upper)<sup>3</sup> . We find that across the transition region, the poorest third of the population in each country is more likely to report that their household was adversely impacted by the crisis (see Table 4). Similarly, household asset ownership (ownership of a car, a secondary residence or a bank account) is negatively correlated with crisis impact. The greater subjective impact of the crisis on poorer households is consistent with the fact that they are much more likely than richer households to have experienced job losses.<sup>4</sup> The subjective impact on the poorest households is greatest in Turkey, the western CIS (BelarusMoldova-Ukraine) sub-region, the new EU member states and the south Caucasus. However, in Russia, Central Asia and south-eastern Europe, the LiTS II data do not suggest a differential subjective impact for the poorest third of households relative to richer households. Lastly, female-headed households<sup>5</sup> and the elderly (or pensioners) do not report a significantly greater crisis impact than other households. These findings based on household perceptions corroborate some of the preliminary or partial information from objective measures but there are also some significant differences. Indeed, consumption data (from Household Budget Surveys) suggest that the crisis affected mostly middle-and upper-income groups (Sulla, 2011). This is consistent with the LiTS II finding that upper- or middle-class households report the largest drops in their social position (relative income). Nevertheless, poorer LiTS households (as measured by their current consumption) also report more being (subjectively) affected by the crisis than richer households.

There could be many reasons why subjective assessments may differ from objective measures. For example, the trauma associated with job-loss can go beyond the associated drop in household income, there may be increased insecurity about the future, and so on. The subjective impact of the crisis may therefore depend on the coping strategies adopted, the intrinsic value of employment, and the extent to which households value leisure, uncertainty or different types of expenditures. Finally, there could be systematic reporting biases if different groups have

> 3 Due to low response rates, actual or imputed rents are not included. Previous analysis showed that the consumption aggregates constructed from LiTS I compare well with conventional measures based on national accounts and household surveys (see Zaidi et al, 2009).

> 4 The greater impact of the crisis on the poor as well as the correlation between job loss and low consumption could be due to greater vulnerability of initially poor households to job loss, or to lower consumption caused by the job loss experienced by the household. The data do not enable a comparison between the two hypotheses as only current consumption is measured in LiTS II.

> 5 Because the LiTS data measure crisis impact at the household level, it is not possible to analyze differences at individual level. The lower likelihood that female-headed households were affected is consistent with the crisis impact on sectors that employed higher proportions of males (for example, construction).

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