The Impact of IFRS 16 on the Financial Reporting Accuracy in the Airline Industry

Abstract

Accounting harmonization enables the comparison of financial reporting and enhances its usefulness. The IFRS was developed to achieve such harmonization in a globalized world. This study focuses on the impact of transitioning from IAS 17 to IFRS 16 within the European airline industry. We conducted a quantitative analysis using non-parametric statistical tests to evaluate the impact on financial and economic ratios and compare the periods before and after the change, covering the period from 2018 to 2024. Our results show that both financial ratios and economic ratios were significantly affected by IFRS 16 adoption; namely, in contrast to prior studies, we show that ROE is significantly and positively affected, an important contribution of this study. Additionally, the size and type of airline company (low-cost, full-service, and flag airline companies) maintain these impacts, representing an innovative approach to this study compared to previous research. Overall, by recognizing both assets and liabilities in financial statements, IFRS 16 allows stakeholders to gain an accurate understanding of a firm’s capital structure and performance. This study contributes empirical evidence to the prior literature about the benefits of the new lease accounting standard and to improving the scientific knowledge on this topic.

1. Introduction

Accounting has the primary function of providing useful, relevant, and reliable information to several stakeholders, allowing for a solid basis for economic decision-making (Francis et al., 2004; Mukhametzyanov & Nugaev, 2016; Jeon & Oh, 2020). The International Financial Reporting Standards (IFRS) were developed in response to the need to provide investors with transparent, consistent and comparable information about different organizations around the world, particularly publicly traded companies. Over the last few years, there has been a significant expansion in their use, especially in 2005, when the European Union also adopted these standards. And in 2010, around 100 countries, including all the major world economies, had adopted IFRS or initiated a harmonization program with these standards (Ramanna, 2013).

Although international standards have significantly contributed to increasing the transparency, reliability, and comparability of financial information between entities, Öztürk and Serçemeli (2016) argue that International Accounting Standard (IAS) 17—“Leases” has been under much criticism because while IAS 17 allowed lessees to split contracts into finance leases or operating leases, in the case of an operating lease, the asset associated was not disclosed on the entity’s balance sheet, nor was the corresponding financial obligation recorded, either as a short-term or long-term liability, in contrast to the finance leases. This issue relied on these criteria to carry out the classification. Thus, within the scope of a project developed jointly with the FASB, the IASB revised the regulations applicable to leases, replacing IAS 17 with IFRS 16, the application of which became mandatory on 1 January 2019.

With the adoption of IFRS 16, the aviation sector was identified as one of the sectors most affected by the implementation of this standard, due to its heavy reliance on lease contracts. In the study conducted by Morales-Díaz and Zamora-Ramírez (2018), as well as by Öztürk and Serçemeli (2016), airlines made extensive use of leasing as a fleet management strategy; consequently, the aviation sector is among those most susceptible to the changes introduced by IFRS 16, thus requiring ongoing analysis over the years.

Christensen et al. (2025) find that the new leasing standard significantly improves their investment efficiency by reducing information asymmetry between top managers and divisional managers. In addition, Öztürk and Serçemeli (2016) argue that the most significant change introduced by IFRS 16 is to ensure greater accuracy, requiring that lease agreements that meet the defined criteria be recorded on the lessee’s balance sheet, simultaneously impacting assets and liabilities, ensuring that nothing is excluded from the accounts presented to stakeholders and other readers of financial information. In the same vein, Kim and Xie (2024) find that financial reporting quality improves after the adoption of the new lease standard, especially in firms with higher information asymmetry. In contrast, the results of Kelten and Perek (2024) suggest that IFRS 16 reduces earnings management in firms that seldom engage in such activities, but increases it in firms that frequently do so.

Given the regulatory transition and the unclear impact of this new standard on financial and economic performance associated with the accounting treatment of leases in previous studies, the primary objective of this study is to analyze the effects of adopting IFRS 16 on financial statements, with particular emphasis on financial indicators in European companies in the airline industry. In addition, this study extends the time frame of analysis to the period between 2018 and 2024, allowing for a clearer and more comprehensive view of the impacts of the regulation, and providing greater robustness and relevance to the results obtained. Therefore, our first research question consists of analyzing whether IFRS 16 adoption produces any impact on financial and economic ratios. Furthermore, based on the evidence from Morales-Díaz and Zamora-Ramírez (2018) that the impact of the adoption of IFRS 16 is more significant for low-cost airline companies, given that they use operational leasing as a strategic means of fleet management, our second research question involves analyzing differences between groups of airlines to determine whether the impact of this standard varies according to different categories of airline companies, as well as analyzing the impact of their size.

For these purposes, this research is based on a comparison between the periods before and after the implementation of IFRS 16, allowing us to identify changes in the ratios of financial autonomy, solvency, return on assets (ROA), return on equity (ROE), Earnings Before Interest, Taxes, Depreciation, and Amortization (EBITDA), indebtedness, and financial leverage (Ini & Damola, 2024). In addition, we carry out non-parametric tests to analyze the impact of different types and sizes of airline companies.

