We limit the data to those that only produced corn and/or soybean crops because we want to focus only at those farmers. The main variables of interest are if the farmer has experienced planting a GMO crop (ADOPT) for the dependent variable, and log of farm size in acres (LNACRES) as well as positive attitude towards GMOs (ATTITUDE) as the main regressors. By the way, as a side note, it's always a good rule of thumb to use the log of a variable if the values of the variable are too large.
We also consider other control variables, such as age of the farmer (AGEGROUP), if the farmer also raises livestock (LIVESTOCK), and if the farmer attends church very often (CHURCH). It is expected that older farmers are less likely to adopt (rely more on traditional farming), that farmers who raise livestock are less likely to adopt, and that farmers who attend church more often are less likely to adopt (probably because more religous farmers view genetic manipulation negatively).
Automatically, we use binary choice models (probit in this case) as the econometric method. The results are as follows:
Well, except for CHURCH, we have the expected results. Older farmers and those that raise livestock are less likely to adopt GMOs. The more important results, however, are the first two regressors: the larger the farm, the more likely the farmer adopts; and (ho-hum) if a farmer has positive attitude towards GMOs, then he or she is more likely to plant GMOs.
Now here's the catch: there might be an endogeneity problem. If you are already planting GMOs, would you say that GMOs are not good for farmers? The answer is no. The fact that you are already adopting GMOs, the more likely you have a positive attitude toward GMOs.
So, for endogeneity problems, we use instrumental variables approach. It is more common to have instrumental variable regressions if the dependent variable is continuous.
What about binary variables such as ADOPT, which is a simple yes if you planted GMOs and no otherwise? Good thing there is also the instrumental variables probit model, which can easily be implemented in STATA with the IVPROB command.
Now, it is given that farmers who are optimistic about the future are likely to have a positive attitude towards GMO adoption, but that this optimism is totally unrelated to farmers' decisions of adopting GMOs or not. And so we use this OPTIMISM as the instrumental variable.
Now like all instrumental variable estimation, there are actually two methods: two-stage least squares estimation (using the TWOSTEP option in STATA) and the simultaneous maximum likelihood estimation (using the MLE option in STATA). We preferred the simplified, reduced-form approach and so we went for the two-stage method. The results are as follows:
Well, surprise surprise. The signs are still there, but only two remain as significant: size of farm and livestock. Age and attitude doesn't affect GMO adoption? We will have to take that with suspicion. And we may be correct. Because if you look at the Wald test for exogeneity (at the bottom of the table), the statistic is insignificant. This only means one thing: THERE IS NO ENDOGENEITY. Well, at least none for this sample. Remember, we just said there MAY be endogeneity. But looking at these results, there is actually none.
So given that, we can go back and report the probit result as the main one. Corn and/or soybean producers that have larger farms are more likely to adopt GM crops. Those producers that have positive attitudes towards GMOs are also more likely to adopt GM crops.
You'll have to put an additional "DUH" on that second one.