Abstract
We investigate the effects of mandatory International Financial Reporting Standards (IFRS) adoption on the comparability of financial accounting information. Using a set of alternative comparability measures, our results suggest that the overall comparability effect of mandatory IFRS adoption is marginal. We hypothesize that firm-level heterogeneity in IFRS compliance explains the limited comparability effect. To test this conjecture, we first hand-collect data on IFRS compliance for a sample of German and Italian firms and find that firm-, region-, and country-level incentives systematically shape IFRS compliance. We then use these compliance determinants to explain the variance in the comparability effect of mandatory IFRS adoption and find that only firms with high compliance incentives experience substantial increases in comparability. Moreover, we document that firms from countries with tighter reporting enforcement experience larger IFRS comparability effects, and that public firms adopting IFRS become less comparable to local GAAP private firms from the same country.
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Notes
By characterizing the capital-market effect of mandatory IFRS adoption as a “second-order effect,” we by no means imply that this effect is of subordinate economic relevance. “Second-order” simply refers to the notion that IFRS adoption potentially causes capital-market effects (second-order) by having an effect on accounting outcomes (first-order). It seems hard to imagine that IFRS could have an effect on capital-markets without affecting accounting outcomes first.
This heterogeneity of the IFRS treatment allows us, together with our pre- and post-IFRS design, to effectively address the issue of serial correlation that affects traditional difference-in-differences designs (Bertrand et al. 2004).
To verify that using annual data for constructing our comparability measures does not drive our results, we construct a sample based on semiannual data. For this, we collect data from Datastream and Compustat Global. A balanced sample with observations for the pre- and post-IFRS period in principle requires consecutive interim financial reporting data over the period 2001–2008. We settle with requiring at least six semiannual observations for each firm and four-year period to balance the sample size with the benefits of using interim data. To augment our semiannual observations, we combine quarterly reporting observations to semiannual observations by appropriately combining two accounting data from two consecutive quarters. This approach yields a final sample of 6,719 firms. Roughly two-thirds of these observations are from the US and Japan, and many European IFRS adopting countries are not well represented in the sample. However, we repeat our analysis for this reduced sample and find our main inferences to be qualitatively unchanged. Also, we verify that our comparability measures based on annual data are consistently linked to firm-level measures of the information environment (analyst following, forecast accuracy, forecast dispersion, and bid-ask spreads) in an economically meaningful way.
Additional details about the sample structure and an illustrative example are provided in "Appendix 1".
Total accruals are calculated as change in current assets minus change in current liabilities minus change in cash plus change in current debt minus depreciation and amortization minus change in provisions. Cash flow from operations is calculated as net income before extraordinary items minus total accruals.
While conceptually cash flow from operations should be unaffected by accounting standards, the indirect method of cash flow calculation and the separation of cash flow from investments from cash flow from operations introduces a modest impact of accounting standards (IAS 7 in the case of IFRS) on the measurement of cash flow from operations. However, rules for calculating cash flow from operations are remarkably similar around the world. Thus we assume the impact of different accounting regimes on the cash flow from operations to be marginal.
This controls for, for example, differences in country-level institutions: a fixed effect for country A will control for the effect of the enforcement system of country A that reduces the variance of accounting outcomes in country A.
Since in some countries voluntary adoption of IFRS was permitted before 2005, our sample of mandatory IFRS adopters could be subject to a reverse selection issue. Given the relatively high frequency of voluntary adopters in Germany, this issue is likely to be relevant for German firms, in particular. To assess the robustness of our findings, we repeat our main tests excluding German firms and find our inferences to be qualitatively unchanged.
To verify that the results of our analyses are insensitive to sample composition, we repeat our main tests using different samples: (i) excluding observations from the US, Japan, and the UK; (ii) restricting the maximum number of observations for each country to 100 randomly chosen observations; (iii) excluding observations from European countries; and (iv) excluding financial institutions. While the results become weaker when we focus on non-European observations, the tenor of our findings is not affected by these design choices.