Overall, the contribution of this study is four-fold: First, we provide evidence that IFRS 16 enables the production of more accurate financial reports, giving stakeholders a fair view of the capital structure and firm performance. This promotes more efficient resource allocation and contributes to the achievement of SDG 8 (Decent Work and Economic Growth) and SDG 12 (Sustainable Consumption and Production). In fact, prior studies that analyze SDG 8 and SDG 12 emphasize the role of liquidity and financial management but do not directly relate to IFRS 16 (Dsouza & Krishnamoorthy, 2026; Dhaigude et al., 2025). Second, our findings suggest that IFRS 16 enhances accounting harmonization, as its impact on financial information is independent of firm size or type, in contrast to Morales-Díaz and Zamora-Ramírez (2018), who find an impact only for low-cost companies. Third, this accounting regulation transition has affected both financial ratios and economic ratios; namely, in contrast to prior studies, we show that ROE is significantly and positively affected, an important contribution of this study. Fourth, we fill the gap of prior studies regarding the impact of IFRS 16 adoption by expanding the period examined.

The remainder of this paper is structured into Section 2, where we provide a literature review that allows us to posit the hypotheses. In Section 3, we develop the empirical methodology concerning sample selection, the model and its variables. Section 4 includes descriptive statistics, other statistical results, and discussions. Section 5 contains the concluding remarks.

2. Literature Review

2.1. Accounting Standards

The informative function of accounting becomes even more relevant in a globalized context, where the decisions of investors, financiers, regulators, and other users of financial information depend on the quality and comparability of the data presented (Mukhametzyanov & Nugaev, 2016; Jeon & Oh, 2020). Standardizing financial information reduces data asymmetry and facilitates the interpretation of financial statements, allowing stakeholders to make more informed decisions (Magli et al., 2018; Othman & Kossentini, 2015; Ball, 2006).

Consistent with Matos and Niyama (2018), IFRS 16 adoption required a substantial modification of internal processes with investments in technology and information, providing a clear base for reporting and assessing the lessees’ contracts. The prior literature suggests that managers have opportunistic motivations when reporting earnings (Francis et al., 2005; Rajgopal & Venkatachalam, 2011; Bhattacharya et al., 2013). In fact, based on the Agency Theory, Cerqueira and Pereira (2017) find a positive association between poor earnings quality and high information asymmetry. Taking into account KPMG (2021), IFRS 16 adoption allows for a more accurate assessment of the financial situation of corporations. This, in turn, means that IFRS 16 allows for the reduction of information asymmetries between insiders and outsiders. Therefore, in this study, we aim to investigate whether IFRS 16 allows the reduction of financial information asymmetries both by integrating leases into financial statements and whether the impacts are persistent regardless of the type and size of companies.

Despite the benefits associated with accounting harmonization, the adoption of IFRS faces significant challenges. The existence of consolidated regulatory systems, aligned with legal traditions, specific cultures, and their own economic structures, increases resistance to the replacement of standards (Procházka & Pelák, 2015). Substantial differences still persist in international accounting practices, which can be attributed to factors such as language, the application of standards, and the political choices of each country. Nobes (2013) further argues that, although these standards are widely adopted, the degree of their use still varies according to the institutional context of each country.

In the case of IAS 17, issued by the IASB, it established guidelines for the accounting treatment of lease contracts, which were split into two types: operating leases and finance leases (IASB, 2003). However, empirical studies have shown that, although this standard has promoted some consistency, it allowed for off-balance sheet practices, that is, values that remained outside the balance sheet, which raised concerns among analysts and regulators regarding the reliability of the financial statements disclosed by companies. Consequently, despite the regulatory intent, the IAS 17 model proved limited in the faithful representation of companies’ contractual obligations, especially in sectors with a strong dependence on leased assets, such as airline, retail and transport (Barone et al., 2014). Consistently, De Martino (2011) argues that the difficulty in establishing objective criteria for distinguishing between leases allowed entities the flexibility to choose the capitalization of the leased asset, according to their accounting presentation purposes. These structural limitations and the increasing use of leases as a form of off-balance sheet financing intensified criticism of the accounting standard and drew the attention of regulators, investors and financial analysts (Fitó et al., 2013; Giner & Pardo, 2018).

This increasing pressure led to the development of IFRS 16. This new standard aims at eliminating the distinction between operating and finance leases on the lessee’s balance sheet, introducing a single recognition model to reflect the assets used and the liabilities assumed by entities during these contracts (Morales-Díaz & Zamora-Ramírez, 2018). The main objective of IFRS 16 is to ensure that lessees recognize the assets and liabilities associated with their lease agreements on their balance sheet, promoting greater financial transparency by eliminating the previous distinction between finance and operating leases for lessees. The recognition of a right-of-use asset and a lease liability aims to strengthen the comparability between entities that lease assets and those that acquire them directly. Thus, the implementation of IFRS 16 required a significant transformation in companies’ internal processes, with investments in technology and training (Matos & Niyama, 2018). In fact, the impact on earnings quality requires firms’ engagement to avoid earnings management activities due to the increase in firms’ leverage (Kelten & Perek, 2024).

In light of the arguments presented in previous studies, there is a need to investigate the real impact of adopting IFRS 16 on financial and economic ratios since its inception until 2024. In fact, Sequeira et al. (2024) find empirical evidence that a significant relationship exists between debt and earnings quality that tends to vary in sign, as the quality of financial information deteriorates with debt, but as debt becomes high, firms tend to increase the quality of earnings. Including financial leases in financial statements enables more informed and accurate decision-making (KPMG, 2021). This contributes to more efficient resource allocation and promotes SDGs 8 and 12.

