Resource allocation and growth strategies in a multi-plant firm:
Kanegafuchi Spinners in the early 20th century
Shotaro Yamaguchi
University of Maryland, Robert H. Smith School of Businessshotaro@umd.edu
Serguey Braguinsky
University of Maryland, Robert H. Smith School of Business, NBER, and Osaka University ISER
sbrag@umd.edu
Tetsuji Okazaki
University of Tokyo, Faculty of Economics
okazaki@e.u-tokyo.ac.jp
Takenobu Yuki
Tohoku University, Faculty of Economics
takenobu.yuki.c1@tohoku.ac.jp
Abstract
Using detailed plant- and individual-level data from a major Japanese cotton spinning company
in the early 20th century, we examine the within-firm allocation of skilled human capital in conjunction
with investment in physical capital, accompanying the firm’s evolving strategic priorities. We showthat the firm leveraged unit-level two-way complementarity between managerial talent and strategicallyimportant plants when the task was achieving large-scale output and positioning for a competitive costadvantage. The task of conducting product differentiation, however, ushered in “three-way
complementarity,” where educated engineering human capital and capable managers needed to bebundled with specialized physical capital. A deeper dive into the “nano-economics” of resource
allocation reveals that educated engineers experiencing product differentiation in pioneering plants
were reallocated to other plants also pursuing product differentiation.
Keywords: resource allocation, firm strategy, multi-plant firms, managerial human capital, engineering humancapitalJune 2022

1. Introduction
How do firms allocate human capital resources, especially skilled human capital, across establishments
to accomplish the desired positioning of the firm, and how do firms create such resource alignment?
Internal allocation and reallocation of skilled human capital is a key issue in strategic management
because its supply is generally limited and cannot be used for multiple uses at the same time due to its
non-scale free nature (Levinthal and Wu, 2010; Wu, 2013). However, few studies have examined how(multiple) competitive and growth strategies translate into strategic (re-)allocation of skilled humancapital within firms (Maritan & Lee, 2017). Partly this is due to a lack of suitable data, making it difficult
to observe (i) individual workers’ internal transfers across establishments and (ii) the heterogeneity inthose workers in terms of educational backgrounds, skills and expertise, and prior work experience.
In this paper, we take a deep dive into within-firm human capital (re-)allocation in conjunction
with investment in physical capital, as it was linked to the firm’s evolving strategic priorities by
examining about two decades of history of Kanegafuchi Spinners (hereafter, Kanebo, after its Japaneseacronym) at the beginning of the 20th century. Kanebo was one of the early private entrants into
Japan’s cotton spinning industry and became one of the “center of gravity” firms by the late-1900s(Agarwal et al., 2020).1 Over the period covered by our data, Kanebo had grown from a single-plant,
standard-product firm to a 16-establishment company with a highly diversified product portfolio,
while shifting its strategic focus from cost advantage based on simple, basic yarns to productdifferentiation involving high-quality, processed yarns and fabrics.2 Importantly, the firm implemented
product differentiation only in select plants and allocated different kinds of human capital accordingly.
We leverage rich and unique archival plant-level data to answer the following research questions in

1 The phenomenal growth of the Japanese cotton spinning industry from the late 19th to the early 20th century has attracted

much attention in economics and management literature. See, for instance, Saxonhouse, 1974; Braguinsky et al., 2015;
Braguinsky & Hounshell, 2016; Agarwal et al., 2020; Braguinsky et al., 2021.
2 Kanebo also diversified into silk spinning, but that part is a separate industry and is outside the scope of our analysis.

1this paper. How does a firm strategically build and (re-)allocate the stock of human capital at different
levels and roles across establishments? How do plant-level competitive and growth strategies map intosuch human capital allocations, and how complementary are human capital and different kinds ofphysical capital? What are the dynamics of human capital allocation and reallocation as a firm initiates
product differentiation in a growing number of plants?
The single-firm case of Kanebo is ideal for addressing these questions for the following
reasons. First, our motivation lies in a firm’s endogenous resource allocation decisions and how such
resource allocation evolves in the process of growth from a small startup to a leading firm in a majorindustry. In our context, we can look at Kanebo’s different unit-level strategies (e.g., cost advantage v.
product differentiation) implemented in different plants at different points in time and accompanying
resource allocation strategies within a single firm so that it controls for unobserved initial conditions
along with firm- and industry-specific characteristics. Second, we believe that unpacking endogenous
resource allocation processes requires embracing a firm’s underlying decision-making as well as
various complementary activities surrounding those decisions, including resource acquisition fromexternal capital and labor markets, product market positioning, learning and procurement of
production technologies, human capital investment, and so on (Maritan and Lee, 2017). Such in-depth
analysis is often challenging in quantitative studies based on a large number of firms.
Third, as described in detail in Section 3 and the online appendix, Kanebo’s archival records
and complementary external sources are exceptionally rich and help us answer the questions above.
The data include plant-level appointments of plant managers, engineers at various levels, and skilled
blue-collar workers trained at its internal vocational school, complemented by university and college
alumni registries and a comprehensive cotton-spinning industry database for their educational
backgrounds and industry careers. We also have detailed plant-level information on the types of
products, inputs and outputs, scale and type of production machines, machine orders, worker turnover,2and plant-level balance sheets and income statements. This unique level of detail allows us to examine
how high-level human capital (managers, engineers, and skilled workers) were (re-)allocated acrossplants that had different capital capacity, had recently been acquired or newly established, and targeted
different types of differentiated products (low-end yarns, high-end yarns, fabrics).
Our in-depth single-firm study provides several key insights related to internal human capital
allocation. First, we find unit-level two-way complementarity between physical capital and managerial
capital in the firm’s positioning for a competitive cost advantage. The best managerial resources
needed to be matched with the largest physical capital to maximize cost efficiency gains, consideredas the within-firm implementation of positive assortative matching between ability and physical capital(Becker, 1973; Agarwal & Ohyama, 2013). Second, high-quality managerial talent was also allocatedto newly acquired plants to resolve the integration challenge and increase productivity (Capron, 1999;
Helfat and Raubitschek, 2000; Mitchell and Shaver, 2003; Zollo and Singh, 2004; Capron and Mitchell,
2009; 2012). Third, product differentiation required matching relevant physical capital (machinesdesigned to produce differentiated products) with managerial and engineering human capital bundled
together. That is, we find a three-way complementarity between managers, engineers, and physicalcapital aimed at differentiated products (Rubens, 2022). Finally, we find cross-plant spillovers in
product differentiation that took place through the reallocation of managers and engineers frompioneering plants where they experienced the production of differentiated products to other plantsthat were newly tasked with product differentiation.
Our paper contributes to the literature on intra-firm resource allocation and reallocation byhighlighting the unit-level complementarity between physical capital (machines and production
technologies) and different kinds of human capital (managers, engineers, and skilled workers). Our indepth examination reveals that even within the same firm, resource allocation decisions to meetdifferent growth strategies exhibit distinct complementarities: two-way complementarity between
3managerial talent and large plants/newly acquired plants for cost advantage and three-way
complementarity between managers, engineers, and high-end machines for product differentiation.
2. Theoretical overview
We build off the theoretical model in Agarwal and Ohyama (2013), which considered the allocationof talent across sectors (industry v. academia) in relation to various complementarities and apply theinsights from that model to the within-firm human capital allocation problem. In doing so, we derive
four theoretical propositions regarding the unit-level complementarity between different kinds ofhuman capital and physical capital that guide our empirical analysis.
We first consider how the allocation of managerial human capital relates to unit-level physicalcapital. If a product is homogeneous and production technologies are common, the key task is to
reduce marginal costs by improving unit-level production efficiency. Unit-level human capital, inparticular, managers of establishments, plays an important role in determining the production
efficiency of the unit (Ployhart and Moliterno, 2011; Crocker and Eckardt, 2013). Given that managers
vary in their quality and only one manager can be assigned to each plant, the optimal allocation
involves positive assortative matching between plant capital capacity and managerial talent (Becker,
1973; Agarwal & Ohyama, 2013).
To elaborate a little more on managers’ assignments to heterogenous plants, consider a firmwith n plants, whose capital capacity is represented by an n-tuple 〈"! , "" , … "# 〉, where "$ denotes
plant k’s stock of physical capital. For now, we assume that the plants are similar in all other respects
except for size. Each plant requires one manager to manage it. Let the n-tuple of available
heterogeneous managers be denoted by 〈'! , '" , … '# 〉, where '% denotes manager i’s managerial
ability (human capital).3 Let the output of plant k managed by manager i be given by (%$ = '% *("$ ),3 We implicitly assume that high-ability managerial human capital is scarce and cannot be easily purchased in the market(e.g., Lucas, 1978). We present the evidence for this from our data below, in Sections 4 and 5.

