definition going to be spread over space to some extent. On the other hand, agglomeration pushes businesses to locate close to consumers or to the source of raw material. Businesses depending on mobile inputs but with higher transport costs for their outputs would tend to have the highest gains from concentrating in particular locations.

Moreover, the location of economic activities across space may be nonlinear. Fafchamps and Shilpi (2003) find for instance that in Nepal agricultural wage employment is concentrated in rural areas close enough to cities to specialize in high-value horticulture, but not so close as to be taken over by unskilled ‘urban’ wage labor opportunities. Non-linearities may also be relevant when city size is found to matter for engagement in non-farm activities (Fafchamps and Shilpi, 2003) or for poverty reduction (Christiaensen et al. 2013). Also, specialization may need a particular market size or specific types of markets to kick-in (Fafchamps and Shilpi, 2004).

Agricultural potential and distance may interact in determining locational advantage, occupational choices and returns to economics activities. Yamano and Kijima (2010) for Uganda and Deichman et al. (2008) for Bangladesh both hint at ways in which the role of agricultural potential in determining household productive choice changes between more or less connected areas. Finally, different patterns of urbanization (megacities versus growth in small towns) may be associated, or drive, development outcomes.

Bringing these arguments and evidence together, it becomes clear that both exogenous physical location, as well as the interaction between sectors (and factor markets) and endogenous issues related to policies (infrastructural as well as sectoral policies) come into play in complex ways that make it less than straightforward to predict the spatial location of economic activities in rural areas.

Taking advantage of newly available data, this paper seeks to gauge the extent of income diversification among rural households in Sub-Saharan Africa, how this diversification compares with other countries, taking into account differences in levels of development. We also seek to understand the role of geography in the level of income diversification, focusing on the role of agricultural potential and distance to urban areas.

In order to answer these questions, we use comparable incomes aggregates from 41 national household surveys with good quality income data from 22 countries from all developing regions, constructed as part of FAO’s Rural Income Generating Activities (RIGA) project. The initial exploration of the RIGA database (Winters et al., 2009; Davis et al., 2010; Winters et al., 2010) highlighted a number of regularities concerning household’s patterns of income diversification in developing countries, and hinted at a specificity of the Sub-Saharan African countries included in that database, which stood out as the only countries for which specialization in farming, as opposed to holding a diversified income portfolio, was the norm.

That analysis was however based on data for only four countries in Sub-Saharan Africa (Madagascar, Malawi, Nigeria, Ghana). This paper takes advantage of more recent data from some of the same countries and data on an additional five countries (Ethiopia, Kenya, Niger, Tanzania, Uganda), collected as part of the Living Standard Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA)<sup>1</sup> project. This new set of countries accounts for 51 percent of the Sub-Saharan African (SSA) population in 2012, as opposed to 26 percent in the initial RIGA sample. While caution is still warranted in treating this sample as representative of SSA as a whole, its coverage is arguably much more complete. Also, we take advantage of the geo-referencing of households and of the focus on agricultural activities that are two of the defining features of the LSMS-ISA datasets, in order to analyze the role of geography in income diversification.

The paper continues as follows. In Section II, we present and describe the construction of the RIGA database. In Section III, we analyze the participation of rural households in income-generating

> 1 See www.worldbank.org/lsms-isa for more information on the LSMS-ISA program of the World Bank, and for full access to the data and documentation.

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