Hitotsubashi University
Institute of Innovation Research

Staying Young at Heart or Wisdom of Age: Longitudinal
Analysis of Age and Performance in US and Japanese Firms

Shotaro Yamaguchi
Ryuji Nitta
Yasushi Hara
Hiroshi Shimizu
IIR Working Paper WP#18-41

2018年6月

一橋大学イノベーション研究センター
東京都国立市中2-1
http://www.iir.hit-u.ac.jp

Staying Young at Heart or Wisdom of Age: Longitudinal Analysis of Ageand Performance in US and Japanese Firms

Shotaro Yamaguchi
Graduate School of Commerce and Management
Hitotsubashi University, Japanyamashou94@gmail.com
Ryuji Nitta
Graduate School of Commerce and Management
Hitotsubashi University, Japanbd181008@g.hit-u.ac.jp
Yasushi Hara
CEAFJP/Michelin Fellow
Foundation France-Japon de l’EHESS, France
yasushi.hara@ehess.fr
Hiroshi Shimizu
Institute of Innovation Research
Hitotsubashi University, Japanshimizu@iir.hit-u.ac.jp1Abstract
How does ageing influence the profitability of firms? This study uses ananalysis of corporations listed on the NYSE and TSE between 1978 and 2015 to highlightclear differences between US and Japanese firms with respect to the relationship between
age and profitability. It shows that US firms lose efficiency, as measured by total asset
turnover as they age, whereas Japanese firms’ ability to generate profit from sales declines.
This study explores the factors underlying these national differences in the relationshipbetween ageing and profitability, focusing on rigidity of resource allocation. By
examining the technological distance, we calculate resource allocation rigidity of thefirms. The results show at all ages, Japanese firms are more rigid than their US
counterparts, although the slope is steeper in the case of US firms. These observations
suggest that Japanese firms’ operating profit margins decline as they age because they areless likely to change how they allocate resources and tend to stay in their existing businesssectors, even if they are underperforming. In other words, these findings suggest thatbeing flexible about allocation of resources mitigates the negative effect of ageing on
profitability. The results suggest that changing patterns of resource allocation retards
learning in the business domains in which a firm is already active.

2

1

Introduction

How does ageing influence the profitability of firms? Just as ageing has
negative effects on human beings, it is reasonable to assume that ageing would have anegative influence on the firms’ performance. Should this be true, it would be importantfor firms to stay young at heart; but on the other hand, just as people can learn as theymature, one might expect firms’ costs to reduce as they mature, due to learning effects at
various levels, which could be called the ‘wisdom of age’.
Directing its attention to the flexibility in resource allocation, this study
examined how the age of a firm influences its profitability, looking at all the corporations
listed on the New York stock exchange (NYSE) and Tokyo stock exchange (TSE)
between 1978 and 2015.
First, descriptive statistics are used to demonstrate the very different
relationships between age and profitability in the US and Japanese firms. The profitabilityof Japanese firms declines consistently from the age of 11 years onwards, whereas US
firms do not suffer until they are more than 100 years old. Second, an estimation strategy
described in earlier research is used to estimate how ageing influences the profitability offirms. This showed that profitability decreased as firms aged in both the US and Japan,
although there were clear national differences in the pattern of ageing effects. Japanesefirms suffered more from ageing than US firms. Return on assets (ROA) can be divided
into return on sales and total asset turnover. The return on sales statistic captures how
efficient a firm is at generating profits from its revenue while total asset turnover
represents how efficient a firm is at deploying its asset to generate revenue. The total asset
turnover of US firms declined with age, whereas the profitability (measured as operating
profit margin) of Japanese firms decreased with age.
Last, this study discusses the causes of the different ageing patterns of US andJapanese firms, in particular it explores resource allocation strategies. Rigidity of resource
allocation is measured as ‘technological distance’ which represents the extent to which afirm’s research and development (R&D) technological portfolio changes over time. Firms
that tend to stay in the same technological area for a long time have a high technologicaldistance. If the firm mobilises its target area flexibly and change R&D portfolio often, itmeans that the rigidity of resource allocation is lower.
US firms tend to withdraw from sectors where they are underperforming andreallocate resources to more promising areas of the business, which mitigates the effects
of ageing on profitability. However, the efficiency with which firms deploying their assets
to generate revenue is negatively associated with age. Japanese firms are suffering fromaging more than U.S. firms due to their relatively inflexible resource allocation. In other3

words, resources are ‘stickier’ in Japan than in the US, which allows more learning effects
in the existing businesses. On the other hands, since Japanese firms are less likely toreallocate resources and more likely to remain investing in existing businesses even if
they are underperforming, their operating profit margin decreases with age.

2

Age and Profitability

The following figure plots the relationship between a firm’s age and its
profitability. Age is measured as the age of a firm as a legal entity and profitability ismeasured as ROA. ROA is calculated from operating profits, taking into accountdepreciation, so it can be interpreted as an indicator of how efficiently a firm uses its
assets to generate profit from its main business.
Figure 1: Firm Age and Profitability
14
12
10
8
6
4
2

0
4
8
12
16
20
24
28
32
36
40
44
48
52
56
60
64
68
72
76
80
84
88
92
96
100
104
108
112
116
120

0

Source: COMPUSTAT database and DBJ Firm Financial Databank* Full-count between 1978-2015 (excluding finance, insurance and real estate sectors)
Figure 1 shows a clear difference between US and Japanese firms. It shows that
US firms retain their profitability better than Japanese firms. US firms’ profitability peaks
at the age of 105 years and decreases marginally with age, whereas Japanese firms’
profitability peaks at the age of 11 years and declines more rapidly with age thereafter.
This figure illustrates that Japanese firms suffer from aging more than US firms. This4

figure provides descriptive evidence of a difference in the pattern of ageing in US andJapanese firms; the rest of this study reports the empirical estimation of the relationshipbetween age and profitability whilst controlling for variance in other factors that would
influence ROA.

3

Why Does Profitability Decrease with Age?

