more likely to be held under individualized tenure forms, we introduce the mean land price in the village, **_Z_** i, to estimate the impact of tenure holding other characteristics constant.

To test whether the passage of the 1998 Land Act has helped reduce the incidence of land conflicts, we use recall data on households’ involvement in such conflicts. We divide the data into three separate three-year periods (1999-2001; 1996-1998; and 1993-1995) and let _Cijt_ be a dummy variable that equals one if, during the 3-year period starting in _t_ , a conflict was initiated but not resolved on plot _j_ owned by household _i_ . Beyond variables included earlier, we add time dummies _T1_ and _T2_ that take a value of one if the observation was from the 1996-1998 or 1999-2001 period and zero otherwise to expand equation (1) as follows: where all right hand side variables are defined as above. The coefficients on the two time dummies, _δ_ and φ _,_ will indicate the probability that a conflict which could not be resolved during the period had occurred during the 1996-1998 or the 1999-2002 period, as compared to the original 1993-1995 time span. The introduction of two 3-year periods of recall allows to minimize the potential error introduced by recall bias and the hypothesis that the 1998 Land Act accomplished its objective of reducing the incidence of land conflicts then translates into φ<δ, which can easily be tested statistically. Other right hand side variables are time invariant plot characteristics as well as other variables for which recall data on the value of the variable at the beginning of the period was available.

To test for the impact of land-related conflict on observed production outcomes, we combine non- and semiparametric evidence with parametric regressions. Non- and semi-parametric methods are appropriate because they provide an opportunity to “let the data speak” and obtain a graphical representation of the possible impact of conflict on productive outcomes without imposing an a priori structure, thereby allowing to capture possible non-linear relationships that may be present in the data. On the other hand, if the underlying assumptions are satisfied, parametric estimation allows to compare results to those obtained by other studies (e.g. with respect to the existence of economies of scale), to obtain a point estimate of the impact, if any, of conflict, and -most importantly- to control for the fact that conflict may not be completely exogenous by using instrumental variable techniques.

The production systems used by rural households in Uganda are characterized by intensive multi-cropping. A production function for physical output of individual crops based would fail to capture the interaction between different crops and thus be seriously mis-specified. Instead, we aggregate total output using prices at the village level,<sup>4</sup> To obtain a non-parametric estimate of the impact of conflict, we split the sample into conflict-affected and conflict-free plots and then estimate nonparametric regressions for each of the two subsamples. Given that the widely used Nadaraya-Watson estimator suffers from large bias when curves are estimated at a boundary region, we apply a local linear fit using the smoother function developed by Fan

> 4 We also tried aggregation using national prices but the results did not differ from those obtained with village level prices.

8