crimes is an important risk to their business. These variables obviously have endogeneity problems, yet we expect these variables to give a sense of in which households and communities HE start-ups mostly happen. Appendix table A9 show the logit start-up regressions of 2003 characteristics at national level, for urban and rural areas, for primary HE start-ups, for secondary HE start-ups, and for terciles of the consumption distribution in 2003.<sup>13</sup> Most specifications showed very few significant variables. In the national, urban, and tercile regressions we only had household characteristics as we could not include community variables. These regressions showed almost no significant variables except for in the tercile regressions. In the tercile specification we did find that education in 2003 was positively associated with start-up in the first tercile, but secondary education was negatively associated with start-up in the top tercile. This is not surprising as with those with secondary education have good opportunities in the wage sector so they might never consider starting an HE. Assets in 2003 were only significant for the third tercile, suggesting that this was not an important predictor of who would be able to start an HE. The community variables used in the rural specification were almost universally not significant.

Our findings mirror those of Vijverberg (2008) using rural investment climate surveys in Nicaragua and Sri Lanka. Vijverberg writes about the regressions results: “few explanatory variables matter and the estimated effects are small”. There are several reasons why we might see this. The community variables are a priori ambiguous. Recent infrastructure projects can improve demand, but if connectivity and information flow increase this could also increase competition. The order of events could be different for different locations thereby diluting coefficients. For instance, a road could have been built in year one leading to an increase in number HEs or the road could have been built in year five just before the survey. In the former case a strong positive coefficient is the correct coefficient, while in the second case no impact would be observed, simply because households have not had time to react yet. The HE sector is very heterogeneous in several aspects; at the household level, at the enterprise level, and at the locality level, so potentially more local models of both sectors and locality might be needed to fully understand the sector. Unfortunately, the current data set is inadequate to analyze these aspects fully. The fact that the investment climate surveys, which were designed with HEs in mind, also finds it difficult to predict HE start-ups illustrates that our understanding of the dynamics of the HE sector is inadequate and that more work and data are needed.

# _What constrains HE growth?_

The panel analysis above was not very helpful in explaining the factors which support or constrain HE start-up. The perceptions data in the enterprise surveys (INFOR2005 and RICs 2010) inform us on why households believe they do not start an HE, why they close their HE, and what they believe

> 13 A first difference regression is better suited at identifying dynamic aspects via changes in characteristics that could impact the household’s decision to start-up a NFE. The drawback of a five year panel is that we do not know causality as we do not know the order of events. A community might have a new market in year three, but we do not know if the NFE was started before or after the new market. We only observe that both the new market and the NFE start-up took place between year one and year five. In any case, we tried a first difference regression and could not find a fit.

20 | P a g e