The effects of contracting costs, bureaucratic costs, and constituencies on state wildlife agency budgets
Parker, Dominic Paul
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In the United States, management of wildlife resources is primarily conducted by private landowners and state wildlife agencies. Landowners must establish and enforce contracts in order to capture the value of wildlife populations that inhabit tracts of land beyond their property boundaries. Bureaucratic agencies have little incentive to maximize the net value of wildlife and their performance is limited by political constraints, employee shirking, and conflict among competing professionals. This thesis examines the role that these costs have in affecting the size of state wildlife agency budgets and allocations of budgets to nongame. The general hypothesis is that budget size and budget allocations to nongame respond to changes in the relative costs of each institution in ways consistent with wealth maximization. Two models are developed to derive a narrower set of hypotheses. The first seeks to explain the determinants of agency budget size. The second seeks to explain the determinants of the percentage of budgets allocated to nongame. Five predictions result. First, increases in private contracting costs will increase agency budget size and decrease nongame allocations. Second, increases in bureaucratic costs will decrease agency budget size. Third, increases in the costs of nongame management to agencies and politicians will decrease budget allocation towards nongame. Fourth, increases in game and nongame demand will increase agency budget size. Fifth, increases in the demand for nongame relative to game will increase budget allocations to nongame. For the empirical tests of each model I use data from a cross-section of U.S. states. Land-use and land ownership data proxy private contracting costs and agency organization and funding variables proxy bureaucratic costs. Agency organization and funding variables are also used to proxy nongame management costs to agencies and politicians. Hunting, fishing, and wildlife-watching data are used to proxy demand for game and nongame. The regression analysis supports all five of the predictions listed above.