Appended Notes
AN 1.2.1 Handling of “services industries” and “professional and business services” in industrial
classification
This is an explanation regarding industrial classification methods used in handling statistical data in
Chapter 1, Section 2.
First, in handling US statistics, “professional and business services,” are, as a rule, “professional and
business services,” classified by the US Department of Labor, Bureau of Labor Statistics as Super Sector
60. In the North American Industry Classification System (NAICS) codes, applied during a 1997 census,
“professional and business services” correspond with the total of NAICS 54 (professional, scientific, and
technical services), NAICS 55 (management of companies and enterprises) and NAICS 56
(administrative and support and waste management and remediation services) (Appended figure 1.2.1).
Appended figure 1.2.1 Classifications of services industries according to the US Department
of Labor and a comparative table of the NAICS Code
Super Sector 07 Services Industries
Super Sector 40 Trade, Transportation and Utilities
NAICS Code 42
Wholesale Trade
NAICS Code 44-45 Retail Trade
NAICS Code 48-49 Transportation and Warehousing
NAICS Code 22
Utilities
Super Sector 50 Information
NAICS Code 51
Information
Super Sector 55 Finance
NAICS Code 52
Finance and Insurance
NAICS Code 53
Real Estate and Rental and Leasing
Super Sector 60 Professional and Business Services
NAICS Code 54
Professional, Scientific, and Technical Services
NAICS Code 55
Management of Companies and Enterprises
NAICS Code 56
Administrative and Support and Waste
Management and Remediation Services
Super Sector 65 Education and Medical Services
NAICS Code 61
Educational Services
NAICS Code 62
Health Care and Social Assistance
Super Sector 70 Recreation and Customer Services
NAICS Code 71
Arts, Entertainment, and Recreation
NAICS Code 72
Accommodation and Food Services
Super Sector 80 Other Services
NAICS Code 81
Other Services (except Public Administration)
Super Sector 90 Public Administration
NAICS Code 92
Public Administration
Super Sector 99 Non-classifiable
NAICS Code 99
Non-classifiable
Note: Bold lettering indicates the industry classifications according to the Bureau of
Labor Statistics, US Department of Labor.
Source: North American Industry Classification System (Census Bureau,
US Department of Commerce).
The “services industries” are “services industries” classified by US Department of Labor, Bureau of
Labor Statistics as Super Sector 07. To see the relationship between “services industries” and their
subdivisions according to the US Department of Labor, Bureau of Labor Statistics and the NAICS codes,
please refer to Appended figure 1.2.1.
-281-
-282-
66-70 Hotels and other lodging
72 Personal services
Finance, insurance, and real estate
Hotels and other lodging
Personal services
79 Amusement and recreational services
80 Health services
82 Educational services
83 Social services
Sum total of the above
Other repairs
Film and video productions
Entertainment
Medical services
Education
Social services
Service industries
L
L
L
L
L
L
L
G
H
L
L
J
K
L
L
L
L
L
L
L
L
L
L
L
L
L
L
L
Service
Service
Service
Service
Service
Service
Service
First division
Electricity, gas, heat and water supply
Transportation and communications
Service
Service
Finance and insurance
Real estate
Service
Service
Service
Service
Service
Service
Service
Service
Service
Service
Service
Service
Service
Service
Service
76
79
79
88
89
91
90
75
72
74
79
79
79
82
83
84
86
92
77
79
78
80
Hotels, boarding houses and other lodging places
Laundry, beauty and bath service
Miscellaneous domestic and personal services
Goods rental and leasing
Goods rental and leasing
Goods rental and leasing
Information services and research
Advertising
Professional services (cannot be classified as other)
Miscellaneous business services
Scientific research institutes
Automobile repair services
Goods rental and leasing
Machine, upholstery, furniture, etc., repair services
Motion picture and video production
Amusement and recreation services, except motion picture and video
production
Goods rental and leasing
Goods rental and leasing
Medical and other health services
Public health services
Education
Social insurance and social welfare
Sum total of the above
81 Broadcasting
87 Waste treatment services
Japan Standard Industrial Classification (1993 revision)
Second division
Source: Japan Standard Industrial Classification (Management and Coordination Agency), North American Industry Classification System (Census Bureau, US Department of Commerce).
