Josee Randriamamonjy

Factors Affecting Performance of Agricultural Extension: Evidence from Democratic Republic of Congo.

Abstract Purpose: As part of the institutional reforms and agricultural restructuring in the Democratic Republic of Congo (DRC), this paper provides an assessment of the performance of the agricultural extension system as well as factors explaining it. Method: This paper involves key informants’ interviews and surveys of 107 extension organizations and 162 extension agents in randomly selected 156 villages, analyzed using qualitative and logistic regression methods. Findings and Practical Implications: Results show that despite having one of the highest extension agent-to-farmer ratio and a pluralistic extension system, DRC fails to deliver knowledge and technologies to rural areas due to lack of coordination, no unified and clear policy and mandate, lack of funding, aging and low competencies of agents, and lack of mobility and interactions of agents with key actors. This paper complements findings by other studies that number of agents is not a sufficient indication of performance, but an effective system needs to focus on the enabling environment for agents to be motivated to work as mandated. In this paper, enabling conditions that are found to be statistically significant are external funding, enforcement of performance targets, systems of rewards and sanctions, mobility to foster linkages, and skills development. Originality: This paper contributes by: (1) analyzing a cross-section of various organizations and agents to identify factors that explain variations in performance in a statistical and systematic approach; (2) providing insights on how to prioritize investments and options for a fragile state like DRC, with weak infrastructure and institutional capacity and with a long history of neglect for their national extension system; and (3) illustrating how a rich and well-cited conceptual framework can be implemented empirically to provide policy options for a country like DRC.

Micro-econometric and Micro-Macro Linked Models: Impact of the National Agricultural Advisory Services (NAADS) Program of Uganda—Considering Different Levels of Likely Contamination with the Treatment

An important problem in causal inference and estimation of treatment effects is identifying a reliable comparison group (control observations) against which to compare those that have been exposed to the treatment (treated observations). It is common knowledge that the estimate obtained by the difference in the values of the indicator of interest associated with the two groups could be biased due to lack of overlap in the covariate distributions or common support between the treated and control observations (Dehejia and Wahba 2002; Imbens and Wooldridge 2009). This is especially problematic with non-experimental control observations (Dehejia and Wahba 2002) in which case combining propensity score matching and regression methods has been suggested to yield more consistent estimates of the treatment effect than using either method alone (Imbens and Wooldridge 2009). Matching removes self-selection bias due to any correlation between the observable (pre-treatment) covariates and the dependent variable, while regression isolates the effect of change in the covariates on change in the dependent variable over the period of the treatment. Using the combined approach, this paper discusses the effect of using different sets of control groups on estimates of treatment effects of the agricultural extension system in Uganda, the National Agricultural Advisory Services (NAADS) program.