The propensity score matching methods use observable characteristics to reduce the bias that is attributable to unobservable factors. The extent to which the bias is reduced depends on the quality of the conditioning variables (Becker and Ichino, 2002). We believe that the diversity of variables we have chosen accomplishes this objective and that the assumption that, conditional on the observable factors, participation in the nonfarm sector is not correlated with unobservable that predict future earnings is satisfied. Our observable characteristics include a rich set of _individual_ , _household_ , _community_ and _provincia_ l level characteristics (see Annex 3). Individuals’ characteristics control for personal differences in endowments and skills, while parental education and experience in the non-farm sector control for possible differences in values and preferences that are passed on across generations. Household level characteristics are a proxy for household wealth, which is likely to influence individuals’ decision to participate in the non-farm sector. We also include community level variables to take account of differences in economic opportunities available to individuals living in disparate communities. We add provincial level dummies to control for natural, political, and economic differences across provinces.<sup>14</sup> We consider that working age individuals can choose among six mutually exclusive alternatives; these consist of five occupational choices as well as a “no occupation” option (see section 5.2). Our interest is in the causal average effect of a choice relative to another choice on some outcome (e.g. earnings). We calculate propensity scores using a standard probit (similar to the one described above) where the binary dependent variable represents the two alternatives that we want to compare.

Next we match participants and non-participants with the same or similar propensity score<sup>15</sup> (see Sianesi (2001) or Becker and Ichino (2002) for more details on the assumptions behind the estimation). We use several matching methods.<sup>16</sup> In the first, the nearest matching estimators with replacement, we compare a participant and a non-participant whose propensity scores are sufficiently close, by recognizing that it is impossible to obtain exact propensity scores for all pairings of participating and non-participating individuals, and that a non-participating individual can be a best match for more than one participating individual. In the second, we try kernel matching. Rather than match one participant with one non-participant, we can use several non-participants, to act as the matches for a participant. The idea is to calculate the average propensity score from a neighborhood of propensity scores of several comparison members (nonparticipants), match this average propensity score to the propensity score of a participant and then proceed to obtain the average participation effect as in equation (2). We use

> 14 This specification is consistent with previous empirical literature (see Woldenhanna and Oskam, 2001; Reardon, 1997; and Lanjouw et al., 2001).

> 15 We have used the Stata program developed by Sianesi (2001). In order to check the robustness of the results, we also used the programs created by Becker and Ichino (2002), the results (not shown) were similar.

> 16 More technical and detailed presentation of these estimators can be found in Becker and Ichino (2002).

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