2.2. Research Hypotheses

The airline industry is greatly affected by the implementation of IFRS 16, due to its strong dependence on leasing contracts (Morales-Díaz & Zamora-Ramírez, 2018). The airlines massively resorted to leasing as a fleet management strategy and to meet cash flow needs, thus preserving liquidity and reducing upfront costs. This is consistent with Öztürk and Serçemeli (2016), who identified the transport sector as one of the most susceptible to regulatory changes. The practice of leasing is justified by the high costs of acquiring aircraft, the need to preserve liquidity, and the flexibility that these contracts provide in fleet management. This reality means that leasing, especially operational leasing, represents a significant part of the financing structure of airlines, and is considered a strategic element for the business model (Fitó et al., 2013; Fülbier et al., 2008). This framework particularly favored low-cost carriers, whose rapid growth was largely sustained by flexible leasing contracts that allowed them to quickly adjust capacity to fluctuations in demand. The structure of leasing in the sector is also distinguished by the predominance of long-term contracts and the importance of operations such as sale and leaseback, where the company sells an aircraft to a lessor and simultaneously leases it back. According to Giner and Pardo (2018), this practice is used not only as an alternative financing mechanism but also as a way of managing risk and optimizing liquidity.

Morales-Díaz and Zamora-Ramírez (2018) show that with IFRS 16, airlines recorded a substantial increase in total assets and financial liabilities, given that contracts previously treated as operating leases were recognized as right-of-use assets and lease liabilities. This adjustment resulted in a significant structural change in the balance sheets, with direct effects on the perception of risk. Fülbier et al. (2008) argue that financial autonomy decreases after including financial leasing in the balance sheet. In the same vein, Bragança (2018) expected a deterioration in financial ratios after IFRS 16 adoption, namely in leverage, liquidity, and asset turnover.

Therefore, in order to answer the first research question, we formulate six hypotheses. Given the studies mentioned regarding the impact on financial autonomy, we posit our first hypothesis:

H1. 

IFRS 16 produces a negative impact on financial autonomy.

Regarding solvency, Fitó et al. (2013) find that the solvency decreases after adopting IFRS 16. Given that the assets associated with leases decrease more rapidly, due to asset depreciation, than the respective liabilities, it is expected that the solvency ratio will decrease. Therefore, we formulate the following hypothesis:

H2. 

IFRS 16 produces a negative impact on the solvency ratio.

Traditionally, operating leases were widely used because, under IAS 17, they did not require the recognition of the asset and liability on the balance sheet, allowing airlines to maintain lower levels of accounting debt and present an apparently stronger financial position (Morales-Díaz & Zamora-Ramírez, 2018; Chen & Wu, 2023). In addition, for the Chinese airline, Yu (2019) finds that the difficulties in obtaining external financing increased, which requires that those companies pay attention to the IFRS 16 effect, and may lead to adjusting the business plan. In the same vein, Giner and Pardo (2018) identify an increase in the debt ratio, resulting from the recognition of lease liabilities. Therefore, we expect an increase in the debt ratio, leading us to the third hypothesis:

H3. 

IFRS 16 produces a negative impact on indebtedness.

With regard to the income statement, although the overall effect is not as significant as in the balance sheet, the change in the classification of lease expenses is fundamental. Under IFRS 16, costs are no longer recorded as rent in External Supplies and Services (ESSs) but are now divided between depreciation of the right-of-use asset and interest. ROA allows for assessing the efficiency of asset use. The literature suggests that ROA tends to decrease, given the increase in total assets resulting from the recognition of the right-of-use model (Morales-Díaz & Zamora-Ramírez, 2018). Then we propose the following hypothesis:

H4. 

IFRS 16 produces a negative impact on the return on assets.

We include ROE, which allows us to assess the ability to generate returns on equity, but ROE may be affected differently depending on each company’s capital structure (Maglio et al., 2018). In the same vein, Öztürk and Serçemeli (2016) argue that the impact of IFRS 16 on ROE depends on the effect on profits or losses, depending on the lease portfolio. Given this inconclusive result of Maglio et al. (2018) and Öztürk and Serçemeli (2016), we further investigate the impact of IFRS 16 adoption on ROE, considering an extended period. By contrast, Veverková (2019) finds evidence that IFRS 16 increases the ROE for the European airline sector. Given these empirical inconsistencies, we formulate our fifth hypothesis:

H5. 

IFRS 16 produces a positive impact on the return on equity.

EBITDA no longer reflects any operating lease expenses, unlike what happened under IAS 17, which translates into a significant increase in this metric and a positive change to be considered in performance analysis (Singh, 2012). Additionally, Giner and Pardo (2018) point out that IFRS 16 introduces greater temporal volatility in financial results, since interest charges are higher in the first years of the contract and decrease progressively, in contrast to the linear recognition foreseen in IAS 17. Among the main impacts, the authors show the growth of EBITDA, since leases previously classified as operating expenses have been replaced by amortizations and interest. Several authors have significantly tested these possible impacts for various sectors of activity, including tourism, energy, services, and the financial sector, among others (Morales-Díaz & Zamora-Ramírez, 2018; Fitó et al., 2013). However, past studies lack information regarding the airline industry, which is one of the major drivers of the European economy and has a diverse range of companies and large economic groups.

