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COLLEGE OF SOCIAL SCIENCE

COLLEGE OF SOCIAL SCIENCE

Another concern is that

Another concern is that the panel nature of the data creates within-city error correlations and heteroscedasticity providing incorrect standard errors (Wooldridge 2002; Cameron and Trivedi 2005). So, to correct standard errors, I used robust standard errors clustered at the city-level. Table 10. Hypothesized Direction and Measurement Variables Hypothesized Direction Institutions Formal Institution - Form of Government + - Type of Election + - Leadership Turnover - Administrative Capacity Informal Institution - Network Bonding Communities (Strong Ties) - Network Flowing Information (Weak Ties) Community Characteristics Environmental Interest - Education - Median Income - Homogeneity - Homeownership Development Interest - Construction - Real Estate Physical Characteristics - Size of City - Population Increase - Population Density - Water Area - Shoreline Moderating Effect - Mayor*Homogeneity - Mayor*Income - Mayor*Homeownership - Election*Homogeneity - Election*Income - Election*Homeownership - RPC Participation*Homogeneity - RPC Participation*Income - RPC Participation*Homeownership Control Variable - EAR - + + + + + + + - - + + + + + Increase the impact of Environmental interests 73 Measurement Strong Mayor-Council 1, others 0 Proportion of council members elected by district % of Number of member changes in councils Comprehensive planning expenditure Expenditure of ILAs If a city participates in RPC meetings or has membership 1, if not 0 % of college degree or above Dollar % of white % of homeowners Proportion Construction Industry Proportion of Real Estate Industry Log population % of population increase people per square miles Ratio of water vs. land Miles of shoreline Adopted year 1 others 0

Institutional Moderating Effects for Both Models The both models imply the important consideration of institutional influences in the IAD framework. As the framework argues, institutions have both direct and moderating effect on community attributes’ influences. In other words, different institutional arrangements are expected to increase the influence of environmental community interests 58 . To test these effects, I estimate models with separate interaction terms between institutional variables and community environmental interests. These interaction terms involve different combinations of environmental interest variables with institutional variables. However, as interaction terms always do, putting all interaction terms in one model create unstable results because there are possible multicollinearity problems among interaction variables and constitute variables. Thus, I put three interaction terms at once in a model to reduce these problems. As the result tables in the next chapter show, interaction models are divided by three models. The first model is without interaction terms (first column in the result table). The second model includes interaction terms between mayor council form of government, and white, income, and homeownership (second column). The third and fourth models respectively include interaction terms on district election and Regional Planning Council participation with same community environmental interest variables as the second model (third and fourth column). 58 Interaction terms in this research are limited to the community interest variables that have been theoretically well developed since interaction terms make the entire model unstable. 74

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