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Research ArticleArticles

An Econometric Model to Predict Participation in Urban and Community Forestry Programs in South Carolina, U.S.

J. Jess Fleming, Thomas J. Straka and Stephen E. Miller
Arboriculture & Urban Forestry (AUF) September 2006, 32 (5) 229-235; DOI: https://doi.org/10.48044/jauf.2006.029
J. Jess Fleming
J. Jess Fleming, Graduate Research Assistant, Department of Forestry and Natural Resources, Clemson University, Box 340317, Clemson, SC 29634-0317, U.S.
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Thomas J. Straka
Thomas J. Straka (corresponding author), Professor, Department of Forestry and Natural Resources, Clemson University, Box 340317, Clemson, SC 29634-0317, U.S.,
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  • For correspondence: [email protected]
Stephen E. Miller
Stephen E. Miller, Professor, Department of Applied Economics and Statistics, Clemson University, Box 340313, Clemson, SC 29634-0313, U.S.,
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  • For correspondence: [email protected]
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Article Figures & Data

Tables

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    Table 1.

    Independent variable definitions.

    VariableVariable
    GenderEnvironment raised
    GenderFemaleAreaRaised1Rural nonfarm
    AgeAreaRaised2Rural farm
    Age1Under 30 yrs oldAreaRaised4Urban
    Age230 to 49 yrs oldAreaResidelRural nonfarm
    Age466 yrs old or olderAreaReside2Rural farm
    Highest level of educationAreaReside4Urban
    Education1Elementary schoolType of household
    Education2High schoolHousehold1Family household without children
    Education3Associate 2-yr degreeHousehold3Female householder with children under 18
    Education4Some collegeHousehold4Male householder with children under 18
    Education6Graduate degreeHousehold5Householder living alone
    Marital statusEmployment duties
    MaritalStatuslNever marriedDuties2Director/coordinator
    MaritalStatus3SeparatedDuties3Consultant
    MaritalStatus4WidowedDuties4Educator
    MaritalStatus5DivorcedDuties5Superintendent/manager
    Region in SC where currently livingDuties6Planner
    Region1UpstateDuties7Other
    Region3Lower Coastal PlainHousehold Income
    Income1$0–30,000 per year
    Income3Greater than $85,000 per year
    • View popup
    Table 2.

    Corresponding β values for the model 1.

    Variableβ’sNumeric value
    Interceptβ0  0.721275
    Genderβ1  0.782774
    Age1β2−1.872991
    Age2βs  0.245537
    Age4β4−0.811006
    Education1β5−8.57872
    Education2β6−1.775104
    Education3β7−0.863352
    Education4β8−1.167792
    Education6β9  0.327203
    MaritalStatus1β10  0.909987
    MaritalStatus3β11−17.808599
    MaritalStatus4β12−29.098726
    MaritalStatus5β13  0.156967
    Region1β14  0.686155
    Region3β15  0.182202
    AreaRaised1β16−0.336653
    AreaRaised2β17−0.273474
    AreaRaised4β18  0.098243
    AreaResidelβ19  0.110472
    AreaReside2β20  0.723733
    AreaReside4β21  0.092644
    Household1β22  0.030136
    Household3β23−1.289152
    Household4β24−0.711543
    Household5β25−0.654883
    Duties2β26−0.615841
    Duties3β27−0.043248
    Duties4β28−2.090003
    Duties5β29−0.551511
    Duties6β30−0.315801
    Duties7β31−1.155163
    Income1β32  1.844188
    Income3β33−0.174308
    • View popup
    Table 3.

    Probabilities for individual parameter estimates.

