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Does following International Accounting Standards reduce firm’s financial constraints ?

Steven VanhaverbekeBenjamin Balsmeier

KU LEUVEN

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Overview• Literature review & Hypotheses

– Why do financial constraints matter ?– What is IFRS ?– How Local GAAP differs from IFRS– High Quality Financial Reporting

• Methodology– Sample– Model

• Results– OLS– Matching

• Discussion

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Why do financial constraints matter?

• Financing activities externally may be costly due to outcome uncertainty, asymmetric information and incomplete appropriability of returns.

• Firms may prefer to exploit internally available funds to finance their R&D investment as much as possible. However, internal funds may be limited as well.

• Financially constrained firms may have to conduct their activities at a sub-optimal level, abandon certain projects or may not be able to operate at all.

- Fazzari et al. (1988)- Bond et al. (2006)- Czarnitzki et al. (2009)

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IFRS

• International Financial Reporting Standards (IFRS) is a set of accounting standards developed by an independent organization called the International Accounting Standards Board (IASB)

• The goal of IFRS is to provide a global framework for how public companies prepare and disclose their financial statements.

• Advantages ?– A business can present its financial statements on the same basis as its

foreign competitors, making comparison easier. – Companies may also benefit using IFRS if they wish to raise capital abroad– Companies with subsidiaries may be able to use one accounting language

company-wide.

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IFRS vs. Local GAAP

IFRS vs Local GAAP ExamplesRecognition and measurement rules -Many countries do not require

accounting for employee benefits, required under IAS 19-Accounting for impairment of assets, required under IAS 36

Disclosure rules -Cash flow Statements-Segment reporting, IAS 14-Related party transactions, IAS 24

Inconsistencies which lead to differences for many enterprises

-Capitalization of research and development costs

Other issues -Differences in accounting for long-term construction contracts (completed contract method is prohibited under IFRS)

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IFRS VS LOCAL GAAP ( Differences that could affect many enterprises (2001))

Russia 42 France 30 Turkey 24 Sweden 18

Switzerland 41 Latvia 30 China 24 New Zealand 17

Spain 38 Brazil 30 Egypt 24 Pakistan 17

Greece 37 Czech Republic 29 Saudi Arabia 24 Israel 16

Luxembourg 37 Slovak Republic 29 Philippines 24 Thailand 16

Poland 36 Portugal 28 Taiwan 23 U.K. 15

Austria 36 Iceland 28 Denmark 23 Ireland 15

Finland 35 India 28 Bulgaria 23 Hong Kong 14

Hungary 34 Belgium 26 Ukraine 23 Korea 14

Chile 34 Japan 26 Australia 23 Singapore 14

Argentina 33 Venezuela 26 Estonia 22 Indonesia 14

Germany 32 Morocco 26 Canada 21 U.S. 13

Italy 31 Malaysia 26 Tunisia 19 Norway 12

Slovenia 31 Lithuania 26 Iran 19The Netherlands 11

Average differences between Local GAAP vs. IFRS in our sample is 27.

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Literature review

Advantages of disclosing high quality financial information

- Internal:– High-quality financial reporting helps business managers to identify good projects and increase

investment efficiency (Chen, Hope, Li & Wang, 2011, McNicholas & Stubben, 2008)

- External:– Disclosure allows providers of capital to better assess the firm’s investment opportunities and

monitor managerial actions(Diamond & Verrechia, 1991; Fama & Jensen, 1983)– Listed firms that adopt IFRS have liquidity improvements and a lower cost of capital (Daske, Hail

and Leuz, 2008; Li, 2010)

=> High-Quality financial reporting should ease external financing constraints by reducing the adverse selection or moral hazard costs associated with information asymmetry

H1: Following IFRS will reduce financial constraints

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Literature review

• Foreign lenders are more familiar with IFRS than local accounting standards=> IFRS-based reporting makes it relatively easier for borrowers to communicate their financial results and credit quality.

• IFRS adopters attract more foreign lenders participating in loan syndicates than non- adopters (Kim, Tsui & Yi, 2011).

=> IFRS-based reporting makes it less costly for foreign lenders to assess borrowers’ credit risk ex ante, to monitor credit quality ex post, and to renegotiate contractual terms subsequent to credit quality changes.

