Recent years have seen considerable interest in the impact of contract farming on farmers in developing countries, motivated out of belief that contract farming spurs transition to modern agriculture. In this article, we provide a thorough review of the empirical literature on contract farming in both developed and developing countries, using China as a special case of the latter. We pay careful attention to broad implications of this research for economic development. We first find empirical studies consistently support the positive contribution of contract farming to production and supply chain efficiency. We also find that most empirical studies identify a positive and significant effect of contract farming on farmer welfare, yet are often unable to reach consistent conclusions as to significant correlates of contract participation.

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractWe consider treatment effect estimation via a difference-in-difference approach for data with local spatial interaction such that the outcome of observed units depends on their own treatment as well as on the treatment status of proximate neighbors. We show that under standard assumptions (common trend and ignorability) a straightforward spatially explicit version of the benchmark difference-in-differences regression is capable of identifying both direct and indirect treatment effects. We demonstrate the finite sample performance of our spatial estimator via Monte Carlo simulations. JEL-classification C21; C53

Empirical growth regressions typically include mean years of schooling as a proxy for human capital. However, empirical research often finds that the sign and significance of schooling depends on the sample of observations or the specification of the model. We use a non‐parametric local‐linear regression estimator and a non‐parametric variable relevance test to conduct a rigorous and systematic search for significance of mean years of schooling by examining five of the most comprehensive schooling databases. Contrary to a few recent articles that have identified significant nonlinearities between education and growth, our results suggest that mean years of schooling is not a statistically relevant variable in growth regressions. However, we do find evidence (within a cross‐sectional framework), that educational achievement, measured by mean test scores, may provide a more reliable measure of human capital than mean years of schooling.

No abstract