4

where *(∙) is a continuous, twice differentiable, and concave production function satisfying all thestandard conditions. This setup implies that to maximize total output (i.e., ∑#$&! (%$ ), the manager
with the highest ability should be assigned to the largest plant (spreading his ability over the largest
capacity), the second-best manager to the second-largest plant, and so on (Becker, 1973, p. 323;
Agarwal and Ohyama, 2013, Lemma 1). That is, we have:
Proposition 1: Plants with larger capital capacity are more likely to have high-quality plant managers (two-way
complementarity between managerial talent and physical capital).
However, firms may not fully enjoy economies of scale if production processes are poorly
operated. This is particularly likely to be the case for newly added plants, such as through acquisitions.
Scholars have pointed out the cost and difficulty of integrating separately held establishments due togaps in routines and cultures (Capron, 1999; Helfat and Raubitschek, 2000; Mitchell and Shaver, 2003;
Zollo and Singh, 2004; Capron and Mitchell, 2009; 2012). Such difficulties can, however, be mitigatedby assigning a capable manager, especially a manager with prior experience managing existing units,
to the newly added unit (Capron et al., 1998). That is, transplanting best practices, which largely involve
tacit knowledge, would require a direct transfer of individuals who possess the relevant skills andknowledge (Argote & Ingram, 2000; Choudhury, 2020; Stadler et al., 2021). Using simple notation
similar to above, denote the temporary cost of integrating a new unit into the firm by some fixedamount of output loss /, and assume that a manager can limit this loss proportionate to his ability.
Then, for some time period during which the plant has not yet been integrated into the firm, the total
output of the newly acquired plant can be expressed as (%' = '% *("' ) − (/ ⁄'% ), where subscript a
denotes a newly added plant (unit). We have:
Proposition 2: For some period of time after they are added to the firm, newly added plants are more likely to have highquality plant managers than other existing plants, even controlling for capital capacity.

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Human capital allocation decisions are likely to become more complicated as the firm initiates
product differentiation based on new technologies. Specifically, when establishments introduce newtechnologies, the allocation of human capital with specialized expertise (such as engineering) becomes
particularly important (Stadler et al., 2021). Rubens (2022) uses historical data from Pennsylvania
mines to demonstrate that there might indeed be strong complementarity between launching a newtechnology and having engineers educated in this technology to oversee its implementation. Thus, inpursuing product differentiation requiring advanced technological knowledge, high-ability engineers
need to be matched with physical capital aimed for such differentiated products. At the same time,
high-quality managerial talent is still required, as penetration into new product spaces requires not justtechnological prowess but also different managerial practices and updates of production routines. We
can express the production function for high-end, differentiated products as (%($ = '% 2( *("$) ),where 2( is engineering human capital of engineer j, and "$) is the stock of physical capital designed
specially to produce high-end, differentiated products. This kind of a production function gives rise
to a three-way complementarity between managers, engineers, and physical capital in productdifferentiation (see Agarwal & Ohyama, 2013, Proposition 1, which establishes complementarity
between physical capital and basic and applied scientists in industry):
Proposition 3: Plants conducting product differentiation are more likely to have high-quality plant managers and engineers
bundled together than those that are not (three-way complementarity between managers, engineers, and physical capital).
Finally, introducing new technologies and expanding the number of units that adopt those
new technologies often requires reallocating the limited and non-scale free stock of human capitalwith suitable skills and knowledge to units tasked with introducing such new technologies, as thoseindividuals serve as the conduits of knowledge transfer (Argote & Ingram, 2000; Choudhury, 2020;
Stadler et al., 2021). Thus, in the dynamic process of a firm’s repositioning, we expect skilled managers

6and engineers with prior experience in working on product differentiation in pioneering plants to bereallocated to newly added plants also conducting product differentiation:
Proposition 4: High-quality managers and engineers who worked in pioneering plants conducting product differentiation
are more likely to be reallocated to other plants initiating product differentiation than those who did not.
We build off these propositions in our empirical examination. Below, in Section 4, we use
quantitative methods to show what Kanebo did to achieve the aforementioned two-way and three-way
complementarities. In Section 5, we then take a deep dive into Kanebo’s history and use detailed
individual-level “nano-economic” data to show how the human capital stock necessary to implement
those complementarities was procured and then (re-)allocated. Before that, in the next section, we
briefly describe the contextual background and data we utilize (more details in Appendix B).
3. Historical context, data, and analytical approach
3.1 Background: The Japanese cotton-spinning industry in the late 19th-early 20th century
The mechanized cotton-spinning industry in Japan started from scratch in the 1870s but quickly
achieved remarkable growth and became a leading export industry by the turn of the 20th century,
contributing to Japan’s rapid ascension to become the first industrialized country in Asia (Saxonhouse,
1974, Braguinsky and Hounshell, 2016). This growth was primarily achieved by a handful of firms that
focused on value creation as opposed to value appropriation (Agarwal et al., 2020). Those firms grew
into “centers of gravity” in the industry by attaining high levels of production and managerial efficiency,
acquiring and restructuring the production systems of other, less efficient firms, and initiating productupgrades and diversification (Braguinsky et al., 2015; Agarwal et al., 2020; Braguinsky et al., 2021).
Kanebo was one of the most successful firms that implemented those growth strategies. Whenits first mechanized cotton spinning plant based in Tokyo started operating in 1889, it had the capacityof over 30,000 spindles, making it the largest startup in the nascent industry at the time of launch. A
few years later, Kanebo received a capital injection from the Mitsui group (one of the largest business7

groups in Japan), which installed a new top management team and dispatched a university-educatedprofessional manager, Sanji Muto (1867-1934) from Mitsui bank, to manage the newly establishedplant in Hyogo (see Figure 1 for the geography of Kanebo plants). In 1900, Muto was given control
over all plant operations of the company and became the de facto general manager even though he wasformally appointed as its CEO only in 1908. As such, he was the key figure behind Kanebo’s strategic
expansion, which went through several phases during the period covered by our data.
Around 1914, Kanebo was the largest firm in the industry in terms of output scale, although
two mega-mergers between its competitors in 1914 and 1918 created two even larger firms. Kanebo’s
firm-level growth strategy enabled by its stable, shared leadership is detailed in Agarwal et al. (2020).
In this paper, we leverage Kanebo’s detailed plant-level data to examine how its strategy translated
into human capital investment and internal (re-)allocation across plants.
3.2 Data
Kanebo left rich archival records, including those related to human capital it employed with detailed
allocation to plants as well as positions held. Those records give us information about when, where,
and how managers and senior engineers were employed in the company. We matched these data withthe university and college alumni data, utilizing our own-constructed panel data on the universe of all
university and technical college graduates in Japan until the 1920 graduation cohorts that include theirjob histories (see Appendix B for the details). This matching process allowed us to add information
about the careers of those individuals before and after they were employed by Kanebo, as well as madeit possible to identify all the educated engineers (university graduates and technical college graduates)
whom Kanebo employed in non-managerial positions. The result is an individual-level panel data onplant managers (511 semi-annual observations on 35 unique individuals) and engineers in all positions(2,314 semi-annual observations on 176 unique individuals) with information about their educational
backgrounds, prior job experience, future careers, and promotion status.
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Apart from the plant managers’ and engineers’ data, the records contain plant-level data on
the number of workers employed in each plant, their turnover, as well as plant-level assignments ofblue-collar workers trained at Kanebo’s own internal vocational school that the company launched in
1906. We also have access to plant-level financial data, such as semi-annual balance sheets and income
statements, and input and output data for different types of products (see Appendix B). Furthermore,
we utilize Kanebo’s plant-level machine orders data from Lancashire archives (Braguinsky et al., 2021
and 2021a) which contain information on machines’ technical characteristics, allowing us to observe
the timing of each plant’s capital expansion (i.e., machine purchases) aimed at producing different
kinds of products. In particular, we are able to distinguish between machines designed to produce
simple, low counts of cotton yarn (“low-end” machines) and machines designed to produce higher
counts’ yarn and further process such as doubling, gassing, and weaving (“high-end” and/or
specialized machines), which required more advanced skills.4 As high-end and specialized machines
embodied novel and more challenging technologies, they required educated engineers to complement
them (Rubens, 2022). We utilize the information about machine orders when examining human capital(re-)allocation following plant-level expansion events. Combining all the plant-level information, weconstruct an unbalanced plant-level panel dataset (465 semi-annual observations on 16 plants). Table
1 lists the plants in our sample, together with their origins (built v. acquired) in column (4), original
capital capacity and its expansion (columns (5)-(7)), and whether and when they were assigned to
implement the product differentiation strategy (columns (8) and (9)).
[Table 1 around here]

