Adewumi _et al._ (2013) is the only study describing impacts of large farm establishment on neighboring smallholder producers in a case study setting we are aware of. To assess direction and size of impacts from large agricultural investments in a more systematic way, complementing case studies with survey-based evidence seems to be very desirable.

Although we know of no studies that have done so for 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 within 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 US industrial plants in the 1990-2002 period, finding significant impacts on toxic air emissions, housing values, and the incidence of low birthweight in the vicinity of these plants. Applying this to gold mines suggests that openings and closings have different effects (Chuhan-Pole _et al._ 2015) and that mining can empower women (Kotsadam and Tolonen 2015).

We build on Ali _et al._ (2015) who use a similar methodology to estimate impacts of large farm establishment on smallholders in Ethiopia over the 2004-14 period. Although the time period covered by our data is much shorter, the TIA includes GPS coordinates for 419 large farms that were either newly established or changed owner after 2012 as well as all smallholders in the 2012 and 2014 rounds. The location of large and small farms is illustrated in figure 1. We use this to create a measure for the distance between any small and the next large farm newly established that was newly established during this period as well as the number of newly established large farms in a concentric circle with inner and outer radius of 0-25, 25-50, and 50-100 km from any small and medium scale farm. Combining this with information on smallholders’ performance in each year (i.e. before and after large farms were established), we run a linear probability model of the form where _Yijt_ is the outcome variable of interest for small farm _i_ in enumeration area (EA) _j_ in year _t_ ; _Lijtd_ is a vector of indicator variables that equal 1 if at least one large farm was established after 2012 in a concentric ring of radius _d_ around small farm _i_ ; _Xijt_ is a vector of control variables;<sup>10</sup> _δt_ is a time trend; _δj_ an EA fixed effect, and _β_ as well as _γ_ are parameter vectors to be estimated. In practice, we limit the distance to be

> 10 Control variables include farm size, household composition (no. of children, adults and old people), head’s gender and age, and highest education. For regressions on crop yields, control variables also include indicator variables for crop-specific use of fertilizer, pesticide and irrigation, hiring of temporary or permanent workers, use of animal traction, crop rotation, inter-cropping, and line sowing at household level, and a rainfall shock.

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