75 Automobile repair, services, and parking
76 Miscellaneous repair services
78 Motion pictures
Automobile repairs, rentals, etc.
Professional and business services
73 Business services
81 Legal services
87 Engineering and management services
60-65 Financial, insurance, and real estate
Utilities, transportation and communications
Definition
US Standard Industrial Classification （SIC 1987）
Code
Industry
40-49 Transportation and public utilities
Appended figure 1.2.2 Comparative table of the Japan Standard Industrial Classification (1993 revision) and the US Standard Industrial Classification System (SIC 1987) in the classification of services industries
795 Sports and recreation goods rental and leasing
799 Miscellaneous goods rental and leasing
794 Automobile rental and leasing
791 General goods rental and leasing
792 Industrial equipment and machinery rental and leasing
793 Office machinery rental and leasing
Third division
One must be careful regarding Figures 1.2.3, 1.2.5, 1.2.6, 1.2.7, 1.2.15, 1.2.16 and 1.2.17, however,
in terms of ensuring comparability between Japan and the US and statistical data restrictions because
industries are classified and defined individually as in Appended figure 1.2.2. Regarding the Japan
Standard Industrial Classification, the reason that 1993 revised classification is used rather than the
newest classification, which was revised in 2002, is that the acquired data is taken from the 1993
classification.
For more information on industrial classification according to the North American Industry
Classification System (NAICS), Standard Industrial Classification (SIC) and the US Department of
Labor, Bureau of Labor Statistics and the Japan Standard Industrial Classification, please refer to the
websites below. Also, please refer to the website below for more information regarding Standard
Occupational Classification (SOC) used in Figure 1.2.14.
(a) North American Industry Classification System (NAICS) and Standard Industrial
Classifications (SIC)
US Department of Commerce, Census Bureau website (http://www.census.gov/epcd/naics02/)
(b) Industrial classification according to the US Department of Labor, Bureau of Labor Statistics
US Department of Labor, Bureau of Labor Statistics website
(http://stats.bls.gov/webapps/legacy/cesbtab1.htm)
(c) Japan Standard Industrial Classification
Ministry of Public Management, Home Affairs, Posts and Telecommunications, Statistics Bureau
website (http://www.stat.go.jp/index/seido/sangyo/)
(d) Standard Occupational Classification (SOC)
US Department of Labor, Bureau of Labor Statistics website (http://stats.bls.gov/soc/home.htm)
AN 2.1.1 Relationship between company sales and non-R&D intellectual assets
1. Method for estimating non-R&D intellectual assets1
(1) Basic model
Y=company sales, K=capital, L=labor (number of employees), R=R&D (research and development
expenditure), e=error term, A=regular non-R&D intellectual assets (can be used for all companies),
FA=non-R&D intellectual assets unique to each company
1
Lev (2003).
-283-
[Production function]
Y = f ( A, FA, K , L, R) = A( FA) KαLβRγe (1)
(2) Estimation of non-R&D intellectual assets
In estimating non-R&D intellectual assets, an annual growth equation (Equation 2) is used in order to
estimate residual output components in the basic model (Equation 1).
Log (Y / Y−1 )
= δ + sπD + αLog ( K / K −1 ) + β Log ( L / L−1 ) + γLog ( R / R−1 ) + Log (e / e−1 ) ( 2)
* Here D is a dummy variable for non-R&D intellectual assets unique to companies, and this coefficient
is the measured value of non-R&D intellectual assets unique to companies.
(3) Monetary conversion of non-R&D intellectual assets
The contribution of sales of non-R&D intellectual assets (RO) is equal to the difference of sales in the
sales estimation in which non-R&D intellectual assets are included (2A) and the estimation in which
non-R&D intellectual assets are not included (2B) (Equation 3).
