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Adoption of the International Financial Reporting Standards (IFRS) on companies’ financing structure in emerging economies
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Finance Research Letters 000 (2015) 1–11
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Adoption of the International Financial Reporting Standards (IFRS) on companies’ financing structure in emerging economies Marco Aurélio dos Santos, Luiz Paulo Lopes Fávero∗, Luiz Fernando Distadio School of Economics, Business and Accounting at University of São Paulo, Department of Accounting and Finance, Av. Prof. Luciano Gualberto, 908 - FEA 3 - room 211, Zip Code: 05508-900, São Paulo, SP, Brazil
a r t i c l e
i n f o
Article history: Received 17 September 2015 Accepted 9 November 2015 Available online xxx JEL classification: C58 F34 F36 G15 G32 Keywords: IFRS Financing structure Multilevel models Emerging market firms
a b s t r a c t This paper aims to study the relationship between the adoption of the International Financial Reporting Standards (IFRS) and the companies’ financing structure in different emerging economies. A linear hierarchical regression model is applied, considering firm, country and region levels, in a database of 150,265 observations of companies from 145 countries between 2003 and 2014. The impact of the adoption of IFRS in financing decisions is heterogeneous among companies from different regions and countries. This effect is clearer when country controls are applied to monitor the legal enforcement and investor safety, such as the quality of the boards and accounting audits. © 2015 Elsevier Inc. All rights reserved.
1. Introduction Several studies identified that country characteristics affect the companies’ funding decision. Since Rajan and Zingales (1995) and La Porta et al. (1998), it is observed that country factors affect the ∗
Corresponding author. Tel.: +55 11 21842046; fax: +55 11 30915820. E-mail addresses: [email protected] (M.A. dos Santos), [email protected] (L.P.L. Fávero), [email protected] (L.F. Distadio). http://dx.doi.org/10.1016/j.frl.2015.11.002 1544-6123/© 2015 Elsevier Inc. All rights reserved.
Please cite this article as: M.A. dos Santos et al., Adoption of the International Financial Reporting Standards (IFRS) on companies’ financing structure in emerging economies, Finance Research Letters (2015), http://dx.doi.org/10.1016/j.frl.2015.11.002
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capital structure and financial performance. Comparing developed and emerging markets, it is observed that country factors influence in a different rate the decision of companies’ funding. While in developed markets the country factors have a less importance in capital structure, comparing to the firm determinants, in emerging economies, country factors are more relevant (de Jong et al., 2008; Kayo and Kimura, 2011; Lucey and Zhang, 2011; Santos, 2013). In economies with low levels of protection for creditors, the initial cost of capital tends to be higher than in economies where this protection exists and, thus, there is a greater impact of tangibility due to the need for a loan, mostly in small companies (González and González, 2008). Big companies, on the other hand, can structure their capital offering other forms of financing, attracting new investors (raising more equity or retained it). In this case, the improvement of quality information is more sensible to equity than credit (Myers and Majluf, 1984). The need for guarantees to grant credit, whose influence is enhanced by the company size, according to Gao and Zhu (2015) and Nikaido et al. (2015), is mainly related to asymmetric information problems. Thus, the greater the adverse selection risks resulting from the creditors’ and investor’s choice of bad investment projects, the higher the costs of capital for the organizations. One way to reduce this cost of capital is the issuing of contracts based on guarantees. Considering difficulties to access credit by new investors or creditors, a stronger trend is observed for governments to adopt policies and institutional improvements that can facilitate the companies’ capitalization through own capital or capital from third parties. The adoption of policies that can improve the credit market, mostly for small companies, has been discussed in recent researches (Clarke et al., 2006; González and González, 2008; Moro and Fink, 2013; Gormley, 2014; Owen and Temesvary, 2014; Nikaido et al., 2015). One of the measures governments have adopted to improve the credit and capital markets is the adaptation of their countries’ accounting models to national generally accepted international standards through the International Accounting Standards Board (IFRS), which are aimed at the harmonization of corporate financial statements in different international markets, favoring these countries’ inclusion in the movements of external capital, as well as the investors and creditors’ greater confidence in the companies. The main importance to adopt the IFRS is the improvement in the informational quality of financial reportings. Daske et al. (2013) and Cristensen et al. (2013) stand out, which identify a significant evolution of the transparency standards in countries that adhere to the international standards, resulting in a greater impact on the capital markets in economics with characteristics such as greater protection of investors. Ahmed et al. (2013), on the other hand, identify no significant changes in the accruals between the former models and the IFRS, despite an improvement in the analysts’ correct assessment of the companies’ future returns in the new model, which indicates the importance of the adoption in financial decision making. Other studies (Houqe et al., 2012; Gao and Sidhu, 2014; Houqe et al., 2014; Naranjo et al., 2015; Beneish et al., 2015) identified positive behaviors in the implementation of the IFRS regarding the improvement of the accounting quality and of the companies’ performance, which ends up creating positive impacts in the concession of credit by the banks. As the IFRS tend to enhance the information quality and, consequently, contribute to the investors’ confidence, the main objective in this paper is to verify whether a relation exists between the adoption of the IFRS in different countries and the companies’ financing decision. That is, if the characteristic ‘adherence to the IFRS’ in one country significantly affects the companies’ ease of getting credit access and the choice of bank financing or investor financing for investments and working capital. Other contribution of this study is to identify the impact of IFRS in financing decision of small and medium firms, emphasizing that this characteristic, not observed in other studies, can influence capital structure and access to credit. In this sense, not only the financing decision theories related to companies’ strategies, such as pecking order and trade-off, are considered, but also the importance of companies’ financing sources and credit access policies in different countries. As observed, there are few studies that analyze the impact of the adoption of IFRS on the organizations’ financing process. Current research, until date, analyzes the correlation between the characteristics of the IFRS adoption and the economic-financial information quality, not advancing on the structuration of the IFRS as a public policy to improve the credit access conditions and, consequently, the companies’ capital structure decisions. Please cite this article as: M.A. dos Santos et al., Adoption of the International Financial Reporting Standards (IFRS) on companies’ financing structure in emerging economies, Finance Research Letters (2015), http://dx.doi.org/10.1016/j.frl.2015.11.002
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The empirical findings suggest that, while country characteristics have an important role in financing decision, the adoption of IFRS does not have impact on investment funding of companies and has impact on working capital decisions. Financing of firms in countries with partial or total adoption tends to be done through retained or new owner funds, as also stated in Myers and Majluf (1984). While Section 2 presents data, variable selection and the proposed econometric models, Section 3 brings discussions of the results. 2. Method 2.1. Sample Considering the country effects in companies’ capital structure, we develop the following hypotheses: H1 : The country/region characteristics affect the capital structure of companies, influencing the financial decision. H1a : The adoption of IFRS by country affects positively the leverage of companies, considering the ease of credit access and the increase of informational quality for the creditors, comparing to firms in countries when the adoption in not present. H1b : The adoption of IFRS by country affects negatively the leverage of companies, considering the ease of financing access by other investors and the increase of informational quality for the investors. For this study, different databases were used, the most important of which was Enterprise Surveys. This database, available on the World Bank (2015), is structured based on interviews in companies of different sizes and institutional designs located in developed and developing countries, between 2003 and 2014. To construct the control variables, firm characteristics were considered, captured through the survey, as well as country characteristics surveyed through economic-institutional data found in databases of the OECD (2005), World Bank (2015) and World Economic Forum (2015). The initial sample consists of 150,625 observations of companies located in 862 different regions of 145 emerging countries, treated according to the tests, as presented in Table 1. 2.2. Variable selection To develop the empirical tests, the dependent (outcome) variables used individually refer to the percentage of investments funded by commercial banks and to the percentage of working capital funded by this same financial agent. Chart 1 shows the variables used in the models, as well as the used sources to obtain them. 2.3. Multilevel modeling Considering the hierarchical characteristics of data and the proposed hypotheses, we use a multilevel framework. The defining feature of multilevel models is their capacity to provide quantification and prediction of random variance due to multiple sampling dimensions (eg. across countries), and thus, these models offer many advantages for analyzing longitudinal data, such as flexible strategies for modeling change and individual differences in change, and the use of all available complete observations (Templin, 2015). In other words, as stated in Raudenbush and Bryk (2002), West et al. (2015) and Fávero (2015), the main advantage of multilevel models over traditional regression models is the consideration of the natural nesting of data. Multilevel models allow the researcher to identify and analyze individual heterogeneities between groups in which these individuals belong to, allowing the specification of random effects in each level of analysis. This fact represents the main difference compared to traditional regression models, which fail to take into account the natural nesting of data and, hence, can produce biased estimators of the parameters. Thus, applications with multilevel models offer researchers new possibilities to test different hypotheses without the risk of violating the premises inherent in other techniques, such as traditional regression models. Please cite this article as: M.A. dos Santos et al., Adoption of the International Financial Reporting Standards (IFRS) on companies’ financing structure in emerging economies, Finance Research Letters (2015), http://dx.doi.org/10.1016/j.frl.2015.11.002
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Chart 1 Structuration and source of variables. Variable Dependent variable END1 Indebtedness investment END2
Indebtedness working capital
Explanatory variable at country level IFRS Adoption of IFRS Control variables at firm level SIZE Size
REV
Total revenue
TANG LEGAL EXP
Tangibility Legal status of firm Export
ORIGIN
Origin of company
Control variables at country level GDP Growth of GDP INFL Inflation TAX Total tax rate SPREAD Total banking spread INVEST BOARDS ENFORC ∗
Degree of legal protection to investor Degree of quality of corporate boards Legal enforcement degree
Proxy used
Source∗
Proportion of investments made funded by banks (%) Proportion of working capital funded by banks (%)
ES
Categorical variables identifying non-adoption, partial adoption or full adoption of IFRS
IFRS
Categorical variable that indicates the firm size per number of employees, less than 20 = small; between 20 and 100 = medium; more than 100 = large Growth in total revenue in function of previous year Company’s volume of fixed assets Type of legal structure of firm Dummy variable indicating whether the company exports or not Dummy variable indicating whether the company is domestic or foreign
ES
Variation of GDP growth (%) Growth of annual inflation (%) Percentage of commercial profits Difference between interests on loans and interests on investments Degree of protection to investor (0–10) Degree of quality of corporate boards (0–10) Degree of legal rights (0–10)
ES
ES ES ES ES ES
WB WB WB WB GCI GCI GCI
ES = Enterprise Surveys of World Bank; WB = World Bank; GCI = World Economic Forum Global Competitiveness Index.