Why does firms’ profitability decline as they age? This section reviews earlier
research on factors found to influence relationship between the age and profitability offirms.
Loderer and Waelchli (2010) published the first study exploring the effects ofageing on profitability. They reported that a firm’s age was negatively associated with its
profitability, expressed in terms of profit margin, ROA and Tobin’s Q (Loderer &Waelchli, 2010). They observed that older firms were less efficient than their younger
industry peers, in that they had higher costs, slower growth, older assets and were lessactive in R&D and investment. They proposed two non-exclusive explanations for thenegative effect of age on profitability: organisational rigidity and rent-seeking behaviour.
Loderer and Waelchi’s (2010) study was the first to explore the relationship between age
and profitability empirically, but their analysis did not control for possible country effects
and, as Figure 1 illustrates, there is a significant difference between US and Japanesefirms with respect to the relationship between age and profitability. Hence the aim of thisstudy was to explore how and why ageing influences US and Japanese firms differently.
One might suppose that firms’ profitability decreases with age because their (ortheir investors’) appetite for risk and uncertainty decreases. Previous literature on risk anduncertainty has suggested that the uncertainty associated with a business decreases withage, because managers and employees learn about their business over time. Therefore,
uncertainty that investors and stakeholders face decreases as the firm ages (Berger &Udell, 1995; James & Wier, 1990). This is consistent with the fact that variability in stock
returns is negatively related with a firm’s incorporation age and listing age (Cheng, 2008).
Less uncertainty on the business implies that the rate of return that the firms are supposed
to provide is declining. It is reasonable, therefore, to assume that firms’ profitability
declines with age because the uncertainty surrounding the business is also declining. This
assumption might also lead one to assume that firms take fewer risks as they get older. In
other words, we are suggesting that older firms tend to take fewer risks because there isless uncertainty surrounding their business. ROA volatility has been used as an index ofrisk-taking (Acharya, Amihud, & Litov, 2011; Faccio, Marchica, & Mura, 2011) and on
this basis Figure 2 examines whether firms are likely to take less risk as they age.
5

Figure 2: ROA Volatility measured by 3Y ROA Volatility
4.5
4
3.5
3
2.5
2
1.5
1
0.5
2
7
12
17
22
27
32
37
42
47
52
57
62
67
72
77
82
87
92
97
102
107
112
117
122
127
132
137
142
147
152
157

0
US

JPN

Source: COMPUSTAT database and DBJ Firm Financial Databank
Figure 2 illustrates that ROA volatility decreases as firms age, both in the US
and in Japan. The ROA volatility of Japanese firms decreases steadily, whereas that of US
firms does not show such steady decline. This figure might lead one to suppose thatcompared with US firms, Japanese firms show a bigger decline in appetite for risk as theyage. However, the level of ROA volatility highly depends on the absolute value of ROA.
If the absolute value of ROA is at the low level, the volatility cannot be greater. Using
ROA volatility as an index of risk-taking underestimates the risk-taking of the firms
whose ROA is relatively low and overestimates the risk-taking of firms whose ROA is
relatively high. Hence it is necessary to use the average of ROA to weight ROA volatility
as an indicator of risk-taking. We calculate the coefficient of variation in three-year ROA
volatility discounted with the three-year moving average of ROA. As Figure 3 illustrates,in the coefficient of variation in ROA is not correlated with age in Japan or the US, whichsuggests that the age-related decrease in ROA in Japanese firms is not the result of riskaverse behaviour, but the result of decreasing profitability.
Figure 3: 3Y Coefficient of Variation in ROA

6

5
4
3
2
1
0
-1
-2
-3
-4
2
7
12
17
22
27
32
37
42
47
52
57
62
67
72
77
82
87
92
97
102
107
112
117
122
127
132
137
142
147
152
157

-5
US

JPN

Source: COMPUSTAT database and DBJ Firm Financial Databank* For the visibility, the figure is marked off at ± 5.0.

Another reason why firms’ profitability decreases as they age might be that theirlevel of diversification into unrelated businesses changes. Firms’ profitability might
depend on their diversification. As the original industry matures, firms tend to diversify
its business based on the resources accumulated in the business (Conner & Prahalad,
1996; Penrose, 1980; Wernerfelt, 2006). Most previous literature on diversification has
observed that profitability tends to fall if a firm diversifies into unrelated areas (Rumelt,
1982). This raises the possibility that Japanese firms tend to conduct more unrelated orconglomerate diversification as they age, compared with US firms. Anecdotal evidence
suggests that Japanese firms tend to diversify their technological base. Japanese firms inthe digital watch industry tend to initiate parallel R&D of competing technologies at amuch earlier stage than US firms (Numagami, 1996). If Japanese firms’ technologicaldiversification is more extensive than that of US firms this might eventually diminish
their profitability. We return to this issue in later analyses in this paper and show that infact there is not much difference in the extent to which US and Japanese firms diversify,
at least in terms of technology.
Why, then, does the performance of Japanese firms decline as they age whereasthat of US firms does not? This study explores flexibility in allocation of managerial
resources. If a firm allocates managerial resources flexibly, shifting them from areas of
the business that are losing their competitive advantage to areas with better prospects it
7

should maintain its profitability as it matures. However, much of the previous literature
indicates that firms’ allocation of resources tends to become less flexible with age, aphenomenon which is referred to as organisational rigidity.
The organisational rigidity hypothesis is often based on analogy with biologicalageing, in particular human ageing. In humans ageing affects various functions, such as
hearing, eyesight, memory, fertility and thermoregulation. Two theories of ageing haverecently proposed. The damage theory attributes symptoms of ageing to the accumulationof damage. For example, cells accumulate free radical damage over time. The
programmed ageing theory posits that ageing is essentially programmed by the internal
processes such as DNA methylation.
As firms age their organisational rigidity increases, which eventually reduces
their performance. Organisational rigidity may arise because past success prompts firms
to codify their approach through organisation and processes. The resource-based view ofthe firm offers a similar explanation: in developing a competitive advantage a firm
accumulates managerial resources and optimises them for its value-chain and customers(Penrose, 1980; Peteraf, 1993). As a result, when the firm is faced with the emergence ofa new technology which threatens future competitiveness it finds it difficult to reallocate
resources to that technology (Christensen, 1997; Christensen & Bower, 1996).
Furthermore, it has previously been observed that there are national differencesin rigidity of resource allocation. Dore is one of the first scholars to have conductedinternational comparative studies and has pointed out the relative flexibility and rigidityof US firms and Japanese firms respectively. For example, Dore explored the Japaneselabour market and in-house training and employment customs in Japan - such as longterm employment - in the 1970s and 1980s, and concluded that managerial resource
allocation in Japanese firms tended to be sticky (Dore, 1973, 1986, 1988). Dore foundthat Japanese firms took a more rigid approach to hiring, firing, and wages than firms in
Anglo-Saxon countries, whose policies tended to be aligned with neo-classical labour
market economists’ assumptions. Furthermore, Dore reported that financial resource
allocation was also less flexible in Japan than in the US (Dore, 2000). Dore attributed
these differences to cultural factors, such as the Japanese emphasis on the workgroup
community, exchange of employee loyalty and employers’ paternalism. In contrast to theculturalists’ viewpoint, economists and economic historians have argued that the rigidity
Dore observed in Japanese firms was the rational outcome of the institutional settings thatdeveloped in the inter-war period (Aoki, 1988, 2001; Okazaki & Okuno, 1999). All ofthese studies observed that Japanese firms tended to exhibit greater organisational rigidity
than US firms.
8Moreover, the previous literature in the new institutional economics has
observed that institutions, which provides incentive as formal rules and informal restraints
to economic agents, have strong influenced the economic performance (Arthur, 1994;
Goldstone, 1998; North, 1990; Pierson, 2000; Williamson, 1985). It argues that the reason
why economic performances across countries has not been converging is the fact that
institutions have strategic complementarity and their changes are path dependent (David,
1989; Mowery & Rosenberg, 1998; Rosenberg, 1994). Small historical events caninfluence the locus of institutional change, although it is not necessarily deterministic.
These studies have suggested that the level of organisational rigidity must vary across thecountries because institutions shape the nature of transactions and resource allocation ina society.
Since Loderer and Waelchli (2010) did not include a country variable in theiranalysis, it does not capture how the relationship between age and profitability varies
across countries. If US firms are less vulnerable to organisational rigidity than their
Japanese counterparts as they age, this would explain the different national patterns inageing and profitability found in this study.