Y ∗ = Y−1exp(sπ )exp(δ )(K/K -1 )α (L/L-1 ) β (R/R -1 ) γ (2A)
Y ∗∗ = Y−1 (K/K -1 )α (L/L-1 ) β (R/R -1 )γ (2B)
RO = Y ∗ − Y ∗∗ (3)
* The values estimated in Equation 2 are used for α, β, etc.
2. Estimation results
(1) US2
(a) Data
・ This analysis covered approximately 250 companies listed in the Information Week 500 between
1991 and 1997.
・ Individual data (sales, capital, number of employees, research and development expenditure) was
obtained from the Compustat Annual Database.
・ Regarding the company data sample used to estimate non-R&D intellectual assets, the sample that
could be obtained for research and development expenditure data for the 1987-2000 period was
2
Lev (2003).
-284-
1,952 and the sample that could not be obtained was 1,246.
・ Research and development expenditure is capitalized and depreciated over five years (annual
depreciation rate=20%)
(b) Results
Appended figure 2.1.1 Estimation results for the US
1. Statistics
Variables
Y
K
L
R
US$ million
US$ million
1,000 people
US$ million
R&D expenditure data available
R&D expenditure data not available
Average Minimum Median Maximum Average Minimum Median Maximum
value
value
value
value
value
value
value
value
9,123
146
4,678
101,781
6,532
3
3,104
191,329
3,433
17
1,302
51,161
1,808
2
632
40,934
42
2
25
813
39
1
16
1,244
1,036
1
323
16,439
2. Coefficient of correlation
Variables
Log（K／K-1）
Log（L／L-1）
Log（R／R-1）
R&D expenditure data available
Log（Y／Y-1）
Log（K／K-1）
Log（L／L-1）
0.61
0.70
0.67
0.41
0.34
0.33
R&D expenditure data not available
Log（Y／Y-1）
Log（K／K-1）
0.56
0.65
0.63
3. Estimation results
R&D expenditure data available R&D expenditure data not available
Coefficient t value
P value Coefficient t value
P value
Intercept
0.03
5.41
0.00
0.02
5.58
0.00
Log（FA／FA-1）
0.02
5.61
0.00
0.02
6.16
0.00
Log（K／K-1）
0.20
8.87
0.00
0.12
4.39
0.00
Log（L／L-1）
0.38
12.47
0.00
0.44
16.12
0.00
Log（R／R-1）
0.19
6.61
0.00
0.6253
0.5813
Final variables (adjusted)
Variables
4. Estimation results for monetary conversion of non-R&D intellectual assets
Average Standard Minimum Median Maximum
Variables
deviation
value
value
value
value
251
777
-2,724
72
8,654
RO＝Y*-Y** US$ million
Y-Y-1
US$ million
576
1,876 -27,425
207
27,379
Source: Lev (2003).
(2) Japan
(a) Data
・ This analysis covered 964 listed companies in Nihon Keizai Shimbun’s corporate database of Nikkei
NEEDS for which items used for estimation3 (sales and operating profit, total tangible fixed assets,
land and the rest of the tangible fixed assets, number of employees, development expenses and
experimental and research expenses) could be continuously obtained for the 1989-2002 estimation
period.
・ Of the 964 companies analyzed, 402 were in manufacturing industries and 562 were in
non-manufacturing industries. Of the companies analyzed in the manufacturing industry, 173 were in
the machinery assembly manufacturing industries4, and of the companies in the non-manufacturing
3
Numerical values are on a non-consolidated basis.
They cover the four sectors of machinery, precision instruments, electrical instruments and transport
equipment under the industry classification of the Tokyo Stock Exchange.
4
-285-
industry, 263 were in retail/service industries, etc.5
・ The company data sample used to estimate non-R&D intellectual assets was 5,628 for
manufacturing industries (including 2,422 for machinery assembly manufacturing industries) and
7,868 for non-manufacturing industries (including 3,682 for retail/service industries, etc.).
・ Research and development capital is defined as the average value of research and development
expenses during the most recent five years.
