# **1 Introduction**

Policies that increase agricultural productivity arguably have a disproportionate effect on poverty reduction (World Bank, 2008). The Green Revolution introduced high-yielding crop varieties, chemical fertilizer, pesticides and other modern agricultural practices to developing countries. However, even decades after their introduction, the take up of these improved inputs and practices has been uneven (Foster and Rosenzweig, 2010). Traditional farming practices remain widespread and yields for smallholder farmers in the developing world remain low.<sup>1</sup> A variety of explanations have been proposed for the observed low rates of technology adoption in the developing world, including limited information on best practices, credit constraints, risk, poor input quality, and behavioral biases.<sup>2</sup> An alternative view is that heterogeneity in returns reflects heterogeneity in a fixed factor (or infrastructure) thus explaining the observed variation in adoption rates (e.g. Suri, 2011).

In this we test several on the role of information while paper hypotheses explicitly recognizing the importance of heterogeneity in soil quality.<sup>3</sup> We test our hypotheses in an accretive manner: first, using our sample of small rainfed farms in Tlaxcala, Mexico, we confirm that heterogeneity in soil quality leads to heterogeneity in the recommended mix of fertilizers. Next, we test whether farmers adopt these tailored recommendations (based on the soil analyses) and whether productivity improves as a result. We then test whether the level of information specificity, that is, whether recommendations are based on the farmer’s own plot or on a larger geographical cluster, matters for adoption and persistence of improved agricultural practices. This is important because evidence suggests that while individually tailored information may be more effective, it is also more expensive than recommendations based on aggregated information, thus introducing a trade-off.<sup>4</sup> We also offer farmers one of two types of in-kind grant to purchase inputs: inflexible (i.e. subsidizing only the recommended inputs) or flexible (i.e. giving farmers the choice of which inputs to purchase). The difference between the two grants allows us to examine the effect of autonomy, defined here as the ability to choose the inputs purchased with the grant. While the broader notion of autonomy and its intrinsic value has received some attention in the literature,

> 1In Mexico for instance, maize yields for rainfed farmers are only about 2–3 metric tons per hectare (mt/ha). By comparison, rainfed maize yields in the United States are approximately 8 mt/ha (Sweeney et al. 2013; Fernández et al. 2012).

> 2On information, see Conley and Udry (2010); Ashraf et al. (2009); on liquidity and credit see e.g. Karlan et al. (2014); on risk see Karlan et al. (2014); Emerick et al. (2016); Giné et al. (2017); on behavioral biases see Duflo et al. (2011); on input quality see Bold et al. (2017). For a recent overview of the evidence see e.g. Magruder (2018) and Macours (2019).

> 3 See Jayne and Rashid, 2013; Goyal and Nash, 2017. Munshi (2004) and Otsuka and Larson (2013), for example, argue that unobserved farm characteristics such as soil quality are important for technology adoption.

> 4See e.g. Madajewicz et al. (2007) on the efficacy of providing localized information in the context of arsenic poisoning in Bangladesh.

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