4Analyses

Our main analyses are divided into two parts. First, like Loderer and Waelchi(2010), this study investigates how a firm’s age is associated with its profitability,
measured by ROA, and how this relationship differs between the two countries. We thendevelop our ideas through further analyses, in which we disaggregate ROA into operating
profit margin (operating income divided by sales revenue) and asset turnover (sales
revenue divided by total assets), and then regress them as dependent variables. This
simple decomposition strategy is used to examine the differences in the associations
between age and profitability and efficiency across firms in both countries.
The second part of this paper focuses on how firms’ R&D activities change as
they age and explores how profitability is related to flexibility in allocating managerial
resources. It is natural to assume that the extent to which firms can change their primary
field of business depends on the national institutional context. This study uses
longitudinal data on the R&D portfolios of US and Japanese firms to argue that ROA
decline in Japanese firms can be attributed to rigidity in resource allocation.
4.1

Impact of ageing on ROA
The sample for our first analysis consisted of all of the US and Japanese firms
listed on the NYSE and TSE respectively for some period between 1978 and 2015. The
9

data on US firms were obtained from COMPUSTAT and the data on Japanese firms from
Corporate Financial Databank provided by the Development Bank of Japan. We
excluded firms in the finance, insurance and real estate sectors (SIC 6000-6999) from thesample. We constructed separate panels of data for the US and Japan and used them toestimate firm-fixed-effect models in order to compare the effects of age in the twocountries. The US data consisted of 22,829 firm-year observations from 958 firms andthe Japanese data consisted of 49,109 firm-year observations from 2,758 firms.
The dependent variable in our models is ROA, which is calculated as operating
profits after depreciation divided by total assets. The independent variable is Age, whichis the number of years elapsed since the legal foundation of the firm. We included in ourestimation models some control variables that represent the firms’ business characteristics,
mostly are the same as the control variables used by Loderer and Waelchi (2010). Size
represents the firms’ size, measured as the logarithm value of total assets; SG&A is selling,
general and administrative expenses and COGS is the cost of goods sold divided by sales
revenue.
Past investment may influence a firm’s profitability, which is captured both bycapital and R&D investment. Capex is the ratio of capital expenses (net of depreciation
and amortisation charges) to the book value of total assets and R&D expense is the amountof internal R&D expenditure. These two variables were entered as one-year lagged values.
Since the Corporate Financial Databank does not provide R&D data, we utilized theindividual data from the Survey of Research and Development carried out by the Japanese
Ministry of Internal affairs and Communications and obtained the firms’ R&D data. The
analysis included the growth of the firms in terms of sales and the number of employees,
Sales_growth and Emp_growth, which are the growth rates from the previous year.
Finally, market characteristics can also influence firms’ profitability
significantly. If market competition in the primary industry to which a firm belongs isfierce one would expect it to be less profitable. This study included a Market Competition
variable to account for the effect of market competition; this was measured as one minus
the

sales

concentration

ratio

in

the

2
∑𝑁𝑁
𝑖𝑖=1 (salesit ⁄market_salest ) .

firm’sprimary

industry:1-

In this equation salesit denotes the sales revenue of firm
i in year t and market_salest denotes the aggregated sales in i’s primary industry in year
t. The primary industry is based on the firm i’s two-digit SIC code.
Data on the capital expenditure, and depreciation and amortisation charges ofJapanese firms, which were used to calculate Capex, are available only from 2001, anddata on the number of employees (needed for Emp_growth) only from 2002, the models
including these variables are based on a much smaller number of observations in the case10

of Japanese firms (20,159 firm-year observations from 2,478 firms). Moreover, since
many of the firms in both the US and Japan do not conduct R&D, for example firms inthe service sector, the number of observations in the models including the R&D variable
is also lower (US: 12,313 firm-year observations from 593 firms; Japan: 7,992 firm-year
observations from 1,090 firms).
Ideally one would include industry dummies in the analyses, because industrial
heterogeneity can significantly influence firms’ profitability; however, we were unable to
do so as we estimated fixed-effect models, which cannot include firm-level variables thatare constant across the time series. Hence, like Loderer and Waelchi (2010), we used thevalues of deviation from the industrial mean so that it can discount industry influence inthe analysis. We used the two-digit SIC codes as the industrial classification variable, andthe industrial mean of all listed firms in the US and Japan, not just those listed on the
NYSE and TSE. The detailed definitions of these variables are given in Table 1.
Table 1: Definition of Variable
Variable (Unit: US/JPN)

Definition

ROA(%)

Operating income after depreciation divided by book value of
total assets.

Age

The number of years elapsed since the year of foundation ofeach firm. The foundation year is obtained from Compustat for
the U.S. firms, and Corporate Financial Databank for Japanesefirms. Since there are many missing values in the databases, we
supplement them manually by looking up the companies’
website.

Size (Thousand dollars / The size variable is the logged value of total assets. We use theMillion yen)value of deviation from the industrial mean (based on 2-digitcode) in each country in each year.

SG&A (Thousand dollars / The book value of selling, general and administrative expenses
Million yen)

We use the value of deviation from the industrial mean (basedon 2-digit code) in each country in each year.

COGS

Cost of goods sold divided by sales revenue. We use the valueof deviation from the industrial mean (based on 2-digit code) in
each country in each year.

