April 5, 2016
WARNING! This is a pretty geeky researcher post. Non-geeks may want to pass.
The data is in from the 1024 household surveys of Rwandan farmers. All 350 questions, which turns into close to 800 variables. It is a large database. The 10 enumerators worked for 10 weeks, working 5 days/week, 7am until 7pm each day, two interviews per day to complete the interviews. (See this post for an up close view of an enumerator's day.) Cleaning and re-coding the data has taken a couple weeks by a team of researchers, some highly experienced in this type of work in SPSS, and some less so (like me). Now we're starting to build the formulas in SPSS that will actually start to analyze what the farmers have told us.
Here's what the beginning of my formulas to calculate cost of production look like:
Looks fun, eh? We have seven cultivation variables (e.g. weeding, pruning, fertilizing, etc.) for both household labor and wage labor. We have one variable for harvest labor (both paid and unpaid), and one for sorting labor (paid and unpaid). Transportation variables include both time required to transport cherry and time required to transport parchment, and fees paid for hired help and fares. To these variables we will add whatever the farmer reported on 10 different types of equipment and 6 types of consumables (pesticide, fertilizer, mulch,...).
In short, the level of detail is serious for this attempt to quantify and understand farmer's cost to produce. Stay tuned for what we learn as we get these formulas going.