222 Wall et al.: Factors Influencing Participation in Urban and Community Forestry Programs shops, and public service materials), and nonprofit administration (volunteer training, workshops, and temporary staffing). Public volunteerism and active citizen participation are the keys to program success (Wates 2000). Participation can be measured in statistics like participating communities, number of days of volunteer assistance, and number of Tree City USA communities. These data are available on a state-by- state basic and show participation levels vary tremendously across the country, even when adjusted for area and popula- tion (USDA Forest Service 2004). For example, number of volunteer days per capita varies by over 300% between ad- jacent states that one would expect to be similar (for example, between Alabama and Mississippi or Indiana and Illinois). Therefore, knowing and understanding the factors that ex- plain this volunteerism and citizen participation will provide invaluable information for U&CF decision-makers and plan- ners and will improve the effectiveness of these programs (Thindwa and Reuben 2003). The purpose of this study was to provide a perspective of the characteristics that are associated with participation in U&CF programs. Econometric methods were used to identify various factors that are correlated with participation. This insight enables state, local, and nonprofit organizations to identify what does and does not motivate individuals, com- munities, county and municipal decision-makers, various in- dustries/professionals, and others to participate in U&CF pro- grams or to embrace and adopt the principles and approaches that improve urban and community forests. STUDY METHODS Econometrics is the branch of economics concerned with the quantitative analysis of economic and social behavior. It in- volves the specification of a model that forecasts or explains this behavior. Most often, the model is based on regression analysis. We developed an econometric or regression model to explain participation in U&CF programs. The model is cross-sectional (i.e., an analysis based on one or more vari- ables collected at a single point in time as opposed to looking at the variables over time). That is, this study was performed to explain the factors which determine U&CF program par- ticipation, not to predict future participation in the program. Data collection was attempted for the contiguous 48 American states for the year 2003. Data for six states were incomplete; this lowered the number of viable observations to 42 (n 42). The variable being explained (dependent or left-hand side variable) in the model is denoted as participa- tion in an individual state. It represents the number of days of public volunteer assistance and participation in the program in a specific state (USDA Forest Service 2004). Table 1 lists the type of factors that were considered for inclusion in the model. They include educational variables (like percent high school graduates), economic variables (like per capital income), and demographic variables (like percent ©2006 International Society of Arboriculture urban population). Sources included the US Census Bureau (2004, 2005), USDA Forest Service (2005), USDA Forest Service Forest Inventory Analysis (2005), Internet sites such as 50states.com (2005), Economagic.com (2005), Demo- graphia.com (2005), National Center for Education Statistics (2003), and the USDA Forest Service U&CF accomplish- ment report for fiscal year 2003 (USDA Forest Service 2004). All variables were computed on either a percentage of population or total land area or a per capita basis. This was done to “even the playing field” because with cross-sectional data, it is not appropriate to compare large states such as Texas and California to smaller states such as Delaware and Rhode Island. The statistical package SAS was used in conjunction with STATA to run several regressions to model volunteer assis- tance days as a function of the independent variables. The most effective model in explaining participation was chosen, evaluated, and interpreted. SAS was used to run the regres- sions and STATA was used to evaluate the models for mul- ticollinearity (high correlation between independent vari- ables), heteroscedasticity (nonconstant variance), and auto- correlation (correlation between successive errors between fitted and actual values). Economic and social data tend to be more interrelated than biologic and physical data. For ex- ample, educational level and per capital income are likely correlated. If both these variables are chosen for the final model, a basic assumption of regression models will be vio- lated. Econometric modeling is particularly prone to these three possible assumption violations; thus, each potential problem is analyzed in detail. RESULTS Econometric modeling involves a series of regression models that test the validity of economic or behavioral theory as to why something happens like the factors that explain partici- pation. The variables first thought to be significant in explain- ing public volunteer assistance days in U&CF programs were Communities, Income, Forestland, WorkingPop, and High School. These variable definitions and the rationale for in- cluding them in the model are discussed subsequently. We discuss the expected impact (positive or negative) of each independent variable before running the regression model. Hopefully, the variable coefficients (positive or negative) will conform to these expectations or further model development would be required. Participation was defined as the number of days of volun- teer assistance in U&CF programs per million people in an individual state. This was the first of many potential models that were evaluated. The econometric process involves run- ning a series of regressions until an optimal explanatory model is found. Each model is evaluated for fit and regression assumption violations until the final version is identified.
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