Conceptually, 29 countries and 73 two-digit SIC industry groups would allow for a total of 29 × 29 × 73 = 61,393 observations for each period. However, we require at least three firms for each country, peer-country, industry group reducing the sample to 16,820 observations covering all countries and 69 industries.
In unreported sensitivity analyses, we also include additional control variables (mean size, mean book-to-market, standard deviation of earnings and cash flows) into our (modified) versions of models (5) and (6). Also, for model (6), we use changes instead of levels. Our inferences are insensitive to any of these design choices.
To mitigate possible sample selection issues, we also collect compliance data from the group financial reports of German voluntary adopters and use propensity-score matching to match German firms to similar Italian firms. We replicate our analysis by comparing the Italian firms with a matched sample of 153 German firms (116 of which voluntary adopters) and check that our results and inferences stay unchanged.
The online appendix is available at http://www.wiwi.hu-berlin.de/rewe/research/cg_online_app.pdf.
Looking more closely at the detailed response data (untabulated), we find that German firms tend (i) to expense development costs and (ii) not to recognize the fair value of derivative financial instruments on their balance sheets. These noncompliant measurement choices are consistent with German firms exhibiting a general tendency towards historical cost (Christensen and Nikolaev 2013). We find a similar result for the German matched sample, although with a somewhat lower level of significance. In contrast, the German matched firms exhibit a higher level of IFRS 2 measurement compliance than Italian firms.
To mitigate omitted variable concerns, we also estimate alternative versions of this model that include leverage, index membership, number of years since the initial public offering, seasoned public offerings (SPO), American Depositary Receipts (ADR), foreign listing, foreign sales, and analyst following as additional independent variables. These additional variables do not change our inferences.
While in all the other tests we define industry with two-digit SIC codes, industry groups in the German and Italian firms’ compliance analysis are based on one-digit SIC codes due to the limited sample sizes.
Since we do not control for the (unobserved) determinants of auditor choice, this effect should not be interpreted as causal.
In untabulated robustness checks, we re-run our analysis by contrasting the Italian sample with a pooled sample of German voluntary and mandatory adopters. Interestingly, German firms show overall higher compliance than Italian firms. Based on the insight from the geographical region results, this finding might be driven by different cultural attitudes towards compliance in general. Our findings are also in line with the commonly held belief that Italian firms tend to “label adopt” IFRS without any serious commitment to transparency because, in a strong insider system like Italy’s, information asymmetries are mainly resolved via means other than publicly disclosing accounting information. This finding is consistent with the argument supported by Daske et al. (2013) and might also be driven by the German enforcement system being more efficient than its Italian counterpart. We explore this intuition in subsequent analyses. Finally, our results show that voluntary adopters provide better disclosure compliance than late adopters.
We refrain from using size as an additional input variable as size usually captures a myriad of different firm-level characteristics, and thus it seems hard to unambiguously interpret any effect related to size. To verify the construct validity of our compliance incentives proxy, we repeat our tests including size in our first principal component analysis as well as using alternative first principle components (i.e., including and excluding, in turn, governmental ownership, board independence, and auditor type) and find our main findings to be robust.
We drop country fixed effects from the analysis to allow the country-level main effect of enforcement to manifest itself in the data. The fact that enforcement effects are only sizeable once country-level fixed effects are omitted from the analysis indicates that we cannot separate a potentially moderating effect of enforcement on the comparability effect of IFRS from other unobservable country-level effects on comparability.
Because the enforcement change indicator introduced by Christensen et al. (2013) is not available for all countries in our sample, we proxy for enforcement levels using the measure developed by Brown et al. (2013) and construct an enforcement change indicator based on their data in the spirit of Christensen et al. (2013). However, using the substantive enforcement change indicator by Christensen et al. (2013) yields qualitatively similar results, albeit for a smaller set of countries.
To test for additional interaction effects, we also estimate a fully saturated version of the model presented in Table 6, including the three-way interactions of BPT_ENF_SCORE, COMPLIANCE and IFRS_EFFECT and find our inferences to be unchanged.
