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University of Copenhagen (2015)

Agricultural technology adoption, food security, poverty and child health : Assessments of an agricultural intervention in Tanzania

Larsen, Anna Folke

Titre : Agricultural technology adoption, food security, poverty and child health : Assessments of an agricultural intervention in Tanzania

Auteur : Larsen, Anna Folke

Université de soutenance : University of Copenhagen

Grade : Doctor of Philosophy (PhD) 2015

Résumé
The three self-contained chapters of this dissertation evolves around different aspects of a Farmer Field School intervention taking place in northern Tanzania, studying the diffusion of agricultural technologies and how agriculture links to food security, poverty and child health. The intervention is called RIPAT (Rural Initiatives for ParticipatoryAgriculturalTransformation)andwasfundedbytheRockwoolFoundation. As a consultant for the Rockwool Foundation I administered a large scale data collection for an impact evaluation of RIPAT.
All three chapters of my dissertation build on these data. The first two chapters are coauthored with Helene Bie Lilleør. A recurrent theme in this dissertation is the identification of causal effects. Since participation in RIPAT is voluntary, the data does not offer direct experimental variation which I can exploit for identification, and there exists no baseline data collected before the implementation of RIPAT I to control for selection. I pursue different identification strategies which are detailed in the three chapters. In the following, I provide a preview of the findings.
Food security and poverty : The first chapter provides a broad impact evaluation of RIPAT with a focus on food security and poverty which were the development objectives stated in the original project documentation. Despite the strong potential of agricultural interventions to affect food security and the poverty status of small scale farmers,existing studies of FarmerFieldSchool interventions focus on short term outcomes such as knowledge, take-up, and agricultural yields. We employ four different estimators to identify the impact of RIPAT on food security and poverty, and they all yield consistent results : The RIPAT households become more food secure in particular in the hungry season, but we do not detect any impacts on our poverty indicators. These finding can be explained by a reallocation of labor resources toward own agricultural production and improved production smoothing which may have improved food security while leaving poverty unaffected.
Child health : The second chapter studies the impact of RIPAT on the health of children below five years of age living in the RIPAT households. Agricultural production is an underlying determinant of child nutrition, and the improvement we found in food security among the RIPAT households has the potential to materialize in better child nutrition and thereby taller children. We find that young children conceived after the phase-in of RIPAT have become 0.8 standard deviations taller and are 17.6 percentage points less likely to be stunted (i.e. severely reduced height-for-age). The large impacts may be explained by the fact that the area was hit by a drought in 2009. The RIPAT project is designed to make the agricultural production more resistant to droughts, and the results show that the children in RIPAT households were indeed better shielded against the adverse consequences of the drought than children from comparison households regardless of whether we compare to comparison households in nearby village sort on on-RIPAT households within RIPAT villages. We investigate if the results could be driven by self-selection into RIPAT based on better drought coping capabilities, but we find no evidence in support of this.
Adoption of technologies : The main component of the RIPAT intervention was the introduction of improved banana cultivation. In the third chapter I assess how the adoption of improved banana cultivation among non-RIPAT farmers in RIPAT villages depends on their links to RIPAT participants who grow improved bananas. In the existing literature on networks and technology adoption, network effects are interpreted associallearning. I show that a farmer’s network can affect the adoption of a new crop not only through social learning, but also by providing necessary inputs for adoption. I setup a simple model for adoption of a new crop where the farmer’s network can provide both information about the expected yield of the new crop and necessary inputs for adoption. I derive the same model implications for how the network affects adoption regardless of whether the network provides inputs or information. Empirically, I find that a farmer is 39 percentage points more likely to adopt banana cultivation if there is at least one farmer growing improved bananas in the farmer’s network. The data suggests that the provision of inputs (banana seedlings) through networks plays an important role for the strong network effects found. This is particularly important for the diffusion of new agricultural technologies in areas that suffer from poor infrastructure which impedes the distribution of agricultural inputs.

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Page publiée le 25 décembre 2015, mise à jour le 17 juillet 2017