The second estimation sample (Older Adults) consists of all observations for individuals over the age of 38.

The first striking difference between youth and older adults is that youth have an average of 2 years more of school level education. In terms of higher education however, the two samples are almost the same, with 91% of youth having no higher education and 92% of adults having no higher education. Secondly, in relation to employment types, a greater proportion of older adults are in self‐employment and government employment, compared to youth, who have a higher presence in private sector employment. While the experience variables for youth and adults seem similar, this does not mean that they have overall experience levels that are similar, as experience prior to 1988 is not recorded. Also in relation to employment are some differences between industries where youth and older adults are employed. A higher proportion of older adults (24%) work in agriculture than youth (14%), even though these individuals are from the same areas and households (with 61% of youth being in urban areas and 58% of adults being in urban areas). While a higher proportion of youth work in manufacturing (23% in contrast to 12% for adults).

In relation to household characteristics, the youth and older adult samples are quite similar as we would expect. The only major differences are that youth are less likely to be a head of household (only 20% are compared to 68% of older adults) and that youth are less likely to be married (66% for youth versus 89% for adults). The average age for the youth observations is about 28, almost half the average age for observations of older adults (about 50). Lastly, for both the youth and older adults, most individuals do not provide a complete retrospective work history across 1988 to 2007.<sup>5</sup> Overall, while the summary statistics presented in table B demonstrate significant variation for both adults and youth in terms of employment categories, work experience and household characteristics, these statistics do not reveal anything about employment quality, mobility or the possibility of scarring. In sections 3.3 and 3.4 we address employment quality and mobility, respectively, while investigating scarring in section 4.

**Table B: summary statistics, IFLS**

||**Youth**|**38 and unde**|**r**|**Ol**|**der Adults**||
|---|---|---|---|---|---|---|
|**Variable**|**Mean**|**Std. Dev.**|**Obs.**|**Mean**|**Std. Dev.**|**Obs.**|
|Net monthly earnings|710860.3|993540.4|11546|783345.60|1553930.00|7783|
|hours worked in a normal week|51.47|12.24|11546|50.07|12.80|7783|
|Years in primary and secondary school|9.18|3.13|11546|6.94|3.78|7783|
|Is a university graduate|0.05|0.23|11546|0.05|0.21|7783|

5 More precisely, this is not because they do not provide a complete retrospective work history, but due to our limitation to only including those with full complete work history (1988‐2007) i.e. the way we generate the variable years at work, so we automatically put household members who started to become adults (15 years or older) or new adult household members in the subsequent IFLS waves after wave 1 as missing in the variable of number of years work. It is important to note that the employment section was only given to adult household members who were 15 years or older at the time of the survey (we include those who were over 15 in at least waves 3 and 4, but possibly under 15 in wave 2 and/or wave1). For these reasons, the Work history data incomplete variable is 0.96 for the youth group.

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