H2: IFRS will increase the propensity to raise foreign capital

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Methodology: Data

Business Environment and Enterprise Performance Survey (BEEPS) of 2004 & 2005• 14,107 firms across 34 countries, which answered over 75 questions about their business

environment, infrastructure services, competition, finance and performance• Random sample of Central- and East European countries.

• CORE QUESTIONS:• IFRS:

– “Does your firm use international accounting standards (IAS) as provided by the International Accounting Standards Board ?”

• FINANCIAL CONSTRAINTS: – “Can you tell me how problematic is access to financing (e.g., colleratal required or financing not

available from banks) for the operation and growth of your business ?” (Scale 1 to 4)

• FOREIGN LOANS: – “What proportion of your firm’s working capital and new fixed investment has been financed from

borrowing from foreign banks, over the last 12 months?”

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ControlsVariables Description

Sales_gr Growth of sales (sales(t-1) – sales(t-3))/sales(t-3)

Internal funds Proportion of working capital financed by internal funds

Log_Productivityratio_l3 Log of Productivity (sales/emp) scaled by the mean productivity of an industry per country

New product dummy 1 if the company developed a major new product line/service, 0 otherwise

Log emp_l3 Size variable, log of # employees

Log_age Log of age of the firm

Univers Percentage of workforce that has a university degree

Auditor dummy 1 if the financial statements are checked by an external auditor, 0 otherwise

Export dummy 1 if the company exports, 0 otherwise

Foreign dummy 1 if the company is foreign owned, 0 otherwise

Year dummy 1 if year = 2005, 0 if year = 2004

Industry dummies Dummy for each 2-digit ISIC code (19 industries)

Country dummies Dummy for each country (25 countries)

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Countries and industriesCountry Freq. Percent ISIC Freq. Percent

Albania 86 2.34 15 426 11.57Armenia 209 5.68 17 304 8.26Bulgaria 89 2.42 20 61 1.66Croatia 53 1.44 22 67 1.82Czech Republic 115 3.12 23 36 0.98Estonia 41 1.11 25 29 0.79FYR Macedonia 40 1.09 26 54 1.47Georgia 19 0.52 27 289 7.85Germany 793 21.54 29 170 4.62Greece 154 4.18 30 51 1.39Hungary 234 6.36 36 91 2.47Kazakhstan 165 4.48 45 494 13.42Kyrgyz Republic 68 1.85 50 163 4.43Latvia 53 1.44 51 291 7.9Lithuania 75 2.04 52 500 13.58Moldova 88 2.39 55 222 6.03Poland 394 10.7 60 235 6.38Portugal 85 2.31 70 144 3.91Romania 266 7.22 72 55 1.49Russia 156 4.24Serbia and Montenegro 50 1.36Slovenia 77 2.09South Korea 47 1.28Turkey 119 3.23Ukraine 206 5.59Total 3,682 100 Total 3,682 100

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Descriptive StatisticsTOTAL IFRS == 1 IFRS == 0