2.3.1. Null model and hypothesis H1 The first model is only based on the grouping process (null model). On the other hand, while the second model refers to a mixed model with random intercepts with firm and country variables, the third model considers random intercepts and slopes. The first model (null model) is as follows:
ENDi jk =
β000 + μ00k + τ0 jk + εi jk
(1)
in which the percentage of indebtedness is determined by a global average, plus an error term corresponding to the group of countries μ00k , regions τ 0jk and the residual idiosyncratic error ɛijk . Through variance decomposition, one can test H1 , studying how much of total variance is due to country, regional, and firm factors. 2.3.2. Random intercepts and random slopes models, and hypotheses H1a and H1b Aiming to test H1a and H1b , we added explanatory variables in the second model. Thus, firm, region and country variables are considered fixed, and our objective is to verify the relationship between the adoption of IFRS (categorical dummy variable) and the outcome variable. The following model is a random intercepts model:
ENDi jk =
β000 + β001 (SIZEi jk ) + β002 (REVi jk ) + β003 (TANGi jk ) + β004 (LEGALi jk ) + β005 (EXPi jk ) + β006 (ORIGINi jk ) + εi jk
(2)
Please cite this article as: M.A. dos Santos et al., Adoption of the International Financial Reporting Standards (IFRS) on companies’ financing structure in emerging economies, Finance Research Letters (2015), http://dx.doi.org/10.1016/j.frl.2015.11.002
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Country
Freq.
Percent
Country
Freq.
Percent
Country
Freq.
Country Slovak Republic Slovenia South Africa South Sudan Spain Sri Lanka St. Kitts and Nevis St. Lucia St. Vincent and the Grenadines Sudan Suriname Swaziland Sweden Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Tonga
748 804 937 738 606 610 150 150 154
0.5 0.54 0.62 0.49 0.4 0.41 0.1 0.1 0.1
662 152 307 600 508 1,099 1,232 1,043 150 155 150
0.44 0.1 0.2 0.4 0.34 0.73 0.82 0.69 0.1 0.1 0.1
Trinidad and Tobago Tunisia Turkey Uganda Ukraine Uruguay Uzbekistan Vanuatu Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe Total
370 592 4,559 1,325 2,556 1,228 1,251 128 820 1,053 835 830 1,204 599 150,265
0.25 0.39 3.03 0.88 1.7 0.82 0.83 0.09 0.55 0.7 0.56 0.55 0.8 0.4 100
Afghanistan Albania Algeria Angola Antigua and Barbuda Argentina Armenia Azerbaijan Bahamas, The
945 960 600 785 151 2117 1128 1196 150
0.63 0.64 0.4 0.52 0.1 1.41 0.75 0.8 0.1
Dominica Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Fiji
150 360 1024 5766 1053 179 804 1128 164
0.1 0.24 0.68 3.84 0.7 0.12 0.54 0.75 0.11
Lesotho Liberia Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania
151 150 1057 982 977 673 1115 850 387
0.1 0.1 0.7 0.65 0.65 0.45 0.74 0.57 0.26
Bangladesh Barbados Belarus Belize Benin Bhutan Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria
2946 150 1034 150 150 250 975 999 610 3444 2500
1.96 0.1 0.69 0.1 0.1 0.17 0.65 0.66 0.41 2.29 1.66
179 174 959 1196 1214 546 153 1,112 223 159 165
0.12 0.12 0.64 0.8 0.81 0.36 0.1 0.74 0.15 0.11 0.11
Mauritius Mexico Micronesia, Fed. Sts. Moldova Mongolia Montenegro Morocco Mozambique Myanmar Namibia Nepal
398 2960 68 1238 722 266 1,066 479 632 909 850
0.26 1.97 0.05 0.82 0.48 0.18 0.71 0.32 0.42 0.6 0.57
Burkina Faso Burundi Cabo Verde Cambodia Cameroon Central African Republic Chad Chile China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Croatia Czech Republic Côte d’Ivoire Djibouti
533 427 254 974 535 150 150 2050 2700 1,942 1228 151 881 1284 846 526 266
0.35 0.28 0.17 0.65 0.36 0.1 0.1 1.36 1.8 1.29 0.82 0.1 0.59 0.85 0.56 0.35 0.18
Gabon Gambia, The Georgia Germany Ghana Greece Grenada Guatemala Guinea Guinea-Bissau Guyana, Co-operative Republic of Honduras Hungary India Indonesia Iraq Ireland Israel Jamaica Jordan Kazakhstan Kenya Korea, Rep. Kosovo Kyrgyz Republic Lao PDR Latvia Lebanon
796 1218 13,515 1,444 756 501 483 376 1076 1,768 1438 598 472 877 630 836 943
0.53 0.81 8.99 0.96 0.5 0.33 0.32 0.25 0.72 1.18 0.96 0.4 0.31 0.58 0.42 0.56 0.63
Nicaragua Niger Nigeria Pakistan Panama Paraguay Peru Philippines Poland Portugal Romania Russian Federation Rwanda Samoa Senegal Serbia Sierra Leone
814 275 4,567 1,841 969 974 1,632 1326 1960 505 1721 5953 453 109 1,107 1102 150
0.54 0.18 3.04 1.23 0.64 0.65 1.09 0.88 1.3 0.34 1.15 3.96 0.3 0.07 0.74 0.73 0.1
Freq.
Percent
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Please cite this article as: M.A. dos Santos et al., Adoption of the International Financial Reporting Standards (IFRS) on companies’ financing structure in emerging economies, Finance Research Letters (2015), http://dx.doi.org/10.1016/j.frl.2015.11.002
Table 1 Observations in the research sample per emerging economy.
