to those national territories whose formal status as a ‘country’ is contested or unwarranted.

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