Variable Obs Mean Std. Dev. Min Max Obs Mean Std. Dev. Min Max Obs Mean Std. Dev. Min MaxDepended VariableFin_con 3682 2.287 1.135 1 4 555 2.097 1.079 1 4 3127 2.320 1.141 1 4Fin_con_hi 3682 0.448 0.497 0 1 555 0.371 0.484 0 1 3127 0.462 0.499 0 1foreign_loans_work 3682 1.103 7.368 0 100 555 2.831 11.459 0 100 3127 0.797 6.327 0 100foreign_loans_work_dum 3682 0.036 0.187 0 1 555 0.092 0.289 0 1 3127 0.026 0.160 0 1Foreign_loans_assets 3682 1.661 10.887 0 100 555 4.234 17.039 0 100 3127 1.204 9.312 0 100Foreign_loans_assets_dum 3682 0.033 0.179 0 1 555 0.085 0.279 0 1 3127 0.024 0.153 0 1Variable of interestIFRS 3682 0.151 0.358 0 1 555 1 0 1 1 3127 0 0 0 0Local GAAP 3682 0.945 0.227 0 1 555 0.638 0.481 0 1 3127 1 0 1 1Controlssales_gr 3682 0.115 0.368 -0.98 4 555 0.152 0.354 -0.95 2.5 3127 0.108 0.370 -0.98 4Internal funds 3682 67.158 36.867 0 100 555 62.083 37.183 0 100 3127 68.059 36.743 0 100productratio_l3 3682 1.024 0.924 0.001 14.262 555 1.147 0.892 0.001 7.624 3127 1.002 0.928 0.001 14.262log_productratio_l3 3682 -0.277 0.791 -7.003 2.658 555 -0.142 0.808 -7.003 2.031 3127 -0.301 0.786 -6.590 2.658new_prod 3682 0.344 0.475 0 1 555 0.492 0.500 0 1 3127 0.318 0.466 0 1emp_l3 3682 74.972 288.624 1 8500 555 172.323 545.298 2 8500 3127 57.694 208.364 1 5200log_emp_l3 3682 2.915 1.463 0.693 9.048 555 3.828 1.555 1.099 9.048 3127 2.753 1.384 0.693 8.557age 3682 12.974 16.172 0 186 555 16.220 20.408 0 186 3127 12.398 15.230 0 177log_age 3682 2.221 0.889 0 5.231 555 2.389 0.916 0 5.231 3127 2.191 0.881 0 5.182univers_l3 3682 0.202 0.256 0 1 555 0.256 0.266 0 1 3127 0.192 0.253 0 1auditor 3682 0.494 0.500 0 1 555 0.723 0.448 0 1 3127 0.454 0.498 0 1exportdum 3682 0.244 0.429 0 1 555 0.459 0.499 0 1 3127 0.206 0.404 0 1foreigndum 3682 0.112 0.315 0 1 555 0.292 0.455 0 1 3127 0.080 0.271 0 1

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ResultsVariable Fin Con Fin Con Hi Foreign Loans Assets Foreign Loans Working CapitalIFRS -0.130** -0.170** 1.902*** 1.090***

(0.059) (0.070) (0.571) (0.389)sales_gr -0.132** -0.116* -0.689 -0.628*

(0.055) (0.063) (0.515) (0.351)Internal funds -0.004*** -0.003*** -0.021*** -0.031***

(0.000) (0.001) (0.005) (0.004)log_productratio_l3 0.015 0.048* 0.651*** 0.160

(0.024) (0.028) (0.232) (0.158)new_prod 0.088** 0.089* 0.334 -0.186

(0.042) 0.049 (0.403) (0.274)log_emp_l3 -0.018 0.002 0.243 0.221**

(0.016) (0.019) (0.157) (0.107)log_age -0.075*** -0.054** -0.287 -0.047

(0.024) (0.028) (0.229) (0.154)univers_l3 -0.024 -0.067 -0.067 -0.036

(0.085) (0.099) (0.805) (0.548)auditor -0.152*** -0.166*** -0.339 -0.054

(0.041) 0.048585 (0.397) (0.271)exportdum 0.035 0.025 0.320 0.622*

(0.051) (0.060) (0.489) (0.334)foreigndum -0.310*** -0.407*** 2.44*** 1.89***

(0.064) (0.076) (0.605) (0.412)year 0.065 0.102 1.581 0.403

(0.205) (0.234) (1.625) (1.106)_cons 0.285 2.146515 1.757*

(0.225) (1.486) (1.011)Industry dummiesCountry dummies

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MATCHING

• Potential Endogeneity issues:– Selection Bias: Best performing companies use

IFRS. They already have less financial constraints

=> Potential Solutions: Difference in Difference, Regression discontinuity design and Matching

– Since we have a cross section we will use propensity score matching approach.

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Descriptive StatisticsTOTAL IFRS == 1 IFRS == 0