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being:
β000 β001 β002 β003 β004 β005 β006
ρ000 + τ0 jk = ρ001 = ρ002 = ρ003 = ρ004 = ρ005 = ρ006 =
(3)
where
ρ000 = γ000 + γ100 (GDPk ) + γ200 (INF Lk ) + γ300 (TAXk ) + γ400 (SPREADk ) + γ500 (INV ESTk ) + γ600 (BOARDSk ) + γ700 (ENF ORCk ) + γ800 (IF RSk ) + μ00k ρ001 = γ001 ρ002 = γ002 ρ003 = γ003 ρ004 = γ004 ρ005 = γ005 ρ006 = γ006
(4)
Replacing (04) and (03) in (02):
ENDi jk =
γ000 + γ100 (GDPk ) + γ200 (INF Lk ) + γ300 (TAXk ) + γ400 (SPREADk ) + γ500 (INV ESTk ) + γ600 (BOARDSk ) + γ700 (ENF ORCk ) + γ800 (IF RSk ) + γ001 SIZEi jk + γ002 REVi jk + γ003 TANGi jk + γ004 LEGALi jk + γ005 EXPi jk + γ006 ORIGINi jk + μ00k + τ0 jk + εi jk (5)
The third model is an extension of the second model, considering the interaction between country and firm variables in the multilevel modeling. This new verification is important to verify how the interaction between explanatory variables could affect the behavior of the outcome variable. The following model is a random intercepts and random slopes model:
ρ000 = γ000 + γ100 (GDPk ) + γ200 (INF Lk ) + γ300 (TAXk ) + γ400 (SPREADk ) + γ500 (INV ESTk ) + γ600 (BOARDSk ) + γ700 (ENF ORCk ) + γ800 (IF RSk ) + μ00k ρ001 = γ001 ρ002 = γ002 + γ102 (GDPk ) + γ302 (INF Lk ) ρ003 = γ003 ρ004 = γ004 ρ005 = γ005 ρ006 = γ006
(6)
Similarly, we can write:
ENDijk
=
γ000 + γ100 (GDPk ) + γ200 (INFLk ) + γ300 (TAXk ) + γ400 (SPREADk ) + γ500 (INVESTk ) + γ600 (BOARDSk ) + γ700 (ENFORCk ) + γ800 (IFRSk ) + γ001 SIZEijk + γ002 REVijk (7) + γ003 TANGijk + γ004 LEGALijk + γ005 EXPijk + γ006 ORIGINijk + γ102 GDPk × REVijk + γ302 INFLk × REVijk + μ00k + τ0jk + εijk
In this model, the countries’ economic performance variables distinctly affect the companies’ earnings behavior, which affects the outcome variable under analysis. Please cite this article as: M.A. dos Santos et al., Adoption of the International Financial Reporting Standards (IFRS) on companies’ financing structure in emerging economies, Finance Research Letters (2015), http://dx.doi.org/10.1016/j.frl.2015.11.002
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3. Analysis and discussion of results 3.1. Null model Table 2 shows the results obtained through the estimation of the null model, what allows us to test H1 . As observed, most of the variances in the model is explained by the firms’ characteristics over time (ICC – intraclass correlation). In addition: 8% of the total variance in the choice of indebtedness through banks is related to characteristics of the country the company is located in, while 3% is related to characteristics of the regions inside the countries where the companies’ headquarters are located, for the sake of bank financing for investment purposes. When compared to working capital financing by banks, the behavior continues, but with a slight increase in the regional characteristics. This regional effect can be linked to specific policies of certain states or provinces in specific countries, as well as greater or lesser regional sophistication of existing credit markets inside a certain country. This country effect behavior has already been discussed in previous papers (Kayo and Kimura, 2011; Santos, 2013), but the regional factor is not identified in the literature, mainly due to unavailability of data. 3.2. Random intercepts and random slopes models Table 3 and 4 show the results obtained through the estimations of the random intercepts model and the random intercepts and random slopes model, respectively, what allows to test H1a and H1b . The analysis of the outputs in Table 3 reveals that there are differences in the estimations when using the indebtedness due to working capital and indebtedness for the sake of investments. When the firm characteristics are analyzed, a positive relation can be verified between the size variable and indebtedness, as large firms tend to possess more tangible resources as a guarantee for debt and lesser bankruptcy risks, a fact that supports the trade-off theory between size and indebtedness. Other explanation for that effect could be the fact that small companies have more information asymmetry than the large ones. Moro and Fink (2013) and Cenni et al. (2015) identified that firm size is an important factor to credit rationing for banks, what is in line with our findings. For investment capital, our evidences show that this effect is more relevant than IFRS effect. In both estimations, it can be observed that export and foreign companies tend to be more levered than national companies, largely in function of the existence of financing structures of transnational companies in different countries. While investor protection and better quality of the boards tend to reduce the financing through banks, given the legal safety for investors, the high level enforcement that the credit system can lend more to the companies (positive signal), identifying the importance of regulatory country characteristics in corporate finance decisions. Table 2 Estimation results of the null model. END1
END2
Null model Observations Coefficient β 000
62,970,00 16,70∗∗∗
118,151,00 12,69∗∗∗
Variance decomposition Countries μ00k Regions τ 0jk Idiossincratic error ε ijk
86,27∗∗∗ 33,34∗∗∗ 942,38∗∗∗
51,19∗∗∗ 33,50∗∗∗ 506,05∗∗∗
Specification model test Chi-square
7,059,55∗∗∗
18,901,41∗∗∗
ICC Across countries Across regions Across firms
0,08 0,03 0,89
0,09 0,06 0,86
∗∗∗
sig. < 0.01.
Please cite this article as: M.A. dos Santos et al., Adoption of the International Financial Reporting Standards (IFRS) on companies’ financing structure in emerging economies, Finance Research Letters (2015), http://dx.doi.org/10.1016/j.frl.2015.11.002
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END2
Random intercepts model Observations Log likelihood Wald test chi2 (20) Coeffcient β 000 Partial IFRS Total IFRS
25,214,00 −123,178,52 495,54∗∗∗ 8,07 0,67 1,99
46,429,00 −209,670,53 1,863,76∗∗∗ 33,64∗∗∗ −2,92∗∗∗ −2,62∗∗∗
Control variables at firm level Medium SIZE Large SIZE REV TANG LEGAL privately held company LEGAL single owner LEGAL participation LEGAL limited participation LEGAL others EXP ORIGIN
5,749267∗∗∗ 9,943441∗∗∗ 0,0235752∗∗∗ 0,0012835 2,641246∗∗∗ 0,696728 −2,114212∗ 2,821139∗∗ 1,522482 0,0169398∗∗∗ −0,06349914∗∗∗
4,080665∗∗∗ 7,601902∗∗∗ 0,0074722∗ 0,0313911∗∗∗ −0,6606194 −3,162193∗∗∗ −3,206033∗∗∗ −0,8211137 −2,337268∗∗ 0,0338774∗∗∗ −0,0428777∗∗∗
Control variables at country level GDP INFL TAX SPREAD INVEST BOARDS ENFORC
−0,2193983∗∗ 0,180867 0,0129004 0,0328941 0,000259 −0,1861866 0,6158791∗
0,1654753 −0,0541023 −0,0412764 0,2053236∗∗ −0,9681399∗∗∗ −5,429542∗∗∗ 1,406586∗∗∗
Variance decomposition Countries μ00k Regions τ 0jk Idiossincratic error ε ijk
80,09793∗∗∗ 31,59392∗∗∗ 1005,114∗∗∗
54,02227∗∗∗ 32,63408∗∗∗ 480,7197∗∗∗
Specification model test Chi-square
1652,23∗∗∗
4327,77∗∗∗
ICC Across countries Across regions Across firms
0,07172055 0,028289537 0,899989913
0,095214223 0,057517549 0,847268227
Obs: ∗ sig. < 0.1; ∗∗ sig. < 0.05; ∗∗∗ sig. < 0.01.
