Agro-environmental schemes (AES), which pay farmers to adopt greener practices, are increasingly important components of environmental and agricultural policies both in the US and the EU. Here we study the French implementation of the EU AES program. We estimate additional and windfall effects of five AESs for a representative sample of individual farmers using difference-in-difference (DID) matching. We derive the statistical assumptions underlying DID-matching from a structural household model and we argue that the economics of the program make it likely that these assumptions hold in our data. We test the implications of the identifying assumptions, provide a lower bound using triple-difference matching, test for crossover effects and insert our estimates of both additionality and windfall effects into a cost-benefit framework. We find that the AESs promoting crop diversity have inserted one new crop into the rotation but on a small part of the cropped area. We also find that the AES subsidizing the planting of cover crops has increased cover crops by 10 ha on the average recipient farm at the expense of almost 7 ha of windfall effect. This AES does not appear to be cost effective. In contrast, we find that the AES subsidizing grass buffer strips could be socially efficient despite large windfall effects. We finally estimate that the AES subsidizing conversion to organic farming has low windfall effects and high additionality

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We test whether social comparison nudges can promote water-saving behaviour among farmers as a complement to traditional CAP measures. We conducted a randomised controlled trial among 200 farmers equipped with irrigation smart meters in SouthWest France. Treated farmers received weekly information on individual and group water consumption over four months. Our results rule out medium to large effect-sizes of the nudge. Moreover, they suggest that the nudge was effective at reducing the consumption of those who irrigate the most, although it appears to have reduced the proportion of those who do not consume water at all.

Consider a policy maker choosing between programs of unknown impact. She can inform her decision using observational methods, or by running a randomised controlled trial (RCT). The proponents of RCTs would argue that observational approaches suffer from bias of an unknown size and direction, and so are uninformative. Our study treats this as an empirical claim that can be studied. By doing so we hope to increase the value of observational data and studies, as well as better inform the choice to undertake RCTs. We propose a large-scale, standardised, hands-off approach to assessing the performance of observational methods. First, we collect and categorise data from a large number of RCTs in the past 20 years. Second, we implement new methods to understand the size and direction of expected bias in observational studies, and how bias depends on measurable characteristics of programmes and settings. We find that the difference between observational estimators and the experimental benchmark is on average zero, but the resulting observational bias distribution has high variance.