(b) Results
Using Lev (2003) as reference, estimation of non-R&D intellectual assets in Japan is carried out
applying company data for each year to Equation 2.
Appended figure 2.1.2
Estimation results for Japan (manufacturing industries, non-manufacturing industries)
1. Statistics
Variables
Y
K
L
R
Million yen
Million yen
People
Million yen
Manufacturing industries
Non-manufacturing industries
Average Minimum Median Maximum Average Minimum Median Maximum
value
value
value
value
value
value
value
value
181,098
1,435
55,419 3,408,251
250,664
518
82,323 9,419,359
41,282
269
12,313
997,139
74,323
10
9,265 4,884,790
2,480
31
1,120
42,375
2,052
7
897
100,090
8,120
8
968
301,298
2. Coefficient of correlation
Variables
Log（K／K-1）
Log（L／L-1）
Log（R／R-1）
Log（Y／Y-1）
0.30
0.35
0.11
Manufacturing industries
Log（K／K-1）
Log（L／L-1）
0.45
0.18
0.15
Non-manufacturing industries
Log（K／K-1）
Log（Y／Y-1）
0.24
0.36
0.32
3. Estimation results
Manufacturing industries
Non-manufacturing industries
Coefficient t value
P value Coefficient t value
P value
Intercept
0.003
2.046
0.041
0.018
13.081
0.000
Log（FA／FA-1）
0.005
1.280
0.201
-0.008
-5.448
0.000
Log（K／K-1）
0.142
12.495
0.000
0.077
12.193
0.000
Log（L／L-1）
0.332
19.102
0.000
0.358
28.574
0.000
Log（R／R-1）
0.030
3.060
0.002
Final variables (adjusted)
0.147
0.148
Variables
4. Estimation results for monetary conversion of non-R&D intellectual assets
Manufacturing industries
Average Standard Minimum Median Maximum Average
Variables
deviation
value
value
value
value
value
784
2,121
-62
188
23,052
1,089
RO＝Y*-Y** Million yen
Y-Y-1
Million yen
1,178
39,188 -780,935
333
581,049
-2,458
Source: Estimates based on Lev (2003).
5
Non-manufacturing industries
Standard Minimum Median Maximum
deviation
value
value
value
15,335
-270,831
1,252
45,952
176,304 -2,825,337
864 6,832,939
They cover the eight sectors of retail trade, services, land transport, maritime transport, air transport,
warehousing/related transportation, information and telecommunications, and electricity and gas under the
industry classification of the Tokyo Stock Exchange.
-286-
Appended figure 2.1.3
Estimation results for Japan (machinery assembly manufacturing industries, retail/service
industries, etc.)
1. Statistics
Variables
Y
K
L
R
Million yen
Million yen
People
Million yen
Machine assembly manufacturing industries
Retail/services industries, etc.
Average Minimum Median Maximum Average Minimum Median Maximum
value
value
value
value
value
value
value
value
234,565
1,435
52,607 3,408,251 183,955
518
68,597 2,482,744
36,437
269
9,237
520,779 125,343
10
14,566 4,884,790
3,339
73
1,268
42,375
2,763
21
1,129 100,090
11,575
18
845
301,298
2. Coefficient of correlation
Variables
Log（K／K-1）
Log（L／L-1）
Log（R／R-1）
Machine assembly manufacturing industries
Log（K／K-1）
Log（L／L-1）
Log（Y／Y-1）
0.26
0.30
0.37
0.09
0.18
0.14
Retail/services industries, etc.
Log（Y／Y-1）
Log（K／K-1）
0.24
0.42
0.32
3. Estimation results
Machine assembly manufacturing
Retail/services industries, etc.
industries
Coefficient t value
P value Coefficient t value
P value
Intercept
0.007
2.607
0.009
0.028
16.113
0.000
Log（FA／FA-1）
0.015
2.020
0.043
0.008
3.151
0.002
Log（K／K-1）
0.150
7.858
0.000
0.058
7.391
0.000
Log（L／L-1）
0.365
11.303
0.000
0.387
24.556
0.000
Log（R／R-1）
0.026
1.582
0.114
Final variables (adjusted)
0.114
0.193
Variables
4. Estimation results for monetary conversion of non-R&D intellectual assets
Retail/services industries, etc.
