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How can Big Data improve the quality of tourism statistics? The Bank of Italy's experience in compiling the "travel" item in the Balance of Payments

Author

Listed:
  • Costanza Catalano

    (Bank of Italy)

  • Andrea Carboni

    (Bank of Italy)

  • Claudio Doria

    (Bank of Italy)

Abstract

In tourism statistics it is becoming more and more important to identify data sources that are more timely and cheaper than the traditional ones, such as surveys. In this paper, we investigate how mobile phone data (MPD), electronic payments data and internet search data (Google Trends) can improve the compilation of tourism statistics and the 'travel' item in the Balance of Payments (BoP). We find that MPD have the potential to improve the estimates of the number of international travelers and can be integrated with surveys, although a constant interaction with the data supplier is required to identify the phenomena to be captured. We highlight the limitations and issues in using electronic payment data for estimating expenditure in tourism statistics, and we propose a model for producing more timely preliminary estimates for BoP purposes. Finally, we point out that Google Trends data can be used to complement the sample estimates of international travelers and to improve the quality of provisional data.

Suggested Citation

  • Costanza Catalano & Andrea Carboni & Claudio Doria, 2023. "How can Big Data improve the quality of tourism statistics? The Bank of Italy's experience in compiling the "travel" item in the Balance of Payments," Questioni di Economia e Finanza (Occasional Papers) 761, Bank of Italy, Economic Research and International Relations Area.
    • Handle: RePEc:bdi:opques:qef_761_23
    as

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    File URL: https://www.bancaditalia.it/pubblicazioni/qef/2023-0761/QEF_761_23.pdf
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    References listed on IDEAS

    as
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    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    big data; international tourism; mobile phone data; payments statistics; Google Trends;
    All these keywords.

    JEL classification:

    • I31 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - General Welfare, Well-Being
    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty
    • D63 - Microeconomics - - Welfare Economics - - - Equity, Justice, Inequality, and Other Normative Criteria and Measurement
    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution

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