considered. All coefficients are very precisely estimated, positive, and highly significant, suggesting that project activities improved soil water content and vegetation cover. Estimated elasticities are highest for SWC on degraded land, followed by SWC on cultivated land, and then tree planting. Information on the marginal cost of specific measures would have allowed us to test how efficiently project resources were allocated. Comparing outcome variables, the largest effects are found for the LSWI, which responds to the project objectives of reducing erosion and improving soil water-holding capacity.

The magnitude of estimated elasticities is comparable to results from similar studies which argue that changes of this size in NDVI are associated with sizeable carbon sequestration that, at commonly assumed carbon values, translate into significant benefits (World Bank 2016). Two factors may impart additional downward bias on our coefficient estimates: First, institution-building and training activities covered the entire river basin rather than just the intervention areas. Anecdotal evidence points towards considerable contamination as the government also supported SLM-related initiatives in the non-project watersheds we used as a control, creating a downward bias. Second, our inability to more precisely locate the areas covered by specific SWC activities suggests that the coefficients in table 4 are averages that conceal possibly vast heterogeneity in terms of treatment type and watershed characteristics. Disaggregated data to more precisely describe the nature of interventions would have made it possible to explore how specific SWC activities affect soil water moisture and vegetation cover in the short and long term. They would also have allowed to assess how these impacts vary with biophysical, socioeconomic, and institutional characteristics in ways that could inform identification of hotspots for SWC intervention and improve understanding of the underlying relationships.

As there is no strong a priori rationale for using a 5 km buffer to define the control area, we explore the robustness of the results to alternative widths by restricting control pixels to fall within 1 and 2-km bands from the treated watershed’s boundary. Results from estimating the model based on these buffers in table 5 are very close to those in table 4, suggesting that buffer width has little effect on either the sign or significance of estimated coefficients. Except for LSWI, point estimates increase slightly with buffer size, a finding that would be consistent with the presence of localized spillovers that peter out as one moves further from the treated watersheds’ boundary. More precise treatment data would allow to explore this hypothesis and is one of several areas where future research is desirable.

Whether SLM interventions -in Ethiopia and elsewhere- are sustainable in the long term has been the subject of considerable debate (Smit _et al._ 2017). Although too little time has elapsed since the project closed to make any meaningful inferences on long-term effects from the data, we can include additional lags of the treatment variable on the right-hand side of (1) to assess sustainability by using variation in the speed of project roll-out across micro-watersheds -assumed to be random- for identification. Doing so for _Tmy-1_ and 12