generations, Conolly, Haeck, and Laliberte (Chapter 10) find that income mobility is also declining in Canada. More evidence of a recent decline in intergenerational mobility is documented in chapter 13 (by Garbinti and Savignac), who observe a decline in wealth mobility (proxied by home ownership mobility) in France.

The book also includes several chapters that study the distribution (and transmission) of wealth. In addition to chapter 13, chapter 5 (by Gale, Gelfond, Fichtner, and Harris) confirms that age is an important factor when it comes to wealth accumulation; in an empirical application to the United States, the authors observe an upward shift in wealth among older individuals but a decline in wealth among younger individuals. Switzerland is shown to stand out with exceptionally low levels of real-estate wealth among the bottom 50 percent (i.e., exceptionally high concentration of real-estate wealth) when compared to other high-income nations; see chapter 4 (by Martinez). In an empirical application to Italy, chapter 6 (by Acciari and Morelli) observes that the value of inheritance as a share of national income has nearly doubled from around 8% in 1995 to 15% in 2016, while tax revenues corresponding to inheritance transfers have declined – hinting at the significance of tax evasion.

In addition to establishing trends in inequality (and intergenerational mobility) and disaggregating these by selected sub-groups of the population, MDMIW also provides several comparative studies that highlight differences observed within the high-income world. Chapter 1 (by Gornick, Milanovic, and Johnson) conducts a comprehensive study of inequality among OECD countries, considering different choices of income and between-group decompositions. The United States is found to come out on top with high levels of inequality relative to its OECD peers. This observation holds up across the different sub-groups and measures of income considered. The results obtained in chapter 11 (by Mitnik, Helso, and Bryant) show that the United States also ranks unfavorably in terms of inequality of opportunity (in addition to inequality of outcomes).

The fifth and last section of MDMIW consists of five chapters that combine macro data (national accounts) with micro data (household surveys). Household survey data are well known to undercover households from the very top of the income distribution for a variety of reasons, while they are the ideal source of data for incomes below the 95<sup>th</sup> percentile. Adequate coverage of top incomes however is crucial for the estimation of income inequality. National accounts on the other hand are an ideal source of data for capturing top incomes but may be a less reliable source of data below the top-income level. That highlights the potential for combining these two sources of data. The concluding chapter (chapter 23 by Dominic Webber, Richard Tonkin, and Martin Shine), for example, develops a novel methodology employed by the UK’s Office for National Statistics (ONS) that utilizes that potential to produce a new top income adjusted series. The UK's statistical agency is the first among its peers to do this. (See also the recent study by Jenkins 2022.) Chapters not highlighted here are no less impressive and we encourage the interested reader to explore these chapters. One chapter that is missing in the book, however, is a concluding 6