Capex

Capex is calculated as follows: (capital expenses - depreciation
and amortization charges) / the book value of total assets. To

11account for the precedency of investment, we use one-year
lagged value.
Sales_growth (%)

The value of sales growth rate in year t is (Sales_t – Sales_t-1)/
Sales_t-1. We use the value of deviation from the industrial
mean (based on 2-digit code) in each country in each year.

Emp_growth (%)

The growth rate of the number of employee in year t is
(Employee_t – Employee_t-1)/ Employee_t-1. We use the valueof deviation from the industrial mean (based on 2-digit code) in
each country in each year.

Market Competition

The sales concentration ratio which is calculated as: 12∑𝑁𝑁
𝑖𝑖=1 (salesit ⁄market_salest ) . Salesit denotes sales revenue offirm i in year t and market_salest denotes aggregated sales in i’s
primary industry (based on 2-digit SIC code) in year t.
R&D Expense (Thousand The absolute amount of internal R&D expenses. For Japanesedollars / Million yen)firms, we use the item of ‘Internal R&D Expenditure’ in theSurvey of Research and Development. We use the value of
deviation from the industrial mean (based on 2-digit code) in
each country in each year. To account for the precedency ofinvestment, we use one-year lagged value.

Table 2 and Table 3 report descriptive statistics for the US and Japanese firms. Table 4shows estimates of ROA. This study used three different models for each country. All
models suggest that a firm’s age is negatively associated with its profitability (significant
at the 1% level), which is consistent with the results of Loderer and Waelchi (2010).
However, the coefficients depended on the model. In Model 1 the coefficient of age was-0.080 for US firms and -0.070 for Japanese firms, but in models 2 and 3 it was much
more negative for the Japanese firms (Model 2: -0.054 for US firms and -0.089 forJapanese firms; Model 3: -0.028 for US firms and -0.084 for Japanese firms). Many of
the firms listed in NYSE and TSE can be considered well-established, so the negative
impact on ROA is lower than in Loderer and Waelchi’s study, as their sample consistedof all listed firms with data on CRSP, COMPUSTAT and COMPUSTAT Industry Segment
between 1978 and 2004 (see Table 4 in Loderer and Waelchi, 2010). However, in ourmodels (Model 2 and 3), Japanese firms listed on the TSE suffered a greater decline inprofitability as they aged than the US firms listed on the NYSE; the impact of ageing on
Japanese firms was closer to that observed in Loderer and Waelchi’s study. These results
indicate that overall age has a negative influence on profitability but the magnitude of the12effect varies with country.

13

Table 2: Descriptive Statistics and Correlation Matrix (US)
Variable

Obs.

Mean

Std. Dev.

Min

Max

1

2

3

4

5

6

7

8

9

1. ROA

22,82910.317

10.187-275.680

187.8101

2. Age22,82961.67343.1390271.000

0.029

1

3. Size

22,829

7.398

1.863

-0.091

13.590-0.0370.266

1

4. SG&A

22,8291217.374

3661.838-283.096915.0

0.025

0.139

0.553

1

5. COGS

22,829

0.640

0.279

0

26.562-0.2540.043-0.052-0.1051

6. Capex

22,634

0.021

0.059

-0.818

1.276

0.247

-0.086-0.070-0.0240.056

1

7. Sales_growth22,82916.581370.670-1069.886

54241.664

0.08

-0.10

-0.057-0.030-0.0170.167

1

8. Emp_growth

21,56823.2101856.397-100.02.70e+05

0.026-0.028-0.007-0.007-0.007

0.019

0.060

1

9. Market

22,829

0.864

0.130

0

0.987

-0.018

0.128

0.149

-0.001-0.091-0.178-0.012-0.001

1

12,831

0.030

0.061

0

3.958

-0.084-0.047

0.043

0.102

0.092

-0.042

0.029

0.007

0.142

10

Competition
10. R&D
expense14

1

Table 3: Descriptive Statistics and Correlation Matrix (JPN)
Variable

Obs.

Mean

Std. Dev.

Min

Max

1

2

3

4

1. ROA

49,109

4.610

5.431

-361.105125.5141

2. Age49,10953.87820.1781.000

170.000-0.142

1

3. Size

49,10918.0881.450

12.83024.207

0.111

0.195

1

4. SG&A

49,1094.31e+01.40e+08

33000.000

3.27e+09

0.036

0.088

0.567

1

5

6

7

8

9

10

7

5. COGS

49,109

0.761

0.143

0.000

5.585

-0.4470.132-0.004-0.1061

6. Capex

26,357-0.0680.051-0.5970.458-0.093-0.044-0.254-0.192-0.021

1

7. Sales_growth49,109-14.9121748.737-9644.885

1.70e+05

0.249-0.011

0.045-0.005-0.048-0.0341

8. Emp_growth

20,1991413.71

6.13e+06-9.74e+06

8.66e+08

0.016

0.025

0.023

0.004

-0.011-0.020

0.019

1

7

9. Market

49,109

0.896

0.105

-0.0000.981-0.014-0.057-0.102-0.142

0.036

0.048

0.000

-0.015

1

15,272

0.003

0.004

0.000

0.150

0.046

-0.066-0.0440.017-0.322-0.035-0.061-0.0570.037

Competition
10. R&D
expense15

1

Table 4: Estimation Result (Dependent Variable: ROA)VARIABLES

Model1Age-0.080***(0.009)-0.148*(0.087)-0.000***(0.000)-0.034***(0.006)

Size
SG&A
COGS

Observations
R-squared
Number of firms-0.070***(0.002)0.281***(0.049)-0.000***(0.000)-47.279***(0.339)-0.875(0.853)16.236***(0.622)14.033***(0.657)

22,8290.012
958

20,6550.020
941

11,8950.030
595-0.000(0.000)R&D_expense
Constant

Model1-0.028***(0.010)-0.592***(0.098)-0.000**(0.000)-0.007(0.005)18.298***(1.698)0.000(0.000)0.001***(0.000)0.310(1.085)-0.007(0.006)13.767***(0.846)

Emp_growthMarket_Competition

Model3-0.054***(0.009)-0.216**(0.091)-0.000***(0.000)-0.024***(0.006)16.341***(1.210)-0.000(0.000)0.000(0.000)-0.285(0.878)

Capex
Sales_growth

US
Model2Japan
Model2Model3-3.683***(0.411)-0.089***(0.009)1.737***(0.133)-0.000***(0.000)-59.035***(0.495)7.894***(0.930)0.000***(0.000)0.000(0.000)-0.699(1.648)11.723***(0.378)11.149***(1.653)-0.084***(0.011)
0.957***(0.223)-0.000***(0.000)-59.721***(1.034)14.231***(1.440)0.001***(0.000)-0.000(0.000)-4.801**(2.363)-85.252***(23.167)
15.179***(2.348)49,1090.341
2,758

