Purpose This study aims to, through a case study of a typical community for rural tourism development in China, (1) measure the types, directions and magnitudes of the trickle-down effect of tourism income distribution; (2) analyze the influencing factors and hierarchical structure that affect this trickle-down effect and (3) explore pathways to narrow tourism income disparities among residents within the community by optimizing the trickle-down mechanism. Design/methodology/approach This study examines Shuanglang, a key tourism destination in Dali, Yunnan Province, China. Using 352 valid questionnaires, the entropy method and the poverty equivalent growth rate (PEGR) index were applied to evaluate the impact in Shuanglang. The Decision-Making Trial and Evaluation Laboratory (DEMATEL) method was used to identify influencing factors, followed by the construction of an interpretive structural model (ISM) to map four key pathways. Finally, the Multiplicative Interdependence Cross Matrix (MICMAC) method was used to analyze optimization paths within this influential system. Findings The results indicate that for the overall external-local trickle-down effect in Dali Shuanglang, the coefficient of the trickle-down growth rate “P” _“H” is 0. 6000, the coefficient of the trickle-down cost-compensated growth rate “P” _“PG” is 2. 1760, where PG stands for Pro-poor Growth compensation, and the coefficient of the growth rate for narrowing inequality within disadvantaged groups “P” _“SPG” is 2. 8953, where SPG stands for Segment-specific Pro-poor Growth. This suggests that inflows of external capital have widened the tourism income gap in Dali Shuanglang. Still, in the long run, the trickle-down effect is gradually mitigating the deterioration of internal inequality. Similar patterns are observed in the core-periphery regional trickle-down effect and the dining/accommodation–tourism/shopping industrial trickle-down effect, in which the income gap first expands and then gradually narrows through the trickle-down mechanism. However, the enterprise-household employment trickle-down effect performs the worst, with coefficients of “P” _“PG” at −0. 5344 and of “P” _“SPG” at −4. 0192, indicating that over the long term, an adverse trickle-down effect may exacerbate the income gap. Among the influencing factors, the control degree of external capital (centrality “C” _“j” = 1. 2379, causality “G” _“j” = 1. 1297) and residents' participation in business opportunities (“C” _“j” = 0. 8021, “G” _“j” = 0. 6010) are identified as key causal factors. In contrast, fairness of tourism resource enjoyment (“C” _“j” = 1. 3699, “G” _“j” = −1. 2760) and residents' dependence on tourism (“C” _“j” = 0. 8749, “G” _“j” = −0. 6983) are key outcome factors. Optimization efforts should therefore focus on these four factors, which rank in the top 30% by centrality, to improve the transmission mechanism and promote a top-down trickle-down process in tourism income distribution. Originality/value Existing research has rarely applied the trickle-down effect to tourism studies, and there is currently no literature directly measuring its impact on tourism income distribution. Related studies on influencing factors remain largely qualitative. This paper hierarchically and categorically defines the concept and theoretical framework of the trickle-down effect in tourism income distribution, extending the theoretical boundaries of the idea. In the empirical analysis, it pioneers the construction of a systematic and comprehensive evaluation indicator system for the trickle-down effect of tourism income distribution. It innovatively applies the PEGR index to micro-level measurement of this effect and, through the case of Dali Shuanglang – a rural Chinese community – responds to international debates surrounding the trickle-down effect theory. Using the DEMATEL method and the ISM model, this study identifies and analyzes the internal structure of the influencing factors related to the trickle-down effect of tourism income distribution from an empirical perspective. Using the MICMAC method, it integrates theory with practice to propose optimization pathways for the transmission mechanism.
Yang et al. (Sat,) studied this question.