No abstract

We report the importance of informal training in introducing new post-harvest technologies in rice farming through informal contacts by exploring answers from both in-residence extension workers ('key farmers') and ordinary farmers in rural Cambodia. We use survey data collected in Cambodian villages between December 2012 and January 2013. While in-residence extension workers are well motivated and informal training plays a crucial role, lack of an appropriate financial incentive for the key farmers might hamper the sustainability of the project. We also emphasize that any intervention for rural development must avoid social exclusion and nepotism.

This study employs the dataset collected for the assessment of a post-harvest technology project in rural Cambodia and focuses on the heterogeneous preferences of project implementers, frequently overlooked in the literature on programme evaluation studies. We focus on the 'implementer effect' on the programme participation of the treated farmers. We demonstrate that the heterogeneous programme participation of ordinary farmers could be induced due to heterogeneity in the characteristics of the project staff. In particular, we indicate that the baseline altruism of the project staff, measured by the dictator game, consistently increases the participation rate and the number of participations in the training sessions of beneficiaries. This type of heterogeneity in project staffs' preferences across treatment sites could be a source of treatment heterogeneity for programmes conducted at a certain cluster level. While few studies have focused on the heterogeneity of programme implementers, our empirical results indicate that the preference of implementers could be a source of treatment heterogeneity and imply the importance of implementation of an actual project.

We propose a new estimation method for heterogeneous causal effects which utilizes a regression discontinuity (RD) design for multiple datasets with different thresholds. The standard RD design is frequently used in applied researches, but the result is very limited in that the average treatment effects is estimable only at the threshold on the running variable. In application studies it is often the case that thresholds are different among databases from different regions or firms. For example thresholds for scholarship differ with states. The proposed estimator based on the augmented inverse probability weighted local linear estimator can estimate the average effects at an arbitrary point on the running variable between the thresholds under mild conditions, while the method adjust for the difference of the distributions of covariates among datasets. We perform simulations to investigate the performance of the proposed estimator in the finite samples.