4 The yarn count expresses the thickness of the yarn, and its number indicates the length of yarndence relative to theweight. The higher the count, the more yards are contained in the pound of yarn, so higher-count yarn is thinner (finer)
than lower-count yarn and sells at a higher price per pound. Producing higher-count (finer) yarn requires better quality
raw cotton as well as different machines and superior technology than producing lower-count (coarser) yarn. High-countyarn is often also improved further by more processing, known as doubling and gassing, which were quite challenging forthe fledgling Japanese cotton spinning mills to master at that time. See Braguinsky et al. (2021) for more details.

9Finally, we also utilize rich qualitative information to bolster our historical analysis, including
regular notice letters, disseminated from Kanebo’s general manager—Sanji Muto—to plant managers(Shihainin Kaisho, 1902-1918), Muto’s biography, his 1901 essay (Muto, 1901) as well as Kanebo’s
company history (Kanebo, 1988). We describe the details of each data source in Appendix B.
3.3 Analytical approach
We adopt a mixed-method approach combining quantitative analysis with a deep dive into businesshistory data (Braguinsky & Hounshell, 2016; Agarwal et al., 2020; Wormald et al., 2021). As describedin the previous section, the uniqueness of our study rests on the comprehensive plant-level databaseof resources, costs, and outputs obtained from Kanebo’s financial reports, and the individual-level
panel data on plant assignment of managers and engineers constructed by various sources. The natureof the database is appropriate for our goal to delineate the process of human capital allocation acrossplants and examine the linkage between heterogeneous skill- and experience-levels of those humancapital and their allocation. Our analytical approach also aligns with history-informed research andabductive methods (Ingram et al., 2012; Kahl et al., 2012; Murmann, 2012; Argyres et al., 2020; Pillai
et al., 2020), whose primary goal is to infer the best explanations for phenomena observed in particular
historical contexts.
4. Growth strategies and resource allocation: an overview and quantitative analyses
In this section, we first present an overview of the main insights supported by quantitative
evidence regarding key differences in resource allocation as driven by the two basic competitive andgrowth strategies––cost leadership based on standard, homogenous products (lower-count, coarser
yarns) coupled with acquisition-based production scaling, and internally developed productdifferentiation based on high-end products (higher-count, finer yarns) and fabrics. Our aim here is topresent the basic evidence, linking it to some well-known theoretical constructions while at the sametime striving to simplify the logic of a deep dive into Kanebo’s business history in the next section.
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4.1 Cost leadership and two-way complementarity of managers and physical capital
We begin with the strategy of achieving a competitive cost advantage. In a multi-plant firm
context, this requires allocating top managerial talent to the most important plants. The plant’s
importance can be driven simply by its size (Proposition 1 above) or the need to integrate a new or
newly acquired plant into the firm (Proposition 2 above).
Strictly speaking, testing Proposition 1 requires ordering managers according to their ability
and showing that the largest plant gets assigned the best manager, and so on. We do not have a perfectmeasure of managerial ability; so instead, we construct four different proxies for several aspects ofsuch ability and examine how the relative size of the plant at any given point in time on average affects
the probability of its manager possessing higher ability as captured by each of those proxies. The four
proxies are: (i) a dummy equal to one if the manager had higher education and zero otherwise; (ii) a
dummy equal to one if the manager had prior experience managing a different plant at Kanebo andzero otherwise; (iii) a dummy equal to one if the manager had both higher education and priorexperience managing a different plant at Kanebo and zero otherwise; and (iv) a dummy equal to one
if the manager was subsequently promoted to the Kanebo board of executives and zero otherwise.5
The idea behind the fourth proxy is that plant managers who later became executives would alreadyhave possessed higher ability than other managers at the time they were appointed.6
The estimation results are presented in Table 2. The dependent variables are the four dummies
above, while the independent variable is the (logged) plant capital capacity (the number of spindles

5 Since some plant managers were appointed midway through the semiannual period, which is the basic unit of ourobservations, we use weighted dummies in case the change happened during the period. For example, if an educatedmanager replaced a manager without formal education in May 1902, the “educated plant manager” variable would be set
to 1/3 for the first half of 1902 because the educated manager oversaw the plant for two months out of six.
6 Experience managing a large plant could have contributed to developing the capability that later led to promotion or
could have simply raised the visibility of the manager. Though such reverse causality is a possibility, note that it is enoughfor our purposes that future promotion is at least partially correlated with inherent managerial ability, which seems to bea plausible assumption.

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installed) in a given plant in a given period, controlling for half-year and plant location fixed effects.7
We employ the linear probability model, but probit and logit specifications yield similar results.
[Table 2 around here]
The estimation results in Table 2 show that higher plant capacity is positively associated withthe probability of the manager possessing each of the four characteristics above.8 The magnitudes are
economically significant; for instance, doubling the number of spindles raises the estimated likelihoodof the plant manager having previous experience managing another plant by 57.3 percent of the mean
(36.0 percentage point increase with the mean of 62.8 percent; p-value=0.003), while it increases thelikelihood of the manager being both educated and having previous experience by 77.2 percent of themean (p<0.001). Also, doubling the number of spindles is associated with an increase in the likelihoodthat the manager allocated was subsequently promoted to an executive by 72.8 percent of the mean(p=0.013). The weakest association is observed between plant capacity size and higher education of
the manager, but this may be because almost 80 percent of all the plant managers in our sample hadhigher education anyway, so there is little variation in this dependent variable.
While, as mentioned, not a strict test of Proposition 1, the results in Table 2 present strong
suggestive evidence that there is indeed a positive assortative matching between plant size andmanagerial ability as we observe consistently positive correlations between relatively larger plant sizeand higher probability of that plant assigned a manager with higher education, prior experience ofmanaging another plant, both these characteristics, or a future executive.

7 Both the number of plants and the number of individuals in our panel data were changing over time, as were many otheraspects of both the internal and external environment surrounding the company. To control for all time-changing variables
affecting the firm and the industry, we include time dummies in all regressions in this paper. We do not include plant or
individual fixed effects for the most part because our focus is on the allocation of heterogeneous individuals acrossheterogeneous plants. We instead include plant location fixed effects to account for geographic variations in the distancein which (re-)allocation can happen. The location variable has four categories: Tokyo (only the Tokyo plant), Kansai region(seven plants), Kyushu region (five plants), and Okayama region (three plants). See Figure 1 for their locations.
8 While the correlations across education, prior plant managing experience, and future promotion are all positive, they are
not large, so they capture different aspects of managerial ability (see Appendix Table A1 for the correlation matrix).

12Now, consider the case where a firm expands by adding a new plant, including but not limited
to acquisition. For the first several years, the firm likely faces the task of integrating the new (acquired)plant into the firm, which often can be quite daunting (Capron & Mitchell, 2012). According to
Proposition 2, we expect the firm to assign superior managerial talent to such new plants, at least
temporarily, to facilitate integration into the firm culture and install its managerial practices. Moreover,this relationship should hold independent of capacity considerations in Proposition 1.
In Table 3, columns (1)-(4), we present the results of estimating a regression where the sameproxies for the quality of the managers as in Table 2 are regressed on a dummy equal to one for the
first five years of a new plant and zero otherwise.9 The estimation equations once again include logged
plant capital capacity and half-year and location fixed effects.
[Table 3 around here]
We do not find a statistical relationship in the simple indicator for the educated managers incolumn (1) of Table 3, but the first five years of new plants are, on average, positively associated witha higher probability of plant managers possessing the other three attributes that proxy for their quality.
In particular, the first five years of new plants are associated with a higher likelihood of the manager
being both educated and possessing experience in managing another plant by 0.189 points (37.5
percent of the mean; p=0.062). This underscores the importance of skilled managers’ prior experiencein newly added plants. Integration periods are also associated with the increase in the likelihood of
appointing managers promoted in the future by 0.330 points (81.9 percent of the mean; p=0.047).
Meanwhile, the coefficients on plant size are similar between Tables 2 and 3, suggesting that the “newplant effect” in Proposition 2 operates independently of the size effect in Proposition 1.