As also stated in Nikaido et al. (2015), while, for investment capital, we observe that guarantees are more preeminent than accounting information demonstrated by IFRS improvement, for working capital we verify that financial improvement through IFRS can attract more equity or incentive the owner to retain funds. Considering previous studies, our findings are different from those obtained by Naranjo et al. (2015) because these authors identified an increase in attracting foreign capital in countries after the adoption of IFRS. On the other hand, the negative relationship between the adoption of IFRS and the reduction of funding for banks, identified in our study, is corroborated by these same authors, who justify this fact by the existence of asymmetric information. As to the variable being studied, the adoption of the IFRS, distinct behaviors are observed in the two estimations. Despite the lack of impact of countries’ adoption of IFRS in the financing process by banks for investment purposes, when resources are captured through banks for use as working capital, countries Please cite this article as: M.A. dos Santos et al., Adoption of the International Financial Reporting Standards (IFRS) on companies’ financing structure in emerging economies, Finance Research Letters (2015), http://dx.doi.org/10.1016/j.frl.2015.11.002
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Table 4 Estimation results of the random intercepts and random slopes model. END1
END2
Random intercepts and slopes model Observations 25,214 Log likelihood −123,187,51 Wald test chi2 (20) 499,20∗∗∗ Coeffcient β 000 7,10 Partial IFRS 0,76 Total IFRS 2,08
46,429 −209,675,88 1,876,95∗∗∗ 34,92∗∗∗ −2,94∗∗∗ −2,70∗∗∗
Control variables at firm level Medium SIZE Large SIZE REV TANG LEGAL privately held company LEGAL single owner LEGAL participation LEGAL limited participation LEGAL others EXP ORIGIN
5,749391∗∗∗ 9,943031∗∗∗ 0,0514846∗∗∗ 0,0012077 2,623741∗∗∗ 0,6716267 −2,140523∗ 2,809171∗∗ 1,553389 0,0167474∗∗∗ −0,0634653∗∗∗
4,086097∗∗∗ 7,59894∗∗∗ −0,0184044∗ 0,0311783∗∗∗ −0,6686575 −3,168713∗∗∗ −3,2044∗∗∗ −0,8255153 −2,358439∗∗ 0,0340308∗∗∗ −0,0428098∗∗∗
Control variables at country level GDP INFL TAX SPREAD INVEST BOARDS ENFORC
−0,1775005 0,1957114∗ 0,0137823 0,0225882 0,0445632 −0,0257721 0,584754∗
0,1475652 −0,0694059 −0,0395677 0,2008515∗∗ −0,9638698∗∗∗ −5,699037∗∗∗ 1,410583∗∗∗
Iterations GDP-REN INFL-REN
−0,0032481 −0,002288
0,0056953∗∗∗ 0,0009338
Variance decomposition Countries μ00k Regions τ 0jk Idiossincratic error ε ijk
78,8301 31,54238 1005,098
54,80336 32,75008 480,5925
Specification model test Chi-square
1652,14∗∗∗
4329,06∗∗∗
ICC Across countries Across regions Across firms
0,070669822 0,028277198 0,90105298
0,096460005 0,057643781 0,845896215
Obs: ∗ sig. < 0.1; ∗∗ sig. < 0.05; ∗∗∗ sig. < 0.01.
that adopt IFRS tend to have less indebtedness of working capital by banks (negative signal) and prefer to capture resources through retained earnings or issuing new stocks. The analysis of the estimators in Table 4 shows no significant changes when compared to the model with random intercepts. The inflation variable should be highlighted, which is significant to explain END1.
4. Conclusions The aim in this article was to verify whether different countries’ adoption of the IFRS positively affected the companies’ access to credit, using multilevel modeling. Please cite this article as: M.A. dos Santos et al., Adoption of the International Financial Reporting Standards (IFRS) on companies’ financing structure in emerging economies, Finance Research Letters (2015), http://dx.doi.org/10.1016/j.frl.2015.11.002
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Its first contribution refers to estimating the influence of firms, countries and global regions’ effects on investment capital and on working capital funded by commercial banks. If the country effect is stronger, which is not the case in this study, a closer look should be taken at the impacts related to the differences among countries to compose percentages of investment capital and working capital. If, on the other hand, the differences among firms explain the largest part of the variance, a focus on strategic management and on differences among organizations needs to be stimulated. In our case, it is observed that the firm size plays a central role in the process of getting access to bank credit, mostly to investment financing, as identified in the capital structure literature. This fact can eliminate the effect of the improvement of the accounting information quality due to the adoption of IFRS. The second contribution of this study is the attempt to add predictive variables at firm and country levels. While some can only study the variance decomposition without evaluating the impacts deriving from the presence of certain variables (Fávero, 2010), the inclusion of explanatory variables at levels 1 (firm) and 2 (country) allowed us to verify the relationship between the adoption of IFRS and the leverage of firms. As implications of the study, we identified a mixed behavior of IFRS influence in financial decisions, driven by application of resources and firm size. When resources are captured for investments in working capital, a negative impact of the adoption of IFRS on indebtedness is observed, which may be related to the easy financing through other financing sources, such as new partners or the offering of new stock. Regulation and institutional protection of countries in financing process are also key points. As the level of investor and creditor protection can influence the capital access, this could also impact IFRS effectiveness policies. Thus, the decision on IFRS adoption can be influenced by control mechanisms and heavy government interference in the financial market. For the sake of future research, more in-depth studies are recommended, specifically related to small and medium companies, in order to verify the effects of the adoption of this policy in companies with restricted access to stock markets and difficulties to get access to credit. Other predictive variables at firm or country level can also be included in the modeling to allow researches and decision makers define new strategies in companies’ financing. Changing the analysis period and even expanding the interval to earlier years can favor a deeper understanding of the mechanisms ruling companies’ financing structure in emerging economies. References Ahmed, K., Chalmers, K., Khlif, H., 2013. A Meta-analysis of IFRS adoption. Int. J. Account. 48, 173–217. Beneish, M.D., Miller, B., Yohn, P., 2015. Macroeconomic evidence on the impact of mandatory IFRS adoption on equity and debt markets. J. Account. Public Policy 34, 1–27. Cenni, S., Monferà, S., Salotti, V., Sangiorgi, M., Torluccio, G., 2015. Credit rationing and relationship lending. Does firm size matter? J. Bank. Financ. 53, 249–265. Clarke, G.R.G., Cull, R., Pería, M.S.M., 2006. Foreign bank participation and access to credit across firms in developing countries. J. Comp. Econ. 34, 774–795. Cristensen, H.B., Hail, L., Leuz, C., 2013. Mandatory IFRS reporting and changes in enforcement. J. Account. Econ. 56, 147–177. Daske, H., Hail, L., Leuz, C., Verdi, R., 2013. Adopting a label: heterogeneity in the economic consequences around IAS/ IFRS adoptions. J. Account. Res. 51, 495–547. De Jong, A., Kabir, R., Nguyen, T.T., 2008. Capital structure around the world: the roles of firm and country specific determinants. J. Bank. Financ. 32, 1954–1969. Fávero, L.P., 2015. Análise de Dados: Modelos de Regressão com Excel, Stata e SPSS. Elsevier, Rio de Janeiro. Fávero, L.P., 2010. The São Paulo Stock Exchange: a multilevel analysis of firm and industry effects on profitability evolution and hedge strategies. Int. J. Financ. Mark. Deriv. 1, 307–325. Gao, W., Zhu, F., 2015. Information asymmetry and capital structure around the world. Pac.-Basin Financ. J. 32, 131–159. Gao, R., Sidhu, B. (2014) Externalities and investment efficiency: the case of mandatory IFRS adoption. Working Paper. Provided by. https://www.business.uq.edu.au/sites/default/files/events/files/externalities_and_investment_efficiency_tina_gao.pdf. (accessed 15.07.18). González, V.M., González, F., 2008. Influence of bank concentration and institutions on capital structure: New international evidence. J. Corp. Financ. 14, 363–375. Gormley, T.A., 2014. Costly Information, entry, and credit access. J. Econ. Theory 154, 633–667. Houqe, M.N., Easton, S., Zijl, T.V., 2014. Does mandatory IFRS adoption improve information quality in low investor protection countries? J. Int. Account., Auditing Tax. 23, 87–97. Houqe, M.N., Zijl, T.V., Dunstan, K., Karim, A.K.M.W, 2012. The effect of IFRS adoption and investor protection on earnings quality around the world. Int. J. Account. 47, 333–355. Kayo, E.K., Kimura, H., 2011. Hierarchical determinants of capital structure. J. Bank. Financ. 35, 358–371. La Porta, R., Lopez-de-Silanes, F., Shleifer, A., Vishny, R., 1998. Law and finance. J. Political Econ. 106, 1113–1155. Lucey, B.M., Zhang, Q., 2011. Financial integration and emerging markets capital structure. J. Bank. Financ. 35, 1228–1238.
