The best forestry money can buy: Efficient contracting for silvicultural expertise (and the limits thereof)

From the typical forest owner’s kitchen window, thoughtful silviculture looks no different from lazy silviculture. Thus, a landowner cannot directly compensate a forester based on their evaluation of the quality of the work performed. Is there an incentive contract, reliant only on observable outcomes, that could induce optimal effort from a skillful forester? We frame this contracting question as a bi-level optimization problem in which the forester (Agent) solves an integer programming problem to choose the individual-tree cutting schedule that will maximize her payoff, net of the cost of her effort, given the contract parameters specified by the forest owner (Principal). The forest owner optimizes his choice of those parameters so as to maximize the value of returns generated from the resulting cutting schedule, net of the forester’s compensation. We apply this approach to empirical data collected from a hardwood forest in northern Vermont, USA. Harvest schedules differ noticeably between a naïve, costless baseline scenario, a scenario in which only management costs (but not contracting distortions) are accounted for, and the bi-level optimal contracting model. We observe not just a transfer of wealth between the landowner and forester, but a deadweight loss as the maximum feasibly contractable gross value production is less than the first-best level of output.