9 When Muto took over the management of all plants owned by Kanebo in 1900, the company had just acquired threeplants from other firms while it had two of its own incumbent plants––the Hyogo plant managed by Muto since he joined
the company, and the Tokyo plant that had been outside of Muto’s control until that time. We include the first five yearsof the Tokyo plant in the category of “new plants” because of strong evidence, noted below, that Muto faced an evenbigger challenge in integrating the Tokyo plant into his management system than the plants just acquired from other firms.

13

As a “placebo test,” we also examined whether the integration process of newly added plants
required the allocation of high-level engineering talent––something that is not predicted by our theory.
In columns (5) and (6), we use the logged number of educated engineers and the product of educatedand experienced plant managers and educated engineers as dependent variables.10 In contrast to plant
managers, the results do not show positive correlations between the new plant dummy and the numberof educated engineers allocated to the plant, either on their own or as a “bundled resource” withmanagers. Thus, while transplanting managerial practices by allocating high-quality managers was a
key to integrating new plants into the firm, there was no need to allocate more engineering talent tosuch plants compared to other plants because technology was less of an issue.11 Consistent with this,in Section 5.1 using individual-level data, we show that Kanebo replaced all managers in the acquired
plants by its own managers but retained many of the educated engineers.
4.2 Product differentiation and three-way complementarity of managers, engineers/skilledworkers, and high-end machines
Turning now to product differentiation, going beyond a simple, homogenous product requires
purchasing machines specially designed for producing higher-count yarns as well as yarn-twisting andgassing equipment. If a plant is also to produce fabrics, it needs more specialized machines such as
power looms and dyeing equipment. These specialized machines were high-tech for the Japaneseindustry at the time, so the firm needed relevant high-level human capital to operate them effectively.
High-level technical human capital was procured by hiring educated engineers from Imperial
Universities (top-tier) and Technical Colleges (second-tier at the time; now equivalent to today’s
Institutes of Technology). In the first half of 1901, Kanebo only employed four educated engineers,

10 Here and in all cases below where a log transformation is employed for a variable that has some zero values, we adopt

the inverse hyperbolic sine (IHS) transformation that better approximates a log function, especially at small values thanthe conventional employed, !"($ + 1). The IHS transformation is defined as !"($ + √$ ! + 1*.
11 We also conducted sensitivity analysis using the first year and the first three years for new plants and the results weresimilar to those presented in Table 3. Those results are reported in Appendix Table A2.

14none of whom was a university graduate. Five years later, right before it embarked on productdifferentiation, it still employed just 14 technical college graduates and eight university graduates (22total educated engineers). By the first half of 1911, however, the total number of educated engineers
employed by Kanebo more than tripled to 71, with 20 of them university graduates, and reached its
peak of 111 (of which 29 were university graduates) in the second half of 1914, after which it leveled
off. Kanebo also started to “build” the stock of skilled human capital by launching an internal
vocational school in 1906, where lower-level workers were trained in specialized spinning skills.
From the resource allocation viewpoint, we expect the bundling of high-level engineering
human capital and high-end, specialized machine. 12 Put it differently, in addition to thecomplementarity between plant size and managerial ability, product differentiation strategy also
generated complementarity with educated engineers (Rubens, 2022). Thus, in product-differentiating
plants, there is a three-way positive assortative matching between machines (especially specialized,
higher-end machines), managerial, and engineering talent (Proposition 3 above).
Table 4 presents suggestive empirical evidence of this three-way complementarity using
regression analysis. It displays estimation results where the dependent variables are the ability of theplant manager (proxied here by the manager having both formal education and previous experiencemanaging another plant, although other proxies work similarly), the number of educated engineers ina given plant-semiannual observation in columns (1) and (2), and the interaction between these twovariables (capturing the resource bundle comprised of a capable manager and educated engineers) incolumn (3). The dependent variables in the last two columns are the logged number of workers trainedat its internal vocational school, and this number interacted with the same proxy for managerial ability.

12 Skilled engineers were actually required already at the ordering stage for new machines. Japan was importing custom-

made textile machinery from England at that time. Hand-written annotations on the orders the authors accessed in British
archives bear witness to Japanese engineers participating in requesting special design features for machines ordered bytheir firms. See photocopies of original orders in the open data repository, Braguinsky et al. (2021a).

15

The independent variable of interest is the dummy set equal to one if the plant had specializedmachinery (machines to produce counts above 20s, twisting and/or gassing machines, and/or powerlooms to produce fabrics) and zero otherwise. This dummy thus identifies plants that Kanebo
designated to implement its product differentiation strategy. As before, the estimation equations also
include logged plant capital capacity and half-year and location fixed effects.
[Table 4 around here]
The estimation results show that the allocation of capable managers and educated engineers
tended to prioritize plants with specialized machines, even controlling for plant size and location.
Plants possessing specialized machines were 33.4 percentage points more likely to have skilled
managers (66.3 percent of the mean; p=0.086; column (1)) and have 91.4 percentage points moreeducated engineers (42.3 percent of the mean; p=0.008; column (2)) than those did not. Most tellingly,
the coefficient on the specialized machines dummy in column (3), where the dependent variable is the“bundle” of educated and experienced managers and educated engineers, implies that plants withspecialized machines had around 2.4 times more educated engineers (=e1.215-1; p=0.013), in
conjunction with a capable manager.
Turning to skilled workers, estimation results in column (4) indicate that plants withspecialized machines have, on average, 40.6 percent more internally trained workers (=e0.341-1;
p=0.014) than those without. In column (5), we again see evidence of bundling them with capablemanagers, with 5.2 times more skilled workers (=e1.821-1; p=0.044) in conjunction with a capablemanager, suggesting that three-way complementarity extended to the non-engineering skilledworkforce in plants conducting product differentiation.13

13 The data on the allocation of internal vocational school graduates are available starting from 1908 which is why the

number of observations in columns (4) and (5) is smaller than in the previous three columns.

16

The specifications in columns (3) and (5) use the weighted dummy variable for educated andexperienced managers and the count variables for the number of engineers / internally trained workers.
The product of these variables may not correctly capture three-way complementarity because if the
manager does not have either higher education or prior experience (i.e., the manager dummy is zero),the dependent variables are always zero, regardless of the number of engineers or internally trained
workers.14 To alleviate this potential concern, in Appendix Table A3 we present a simple two-by-two
tabulation of the number of observations by plants with and without specialized machines. Regarding
managerial talent, plants are divided into those overseen by educated and experienced managers andthose overseen by managers without higher education or previous experience managing a plant.
Regarding engineering talent, the plants are divided into those above and below the median numberof educated engineers. In plants with specialized machines, observations are heavily concentrated in
the cell that has both educated and experienced managers and an above-the-median number ofengineers (63.2 percent of observations are in this cell), while in plants with no specialized machines,
59.7 percent of observations are in the cell with a manager who does not possess either higher
education or previous experience managing a plant, and a below-the-median number of engineers.
Regression estimations using those median-cut dummy variables, reported in Table A4, produce
similar results.
While the cross-plant (i.e., pooled OLS) regressions with time fixed effects presented in Table
4 fit our research design to compare the allocation of resources across heterogeneous plants at a given
point in time, we also employed regressions using capital expansion events (i.e., machine orders) while
including plant fixed effects to examine within-plant variations in human capital allocation following
14 Suppose, just as an example, that the average number of engineers are five in product-differentiation plants and one in

non-product-differentiation plants, regardless of the managers’ quality. The manager dummy is one with the probability
0.5 and zero with the probability 0.5, regardless of product differentiation and engineers. In this case, the conditional meanof the dependent variable becomes 2.5 in product-differentiation plants and 0.5 in non-product-differentiation plants sothat we find the positive correlation between the dependent variable and product differentiation, despite the fact there isno three-way complementarity. We thank Atsushi Ohyama for bringing this to our attention.

