Measurement errors in agricultural data and their implications on marginal returns to modern agricultural inputs uri icon

abstract

  • The low uptake of modern agricultural technologies in sub-Saharan African countries has encouraged researchers to revisit the returns to (or profitability of) these agricultural inputs. A related strand of literature is exploring the allocative efficiency of these factors of production in African agriculture. However, all these studies rely on self-reported agricultural data, which are prone to nonclassical measurement errors, the errors in these data are correlated with the true values of variables of interest. In this article we investigate the implication of measurement errors in self-reported agricultural input and production data on marginal returns to these modern agricultural inputs. We consider a generic two-sided measurement error problem where both production and inputs can be measured with error, and these errors can be correlated. We employ both self-reported and objective measures of production and plot size to compute output elasticities under these alternative measurement scenarios. We find that using self-reported production and plot size overestimates output elasticities and hence marginal returns to modern agricultural inputs (including chemical fertilizer and improved seed). These results are noteworthy in terms of informing conventional technology diffusion strategies as well as in view of revisiting existing presumptions about the profitability of modern agricultural inputs.

publication date

  • 2020
  • 2020