educational attainment of the enterprise owners is very low: 64% of them never attended school and none have attended college, and very few managers are able to perform basic arithmetic operations.

The proportion of female headed households who own an enterprise is high, consistent with earlier results. Furthermore, enterprise ownership differs depending on geographical characteristics. Households with an enterprise tend to be located in less remote locations, and closer to roads, markets and financial centers, than non-enterprise households. Finally, it is noteworthy that average log annual expenditure (on an adult equivalence scale) is very similar across households with and without enterprises.<sup>8</sup> The average of the log is about 7.3, which corresponds to about 1,500 Birr or about $160 per year. This is a stark reminder of how poor most of the households in the sample are. Figure 5 shows how the ratio of total enterprise profits to household expenditure varies with total household expenditure. For enterprise-households this ratio varies around 0.4, indicating that total profits are less than half of the value of household expenditure on average, consistent with the earlier finding that enterprise-households tend not to rely exclusively on enterprise activities as a source of income. The graph further shows that there is no systematic relationship between the relative importance of enterprise profits and total household expenditure.

Table 14 shows estimates from probit regressions in which we model the likelihood that a household has at least one nonfarm enterprise (columns 1 and 2) and the likelihood that a household has _started_ a nonfarm enterprise during the last three years (columns 3 and 4). Columns (1) and (3) include in the set of repressors basic characteristics of the household and the household head, and an indicator of predicted crop performance based on the availability of water during the growing season, in 2006. We consider this variable, known as the Water Requirement Satisfaction Index (WRSI), a proxy for local demand, but are aware that this variable might also be capturing relative price effects or an increase in the availability of inputs.<sup>9</sup> Columns (2), and (4) add to the basic specification geographical variables, a measure of the opportunity cost of labor (proxied as the daily wage for casual agricultural workers in the community), various measures of the quality of the local investment climate, various shocks, and a proxy for the accessibility of credit in the community.

First consider the results for the models in which participation (i.e. regardless of time of entry) is the dependent variable. The results in columns (1) confirm patterns that are familiar by now: the likelihood of enterprise ownership is relatively high if the household head is young, female and educated. A new result emerging here is that divorced or separated individuals are more likely to own an enterprise than other individuals and that the probability of owning an enterprise is positively related to the number of adult women in the household and negatively associated with the number of children under 5. Predicted crop performance in the community is not significantly associated with

> 8 We focus on household expenditure rather than household income, as the latter variable is very difficult to measure in Ethiopia.

> 9 We thank Måns Nerman for offering this suggestion.

-9-