3 that have some correlation across groups. Rural India provides a similar context, with an organization of social interactions within castes. Although social mobility and social mixing have increased in urban India, life in villages is still organized along caste lines (Damodaran 2008; Munshi 2011; Munshi, Myaux, and Rosenzweig 2016). Education spillovers are therefore expected to occur within caste, while cross-caste effects should be absent.

This prediction is tested using data from the 2006 round of the Additional Rural Incomes Survey and Rural & Demographic Survey (ARIS-REDS), a household survey conducted in rural India. This dataset has several advantages over the datasets used in previous literature. First, the round of 2006 has a _listing_ of every household in the surveyed villages with basic demographic characteristics information on households and heads of household, such as their caste, gender, age, education level, size of the household and main occupation. This is very valuable information as it makes it possible to compute the average level of education of members from the same caste using all the heads of households from the same caste in the village and not only the heads of household that have also been sampled as is usually done in the literature. The education of neighbors is therefore very precisely measured. The other advantage of the village listing is that each sampled household is surveyed twice, once in the survey, and once in the listing, which is useful when dealing with potential measurement error as explained in section 4. Second, households report their subcaste name, which makes it possible to define the caste group at a very thin level. Finally, this round is part of a panel with earlier rounds that are exploited for robustness checks.

The findings show that the education level of households from the same caste is positively correlated with farm productivity, but there are no cross-caste effects, and no effects from members from the same caste that do not have agriculture as their main activity. These results are robust to changes in specifications, and in particular to a specification with household fixed effects, using the round of 1982. The absence of cross-caste and cross-occupation effects rules out the possibility that the estimated education spillover effect is driven by village or caste-level unobservables that are correlated within castes. Indeed, as shown by Munshi and Myaux (2006), and further elaborated on in section 2, these results can only be obtained without any education spillover if the omitted determinants of farm productivity are totally uncorrelated across castes, as well as within castes across occupations. The panel specifications also rule out that the positive correlation between education of the caste and productivity is driven by unobservables that are fixed over time. Additionally, there is an effect of the education of members from the same caste only for wheat growers, while there is no effect for rice growers. This pattern is consistent with a spillover effect as argued in section 4 and highlights that the omitted factors correlated with neighbors’ education should only determine wheat production but not rice production to explain the positive effect of neighbors’ education on productivity. Finally, the effect of the education of members from the same caste solely comes from members from the same subcaste, which shows that interactions with respect to learning happen at the level of the subcaste in rural India.

Section 5 discusses the properties that the unobservables need to have to explain away the results. In particular, the methodology suggested by Oster (2017) is used to compute the relative degree of selection on unobservables to selection on observables for which the true effect of the mean education level of the group would be zero. In most cases the selection on unobservables needs to be larger than the selection on observables, which can be interpreted as an indication that the true effect of the education of members from the same group is nonzero, as suggested by Oster (2017).

This paper therefore provides evidence showing that there is an impact of neighbors’ education when neighbors are adequately defined and measured, and that this result is unlikely to be driven by omitted variables. It is, however, important to keep in mind that the identification relies on the assumption that time-varying omitted variables correlated with the educational level of groups and to agricultural productivity are also correlated within villages across castes or within castes across occupations. If this is not the case, it is possible that the education level of members from the same caste proxies for other castelevel unobservable characteristics, and causality cannot be inferred.