Machine assembly manufacturing industries
Average Standard Minimum Median Maximum Average Standard Minimum Median Maximum
Variables
deviation
deviation
value
value
value
value
value
value
value
value
2,037
5,330
-470
378
49,270
5,189
10,720
9
1,767
96,462
RO＝Y*-Y** Million yen
Y-Y-1
Million yen
2,942
53,532 -780,935
533 581,049
3,987
20,822 -249,346
980
468,117
Source: Estimates based on Lev (2003).
AN 2.1.2 Provisional methods to evaluate intellectual assets
1. Companies analyzed
The analysis was carried out using OSIRIS, a financial database by Bureau van Dijk of listed
companies in countries around the world. Of the 8,437 manufacturing industry companies (in 21
countries) in OSIRIS, 7,897 companies were analyzed and 540 companies were excluded based on the
standards below. Please refer to Appended figure 2.1.4 for the number of companies analyzed by
industry and by country.
・ Data of companies with contradictions such as total capital being negative, etc.
・ Data of companies whose recent sales are unknown or below 100 million yen.
-287-
Food
121 113
Tobacco
1
6
Textiles
47
28
Clothing
28
48
Lumber and wood
14
23
Furniture and accessories
11
29
Paper and pulp
31
40
Printing and publishing
31
70
Chemicals
177 363
Petroleum and coal
5
20
Rubber and plastics
62
60
Leather
4
23
Ceramic, stone and clay
54
25
Iron and steel, non-ferrous metal
86
72
Metal products
92
71
General machinery
218 339
Electrical machinery
199 543
Transport machinery
94 126
Precision machinery
89 393
Other
19
66
(All manufacturing industries) 1,383 2,458
38
1
2
4
13
7
11
13
46
4
8
2
9
11
14
34
41
15
8
11
292
37
3
12
14
2
6
9
32
60
1
14
5
20
8
18
41
59
30
31
18
420
34
0
8
13
4
4
9
7
34
1
11
0
19
5
10
62
46
23
27
10
327
37
0
12
17
5
5
8
6
32
3
15
2
10
15
25
33
30
19
13
8
295
6
0
7
6
0
1
3
8
7
1
1
0
7
2
1
12
9
6
5
3
85
3
0
1
1
5
4
7
3
7
0
2
0
0
4
6
10
13
7
9
2
84
4
0
0
1
1
3
1
4
2
0
0
0
0
3
1
6
7
4
2
0
39
5
0
3
0
1
1
4
6
4
0
5
0
3
3
1
11
14
1
3
4
69
7
0
2
2
3
3
4
3
10
0
1
0
3
1
0
8
10
2
5
2
66
8
0
3
3
0
3
3
5
8
1
4
0
2
2
5
8
10
2
2
1
70
40
1
53
25
1
1
19
3
171
18
12
3
35
52
13
151
70
44
9
6
727
24
1
13
16
3
3
7
3
48
2
8
5
21
21
10
25
79
33
6
3
331
6
0
23
6
0
1
4
2
24
2
12
2
7
11
5
55
79
6
0
8
253
14
1
8
16
4
2
2
8
18
1
5
8
3
3
4
10
40
2
7
18
174
12
0
2
2
2
2
4
10
3
2
7
0
7
8
9
29
41
13
2
3
158
34
0
17
2
3
3
5
8
16
1
10
4
9
10
8
9
17
3
0
7
166
14
3
12
6
4
1
6
2
23
0
11
2
7
8
5
6
3
1
0
7
121
38
3
8
15
30
7
18
8
25
2
22
1
30
25
22
21
46
19
4
12
356
8
0
0
0
0
0
0
1
1
1
2
0
3
0
1
2
3
1
0
0
23
Overall
amount
Philippines
Malaysia
Indonesia
Thailand
Singapore
Hong Kong
Taiwan
Korea
China
Netherlands
Denmark
Finland
Norway
Sweden
Italy
France
Germany
UK
Canada
US
Japan
Appended figure 2.1.4 Number of companies analyzed by industry and by country
603
20
261
225
118
97
195
233
1,079
65
272
61
274
350
321
1,090
1,359
451
615
208
7,897
2. Handling of data in the process of indexation of proxy variables
(1) Deviation values of proxy variables
Indexes (deviation values) are calculated for each proxy variable according to procedures shown in
(a)-(j) below, with the average value 50 and changes of 10 for each standard deviation. In the calculation
results, when the deviation value is 75 and above or 25 and below, the deviation value sought is either 75
or 25. Please refer to Appended figure 2.1.5 for the average values of proxy variables and cover rates,
etc.
