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The voluntary adoption of International Financial Reporting Standards and loan contracting around the world

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Abstract

Using a sample of non-U.S. borrowers from 40 countries during 1997 through 2005, this paper investigates the effect of the voluntary adoption of International Financial Reporting Standards (IFRS) on price and nonprice terms of loan contracts and loan ownership structure in the international loan market. Our results reveal the following. First, banks charge lower loan rates to IFRS adopters than to non-adopters. The difference in loan rates in excess of a benchmark rate between the two groups is about 20 basis points for all loans and nearly 31 basis points for London Interbank Offered Rate (LIBOR)-based loans. Second, banks impose more favorable nonprice terms on IFRS adopters, particularly less restrictive covenants. We also provide evidence suggesting that banks are more willing to extend credit to IFRS adopters through larger loans and longer maturities. Finally, IFRS adopters attract significantly more foreign lenders participating in loan syndicates than non-adopters.

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Notes

  1. For example, over the past decade, about $780 billion in new debt securities were issued in the U.S. market, while only $2 billion in new equity securities were issued. About 54% of debt issues were bank loans (Graham et al. 2008).

  2. Our sample includes loans made by both commercial banks and private lenders such as investment banks and insurance companies. We use the terms banks and lenders interchangeably.

  3. For example, it is difficult for IFRS adopters to reverse the decision, once made, and IFRS adoptions require nontrivial efforts and resources on the part of the preparers of financial statements and their auditors.

  4. Diamond (1991) shows that low- and high-risk borrowers prefer short-term loans because low-risk borrowers can roll over their loans without incurring high renegotiation costs and lenders may hesitate to offer long-term loans to high-risk borrowers with high default risk. This author’s analysis further indicates that intermediate-risk borrowers prefer long-term loans to minimize refinancing or renegotiation costs.

  5. Evidence shows that voluntary IFRS adoption not only increases the quantity and quality of financial disclosures but also reduces accounting flexibility by restricting the choice of measurement methods (e.g., Ashbaugh and Pincus 2001). Bharath et al. (2008) provide evidence suggesting that lenders use more stringent (nonprice) contractual terms for borrowers with poor reporting quality. IFRS adoption can decrease the agency cost of debt to the extent that the resulting reduced accounting flexibility increases reporting quality and thus enables lenders to save ex post costs associated with loan monitoring and re-contracting. In this regard, lenders are also likely to offer more favorable nonprice terms or impose less restrictive covenants for IFRS adopters than for non-adopters.

  6. Ball et al. (2008) and Kim and Song (2010) show that the lead arranger of a loan syndicate retains a smaller portion of new loans when the information asymmetry between the lead arranger and other syndicate participants is lower.

  7. Information asymmetry exists among loan participants because the lead arranger is better informed about borrower credit quality than the other syndicate participants. This information asymmetry creates standard agency problems of adverse selection and moral hazard in loan contracting. For more discussions, see Holmstrom and Tirole (1997) and Ball et al. (2008).

  8. Implicit here is the assumption that at the time of loan contracting in year t, financial statements for year t are not publicly available. Examining the contemporaneous relation between DIFRS in year t and Loan Features in year t can create an endogeneity concern, because voluntary IFRS adoptions are likely to be endogenous.

  9. In our DealScan sample, the most popular benchmark rate is the LIBOR. We notice, however, that some loans are priced in excess of non-LIBOR benchmark rates, such as the Hong Kong Interbank Offered Rate (HIBOR), the Tokyo Interbank Offered Rate (TIBOR), the Singapore Interbank Offered Rate (SIBOR), and the Euro Interbank Offered Rate (EURIBOR). As will be further discussed in the next section, we include in our sample loans priced in excess of either LIBOR or non-LIBOR. We report the regression results using both LIBOR- and non-LIBOR-based spreads and those using only LIBOR-based spreads, separately.

  10. Prepayment restrictions include asset sweep, excess cash flow sweep, debt issue sweep, equity issue sweep, and insurance proceeds.

  11. Since the IFRS adoption indicator, DIFRS (as well as all borrower-specific financial statement variables) is measured in year t − 1 and the dependent variable, Loan Feature, is measured in year t, two-way causation is unlikely between DIFRS (our test variable) and Loan Feature (our dependent variable). This approach mitigates concerns over reverse causality in Eq. (1) with respect to the relation between Loan Feature and DIFRS. Nevertheless, Sect. 6 also reports the results of Heckman-type two-stage regressions to control for potential self-selection bias associated with a borrower’s decision to adopt IFRS voluntarily.

  12. The MSCI Index is a world market index constructed using the prices of representative stocks listed on 22 stock markets in North America, Europe, and the Asia/Pacific region weighted by the market capitalization of each constituent stock market.

  13. We also consider an additional borrower-specific variable, namely, asset maturity (ASM), when Eq. (1) is estimated using loan maturity (Maturity) as the dependent variable, because previous research shows a positive relation between the two (Barcley and Smith 1995; Bharath et al. 2008). Unreported results show that the coefficient for ASM is insignificant at the 10% level in all cases.

  14. Worldscope has a data field, 07536, that describes the accounting standards followed by a specific firm. This data field identifies 22 different accounting standards adopted by non-U.S. firms, including local standards (01), International Accounting Standards, hereafter IAS (02), U.S. standards (03), IAS with EU guidelines (06), and IFRS (23). In this paper, we classify firms with accounting standards codes 02, 06, and 23 as IFRS adopters.

  15. Other papers using the Loan Pricing Corporation’s DealScan database include Strahan (1999), Bae and Goyal (2009), Bharath et al. (2008), Asquith et al. (2005), Ivashina et al. (2008), and Kim et al. (2011).