19,805
0.469
2,448

7,873
0.419
1,062

0.000***(0.000)Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1
Assuming that the negative effect of age on a firm’s profitability does indeed
vary by country, why is this? We hypothesise that it is due to the mechanism by which
age influences profitability. To explore this idea we decomposed ROA into profit margin(operating profits after depreciation divided by sales revenue) and asset turnover (sales
revenue divided by total assets) and then regressed these variables on the same variables
16

as those in the models in Table 4. Table 5 and Table 6 report the results of these analyses.
It can be seen in Table 5 that although age was not correlated with profit margin in US
firms, there was a negative correlation (significant at the 1% level) between age and profit
margin for the Japanese firms, in all models. The opposite pattern of results was observed
when asset turnover was the dependent variable (see Table 6). In the case of the US firm
all models suggested that age had a negative influence on asset turnover, but the results
for the Japanese firms were inconsistent. In the case of the Japanese firms the coefficientof age was negative in Model 1, positive in Model 2 and non-significant in Model 3.

17

Table 5: Estimation Result (Dependent Variable: Profit Margin)
US
Model2Model3Model10.024(0.023)
1.572***(0.223)-0.000**(0.000)-0.037**(0.015)13.504***(2.961)0.000(0.000)0.000(0.000)0.006(0.011)1.202***(0.113)-0.000**(0.000)-0.009(0.006)4.449**(1.944)-0.000(0.000)0.001***(0.000)-0.068***(0.003)1.440***(0.065)-0.000***(0.000)-68.860***(0.446)-9.773***(3.626)-9.797***(2.147)14.852***(2.645)13.600***(1.606)-0.837(1.242)0.002(0.007)7.858***(0.969)22,8290.004
958

20,6550.006
941

11,8950.017
595

VARIABLES

Model1Age-0.024(0.039)2.573***(0.369)-0.000**(0.000)-0.083***(0.027)

Size
SG&A
COGS
Capex
Sales_growth

0.000(0.000)

Emp_growthMarket_Competition
R&D_expense
Constant

Observations
R-squared
Number of firms

Japan
Model2Model3-0.103***(0.010)2.773***(0.150)-0.000***(0.000)-92.383***(0.558)10.288***(1.050)0.000***(0.000)0.000(0.000)-0.109***(0.012)2.105***(0.245)-0.000***(0.000)-84.624***(1.136)18.492***(1.581)0.001***(0.000)-0.000(0.000)-1.382**(0.541)2.140(1.860)9.618***(0.497)9.714***(1.866)-2.394(2.595)-209.435***(25.440)
15.231***(2.578)49,1090.365
2,758

19,805
0.625
2,448

7,873
0.532
1,062

0.000***(0.000)Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

18

Table 6: Estimation Result (Dependent Variable: Asset Turnover)US
Model2Model3Model1-0.006***(0.000)-0.126***(0.004)0.000(0.000)-0.001**(0.000)-0.126**(0.056)-0.000(0.000)-0.000(0.000)-0.005***(0.000)-0.166***(0.005)0.000**(0.000)-0.000*(0.000)-0.204**(0.087)0.000(0.000)-0.000(0.000)-0.003***(0.000)-0.181***(0.003)0.000***(0.000)0.459***(0.024)0.297***(0.040)0.320***(0.041)

Constant

1.515***(0.029)1.541***(0.031)

0.048(0.056)-0.001***(0.000)1.911***(0.043)Observations
R-squared
Number of firms

22,8290.101
958

20,6550.109
941

11,8950.191
595

VARIABLES

Model1Age-0.006***(0.000)-0.119***(0.004)-0.000(0.000)-0.001***(0.000)

Size
SG&A
COGS
Capex
Sales_growth-0.000(0.000)

Emp_growthMarket_Competition
R&D_expenseJapan
Model2Model30.005***(0.000)-0.265***(0.007)0.000***(0.000)0.180***(0.026)
0.278***(0.050)0.000***(0.000)0.000(0.000)0.001(0.001)-0.241***(0.010)0.000***(0.000)-0.100**(0.048)0.167**(0.067)0.000***(0.000)-0.000(0.000)-0.183***(0.029)-0.588***(0.088)1.449***(0.026)1.432***(0.088)-0.202*(0.110)-11.958***(1.083)
1.219***(0.110)49,1090.119
2,758

19,805
0.093
2,448

7,873
0.138
1,062

0.000***(0.000)Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1
To sum up, all models in Table 4 supports the claim that ageing is negatively
associated with profitability in both US and Japanese firms, although the magnitude of
the impact of age varies. Japanese firms seem to be more strongly influenced by age thanUS firms. Furthermore, the way in which age influences profitability also differs between
19the two countries. In Japanese firms ageing is associated with a decline in the efficiency
with which profits are generated from sales revenue, whereas in the US a firm’s age has
more impact on the efficiency with which assets are deployed to generate revenue.
What reasons are there for these differences? National differences ininstitutional behaviour, especially with respect to the flexibility of resource allocation, are
one possible explanation, as noted in Section 2. Because allocation of managerial
resources is relative flexible in the US, it is easier for the firms to shift resources quickly
from unprofitable business fields to more promising ones. Of course, the swift shift might
incur some costs: for example, a firm may need to rewrite its business plan, learn about a
new business environment and develop new skills when it enters a new area. Thus, US
firms’ flexible resource allocation may make them less efficient at using their assets
although it protects them from loss of efficiency in generating profits from revenue.
In contrast resource allocation in Japan is relatively inflexible. This inflexibility
may help companies to generate sales through efficient use of their assets, as they operate
in areas where they have established competence. But as a sector matures pricecompetition becomes fierce as so companies may become less efficient in the sense that
their business becomes less profitable. Our results on Japanese firms, showing that ageing
is more negatively associated with Profit Margin than with Asset Turnover, may reflect
such a process. The next step in our analysis was an exploration of flexibility of resource
allocation through examination of longitudinal data on R&D rigidity.
4.2

