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This article provides new global evidence for the causal relationship between international tourist arrivals (TA) and economic growth (EG). The analysis considers 23 developing and developed countries and covers the period from January 1981 to December 2017. The causal relationship between TA and EG is determined using a bootstrap mixed-frequency Granger causality approach adopting a rolling window technique to evaluate its stability and persistency over time. Empirical results show that causality is time-varying in both the short-term and the long-term. We illustrate our results by constructing a new global connectivity index (GCI). The GCI shows that international TA remain a leading indicator for future EG in a global perspective, especially during the global financial crisis (GFC). Our findings suggest that tourism sector plays an important part in the future EG in developing countries after the GFC. Similarly, the period after the GFC is characterised by one of the highest values of the tourism-led EG in developed countries according to the GCI; however, this effect is temporal and quickly eradicates. Overall, we find that tourism sector in developing countries remains a primary contributor to future EG, which is not the case in developed countries.

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This paper evaluates the impact of tourism on poverty alleviation using a new panel quantile fixed effects method that allows regressors to affect the entire conditional distribution of the dependent variable providing substantial information gains. Our results show statistically significant negative marginal effects of tourism on both absolute poverty measures and Gini income inequality across all quantiles, including the poorest 10%. We also find evidence that international tourism can mitigate the slow improvement in domestic income level for poverty reduction. From a policy perspective, our findings can provide insights into developing targeted tourism policies and strategies to achieve better solutions on poverty alleviation. We also call for special attention to policymakers in developing countries to continue working on tourism product differentiation and targeting a smaller but reachable market in the post COVID-19 recovery era, to prevent the adverse effect of the worldwide income growth stagnation on their poverty rates.

This paper explicitly models four different corruption regimes according to the way in which corruption is practised. It distinguishes between organized and disorganized, collusive and non‐collusive corruption. The implications of these are compared and contrasted to provide ranking regarding their impacts on growth. Corruption is always bad, but the extent of the detrimental effect on growth is sensitive to the corruption regime observed. The least (or most) damaging regime is the one in which corruption is both organized and collusive (or disorganized and non‐collusive), as broadly characterizes the situation in China and its fast‐growing neighbours (or some African countries). An effective anti‐corruption policy should focus on fighting embezzlement and discretionary rent‐seeking first, which will dramatically reduce the adverse effect of corruption on growth.