    Approximate probability > l t 1Significance levelHypothesis test Ho:
    βn = 0
    Ha: βn ≠ 0
    Intercept  0.31305%Fail to reject
    Gender  0.09715%Fail to reject
    Age1  0.05405%Fail to reject
    Age2  0.59875%Fail to reject
    Age4  0.24695%Fail to reject
    Education1<0.00015%Reject
    Education2  0.04535%Reject
    Education3  0.28885%Fail to reject
    Education4  0.11315%Fail to reject
    Education6  0.46435%Fail to reject
    MaritalStatus1  0.46125%Fail to reject
    MaritalStatus3  0.99785%Fail to reject
    MaritalStatus4  ,.00015%Reject
    MaritalStatus5  0.88975%Fail to reject
    Region1  0.17025%Fail to reject
    Region3  0.68365%Fail to reject
    AreaRaised1  0.52565%Fail to reject
    AreaRaised2  0.65415%Fail to reject
    AreaRaised4  0.88675%Fail to reject
    AreaReside1  0.84125%Fail to reject
    AreaReside2  0.33475%Fail to reject
    AreaReside4  0.87555%Fail to reject
    Household1  0.94865%Fail to reject
    Household3  0.56755%Fail to reject
    Household4  0.67285%Fail to reject
    Household5  0.57255%Fail to reject
    Duties2  0.32115%Fail to reject
    Duties3  0.97715%Fail to reject
    Duties4  0.00235%Reject
    Duties5  0.40595%Fail to reject
    Duties6  0.64115%Fail to reject
    Duties7  0.06465%Fail to reject
    Income1  0.05805%Fail to reject
    Income3  0.68255%Fail to reject
    • View popup
    Table 4.

    Pearson correlation coefficients.

    Variable&VariableCorrelation
    Age1&MaritalStatusl  0.45848
    Age2&Household1−0.37135
    Age2&Age4−0.32868
    Age4&Region1  0.30745
    Age4&Duties7  0.34561
    Age4&Income1  0.33776
    Age4&MaritalStatus4  0.31811
    Education1&MaritalStatus4  0.57429
    Education3&Income1  0.3072
    Education6&Duties4  0.32459
    MaritalStatusl&Household5  0.53809
    MaritalStatus3&Household3  0.40063
    MaritalStatus3&Household4  0.40063
    MaritalStatus5&Household5  0.47004
    Region3&Region1−0.51711
    AreaRaised1&AreaRaised2−0.30023
    AreaRaised2&AreaReside2  0.47169
    AreaRaised4&AreaReside4  0.34715
    AreaReside1&AreaReside4−0.30582
    Household1&Household5−0.40581
    • View popup
    Table 5.

    Pseudo R2 values.

    MeasureModel lModel 2Formula
    Cragg–Uhler0.29550.2407(1 – exp(−R/N))/(1 – exp(−U/N))
    McFadden0.18130.1441R/U
    • View popup
    Table 6.

    Corresponding β values for model 2.

    Variableβ’sNumeric value
    Interceptβ0  0.507417
    Genderβ1  0.649682
    Age1β2−1.450874
    Age2β3  0.393127
    Age4β4−1.057676
    Education2β6−1.677965
    Education3β7−0.641793
    Education4β8−1.219091
    Education6β9  0.222361
    Region1β14  0.642748
    Region3β15  0.378596
    AreaRaised1β16−0.22198
    AreaRaised2β17−0.273058
    AreaRaised4β18  0.182307
    AreaReside1β19  0.088404
    AreaReside2β20  0.961657
    AreaReside4β21  0.043463
    Household1β22  0.101815
    Household3β23−1.5387l5
    Household4β24−1.468615
    Household5β25−0.150528
    Duties2β26−0.530383
    Duties3β27  0.311920
    Duties4β28−1.891497
    Duties5β29−0.461981
    Duties6β30−0.263164
    Duties7β31−1.154150
    Income1β32  1.047749
    Income3β33−0.135845
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Arboriculture & Urban Forestry (AUF): 32 (5)
Arboriculture & Urban Forestry (AUF)
Vol. 32, Issue 5
September 2006
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An Econometric Model to Predict Participation in Urban and Community Forestry Programs in South Carolina, U.S.
J. Jess Fleming, Thomas J. Straka, Stephen E. Miller
Arboriculture & Urban Forestry (AUF) Sep 2006, 32 (5) 229-235; DOI: 10.48044/jauf.2006.029

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An Econometric Model to Predict Participation in Urban and Community Forestry Programs in South Carolina, U.S.
J. Jess Fleming, Thomas J. Straka, Stephen E. Miller
Arboriculture & Urban Forestry (AUF) Sep 2006, 32 (5) 229-235; DOI: 10.48044/jauf.2006.029
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