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Myers, S.C., Majluf, N.S., 1984. Corporate financing and investment decision when firms have information that investors do not have. J. Financial Econ. 13, 187–221. Moro, A., Fink, M., 2013. Loan managers’ trust and credit access for SMEs. J. Bank. Financ. 37, 927–936. Naranjo, P., Saavedra, D., Verdi R.S. (2015) Financial reporting regulation and financing decisions. Working Paper. Provided by (accessed 15. 07. 15). Nikaido, Y., Pais, J., Sarma, M., 2015. What hinders and what enhances small enterprises’ access to formal credit in India? Rev. Dev. Financ. (in press). . OECD – Organization for Economic Co-operation and Development, 2005. OECD SME and entrepeneurship outlook. OECD Publication Service. Owen, A.L., Temesvary, J., 2014. Heterogeneity in the growth and finance relationship: How does the impact of bank finance vary by country and type of lending? Int. Rev. Econ. Financ. 31, 275–288. Rajan, R.G., Zingales, L., 1995. What do we know about capital structure? Some evidence from international data. J. Financ. 50-5, 1421–1460. Raudenbush, S.W., Bryk, A.S., 2002. Hierarchical Linear Models, 2nd ed. Sage Publications, Thousand Oaks. Santos, M.A. (2013) Determinantes da estrutura de capital de empresas em diferentes cenários econômicos e institucionais: um estudo comparativo. Master’s degree dissertation in Accounting and Finance at University of São Paulo. Provided by (accessed 15.07. 01). Templin, J. (2015) Multilevel models for cross sectional and longitudinal data. Provided by http://jonathantemplin.com/teaching/ academic-courses/multilevel-models/multilevel-models-cross-sectional-longitudinal-data-summer-2015-icpsr/. (assessed 15.10.26). The World Bank. (2015) Enterprise surveys. Provided by . (accessed 15. 07. 12). The World Bank. (2015) World databank. Provided by http://data.worldbank.org/. (accessed 15. 07. 12). The World Economic Forum (2015) Global competitiveness report. Provided by (accessed 15. 07. 12). West, B.T., Welch, K.B., Gałecki, A.T., 2015. Linear Mixed Models: A Practical Guide using Statistical Software, 2nd ed. Chapman & Hall/CRC Press, Boca Raton.
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Finance Research Letters 000 (2015) 1–11
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Adoption of the International Financial Reporting Standards (IFRS) on companies’ financing structure in emerging economies Marco Aurélio dos Santos, Luiz Paulo Lopes Fávero∗, Luiz Fernando Distadio School of Economics, Business and Accounting at University of São Paulo, Department of Accounting and Finance, Av. Prof. Luciano Gualberto, 908 - FEA 3 - room 211, Zip Code: 05508-900, São Paulo, SP, Brazil
a r t i c l e
i n f o
Article history: Received 17 September 2015 Accepted 9 November 2015 Available online xxx JEL classification: C58 F34 F36 G15 G32 Keywords: IFRS Financing structure Multilevel models Emerging market firms
a b s t r a c t This paper aims to study the relationship between the adoption of the International Financial Reporting Standards (IFRS) and the companies’ financing structure in different emerging economies. A linear hierarchical regression model is applied, considering firm, country and region levels, in a database of 150,265 observations of companies from 145 countries between 2003 and 2014. The impact of the adoption of IFRS in financing decisions is heterogeneous among companies from different regions and countries. This effect is clearer when country controls are applied to monitor the legal enforcement and investor safety, such as the quality of the boards and accounting audits. © 2015 Elsevier Inc. All rights reserved.
1. Introduction Several studies identified that country characteristics affect the companies’ funding decision. Since Rajan and Zingales (1995) and La Porta et al. (1998), it is observed that country factors affect the ∗
Corresponding author. Tel.: +55 11 21842046; fax: +55 11 30915820. E-mail addresses: [email protected] (M.A. dos Santos), [email protected] (L.P.L. Fávero), [email protected] (L.F. Distadio). http://dx.doi.org/10.1016/j.frl.2015.11.002 1544-6123/© 2015 Elsevier Inc. All rights reserved.
Please cite this article as: M.A. dos Santos et al., Adoption of the International Financial Reporting Standards (IFRS) on companies’ financing structure in emerging economies, Finance Research Letters (2015), http://dx.doi.org/10.1016/j.frl.2015.11.002
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capital structure and financial performance. Comparing developed and emerging markets, it is observed that country factors influence in a different rate the decision of companies’ funding. While in developed markets the country factors have a less importance in capital structure, comparing to the firm determinants, in emerging economies, country factors are more relevant (de Jong et al., 2008; Kayo and Kimura, 2011; Lucey and Zhang, 2011; Santos, 2013). In economies with low levels of protection for creditors, the initial cost of capital tends to be higher than in economies where this protection exists and, thus, there is a greater impact of tangibility due to the need for a loan, mostly in small companies (González and González, 2008). Big companies, on the other hand, can structure their capital offering other forms of financing, attracting new investors (raising more equity or retained it). In this case, the improvement of quality information is more sensible to equity than credit (Myers and Majluf, 1984). The need for guarantees to grant credit, whose influence is enhanced by the company size, according to Gao and Zhu (2015) and Nikaido et al. (2015), is mainly related to asymmetric information problems. Thus, the greater the adverse selection risks resulting from the creditors’ and investor’s choice of bad investment projects, the higher the costs of capital for the organizations. One way to reduce this cost of capital is the issuing of contracts based on guarantees. Considering difficulties to access credit by new investors or creditors, a stronger trend is observed for governments to adopt policies and institutional improvements that can facilitate the companies’ capitalization through own capital or capital from third parties. The adoption of policies that can improve the credit market, mostly for small companies, has been discussed in recent researches (Clarke et al., 2006; González and González, 2008; Moro and Fink, 2013; Gormley, 2014; Owen and Temesvary, 2014; Nikaido et al., 2015). One of the measures governments have adopted to improve the credit and capital markets is the adaptation of their countries’ accounting models to national generally accepted international standards through the International Accounting Standards Board (IFRS), which are aimed at the harmonization of corporate financial statements in different international markets, favoring these countries’ inclusion in the movements of external capital, as well as the investors and creditors’ greater confidence in the companies. The main importance to adopt the IFRS is the improvement in the informational quality of financial reportings. Daske et al. (2013) and Cristensen et al. (2013) stand out, which identify a significant evolution of the transparency standards in countries that adhere to the international standards, resulting in a greater impact on the capital markets in economics with characteristics such as greater protection of investors. Ahmed et al. (2013), on the other hand, identify no significant changes in the accruals between the former models and the IFRS, despite an improvement in the analysts’ correct assessment of the companies’ future returns in the new model, which indicates the importance of the adoption in financial decision making. Other studies (Houqe et al., 2012; Gao and Sidhu, 2014; Houqe et al., 2014; Naranjo et al., 2015; Beneish et al., 2015) identified positive behaviors in the implementation of the IFRS regarding the improvement of the accounting quality and of the companies’ performance, which ends up creating positive impacts in the concession of credit by the banks. As the IFRS tend to enhance the information quality and, consequently, contribute to the investors’ confidence, the main objective in this paper is to verify whether a relation exists between the adoption of the IFRS in different countries and the companies’ financing decision. That is, if the characteristic ‘adherence to the IFRS’ in one country significantly affects the companies’ ease of getting credit access and the choice of bank financing or investor financing for investments and working capital. Other contribution of this study is to identify the impact of IFRS in financing decision of small and medium firms, emphasizing that this characteristic, not observed in other studies, can influence capital structure and access to credit. In this sense, not only the financing decision theories related to companies’ strategies, such as pecking order and trade-off, are considered, but also the importance of companies’ financing sources and credit access policies in different countries. As observed, there are few studies that analyze the impact of the adoption of IFRS on the organizations’ financing process. Current research, until date, analyzes the correlation between the characteristics of the IFRS adoption and the economic-financial information quality, not advancing on the structuration of the IFRS as a public policy to improve the credit access conditions and, consequently, the companies’ capital structure decisions. Please cite this article as: M.A. dos Santos et al., Adoption of the International Financial Reporting Standards (IFRS) on companies’ financing structure in emerging economies, Finance Research Letters (2015), http://dx.doi.org/10.1016/j.frl.2015.11.002
