Does following International
Accounting Standards reduce
firm’s financial constraints ?
Steven Vanhaverbeke
Benjamin Balsmeier
KU LEUVEN
1
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
2
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)
3
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.
4
IFRS vs. Local GAAP
IFRS vs Local GAAP
Examples
Recognition 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)
5
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
12
Slovenia
31 Lithuania
26 Iran
19
Norway
The
Netherlands
Average differences between Local GAAP vs. IFRS in our sample is 27.
11
6
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
7
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
8
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?”
9
Controls
Variables
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)
10
Countries and industries
Country
Albania
Armenia
Bulgaria
Croatia
Czech Republic
Estonia
FYR Macedonia
Georgia
Germany
Greece
Hungary
Kazakhstan
Kyrgyz Republic
Latvia
Lithuania
Moldova
Poland
Portugal
Romania
Russia
Serbia and Montenegro
Slovenia
South Korea
Turkey
Ukraine
Total
Freq.
Percent
86
209
89
53
115
41
40
19
793
154
234
165
68
53
75
88
394
85
266
156
50
77
47
119
206
3,682
ISIC
2.34
5.68
2.42
1.44
3.12
1.11
1.09
0.52
21.54
4.18
6.36
4.48
1.85
1.44
2.04
2.39
10.7
2.31
7.22
4.24
1.36
2.09
1.28
3.23
5.59
100 Total
Freq.
15
17
20
22
23
25
26
27
29
30
36
45
50
51
52
55
60
70
72
Percent
426
304
61
67
36
29
54
289
170
51
91
494
163
291
500
222
235
144
55
11.57
8.26
1.66
1.82
0.98
0.79
1.47
7.85
4.62
1.39
2.47
13.42
4.43
7.9
13.58
6.03
6.38
3.91
1.49
3,682
100
11
Descriptive Statistics
TOTAL
Variable
IFRS == 1
Obs Mean Std. Dev. Min
Max
Obs Mean
IFRS == 0
Std. Dev. Min
Max Obs Mean
Std. Dev. Min
Max
Depended Variable
Fin_con
Fin_con_hi
foreign_loans_work
foreign_loans_work_dum
Foreign_loans_assets
Foreign_loans_assets_dum
3682
3682
3682
3682
3682
3682
2.287
0.448
1.103
0.036
1.661
0.033
1.135
0.497
7.368
0.187
10.887
0.179
1
0
0
0
0
0
4
1
100
1
100
1
555
555
555
555
555
555
2.097
0.371
2.831
0.092
4.234
0.085
1.079
0.484
11.459
0.289
17.039
0.279
1
0
0
0
0
0
4
1
100
1
100
1
3127
3127
3127
3127
3127
3127
2.320
0.462
0.797
0.026
1.204
0.024
1.141
0.499
6.327
0.160
9.312
0.153
1
0
0
0
0
0
4
1
100
1
100
1
3682 0.151
3682 0.945
0.358
0.227
0
0
1
1
555
555
1
0.638
0
0.481
1
0
1
1
3127
3127
0
1
0
0
0
1
0
1
0.152
62.083
1.147
-0.142
0.492
172.323
3.828
16.220
2.389
0.256
0.723
0.459
0.292
0.354
37.183
0.892
0.808
0.500
545.298
1.555
20.408
0.916
0.266
0.448
0.499
0.455
-0.95
0
0.001
-7.003
0
2
1.099
0
0
0
0
0
0
2.5
100
7.624
2.031
1
8500
9.048
186
5.231
1
1
1
1
3127
3127
3127
3127
3127
3127
3127
3127
3127
3127
3127
3127
3127
0.108
68.059
1.002
-0.301
0.318
57.694
2.753
12.398
2.191
0.192
0.454
0.206
0.080
0.370
36.743
0.928
0.786
0.466
208.364
1.384
15.230
0.881
0.253
0.498
0.404
0.271
-0.98
0
0.001
-6.590
0
1
0.693
0
0
0
0
0
0
4
100
14.262
2.658
1
5200
8.557
177
5.182
1
1
1
1
Variable of interest
IFRS
Local GAAP
Controls
sales_gr
Internal funds
productratio_l3
log_productratio_l3
