Can medium-scale farms support smallholder commercialisation and improve welfare? Evidence from Nigeria

In spite of mounting evidence about the growth of medium-scale farms (MSFs) across Africa, there is limited empirical evidence on their impact on neighbouring small-scale farms (SSFs). We examine the relationships between MSFs and SSFs, with particular focus on the specific mechanisms driving potential spillover effects. First, we develop a theoretical model explaining two propagating mechanisms: learning effects (training) and cost effects (reduced transactions cost). An empirical application to data from Nigeria shows that SSFs with training from MSFs tend to use higher levels of modern inputs (have higher productivity), and receive higher prices and income. The results also show that purchasing inputs from MSFs reduces the costs of accessing modern inputs and is associated with higher inorganic fertiliser use by SSFs. Our results suggest that the benefits of receiving training and purchasing inputs from MSFs are particularly important for very small-scale producers, operating less than 1 hectare of land. This implies that policies which promote the efficient operation of MSFs and encourage their interaction with SSFs can be an effective mechanism for improving the productivity and welfare of smallholder farms, hence reducing their vulnerability to extreme poverty.     

Analysing inefficiency in a non-parametric spatial-dynamic by-production framework: A k-nearest neighbour proposal

This paper accounts for spatial effects by benchmarking farms against their k-nearest neighbours (KNN) and measuring their inefficiency in a non-parametric dynamic by-production setting. The optimal number of neighbours $k$ against which farms are compared corresponds to the value of $k$ that maximises the Moran I test for spatial autocorrelation of the good and the bad output of the farms' two sub-technologies. The inefficiency scores for farms' good output, variable inputs, investments and bad outputs are then computed and compared with those calculated based on a global technology, which benchmarks all farms together. The application focuses on an unbalanced panel of specialised Dutch dairy farms over the period 2009–2016 that contains information on their exact geographical locations. The results suggest that the inefficiency scores exhibit statistically significant differences between the KNN and the global model. Specifically, the inefficiencies are generally deflated when a KNN technology is considered, suggesting that ignoring spatial effects can overestimate inefficiency.