selection bias which is time-invariant. If this is the case, the _DD_ estimator is unbiased and the program impact is correctly estimated (Ravallion 2008). In our case, we can relax the time-invariant assumption given that we do not use group A as a comparison group but a match of group B extracted from group A. One problem may instead arise from the fact that we need to use the panel component of the surveys rather than the two full cross-section surveys.<sup>6</sup> We need to trace the same households between period 1 and 2. This implies a certain attrition resulting in a loss of observations and a possible loss of sample representativeness. This potential loss will be assessed by testing means equality of the balanced and unbalanced panels.

The third step will be to exploit the availability of a four years period for a longitudinal parametric analysis. We regress the intensity of benefits (conditional on a number of other variables) on a measure of welfare and the social benefits status (conditional on a number of variables) on the poverty status as follows: where α _i_ , β, γ are parameters, ε _it_ is the error term, _Poorit_ is a binary variable with 1 = _Poor_ and 0 = _Non_ Poor_ , _yit_ is a continuous measure of welfare (described in the next section), _Bit_ is a continuous measure of benefits (described in the next section), _Pit_ represents program participation and _i_ and _t_ stand respectively for units of observation and time. We call [4] the welfare equation and [5] the poverty equation. The advantage of this approach is that we can exploit the ability of longitudinal models to treat non-observed time-variant and time-invariant factors under various assumptions and address the problems of unobserved heterogeneity and endogeneity.

The choice of panel models estimators is extremely rich. We opted to use Fixed effects (FE), Between Effects (BE) and Random Effects (RE) models addressing separately the question of consistency of results across specifications with different sets of regressors and the question of consistency of results across these three models (more on the variables used in the next section).<sup>7</sup> To check for results consistency across models specified with different sets of regressors we use

> 6Note that the standard _DD_ approach does not require a panel structure (Ravallion 2008).

> 7We excluded the pooled OLS because this estimator would provide bias estimates given that we expect significant endogeneity due to unobserved heterogeneity. We also excluded dummy variable regressions (LSDV) because this is practical only when _N_ is small and Instrumental Variables (IV) models because they rely on assumptions about the instrument that cannot be tested. Dynamic panel models were excluded because they would require IV specifications to be unbiased. A conditional fixed-effects logit model was also excluded because it drops observations where the treatment variable does not change over time. In our data, these are groups A and D which would mean wasting most of the dataset. Among error components models we opted to use both FE and RE models. A RE model assumes no covariance between the explanatory variables and the error term that represents person specific and time invariant unobserved heterogeneity. This would be a rather unrealstic assumption. In a country like Moldova, unobserved factors such as individual contacts matter a great deal for finding a job or accessing cash benefits. However, this model has the advantage of accomodating all types of regressors including time invariant and cross-section invariant regressors. This is important because we want to test the model conditional on variables such as rural location or the season when the interview took place which would be dropped in a FE model.

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