where _Yit_ is an outcome in protected area _i_ in calendar year _t_ , _αi_ are the protected area fixed effects (binary indicator variables for each protected area), _λt_ are the calendar year fixed effects, _δel_ represents the treatment effect for cohort _e_ in relative period _l_ , **1** _{Ei_ = _e}_ is a binary indicator that equals 1 if protected area _i_ belongs to cohort _e_ , _Dit_<sup>_l_isabinaryindicatorthatequals1ifprotectedarea</sup><sup>_i_is</sup><sup>_l_yearsawayfrom</sup> transference to AP management, _Xit_ is a matrix of weather control variables (defined below), and _εit_ is the error term [49]. The indicator _Dit_<sup>_l_alwaysequals0forprotected</sup> areas always managed by governments. This regression avoids forbidden comparisons by estimating a separate effect for every cohort and relative period combination. The regression omits the indicator for the year immediately preceding transference ( _l_ = _−_ 1) to avoid multicollinearity. The protected area fixed effects control for all timeinvariant characteristics of each protected area, such as location and inherent habitat features, while the calendar year fixed effects account for time-varying factors that affect all protected areas uniformly, such as global economic trends and global demand for elephant ivory.

The second step estimates weights for the treatment effect coefficients ( _δel_ ) based on the proportion of observations each cohort represents in each relative period. The final step computes the weighted average treatment effect for each relative period. We use the commands feols and sunab from the R package fixest to perform the estimation procedure [94]. We present all relative period estimates in the SI figures. The Average Treatment effect on the Treated (ATT) estimates shown in the main text tables are the weighted averages over the post-transference relative treatment period coefficients (0 _≤ l ≤_ 10), where the weights for each relative period coefficient are based on the proportion of the treated group (areas managed by AP) in the overall population during that specific relative period [49].

Our control group comprises protected areas that AP has determined meet their established criteria for potential future management, which AP refers to as “anchor areas.” Polygons demarcating the boundaries of AP’s anchor areas are publicly available [51]. We identify individual protected areas in our control group in three steps. First, we used Quantum GIS to manually select all polygons from each of the World Database of Protected Areas’ (WDPA) shapefiles that overlapped with the boundaries of AP’s anchor areas. Second, we eliminated duplicate entries from the selected WDPA boundaries. Finally, we manually validated the remaining WDPA boundaries to confirm that protected areas in our control group match the set of polygons displayed on AP’s map of anchor areas. In three cases, we used QGIS and georeferenced polygons from either UNESCO or the literature [95, 96] to manually create shapefiles for AP anchor areas that did not have shapefiles in the WDPA.

Several protected areas in our control group are partly managed by governments and partly managed by an NGO other than AP. We retain these protected areas in our control group because they do not employ a “delegated” management model like AP, where the NGO has full control over management decisions [29]. Several of AP’s 15