H6. 

IFRS 16 produces a positive impact on EBITDA.

To further develop this research, we analyze whether company type and size influence the scale of the impact on financial ratios after the adoption of IFRS 16, based on the evidence from Morales-Díaz and Zamora-Ramírez (2018) that the impact of the adoption of IFRS 16 is more significant for low-cost airline companies. This investigation allows for determining whether the impact of the lease accounting standard is similar regardless of the size and type of company, thereby contributing to increasing accounting harmonization. Then, to answer the second research question, we formulate the following hypothesis:

H7. 

The impact of adopting IFRS 16 is greater for low-cost airlines than for flag and full-service airlines.

3. Empirical Methodology

Data was collected through a comprehensive review of the consolidated financial statements of companies in the airline industry, covering the period from 2018 to 2024. This approach allowed us to capture not only the immediate impact of the regulatory change but also the evolution of companies’ adaptations over time, offering a clearer and more substantiated view of how the sector has adapted to the new accounting requirements.

3.1. Sample Selection

This selection was based on the fact that airline companies are subject to the application of IAS/IFRS in accounting and preparation of financial information, and consequently, have adopted IFRS 16 as of 1 January 2019, as established in European Commission Regulation (EU) 2017/1986 of 31 October 2017.

The companies were included if they provided the financial information on their official websites, ensuring the reliability and consistency of the data used in the analysis. Furthermore, companies have to operate in the airline industry and apply IFRS in their financial statements. From a total of 15 companies, we excluded 6, namely Air Europa, because it does not make available, on its official website, the annual reports and accounts corresponding to the analysis periods considered.

In addition, companies that present their accounts in different currencies were excluded, namely Jet2 and EasyJet (British pounds), SAS (Swedish krona), Norwegian (Norwegian krona), and Turkish Airlines (US dollars). This decision also reduces the complexity associated with the analysis of historical trends, since exchange rate fluctuations could distort the evolution of financial indicators. The inclusion of these entities would require converting the values to the Euro, introducing the need to define reliable exchange rates for each period and generating potential inconsistencies due to exchange rate volatility between years. Given that we focus on the impact of IFRS 16 adoption on the financial and economic ratios, such conversions could directly affect the calculation of economic and financial ratios, altering indicators such as indebtedness, liquidity, profitability, and operational efficiency metrics, compromising comparability with the other companies in the sample.

During the sample selection, the inclusion of relevant international airlines, such as Qantas Airways (Australia), Singapore Airlines (Singapore), and LATAM Airlines Group, was also considered. All of these entities apply IFRS or use it as a means of consolidation and could enrich the analysis due to their size and impact on the sector. However, it was found that including these companies could compromise the homogeneity of the sample, not only due to exchange rate and contextual differences, but above all because they operate in regulatory and economic frameworks different from those applied in continental Europe.

Therefore, the sample includes a set of 9 European airlines representing different business models and sizes in the airline industry: The International Consolidated Airlines Group, S.A. (IAG), which includes Iberia, British Airways, Vueling, and Aer Lingus, as well as Air France–KLM Group and Deutsche Lufthansa AG (Lufthansa Group). In order to capture the perspective of low-cost carriers, Ryanair Holdings plc and Wizz Air Holdings plc were incorporated into the study, both of which are highly relevant due to their strong expansion in the European market and the impact that their business model has on the sector’s competitiveness. The sample was further complemented by national carriers such as TAP Air Portugal, S.A., Finnair Oyj and Aegean Airlines S.A., which, although they have different sizes compared to the large groups, are fundamental to capturing the structural diversity of the European sector. Finally, TUI Group AG, with air operations associated with tourism, was included as it represents a hybrid model, where air transport is combined with tourism activities.

3.2. Research Design

In order to test the first hypothesis, we carried out a comparative analysis of the economic–financial ratios and checked the normality of the statistical distribution of the ratios, namely the Shapiro–Wilk and Kolmogorov–Smirnov tests, which are statistically adequate for small- or medium-sized samples, as is the case in the present study. These tests allowed us to conclude that the financial autonomy and debt ratios show a distribution consistent with normality. In contrast, the solvency, ROA, ROE, EBITDA margin, and financial leverage ratios do not follow a normal distribution. Then, we compare these ratios between the period prior to the implementation of the standard, the year 2018, still under the framework of IAS 17, and the years following its adoption. This methodological strategy allows us to identify not only the size of the variations but also their statistical relevance.

To investigate the second research hypothesis regarding possible differences in the impact of IFRS 16 depending on company characteristics, the sample was organized into three distinct groups: low-cost, regional or “flag carriers,” which represent the country in this sector, and top-tier/full-service carriers, composed of companies with high revenue volumes, higher margins compared to the competition, and a vast network of air routes. This classification allows us to assess whether the effect of the accounting standard differs according to the business model, cost structure, and size of the airlines, factors that directly influence the economic and financial ratios. The lack of specific studies on this approach highlights a gap in the literature that this work aims to address. The method adopted consists of a descriptive statistical analysis of the main items in the balance sheet and income statement, namely assets, liabilities, equity, interest costs, depreciation and amortization. Through these data, it will be possible to calculate and present the relevant economic–financial ratios, namely financial autonomy, solvency, ROA, ROE, EBITDA margin and indebtedness, allowing the identification of patterns of variation in this sector over time.

