Buyouts and Agglomeration Bonuses in Wildlife Corridor Auctions

Abstract

We present a general model of bidding behavior in wildlife corridor auctions. Given (i) some spatial configuration of landowners in a landscape, (ii) the landowners' opportunity costs, and (iii) the value of establishing the corridor, our model predicts individual landowners' bids, overall participation rates in the auction, as well as the expected net benefits to the conservation agency. We find that market efficiency of the auction increases in the number of potential corridors. We use simulations to compare a benchmark auction with two policy scenarios in a hypothetical landscape. In one scenario, members of a winning corridor receive an agglomeration bonus. In the other scenario, we buy out a pivotal landowner (one that is part of many potential corridors) prior to the auction. Given equal budgets for the agglomeration bonus and the buyout policy, an agglomeration bonus is less efficient than a buyout for conservation agencies facing low budgets, and vice versa. A risk-averse conservation agency is however always better off using a buyout policy.

Landscape‐level determinants of the performance of an agglomeration bonus in conservation auctions

“…However, it is not always the case that the average reduction in bid amount significantly offsets the bonus payments. A low level of bonus capitalisation in bids would likely erode AB performance (Banerjee et al., 2021; Dijk et al., 2017; Fooks et al., 2016). These findings are contrary to those of another stream of the literature, which suggests that AB can improve spatial coordination and auction cost‐effectiveness (see e.g., Banerjee et al., 2014; Kuhfuss et al., 2022; Parkhurst et al., 2002; Parkhurst & Shogren, 2007, 2008).…”

The influence of farmland distribution on the performance of the agglomeration bonus

“…Since its first formulation, many research papers have been published about the AB. These comprise modelling studies (Albers et al, 2008;Bell et al, 2016;Delacote et al, 2016;Iftekhar and Tisdell, 2016;Dijk et al, 2017;Arora et al, 2021;Bareille et al, 2023), empirical analyses (Bell et al, 2018;Krämer and Wätzold, 2018;Huber et al, 2021), and lab experiments (Parkhurst et al, 2002;Parkhurst and Shogren, 2007;Banerjee et al, 2012;Banerjee et al, 2014;Krawczyk et al, 2016;Parkhurst et al, 2016;Banerjee et al, 2017;Panchalingam et al, 2019;Kuhfuss et al, 2022).…”

“…An important assumption that is likely to drive their results but has so far received no attention is the spatial extent and distribution of the landowners' (or players' or model agents') properties. One large group of experimental and modelling studies (e.g., Albers et al, 2008;Banerjee et al, 2012;Banerjee et al, 2014;Bell et al, 2016;Delacote et al, 2016;Iftekhar and Tisdell, 2016;Banerjee et al, 2017;Dijk et al, 2017;Arora et al, 2021) assumes that each agent or player, respectively, owns a single land parcel that is neighboured to a number of land parcels owned by other agents or players.…”