Detecting tax evasion in the hospitality and tourism sector

  • 147 Views
  • 54 Downloads

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License

One of the industries with the fastest development is the hospitality and tourism (HoReCa) sector. However, there is also a growing trend in this sector to evade some state taxes. Despite promises that digitalization will reduce tax evasion, this practice nevertheless is a serious threat to the economy and the state. This study aims to process a comprehensive model for screening and risk assessment of tax fraud in the HoReCa sector in Romania. In this sense, an empirical study was conducted using an econometric model to detect tax evasion in the HoReCa sector in Romania, based on a sample of 50 firms for each sub-sector (hotels, restaurants, cafes), analyzing the period 2018–2022. The dependent variable of the model was the tax evasion risk indicator, calculated as the difference between the average financial ratios of each firm and the average for the entire sector. The results show that the leverage ratio has the strongest positive impact on the tax evasion risk indicator. The fixed asset turnover ratio and the accounts receivable turnover ratio also have a significant impact, indicating false sales reports or collection irregularities. The solvency ratio and the immediate liquidity ratio show positive effects on the risk of tax fraud, while the net rate of return is the only one with a negative effect, suggesting that profitable entities are less prone to tax evasion. The proposed model provides a solid basis for identifying high-risk companies directing tax authorities to improve supervision in the HoReCa industry. The findings also highlight the importance of further automating tax reporting systems to reduce the risks of evasion.

view full abstract hide full abstract
    • Table 1. Average financial ratios by sector, %
    • Table 2. Summary of the econometric model
    • Table 3. Coefficients
    • Table 4. Type and order of influence of independent variables
    • Conceptualization
      Andrei Dumitriu, Veronica Grosu, Cristina Gabriela Cosmulese
    • Data curation
      Andrei Dumitriu
    • Methodology
      Andrei Dumitriu, Veronica Grosu, Cristina Gabriela Cosmulese
    • Visualization
      Andrei Dumitriu
    • Writing – original draft
      Andrei Dumitriu, Veronica Grosu, Cristina Gabriela Cosmulese
    • Formal Analysis
      Veronica Grosu, Cristina Gabriela Cosmulese
    • Investigation
      Veronica Grosu, Cristina Gabriela Cosmulese
    • Resources
      Veronica Grosu, Cristina Gabriela Cosmulese
    • Supervision
      Veronica Grosu, Cristina Gabriela Cosmulese
    • Project administration
      Cristina Gabriela Cosmulese
    • Validation
      Cristina Gabriela Cosmulese
    • Writing – review & editing
      Cristina Gabriela Cosmulese