# **1.  Introduction**

Understanding the extent of mobility – defined here as changes in individuals’ relative incomes – is crucial to interpreting movements over time in average incomes in different parts of the income distribution. For example, the policy implications, and even the political acceptability, of a given change in average income in the top 1 percent of the income distribution depends crucially on whether the identity of those in the top income group is stable over time, or instead whether some of the initially rich fall out of the top group and are replaced with those who were initially poorer. This distinction also matters at the lower end of the income distribution. Consider for example the World Bank’s declared goal of promoting “shared prosperity”, defined as growth in the bottom 40 percent of a country’s income distribution.<sup>1</sup> This emphasis on the bottom 40 percent sits well with the World Bank’s focus on the conditions of the poorest in a country. However, to the extent that the role of such a goal is to evaluate the efficacy of interventions targeted towards the initially-poor members of the bottom 40 percent, it is necessary to know whether membership in this group is stable over time, or whether the initially-poor beneficiaries of these interventions become richer and move out of the bottom 40 percent and are replaced by those who started out richer and became relatively poorer.

The ability to document and analyze income distribution dynamics at the individual or household level is severely constrained by the scarcity of panel data sets that track individual or household incomes over time.<sup>2</sup> In advanced economies such as the United States, long and high-quality panel data on household incomes is available in the Panel Study on Income Dynamics (PSID). However, such high-quality long panel data sets in developing countries are rare. Instead, it is much more common for survey data to be available in the form of repeated cross-sections, with different samples of individuals appearing in each round of the survey. In cases where researchers have access to record-level survey data, it is possible to deploy a variety of pseudo-panel estimation techniques to obtain approximations to income distribution dynamics at the individual level.<sup>3</sup> In many cases, however, access to record-level data from

> 1 See for example Basu (2013).

> 2 The different datasets we work with in this paper have different units of observation at the micro level, including individuals, households, and tax units.  They also differ in whether they measure consumption or income.  For terminological convenience we will refer to _income_ distribution dynamics at the _individual_ level wherever it is possible to do so without confusion.

> 3 See for example Dang, Lanjouw, Luoto and McKenzie (2014) for a recent application of such techniques to transitions into and out of poverty. Another approach is to move away from measures of income mobility and instead focus on measures of occupational and/or educational mobility, based on comparison of occupations and/or education levels of parents and children, using individual surveys in which information on the occupations and/or education of multiple generations in the same family can be obtained at a single point in time.  See for example Long and Ferrie (2013) for a comparison of the US and Great Britain, and Sinha (2017) for evidence from India.

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