Earnings management and impression management: European evidence

  • Received December 29, 2021;
    Accepted March 24, 2022;
    Published April 1, 2022
  • Author(s)
  • DOI
  • Article Info
    Volume 20 2022, Issue #1, pp. 459-472
  • Cited by
    5 articles

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

This study explores the relationship between Earnings Management and Impression Management in the context of some European listed companies. The analysis focuses on the readability of annual reports, measured by the file size. Earnings management is assessed using the modified Jones model. The sample consists of 2,953 listed companies from 17 industries of 24 European countries between 2012 and 2018 resulting in 13,020 firm-year observations. It has been found that one standard deviation increase in financial reports file size increases discretionary accruals in around 4%. These results are robust across different sample specifications in terms of firms’ size, industry and country. The findings show that increased intensity in the use of discretionary accruals is obfuscated by the disclosure of less readable annual reports, implying that Earnings Management and Impression Management are used complementarily. The conclusions have impact both for investment management and for policy, preventing inefficient allocation of capital budgeting and providing additional information that improves regulation on financial reporting transparency.

The authors are grateful to financial support from FCT – Fundação para a Ciência e Tecnologia (Portugal), national funding through research grant (UID/SOC/04521/2020).

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    • Figure 1. Average electronic file size by country
    • Figure 2. Average electronic file size by industry
    • Table 1. Descriptive statistics
    • Table 2. Relationship between earnings management and impression management
    • Table 3. Influence of predominant countries and industries, company size and reporting operating profit or loss
    • Table A1. Variables description
    • Conceptualization
      Tiago Goncalves, Cristina Gaio, Pedro Ramos
    • Data curation
      Tiago Goncalves, Pedro Ramos
    • Formal Analysis
      Tiago Goncalves, Pedro Ramos
    • Funding acquisition
      Tiago Goncalves, Cristina Gaio
    • Investigation
      Tiago Goncalves, Cristina Gaio, Pedro Ramos
    • Methodology
      Tiago Goncalves, Pedro Ramos
    • Project administration
      Tiago Goncalves, Cristina Gaio
    • Resources
      Tiago Goncalves, Cristina Gaio
    • Supervision
      Tiago Goncalves, Cristina Gaio
    • Validation
      Tiago Goncalves, Cristina Gaio, Pedro Ramos
    • Visualization
      Tiago Goncalves, Cristina Gaio, Pedro Ramos
    • Writing – review & editing
      Tiago Goncalves
    • Writing – original draft
      Cristina Gaio, Pedro Ramos
    • Software
      Pedro Ramos