the poverty line ( _B_ ) for benefits recipients has instead declined between 2001 and 2004 from 0.16 to 0.14. However, this decline has been accompanied by a remarkable growth in coverage (from 5.7% to 19%) which explains the growth of average benefits per capita in the population (Table 1). The government clearly opted to increase expenditure and coverage rather than focusing on proper targeting based on means tests and the fine tuning of individual transfers.

The transition probability matrices would suggest a positive incidence of social benefits on poverty. Table 2 reports the transition probabilities between poor and non poor. The first two columns in Table 3 report the difference between these probabilities estimated with and without benefits. Positive values indicate an increased probability of moving across poverty groups in the presence of social benefits and vice-versa for negative values. To check for consistency, we repeated the estimations using the balanced and unbalanced four years panels and also the balanced panels for the three periods 2001-2002, 2002-2003 and 2003-2004. We can see that the values for the transition from poor to non-poor (P-NP) are all positive while the values for the transition from non poor to poor (NP-P) are all negative.

The PROT and PROM tests proposed by Ravallion (1995) confirm these findings with positive values for both PROT and PROM for all estimates (Table 3, columns 3 and 4).<sup>12</sup> The poverty headcount index in the presence of social benefits is also lower than in the absence of benefits for all estimates (Table 3, column 5). In substance, the presence of social benefits enhances the probability of moving from poor to non-poor and reduces the probability of moving from non-poor to poor. As already mentioned, the problem with this procedure is that we ignore the issue of the reference group using the untreated individuals as comparison group unconditional on household characteristics.

The double difference estimates with matched samples provide a rather different picture. Table 4 reports the relevant statistics for each group and the respective match over each of the three periods considered. When we compare those who joined the social benefits scheme (group B) with a matched sample extracted from those who did not join (group A) we find that all _DD_ estimates are negative. This indicates that joining the scheme did not improve welfare for the treated everything else being equal. As a counterfactual, we estimated the _DD_ measures comparing those who left the scheme (group C) with a matched sample extracted from those who continued to stay in the scheme (group D). Leaving the scheme seems to improve welfare everything else being equal. From these estimates, social benefits seem to have a negative effect on welfare.

The disadvantage of the double difference approach as we designed it is that we constrain the sample to a rather small number. This is due to the fact that coverage is initially low and that the treated group is small. On the other hand, if the matched sample is valid, estimates should be

> 12Note that columns 1 and 4 in table 3 report the same values. That is because the PROM test is reduced to the difference of the transition probabilities between estimates with and without benefits.

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