Investment diversity and rigidity in R&D
To estimate the rigidity of firms’ resource allocation this study investigated thedependence of R&D activities on past investment. To look at temporal changes inresource reallocation we calculated technological distance between firms’ technologicalportfolios in the recent and more distant past. As discussed earlier, profitability could also
be related to diversification; to examine this hypothesis we estimated diversification in
R&D and compared figure for the US and Japan. We used patent data to estimate firms’
R&D investment activities. Data on patents given by the United States Patent andTrademark Office (USPTO) patents were obtained from PatentsView and the Derwent
Innovations Index and data on patents given by Japan Patent Office (JPO) patents fromthe IIP Patent Database. The observation period was from 1956 to 2017 for US firms
listed on NYSE and from 1964 to 2012 for Japanese firms listed on the TSE.
One obstacle to be overcome in collecting data on patents acquired byconsolidated firms is that information about consolidation is required. In most cases
patent applications only give the name of the individual firm concerned, so information
20

about firm ownership must be obtained from other sources in order to constructconsolidated-level information about patents. As ownership structures can change overtime, for example through mergers and acquisitions (M&A), time-series information
about ownership was needed for this study.
Moody’s / Mergent Industrial Manual and the NISTEP firms’ name dictionary
were used to compile information about consolidation. Moody’s / Mergent Industrial
Manual gives the names of the subsidiaries owned by each firm, which we used to searchfor patents. Since the descriptions on individual firm information provided in Moody’s /Mergent Industrial Manual are not necessarily fully standardised, we manually looked up
the subsidiaries and collected lists of names according to standardised rules 1 . We
collected information on subsidiaries from Moody’s / Mergent Industrial Manual with
five-year time window. For example, if the 2000 Mergent Industrial Manual reported thatparent firm A owns subsidiaries B and C, we recorded patents obtained by firms A, B andC between 1996 and 2000 as A’s consolidated patents.
Consolidation information about Japanese firms was obtained from the NISTEP
firms’ name dictionary. This database includes consolidated firms’ names, changes ofname, and historical information about M&A and their timing, which we used to constructtime-series data on consolidation.
Before examining R&D rigidity, we compared the R&D diversification of US
and Japanese firms. Diversification may influence a firm’s profitability and the rigidityof its R&D activities, as discussed in Section 2. We estimated technologicaldiversification by calculating the patent-HHI of each firm. Patent-HHI of firm i is2
calculated as ∑𝑁𝑁
𝑗𝑗=1 (NPijt ⁄NPit ) , where NPit denotes the total number of patents obtainedby firm i in year t, and NPijt denotes the number of patents obtained by firm i in field j inyear t. Technological field data were based on the four-digit International PatentClassification (IPC) subclasses.
We used technological distance as an indicator of R&D rigidity. The
technological distance construct, proposed by Jaffe (1986), was originally intended as ameasure of the degree of similarity between technological investment portfolios (which
Jaffe called ‘technological positions’) of two different firms. Thus, we estimated R&D
rigidity by calculating the similarity between a firm’s current and previous technologicalinvestment portfolio. The more similar a firm’s current and previous portfolios, the morerigid its R&D resource allocation. Technological distance was calculated as follows.
1

We collect the names of all firms listed as ‘subsidiaries’, ‘affiliations’ ‘affiliated companies’ or‘consolidated companies’ excluding firms as follows. 1. Firms listed as ‘unconsolidated’ or with
notes specifying that they were unconsolidated. 2. Firms where less than 51% of the stock was heldby the parent firm. 3. Firms owned by a parent firm’s subsidiary (i.e. subsidiaries of a subsidiary).

21

The vector for the technological position of firm i in year t is written as（1） 𝐹𝐹𝑖𝑖𝑖𝑖 = �

𝑁𝑁𝑁𝑁𝑖𝑖𝑖𝑖𝑖𝑖
𝑁𝑁𝑁𝑁𝑖𝑖1𝑡𝑡 𝑁𝑁𝑁𝑁𝑖𝑖2𝑡𝑡
,
,
⋯
,
�
𝑁𝑁𝑁𝑁𝑖𝑖𝑖𝑖 𝑁𝑁𝑁𝑁𝑖𝑖𝑖𝑖
𝑁𝑁𝑁𝑁𝑖𝑖𝑖𝑖

where Fit is 1 ☓ j vector, NPit denotes the number of patents obtained by firm i in year t
and NPijt is the number of patents obtained by firm i in field j in year t. The technologicaldistance (Pit) between firm i’s technological position in year t (Fit) and year t-1 (Fit-1) is
obtained from the following equation:
（2）

′

′

′

1/2

𝑃𝑃𝑖𝑖𝑖𝑖 = 𝐹𝐹𝑖𝑖𝑖𝑖 ∙ 𝐹𝐹𝑖𝑖𝑖𝑖−1 ���𝐹𝐹𝑖𝑖𝑖𝑖 ∙ 𝐹𝐹𝑖𝑖𝑖𝑖 � �𝐹𝐹𝑖𝑖𝑖𝑖−1 ∙ 𝐹𝐹𝑖𝑖𝑖𝑖−1 ��Technological distance assumes a value between 0 and 1, and it is unity if the two vectors
are identical, which implies that the firm did not changes its investment profile at all
between t-1 and t, and zero if the two vectors are orthogonal, which implies that the firmchanged its technological position completely.
We carried out sampling with the following procedure in order to capture theconsolidated-level patents as precisely as possible. First, we limited our samples to firms
belonging to R&D-intensive sectors such as chemicals, metal products, machinery,devices and so on 2. Second, in the case of US firms we considered only firms that wereover a hundred years old in 2015. The primary reason for focusing on these very old firms
was that we aimed to observe those which were mostly likely to be rigid in their resource
allocation. If ageing is associated with organisational rigidity, as Loderer and Waelchi(2010) argued, one would expect very old firms to exhibit much more rigid resource
allocation than young firms. If, however, we found that Japanese firms’ resource
allocation was more rigid than that of even very old US firms, our hypothesis of therigidity in resource allocation would be more persuasive. In other words, limiting oursampling of US firms to very old firms were a deliberately conservative analytical strategy.
Another reason is that in order to investigate the technological diversificationand switching of US firms at a consolidated level, we had to look up their subsidiaries
manually in Moody’s / Mergent Industrial Manual and work out the exact history of theirownership structure. This task compelled us to reduce the US sample size significantly.
In the end, our US sample for this analysis consisted of 112 very old firms, including
General Electric, 3M, Ford, and Du Pont, and the Japanese sample of 586 firms in R&Dintensive sectors.
Figure 4 shows the sample means of Patent-HHI by year, and Figure 5 shows2

US firms with the following codes: US SIC 2600-2699; 2800-2899; 3300-3899; and 9997
(Conglomerate) and Japanese firms with the following codes: Japanese SIC 1100-1199; 1400-1499;
1600-1699; 2200-3199.