  16. For instance, a deal can comprise a line of credit facility and a term loan.

  17. Qian and Strahan (2007) use a similar approach. As shown in their Tables II and III, the number of facility-years used in their regression analyses is 4,322 for the number of lenders and 1,255 for the drawn all-in spread.

  18. The percentage of IFRS adopters in our sample, about 4.0%, is smaller than that in the sample of Covrig et al. (2007). These authors use a total sample of 24,592 firm-years with both IFRS adopters and non-adopters from 29 countries in the period 1992 through 2002 to examine the effect of IFRS adoption on foreign mutual fund holdings in the global equity market. In their total sample, the percentage of IFRS adopters is about 5% (see their Table 1). Their focus is on the global equity market, while ours is on the international market for private debts.

  19. We estimate our main regressions after excluding observations from 21 countries with no IFRS adopters. Results using this reduced sample are qualitatively similar to those reported in this paper. Covrig et al. (2007) also include in their sample observations from nine (out of 29) countries with no IFRS adopters when examining the effect of IFRS adoption on foreign mutual fund holdings.

  20. Following Berkowitz et al. (2003), we aggregate five legal proxies into the single legal enforcement index as follows: LEnforce = 0.381*(Efficiency of Judiciary) + 0.578*(Rule of Law) + 0.503*(Absence of Corruption) + (0.347*Risk of Expropriation) + 0.384*(Risk of Contract Repudiation).

  21. In columns 3 to 5 of Table 5, where the dependent variable is a binary variable, Country Indicators is excluded to avoid the problem of quasi-complete separation, which stops us from estimating the probit regressions (Albert and Anderson 1984). This problem often arises when there is an independent dummy variable, x, such that for one value of x, either every case has 1 on the dependent variable, or every case has 0 (Allison 1999). In our case, in some countries none of the loans offered to firms have collateral and financial or general covenants. We therefore do not include Country Indicators in columns 3, 4, and 5 of Table 5.

  22. Following previous research (for example, Graham et al. 2008, Kim et al. 2011), we apply a Poisson regression when NCov is the dependent variable, where NCov, that is, the number of both financial and general covenants, is a countable number with a minimum of zero and a maximum of 13, and thus its distribution is better described by a Poisson distribution.

  23. Graham et al. (2008) also document a positive relation between leverage and loan maturity in their U.S. sample.

  24. We performed the Shapiro–Francia test for normality and rejected the null hypothesis that these variables are normally distributed at less than the 1%. These variables do not follow a normal distribution because they are a discrete random variable ranging from zero to finite maxima of 61, 56, and 56, respectively. As a robustness check, we also ran OLS regressions to test H4 and obtained qualitatively similar results to the ones reported in Table 6.

  25. Tabulated results are available from the authors upon request.

  26. These variables are chosen based on prior studies on IFRS adoption or cross-listing decisions (e.g., Pagano et al. 2002, Barth et al. 2008, Kim and Shi 2010). The first-stage probit estimation (untabulated) shows that firm size and the percentage change in equity financing and that of debt financing are significant determinants of the demand for IFRS-based reporting at the 1%, 5%, and 10% levels, respectively.

  27. We do not re-estimate Eq. (1) with the inverse Mills ratio added when the dependent variable is NCov, NLender, NForeign, or NDomestic, because these variables (and thus the associated error terms) are Poisson distributed. An important assumption underlying the Heckman-type two-stage treatment effect regression is that the error terms in both the first- and second-stage regressions follow a normal distribution with zero mean and constant variance.

  28. When the dependent variable is NCov in the poor information environment (Panel A of Table 7) and for low participation of foreign lenders (Panel C), maximum likelihood estimates for Poisson regressions do not exist, because the data have a large number of zeros (Santos Silva and Tenreyro 2009). For example, in the poor information environment subsample (Panel A), NCov is zero for all loans made to IFRS adopters, which precludes estimating Poisson regressions.

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Acknowledgments

We thank Agnes Cheng, Christopher Hodgdon, Cam Morrill, Jim Ohlson, Andrews Oppong, Annie Qiu, Joshua Ronen, Byron Song, Haina Shi, Dushvant Vyas, participants of research workshops at the City University of Hong Kong, Renmin University of China, Seoul National University, Xiamen University, the 2007 Accounting Research Camp of the John Molson School of Business at Concordia University, the 2007 CAAA Annual Conference, and the 2007 AAA Annual Meeting, and, in particular, an anonymous referee for useful comments on earlier versions of this paper. Special thanks go to Katherine Schipper (editor) for her insightful comments and detailed suggestions, which helped us improve the paper substantially. We acknowledge financial support for this research obtained from the 2006 Competitive Earmarked Research Grant of the Hong Kong SAR Government. Any remaining errors and omissions are, of course, ours.

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Authors and Affiliations

  1. Department of Accountancy, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong

    Jeong-Bon Kim

  2. School of Accounting and Finance, Hong Kong Polytechnic University, Hung Hom, Hong Kong

    Judy S. L. Tsui & Cheong H. Yi

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  1. Jeong-Bon Kim
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  2. Judy S. L. Tsui
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Correspondence to Jeong-Bon Kim.

Appendix

Appendix

See Table 8.

Table 8 Variable definitions
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Kim, JB., Tsui, J.S.L. & Yi, C.H. The voluntary adoption of International Financial Reporting Standards and loan contracting around the world. Rev Account Stud 16, 779–811 (2011). https://doi.org/10.1007/s11142-011-9148-5

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