A GROUP RANDOM COEFFICIENT APPROACH TO MODELING HETEROGENEITY IN TECHNOLOGY ADOPTION
In an influential paper, Suri (2011) provided a new approach to identifying correlated random coefficient (CRC) models. Suri applied the method to an important empirical puzzle: given that improved technologies exist, and given that these technologies have high average returns, why do many sub-Saharan farmers still use traditional farming technologies? We revisit Suri's econometric model and propose an improved estimation strategy using GMM and drawing on recent developments in the non-parametric panel data identification literature.
Collaborators: Oscar Barriga Cabanillas (UC-Davis), Dalia Ghanem (UC-Davis), Travis J. Lybbert (UC-Davis), Aleksandr Michuda (UC-Davis), and Emilia Tjernström (University of Wisconsin)
 
NECESSARY AND SUFFICIENT CONDITIONS FOR ADOPTION OF IMPROVED GROUNDNUT VARIETIES
While crop improvement programs invest vast resources to develop new seed technology, the adoption of improved varieties by farmers remain low. Drawing on survey data from across Tanzania, we argue that a lack of awareness and attractiveness remain the biggest barriers to adoption of improved groundnut varieties by farmers. Focusing on accessibility and affordability of improved groundnut varieties are important but insufficient drivers of adoption. Reforms to crop improvement program requires researchers to reframe the conditions for adoption.
Collaborators: Michael Hauser (ICRISAT) and Kai Mausch (ICRAF)
Funding: Bill & Melinda Gates Foundation
 
NUTRITIONAL AND ENVIRONMENTAL IMPACTS OF SOLAR STOVE ADOPTION
Much of the population in rural sub-Saharan African relies of firewood or charcoal to prepare food. Population pressure is speeding the rate of deforestation, raising the monetary and opportunity costs of cooking meals. We use a field experiment in Zambia to investigate the impact of solar cook stoves on the money households spend on charcoal and the time allocated to collecting firewood. Additionally, we examine changes in diet that result from the reduction in the cost of meal preparation.
Collaborators: Natalia Estrada Carmona (Bioversity) and Vanessa Ocampo (Bioversity)
Funding: Standing Panel on Impact Assessment (SPIA) for the CGIAR
 
USING REMOST SENSING TO EVALUATE THE IMPACT OF STRESS-TOLERANT RICE VARIETIES
This study assesses the impact of key stress-tolerant rice varieties (STRVs) in South Asia and West Africa. We combine administrative data on variety release with remote sensing data on rainfall, temperature, and the density of green vegetation (NDVI). Using historic data on rainfall and temperature, we can determine the impact of these exogenous weather conditions on the NDVI. Then, using district level data on the release of STRVs and a difference-in-difference framework we can then measure the change in NDVI that occurs after the introduction of STRVs.
Collaborators: Aminou Arouna (Africa Rice), Anna Josephson (University of Arizona) and Val Pede (IRRI)

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