CROP PRODUCTION AND ROAD CONNECTIVITY IN SUB-SAHARAN AFRICA The hypothesis to be tested is that better road connectivity increases crop production (or productivity) after controlling for other factors. This effect is assumed to take place through reduction in transport costs of goods and services that raise crop producers’ prices (depending on the elasticity of demand as well as supply).<sup>17</sup> Reduced transport costs also lower producers’ input costs (increasing profitability and supply) and may increase access to technology, including extension services.

There are also possible compositions of production effects, as lower transport costs result in a greater percentage reduction in the price of perishable, bulky items such as vegetables, thus increasing their relative profitability. Finally, where the transport cost reduction is sufficiently large and widespread, there are potential general equilibrium effects on rural and urban nonfarm sectors, wages, and overall incomes (Diao and others 2006).

Thus, the basic model, expressed as a reduced-form crop-production function, is: _Crop production_ **ijl** _= f(agronomic potential_ **ijl** _, local market size_ **i** _, road connectivity_ **i** _, Ω)_ Agronomic potential, or potential crop production, reflects the supply-side constraints on the quantity and quality of suitable land. It measures the maximum crop-production estimates, given suitable land size and quality, in a pixel. In estimation, we multiply the suitable land size by corresponding potential yield (of each crop and production system) in a pixel. Data for both variables are from FAO/IIASA and are used as inputs in the IFPRI SPAM.

To control for the local market size, a main determinant of crop demand, we propose two variables: population count in a pixel and the distance-weighted population aggregate in neighboring areas (up to a 100-km radius). Road connectivity is measured by the travel time to the nearest city with (a) 25,000 people or more, (b) 100,000 people or more, or (c) 500,000 people or more. We experiment with these three road-connectivity measures in turn. Finally, we add country dummies to control for (unobserved) country fixed effects.

# Econometric issues

First, it is necessary to correct for bias in the regression estimates arising because the dependent variables (crop production and productivity) are left-censored data (that is, by definition, their values are never less than a certain value, in this case zero).<sup>18</sup> To overcome this potential bias, we estimate the equations using a Tobit (censored regression) model. In addition, we have dropped observations (pixels) that are unsuitable for agricultural production from our regression.

Second, in the basic model, one of the explanatory (right-hand-side) variables—road connectivity— can be endogenous, since it may be determined by unobserved local factors that also influence crop

> 17 The price gain to producers increases as demand becomes more elastic.

> 18 A crop is produced in an area only when the climate and soil are suitable for the crop and when its production is profitable (the revenue generation from the crop production should be greater than the costs). In mathematical terms, we may express this in the crop-production function: yi* = β’xi + εi. We observe crop production in location i (yi=yi* if yi*>0) only when it is profitable; otherwise, there is no production in that location (yi=0 if yi*<0). Standard regression methods fail to account for the qualitative difference between limit (zero) observations and nonlimit (continuous) observations and thus produce biased parameter estimates (Greene 2003). In our data set, there are in fact many locations where production of certain crops is zero.

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