The recent proliferation of mobile phones in rural Africa has also led to increased interest in mobile financial services (MFS), such as mobile money and mobile banking. Such services are often portrayed as promising tools to improve agricultural finance, especially among smallholders who are typically underserved by traditional banks. However, empirical evidence on the actual use of MFS for agricultural activities is thin. Here, we use nationally representative data from Kenya to analyze the use of mobile payments, mobile savings, and mobile credit among the farming population.
Mobile phone based money services have spread rapidly in many developing countries. We analyze micro level impacts using panel data from smallholder farmers in Kenya. Mobile money use has a large positive net impact on household income. One important pathway is through remittances, which contribute to income directly but also help to reduce risk and liquidity constraints, thus promoting agricultural commercialization. Mobile money users apply more purchased inputs, market a larger proportion of their output, and have higher farm profits.
Enhancing the diversity of agricultural production systems is increasingly recognized as a potential
means to sustainably provide diversified food for rural communities in developing countries, hence
ensuring their nutritional security. However, empirical evidences connecting farm production
diversity and farm-households’ dietary diversity are scarce. Using comprehensive datasets of
market-oriented smallholder farm households from Indonesia and Kenya, and subsistence farmers
Recent research has analyzed whether higher levels of farm production diversity contribute to improved diets in smallholder farm households. We add to this literature by using and comparing different indicators, thus helping to better understand some of the underlying linkages. The analysis builds on data from Indonesia, Kenya, and Uganda. On the consumption side, we used 7-day food recall data to calculate various dietary indicators, such as dietary diversity scores, consumed quantities of fruits and vegetables, calories and micronutrients, and measures of nutritional adequacy.
Background
Weather risk is a serious issue in the African small farm sector that will further increase due to climate change. Farmers typically react by using low amounts of agricultural inputs. Low input use can help to minimize financial loss in bad years, but is also associated with low average yield and income. Increasing small farm productivity and income is an important prerequisite for rural poverty reduction and food security. Crop insurance could incentivize farmers to increase their input use, but indemnity-based crop insurance programs are plagued by market failures.
Sustainable intensification of agriculture will have to build on various innovations, but synergies between different types of technologies are not yet sufficiently understood. We use representative data from small farms in Kenya and propensity score matching to compare effects of input-intensive technologies and natural resource management practices on household income. When adopted in combination, positive income effects tend to be larger than when individual technologies are adopted alone.
Supermarkets and high-value exports are currently gaining ground in the agri-food systems of many developing countries. While recent research has analyzed income effects in the small farm sector, impacts on farming efficiency have hardly been studied. Using a survey of Kenyan vegetable growers and a stochastic frontier approach, we show that participation in supermarket channels increases mean technical efficiency by 19%. This gain is bigger at lower levels of efficiency, suggesting the potential for positive income distribution effects.
Classical innovation adoption models implicitly assume homogenous information flow across farmers, which is often not realistic. As a result, selection bias in adoption parameters may occur. We focus on tissue culture (TC) banana technology that was introduced in Kenya more than 10 years ago. Up till now, adoption rates have remained relatively low.
Most micro-level studies on the impact of agricultural technologies build on cross-section data, which can lead to unreliable impact estimates. Here, we use panel data covering two time periods to estimate the impact of tissue culture (TC) banana technology in the Kenyan small farm sector. TC banana is an interesting case, because previous impact studies showed mixed results. We combine propensity score matching with a difference-in-difference estimator to control for selection bias and account for temporal impact variability.