Objectives:
State and local governments in the U.S. have sought to preserve open-space, but such programs operate within a complex and decentralized public choice environment. This paper offers a theoretical framework for evaluating the links between land-use decisions, local tax and spending policies, intergovernmental grants, and household decisions regarding location, lot-size, housing, and other consumption. Of particular interest is the extent to which the consumption of “private open-space,” driven by large-lot zoning requirements, tends to substitute for or complement land zoned for open space.
Methods:
The budget-constrained representative household maximizes utility by choosing land zoned for residential use (lot-size), structure, and other goods. Utility also responds to local government’s provision of a local public good, community open-space, and (via the budget constraint) the local property tax rate. Equilibrium lot-size and other consumption patterns, local land prices, and community population size are determined by an “open-city” condition that requires the representative household’s utility to match an outside norm. Information from this household sub-model then is imbedded in the local government’s optimal choice of: local public spending, property tax rate, open-space, and minimum zoning restrictions on lot-size and structure.
Local government data from Connecticut are used to calibrate the model, which is then used to examine: (1) the links between the amounts of local land zoned for open-space (or other low-density uses) versus residential use, zoning restrictions on lot-size and structure, public spending, and property taxes, and (2) how these endogenous local decisions are affected by higher-level (state) land use and fiscal policies as well as community attributes.
Results:
Sensitivity analysis shows that higher-level fiscal policies influence local fiscal and land-use policies, including land-use zoning and density zoning decisions, and that both the magnitude and direction of these effects are sensitive to underlying household parameters (income, preferences) and community characteristics (location, geographic size, etc.).