Variable Obs Mean Std. Dev. Min Max Obs Mean Std. Dev. Min Max Obs Mean Std. Dev. Min MaxDepended VariableFin_con 1104 2.236 1.118 1 4 552 2.103 1.079 1 4 552 2.370 1.142 1 4Fin_con_hi 1104 0.436 0.496 0 1 552 0.373 0.484 0 1 552 0.498 0.500 0 1foreign_loans_work 1104 1.776 8.797 0 100 552 2.846 11.488 0 100 552 0.707 4.543 0 40foreign_loans_work_dum 1104 0.064 0.245 0 1 552 0.092 0.290 0 1 552 0.036 0.187 0 1Foreign_loans_assets 1104 3.125 15.170 0 100 552 4.257 17.083 0 100 552 1.993 12.894 0 100Foreign_loans_assets_dum 1104 0.064 0.245 0 1 552 0.085 0.279 0 1 552 0.043 0.204 0 1Variable of interestIFRS 1104 0.5 0.500 0 1 552 1 0 1 1 552 0 0 0 0Local GAAP 1104 0.819 0.385 0 1 552 0.638 0.481 0 1 552 1 0 1 1Controlssales_gr 1104 0.170 0.400 -0.95 3 552 0.153 0.354 -0.95 2.5 552 0.187 0.441 -0.78 3Internal funds 1104 60.764 37.384 0 100 552 62.042 37.218 0 100 552 59.487 37.540 0 100productratio_l3 1104 1.166 0.942 0.001 8.185 552 1.145 0.894 0.001 7.624 552 1.186 0.988 0.007 8.185log_productratio_l3 1104 -0.135 0.814 -7.003 2.102 552 -0.145 0.810 -7.003 2.031 552 -0.124 0.820 -4.919 2.102new_prod 1104 0.520 0.500 0 1 552 0.491 0.500 0 1 552 0.549 0.498 0 1emp_l3 1104 195.988 562.955 1 8500 552 171.652 546.652 2 8500 552 220.324 578.270 1 5200log_emp_l3 1104 3.870 1.627 0.693 9.048 552 3.818 1.553 1.099 9.048 552 3.921 1.698 0.693 8.557age 1104 17.473 23.391 0 186 552 16.210 20.453 0 186 552 18.736 25.957 0 172log_age 1104 2.392 0.982 0 5.231 552 2.387 0.917 0 5.231 552 2.396 1.043 0 5.153univers_l3 1104 0.254 0.268 0 1 552 0.256 0.266 0 1 552 0.251 0.270 0 1auditor 1104 0.722 0.448 0 1 552 0.721 0.449 0 1 552 0.723 0.448 0 1exportdum 1104 0.458 0.498 0 1 552 0.457 0.499 0 1 552 0.460 0.499 0 1foreigndum 1104 0.306 0.461 0 1 552 0.288 0.453 0 1 552 0.324 0.469 0 1

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Differences before and after matchingDepended IFRS

t test Before Matching

t testAfter Matching

t test Before Matching

t testAfter Matching

Variables Sign P>z P>z Variables Sign P>z P>z

audit + p < 0.001 0.961 isic21 + P = 0.9139 0.637sales_gr + p < 0.008 0.324 isic22 - P < 0.001 0.572Internal funds - p < 0.001 0.404 isic24 + P = 0.7946 1,000log_productratio_l3 + p < 0.001 0.760 country1 + P < 0.10 0.822new_prod + p < 0.001 0.155 country2 - P = 0.7622 0.615log_emp_l3 + p < 0.001 0.446 country3 + P < 0.001 0.549log_age + p < 0.001 0.905 country5 + P < 0.005 1.000univers_l3 + p < 0.001 0.817 country7 - P < 0.003 0.547exportdum + p < 0.001 0.929 country8 - P < 0.004 0.298foreigndum + p < 0.001 0.339 country10 - P < 0.001 0.859year + p < 0.001 0.563 country11 - P < 0.001 0.782 country12 + P = 0.4043 0.812isic5 + p < 0.001 0.861 country13 - P < 0.008 0.659isic6 - p = 0.5092 0.663 country14 - P = 0.1355 0.637isic7 + p = 0.2350 0.999 country17 + P = 0.4369 0.353isic8 + P = 0.7646 0.334 country18 + P < 0.001 0.841isic9 + P = 0.3066 0.682 country19 + P < 0.001 0.929isic10 + P = 0.2623 0.239 country20 + P < 0.001 0.691isic11 - P = 0.1469 1.000 country21 + P < 0.001 0.397isic12 - P = 0.4160 1.000 country22 - P < 0.1 0.695isic13 - P = 0.8900 1.000 country23 + P < 0.1 0.766isic14 + P < 0.01 0.896 country24 + P = 0.6020 0.144isic15 + P = 0.9336 0.607 country26 - P = 0.8421 1.000isic16 - P < 0.01 0.376 country28 - P < 0.001 1.000isic17 - P = 0.1692 0.800 country29 - P < 0.001 0.259isic18 + P < 0.02 0.496 country31 + P = 0.1803 0.325isic19 - P < 0.001 0.526 country33 + P = 0.7211 P = 1.000