The group of low-cost carriers includes companies whose business model is based on maximizing costs and intensive use of leased aircraft, including Ryanair Holdings plc and Wizz Air Holdings plc. These companies have high levels of operating leases and low margins per passenger and are particularly sensitive to changes affecting the recognition of lease assets and liabilities. Analysis of these operators reveals how IFRS 16 impacts companies with a fleet structure concentrated on leasing and cash flow highly dependent on operational efficiency. The second group, the regional airlines, includes carriers that operate predominantly in local or medium-haul markets, such as TAP Air Portugal, S.A., Finnair Oyj, and Aegean Airlines S.A. These companies have a mix of owned and leased aircraft, and their business model combines domestic and short-haul international flights. Their inclusion allows for an analysis of the impact of IFRS 16 on companies with less concentrated operations and smaller scales, verifying whether the size and geographical diversity of the fleet influence the magnitude of the accounting effects. The third group, the top-tier/full-service airlines, includes large aviation conglomerates such as International Consolidated Airlines Group, S.A. (IAG), Air France–KLM Group, Deutsche Lufthansa AG (Lufthansa Group), and TUI Group AG. These companies are characterized by extensive fleets, global operations, and full passenger services, integrating multiple market segments. Segmenting these air operators allows us to assess whether IFRS 16 has a more significant impact on companies with high operational diversity, multiple leasing contracts, and complex financial reporting structures. This methodological categorization enables us to conduct a quantitative analysis that can identify systematic differences between the groups.

4. Results

4.1. Descriptive Statistics

We use the R Studio software 2026.05.0 to achieve the results. The following six tables contain descriptive statistics for the financial and economic ratios for the entire sample period. The year 2018 is pre-adoption, while the other years are post-adoption.

As we can see in

Table 1. Descriptive statistics of financial autonomy—H1.

Year	Mean	Median	Standard Deviation	Minimum	Maximum
2018	0.28	0.28	0.16	0.05	0.58
2019	0.25	0.25	0.17	0.03	0.60
2020	0.07	0.04	0.20	−0.23	0.33
2021	0.08	0.11	0.16	−0.12	0.38
2022	0.11	0.07	0.13	−0.08	0.37
2023	0.13	0.12	0.11	−0.05	0.34
2024	0.16	0.14	0.13	0.02	0.44

, between 2018 and 2024, there is a possible reduction in the average financial autonomy ratio (H1) from the average of 28% in 2018, with IAS 17 still in force. Furthermore, the more abrupt impact during the first years of application of IFRS 16 should be highlighted, coinciding with the COVID-19 pandemic, with averages of 7% and 8%. Nevertheless, financial autonomy in 2024 was almost half that of 2018. The median shows positive values throughout the analysis periods, justifying the high financial autonomy of the companies in the sample. These results show an evolution consistent with Fülbier et al. (2008), who argue in favor of an decrease in financial autonomy after including financial leasing in the balance sheet.

Based on

Table 2. Descriptive statistics of solvency—H2.

Year	Mean	Median	Standard Deviation	Minimum	Maximum
2018	0.47	0.38	0.40	0.05	1.38
2019	0.42	0.33	0.44	0.03	1.48
2020	0.11	0.05	0.24	−0.19	0.50
2021	0.12	0.12	0.22	−0.11	0.61
2022	0.14	0.08	0.19	−0.07	0.58
2023	0.17	0.14	0.17	−0.05	0.52
2024	0.22	0.16	0.24	0.02	0.80

, in the case of the solvency indicator (H2), the average tended to decrease over the years, with 47% in 2018, versus values ranging from 11% to 22% (0.22 × 100%) in the later years analyzed. Once again, the COVID-19 pandemic intensified the impact, but the solvency in 2024 was less than half of 2018. The presence of outliers at the upper end of the distribution, i.e., exceptionally high solvency values, exerts a significant influence on the mean value. The lowest values were once again reached during the pandemic period. The standard deviation reveals heterogeneity of solvency data. In the initial period, between 2018 and 2019, the standard deviations were high, 40% and 44%, respectively, indicating significant heterogeneity in the sample. This suggests that solvency varies considerably between entities. However, in 2020, the standard deviation decreased drastically, reflecting a convergence towards homogeneity, and since then, values have remained similar for the remaining downstream periods. These results are consistent with Fitó et al. (2013), who find a decrease in the solvency ratio after adopting IFRS 16.

As documented in

Table 3. Descriptive statistics of indebtedness—H3.

Year	Mean	Median	Standard Deviation	Minimum	Maximum
2018	0.72	0.72	0.16	0.42	0.95
2019	0.75	0.75	0.17	0.40	0.97
2020	0.93	0.96	0.20	0.67	1.23
2021	0.92	0.89	0.16	0.62	1.12
2022	0.89	0.93	0.13	0.63	1.08
2023	0.87	0.88	0.11	0.66	1.05
2024	0.84	0.86	0.13	0.56	0.98

, between 2018 and 2024, the debt ratio (H3) tended to increase shortly after the introduction of IFRS 16, particularly during the pandemic periods, where average values were 93% and 92% in 2020 and 2021, respectively. These values decrease to 87% in 2023 and 84% in 2024, although they are still significantly higher than those recorded during the period when IAS 17 was still in effect, with 72%. These results are consistent with those found by Chen and Wu (2023), showing an increase in the debt ratio.

