Financial development and tourism: a century of evidence from Germany

ABSTRACT

This article presents findings from the first study to examine the direct effects of financial development on tourism. Using a unique historical dataset for Germany covering 1870 to 2016, we apply an autoregressive distributional lag (ARDL) model with structural breaks. To identify the lead–lag relationship between financial development and tourism, we adopt the wavelet coherence method and the most recently developed Shi, Hurn, and Phillips (2020) time-varying causality test. The ARDL results suggest that, on average, financial development is associated with an increase in tourist arrivals. The wavelet coherence results unveil a significant positive correlation between financial development and tourism in both short- and medium-terms, and financial development leads to tourism growth in Germany. Moreover, the causality results indicate that the positive effect of financial development on tourism is most evident from 2009 onward. Our study provides important implications for policymakers.

KEYWORDS:

• ARDL
• financial development
• time-varying causality
• tourist arrivals
• wavelet coherence

JEL CLASSIFICATION:

• O40
• Z32

Acknowledgments

We thank the anonymous referee for the insightful comments, which helped to improve the quality and readability of the article. All the remaining errors are our own.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

¹ The Global Financial Data database is available at http://www.globalfinancialdata.com/.

² The bank loans include ``lending by various types of financial institutions such as commercial banks, savings banks, postal banks, credit unions, mortgage banks, insurance companies, and building societies” (Awaworyi Churchill et al. 2020, 7).

³ Between 1941 and 1949, there are some missing observations, which we use linear interpolation techniques to interpolate the missing data. The data is freely available online at: https://figshare.com/articles/German_Time_Series_Dataset_1834_2012/1450809/1.

⁴ The data is available at http://www.macrohistory.net/data/. Details how the variables are constructed can be found in Jordà, Schularick, and Taylor (2017) and Jordà et al. (2019). Inflation is calculated as the change in consumer price index (CPI).

⁵ This is the only unit root test that takes into account heteroscedasticity in the data series.

⁶ The number of regressions estimated by ARDL model, in this article, is 12,500.

⁷ Our results are robust from 1 lag to 10 lags.

⁸ All the Cusum test results, in this article, are available upon request.