new_prod
emp_l3
log_emp_l3
age
log_age
univers_l3
auditor
exportdum
foreigndum
3682
3682
3682
3682
3682
3682
3682
3682
3682
3682
3682
3682
3682
0.115 0.368 -0.98
4
555
67.158 36.867
0
100 555
1.024 0.924 0.001 14.262 555
-0.277 0.791 -7.003 2.658 555
0.344 0.475
0
1
555
74.972 288.624
1
8500 555
2.915 1.463 0.693 9.048 555
12.974 16.172
0
186 555
2.221 0.889
0
5.231 555
0.202 0.256
0
1
555
0.494 0.500
0
1
555
0.244 0.429
0
1
555
0.112 0.315
0
1
555
12
Results
Variable
IFRS
sales_gr
Internal funds
log_productratio_l3
new_prod
log_emp_l3
log_age
univers_l3
auditor
exportdum
foreigndum
year
_cons
Industry dummies
Country dummies
Fin Con
-0.130**
(0.059)
-0.132**
(0.055)
-0.004***
(0.000)
0.015
(0.024)
0.088**
(0.042)
-0.018
(0.016)
-0.075***
(0.024)
-0.024
(0.085)
-0.152***
(0.041)
0.035
(0.051)
-0.310***
(0.064)
0.065
(0.205)
Fin Con Hi
-0.170**
(0.070)
-0.116*
(0.063)
-0.003***
(0.001)
0.048*
(0.028)
0.089*
0.049
0.002
(0.019)
-0.054**
(0.028)
-0.067
(0.099)
-0.166***
0.048585
0.025
(0.060)
-0.407***
(0.076)
0.102
(0.234)
0.285
(0.225)
Foreign Loans Assets Foreign Loans Working Capital
1.902***
(0.571)
-0.689
(0.515)
-0.021***
(0.005)
0.651***
(0.232)
0.334
(0.403)
0.243
(0.157)
-0.287
(0.229)
-0.067
(0.805)
-0.339
(0.397)
0.320
(0.489)
2.44***
(0.605)
1.581
(1.625)
2.146515
(1.486)
1.090***
(0.389)
-0.628*
(0.351)
-0.031***
(0.004)
0.160
(0.158)
-0.186
(0.274)
0.221**
(0.107)
-0.047
(0.154)
-0.036
(0.548)
-0.054
(0.271)
0.622*
(0.334)
1.89***
(0.412)
0.403
(1.106)
1.757*
(1.011)
13
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.
14
Descriptive Statistics
TOTAL
Variable
Obs
Mean
IFRS == 1
Std. Dev. Min
Max Obs
Mean
IFRS == 0
Std. Dev. Min
Max Obs
Mean
Std. Dev. Min
Max
Depended Variable
Fin_con
Fin_con_hi
foreign_loans_work
foreign_loans_work_dum
Foreign_loans_assets
Foreign_loans_assets_dum
1104
1104
1104
1104
1104
1104
2.236
0.436
1.776
0.064
3.125
0.064
1.118
0.496
8.797
0.245
15.170
0.245
1
0
0
0
0
0
4
1
100
1
100
1
552
552
552
552
552
552
2.103
0.373
2.846
0.092
4.257
0.085
1.079
0.484
11.488
0.290
17.083
0.279
1
0
0
0
0
0
4
1
100
1
100
1
552
552
552
552
552
552
2.370
0.498
0.707
0.036
1.993
0.043
1.142
0.500
4.543
0.187
12.894
0.204
1
0
0
0
0
0
4
1
40
1
100
1
1104
1104
0.5
0.819
0.500
0.385
0
0
1
1
552
552
1
0.638
0
0.481
1
0
1
1
552
552
0
1
0
0
0
1
0
1
Variable of interest
IFRS
Local GAAP
Controls
sales_gr
Internal funds
productratio_l3
log_productratio_l3
new_prod
emp_l3
log_emp_l3
age
log_age
univers_l3
auditor
exportdum
foreigndum
1104 0.170
0.400 -0.95
3
1104 60.764 37.384
0 100
1104 1.166
0.942 0.001 8.185
1104 -0.135
0.814 -7.003 2.102
1104 0.520
0.500
0
1
1104 195.988 562.955
1 8500
1104 3.870
1.627 0.693 9.048
1104 17.473 23.391
0 186
1104 2.392
0.982
0 5.231
1104 0.254
0.268
0
1
1104 0.722
0.448
0
1
1104 0.458
0.498
0
1
1104 0.306
0.461
0
1
552
0.153
0.354 -0.95 2.5
552 62.042 37.218
0 100
552
1.145
0.894 0.001 7.624
552 -0.145
0.810 -7.003 2.031
552
0.491
0.500
0
1
552 171.652 546.652
2 8500
552
3.818
1.553 1.099 9.048
552 16.210 20.453
0 186
552
2.387
0.917
0 5.231
552
0.256
0.266
0
