the changes over time in position of individuals in the distribution. Consequently, the results in terms of inequality or pro-poorness of growth might differ substantially between the conventional poverty analysis which relies on cross section data and the mobility profiles approach (or mobility statistics), based on longitudinal data. Jenkins and van Kerm (2008) show in the case of Britain that whether patterns of income growth became more pro-poor under the labour government in the year 1999-2003 (with respect to the Conservative period 1992-1996) depend on the perspective used (and the definition of income growth). According to conventional analysis using a cross-sectional perspective, income growth became more pro-poor using both absolute and proportionate growth definitions. However, from a longitudinal perspective, an increase in progression of income growth is most clearly apparent using absolute income growth definition. The picture of greater progressivity is more muted when viewed in terms of proportionate changes<sup>10</sup> .

The sensitivity of the conclusions to how income growth is defined raises questions about how changes in income distribution over time should be assessed. In this context it is also important to bear in mind that the negative slope of the mobility profiles, which is associated with a pro-poor definition, describes a form of regression to the mean which can reflect also the effects of measurement errors and transitory variations. It is therefore essential to apply methods to mitigate the cause of potential spurious impact in the analysis.

# **_Measurement errors in the analysis of mobility based on panel surveys_**

All income distribution statistics are sensitive to measurement errors and transitory variations in income, but the issue is particularly relevant when estimators are based on change measured at the household level. The observed mobility or growth rates for poor versus non-poor can be genuine – attributable to genuine economic phenomena – or reflecting the effects of measurement errors and transitory variations in income. If there is measurement error of the "classical" form (uncorrelated with the true value and over time), then the expected income increase is positive for someone with a below-average income and negative for someone with an above-average income. As a result, some of the observed progressivity in income growth may be spurious.<sup>11</sup> As Baulch and Hoddinott (2000) phrase it, “... some of the observed movements in and out of poverty will be a statistical artifact.” This is also known as “Galton fallacy”.

> 10  In their analysis there is no contradiction between the results of cross-sections and panel.  Egypt data analyzed in this paper demonstrates a more extreme case of diametrically opposite results from two types of analysis. 11 Van Kerm and Jenkins (2008) use an elegant metaphor to illustrate this point.  If one rolls a standard die, the expected number of spots at any roll is 3.5 (the sum of the possible scores divided by six). If the first roll in fact produces a 1, then the expected increase in the score when the die is rolled again is positive (+2.5). By contrast, if a

> 6 comes up first, the expected gain at the second roll is negative (–2.5). So, despite there being no association

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