While we know of no studies that systematically explore firm-level externalities in agricultural production, a number of studies have done so in an industrial context. Henderson (2003) uses panel data for machinery and high-tech industries to estimate firm-level production functions that allow for scale externalities from other local plants in the same industry and from the diversity of local economic activity, finding strong evidence of information spillovers for high tech industries only and little evidence of benefits from diversity beyond the own industry. Moretti (2004) looks at human capital externalities, finding that in a city spillovers will be larger between industries that are economically closer. Currie _et al._ (2015) link firm-level with other data to assess the impact of opening or closing of 1,600 U.S. industrial plants in the 1990-2002 period, finding impacts on toxic air emissions, housing values, and the incidence of low birthweight in the vicinity of these plants. Applying this to mines suggests that openings and closings have different effects (ChuhanPole _et al._ 2015) and that mining can empower women (Kotsadam and Tolonen 2015).

# **2.3 Data sources and definitions**

Before developing the econometric approach, we discuss data. For large farms, we use the commercial farm survey regularly conducted by Ethiopia’s Central Statistical Agency (CSA). This survey covers a sample of 10-50 ha farms and the universe of operational farms cultivating 50 ha or more. Farm level information is provided on input use, output, and year of establishment. GPS coordinates taken for every field allow us to map all farms above 50 ha that were operational in 2014.<sup>6</sup> For any small farm and year, this allows us to compute distance to the nearest large farm or the nearest large farm growing the same crop and the total area cultivated by large farms or devoted to large farm cultivation of a specific crop within a certain radius.

Information on smallholders comes from 11 years (2004-14) from CSA’s smallholder survey which had been conducted annually since 1980 by resident enumerators on a sample of some 1,400 kebeles nationwide. Figure 1 illustrates the location of the kebeles included in the 2013/14 round as well as that of large farms above 50 ha. As sample kebeles were changed only in 2007/8, this provides us with a panel of kebeles in the 2003/4-2006/7 and the 2007/8-20013/14 period. Recovery of kebele codes, properly adjusting for splits, mergers, etc. was, however, possible only for about half the kebeles included in the earlier period, providing us with data from about 500 and 2,000 kebeles before and after 2006/7, respectively. Information on inputs is based on a random sample of 20-40 farmers per kebele, resulting in a coverage of 28,000 to 56,000 farmers per year. Data on yield is based on crop cuts of randomly selected fields in each EA, i.e. not those of the farmers interviewed, limiting the ability to for example estimate production functions.

We complement these surveys with two data sources. First, as the smallholder production survey lacks data on labor use, we use data on labor supply at individual level from the 2011/12 and 2013/14 rounds of the

> 6 Information on the year of establishment is used to reconstruct the inter-temporal evolution of large farms, following Ali _et al._ (2015). As nonoperational farms are not included in CSA’s sample, this implies that our results are valid for operational farms.

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