isic20 - P = 0.7736 0.329 country34 + P < 0.001 P = 0.839

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Financial Constraints

Two-sample t test with unequal variances

Group Obs Mean Std. Err. Std.Dev. [ 95% conf. Interval]0 552 2.370 0.049 1.142 2.274 2.4651 552 2.103 0.046 1.079 2.013 2.193

Combined 1104 2.236 0.033 1.118 2.170 2.302

Diff 0.266 0.067 0.135 0.397

Diff = mean(0) –mean(1) T = 3.983

H0: diff = 0 Satterhwaite’s degree of freedom = 1098.43

Ha: diff < 0 Ha: diff!= 0 Ha: diff > 0

Pr(T<t) = 1.00 Pr(|T|>|t|) = 0.000 Pr(T>t) = 0.000

Lechners Approximation:

Alpha: -0.266 Std. Err: 0.092 t-value: -2.898

P-value: 0.004

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Financial constraints (HIGH)

Two-sample t test with unequal variances

Group Obs Mean Std. Err. Std.Dev. [ 95% conf. Interval]0 552 0.498 0.021 0.500 0.456 0.5401 552 0.373 0.021 0.484 0.333 0.414

Combined 1104 0.436 0.015 0.496 0.406 0.465

Diff 0.125 0.030 0.067 0.183

Diff = mean(0) –mean(1) T = 4.218

H0: diff = 0 Satterhwaite’s degree of freedom = 1100.78

Ha: diff < 0 Ha: diff!= 0 Ha: diff > 0

Pr(T<t) = 1.000 Pr(|T|>|t|) = 0.000 Pr(T>t) = 0.000

Lechners Approximation:

Alpha: -0.125 Std. Err: 0.041 t-value: -3,086

P-value: 0.002

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Foreign Loans Assets

Two-sample t test with unequal variances

Group Obs Mean Std. Err. Std.Dev. [ 95% conf. Interval]0 552 1.993 0.549 12.894 0.915 3.0711 552 4.257 0.727 17.083 2.829 5.685

Combined 1104 3.125 0.457 15.169 2.229 4.021

Diff -2.264 0.911 -4.052 -0.477

Diff = mean(0) –mean(1) T = -2.486

H0: diff = 0 Satterhwaite’s degree of freedom = 1024.98

Ha: diff < 0 Ha: diff!= 0 Ha: diff > 0

Pr(T<t) = 0.006 Pr(|T|>|t|) = 0.013 Pr(T>t) = 0.994

Lechners Approximation:

Alpha: 2.264 Std. Err: 1.156 t-value: 1.959

P-value: 0.050

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Foreign Loans Assets

Two-sample t test with unequal variances

Group Obs Mean Std. Err. Std.Dev. [ 95% conf. Interval]0 552 0.707 0.193 4.543 0.327 1.0861 552 2.846 0.489 11.488 1.885 3.807

Combined 1104 1.776 0.265 8.797 1.257 2.296

Diff -2.139 0.526 -3.172 -1.107

Diff = mean(0) –mean(1) T = -4.069

H0: diff = 0 Satterhwaite’s degree of freedom = 719.244

Ha: diff < 0 Ha: diff!= 0 Ha: diff > 0

Pr(T<t) = 0.000 Pr(|T|>|t|) = 0.000 Pr(T>t) = 1.000

Lechners Approximation:

Alpha: 2.139 Std. Err: 0.583 t-value: 3.673

P-value: 0.000

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Conclusions and Future Research

• We have shown that following IFRS reduces financial constraints and increases the possibility to have foreign loans.

• We contribute to the literature on the role of financial information, firm characteristics, and country-level institutions for an important and interesting group of firms.

• Future developments:– Restrict Matching procedure within countries and industries– Use of Subsample:

• Differences between Local GAAP; • Differences between innovating firms• Differences between other Institutional factors