17

capital expansion. The results, presented in Appendix Table A5, confirm that we find within-plants
three-way complementarity between managers, engineers, and high-end machines (though we do notfind it for internally trained workers).
Finally, working with higher-level specialized machines helps develop the skills of engineers.
These engineers can then be reallocated to other plants that introduce such machines later to apply
their knowledge there (Proposition 4 above). In Table 5, we utilize the individual-level panel data on
educated engineers to examine how the experience of working at a pioneering product-differentiating
plant was associated with subsequent assignment to another plant conducting product differentiation
later in the sample. More specifically, the dependent variable in columns (1)-(3) is a dummy equal to
one if an engineer was transferred from a plant to another plant that was producing or later began
producing differentiated products. The explanatory variable of interest is the dummy set equal to one
if the engineer had previous experience working at one of the three plants that pioneered productdifferentiation strategy at Kanebo (the Tokyo, Hyogo, and Sumoto plants; see below Section 5.2 formore details). The estimation also controls for the total number of plants the engineer had worked at,
as well as the (logged) capacity of the plant from which the engineer was relocating.
[Table 5 around here]
The results in columns (1)-(3) suggest that engineers’ prior experience in pioneering productdifferentiating plants is positively associated with the likelihood they were later reallocated to anotherproduct-differentiating plant. Specifically, in the most saturated specification in column (3), where we
include controls for the number of plants the engineer had worked for before the focal transfer, thefocal plant size, as well as individual, half-year, and plant location fixed effects, the corresponding
association is estimated to be 9.8 percentage points (2.6 times the mean probability; p=0.001). As aplacebo test, in columns (4)-(6), we employ the same regressions for the engineers’ reallocation to aplant that did not produce differentiated products. In this case, we do not find an economically or18

statistically meaningful association between previous experience in a pioneering productdifferentiating plant and the probability of reallocation, consistent with the claim that educatedengineers carried their knowledge from experience in product differentiation to a different
establishment also pursuing product differentiation.15 These findings shed light on a possible channelof “spillovers” from product innovation to the overall growth in firm output noted in Braguinsky etal. (2021). Note also that controlling for individual fixed effects, the number of different plants theengineer had previously worked at positively affects the probability of another reallocation, both to a
product-differentiating and a nonproduct differentiating plant (see the coefficients on the number ofplants previously worked at in columns (3) and (5)).
5. Growth strategies and resource allocation: a deeper dive into historical evidence
In this section, we leverage historical data, in which Kanebo adopted different strategies at different
periods, to examine the dynamics of resource allocation associated with each strategy. Morespecifically, our goal here is to deepen the understanding of the two-way and three-way
complementarities revealed by the quantitative examination in the previous section through a “nanoeconomic” analysis of the ways in which (a) managerial talent was recruited and allocated to plants inneed of being integrated into the company; (b) educated engineers were recruited and allocated toplants tasked with implementing the product differentiation strategy; and (c) human capital at various
levels was recruited, trained and (re)-allocated as the firm became large and diversified.
Table 6 summarizes the transition of Kanebo’s strategies and accompanying resource
allocation in three different phases. From the late 1890s until about 1905, Kanebo’s focus was on
scaling the output of homogenous products, mostly through horizontal acquisitions, and theassociated cost reduction (Yuki, 2014). Then, from 1906 to 1910, the primary focus switched to

15 We also conducted another “placebo test” using the sample of engineers without formal education and confirmed thatthe relationships seen in Table 5 columns (1)-(3) do not hold in this sample either (see Appendix Table A6).

19

product differentiation. This sequencing was closely related to the fundamental “build or buy” growth
dilemma (Capron and Mitchell, 2012). Through both these stages, Kanebo had to decide on how to
procure the necessary human capital resources even before it could determine how to allocate themas its own internal resource base was yet inadequate. By the 1910s, however, the internal resource
constraint had been by and large removed, allowing the firm to start pursuing a more balanced strategy
consisting of both “build” and “buy.” This new phase ushered in an increased emphasis on meetingchanging plant-level needs through internal resource reallocation, as well as utilizing knowledgetransfer from the highest-level educated engineers (university graduates) to second-tier educatedengineers (technical college graduates) and skilled blue-collar workers. Such transfer allowed the firm
to increasingly substitute technical college graduates and skilled workers for university-educatedengineers. In what follows, we describe the resource acquisition and allocation decisions
corresponding to each phase.
[Table 6 around here]
5.1 Cost-leadership, resource acquisition, and within-firm resource allocation
Kanebo’s competitive strategy in the first phase was to increase output scale and achieve costadvantages for low-end (i.e., yarns of counts 20s and below) products. Horizontal acquisitions of
struggling cotton spinners as the industry was going through the shakeout phase were an effective
strategy to accomplish these tasks. In his essay, “On the Large Mergers of Cotton Spinners” (Muto,
1901), Muto went as far as to suggest that all Japanese cotton spinners should merge into a single trust
to improve production efficiency:“The fundamental spirit of ‘Trust’ [large-scale mergers – authors] … consists of merging
separate businesses of the same kind to achieve capital concentration and sedulous
management to lower production costs and prices, and thereby to increase capital profits andwages of workers working in production as well as provide cheaper goods for the public.”(Muto, 1901, p.7; translated by the authors)

20This grand design never materialized, but Kanebo itself went on an acquisition spree.16 Since mostacquisitions consummated by Kanebo at this time were takeovers of plants run by poorly managed
firms, the primary task was to improve the way the acquired plants had been operated.17
Muto recognized that human capital was the key to success (Kanebo, 1988). In particular, hegave substantial discretion to plant managers in plant operations, worker empowerment and retention,
cost-saving, and quality controls (Yuki, 2013; see also Appendix C2.1-2.6). Since Kanebo did not yet
have sufficient stock of high-level human capital, the primary need in this phase was the acquisitionof skilled managers and engineers to whom Muto could entrust the task of operating the acquired
plants. Table 7 summarizes the educational and previous experience backgrounds of plant managers
and chief engineers in charge of Kanebo plants from 1900-1905 at the individual-semi-annual panel
level. The total number of observations on plant managers is 108, while the number of unique
individuals observed managing a plant at least once is 15. The corresponding numbers for chiefengineers are 93 and 18, respectively.18[Table 7 around here]
The major takeaways from Table 7 are four-fold. First, Kanebo did not rely on managers
retained from acquired plants at all but rather allocated its own managers to them. This clearly reflects
the need to radically overhaul operations by appointing new leadership that would implement thecompany’s strategy and resolve the issues of integrating all plants into the new culture Muto wanted

16 The series of acquisitions started in late 1899 when Kanebo acquired two middle-sized firms in Osaka and another firmon an island across the strait from its flagship plant in Hyogo in January 1900. Shanghai Spinners, a full subsidiary of
Kanebo to begin with, was also formally acquired in 1899 and its facilities integrated into the Hyogo plant. This wasfollowed in late 1902 by the acquisition of all firms that actively operated at the time on Japan’s southernmost island of
Kyushu (three firms and five plants total). See Table 1 for the details of the acquisitions consummated by Kanebo until
1920 (including later ones in 1907, 1911, and 1913) and Figure 1 for the geographic distribution of the company’s plants.
17 Muto took this task seriously and informed plant managers that the acquired plants lacked efficient operational practices
and experienced staffs, so they needed to be resolved urgently (see Appendix C1.1-1.4).
18 We focus on the number of observations because in some cases, Kanebo would appoint a manager to a newly acquired
plant only to replace him within a year by a different one. Suppose the first manager did not have higher education while
the second one did. Not weighing individuals by the number of periods they were in charge would lead to misleading
results in such cases. Using the number of observations assigns weights to each manager corresponding to his tenure.