According to

Table 4. Descriptive statistics of return on assets—H4.

Year	Mean	Median	Standard Deviation	Minimum	Maximum
2018	0.08	0.08	0.05	−0.01	0.14
2019	0.06	0.05	0.03	0.01	0.12
2020	−0.08	−0.14	0.14	−0.19	0.15
2021	−0.10	−0.08	0.09	−0.32	0.02
2022	0.01	0.03	0.05	−0.09	0.07
2023	0.06	0.06	0.05	−0.07	0.10
2024	0.06	0.06	0.03	0.03	0.12

, the evolution of the average ROA (H4) between 2018 and 2024 shows three distinct moments in the performance of the companies in the sample. In the initial period, corresponding to 2018 and 2019, the values were 8% and 6%, respectively, revealing positive and relatively stable profitability levels. From 2020 onwards, a significant change in this trend can be observed, as the average ROA fell to −8%, reflecting the adverse effects of the pandemic crisis, which severely compromised companies’ abilities to generate positive results. In 2021, the situation worsened, with the indicator reaching −10%, the lowest value of the entire period under analysis. This confirms the deep impact of the unfavorable economic and operational environment. In later years, the ROA recorded a recovery to levels close to pre-adoption. These results are consistent with those from Morales-Díaz and Zamora-Ramírez (2018) and Fitó et al. (2013), showing a decreasing tendency in ROA.

As we can see in

Table 5. Descriptive statistics of return on equity—H5.

Year	Mean	Median	Standard Deviation	Minimum	Maximum
2018	0.15	0.22	0.28	−0.56	0.37
2019	0.08	0.13	0.31	−0.71	0.35
2020	−2.81	−0.58	4.99	−14.40	1.31
2021	0.49	−0.22	2.72	−3.47	5.93
2022	−0.39	−0.04	0.89	−2.43	0.31
2023	0.67	0.40	0.64	0.17	1.98
2024	0.53	0.26	0.77	0.06	2.52

, regarding ROE (H5), it shows great volatility between 2018 and 2024. In the initial years, 2018 and 2019, the average was 15% and 8%, respectively, indicating positive returns, although slightly lower in the first year of using IFRS 16, which reflected a moderation in the return generated on equity. In 2020, an extreme change occurred, with the average falling to a negative value of −281%, a direct consequence of the pandemic crisis and its severe effect on the sector’s profitability. However, in 2021, the average recovered significantly to a positive value of 49%, although there is still a strong dispersion in the values of this financial indicator. Between 2023 and 2024, average values stabilized around 53% to 67%, indicating that, overall, companies began to achieve more consistent levels with higher average ROE profitability than under IAS 17. In 2020, the median was −58%, significantly higher than the average of −281%, confirming the existence of negative outliers at the extremes that distorted the average. In the years 2022 to 2024, the median progressively approaches the average, reflecting greater homogeneity in the results and stabilization of the core profitability of the companies in the sample. These results contrast with those from Maglio et al. (2018) and Öztürk and Serçemeli (2016) because their results are inconclusive, while ours show a tendency to increase ROE, which is consistent with Veverková (2019).

Table 6. Descriptive statistics of EBITDA margin—H6.

Year	Mean	Median	Standard Deviation	Minimum	Maximum
2018	0.16	0.16	0.08	0.06	0.31
2019	0.16	0.16	0.05	0.07	0.21
2020	−0.13	−0.17	0.22	−0.36	0.26
2021	−0.15	−0.16	0.27	−0.72	0.27
2022	0.11	0.12	0.07	−0.01	0.22
2023	0.16	0.17	0.06	0.04	0.24
2024	0.18	0.19	0.06	0.09	0.24

documents that, in 2018 and 2019, the average EBITDA margin (H6) remained stable at 16%, reflecting positive and consistent levels of operating profitability. The median, also equal to 16%, indicates that most companies had balanced margins. From 2020 onwards, a sharp drop in operating profitability can be observed, with the average falling to −13%. In the years 2022 to 2024, a gradual and consistent recovery was observed, with the average rising to 11% and the median to 12% in 2022, with a minimum of −1% and a maximum of 22%, suggesting a decrease in dispersion and an improvement in EBITDA margin in most companies. Subsequently, in 2023 and 2024, the average increased to 16% and 18%, while the median stood at 17% and 19% (0.19 × 100%), with positive minimums and maximums in both years. These figures indicate not only the consolidation of operating margins but also a progressive homogenization of performance, with several companies exceeding pre-crisis and pre-IFRS 16 levels. Regarding its standard deviation, it shows generally homogeneous behavior among the companies in the sample, with relatively low values in most years. These results are consistent with the results of Singh (2012), suggesting an increase in EBITDA margin.

This empirical evidence allows for supporting the expected evolution, taking into account previous studies for all hypotheses from H1 to H6.