Our sample is larger than the sample in Yip and Young (2012, Table 3 Panel B, right-most column) (939 observations), first of all, because we study more countries. Secondly, even when we limit our sample only to the countries investigated in Yip and Young (2012), our sample is still slightly larger (1,367 observations).
Our results differ from the main findings presented by Yip and Young (2012, Table 3), who document a positive impact of IFRS adoption on the degree of information transfer. A reason for this divergence might be that we use a difference-in-differences design with a control group of firms from non-adopting countries while the analyses in Yip and Young (2012) rely on an interrupted time series approach without a control group.
We thank the anonymous referee for this suggestion.
While this finding seems to be driven by the comparability of private firms decreasing, the comparability of public firms remains more or less constant, which we regard as evidence consistent with a marginal comparability effect of mandatory IFRS adoption.
Since a control sample of non IFRS-adopting countries is not available for this test, we cannot rule out that the coefficient for POST_2004 might be also capturing a general time trend.
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Acknowledgments
We thank Lakshmanan Shivakumar (editor) and an anonymous referee for their insightful suggestions and constructive feedback. We acknowledge the helpful comments of Rashad Abdel-Khalik, Jan Barton, Sudipta Basu, Katheryn Bewley, Ulf Brüggemann, Maria Correia, Lucie Courteau, Dan Givoly, Steven Huddart, Bjørn Jørgensen, Bin Ke, Urška Kosi, Garen Markarian, Peter Pope, Karthik Ramanna, Bill Rees, Shyam Sunder, İrem Tuna, Pauline Weetman, Steven Zeff, and seminar participants at the University of Amsterdam, University of Bari, ESMT Berlin, Bocconi University, Cass Business School, University of Cologne, Edinburgh University, HEC Paris, IE Madrid, London School of Economics, University of Naples Federico II, Rotterdam School of Management as well as conference participants at the 2009 AAA International Accounting Section Mid-Year Meeting, the 2009 EAA Conference, the 2009 AAA Annual Meeting, and the 2010 Pennsylvania State University Annual Conference. We thank Katarina Wilhelm for excellent research assistance. Joachim Gassen acknowledges the financial support of the German research foundation (DFG) under project A7 of the collaborative research center SFB 649 at Humboldt University. Stefano Cascino thanks the SFB 649 for co-founding his research visit at Humboldt University.
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Appendices
Appendix 1: Additional details on sample construction and test design
The concept of comparability is based on a comparison of firm pairs. To assess the treatment effect of IFRS adoption on comparability, we need to compare sets of firms whose level of comparability is likely to be affected by IFRS adoption. We attempt to achieve this goal by comparing firms within the same SIC two-digit industry across countries. Using the DKVCOMP and CFCOMP measures constructed as described in the research design section, we observe the average comparability of firms from one country (e.g., the US) with firms from another country (e.g., the UK), separately for each two-digit industry group with sufficient data and for the pre- and post-IFRS regime change period. This procedure yields us a dataset with the following structure:
COUNTRY | PCOUNTRY | IND | PERIOD | GAAP_PROX | DKVCOMP | CFCOMP |
|---|---|---|---|---|---|---|
UK | US | 20 | PRE | −0.167 | −0.042 | −0.071 |
UK | US | 20 | POST | −0.222 | −0.046 | −0.070 |
UK | Germany | 20 | PRE | −0.556 | −0.056 | −0.111 |
UK | Germany | 20 | POST | 0.000 | −0.058 | −0.094 |
… | … | … | … | … | … | … |
COUNTRY and PCOUNTRY indicate the two countries that are being compared. IND stands for the two-digit SIC code of the respective firms. PERIOD indicates the period pre (2001–2004) and post (2005–2008) IFRS adoption. GAAP_PROX captures the country-pair GAAP distance by summing up differences between two countries based on the GAAP differences measure presented in Bae et al. (2008, Table 1). The variable is multiplied by minus one and recoded to be distributed between −1 and 0 so that larger (less negative) values indicate more similar accounting regimes. DKVCOMP and CFCOMP are our comparability measures. For both measures, larger (less negative) values indicate more comparable financial accounting information.