21

to install (see Appendix C1.1-1.2). However, Kanebo did not yet have a deep enough pool of own
managers. The row “At Kanebo pre-1900” in the left panel of Table 7 shows that plant managers who
had been with the company before 1900 were only 31 percent of the observations in this period. Thus,
Muto had to procure the necessary resources from outside the firm.19
Second, most of the newly appointed managers (81 percent of all observations or 11 out of 15
unique individuals) had formal higher education, with degrees in economics or business.20 College
degree holders were a scarce resource during that era, and most firms did not have any cadres withhigher education. Also, all degreed managers had graduated before 1900, so no freshly minted
graduates were assigned as plant managers during those years. That underscores the importance ofboth managerial skills and experience as the firm faced the task of plant restructuring.
Third, Muto leveraged his network to acquire managerial talent. Muto himself graduated from
Keio University, the first private university in Japan and the only one that provided education inpractical managerial skills. More than half of the managers were drawn from the Keio alumni network.
In particular, all the managers initially appointed to manage the three plants acquired in 1899-1900
were recent graduates from Keio University.21 Muto also relied on the Mitsui network (around 27
percent of the observations), the business group that owned about 50 percent of Kanebo’s shares
during this period and where Muto himself had been employed prior to being transferred to Kanebo.
Significantly, the channels of acquiring engineering human capital during this period were very
different from plant managers. To begin with, 31 percent of all observations on chief engineers (fiveout of 15 unique individuals) were those retained from acquired firms. Thus, while Kanebo wanted

19 As can be seen from the last row in Table 7, we do not have information on the exact previous experience for 44 percent

of all observations on plant managers, but there is no indication that any of those managers had worked at Kanebo prior
to 1900, so in all probability, they also came from outside of the firm.
20 It is possible that some among those for whom we do not know the education background also had higher education,
so the estimate in Table 7 is conservative.
21 After 1890, Keio University had economics, law/politics, and literature departments. All Keio graduates who became
plant managers at Kanebo except one (Shingo Tsuda, who joined in 1907) hailed from the economics department. We
thank Fukuzawa Memorial Center for Modern Japanese Studies, Keio University, for the data on graduation departments.

22none of the acquired firms’ management practices, it was willing to retain the technological expertise.
Also, 47 percent of all observations are on chief engineers with no formal education (53 percent if we
add those for whom we don't know for sure). Engineering talent available to the industry at that time,
especially formally trained engineers, was still extremely scarce (Agarwal et al., 2020). In contrast toplant managers whose procurement heavily rested on university education and Muto’s personal
network, a large share of chief engineers was procured through hiring from competitors.
Even though the primary need in this stage was to procure the necessary resources to manage
plant expansion, there were already some strategic (re-)allocations of managerial talent. As Kanebo
acquired five more plants in 1902, the company immediately reallocated two of the managers in chargeof previously acquired plants to the two largest and most important among the newly acquired plants,
and one more such manager was reallocated to another newly acquired plant two years later. This
corroborates Proposition 2 and regression results in Table 3. Also interestingly, we observe two
managers, initially put in charge of acquired plants, promoted to manage the company’s main plants
in Tokyo and Hyogo, apparently in recognition of the work they had done with acquired plants. In
particular, although technically not an acquisition, the Tokyo plant was in dire need of restructuring.22
The outcomes of this early strategy are presented in Appendix Figure A1. The first two panels
depict the dynamics of two key metrics of the cost efficiency of production––the ratio of operating
expenses to output and the ratio of wage expenses (total plant-level wage bill) to output measured inweight units (pounds), adjusted to the 20s count as in Braguinsky et al. (2021). The data are aggregatedby three categories of plants: Kanebo’s own Tokyo and Hyogo plants, the three plants in the Kansai
region acquired in the late 1899-early 1900, and the five Kyushu plants acquired in 1902 (see Appendix
22 The plant manager in charge of this restructuring, Masazumi Fuji, was relocated to the Tokyo plant after a brief stint at

an early acquired plant. Fuji had graduated from Keio University and overlapped with Muto at Mitsui bank in 1893-94. He
then joined Kanebo as a middle manager in 1897 (Mita Shogyo Kenkyukai, 1909, pp. 645-646). Once in charge of theTokyo plant, Fuji implemented various managerial innovations, from improving machine maintenance and working
conditions to such small but important things as leveling the plant floor to avoid wasting lubricating oils (Kinugawa, 1939,
pp. 476-483; cf. Bloom et al., 2013). Fuji was rewarded by being promoted to the company Board of Directors in 1907.

23

Figures A2 and A3 for each plant separately). We observe a rapid decrease in both operating expenses
to output and wages to output ratios across all plants, but especially in acquired plants after acquisitions,
resulting in almost full convergence by around 1904 (see also Yuki, 2013).23
Improved management practices can also be seen in large reductions in worker turnover (quit)
rates, which was a serious problem as it hindered human capital accumulation.24 Panels C and D in
Figure A1 show that both Kanebo’s original plants and acquired plants (post-acquisition) succeededin reducing turnover rates, with an average decrease rate of over 50 percent over 1900-1905 (seeAppendix Figure A5 for each plant separately).
5.2 Product differentiation, resource building, and within-firm resource allocation
The second phase of Kanebo’s growth strategy started around 1906 and continued until 1910. During
this period, the focus shifted from scale expansion and cost reduction to upgrading and diversifying
the product portfolio. This strategic shift was due to the realization by the company’s top brass that
there were immense unexploited profitable opportunities in the markets for finer, higher-quality, andmore processed yarn, such as gassed yarn, as well as in mechanized fabric production. The first majordecision taken by the company in the second half of 1906 was to procure brand-new machines to
produce yarns of high counts (60s and 80s), as well as yarn gassing equipment for its newly restructured
Tokyo plant. The decision to prioritize this project among a slew of others (see Appendix C4.1) was
based on the recognition of its high profitability seen in the letter sent by Muto to all plant managers:“We are rushing full operation of the Tokyo plant No. 3 [the high-count yarnplant] because the profitability of gassed yarn is high. I would like all plant
managers to understand this goal.” (Shihainin Kaisho, 02/02/1908; translatedby the authors)
23 For example, the pre-acquisition rates of operational and wage expenses in the acquired plants in Kyushu wererespectively 53.2 percent and 35.9 percent higher than in the original plants, while those gaps decreased to 16.0 percent
and -0.002 percent after acquisition. Appendix Figure A4 shows that the decline in wage expenses to output ratio did notcome from lower wages; instead, this decline resulted from improved productivity accompanied by higher, not lower wages.
24 Worker turnover was a serious problem for all Japanese cotton spinning firms at that time (Saxonhouse, 1974).
Recognizing that high worker turnover rates hindered the accumulation of skilled human capital, Muto repeatedly
instructed plant managers to improve worker retention (see Appendix C3.1-3.7).

24

In February 1908, Kanebo placed three orders for new machines designed to produce middlecount yarns (up to 45s count) in the Sumoto plant that tripled and drastically upgraded its capacity.
This plant also diversified downstream by adding loom machines to produce fabrics. The next year
Kanebo’s flagship plant in Hyogo diversified downstream and added some more spindles, and so did
the two formerly acquired plants in Kyushu, Nakatsu (downstream diversification in 1909) and Hakata(downstream diversification and middle-count yarn producing machines added in 1910).
At this stage, Kanebo did not add any new plants through acquisitions, so the firm could now
focus more on (re-)allocating its internally accumulated managerial resources in accordance with the
new strategic priorities. Table 8 presents the characteristics of plant managers during the second phase,comparing plants that were conducting product differentiation to those that at this stage were only
producing low-end products and not (yet) part of implementing the product differentiation strategy.
The first thing to note is that in all observations on plants conducting product differentiation, plant
managers were formally educated ones, with 85 percent of those being Keio University graduates, as
opposed to just 45 percent of Keio University graduates in other plants. Product-differentiating plants
were also assigned experienced managers: in 78 percent of observations on such plants, managers had
worked for Kanebo prior to 1906, compared to just 20 percent in other plants. Thus, the cadres chosen
to oversee the beginning of the product differentiation strategy implementation were managers who
were both educated and experienced (see also Table 4 above).
[Table 8 around here]
Second, and in contrast to the previous phase, the assignment of educated and experienced
managers to plants selected for product differentiation was largely accomplished through internal
reallocation. The data presented in Table 8 show that in 70 percent of observations, pioneering plants
involved in product differentiation were assigned managers with previous experience at another plant.
For instance, as aforementioned Masazumi Fuji (see footnote 22) was promoted to the company board25

