No abstract

In many Sub-Saharan African countries, farmers typically have a choice between selling their products to traders who travel between villages and markets and transporting their products to the nearest market themselves. Because of communities' remoteness and poor communications with marketplaces, farmers' uncertainty about market prices is usually high. Traders may take advantage of farmers' ignorance of the market price and extract a rent from them by oering very low prices for their products. In this article, we model bargaining interactions between a farmer and a trader who incur dierent transportation costs, and we study how price information aects the bargain and the balance of power. We then estimate the causal eect of a Market Information System (MIS) working through mobile phone networks on Ghanaian farmers' marketing performances. We nd that farmers who have beneted from the MIS program received signicantly higher prices for maize and groundnuts: about 12.7% more for maize and 9.7% more for groundnuts than what they would have received had they not participated in the MIS program. These results suggest that the theoretical conditions for successful farmer use of MIS may be met in eld.

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

We estimate the early effects of the pilot project to Reduce Emissions from Deforestation and forest Degradation (REDD+) in the Brazilian Amazon. This project offers a mix of interventions, including conditional payments, to reduce deforestation by smallholders who depend on swidden agriculture and extensive cattle ranching. We collected original data from 181 individual farmers. We use difference‐in‐difference (DID) and DID‐matching approaches and find evidence that supports our identification strategy. We estimate that an average of 4 ha of forest were saved on each participating farm in 2014, and that this conservation came at the expense of pastures rather than croplands. This amounts to a decrease in the deforestation rate of about 50%. We find no evidence of within‐community spillovers.

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.