consistent with those obtained by OLS, we do not report them (they are available from the authors upon request) and proceed directly to a discussion of the parametric estimates.

Results from parametric estimation, using both OLS and IV, are reported in table 5.<sup>10</sup> We find that, after controlling for other factors, output on a plot affected by conflict is significantly below what would be obtained without conflict. More importantly, and consistent with the order of magnitude of losses that emerged from descriptive evidence, the coefficients point to a large reduction in output due to conflict. While the point estimate for the magnitude of this reduction is 0.33 according to the OLS estimates, it is 2.3 in the case of the IV estimates. The IV results also point towards a lower reduction in output through conflicts on public lands (0.46) and those related to tenancy (1.15). To put this into perspective, recall that, based on our sample, the estimated frequency of land conflicts in the total population is between 2.5%-5%.<sup>11</sup> Using the IV estimates, this would imply a conflict-induced loss of agricultural production of between 5.5% and 11%. As it does neither consider other outlays (e.g. administrative expenses or the destruction of assets) that may be associated with the conflict nor make adjustments for the fact that plots lost due to conflict may not be under production at all, this figure is likely to constitute a lower bound for the true losses. From a policy perspective, this implies that dealing with land conflict can have significant impacts on productivity.

A second finding of considerable interest is that the coefficient on land owned is negative, pointing towards an inverse relationship between farm size and productivity, something that has been hypothesized a long time ago (Bhalla and Roy 1988) and in the meantime been supported by a large literature (Deininger and Feder 2001). While it has repeatedly noted that controlling for soil quality may weaken this relationship (Frisvold 1994, Benjamin 1995), the fact that the coefficient remains negative even once soil quality is controlled for adds to the empirical evidence in this regard (Deininger 2003). While a more in-depth investigation of this issue would transcend the scope of this paper, it does suggest that, in the case of Uganda, increasing farm sizes will not automatically lead to greater productivity and that further exploration of the underlying factors may be an important area for future research.

Coefficients on other variables of interest are in line with expectations: Ownership of livestock, both cattle and pigs, has a strongly output-enhancing effect that is most likely to come about through the ability to use the associated manure, in line with the very limited use of inputs which is consistent with the broader situation in Uganda (Deininger and Okidi 2001). Bad soil quality reduces output by about 27%. In addition to the coefficients on the regional dummies, which are significantly higher for the West and the Center where coffee is grown than for the East and the North, having coffee on a plot is estimated to increase output by about 27% in the OLS regression.

> 10 Note that, if some plots are not cultivated because of conflict (as is indeed suggested by the descriptive evidence), the impact estimated here is a lower bound for the true value given that plots without output are not included in the production function estimation. In view of the limited number of plots per households and the fact that conflict is often household-rather than plot specific, it was impossible to use a regression with household fixed effects.

> 11 Note that this is consistent with evidence from the National Household Survey where, to overcome some of the difficulties discussed earlier, information on land conflicts was obtained only at the community level. Results from this survey indicate that two thirds of communities were affected by land conflicts but the number of conflicts reported per community was relatively low.

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