of executives in 1907, he was replaced by the manager who had been in charge of the largest plant
among those Kanebo acquired from Kyushu Spinners in 1902 (and also a Keio University graduate).
Another experienced manager with a Keio University degree who had overseen a plant Kanebo
acquired in 1899 was relocated to manage the Sumoto plant in 1907, right before it started upgrading
its capacity as mentioned above. Yet another manager with experience in managing a previously
acquired plant was sent to manage the upgrading at the Nakatsu plant in 1909.
Implementing the new strategy also involved non-trivial technological challenges, which
required expanding and upgrading not just the stock of machines but also the stock of engineering
human capital and skilled operatives. In contrast to managerial resources, Kanebo’s stock of educatedengineers at the start of the product differentiation strategy was still quite thin, and Kanebo had nointernal worker training facility yet. During 1906-1910, the firm more than tripled the number ofdegreed engineers it employed, mostly by hiring new graduates of Imperial Universities and TechnicalColleges. It also reallocated skilled blue-collar workers to plants tasked with product differentiation.
Figure 2 shows the allocation of educated engineers across plants implementing productdifferentiation at this stage and other plants. Plants conducting product differentiation on average had
1.4 degreed engineers in senior positions compared to 0.6 engineers in other plants. Furthermore,
plants that conducted product differentiation were prioritized compared to other plants in assigningdegreed engineers at all levels, engineers hired out of school (those who graduated in 1905 or later)
and those with experience working for Kanebo before this period (although in the latter case the 95percent confidence intervals largely overlap). The aforementioned Sumoto plant serves as a case inpoint: it had no degreed engineers until 1902 and only one such engineer for several more years. As itwas selected for product differentiation around 1909, however, the firm immediately assigned fivedegreed engineers to this plant, increasing to 10 by 1911.
[Figure 2 around here]26

In addition to hiring educated engineers out of universities and colleges (i.e., “buying”
specialized human capital), Kanebo also embarked on “building” its own stock of skilled blue-collar
workers by launching its internal vocational school in 1905. The school sought applicants from all the
Kanebo’s plants and trained them in advanced spinning skills under a one-year program.25 Despitehigh turnover rates, this school had increased the stock of lower-level skilled human capital (Appendix
Figure A6), with evidence that plants conducting product differentiation were prioritized in allocating
those workers, as well as educated engineers (Table 4 above; see also Appendix Figure A7).
Interestingly, the shift to product differentiation appears to have forced the company to set
aside, at least temporarily, the goal of reducing operating expenses. Recall that the ratio of operating
expense to output went down dramatically in the previous phase (see Appendix Figure A1 andAppendix C1.3-1.4). This trend was completely reversed in this new phase until it was brought
somewhat under control in 1910 (Appendix Figure A8). Changes in company operations
accompanying product differentiation may have partially contributed to this. For instance, high-end
machines likely required more frequent and expensive maintenance (see Appendix C5 for somesuggestive evidence). They also needed raw cotton inputs imported from the U.S. and Egypt rather
than Chinese or Indian cotton. 26 Also, higher-count (thinner) yarn presumably requires more
packaging and shipping expenses per same weight.
5.3. Balanced growth strategy and complementarity of resources
The third and final phase we analyze in this paper covers the period from 1911-1918. A majordifference from the first two stages was that by this time, Kanebo had internally accumulated
substantial stocks of both managerial and engineering human capital. This allowed the firm to pursue

25 The curriculum included classes of mixing, blowing, carding, first spinning, fine spinning, and finishing (bundling), andthe use of machines. The detailed guidelines disseminated to plant managers for its launch are described in Appendix C5.1.
Appendix C5.2 shows that the curriculum was constantly updated in response to the deficiency of necessary skills.
26 While cotton input is not part of operating expenses, delivery costs are.

27

growth along multiple dimensions, from continued product upgrading and diversification to new plant
construction through more acquisitions, and to allocate and reallocate internal resources in doing so.
The key focus remained on expanding the variety of the product portfolio, but rather thanbreaking into some even newer product spaces, the firm focused on expanding the overall output scale
while steadily increasing the share of high value-added high-count and processed yarn. As seen in
Figure 3, in 1910, the combined share of yarn of counts above 20s (i.e., middle-end plus high-end
products) was still only about 30 percent of total output. This had increased to almost 60 percent
toward the end of our sample. Most pronounced was the growth in middle yarn counts (from 21s to51s), which almost caught up with the output of low-end yarns by 1918. The number of new productvarieties in this “middle range” also rapidly increased (Braguinsky et al., 2021).
[Figure 3 around here]
The increase in high-count and middle-count output was achieved both by adding new, highend machines to existing plants and by acquisitions. Kanebo installed such machines in its flagship
Hyogo plant in 1913, with the capacity of one-third of its pre-existing, low-end machine capacity. The
firm also constantly kept purchasing more high-end machines for the Sumoto plant, whose production
scale of middle yarn counts became 2.45 times larger than its low-end production by 1918. However,other plants, including the brand-new Takasago plant, remained in the low-count product space anddid not engage in downstream diversification either (see Table 1).
The change in the nature of acquisitions is especially noteworthy––while increasing production
efficiency through improved management remained a goal, Kanebo started targeting plants that would
increase its capacity to produce diversified products. In 1911, Kanebo acquired four plants in thewestern part of Honshu operated by Kenshi Spinners and a plant in Osaka owned by Asahi Spinners& Weavers in 1913. As seen in Table 1, most of those newly acquired plants already had high-end
machines and looms for producing fabrics installed by the previous owners. Especially the Osaka plant28

was brand-new as its previous ownership acquired all the high-end machines and looms in 1912-13
but could not start operations and instead sold the plant to Kanebo. In its turn, Kanebo added highend yarn production to the Saidaiji plant and expanded both the low-end and high-end production
capacity of most plants it acquired. Also, in conjunction with the launch of operations by the Osaka
plant in 1915, it built a specialized dyeing and finishing plant nearby (the Yodogawa plant, not listed
in Table 1 because it did not produce any yarn or fabrics on its own).
By that time, Muto had a lot of personnel to choose from to manage important new plants
such as this Yodogawa plant. In this case, the choice fell on Shingo Tsuda, a maverick Keio graduate
who first joined the company in 1907 as a rank-and-file worker and later oversaw the expansion of
the Saidaiji plant after being appointed to manage it in 1911. According to Tsuda’s biography:“Kanebo needed a large-sized plant for dyeing and finishing for its future growth … Muto planned toconstruct a large-scale plant that could surpass British plants. Accordingly, the construction of the
Yodogawa plant required new knowledge and an outstanding and capable person. … Tsuda waseventually chosen for this role following several rounds of selection.” (Ishiguro, 1960, p. 60; translatedby the authors)Thus, the selection of managers was indeed a product of the top manager’s deliberate decisions, andTsuda was selected for his capabilities. He later went on to become the second company president.
There are various instances where educated managers with prior experience in managing plants
where they already oversaw product differentiation were reallocated to newly acquired plants tasked
with expanding the scope of Kanebo’s product portfolio. For example, Toshijiro Sato, a graduate of
Keio University who oversaw the upgrading and diversification of the Sumoto plant (one of thepioneering plants tasked with product differentiation), was appointed to manage the two largest plants
among those acquired from Kenshi Spinners in 1911 and oversaw a big expansion of the high-end
capacity in one of those. Another Keio-educated and experienced manager, Gota Miyake, whooversaw the launch of the weaving division and middle-count yarn market penetration in the Nakatsu
plant in 1909-1913, was put in charge of the newly acquired Osaka plant in the second half of 1913
29

and oversaw the launch of its operations. Both Sato and Miyake were later promoted to executive
positions in the company. On the contrary, plants not involved in product differentiation were still
often assigned managers without any previous managerial experience and higher education (such as
the Wakayama plant, also acquired from Kenshi spinners alongside other three plants but tasked with
low-end yarn production). These individual cases illustrate that talented managers with relevant
experiences were selectively allocated to priority plants.
Turning to the allocation of engineering talent, Figure 4, Panel A shows that while plants that
conducted product differentiation generally had more university-educated engineers per plant thanplants not involved in product differentiation, their employment was concentrated in the early years(when product differentiation was just starting) while such engineers were later released.27 In contrast,
as can be seen in Panel B, employment of technical college graduates (also educated engineers butwith less prestigious education and thus significantly cheaper than university graduates) picked up inplants conducting product differentiation a little later but remained at a high level throughout oursample. Finally, Panel C shows that the number of internally trained skilled workers kept increasing inboth plants, while the levels remained much higher for the former plants.
[Figure 4 around here]
How can we interpret these dynamics? Why did university-educated engineers decline in the
1910s while technical college-educated engineers and internally trained workers remained at highlevels? One explanation, which would be consistent with knowledge transfer from product upgrading
to product diversification (Braguinsky et al., 2021), could be as follows. New technologies require thehighest-level human capital (embodied in university-educated engineers), especially when they are just27 The first peak in the number of university-educated engineers in 1906-1909 corresponds to the initial phases of

introducing high-end machines and looms in pioneering product-differentiation plants (Tokyo, Sumoto, Hyogo), while
the second peak around 1913-1915 corresponds to product differentiation involving plants acquired in 1911-1913
mentioned immediately above (with the Osaka plant actually starting operations in 1915).