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The empirical findings suggest that, while country characteristics have an important role in financing decision, the adoption of IFRS does not have impact on investment funding of companies and has impact on working capital decisions. Financing of firms in countries with partial or total adoption tends to be done through retained or new owner funds, as also stated in Myers and Majluf (1984). While Section 2 presents data, variable selection and the proposed econometric models, Section 3 brings discussions of the results. 2. Method 2.1. Sample Considering the country effects in companies’ capital structure, we develop the following hypotheses: H1 : The country/region characteristics affect the capital structure of companies, influencing the financial decision. H1a : The adoption of IFRS by country affects positively the leverage of companies, considering the ease of credit access and the increase of informational quality for the creditors, comparing to firms in countries when the adoption in not present. H1b : The adoption of IFRS by country affects negatively the leverage of companies, considering the ease of financing access by other investors and the increase of informational quality for the investors. For this study, different databases were used, the most important of which was Enterprise Surveys. This database, available on the World Bank (2015), is structured based on interviews in companies of different sizes and institutional designs located in developed and developing countries, between 2003 and 2014. To construct the control variables, firm characteristics were considered, captured through the survey, as well as country characteristics surveyed through economic-institutional data found in databases of the OECD (2005), World Bank (2015) and World Economic Forum (2015). The initial sample consists of 150,625 observations of companies located in 862 different regions of 145 emerging countries, treated according to the tests, as presented in Table 1. 2.2. Variable selection To develop the empirical tests, the dependent (outcome) variables used individually refer to the percentage of investments funded by commercial banks and to the percentage of working capital funded by this same financial agent. Chart 1 shows the variables used in the models, as well as the used sources to obtain them. 2.3. Multilevel modeling Considering the hierarchical characteristics of data and the proposed hypotheses, we use a multilevel framework. The defining feature of multilevel models is their capacity to provide quantification and prediction of random variance due to multiple sampling dimensions (eg. across countries), and thus, these models offer many advantages for analyzing longitudinal data, such as flexible strategies for modeling change and individual differences in change, and the use of all available complete observations (Templin, 2015). In other words, as stated in Raudenbush and Bryk (2002), West et al. (2015) and Fávero (2015), the main advantage of multilevel models over traditional regression models is the consideration of the natural nesting of data. Multilevel models allow the researcher to identify and analyze individual heterogeneities between groups in which these individuals belong to, allowing the specification of random effects in each level of analysis. This fact represents the main difference compared to traditional regression models, which fail to take into account the natural nesting of data and, hence, can produce biased estimators of the parameters. Thus, applications with multilevel models offer researchers new possibilities to test different hypotheses without the risk of violating the premises inherent in other techniques, such as traditional regression models. Please cite this article as: M.A. dos Santos et al., Adoption of the International Financial Reporting Standards (IFRS) on companies’ financing structure in emerging economies, Finance Research Letters (2015), http://dx.doi.org/10.1016/j.frl.2015.11.002
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Chart 1 Structuration and source of variables. Variable Dependent variable END1 Indebtedness investment END2
Indebtedness working capital
Explanatory variable at country level IFRS Adoption of IFRS Control variables at firm level SIZE Size
REV
Total revenue
TANG LEGAL EXP
Tangibility Legal status of firm Export
ORIGIN
Origin of company
Control variables at country level GDP Growth of GDP INFL Inflation TAX Total tax rate SPREAD Total banking spread INVEST BOARDS ENFORC ∗
Degree of legal protection to investor Degree of quality of corporate boards Legal enforcement degree
Proxy used
Source∗
Proportion of investments made funded by banks (%) Proportion of working capital funded by banks (%)
ES
Categorical variables identifying non-adoption, partial adoption or full adoption of IFRS
IFRS
Categorical variable that indicates the firm size per number of employees, less than 20 = small; between 20 and 100 = medium; more than 100 = large Growth in total revenue in function of previous year Company’s volume of fixed assets Type of legal structure of firm Dummy variable indicating whether the company exports or not Dummy variable indicating whether the company is domestic or foreign
ES
Variation of GDP growth (%) Growth of annual inflation (%) Percentage of commercial profits Difference between interests on loans and interests on investments Degree of protection to investor (0–10) Degree of quality of corporate boards (0–10) Degree of legal rights (0–10)
ES
ES ES ES ES ES
WB WB WB WB GCI GCI GCI
ES = Enterprise Surveys of World Bank; WB = World Bank; GCI = World Economic Forum Global Competitiveness Index.
2.3.1. Null model and hypothesis H1 The first model is only based on the grouping process (null model). On the other hand, while the second model refers to a mixed model with random intercepts with firm and country variables, the third model considers random intercepts and slopes. The first model (null model) is as follows:
ENDi jk =
β000 + μ00k + τ0 jk + εi jk
(1)
in which the percentage of indebtedness is determined by a global average, plus an error term corresponding to the group of countries μ00k , regions τ 0jk and the residual idiosyncratic error ɛijk . Through variance decomposition, one can test H1 , studying how much of total variance is due to country, regional, and firm factors. 2.3.2. Random intercepts and random slopes models, and hypotheses H1a and H1b Aiming to test H1a and H1b , we added explanatory variables in the second model. Thus, firm, region and country variables are considered fixed, and our objective is to verify the relationship between the adoption of IFRS (categorical dummy variable) and the outcome variable. The following model is a random intercepts model:
ENDi jk =
β000 + β001 (SIZEi jk ) + β002 (REVi jk ) + β003 (TANGi jk ) + β004 (LEGALi jk ) + β005 (EXPi jk ) + β006 (ORIGINi jk ) + εi jk
(2)
Please cite this article as: M.A. dos Santos et al., Adoption of the International Financial Reporting Standards (IFRS) on companies’ financing structure in emerging economies, Finance Research Letters (2015), http://dx.doi.org/10.1016/j.frl.2015.11.002
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Country
Freq.
Percent
Country
Freq.
Percent
Country
Freq.
Country Slovak Republic Slovenia South Africa South Sudan Spain Sri Lanka St. Kitts and Nevis St. Lucia St. Vincent and the Grenadines Sudan Suriname Swaziland Sweden Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Tonga
748 804 937 738 606 610 150 150 154
0.5 0.54 0.62 0.49 0.4 0.41 0.1 0.1 0.1
662 152 307 600 508 1,099 1,232 1,043 150 155 150
0.44 0.1 0.2 0.4 0.34 0.73 0.82 0.69 0.1 0.1 0.1
Trinidad and Tobago Tunisia Turkey Uganda Ukraine Uruguay Uzbekistan Vanuatu Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe Total
370 592 4,559 1,325 2,556 1,228 1,251 128 820 1,053 835 830 1,204 599 150,265
0.25 0.39 3.03 0.88 1.7 0.82 0.83 0.09 0.55 0.7 0.56 0.55 0.8 0.4 100
Afghanistan Albania Algeria Angola Antigua and Barbuda Argentina Armenia Azerbaijan Bahamas, The
945 960 600 785 151 2117 1128 1196 150
0.63 0.64 0.4 0.52 0.1 1.41 0.75 0.8 0.1
Dominica Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Fiji
150 360 1024 5766 1053 179 804 1128 164
0.1 0.24 0.68 3.84 0.7 0.12 0.54 0.75 0.11
Lesotho Liberia Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania
151 150 1057 982 977 673 1115 850 387
0.1 0.1 0.7 0.65 0.65 0.45 0.74 0.57 0.26
Bangladesh Barbados Belarus Belize Benin Bhutan Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria
2946 150 1034 150 150 250 975 999 610 3444 2500
1.96 0.1 0.69 0.1 0.1 0.17 0.65 0.66 0.41 2.29 1.66
179 174 959 1196 1214 546 153 1,112 223 159 165
0.12 0.12 0.64 0.8 0.81 0.36 0.1 0.74 0.15 0.11 0.11
Mauritius Mexico Micronesia, Fed. Sts. Moldova Mongolia Montenegro Morocco Mozambique Myanmar Namibia Nepal
398 2960 68 1238 722 266 1,066 479 632 909 850
0.26 1.97 0.05 0.82 0.48 0.18 0.71 0.32 0.42 0.6 0.57
Burkina Faso Burundi Cabo Verde Cambodia Cameroon Central African Republic Chad Chile China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Croatia Czech Republic Côte d’Ivoire Djibouti
533 427 254 974 535 150 150 2050 2700 1,942 1228 151 881 1284 846 526 266
0.35 0.28 0.17 0.65 0.36 0.1 0.1 1.36 1.8 1.29 0.82 0.1 0.59 0.85 0.56 0.35 0.18
Gabon Gambia, The Georgia Germany Ghana Greece Grenada Guatemala Guinea Guinea-Bissau Guyana, Co-operative Republic of Honduras Hungary India Indonesia Iraq Ireland Israel Jamaica Jordan Kazakhstan Kenya Korea, Rep. Kosovo Kyrgyz Republic Lao PDR Latvia Lebanon
796 1218 13,515 1,444 756 501 483 376 1076 1,768 1438 598 472 877 630 836 943
0.53 0.81 8.99 0.96 0.5 0.33 0.32 0.25 0.72 1.18 0.96 0.4 0.31 0.58 0.42 0.56 0.63
Nicaragua Niger Nigeria Pakistan Panama Paraguay Peru Philippines Poland Portugal Romania Russian Federation Rwanda Samoa Senegal Serbia Sierra Leone
814 275 4,567 1,841 969 974 1,632 1326 1960 505 1721 5953 453 109 1,107 1102 150
0.54 0.18 3.04 1.23 0.64 0.65 1.09 0.88 1.3 0.34 1.15 3.96 0.3 0.07 0.74 0.73 0.1
Freq.
