Externalities, asset allocation, and land fragmentation at the tropical moist forest frontier

Market asset allocation and land fragmentation at the tropical moist forestal frontier are often inadequate because limited and short-term human interests often do not agree with ecological and long-term considerations. This work explores how the asset allocation changes when a social decision-maker considers neighbor externalities or the social price for stock and how limited rationality can limit efforts to internalize those externalities if agents can learn new information. An intertemporal dynamic model in steady-state equilibrium analyzes the asset allocation at the tropical moist forestal frontier, where forest, agriculture, and livestock use land. Assuming Shaefer production functions and certain conditions, the internalization of stock externalities from forest or the social price for forestal stock results in greater stock and unit stock, and perhaps smaller land use and greater output, that is, forestal reserves with great stock and small area, expansion of forests, or conservation. The work also shows conditions for changes caused by indirect or feedback effects from internalization of neighbor externalities. Using a model to analyze learning in a sequential game between a coordinator, who observes foresters' actions imperfectly and internalizes an external damage using Pigouvian axation, and foresters, who suffer from neighbor externalities we find: Time dependency and separation of foresters' and coordinator's problems depend on uncertainty and used strategies. If noise in the probabilities of damage or logging signal increases with logging, use of Bayesian strategies can imply time dependency and non-separability of the coordinator's problem in periods previous to the last one. Time dependency can exist besides the one from the irreversability factor. If noise in the probability of damage increases with logging, learning can result in higher taxes and logging during the first period. If noise in the probabilities of damage and the logging signal are independent of logging, logging and taxes are the same independently of the sophistication of agents and learning does not matter. Optimal logging increases and taxes decrease with the absolute risk aversion of the coordinator. In the Mexican tropical moist forestal region, great damage was observed during the 1980s, which resulted in policies that give incentives, although imperfectly, for more conservation.*.;*Originally published in DAI Vol. 57, No. 5. Reprinted here with corrected author name.