Moldova - Value Chain Training

Anonymized dataset for public distribution

The evaluation of the GHS value chain training subactivity wwas designed to measure the extent, if any, to which the training activities improved the productivity and profitability of participants. In particular, the evaluation sought to address the following research questions:

1. What is the impact of GHS value chain training on adoption of new practices and production (yield) within the context of a value chain project? Do these impacts vary by value chain? Were some practices or combinations of practices adopted more than others, and why or why not?

2. Does distance from an GHS value chain training site affect participation in GHS value chain training? What other factors affect participation?

3. To what degree are new practices adopted by value chain participants who do not themselves participate in GHS value chain training activities? Can adoption by nonparticipants be attributed to program ripple effects, rather than broader trends?

4. How does the impact of value chain training on adoption of new practices and production vary with the characteristics of farm operators and farm households?

The impact evaluation of the GHS value chain training subactivity will use a random assignment evaluation design. Eighty potential training sites were randomly assigned to a treatment group (48 sites)--at which training activities will be conducted--or to a control group (32 sites)--at which training activities will not be conducted. Though random assignment will determine where GHS value chain training activities are held, it will not necessarily determine which farmers participate in training. Farmers living in communities that are near control sites will be free to attend trainings held in other communities and may travel to do so; likewise, not all farmers living near treatment sites will attend trainings. If all farmers in treatment sites attended training while all farmers in control sites did not, the impacts of training could be estimated by comparing the outcomes of treatment group farmers to the outcomes of control group farmers at follow-up. If instead some farmers living near treatment sites choose not to attend training while some farmers living near control sites do attend training--which is our expectation--the evaluation approach will have to account for this phenomenon.

The evaluator will be able to measure the impacts of the GHS value chain training subactivity as long as farmers living near treatment sites are more likely to attend GHS value chain training activities than farmers who live near control sites. The estimation approach will exploit the variation in the likelihood of attending GHS value chain training activities induced by random assignment. In particular, the impact of the GHS value chain training subactivity will be estimated using an instrumental variables (IV) framework, using distance from training as an instrument for participation in training. In this context, using an IV approach is not unlike a comparison of farmers in treatment and control sites, except that it adjusts for the fact that some control farmers will participate in GHS value chain training activities and some treatment farmers will not participate.

The IV approach is credible in this context because training sites were assigned randomly. Because training locations were assigned randomly, we can assume that farmers near treatment sites are the same, on average, as farmers living near control sites (before training activities take place). The IV approach isolates the component of participation that is driven by the instrument (here, distance). The IV estimates can be interpreted as the impact for a key group affected by the training subactivity--farmers who undertake training if it is offered nearby, but not if it is offered far away.

This evaluation design will enable the evaluator to measure the impacts of participating in GHS value chain training activities. Importantly, all value chain participants could benefit from the activities, whether or not they participate in training; furthermore, other activities in the value chain could amplify the benefits of training. Therefore, impacts measured through the evaluation will tell us the impacts of training in an environment in which other value chain barriers are addressed; they will not tell us the full impact of all of the activities or what the impact of training would be in the absence of other, related activities.

Randomization Farms

Sample survey data [ssd]

The evaluation will draw on three key sources of data.

The first is longitudinal survey data from farm operators living near treatment and control sites that will enable us to track outcome changes over time. This survey, the Moldova Farm Operator Survey, included two questionnaires: one questionnaire for small and medium farms (< 100 Ha), and a separate questionnaire for large farms (>= 100 Ha). The questionnaires were provided in Romanian language (though English translations are available). In some cases, the interview may have been conducted in Russian instead of Romanian. The questionnaire includes numerous domains, including household/farm characteristics, production, sales, farm income, use of agricultural practices, participation in agricultural training, and credit. In general, the questionnaire focused on outcomes from the 2012 agricultural season.

For the impact analysis, these survey data will be linked to a second source, which is implementation data about GHS value chain training activities--such as locations, value chains and topics covered, and dates. The final source is qualitative data from focus groups and interviews.

Data are collected from farmers in communities spread throughout rural Moldova, but only from communities that were considered for training (but may not have necessarily had training offered, such as for communities randomly assigned to the control group).