# **1 Introduction**

Technology adoption can generate externalities whose monetary value is difficult to measure: since they are not sold on the market, they lack a price tag. Yet, knowing the value of externalities is crucial for two reasons. First, it helps governments decide _whether_ to intervene: an intervention may be worth it only once its social benefit exceeds a certain threshold. Second, if the threshold is met, knowing how externalities are produced helps governments decide _how_ to intervene: typically governments have a menu of policies to choose from, and may want to prioritize those with the highest social benefits.

Externalities are commonplace, and notably in agriculture. Many of the agricultural technologies available to farmers generate positive externalities, like pest control or wildfire prevention, that cause equilibrium adoption rates to be lower than socially optimal if agents do not internalize the social benefit of adoption (Pigou, 1920); this may contribute to explaining the persistent low adoption rates observed in many lower income countries (Bandiera and Rasul, 2006; Foster and Rosenzweig, 1995; World Bank, 2007).

In this paper I design a field experiment to uncover how much the externalities of one technology are worth and what governments should do to harness their benefits. Specifically, I study the externalities of a new agricultural pest-control technology in Uganda, consisting of a training on pest-control practices for a new pest. I measure the externalities of the training by asking farmers how much they are willing to pay for _only one specific farmer_ in their village to be trained on pest-control practices for the pest fall armyworm. The willingness-to-pay elicitation mechanism, a variant of the Becker-deGroot-Marschak or BDM (Becker et al., 1964), is incentive-compatible and yields a measure of the externalities generated by one trained farmer keeping others’ actions constant.<sup>1</sup> The feature of keeping others’ actions constant is achieved by allowing a maximum of one farmer to receive training per village through a lottery; it allows to shut down complementarities and substitutabilities in demand that would confound the observed willingness-to-pay for others. The upside of such a design choice is that it ensures I measure the externalities produced by one farmer (at the margin) neatly, although at the expense of measuring how externalities change when more than one farmer is trained in a village. I find farmers anticipate substantial positive spillovers from others: the median value a farmer attaches to another farmer receiving the training is equivalent to the wage received from two days of agricultural labor (or $8.6), about half the median willingness-to-pay for themselves (equal to $17.3).

I use my valuation data to show that targeting choices can produce large and quantifiable social gains if they account for externalities. I identify which targeting criterion for the training produces a higher social benefit in two steps. First, I calculate the per capita externality generated by training one farmer - the average willingness-to-pay of

> 1This setup rests on the assumption, supported by the data, that farmers are self-interested and do not engage in altruistic behavior in this setting.

1