We focus on these as the sectors that identify more univocally engagement in activities outside of agriculture.

The graphs convey the combined effect of the quadratic and interaction terms that are otherwise difficult to read in a standard table of coefficients. The first result that emerges is that non-linearities are clearly present in most of the estimated relationships. For most countries and ‘sectors’ of specialization, the role of distance changes markedly with potential and with city size.

Looking at the graphs for these two categories, the impact of distance from cities appears to be a lot more muted in areas of higher agricultural potential. This is shown for instance in the nonagricultural wage graphs for Malawi and Tanzania, and in the self-employment graphs for Malawi and Niger (it is worth recalling that in Niger the share of non-agricultural wage specializers is extremely low, so that it is not surprising not to find high variability for non-agricultural wage in the Niger graphs).

Uganda is the one country that bucks these broad regularities and displays a U-shaped relationship for self-employment and for non-agricultural wage (the latter limited to cities greater than 500 thousand inhabitants) for both high and low potential areas. In Uganda the lines for high and low potential areas tend to cross around the middle range of the distance variable distribution. Agriculture goes hand-in-hand with off farm diversification in highly integrated areas, whereas low potential areas tend to ‘dominate’ high potential in terms of the odds of households specializing outside of agriculture as distance increases.

In Tanzania, the country in this sample with relatively more abundant land, the difference in potential is only apparent for non-agricultural wage specialization when large cities are considered. Otherwise, similar trends are observed as distance increases, regardless of potential.

An additional observation concerns the fact that indeed the size of the city one considers the distance from does matter in this type of analysis. The forces puling households out of agriculture are in general smaller for small (and medium size) towns. In rural areas close to small towns (20,000 people) one does not observe substantial differences in the pull from off-farm activities, except for self-employment in Niger where households are in fact more likely to specialize in self-employment where agricultural potential is higher.

The story therefore appears to be one of the interaction of smaller towns and potential being more relevant for explaining diversification (as defined here), while larger towns being more relevant when specialization into non-farm is considered.

These findings speak of really different dynamics when the role of small towns is considered and when large cities come into play. For small towns, we find support to the hypothesis that highpotential, low-integration areas see less specialization in off farm activities, the reverse being true for high-integration low-potential areas. These were the two cells in Figure 22 for which we had clear a priori expectations, but we also found that the role of potential is not particularly strong at least when the off-farm specialization categories are considered. The two cells where we had unclear expectations were the high potential-high integration, and low-potential low-integration areas. For the former we find that at least in Tanzania and Uganda the combination of favorable conditions for agriculture and lower distance from urban centers tends to create the conditions for more households to specialize in off-farm activities. When integration is lower and agricultural conditions more difficult, the picture is mixed, with households more likely to engage more fully in non-farm activities in Niger and Malawi, but less likely to do so in Uganda and Tanzania.

When distance to large cities is considered, the impact of distance in low potential areas is much more marked, as signaled by the relatively steep negative slope found in all countries for either selfemployment or non-agricultural wage work, even though a U-shape is still found for Uganda. In other words, the impact of proximity to large cities is highest in low potential areas, as expected. In lowpotential, low-integration areas the sign was uncertain a priori and we find that the impact of 25