factors of production – labor days, capital and materials - are significant, and constant returns to scale cannot be rejected. The estimated coefficient on labor is equal to 0.54, which is in line with the literature on microenterprises (see e.g. World Bank 2007 for comparable estimates for Tanzanian microenterprises). The coefficients on capital and inputs are rather modest. Turning to characteristics of the manager, male operated enterprises are much more productive than enterprises managed by females. In addition, there seems to be a convex relationship between enterprise profitability and the education of the manager, as well as between enterprise profitability and the age of the manager, though the latter relationship is less pronounced; since the “turning point” is at about 50 years of age, it seems safe to conclude that age is productivity is inversely related to the age of the manager, except for the very oldest managers The positive and significant coefficient on WRSI implies that prospect of a good crop raises productivity among nonfarm enterprises, probably because of higher local demand. The coefficients on the industry dummies suggest that manufacturing activities are among the least productive activities while trading activities, such as wholesale and retail, are very productive. The high “productivity” of trading activities could reflect arbitrage opportunities due to limited economic integration.

Adding explanatory variables to the parsimonious specification has quite small effects on the results already established. There is evidence here that enterprises in rural towns are more productive than enterprises elsewhere. None of the other additional explanatory variables have statistically significant effects on productivity at the conventional 5% significance, which is rather striking.

Finally, sample selection bias does not appear to be a serious problem. While the Inverse Mills Ratios is significant at the 10% level in our basic specification and at the 5% level in our extended specification, the magnitude of the bias seems modest; individual coefficient estimates seem rather robust and the coefficient on the inverse Mills ratio is small.

# **4. Conclusions**

This paper uses matched household, enterprise and community data from rural Ethiopia to analyze the performance and constraints of nonfarm enterprises in the country. Ethiopia is an interesting case study for several reasons. The agricultural sector is very large and has performed poorly over a long period, and far-reaching income diversification is often referred to as a promising way of reducing poverty in the country. Indeed, private initiatives and income diversification among rural households are acknowledged by the Ethiopian government as central mechanisms for alleviating poverty, as evidenced by the Plan for Accelerated and Sustainable Development to End Poverty (PASDEP). Yet very little is known about the basic characteristics, the constraints and the performance of the rural nonfarm enterprise sector in Ethiopia. One important contribution of this paper is to fill this information gap.

While the rural economy remains dominated by agriculture, the nonfarm enterprise sector in Ethiopia is economically important, particularly for women and especially in the low season for

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