22them by age. We took averages of the last three years (including the focal year) to smooththe lines. It can be seen from Figure 4 that although the Japanese firms had higher values,
implying a greater concentration of technological investment, in some years, overall theextent of technological diversification in the two countries was very similar. The US andJapanese age cohorts are so different that comparison was difficult, but Figure 5 showsthat the US and Japanese firms in the 60- to 90-year-old cohort had similar levels oftechnological diversification. Interestingly, in the case of US firms Patent-HHI increases
from the age of 100 years, whereas in the case of Japanese firms it continues to decline.
This implies that US firms that have been in existence for over 100 years are more likely
to concentrate their R&D resources in certain fields, whereas even very old Japanesefirms consistently continue to diversify their R&D resources. The important finding is,however, around the age of 70, which is the cohort with the highest density in the
population of Japanese firms as illustrated by Figure 6, US and Japanese firms have avery similar level of diversification. As there are few firms around the age of 100, they
do not have much effect on the overall effect of age on profitability. In contrast to the
anecdotal evidence (Numagami, 1996), these results show that it is difficult to explain the
difference between US and Japanese firms’ profitability in terms of technologicaldiversification.

23

Figure 4: Technological Diversification by Year (3-year average)
0.5
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
1958
1960
1962
1964
1966
1968
1970
1972
1974
1976
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
2010
2012
2014
2016

0

US

JPN

Source: PatentsView and Derwent Innovations Index for USPTO patents, and IIP Patent
Database for JPO patents.
Figure 5: Technological Diversification by Age (3-year average)
0.6
0.5
0.4
0.3
0.2
0.1

3
10
17
24
31
38
45
52
59
66
73
80
87
94
101
108
115
122
129
136
143
150
157
164
171
178
185
192
199
206

0
US

JPN

Source: PatentsView and Derwent Innovations Index for USPTO patents, and IIP Patent
Database for JPO patents.

24

Figure 6: Cohort of the Firms in the U.S. and Japan
165
154
143
132
121
110
99
88
77
66
55
44
33
22
11
0

0

200

400

600

800

US

1000

1200

1400

JPN

Source: COMPUSTAT database and DBJ Firm Financial Databank* Full count during the periods between 1978-2015
We now move on to the analysis of R&D rigidity, which was measured astechnological distance. We calculated three technological distance variables for each firm:
the distances between technological positions in year t and years t-1, t-3, t-5. Figures 7,8, and 9 show the sample means of technological distance by year. In these figures all thedata for both countries show a pattern of gradual increase until the 1990s, which implies
that the firms became gradually less flexible in resource allocation as they expand, so thateventually their technological distance between different periods became similar. Afterthe 1990s, however, the courses taken by US and Japanese firms begin to diverge. In
Japanese firms, technological distance from the prior period continues to increase,
whereas in US firms, it starts to level off; this divergence becomes more marked in the
2000s.

25

Figure 7: R&D Rigidity by Year (t-1, 3-year average)
0.9
0.8
0.7
0.6
0.5
0.4

US

2015

2013

2011

2009

2007

2005

2003

2001

1999

1997

1995

1993

1991

1989

1987

1985

1983

1981

1979

1977

1975

1973

1971

1969

1967

1965

1963

1961

1959

0.3

JPN

Source: PatentsView and Derwent Innovations Index for USPTO patents, and IIP Patent
Database for JPO patents.
Figure 8: R&D Rigidity by Year (t-3, 3-year average)
0.9
0.8
0.7
0.6
0.5
0.4

US

2015

2013

2011

2009

2007

2005

2003

2001

1999

1997

1995

1993

1991

1989

1987

1985

1983

1981

1979

1977

1975

1973

1971

1969

1967

1965

1963

1961

0.3

JPN

Source: PatentsView and Derwent Innovations Index for USPTO patents, and IIP Patent
Database for JPO patents.

26

Figure 9: R&D Rigidity by Year (t-5, 3-year average)
0.8
0.75
0.7
0.65
0.6
0.55
0.5
0.45
0.4
0.35

US

2015

2013

2011

2009

2007

2005

2003

2001

1999

1997

1995

1993

1991

1989

1987

1985

1983

1981

1979

1977

1975

1973

1971

1969

1967

1965

1963

0.3

JPN

Source: PatentsView and Derwent Innovations Index for USPTO patents, and IIP Patent
Database for JPO patents.
Figures 10, 11 and 12 show the sample means of technological distance for US
and Japanese firms. These figures clearly show that as firms get older, their technologicalpositions change less rapidly, in other words their way to allocate resources becomes morerigid. The figures also illustrate that Japanese firms tend to change their technologicalposition more during a given period than US firms of a similar age. The discrepancy isgreatest when comparing technological distances between t and t-5 in Figure 12. The
distances of Japanese firms that are around 30 years old are similar to those of US firms
around the age of 90 or 100 years, and the distances of 110-year-old Japanese firms are
similar to those of 170-year-old US firms. There is a lag of 60-70 years between the twocountries. This analysis remains to be time-series observation, in which the causal
relationship between the rigidity in resource allocation and the profitability is not
specified, but it does confirm that age is associated with rigidity of resource allocation
across a variety of institutional contexts.

27

Figure 10: R&D Rigidity by Age (t-1, 3-year average)
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
2
9
16
23
30
37
44
51
58
65
72
79
86
93
100
107
114
121
128
135
142
149
156
163
170
177
184
191
198
205

0
US

JPN

Source: PatentsView and Derwent Innovations Index for USPTO patents, and IIP Patent
Database for JPO patents.
Figure 11: R&D Rigidity by Age (t-3, 3-year average)
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
4
11
18
25
32
39
46
53
60
67
74
81
88
95
102
109
116
123
130
137
144
151
158
165
172
179
186
193
200
207

0
US

JPN

Source: PatentsView and Derwent Innovations Index for USPTO patents, and IIP Patent
Database for JPO patents.
28

Figure 12: R&D Rigidity by Age (t-5, 3-year average)
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
1
8
15
22
29
36
43
50
57
64
71
78
85
92
99
106
113
120
127
134
141
148
155
162
169
176
183
190
197
204

0
US

JPN

Source: PatentsView and Derwent Innovations Index for USPTO patents, and IIP Patent
Database for JPO patents.

