**Findings in this study mostly relate to the low mobility scenario and describe a more conservative view**

**of economic mobility.** Under the assumption that returns to non-observable factors such as social networks or non-cognitive ability are highly correlated over time, the low mobility scenario underestimates true economic mobility but overestimates immobility in society (those who remain in the same welfare status over time including chronic poverty). As a result and similar to the work pursued by the LAC team, the lower mobility bound generates poverty transition matrixes which describe a conservative view of economic mobility.

**The identification of determinants correlated to transitions out of poverty is based on actual household consumption for the first period and imputed consumption for the second period.** Here, the imputed consumption level for the second period relates to the low mobility scenario, which assumes perfect correlation in error terms between the first and second period (more specifically _Ĉ2=F2(X1)+e1_ ). The regression model then distinguishes between two different groups of household: first, households which remained in poverty for the first and second period; and second, households which managed to escape poverty between the first and second period. Using a set of time-invariant and time-variant individual and household characteristics as independent variables the analysis highlights systematic differences between the first and second group of households, which can be used to better understand poverty dynamics on the level of households.

**Estimates of economic mobility are also possible during the crisis period.** The synthetic panel methodology identifies the functional relationship between consumption and time-invariant characteristics for each period separately. Thus structural changes during the crisis period are well accounted for when determining patterns of economic mobility. Yet, uncertainty about non-observable factors during the crisis period increases. Do social networks still allow for higher incomes and consumption or do friends and relatives loose in importance as economic or political instability impact the structure of society? Under the second scenario, the economy moves to a high(er) mobility scenario and the true estimate of economic mobility will be relatively closer to the upper bound.

**In response to methodological challenges,** Dang and Lanjouw (2013) **introduce a new approach to determine bounds of economic mobility based on point estimates and confidence intervals.** Compared to the lower and upper bound estimates, point estimates have the potential to generate a more precise measure of mobility which accounts explicitly for sampling and modeling errors. For the lower bound approach, the degree of bias depends on how far the true correlation between the error terms in both periods is from 0. If the true correlation term is close to 0, we have better estimates, and vice versa. Here, the point estimates approach incorporates the true correlation of error terms. One of the key caveats behind the point estimates approach is that the current framework can be used to obtain the profiling of household poverty mobility (i.e., classifying households into different socioeconomic groups), but cannot be used to estimate mobility measures that are constructed based on the continuous variable of imputed consumption level.

**To assess economic mobility in Moldova in the periods of analysis, this paper relies on the “synthetic panel” methodology** . Although Moldova has a panel component in its HBS data, the paper will rely on this method to eliminate concerns of panel attrition and to explore longer periods of time than what the panel component allows (see Annex A1 and Annex A2 for more details).  The study makes use of the available panel data, however, to further add to the literature testing the validity of the “synthetic

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