<p>This paper compares the Bank of Italy and ISTAT data to measure the main categories of foreign tourists neglected by accommodation data. Supply-side data, focusing on tourists who spend at least one night in official accommodations, may underestimate overall tourism flows because they do not account for same-day visitors and those who do not stay in official facilities. To address this limitation, we used spectrum and cointegration analyses to supplement accommodation data with specific categories of visitors identified through demand surveys, obtaining a first strong result according to which for every two arrivals recorded by supply-side data, there is one unreported arrival. We quantified two main components of unobserved tourism: unmeasured and underground. The latter is a proxy for the portion of tourism contributing to the shadow economy. We demonstrated that the two components exhibit different underlying stochastic processes, so they should be analyzed separately to make reliable forecasts of the entire phenomenon. The forecasts obtained through a SARIMA model show that underground tourism continues to have, after the COVID-19 pandemic, a relevance similar to the pre-pandemic years. This research contributes to creating more reliable databases that may help decision-makers define proper policy measures to promote tourism development.</p>

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Accommodation Statistics and Unobserved Inbound Tourism: The Italian Case Study

  • Anna Serena Vergori,
  • Giorgio Colacchio,
  • Domenico Suppa

摘要

This paper compares the Bank of Italy and ISTAT data to measure the main categories of foreign tourists neglected by accommodation data. Supply-side data, focusing on tourists who spend at least one night in official accommodations, may underestimate overall tourism flows because they do not account for same-day visitors and those who do not stay in official facilities. To address this limitation, we used spectrum and cointegration analyses to supplement accommodation data with specific categories of visitors identified through demand surveys, obtaining a first strong result according to which for every two arrivals recorded by supply-side data, there is one unreported arrival. We quantified two main components of unobserved tourism: unmeasured and underground. The latter is a proxy for the portion of tourism contributing to the shadow economy. We demonstrated that the two components exhibit different underlying stochastic processes, so they should be analyzed separately to make reliable forecasts of the entire phenomenon. The forecasts obtained through a SARIMA model show that underground tourism continues to have, after the COVID-19 pandemic, a relevance similar to the pre-pandemic years. This research contributes to creating more reliable databases that may help decision-makers define proper policy measures to promote tourism development.