Percent
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Please cite this article as: M.A. dos Santos et al., Adoption of the International Financial Reporting Standards (IFRS) on companies’ financing structure in emerging economies, Finance Research Letters (2015), http://dx.doi.org/10.1016/j.frl.2015.11.002
Table 1 Observations in the research sample per emerging economy.
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being:
β000 β001 β002 β003 β004 β005 β006
ρ000 + τ0 jk = ρ001 = ρ002 = ρ003 = ρ004 = ρ005 = ρ006 =
(3)
where
ρ000 = γ000 + γ100 (GDPk ) + γ200 (INF Lk ) + γ300 (TAXk ) + γ400 (SPREADk ) + γ500 (INV ESTk ) + γ600 (BOARDSk ) + γ700 (ENF ORCk ) + γ800 (IF RSk ) + μ00k ρ001 = γ001 ρ002 = γ002 ρ003 = γ003 ρ004 = γ004 ρ005 = γ005 ρ006 = γ006
(4)
Replacing (04) and (03) in (02):
ENDi jk =
γ000 + γ100 (GDPk ) + γ200 (INF Lk ) + γ300 (TAXk ) + γ400 (SPREADk ) + γ500 (INV ESTk ) + γ600 (BOARDSk ) + γ700 (ENF ORCk ) + γ800 (IF RSk ) + γ001 SIZEi jk + γ002 REVi jk + γ003 TANGi jk + γ004 LEGALi jk + γ005 EXPi jk + γ006 ORIGINi jk + μ00k + τ0 jk + εi jk (5)
The third model is an extension of the second model, considering the interaction between country and firm variables in the multilevel modeling. This new verification is important to verify how the interaction between explanatory variables could affect the behavior of the outcome variable. The following model is a random intercepts and random slopes model:
ρ000 = γ000 + γ100 (GDPk ) + γ200 (INF Lk ) + γ300 (TAXk ) + γ400 (SPREADk ) + γ500 (INV ESTk ) + γ600 (BOARDSk ) + γ700 (ENF ORCk ) + γ800 (IF RSk ) + μ00k ρ001 = γ001 ρ002 = γ002 + γ102 (GDPk ) + γ302 (INF Lk ) ρ003 = γ003 ρ004 = γ004 ρ005 = γ005 ρ006 = γ006
(6)
Similarly, we can write:
ENDijk
=
γ000 + γ100 (GDPk ) + γ200 (INFLk ) + γ300 (TAXk ) + γ400 (SPREADk ) + γ500 (INVESTk ) + γ600 (BOARDSk ) + γ700 (ENFORCk ) + γ800 (IFRSk ) + γ001 SIZEijk + γ002 REVijk (7) + γ003 TANGijk + γ004 LEGALijk + γ005 EXPijk + γ006 ORIGINijk + γ102 GDPk × REVijk + γ302 INFLk × REVijk + μ00k + τ0jk + εijk
In this model, the countries’ economic performance variables distinctly affect the companies’ earnings behavior, which affects the outcome variable under analysis. Please cite this article as: M.A. dos Santos et al., Adoption of the International Financial Reporting Standards (IFRS) on companies’ financing structure in emerging economies, Finance Research Letters (2015), http://dx.doi.org/10.1016/j.frl.2015.11.002
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3. Analysis and discussion of results 3.1. Null model Table 2 shows the results obtained through the estimation of the null model, what allows us to test H1 . As observed, most of the variances in the model is explained by the firms’ characteristics over time (ICC – intraclass correlation). In addition: 8% of the total variance in the choice of indebtedness through banks is related to characteristics of the country the company is located in, while 3% is related to characteristics of the regions inside the countries where the companies’ headquarters are located, for the sake of bank financing for investment purposes. When compared to working capital financing by banks, the behavior continues, but with a slight increase in the regional characteristics. This regional effect can be linked to specific policies of certain states or provinces in specific countries, as well as greater or lesser regional sophistication of existing credit markets inside a certain country. This country effect behavior has already been discussed in previous papers (Kayo and Kimura, 2011; Santos, 2013), but the regional factor is not identified in the literature, mainly due to unavailability of data. 3.2. Random intercepts and random slopes models Table 3 and 4 show the results obtained through the estimations of the random intercepts model and the random intercepts and random slopes model, respectively, what allows to test H1a and H1b . The analysis of the outputs in Table 3 reveals that there are differences in the estimations when using the indebtedness due to working capital and indebtedness for the sake of investments. When the firm characteristics are analyzed, a positive relation can be verified between the size variable and indebtedness, as large firms tend to possess more tangible resources as a guarantee for debt and lesser bankruptcy risks, a fact that supports the trade-off theory between size and indebtedness. Other explanation for that effect could be the fact that small companies have more information asymmetry than the large ones. Moro and Fink (2013) and Cenni et al. (2015) identified that firm size is an important factor to credit rationing for banks, what is in line with our findings. For investment capital, our evidences show that this effect is more relevant than IFRS effect. In both estimations, it can be observed that export and foreign companies tend to be more levered than national companies, largely in function of the existence of financing structures of transnational companies in different countries. While investor protection and better quality of the boards tend to reduce the financing through banks, given the legal safety for investors, the high level enforcement that the credit system can lend more to the companies (positive signal), identifying the importance of regulatory country characteristics in corporate finance decisions. Table 2 Estimation results of the null model. END1
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Null model Observations Coefficient β 000
62,970,00 16,70∗∗∗
118,151,00 12,69∗∗∗
Variance decomposition Countries μ00k Regions τ 0jk Idiossincratic error ε ijk
86,27∗∗∗ 33,34∗∗∗ 942,38∗∗∗
51,19∗∗∗ 33,50∗∗∗ 506,05∗∗∗
Specification model test Chi-square
7,059,55∗∗∗
18,901,41∗∗∗
ICC Across countries Across regions Across firms
0,08 0,03 0,89
0,09 0,06 0,86
∗∗∗
sig. < 0.01.
