Modelling the economic effect of inbound birth tourism: a random forest algorithm approach

被引：0

作者：

Solarin S.A. ^{[1
]}

Gorus M.S. ^{[2
]}

Ozgur O. ^{[2
]}

机构：

[1] Faculty of Business, Multimedia University, Melaka

[2] Department of Economics, Faculty of Political Sciences, Ankara Yildirim Beyazit University, Ankara

来源：

Quality & Quantity | 2024年 / 58卷 / 5期

关键词：

Birth tourism; Economic growth; Machine learning; New Zealand; Random forest algorithm; Tourism-led hypothesis;

D O I：

10.1007/s11135-024-01852-7

中图分类号：

学科分类号：

摘要：

Due to the tourism sector being one of the main drivers of economic growth in several countries, many studies have been conducted on the economic impact of the tourism sector. However, the economic effect of the different tourism activities, especially birth tourism has not been sufficiently explored in the existing literature. Using different proxies that capture the development of birth tourism, we examine the economic effect of birth tourism on economic growth in New Zealand for the period, 1980–2019. Novel machine-learning techniques including random forest algorithm and partial dependence plots have been used to estimate the relationship between the variables in a framework, which also include capital stock and financial development. The empirical findings of this study show that there is a non-linear relationship between per capita income and birth tourism-related variables in New Zealand. These results offer fresh insights to policymakers on the importance of birth tourism. © The Author(s), under exclusive licence to Springer Nature B.V. 2024.

引用

页码：4223 / 4240

页数：17

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