4.2. Regulatory Transition Impact

In order to proceed with the comparative analysis of the economic–financial ratios and with the aim of answering the first and second research questions, it proved necessary to verify the normality of the distribution of the ratios. Then, we carried out the Shapiro–Wilk and Kolmogorov–Smirnov tests, which are statistically adequate for small- or medium-sized samples, as is the case in the present study. These tests allowed us to conclude that the financial autonomy and debt ratios show a distribution consistent with normality. In contrast, the solvency, ROA, ROE, EBITDA margin, and financial leverage ratios do not follow a normal distribution.

For the first research question, we carry out a comparative analysis between the period prior to the implementation of the standard, the year 2018, still under the framework of IAS 17, and the years following its adoption. This methodological strategy allows us to identify not only the size of the variations but also their statistical relevance.

The selection of statistical tests began with the prior assessment of the data distribution. Where ratios showed behavior close to normality, parametric tests were used, specifically the t-test. Conversely, when the normality assumptions were not met, non-parametric tests were chosen, specifically the Wilcoxon test. This methodological differentiation ensures the consistency of the results and, simultaneously, prevents biased conclusions that could arise from the inappropriate use of parametric tests in distributions that do not meet the normality assumption.

Table 7. Significant differences in ratios before and after IFRS 16 adoption.

Years	Financial Autonomy	Solvency	ROA	ROE	EBITDA Margin	Indebtedness
2018 vs.	p-value	p-value	p-value	p-value	p-value	p-value
2019	0.14171	0.31394	0.02840 *	0.00769 **	0.95277	0.13587
2020	0.00015 ***	0.00769 **	0.01079 *	0.138640	0.01079 *	0.00015 ***
2021	0.00062 ***	0.01086 *	0.00769 **	0.76710	0.02088 *	0.00062 ***
2022	0.01382 *	0.02088 *	0.03815 *	0.08583	0.26039	0.01374 *
2023	0.04978 *	0.02840 *	0.10974	0.038151 *	0.90561	0.04946 *
2024	0.08814	0.08583	0.10974	0.19213	0.51467	0.08750

Tests are based on the t-statistic used for financial autonomy and indebtedness, and the Wilcoxon test for solvency, ROA, ROE and EBITDA margin. ***, **, *, represents statistical significance at the level of 1%, 5%, 10% respectively.

documents these results, offering a clearer perspective on the effects of the standard on both the financial structure and the economic performance of the companies under study.

The financial autonomy ratio shows that the adoption of IFRS 16 created statistically significant impacts in several years when compared to the pre-standard period. In particular, a strong significance is observed in 2020 and 2021, while in 2024, no statistically significant result was found. The solvency ratio analysis reveals statistically significant changes, particularly in the period between 2020 and 2023. This indicates that the adoption of IFRS 16 had a direct impact on companies’ capital structure, suggesting that the ability of airlines to meet their medium- and long-term liabilities was significantly affected in the first years after the regulatory transition. The ROA indicator exhibits statistically significant differences between 2019 and 2022, particularly highlighting the year 2021. This behavior indicates that the return on assets was strongly influenced in the first years after the adoption of IFRS 16, reflecting both the recognition of lease contracts under the right-of-use heading and the adverse effects of the pandemic situation on operating profitability. In the case of ROE, the results show more irregular behavior compared to other profitability ratios, with statistical significance observed in 2019 and again in 2023, while in the remaining years, no relevant differences were observed in relation to 2018. These results suggest that the impact of IFRS 16 on return on equity was not uniform throughout the analyzed period. The results for EBITDA margin data indicate statistically significant differences in the years 2020 and 2021 in relation to the period of validity of IAS 17, while in the remaining years, no relevant changes were observed. Regarding the debt ratio, the results show statistically significant changes in 2020 and 2021when compared to the reference year 2018. These differences clearly reflect the combined impact of the adoption of IFRS 16 and the unfavorable economic conditions caused by the pandemic, which influenced the capital structure of the companies analyzed. In the following years, 2022 and 2023, although the values continue to show some significance, the effect seems to diminish, suggesting a gradual adaptation of companies to the new accounting standard. In 2024, the ratio does not show significant changes compared to 2018, indicating a stabilization of the financial structure in the most recent period.

By combining these results before and after IFRS 16 adoption and the evolution of financial and economic ratios presented in the previous section, it is possible to support all of the hypotheses from H1 to H6, which are in line with the work of previous authors (Fülbier et al., 2008; Singh, 2012; Fitó et al., 2013; Morales-Díaz & Zamora-Ramírez, 2018; Maglio et al., 2018; Chen & Wu, 2023).

4.3. The Impact of Business and Size of Airline Companies

In this section, we present the results in

Table 8. Differences in ratios for financial autonomy and debt by group, year, and group/year using the ANOVA test.

Ratio	Issue	p-Value	Significance
Financial autonomy	Group	0.28075
Financial autonomy	Year	0.00506	**
Financial autonomy	Group/Year	0.99625
Indebtedness	Group	0.28231
Indebtedness	Year	0.00502	**
Indebtedness	Group/Year	0.99630

** represents statistical significance at the level of 5%.

and

Table 9. Differences in ratios for solvability, ROA, ROE and EBITDA margin between groups of companies and years using the Kruskal–Wallis test.