Appended figure 2.1.5 Basic data for each proxy variable
Component
Business structure
reform capacity
Business
efficiency
Proxy variable
Changes in turnover ratio of
total capital
Changes in the ratio of
operating profit to sales
Inventory asset turnover
ratio
Turnover ratio of tangible
fixed assets
Cumulative R&D expenses
Technological
capacity
Average value
Standard deviation
Maximum value
Minimum value
Number of
Coverage
covered
rates
companies
0.02 times
0.27 times
1.08 times
-1.03 times
7,052
89.3%
-2.3%
10.3%
39.0%
-44.5%
6,477
82.0%
8.78 times
7.60 times
61.34 times
0.81 times
7,458
94.4%
6.97 times
53.61 times
0.23 times
7,694
97.4%
159.729 bn yen per 2.126578 bn yen per 2 m yen per company
company per year
company per year
per year
2,269
28.7%
5.54 times
36.517 bn yen per
company per year
Cumulative sales /
Cumulative R&D expenses
48.18 times
435.50 times
17,312.35 times
0.04 times
1,635
20.7%
Credibility
0.72 times
0.65 times
5.44 times
0.06 times
6,146
77.8%
Sales share
0.25%
1.19%
46.76%
0.00007%
7,897
100.0%
26.039m yen/person 21.323m yen/person 192.595m yen/person 139,000 yen/person
5,565
70.5%
4,635
58.7%
Marketing capacity
Organizational
capacity
Employee productivity
Changes in number of
1.01 times
employees
Note: Denominator for "coverage rate" is the 7,897 companies that are analyzed.
0.37 times
12.60 times
0.00 times
(a) Changes in turnover ratio of total capital (the latest term, two terms before the latest term)
・ 655 companies for which the turnover ratio of total capital in both the latest term and two terms
before the latest term could not be calculated were excluded from the analysis.
-288-
・ The average value and standard deviation were sought for the numerical value of the turnover ratio
of the total capital two terms before the latest term subtracted from the ratio in the latest, and the
samples of 190 companies for which there was a divergence of 2.5 times or more the standard
deviation from the average value were regarded as outlying values and excluded.
・ A recalculated average value and standard deviation were sought for the numerical values of the
7,052 companies remaining after the above process, and the deviation value of each company was
calculated based on this.
(b) Changes in the ratio of operating profit to sales (the latest term, two terms before the latest
term)
・ 652 companies for which the ratio of operating profit to sales in both the latest term and the two
terms before the latest could not be calculated were excluded from the analysis.
・ The samples of 487 companies for which the numerical value of the ratio of operating profit to sales
two terms before the latest term subtracted from the ratio in the latest is not within the range of
±100% were regarded as outlying values and excluded.
・ The average value and standard deviation were sought for the numerical value of the ratio of
operating profit to sales two terms before the latest term subtracted from the ratio in the latest, and
the samples of 281 companies with a divergence of 2.5 times or more the standard deviation from
the average value were regarded as outlying values and excluded.
・ A recalculated average value and standard deviation were sought for the numerical values of the
6,477 companies remaining after the above process, and the deviation value of each company was
calculated based on this.
(c) Inventory asset turnover ratio (the latest term)
・ 208 companies for which inventory asset turnover ratio in the latest term could not be calculated
were excluded from the analysis.
