. 22 and mF, mm are the numbers of females and males In the household and I<sup>>a>0 Is a parameter. The case a=1</sup> corresponds to the case In which all self-emp!oyment opportunity sets are Independent across household members. The particular parametrization (3.11) has been cr,osen for computational<sup>convenience. Finally, we have Introduced</sup> g, which Is the fraction of feasible self-employment positions that<sup>yields positive profit during a period (one year).</sup> The rationale behind g9 is that In addition to a llmited set of feasible<sup>self-employment positions Is the fact that a</sup> successful businesa does not necessarily yield positive profRt through every period. In fact the data demonstrates that profit is negative for some households during the period of<sup>the data collection. We may interpret alternatively</sup> as the (average) traction of the year the business Is likely to operate wnh<sup>positive profit. A rigorous treatment of the</sup> choice of self-employment activity would of course require a model<sup>for decision under uncertainty.</sup> The offered wage densities are assumed log normal where the means<sup>depend on experience, SPLYRSC1,</sup> SPLYRSC2, SPLYRSC3 where experience Is defined as age minus length of<sup>schooling minus 6 and</sup> r (X,O,O) if x t 5 (SPLYRSC1, SPLYRSC2, SPLYRSC3) = . (5,x-5,0) if 5 < x 5 _10_ ! (5,5,x-10) if x > 10. **L**

# 4. Summar statcs and p eter esimates

The summary statistics of the variables generated from the household survey data are presented<sup>in Table</sup> 8 for Lima and rural areas. There are differences In observed household behavior<sup>In production and consumption,</sup> and in inovidual and household attributes. For example, consumption (income)<sup>per capita is much higher In Uma</sup> than in rural areas. Women in Uma spend more hours In wage work than<sup>women in rural areas; women In rural</sup> areas spend more hours in self-employment. However rural households<sup>record higher profits from seit-employment</sup> than do households In Uma. This is not surprising given that nontarrn<sup>production is dominant in Uma while</sup> agricultural production is the leading activity in the self-employment category.<sup>Interestingly, female wage workers</sup> earn more In rura: areas than male wage workers. For more details see section<sup>2 and Aaberge and Dagsvik (1990).</sup> The parameters of the opportunity density and of the utility function are estimated<sup>simultaneously by a</sup> modified maximum likelihood procedure. The estimates of the utility<sup>function are presented in Table 9, while the</sup> estimates of the opportunity density are given in Table 12. The estimates<sup>of the wage and profit functions are shown</sup> in Tables 10 and 11 respectively. We have also estimated the wage equations<sup>and the profit function by ordinary</sup> least squares. This procedure may lead to biased estimates since it does<sup>not account for the fact that households</sup> do not maximize profit but the utility of consumption and leisure. Consequently,<sup>the conditional expectation of the</sup> error term in the profit firnction given the hours is in general a function of<sup>these hours because they enter the utility</sup> function through consumption and leisure. The results are reported here only<sup>for Lima and rural areas.</sup>