TY - JOUR
T1 - Correlated discrete and continuous outcomes with endogeneity and lagged effects
T2 - past season yield impact on improved corn seed adoption
AU - Muse, Rhoda Nandai
AU - Aradhyula, Satheesh
N1 - Funding Information:
The authors would like to thank Tegemeo Institute in Kenya for providing the data used in this study.
Publisher Copyright:
© 2020 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2021
Y1 - 2021
N2 - Farmers in Sub-Saharan Africa have lower agricultural technology adoption rates compared to the rest of the world. It is believed that the past season yield affects a farmer's capacity to take on the riskier improved seed variety; but this effect has not been studied. We quantify the effect of past season yield on improved corn seed use in future seasons while addressing the impact of the seed variety on yield. We develop a maximum likelihood method that addresses the fact that farmers self-select into a technology resulting in its effect on yield being endogenous. The method is unique since it models both lagged and endogenous effects in correlated discrete and continuous outcomes simultaneously. Due to the prescence of the lagged effect in a three year dataset, we also propose a solution to the initial conditions problem and demonstrate with simulations its effectiveness. We used survey longitudinal data collected from Kenyan corn farmers for three years. Our results show that higher past season yield increased the likelihood of adoption in future seasons. The simulation and empirical studies indicate that ignoring the self selection of improved seed use biases the results; we obtain a different sign in the covariance.
AB - Farmers in Sub-Saharan Africa have lower agricultural technology adoption rates compared to the rest of the world. It is believed that the past season yield affects a farmer's capacity to take on the riskier improved seed variety; but this effect has not been studied. We quantify the effect of past season yield on improved corn seed use in future seasons while addressing the impact of the seed variety on yield. We develop a maximum likelihood method that addresses the fact that farmers self-select into a technology resulting in its effect on yield being endogenous. The method is unique since it models both lagged and endogenous effects in correlated discrete and continuous outcomes simultaneously. Due to the prescence of the lagged effect in a three year dataset, we also propose a solution to the initial conditions problem and demonstrate with simulations its effectiveness. We used survey longitudinal data collected from Kenyan corn farmers for three years. Our results show that higher past season yield increased the likelihood of adoption in future seasons. The simulation and empirical studies indicate that ignoring the self selection of improved seed use biases the results; we obtain a different sign in the covariance.
KW - Lagged effects
KW - correlated discrete and continuous outcomes
KW - endogeneity and initial conditions problem
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U2 - 10.1080/02664763.2020.1757050
DO - 10.1080/02664763.2020.1757050
M3 - Article
AN - SCOPUS:85084315425
SN - 0266-4763
VL - 48
SP - 1128
EP - 1153
JO - Journal of Applied Statistics
JF - Journal of Applied Statistics
IS - 6
ER -