Market opportunities for African agriculture

"Rapid growth in the agricultural sector is central to any strategy for slashing poverty and hunger on the African continent. Yet investments aimed at increasing agricultural productivity need to be linked to market opportunities if they are not to depress commodity prices and farm incomes. It is widely perceived that high market transaction costs, weak domestic consumer demand, and lack of export possibilities are major constraints on agricultural growth prospects for Africa. But just how severe are these constraints, and what can be done to enhance market opportunities to enable agriculture to become a more powerful engine of growth for the continent? This study addresses these questions. It concludes that non-traditional exports have the fewest constraints and remain the most profitable option for increasing export earnings.....Thus, investments in agriculture and other efforts to promote higher agricultural productivity growth need to be complemented with policies and investments to spur non-agricultural growth. More generally, investments in rural infrastructure can help to maximize positive linkage effects of agricultural growth. Agricultural growth can play a major role in increasing food supply, but sustained increases in incomes and reductions in poverty are likely to require a combination of labor-intensive growth in both agricultural and nonagricultural activities." from Authors' Abstract

Inference under Covariate-Adaptive Randomization with Multiple Treatments

This paper studies inference in randomized controlled trials with covariate-adaptive randomization when there are multiple treatments. More specifically, we study inference about the average effect of one or more treatments relative to other treatments or a control. As in Bugni et al. (2018), covariate-adaptive randomization refers to randomization schemes that first stratify according to baseline covariates and then assign treatment status so as to achieve balance within each stratum. In contrast to Bugni et al. (2018), we not only allow for multiple treatments, but further allow for the proportion of units being assigned to each of the treatments to vary across strata. We first study the properties of estimators derived from a fully saturated linear regression, i.e., a linear regression of the outcome on all interactions between indicators for each of the treatments and indicators for each of the strata. We show that tests based on these estimators using the usual heteroskedasticity-consistent estimator of the asymptotic variance are invalid; on the other hand, tests based on these estimators and suitable estimators of the asymptotic variance that we provide are exact. For the special case in which the target proportion of units being assigned to each of the treatments does not vary across strata, we additionally consider tests based on estimators derived from a linear regression with strata fixed effects, i.e., a linear regression of the outcome on indicators for each of the treatments and indicators for each of the strata. We show that tests based on these estimators using the usual heteroskedasticity-consistent estimator of the asymptotic variance are conservative, but tests based on these estimators and suitable estimators of the asymptotic variance that we provide are exact. A simulation study illustrates the practical relevance of our theoretical results.Comment: 33 pages, 8 table