・ The average value and standard deviation were sought for the logarithmically transformed numerical
value of the inventory asset turnover ratio in the latest term, and the samples of 231 companies with
a divergence of 2.5 times or more the standard deviation from the average value were regarded as
outlying values and excluded.
・ A recalculated average value and standard deviation were sought for the numerical values of the
7,458 companies remaining after the above process, and the deviation value of each company was
calculated based on this.
(d) Turnover ratio of tangible fixed assets (the latest term)
・ 22 companies for which the turnover ratio of tangible fixed assets in the latest term could not be
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calculated were excluded from the analysis.
・ The average value and standard deviation were sought for the logarithmically transformed numerical
value of the turnover ratio of tangible fixed assets in the latest term, and the samples of 181
companies with a divergence of 2.5 times or more the standard deviation from the average value
were regarded as outlying values and excluded.
・ A recalculated average value and standard deviation were sought for the numerical values of the
7,694 companies remaining after the above process, and the deviation value of each company was
calculated based on this.
(e) Cumulative R&D expenses (three latest terms)
・ 5,628 companies for which the numerical value of R&D expenses in one or more of the latest three
terms could not be obtained were excluded from the analysis.
・ The average value and standard deviation were sought for the numerical values of the 2,269
remaining companies, and the deviation value of each company was calculated based on this.
(f) Cumulative sales (three latest terms) / cumulative R&D expenses (three terms before-five terms
before)
・ 6,262 companies for which one or more data was lacking for the data on sales from the three latest
terms and data on cumulative R&D expenses from three terms before to five terms before were
excluded from the analysis.
・ The average value and standard deviation were sought for the numerical values of the 1,635
remaining companies, and the deviation value of each company was calculated based on this.
(g) Credibility＝trade payables (the latest term) / trade receivables (the latest term)
・ 1,583 companies for which credibility could not be calculated in the latest term were excluded from
the analysis.
・ The average value and standard deviation were sought for the logarithmically transformed numerical
value of credibility in the latest term, and the samples of 168 companies with a divergence of 2.5
times or more the standard deviation from the average value were regarded as outlying values and
excluded.
・ A recalculated average value and standard deviation were sought for the numerical values of the
6,146 companies remaining after the above process, and the deviation value of each company was
calculated based on this.
(h) Sales share (the latest term)
・ Companies with the same two-digit US-SIC code are placed into groups of companies in the same
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industry, and sales share is a company’s sales ratio of the total sales in the group of companies in the
same industry in the latest term.
・ The average value and standard deviation were sought for the sales share of the 7,897 companies
analyzed, and the deviation value of each company was calculated based on this.
(i) Sales per employee (the latest term)
・ 2,307 companies for which sales per employee could not be calculated in the latest term were
excluded from the analysis.
・ The average value and standard deviation were sought for the logarithmically transformed numerical
value of sales per employee in the latest term, and the samples of 25 companies with a divergence of
2.5 times the standard deviation from the average value were regarded as outlying values and
excluded.
・ A recalculated average value and standard deviation were sought for the numerical values of the
5,565 companies remaining after the above process, and the deviation value of each company was
calculated based on this.
(j) Changes in number of employees (Number of employees <the latest term + one term before the
latest> / number of employees <one term before the latest+ two terms before>)
・ 3,259 companies for which the number of employees could not be calculated from the latest term to
two terms before were excluded from the analysis.
・ The average value and standard deviation were sought for the numerical value of changes in the
number of employees, and the samples of three companies with a divergence of 2.5 times the
standard deviation from the average value were regarded as outlying values and excluded.
・ A recalculated average value and standard deviation were sought for the numerical values of the
4,635 companies remaining after the above process, and the deviation value of each company was
calculated based on this.
(2) Integration of components of each proxy variable
A simple average of the proxy variables from which deviation values were derived was taken for
each company by component (business structure reform capability, business efficiency, technological
capability, marketing capacity, and organizational capacity), and a score is given for each component. If
two of the proxy variables for a component were not complete, then the score was excluded only in the
component.
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