Gille 2 areas, of which 72 percent depends on agriculture (Census of India, 2011). Agriculture accounts for 17 percent of GDP and employs 51 percent of the total labor force (World Bank, 2010).<sup>2</sup> This paper studies the productivity effects of the mean education of neighbors in the agricultural sector, which can take place through learning spillovers. First, farmers can learn from their neighbors about agricultural technology. The fact that there are learning spillovers when there is a technological change has been widely asserted in the literature (see for example in the case of agriculture in developing countries Foster and Rosenzweig [1995]; Munshi [2004]; Conley and Udry [2010]). Second, neighbors’ education can influence the _adoption_ of technologies, through their own adoption of technology. It can also have a positive impact on the _use_ of technologies, or more generally, neighbors’ education can increase efficiency in the use of inputs (Weir and Knight 2007). It can be the case if all farmers are not on the production frontier, which would be a signal that certain inputs, such as fertilizers, manure or grains are over- or underused. Finally, neighbors can have an _allocative effect_ : their education level would have an impact on farmers through learning about which input or output to choose, given their relative prices (Kumbhakar and Lovell 2003).

The existing empirical literature mainly focuses on education externalities in cities or in firms in developed countries (see for example Acemoglu and Angrist 1999; Moretti 2004a, b). Very few papers study education externalities in agriculture in developing countries, with a majority subject to limitations inherent to the study of peer effects, namely the problem of definition of peers, and the identification challenge due to group-level unobservables. Appleton and Balihuta (1996) introduce neighbors’ average level of education in the production function of farmers in rural Uganda. They find that the proportion of farmers with primary schooling is positively and significantly related to farmers’ productivity. However, they cannot control for unobserved community effects by adding community dummies as neighbors’ education is calculated at this level. Weir and Knight (2007) estimate the average and stochastic frontier production function with neighbors’ education as a control variable in rural Ethiopia. Here, neighbors are defined as groups of households within «sites», so they are able to control for site dummies. They find a positive relation between neighbors’ education and average production, but they do not find any impact of neighbors’ education on farmers’ efficiency. Again, although some unobservables are controlled for by the site dummies, it cannot be ruled out that the observed relation between neighbors’ education and average production is driven by unobservables at a smaller level than the site. Asadullah and Rahman (2009) use the same methodology to study external returns of education in agriculture in Bangladesh. They control for village fixed effects while defining neighbors at a smaller level called «Bari». In contrast to Appleton and Balihuta (1996) and Weir and Knight (2007), they find no evidence of external returns of education on farm productivity. However, the (absence of) results may be driven by the sample design of the data: their database only reports information on two households per Bari. This definition of neighbors allows for village dummies but may be too restrictive to capture any external effect. Gille (2012), using spatial econometrics tools to study education externalities in farm productivity in India, also finds evidence of a positive correlation between neighbors’ education and productivity. But the strategy used does not rule out unobservables at the group level. These four papers illustrate the current trade-off in the empirical literature: It is necessary to control for geographical fixed effects to prove that the captured effect does not reflect a spurious correlation, but controlling for geographical effects may lead to a too restrictive definition of neighbors.

This paper overcomes this trade-off by following the strategy of Munshi and Myaux (2006), who study the impact of other women’s behavior towards contraceptives on own adoption of contraceptives. The authors take advantage of the fact that women in rural Bangladesh interact solely within their religious group to define women’s reference group and to rule out that the results are driven by group unobservables 2 Agriculture represented 1.8 percent of total GDP in France in 2009 and 1.2 percent in the United States in 2011.