Table 5 Multi-level modeling parameter...

Table 5

Multi-level modeling parameter estimates.^{^a}

	Semilog (log-linear)			Linear-log
	Coef.	Robust Std. Err.	P > \|z\|	Coef.	Robust Std. Err.	P > \|z\|
Conservation type (T)
Forest restoration (β₁)	–1.083	0.363	0.003	–1.054	0.388	0.007
Freshwater restoration (β₂)	–0.642	0.335	0.055	–0.557	0.329	0.091
Conservation scope (S)
Program low (β₅)	1.348	0.356	0.000	1.382	0.388	0.000
Program high (β₆)	1.703	0.618	0.006	1.764	0.654	0.007
Valuation characteristics
Sample size (β₅)	0.002	0.001	0.201
Log sample size (β₆)				0.608	0.332	0.067
Elicitation format
Choice experiment (β₇)	0.610	0.336	0.069	0.516	0.348	0.138
Contingent valuation (β₈)	0.501	0.362	0.167	0.412	0.394	0.295
ASC × CE (β₉)	–0.235	0.421	0.577	–0.292	0.443	0.509
Payment vehicle
Tex (β₁₀)	–1.483	0.387	0.000	–1.574	0.411	0.000
Voluntary (β₁₁)	–2.443	0.682	0.000	–2.364	0.754	0.002
Payment frequency (β₁₂)	1.047	0.439	0.017	0.928	0.512	0.070
WTP form (β₁₃)	0.016	0.264	0.953	0.112	0.267	0.675
Context characteristics
Trend (β₁₄)	–0.079	0.031	0.010	–0.054	0.032	0.098
USA (β₁₅)	–1.088	0.517	0.035	–0.750	0.514	0.144
GDP per capita (β₁₆)	0.061	0.014	0.000
Log GDP per capita (β₁₇)				1.306	0.326	0.000
Constant (β₀)	2.658	0.958	0.006	–3.178	2.387	0.183
Random-effects parameters
GID: Identity
var $(constant) = σ_{u}^{2}$	0.138	0.232		0.193	0.223
$v a r (e) = σ_{e}^{2}$	0.643	0.197		0.618	0.179
Model statistics
Snijders/Bosker R–squared Level 1	0.600			0.585
Snijders/Bosker R–squared Level 2	0.758			0.725
AIC	356.337			355.478
BIC	407.533			406.673
Log-likelihood at convergence	–160.169			–159.739
Wald χ² (15)	1664.990			1426.090
Prob > χ²	0.000			0.000
Number of observations	127			127
Number of groups	22			22

	Semilog (log-linear)			Linear-log
	Coef.	Robust Std. Err.	P > \|z\|	Coef.	Robust Std. Err.	P > \|z\|
Conservation type (T)
Forest restoration (β₁)	–1.083	0.363	0.003	–1.054	0.388	0.007
Freshwater restoration (β₂)	–0.642	0.335	0.055	–0.557	0.329	0.091
Conservation scope (S)
Program low (β₅)	1.348	0.356	0.000	1.382	0.388	0.000
Program high (β₆)	1.703	0.618	0.006	1.764	0.654	0.007
Valuation characteristics
Sample size (β₅)	0.002	0.001	0.201
Log sample size (β₆)				0.608	0.332	0.067
Elicitation format
Choice experiment (β₇)	0.610	0.336	0.069	0.516	0.348	0.138
Contingent valuation (β₈)	0.501	0.362	0.167	0.412	0.394	0.295
ASC × CE (β₉)	–0.235	0.421	0.577	–0.292	0.443	0.509
Payment vehicle
Tex (β₁₀)	–1.483	0.387	0.000	–1.574	0.411	0.000
Voluntary (β₁₁)	–2.443	0.682	0.000	–2.364	0.754	0.002
Payment frequency (β₁₂)	1.047	0.439	0.017	0.928	0.512	0.070
WTP form (β₁₃)	0.016	0.264	0.953	0.112	0.267	0.675
Context characteristics
Trend (β₁₄)	–0.079	0.031	0.010	–0.054	0.032	0.098
USA (β₁₅)	–1.088	0.517	0.035	–0.750	0.514	0.144
GDP per capita (β₁₆)	0.061	0.014	0.000
Log GDP per capita (β₁₇)				1.306	0.326	0.000
Constant (β₀)	2.658	0.958	0.006	–3.178	2.387	0.183
Random-effects parameters
GID: Identity
var $(constant) = σ_{u}^{2}$	0.138	0.232		0.193	0.223
$v a r (e) = σ_{e}^{2}$	0.643	0.197		0.618	0.179
Model statistics
Snijders/Bosker R–squared Level 1	0.600			0.585
Snijders/Bosker R–squared Level 2	0.758			0.725
AIC	356.337			355.478
BIC	407.533			406.673
Log-likelihood at convergence	–160.169			–159.739
Wald χ² (15)	1664.990			1426.090
Prob > χ²	0.000			0.000
Number of observations	127			127
Number of groups	22			22

STATA multi-level model syntax: xtmixed dependent variable independent variable(s) || GID:, variance robust.

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