based on pre-intervention characteristics is thus used to improve comparability between the two groups. Once households with missing data (including attrition at follow-up) or those with formal certificates at baseline are dropped, we are left with 1,215 valid observations (775 and 440 from treatment and control, respectively). Using kernel-based matching eliminates 58 off-support treated households, resulting in a matched sample of 1,157 (717 treatment and 440 control) and also reduces the mean (median) percentage bias from 17.6 (15.5) to 3.8 (3.2), creating balance between treatment and control in almost all variables (appendix table 1).<sup>13</sup> A difference-in-differences (DID) approach on the matched sample would then allow assessing LARP impacts on outcome variables such as female access to land rights and land as well as investment and credit-market participation. Letting _i_ denote households and _t_ time, the equation to be estimated is i 1 2 ∗F i i (1) where _T_ is the treatment dummy (YY = ααii + δδ TT _T_ ii=1 for treatment group, ∗PPii + δδ TTii ∗PPii + λλPP _T_ =0 for control group), ii + XX ββ+ εε _P_ is a time dummy ( _P_ =1 for post treatment period, _P_ =0 for the pre-treatment period) to control for common time-trend, captures observed and unobserved time invariant individual heterogeneity, _FH_ is a dummy for female head, **_X_** is a vector of time varying control variables (including land size and annual per capita αα expenditure), 1 is the key parameter of interest measuring the average effect of the land tenure regularization program and 2 captures heterogeneity effect by gender of the head of the household. δδ Outcome variables i focus on participation in land markets and land-related investment as well as δδ credit market access. YY Aerial photography from before (2012) and after (2016) the intervention allows extracting of finished or unfinished structures at two points in time as an ‘objective’ measure of investment that may suffer less from bias than household survey responses.<sup>14</sup> Appendix figure 3 provides some examples for such changes. We use a main road separating treatment and control in the area of interest (figure 9) to implement a spatial discontinuity design along this road by estimating (1) with area under finished or unfinished structures as outcome variable for different bandwidths. This is a plausible design if, before the project, households either side were similar in observed and unobserved characteristics.

> 13 Estimated mean and median bias are lowest for kernel-based vs. to one-to-one or two-nearest neighbor matching, an impression confirmed by visual inspection. Standardized percentage biases, defined as the percentage difference of sample means in treatment and control groups (unmatched or matched) as a percentage of the square root of the average of the sample variances in the treatment and control groups (Rosenbaum and Rubin, 1983), for different covariates as displayed in appendix figures 1 and 2 illustrate that, compared to being widely spread for the unmatched sample, biases are concentrated around zero in the matched sample. 14 Imagery was available for 2012 (at 20 cm resolution) and 2016 (at 35 cm resolution) to guide program implementation and census mapping, respectively. Finished and unfinished structure were hand digitized. Unfortunately, the resolution proved insufficient to robustly discern changes in characteristics such as presence of fences, walkways, and complexity or type of roofs that could be made out from drone imagery using machine learning (Gevaert _et al._ 2020).

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