We now turn to a more detailed analysis of the correlates of participation in the nonfarm enterprise sector. We use a probit to model the likelihood that a household has an enterprise, as a function of characteristics of the household head, distances from roads and markets, and self-reported constraints to operating an enterprise. Results are presented in Table 4. Owning a nonfarm enterprise is more likely if the household is relatively large, located close to an all weather road or the market, and if the household head is young and female. The latter result is consistent with the analysis by Bardasi and Getahun (2007). In addition, working in a nonfarm enterprise is much more likely to be a primary source of employment for women than for men, which may reflect cultural gender biases against women’s engagement in certain activities.

Documenting the role of education for entrepreneurship in the nonfarm sector is of obvious policy interest. Recall from Table 3 that the average number of years of education is somewhat larger than 2, hence there is, of course, an enormous scope for expansion of education in rural Ethiopia. Can we say anything based on the data available regarding the likely effects of such an expansion on nonfarm entrepreneurship? We suspect the effects of education to be nonlinear, and so we include in the regressions education squared, in addition to the levels term. The results, shown in Table 4, suggest the effects are indeed nonlinear: increasing from 0 to about 5 years of education, then decreasing. The effects are highly statistically significant and not quantitatively negligible. For example, the results imply that an expansion in the average years of education from 2 to 5 would increase the proportion of households with a nonfarm enterprise from 0.28 to 0.32, which amounts to an increase in the number of enterprises in the economy by about 15%. The fact that the likelihood of running an enterprise starts to fall as schooling increases beyond 5 years is probably driven by better access to wage jobs, which tend to be better paid. In any case, very few individuals in the sample have more than 5 years of education.

A major objective of the fielded surveys is to document the nature and severity of various constraints associated with running an enterprise. The upper panel of Table 5 summarizes selfreported data on the most severe constraint to enterprise start-up, distinguishing between households with and without an enterprise. Credit, markets and transportation are the most commonly cited constraints for both groups. The lower panel of Table 5 shows a breakdown by region and location of constraints to running – as distinct from starting - an enterprise, as perceived by existing enterprises. The results are very similar to those for startup, with markets, finance and transport being the most frequently cited problems. In general, the use of formal credit is very limited among the enterprises surveyed. Figure 2 shows that income from agriculture constitutes the primary source of finance of enterprise startup, while credit from formal banks and cooperatives was the most important source of finance for only 2% of all enterprises. The rankings of constraints do not differ very much across regions, more so across sectors. In particular, firms engaging in trade consider financial services and transportation disproportionately problematic, while they are less likely to consider demand (markets)

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