1
552
0.721
0.449
0
1
552
0.457
0.499
0
1
552
0.288
0.453
0
1
552 0.187
0.441 -0.78
3
552 59.487 37.540
0 100
552 1.186
0.988 0.007 8.185
552 -0.124
0.820 -4.919 2.102
552 0.549
0.498
0
1
552 220.324 578.270
1 5200
552 3.921
1.698 0.693 8.557
552 18.736 25.957
0 172
552 2.396
1.043
0 5.153
552 0.251
0.270
0
1
552 0.723
0.448
0
1
552 0.460
0.499
0
1
552 0.324
0.469
0
1
15
Differences before and after matching
t test
t test
Before Matching After Matching
Depended IFRS
Variables
Sign
P>z
P>z
audit
sales_gr
Internal funds
log_productratio_l3
new_prod
log_emp_l3
log_age
univers_l3
exportdum
foreigndum
year
+
+
+
+
+
+
+
+
+
+
p < 0.001
p < 0.008
p < 0.001
p < 0.001
p < 0.001
p < 0.001
p < 0.001
p < 0.001
p < 0.001
p < 0.001
p < 0.001
0.961
0.324
0.404
0.760
0.155
0.446
0.905
0.817
0.929
0.339
0.563
isic5
isic6
isic7
isic8
isic9
isic10
isic11
isic12
isic13
isic14
isic15
isic16
isic17
isic18
isic19
+
+
+
+
+
+
+
+
-
p < 0.001
p = 0.5092
p = 0.2350
P = 0.7646
P = 0.3066
P = 0.2623
P = 0.1469
P = 0.4160
P = 0.8900
P < 0.01
P = 0.9336
P < 0.01
P = 0.1692
P < 0.02
P < 0.001
0.861
0.663
0.999
0.334
0.682
0.239
1.000
1.000
1.000
0.896
0.607
0.376
0.800
0.496
0.526
isic20
-
P = 0.7736
0.329
Variables
isic21
isic22
isic24
country1
country2
country3
country5
country7
country8
country10
country11
country12
country13
country14
country17
country18
country19
country20
country21
country22
country23
country24
country26
country28
country29
country31
country33
country34
t test
Before Matching
t test
After Matching
Sign
P>z
P>z
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
P = 0.9139
P < 0.001
P = 0.7946
P < 0.10
P = 0.7622
P < 0.001
P < 0.005
P < 0.003
P < 0.004
P < 0.001
P < 0.001
P = 0.4043
P < 0.008
P = 0.1355
P = 0.4369
P < 0.001
P < 0.001
P < 0.001
P < 0.001
P < 0.1
P < 0.1
P = 0.6020
P = 0.8421
P < 0.001
P < 0.001
P = 0.1803
P = 0.7211
0.637
0.572
1,000
0.822
0.615
0.549
1.000
0.547
0.298
0.859
0.782
0.812
0.659
0.637
0.353
0.841
0.929
0.691
0.397
0.695
0.766
0.144
1.000
1.000
0.259
0.325
P = 1.000
+
P < 0.001
P = 0.839
16
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
2.465
1
552
2.103
0.046
1.079
2.013
2.193
Combined
1104
2.236
0.033
1.118
2.170
2.302
0.266
0.067
0.135
0.397
Diff
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
17
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
0.540
1
552
0.373
0.021
0.484
0.333
0.414
Combined
1104
0.436
0.015
0.496
0.406
0.465
0.125
0.030
0.067
0.183
Diff
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
18
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
3.071
1
552
4.257
0.727
17.083
2.829
5.685
Combined
1104
3.125
0.457
15.169
2.229
4.021
-2.264
0.911
-4.052
-0.477
Diff
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
19
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
1.086
1
552
2.846
0.489
11.488
1.885
3.807
Combined
1104
1.776
0.265
8.797
1.257
2.296
-2.139
0.526
-3.172
-1.107
Diff
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
20
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
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