Please cite this article as: M.A. dos Santos et al., Adoption of the International Financial Reporting Standards (IFRS) on companies’ financing structure in emerging economies, Finance Research Letters (2015), http://dx.doi.org/10.1016/j.frl.2015.11.002
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ARTICLE IN PRESS [m1G;December 3, 2015;11:19] M.A. dos Santos et al. / Finance Research Letters 000 (2015) 1–11 Table 3 Estimation results of the random intercepts model. END1
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Random intercepts model Observations Log likelihood Wald test chi2 (20) Coeffcient β 000 Partial IFRS Total IFRS
25,214,00 −123,178,52 495,54∗∗∗ 8,07 0,67 1,99
46,429,00 −209,670,53 1,863,76∗∗∗ 33,64∗∗∗ −2,92∗∗∗ −2,62∗∗∗
Control variables at firm level Medium SIZE Large SIZE REV TANG LEGAL privately held company LEGAL single owner LEGAL participation LEGAL limited participation LEGAL others EXP ORIGIN
5,749267∗∗∗ 9,943441∗∗∗ 0,0235752∗∗∗ 0,0012835 2,641246∗∗∗ 0,696728 −2,114212∗ 2,821139∗∗ 1,522482 0,0169398∗∗∗ −0,06349914∗∗∗
4,080665∗∗∗ 7,601902∗∗∗ 0,0074722∗ 0,0313911∗∗∗ −0,6606194 −3,162193∗∗∗ −3,206033∗∗∗ −0,8211137 −2,337268∗∗ 0,0338774∗∗∗ −0,0428777∗∗∗
Control variables at country level GDP INFL TAX SPREAD INVEST BOARDS ENFORC
−0,2193983∗∗ 0,180867 0,0129004 0,0328941 0,000259 −0,1861866 0,6158791∗
0,1654753 −0,0541023 −0,0412764 0,2053236∗∗ −0,9681399∗∗∗ −5,429542∗∗∗ 1,406586∗∗∗
Variance decomposition Countries μ00k Regions τ 0jk Idiossincratic error ε ijk
80,09793∗∗∗ 31,59392∗∗∗ 1005,114∗∗∗
54,02227∗∗∗ 32,63408∗∗∗ 480,7197∗∗∗
Specification model test Chi-square
1652,23∗∗∗
4327,77∗∗∗
ICC Across countries Across regions Across firms
0,07172055 0,028289537 0,899989913
0,095214223 0,057517549 0,847268227
Obs: ∗ sig. < 0.1; ∗∗ sig. < 0.05; ∗∗∗ sig. < 0.01.
As also stated in Nikaido et al. (2015), while, for investment capital, we observe that guarantees are more preeminent than accounting information demonstrated by IFRS improvement, for working capital we verify that financial improvement through IFRS can attract more equity or incentive the owner to retain funds. Considering previous studies, our findings are different from those obtained by Naranjo et al. (2015) because these authors identified an increase in attracting foreign capital in countries after the adoption of IFRS. On the other hand, the negative relationship between the adoption of IFRS and the reduction of funding for banks, identified in our study, is corroborated by these same authors, who justify this fact by the existence of asymmetric information. As to the variable being studied, the adoption of the IFRS, distinct behaviors are observed in the two estimations. Despite the lack of impact of countries’ adoption of IFRS in the financing process by banks for investment purposes, when resources are captured through banks for use as working capital, countries Please cite this article as: M.A. dos Santos et al., Adoption of the International Financial Reporting Standards (IFRS) on companies’ financing structure in emerging economies, Finance Research Letters (2015), http://dx.doi.org/10.1016/j.frl.2015.11.002
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Table 4 Estimation results of the random intercepts and random slopes model. END1
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Random intercepts and slopes model Observations 25,214 Log likelihood −123,187,51 Wald test chi2 (20) 499,20∗∗∗ Coeffcient β 000 7,10 Partial IFRS 0,76 Total IFRS 2,08
46,429 −209,675,88 1,876,95∗∗∗ 34,92∗∗∗ −2,94∗∗∗ −2,70∗∗∗
Control variables at firm level Medium SIZE Large SIZE REV TANG LEGAL privately held company LEGAL single owner LEGAL participation LEGAL limited participation LEGAL others EXP ORIGIN
5,749391∗∗∗ 9,943031∗∗∗ 0,0514846∗∗∗ 0,0012077 2,623741∗∗∗ 0,6716267 −2,140523∗ 2,809171∗∗ 1,553389 0,0167474∗∗∗ −0,0634653∗∗∗
4,086097∗∗∗ 7,59894∗∗∗ −0,0184044∗ 0,0311783∗∗∗ −0,6686575 −3,168713∗∗∗ −3,2044∗∗∗ −0,8255153 −2,358439∗∗ 0,0340308∗∗∗ −0,0428098∗∗∗
Control variables at country level GDP INFL TAX SPREAD INVEST BOARDS ENFORC
−0,1775005 0,1957114∗ 0,0137823 0,0225882 0,0445632 −0,0257721 0,584754∗
0,1475652 −0,0694059 −0,0395677 0,2008515∗∗ −0,9638698∗∗∗ −5,699037∗∗∗ 1,410583∗∗∗
Iterations GDP-REN INFL-REN
−0,0032481 −0,002288
0,0056953∗∗∗ 0,0009338
Variance decomposition Countries μ00k Regions τ 0jk Idiossincratic error ε ijk
78,8301 31,54238 1005,098
54,80336 32,75008 480,5925
Specification model test Chi-square
1652,14∗∗∗
4329,06∗∗∗
ICC Across countries Across regions Across firms
0,070669822 0,028277198 0,90105298
0,096460005 0,057643781 0,845896215
Obs: ∗ sig. < 0.1; ∗∗ sig. < 0.05; ∗∗∗ sig. < 0.01.
that adopt IFRS tend to have less indebtedness of working capital by banks (negative signal) and prefer to capture resources through retained earnings or issuing new stocks. The analysis of the estimators in Table 4 shows no significant changes when compared to the model with random intercepts. The inflation variable should be highlighted, which is significant to explain END1.
4. Conclusions The aim in this article was to verify whether different countries’ adoption of the IFRS positively affected the companies’ access to credit, using multilevel modeling. Please cite this article as: M.A. dos Santos et al., Adoption of the International Financial Reporting Standards (IFRS) on companies’ financing structure in emerging economies, Finance Research Letters (2015), http://dx.doi.org/10.1016/j.frl.2015.11.002
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Its first contribution refers to estimating the influence of firms, countries and global regions’ effects on investment capital and on working capital funded by commercial banks. If the country effect is stronger, which is not the case in this study, a closer look should be taken at the impacts related to the differences among countries to compose percentages of investment capital and working capital. If, on the other hand, the differences among firms explain the largest part of the variance, a focus on strategic management and on differences among organizations needs to be stimulated. In our case, it is observed that the firm size plays a central role in the process of getting access to bank credit, mostly to investment financing, as identified in the capital structure literature. This fact can eliminate the effect of the improvement of the accounting information quality due to the adoption of IFRS. The second contribution of this study is the attempt to add predictive variables at firm and country levels. While some can only study the variance decomposition without evaluating the impacts deriving from the presence of certain variables (Fávero, 2010), the inclusion of explanatory variables at levels 1 (firm) and 2 (country) allowed us to verify the relationship between the adoption of IFRS and the leverage of firms. As implications of the study, we identified a mixed behavior of IFRS influence in financial decisions, driven by application of resources and firm size. When resources are captured for investments in working capital, a negative impact of the adoption of IFRS on indebtedness is observed, which may be related to the easy financing through other financing sources, such as new partners or the offering of new stock. Regulation and institutional protection of countries in financing process are also key points. As the level of investor and creditor protection can influence the capital access, this could also impact IFRS effectiveness policies. Thus, the decision on IFRS adoption can be influenced by control mechanisms and heavy government interference in the financial market. For the sake of future research, more in-depth studies are recommended, specifically related to small and medium companies, in order to verify the effects of the adoption of this policy in companies with restricted access to stock markets and difficulties to get access to credit. Other predictive variables at firm or country level can also be included in the modeling to allow researches and decision makers define new strategies in companies’ financing. Changing the analysis period and even expanding the interval to earlier years can favor a deeper understanding of the mechanisms ruling companies’ financing structure in emerging economies. References Ahmed, K., Chalmers, K., Khlif, H., 2013. 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Please cite this article as: M.A. dos Santos et al., Adoption of the International Financial Reporting Standards (IFRS) on companies’ financing structure in emerging economies, Finance Research Letters (2015), http://dx.doi.org/10.1016/j.frl.2015.11.002
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Please cite this article as: M.A. dos Santos et al., Adoption of the International Financial Reporting Standards (IFRS) on companies’ financing structure in emerging economies, Finance Research Letters (2015), http://dx.doi.org/10.1016/j.frl.2015.11.002