Overall, our sample comprises 11,064 enterprises in 8,115 rural households, implying an average of 1.36 enterprises per household. The share of households operating a nonfarm enterprise in rural Africa varies widely: from a relatively low share of 17 percent of households in rural Malawi, to almost 62 percent in rural Niger.<sup>4</sup> Despite the usefulness and strengths of the data, it is also subject to a number of limitations for our present purposes. First, a comprehensive analysis of enterprise survival and failure has to be cursorily due to a lack of data on failed enterprises. Second, enterprises are not explicitly tracked over the survey rounds in Tanzania and Uganda. Therefore we cannot follow the enterprises over each survey round to study their individual performance over time. Duration analysis is therefore not possible to conduct at present, and instead we have to accept less precise estimations at different aggregation levels. Third, we can only calculate labor productivity, and not total factor productivity. The reason is that information to estimate production functions and technical efficiency is not available. Fourth, we do not have information of time-use of each household member, which can lead to biased estimates. For example in the case of female-owned enterprises, we may underestimate their enterprises productivity, as the unpaid household work of women is not accounted for.

Given these limitations we propose a number of modifications and additions to the questionnaires. A full elaboration of these fall outside the scope of the present paper, however we want to briefly mention them as an illustration of the type of current shortcomings we face. Apart from clearly tracking individual enterprises over various survey rounds, discontinued enterprises should equally be registered and information collected. We suggest to add specific questions to know more about these discontinued enterprises, e.g. when exactly the enterprise was discontinued (year and month), and the reason for stopping enterprise operations. Adding modules to the questionnaires to collect information that allow to estimate production functions and technical efficiency, including price information, information about the final product(s) and their prices, wages paid per worker, input costs (intermediate inputs), overhead (transport, security), fixed and working assets employed in the enterprise (to know more about the capital intensity), would greatly facilitate future analyses of the rural non-farm enterprise sector.

# **3.2 Methodology**

In addition to various descriptive statistics, including kernel density estimates of labor productivity, we employ a number of regression techniques to provide answers for the first two questions. To identify the determinants of rural household choice to operate a nonfarm enterprise, we use a discrete-choice estimator, a probit model (see section 3.2.1), and to analyze the type of business activity that a household operates we use a multinomial logit model (see section 3.2.2). To estimate the determinants of the labor productivity in rural enterprises we use both a Heckman selection model, and fixed and random-effects panel-data regression estimators (see sections 3.2.3 and 3.2.4). The variables are selected from the LSMS-ISA database and are based on the literature review.

> 4 See Tables 10 and 11 in Appendix B.1 for the prevalence of non-farm entrepreneurship in both rural and urban Africa.

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