These example data show that the comparability of UK firms with US firms can be expected to be affected by the adoption of IFRS in the UK: after IFRS adoption the accounting regime in the UK becomes more dissimilar to the accounting regime of the US while becoming identical with the accounting regime of Germany. To capture this relation, our main treatment variable is the change of GAAP_PROX between the 2004 and 2008 periods (IFRS_EFFECT). While this variable is zero for country pairs where neither country has adopted IFRS (our control group), it differs from zero whenever at least one country has adopted IFRS (our treatment group).
As stated in the research design section, our tests are based on the following change analysis:
Since the samples are organized by country, peer-country and industry, we can use country fixed effects for both country dimensions as well as industry fixed effects in our regressions. Note that we do not use the interaction of country and peer-country fixed effects since this would effectively remove from the system all the variation of IFRS_EFFECT or GAAP_PROXIMITY that we need for identification. The country-level fixed effects allow us to effectively control for country-level institutions that might affect the overall rigidity of a country’s accounting regime (such as enforcement, efficiency of the auditing process, etc.). This approach is conceptually similar to an estimation of a firm-year panel using firm and year fixed effects. Using firm and year fixed effects removes the average cross-sectional effects and the average time effects from the system. As in this standard setting, our identification comes from the interaction of country and peer-country and not from the average effects of country and peer-country per se.
Using IFRS_EFFECT as our heterogeneous treatment enhances the power of our tests compared to a traditional difference-in-differences setting where the treatment is modeled as a binary state variable. In addition, collapsing time series data in a pre- and post-period avoids the problem of inconsistent standard errors caused by serial correlated outcomes (Bertrand et al. 2004).
Appendix 2: Instrument for IFRS compliance test
The objective of the data collection process is to collect data about the level of compliance with IFRS for German and Italian firms. Based on prior literature, we expect compliance to be particularly problematic whenever local GAAP deviates significantly from IFRS. In addition, prior research has documented disclosure compliance to be easier to assess than measurement compliance. Thus we design our data collection instrument focusing on disclosure rules and those accounting issues where German and Italian GAAP exhibit sizeable differences between each other and IFRS. For this reason, we identify the subset of IFRS that covers those specific accounting issues. Measurement and disclosure compliance are assessed via a number of check-list-type of questions directly drawn from each of the surveyed accounting standards. Where applicable, to construct the compliance score we assign the value of one (zero) in case the company meets (does not meet) the compliance threshold level for the standard investigated. We express our compliance index (CSCORE) as a percentage. If a firm satisfies all the applicable disclosure compliance questions on the checklist, the compliance index is equal to 100 %. Further details about the construction of the instrument and the data collection process are provided in the online appendix to this paper available at the following link: http://www.wiwi.hu-berlin.de/rewe/research/cg_online_app.pdf.
Accounting standard | Accounting topic covered | Measurement compliance (number of questions) | Disclosure compliance (number of questions) |
|---|---|---|---|
IFRS 2 | Share-based payments | 1 | 3 |
IAS 11 | Construction contracts | 1 | 3 |
IAS 17 | Leases | 1 | 3 |
IAS 19 | Employee benefits | 1 | 4 |
IAS 33 | Earnings per share | 0 | 4 |
IAS 36 | Impairment of assets | 2 | 3 |
IAS 38 | Intangible assets | 2 | 3 |
IAS 39 | Financial instruments | 5 | 3 |
Total | 13 | 26 |
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Cascino, S., Gassen, J. What drives the comparability effect of mandatory IFRS adoption?. Rev Account Stud 20, 242–282 (2015). https://doi.org/10.1007/s11142-014-9296-5
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DOI: https://doi.org/10.1007/s11142-014-9296-5