corresponding to respondents whose mother tongue is Quechua, Aymara, and one of the native languages spoken in the selva, as well as dummy variables for the sierra and the selva. The second specification supplements these with district-level measures of population, mean income, and expenditures made by FONCODES in the twelve months prior to the survey." The third specification is a fixed-effects specification which includes dummy variables for every cluster in the survey. Note that the sample sizes in this cluster fixed-effects specification are smaller because all clusters in which no-one (or everyone) reports having volunteered have to be dropped. Also, the regional dummies and the district-level measures have to be excluded from the regression to avoid perfect colinearity.

Table 3 confirms that individuals with more schooling are significantly more likely to volunteer. Every additional year of education increases the probability of volunteering by between .004 and .007-equivalent to between 6.0 and 5.1 percentile points of the predicted probability of voluInteering at the means of other variables.'<sup>2</sup> Again, this does not seem to be consistent with an explanation in which volunteering is primarily done by individuals with a low opportunity cost of time.<sup>13</sup> To further explore the relationship between opportunity cost of time and volunteering, I graph the probabi I ity of volunteering as a function of the number of hours worked (for a similar graph, see Freeman). A priori, one might expect to find evidence of substitution between work time and the probability of volunteering: working long hours should be an indication of a high opportunity cost of time-both because people who work more are likely to have a high marginal return to work, and because the value of leisure increases as more hours are spent on the job. But Figure I shows that the probability of volunteering generally _increases_ with the number of hours worked, at least for men. Like the results in Tables I and 3, Figure 1 seems to confirm that, if anything, individuals with a high opportunity cost of time volunteer more.<sup>14</sup> Figure 1, finally, shows that men are much more likely to volunteer than women in rural Peru.

> 10 Because the ratio of male to female volunteers is approximately three to one, however, males are actually more likely to volunteer in every category except medical services, social work, and cooking.

> " This is an imputed measure of income, estimated by combining information from the 1993 census with a 1995 household survey. For a discussion of the general methodology see Hentschel et. al. 2000. For its application in Peru see INEI 1996 and Schady 2000b.

12 For example, in the first specification the predicted probability of volunteering for those with x years of schooling is .0726315, while the predicted probability for those with x + 1 years of schooling is (.0726315 + .0043496) = .0769811. The change in probability as a fraction of the predicted probability itself is therefore (.0043496 / .0726315) = .05989 = 6.0%.

**13** When per capita expenditures are included in these regressions the coefficient on per capita expenditures is indistinguishable from zero (z-statistics are -0.54, -0.36, and 0.18), while the coefficient on years of education remains essentially unchanged.

**14** Freeman suggests that motivation is an omitted variable from the volunteering equation (as well as from standard earnings or labor supply equations). More motivated individuals work more in the job market and volunteer more.

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