Machine learning for characterizing growth in tourism employment in developing economies: an assessment of tourism employment in Sri Lanka

ABSTRACT

Understanding the influence of tourism-linked factors on direct and indirect employment is important for tourism planning, particularly for tourism-dependent developing economies. Yet, related studies on developing countries are scant. This research considers trends of tourism growth in Sri Lanka over 1972–2018 using state-of-the-art machine learning methods: Classification and Regression Tree (CART), Boruta, hyperparameter tuning, grid search, novel robustness check strategies, and Random Forest. Our analysis confirmed that the growth in both direct and indirect tourism employment in Sri Lanka is influenced by three factors – total tourist arrivals, tourism receipts, and arrivals in the last quarter. The findings also reveal a notable seasonality impact on tourism employment, especially the growth of arrivals during the fourth quarter, for the country. Random Forest models suggest that an increase of tourist arrivals during the fourth quarter can largely compensate any detrimental impact on the growth of direct and indirect employment from a decrease in total tourist arrivals and tourism receipts. Overall, the article demonstrates that a systematic combination of machine learning approaches can provide rich insights from macro-level tourism statistics reported by tourism authorities, which in turn can guide policy formulation to boost tourism in the post-COVID-19 era.

KEYWORDS:

• CART
• Boruta
• machine learning
• tourism employment
• Sri Lanka

Acknowledgements

The authors acknowledge the SLTDA for confirming permission of the use of information.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Dataset availability statement

The used data are extracted from the publicly available SLDTA annual reports: https://sltda.gov.lk/annual-statistical-report.

Notes

1 Further, Boruta algorithm may tag some predictors as tentative, implying these predictors are neither confirmed nor rejected as important. The Boruta package in R (Kursa & Rudnicki, 2010), however, provides an additional function to decide about tentative predictors and the function has been utilized in this research.