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COLLEGE OF SOCIAL SCIENCE

COLLEGE OF SOCIAL SCIENCE

Table 9. Variables and

Table 9. Variables and the Data Sources Variables Data Sources Dependent Variables - Conservation Amendment - Ratio of Large to small scale Amendments of Future Land Use Map Independent Variables Institutions - Formal Institutions Form of Government Types of Election Leadership Turnover Administrative Capacity - Informal Institutions Network Bonding Communities Network Flowing Information Community Characteristics - Environmental Interests Education Median Income Homogeneity Homeownership - Development Interests Construction Real Estate Physical Characteristics Population Population change Population density Water Area Shoreline Control Variable Evaluation Appraisal Report NSF supported study by Feiock and Lubell of Local Land Use Politics NSF supported study by Feiock and Lubell of Local Land Use Politics Devoe Moore Center Survey of political institutions in 2002 Devoe Moore Center Survey of political institutions in 2002 The Membership directory of Florida Municipal League Florida Department of Financial Services Interlocal Service Delivery Reports of DCA RPC Board meeting minutes / RPC annual reports U.S. Census 2000 U.S. Census 2000 U.S. Census 2000 U.S. Census 2000 U.S. Census 2000 Zip Code Business Patterns U.S. Census 2000 Zip Code Business Patterns U.S. Census 2000 U.S. Census 2000 U.S. Census 2000 Florida Department of Environmental Protection Florida Department of Environmental Protection DCA Archival file of EAR reports 69

Research Design Panel Probit Model for Conservation Amendments of Florida Cities For this model, I used panel probit model for the binary nature of the dependent variable. This model explains the dependent variable as a function of various institutions, community, and physical attributes. I estimate random effects probit models for whether or not a city offered conservation amendments in a certain year. General rule of thumb to use random effect model is that if the key explanatory variable is constant over time, we cannot use fixed effect to estimate its effect on the dependent variable (Wooldridge 2002). The specification is: yit = xit β + εit, i = 1,…..N ; t = 1,….9 yit = {1 if y* it > 0, 0 else} where yit is a dichotomous variable that indicates whether a city offered conservation amendments while xit is the set of independent variables, and εi = γi + μit. Since I use Random Effect model, I have: E(γi | Xi) = E(μit | Xi) = 0 Var(εit | Xi) = σγ 2 + σμ 2 This model assumes that error terms are random and therefore uncorrelated to the regressors, and thereby yield unbiased and consistent estimates. I estimates this model with robust standard errors clustered at the city level to provide correct standard errors. My econometric model specification is: Whether or not amendment offered = β0 + β1tMayor + β2tDistrict + β3tTurnover + β4tExpenditure + β5tRPC + β6tILA + β7tConstruction + β8tReal Estate + β9tIncome + β10tWhite + β11tHomeownership + β12tDegree + β 70

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