Ratio	Years with Significant Differences	Most Affected Groups
Solvability	None	No differences between groups
ROA	None	No differences between groups
ROE	None	No differences between groups
EBITDA margin	None	No differences between groups

because the ratios for financial autonomy and indebtedness present a normal distribution, and the test carried out was an ANOVA, while the solvency, ROA, ROE and EBITDA margin present a non-normal distribution, so we carried out the Kruskal–Wallis test.

Concerning the second research question, we focus on the type and size of the airline companies. Table 8 contains the results for the ratios that present a normal distribution.

The analysis of variances applied to financial autonomy and debt ratios reveals relevant results regarding the temporal dimension. For both indicators, the year factor shows highly significant p-values, revealing that there are statistically significant differences in the evolution of these ratios over the period under analysis. On the other hand, the group factor, which categorizes airline types into low-cost, regional, and top-tier/full-service airlines, is not statistically significant in any of the ratios analyzed. No significant differences were recorded between the different business models regarding levels of financial autonomy and indebtedness; that is, IFRS 16 appears to have had an impact on all groups, without markedly distinguishing between types of airlines.

In addition, the “group and year” interaction was not significant, reinforcing the idea that the temporal evolution of the ratios followed similar patterns among the groups analyzed. The result indicates that, despite the structural differences between business models, the temporal dynamics induced by the new accounting standard affected the sector in a relatively homogeneous way.

To analyze the financial ratios with a non-normal distribution, we apply the Kruskal–Wallis test, which enables us to evaluate the differences in the impact of adopting IFRS 16 on the solvability, ROA, ROE and EBIT margin ratios between the three groups of companies each year.

In general, the results show that ratios have no statistically significant differences between the groups over the analyzed period. These results do not support H7, which contrasts with the evidence of Morales-Díaz and Zamora-Ramírez (2018) that the low-cost airline companies were more significantly impacted by IFRS 16 adoption. Although these results allow us to consider that IFRS 16 contributes to accounting harmonization, no significant differences in financial ratios were calculated from financial reporting statements, in line with more earning quality found in the studies by Kim and Xie (2024) and Kelten and Perek (2024).

5. Discussion

The statistical analysis carried out allows us to validate the first research question, given that the adoption of IFRS 16 had a significant impact on the main economic and financial ratios consistent with expected base on prior studies (Fülbier et al., 2008; Singh, 2012; Fitó et al., 2013; Morales-Díaz & Zamora-Ramírez, 2018; Maglio et al., 2018; Chen & Wu, 2023).

These results show that this standard impacts financial statements, which, in turn, show that it is able to surpass the weakness of the previous standard for leases, and financial reporting becomes more transparent and accurate.

An additional contribution of this study is related to the inconclusive evidence of previous studies regarding the impact of the adoption of IFRS 16 on ROE. We found a positive and significant impact on ROE.

Regarding the second research question, the ANOVA and Kruskal–Wallis statistical tests did not find statistically significant differences between the three groups of airlines, although low-cost companies theoretically present a greater dependence on operating leases as argued by Morales-Díaz and Zamora-Ramírez (2018). In contrast, our results suggest that the effect of IFRS 16 is transversal and homogeneous, regardless of the size or business model of airline companies. The absence of significant variation reinforces the idea that the accounting framework introduced by IFRS 16 has superseded the specific characteristics of companies, demonstrating a widespread effect of capitalization and transparency in financial reporting.

Our results provide evidence that contributes to better decision-making of stakeholders and promotes efficient resource allocation and the achievement of SDGs 8 and 12. In addition, these results are an important contribution to regulators and accounting harmonization.

6. Conclusions

This study confirmed that the adoption of IFRS 16 represented a significant structural change in the financial reporting of airlines. The capitalization of operating lease contracts resulted in a substantial impact on economic and financial ratios for a longitudinal analysis up to 2024 that confirmed that this standard had consolidated itself as a more rigorous financial reporting instrument, providing stakeholders with a more realistic view of the capital structure of airline companies. This evidence is consistent with the existing literature, which highlights the increased transparency and improved comparability between companies. Additionally, our findings show that the impacts of applying IFRS 16 were homogeneous among the different groups of airlines, reinforcing the idea that this standard produced a systemic and transversal impact, acting as a standardization factor and mitigating the differences arising from different business models.

Overall, this study contributes to a deeper understanding of the accounting and financial impacts resulting from the adoption of IFRS 16, addressing the pandemic crisis period and presenting readers with a study using data updated to 2024. The analysis also clearly highlights the changes introduced by the standard in economic and financial ratios, which provide more accurate financial information about the airline companies, increasing the efficiency of resource allocation and contributing to SDGs 8 and 12.

We find some limitations during this study; namely, there are some companies operating in this sector that would be important to include. In addition, with this study being a quantitative analysis, it does not provide insights into strategy management. In this sense, future research could benefit from a qualitative approach, based on interviews with financial managers, administrators and fleet management managers, in order to capture how IFRS 16 influenced the formulation of business strategies, the renegotiation of leasing contracts and decision-making processes. In addition, the development of a data panel regression analysis would bring new insights into the determinants of the impact of IFRS 16 adoption on financial statements. Another relevant future research area would be to develop a study of the interaction between the implementation of IFRS 16 and the context of the COVID-19 pandemic, given that the evidence for the years 2020 and 2021 proved to be particularly critical, amplifying the effects of the new standard and accentuating existing weaknesses in the sector.