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Note: All computations for the BMA are based on birth—death Markov-chain Monte Carlo (MCMC), Zellner’s g benchmark prior structure, uniform model prior with 100,000 burn-ins and 2,000,000 iterations. PCC = partial correlation coefficient; PIP = posterior inclusion probability; GDP = gross domestic product; OLS = ordinary least squares; GLS = generalized least squares; DOLS = distributed ordinary least squares; ARDL = autoregressive distributed-lagged model; GMM = generalized method of moment; VECM = vector error correction model; ECM = error correction model; FMOLS = fully modified ordinary least square.

Table 7 Note: All computations for the BMA are based on birth—death Markov-chain Monte Carlo (MCMC), Zellner’s g benchmark prior structure, uniform model prior with 100,000 burn-ins and 2,000,000 iterations. PCC = partial correlation coefficient; PIP = posterior inclusion probability; GDP = gross domestic product; OLS = ordinary least squares; GLS = generalized least squares; DOLS = distributed ordinary least squares; ARDL = autoregressive distributed-lagged model; GMM = generalized method of moment; VECM = vector error correction model; ECM = error correction model; FMOLS = fully modified ordinary least square.

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Table |. Literature Sources.
Table |. Literature Sources.
Figure |. Forest plot of individual estimates.
Figure |. Forest plot of individual estimates.
Figure 2. Funnel plot for tourism estimates. Note: Dashed vertical line indicates a zero partial correlation coefficient.
Figure 2. Funnel plot for tourism estimates. Note: Dashed vertical line indicates a zero partial correlation coefficient.
Table 2. Funnel Asymmetry Test. Note: The response variable is the t statistic of the estimated coefficient on tourism, which are estimated using the mixed effects model with robust standard error. ED < 0.01; ***p < 0.001. total number of moderator variables; and gi is the error term. However, equation (3) is heteroscedastic (see Valickova, Havranek, and Horvath 2015). Following Havranek and IrSova (2011) and Valickova, Havranek, and Horvath (2015), to correct for heteroscedasticity, we divide equation (3) by the standard error of the respective PCC. This method is known as the weighted least square (WLS) method.
Table 2. Funnel Asymmetry Test. Note: The response variable is the t statistic of the estimated coefficient on tourism, which are estimated using the mixed effects model with robust standard error. ED < 0.01; ***p < 0.001. total number of moderator variables; and gi is the error term. However, equation (3) is heteroscedastic (see Valickova, Havranek, and Horvath 2015). Following Havranek and IrSova (2011) and Valickova, Havranek, and Horvath (2015), to correct for heteroscedasticity, we divide equation (3) by the standard error of the respective PCC. This method is known as the weighted least square (WLS) method.
Figure 3. Galbraith plot for tourism estimates. Note: Dashed horizontal lines indicate the thresholds of two-sided critical values at the 10% significance level +1.65.
Figure 3. Galbraith plot for tourism estimates. Note: Dashed horizontal lines indicate the thresholds of two-sided critical values at the 10% significance level +1.65.
Figure 4. Country heterogeneity in the estimates of the impact of tourism on economic growth.
Figure 4. Country heterogeneity in the estimates of the impact of tourism on economic growth.
Figure 5. Method heterogeneity in the estimates of the impact of tourism on economic growth.
Figure 5. Method heterogeneity in the estimates of the impact of tourism on economic growth.
Note: Data Sources: World Tourism Organization; World Development Indicators; international financial statistics www.cepii.fr and www.bruegel. org. For country-level variables, we used values for 2011, the median year of the data used in the primary studies; multicollinearity: to cater for multicollinearity, the variables under each category (e.g., country characteristics, data characteristics, and specification characteristics) include only those that were retained after removal of those variables that had the least number of observations. PCC = partial correlation coefficient. Table 3. (continued)
Note: Data Sources: World Tourism Organization; World Development Indicators; international financial statistics www.cepii.fr and www.bruegel. org. For country-level variables, we used values for 2011, the median year of the data used in the primary studies; multicollinearity: to cater for multicollinearity, the variables under each category (e.g., country characteristics, data characteristics, and specification characteristics) include only those that were retained after removal of those variables that had the least number of observations. PCC = partial correlation coefficient. Table 3. (continued)
Table 4. Bayesian Model Averaging.
Table 4. Bayesian Model Averaging.
Table 5. Meta-regression Results (With Robustness Check). Note: The regression model includes only variables with a PIP greater than 0.1. ML = maximum likelihood; OLS = ordinary least squares; IV-2SLS = instrumental variable for two-staged least squares; GDP = gross domestic product; GLS = generalized least squares; ARDL = autoregressive distributed-lagged model; ECM = error correction model; FMOLS = fully modified ordinary least square. *, **, and **** indicate significance at 10%, 5%, and 1%, respectively.
Table 5. Meta-regression Results (With Robustness Check). Note: The regression model includes only variables with a PIP greater than 0.1. ML = maximum likelihood; OLS = ordinary least squares; IV-2SLS = instrumental variable for two-staged least squares; GDP = gross domestic product; GLS = generalized least squares; ARDL = autoregressive distributed-lagged model; ECM = error correction model; FMOLS = fully modified ordinary least square. *, **, and **** indicate significance at 10%, 5%, and 1%, respectively.
Figure 6. Bayesian model averaging (BMA) model inclusion for the tourism and economic growth nexus. Note: Response variable: partial correlation coefficient (PCC). Columns denote individual models. The variables are sorted in descending order of posterior inclusion probabilities. A blue-shaded cell implies that the variable is included with a positive estimated sign; a red-shaded cell implies that the variable is included with a negative estimated sign; and a blank cell implies that the variable is not included.
Figure 6. Bayesian model averaging (BMA) model inclusion for the tourism and economic growth nexus. Note: Response variable: partial correlation coefficient (PCC). Columns denote individual models. The variables are sorted in descending order of posterior inclusion probabilities. A blue-shaded cell implies that the variable is included with a positive estimated sign; a red-shaded cell implies that the variable is included with a negative estimated sign; and a blank cell implies that the variable is not included.
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