Including HEs as both primary and secondary employment provides a more complete view of upward mobility related to HE start-ups, as we avoid the reporting bias related to success of the enterprise by including all HEs. Using this definition we still observe significant higher poverty reduction in rural areas from HE start-ups and Survivors than among Never HEs (25 and 42 percent compared to 17 percent for Never HEs), but the difference is smaller than the effect observed when analyzing primary employment. This is consistent with the results observed in the table 4, indicating that a secondary HE by itself does not lead to poverty reduction. Also consistent with table 4 and secondary HEs being a stepping stone out of poverty, we see substantial poverty reduction observed among Survivors that now can be a primary or a secondary HE. In urban areas we still do not observe any poverty reduction associated with HE start-ups, but the smaller likelihood of falling into poverty still persists.

# **Starting up and sustaining HEs**

The evidence thus far shows that adding an HE to the household livelihood leads to relative higher welfare. However, the share of households with HEs is still low. In this section, we use the panel data to try to analyze which factors measured in the survey data supported the creation of HEs. What determines where and which households that are capable of taking advantage of HEs to enhance their livelihood?

Household opportunities for starting up an HE depend on many factors including those of the individual owner (for instance education and skills, other responsibilities such as child care), the household (for instance assets, employment situation of family members and connections) and community aspects (for instance infrastructure, local governance and access to markets and products), not to mention overall demand for goods and services sold. These aspects may vary over time and location, and may be mutually correlated. Some may not be easily measureable (i.e. entrepreneurial interest of household members). This makes identification of the necessary conditions for successful HE start-ups challenging. What the NPS data do allow is multivariate analysis of this question, controlling for initial conditions.

To identify factors at the household and community level which are correlated with HE start-ups, we ran logit regressions of HE start-ups on household and community characteristics in 2003. We only included the start-ups and the Never HE in this regression so the dummy dependent variable has a value one for an HE start-up household and zero for Never HEs. The household characteristics are the same ones used in previous regressions (age of head, education level of household members, other income sources, assets in 2003). The community level variables for 2003 are available for rural areas only. We include presence of running water and electricity, presence of a market, distance to market (if not present at location), distance to landline phone, subjective questions on direction of the community in general and in terms of employment (is it easier/more difficult to find employment now than in the past etc.), recent infrastructure projects including irrigation, phone lines, and how safe the community is based on theft, burglaries, other crimes, and subjective questions on safety.<sup>12</sup> Safety is included because Kweka and Fox (2011) find in qualitative interviews with HE operators in Tanzania that theft and other

> 12 Factor analysis is used to compile these different questions into a safety index.

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