5

Conclusion

This study uses an analysis of corporations listed on the NYSE and TSE
between 1978 and 2015 to highlight clear differences between US and Japanese firms
with respect to the relationship between age and profitability. It has shown that US firms
are less affected by age than Japanese firms. The empirical analysis showed that operating
profit margins decline more sharply with age in Japanese firms, although both US andJapanese firms suffer negative ageing effects. On the other hand, US firms show a decline
in the efficiency with which assets are deployed to generate revenue, which does not occur
consistently in Japanese firms. In short, the results show that US firms lose efficiency, as
measured by total asset turnover as they age, whereas Japanese firms’ ability to generate
profit from sales declines.
This study explored the factors underlying these national differences in therelationship between ageing and profitability, focusing on rigidity of resource allocation.
The distance between firms’ technological positions at two time points (technologicaldistance) was as a proxy for rigidity of resource allocation. Technological distancecaptures the extent to which a firm changed its allocation of R&D resources during therelevant period.
29

The results of our analysis showed a clear difference in the technologicaldistance and the ageing. At all ages, Japanese firms are more rigid than their US
counterparts, although the slope is steeper in the case of US firms. One hundred-year-old
US firms have a rigidity equivalent to that of 30-year-old Japanese firms.
These observations suggest that Japanese firms’ operating profit margins
decline as they age because they are less likely to change how they allocate resources andtend to stay in their existing business sectors, even if they are underperforming. In otherwords, these findings suggest that being flexible about allocation of resources mitigates
the negative effect of ageing on profitability. More concretely, it implies that firms whichhave a flexible approach to allocation of human and financial resources find it easier towithdraw from unprofitable sectors and acquire personnel for prospective businesses. The
other side of this coin is that changing patterns of resource allocation retards learning in
the business domains in which a firm is already active.
As previous research has noted, institutions such as worker protections,
property rights, the financial market and government funding for R&D differ greatly
between countries. One would expect these variables to influence the flexibility of firms’
resource allocation strategies. Our findings confirm that this flexibility differs between
US and Japanese firms and that this difference may influence the way in which firms age
in the two countries. Our findings imply a trade-off between young at heart and wisdomof age exists and it is intervened by the flexibility in resource allocation.
It must be noted that the findings of this study relate to associations between
age and performance rather than causal relationships. The precise causal mechanisms bywhich age influences a firm’s performance remain to be elucidated.

30

6References

Acharya, V. V., Amihud, Y., & Litov, L. 2011. Creditor Rights and Corporate Risk-Taking. Journal of
Financial Economics, 102(1): 150-166.
Aoki, M. 1988. Information, Incentives, and Bargaining in the Japanese Economy. Cambridge; New
York: Cambridge University Press.
Aoki, M. 2001. Toward a Comparative Institutional Analysis. Cambridge, Massachusetts: MIT Press.
Arthur, W. B. 1994. Increasing Returns and Path Dependence in the Economy. Ann Arbor: the Universityof Michican Press.
Berger, A. N., & Udell, G. F. 1995. Relationship Lending and Lines of Credit in Small Firm Finance.
Journal of Business, 68(3): 351-381.
Cheng, S. 2008. Board Size and the Variability of Corporate Performance. Journal of Finaicial Economics,
87(1): 157-176.
Christensen, C. M. 1997. The Innovator's Dilemma: When New Technologies Cause Great Firms to Fail.
Boston, Mass: Harvard Business School Press.
Christensen, C. M., & Bower, J. L. 1996. Customer Power, Strategic Investment, and the Failure of Leading
Firms. Strategic Management Journal, 17(3): 197-218.
Conner, K. R., & Prahalad, C. K. 1996. A Resource-based Theory of hte Firm: Knowledge Versus
Opportunism. Organization Science, 7(5): 477-501.
David, P. 1989. Path Dependence and Predictability in Dynamic Systems with Local Network Externalities:
A Paradigm for Historical Economics. High Technology Impact Program Working Paper, March.
Dore, R. P. 1973. British Factory, Japanese Factory; the Origins of National Diversity in Industrial
Relations. London: Allen & Unwin.
Dore, R. P. 1986. Flexible Rigidities: Industrial Policy and Structural Adjustment in the Japanese
Economy, 1970-80. Stanford, California: Stanford University Press.
Dore, R. P. 1988. Rigidities in the Labour Market. Government and Opposition, 23(4): 395-412.
Dore, R. P. 2000. Stock Market Capitalism: Welfare Capitalism : Japan and Germany Versus the AngloSaxons. Oxford, UK; New York: Oxford University Press.
Faccio, M., Marchica, M.-T., & Mura, R. 2011. Large Shareholder Diversification and Corporate RiskTaking. Review of Financial Studies, 24(11): 3601-3641.
Goldstone, J. A. 1998. Initial Conditions, General Laws, Path Dependence and Explanation in Historical
Sociology. American Journal of Sociology, 104(3): 829-845.
Jaffe, A. B. 1986. Technological Opportunity and Spillovers of R&D: Evidence from FIrms' Patents, Profits,
and Market Value. The American Economic Review, 76(5): 984-1001.
James, C., & Wier, P. 1990. Borrowing Relationships, Intermediation, and the Cost of Issuging Public
Securities. Journal of Finaicial Economics, 28(1-2): 149-171.

31

Loderer, C., & Waelchli, U. 2010. Firm Age and Performance. MPRA Paper, 26450.
Mowery, D. C., & Rosenberg, N. 1998. Paths of Innovation: Technological Change in 20th-Century
America. Cambridge; New York: Cambridge University Press.
North, D. C. 1990. Institutions, Institutional Change, and Economic Performance. Cambridge ; New
York: Cambridge University Press.
Numagami, T. 1996. Flexibility Trap: A Case

Analysis of U.S. and Japanese Technological Choice in the

Digital Watch Industry. Research Policy, 25(1): 133-162.
Okazaki, T., & Okuno, M. 1999. The Japanese Economic System and its Historical Origins. Oxford; New
York: Oxford University Press.
Penrose, E. T. 1980. The Theory of the Growth of the Firm. White Plains, N.Y.: M. E. Sharpe.
Peteraf, M. A. 1993. The Cornerstones of Competitive Advantage: A Resource-Based View. Strategic
Management Journal, 14(3): 179-191.
Pierson, P. 2000. Increasing Returns, Path Dependence, and the Study of Politics. The American Political
Science Review, 94(2): 251-267.
Rosenberg, N. 1994. Path-Dependent Aspects of Technological Change. In N. Rosenberg (Ed.), Exploring
the Black Box: Technology, Economics, and History: 9-23. Cambridge: Cambridge University
Press.
Rumelt, R. P. 1982. Diversification Strategy and Profitability. Strategic Management Journal, 3(4): 359369.
Wernerfelt, B. 2006. A Resource-Based View of the Firm. Strategic Management Journal, 5(2): 171-180.
Williamson, O. E. 1985. The economic institutions of capitalism : firms, markets, relational contracting.
New York: Free Press ; London : Collier Macmillan.

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