Crop Monitoring as a Tool for Modelling the Genesis of Millet Prices in Senegal

Abstract:

Food security in Sahelian countries strongly relies on the ability of markets to transfer staples
from surplus to deficit areas. Market failures, leading to the inefficient geographical allocation of food,
are expected to emerge from high transportation costs and information asymmetries that are common
in moderately developed countries. As a result, important price differentials are observed between
producing and consuming areas which damages both poor producers and food insecure consumers. It
is then vital for policy makers to understand how the prices of agricultural commodities are formed by
accounting for the existing market imperfections in addition to local demand and supply considerations.

To address this issue, we have gathered an unique and diversified set of data for Senegal and
integrated it in a spatially explicit model that simulates the functioning of agricultural markets, that is
fully consistent with the economic theory. Our departure point is a local demand and supply model
around each market having its catchment areas determined by the road network. We estimate the local
supply of agricultural commodities from satellite imagery while the demand is assumed to be a function
of the population living in the area. From this point on, profitable transactions between areas with low
prices to areas with high prices are simulated for different levels of per kilometer transportation cost
and information flows (derived from call details records i.e. mobile phone data). The simulated prices are then compared
with the actual millet prices.

Despite the parsimony of the model that estimates only two parameters, i.e. the per kilometer
transportation cost and the information asymmetry resulting from low levels of mobile phone activity
between markets, it impressively explains more than 80% of the price differentials observed in the 40
markets included in the analysis. In one hand these results can be used in the assessment of the social
welfare impacts of the further development of both road and mobile phone networks in the country. On
the other hand, the model could be further developed as a precious tool for the prediction of future
staple prices in the country.