30

being introduced. However, as such technologies become more familiar and the novel production
process becomes routinized, the knowledge can now be transferred within establishments to lowerlevel engineers and even to skilled blue-collar workers who become technologically competent enoughto run the production process on their own. Such knowledge transfer enabled university-educatedengineers (and partly technical college-educated engineers) to be released and/or reallocated to otherplants.28 Consistent with this notion, Appendix Figure A9 shows that within each pioneering plant—
Tokyo, Hyogo, and Sumoto, the number of university-educated engineers assigned did not increaseand even declined a few periods after the initiation of product differentiation, while the number oftechnical college graduates and skilled workers kept increasing. Appendix Table A7 summarizes the
allocation of the educated engineers after their appointment to the pioneering plants. It shows thegeneral pattern of reallocation from a pioneering plant to another product-differentiating plant.
6. Conclusion
Using detailed plant- and individual-level panel data from a major Japanese cotton spinning company
over the first two decades of the 20th century, we showed that there was two-way complementarity
between managerial talent and important plants when the strategic task was achieving large-scale
output and positioning for a competitive cost advantage. The task of conducting productdifferentiation, however, required bundling high-end machines (specialized physical capital) withhighly skilled (educated) engineering human capital while still requiring the assignment of the mostcapable (educated and experienced) managers, giving rise to a “three-way complementarity” between
machines, managers, and engineers (as well as skilled workers).
Leveraging the unique historical nature of our data, we dug deeper into the “nano-economics”
of resource allocation associated with each strategy. The clear contrast is that in the earlier period, the28 For instance, as mentioned, while the Sumoto plant constantly expanded its high-end machines over the years, thenumber of university-educated engineers reached the peak of three in 1910-11 and subsequently fell to just one by 1913.
However, the number of technical-college educated engineers grew to nine by 1913 and then stayed at this level.

31

firm only had a meager internal resource base and thus had to pursue the two strategies above one byone, while in the later period, the firm had accumulated an adequate internal resource base and could
pursue both strategies at the same time. This analysis further uncovered that (i) the firm paid special
attention to recruiting and allocating the right kind of managerial resources to new plants that needed
to be integrated into the firm; (ii) the initial phase of product differentiation strategy entailed building
up the stock of highly educated engineers and allocating them to pioneering plants conducting productdifferentiation in conjunction with the best managers; and (iii) once the initial difficulties of masteringhigh-end technologies had been overcome, knowledge embodied in the most capable engineers hiredfrom Imperial Universities could be transferred to second-tier engineers and internally trained skilledworkers who could then substitute for expensive university-educated engineers.
Our research setting has a limitation in that it rests on a single firm case. While we leverage aunique opportunity to highlight the dynamic transition of the allocation process in different growthphases, the generalizability to a different industry, country, and time remains an open question.
Moreover, all managers and engineers in our data are Japanese males, so we only observe heterogeneity
in terms of educational background and job experience. Future studies dealing with more diverse
samples in terms of demographics can shed light on how firms allocate human capital considering
multiple facets of human capital.
Decisions about human capital (re-)allocation are, of course, part of a broad strategy decisionmaking process. In line with the call for papers for this special issue, we highlighted the endogenous
process of the firm’s resource allocation decisions in conjunction with physical capital investment andstrategic implementation. If the focus is on empirical identification, future studies may leverage
exogenous sources of variation that force internal human capital to be transferred to explore theinteraction of different kinds of resources and their consequences. Such “forced” (re-)allocation of
resources due to factors outside of the firm’s control, however, may have very different consequences
32compared to deliberate (strategic) allocation decisions. We believe that in studies like this, endogeneityof the resource allocation process should be embraced as part of the mechanism of interest.
This paper makes several contributions to the resource allocation literature. First, we fill the
gap in this literature by providing empirical evidence on the allocation of different kinds of skilledhuman capital (plant managers, engineers, and skilled workers) within a single-industry firm (Ahuja &Novelli, 2016; Maritan and Lee, 2017). In doing so, we highlight complementarities at the plant level
between physical capital related to new product markets and human capital with relevant skills. Usingmachine orders data, we show that skilled human capital was particularly relevant for plants
conducting high-end capital expansion. While the complementarity between skilled human capital andnew technologies has been previously documented (Stadler et al., 2021; Rubens, 2022), we particularly
highlight the three-way complementarity of managers, engineers, and new technologies in productdifferentiation in which skilled managers and engineers were likely to be allocated in tandem.
Second, our paper contributes to the stream of the literature employing historical methods instrategy research (Ingram et al., 2012; Kahl et al., 2012; Murmann, 2012; Argyres et al., 2020; Pillai etal., 2020). Specifically, we show the usefulness of historical methods in studying resource allocation.
We leverage the “nano-economic” approach to identify each individual manager and engineer’s
educational backgrounds and prior job experience to shed light on the allocation process ofheterogeneous human capital across different establishments (Braguinsky and Hounshell, 2016).
Our study tells the story of Kanebo’s internal resource allocation, starting with the period
when it only had two plants producing simple basic products, with a narrow and limited stock of bothmachines and human capital. We follow it through a remarkable journey involving growing scale andnumber of establishments, expanding the firm’s technological frontier, and building up and(re-)allocating managerial, engineering, and skilled labor resources required to make this multifaceted
expansion possible. In retrospect, Kanebo’s was an amazing accomplishment. The granular data33

employed in this study allows us to unpack the endogenous resource allocation process that led to
Kanebo becoming one of the most important firms in this critically important industry.
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35

Figures and Tables
Figure 1: Location of Kanebo’s plants
Panel A: Ten plants built or acquired in 1887-1902

HakataNakatsu

Panel B: Six plants built or acquired in 1907-1913

Saidaiji

Tokyo
Hyogo

Okayama

Osaka

Suminodo

KurumeWakayama

Nakajima
Miike

Takasago

Bizen

SumotoKumamoto

Figure 2. Number of engineers per plant-semiannual observations by plants conducting product differentiation or not, 1906-10

Note: The blue lines are 95% confidence intervals of the sample means.

36Figure 3. Kanebo total output by type of yarn.

Note: The figure depicts the count-adjusted total amount of output (in pounds) in each category. “High-end yarn”
are counts 52s and higher. “Middle-end yarn” are counts 21s-51s. “Low-end yarn” are counts 20s and below.

Figure 4.
Educated engineers and workers in plants involved and not involved in product differentiation.

Note: In all panels, each bar represents the number of engineers/vocational school graduates per plant acrossplants that conducted or did not conduct product differentiation.

37

Table 1: Summary of Kanebo’s plant history from the 1880s to the 1910s.
(1)
Plant
Tokyo
Hyogo
Suminodo
Nakajima
SumotoMiike
KurumeKumamoto
Nakatsu(2)
Year oflaunch/acquisition
1887
1894
1899
1900

1902

(4)

(5)

(6)

(7)

(8)

(9)

Built or
Acquired

Acquired firm

Initial capacityof spindles

Capacity in
1918-2

Capacitychange (%)

High-end yarn
production

Downstream
integration

Built
Built
Acquired
Acquired
Acquired

Kashu Spinners
Kunijima Spinners
Awaji Spinners

Acquired

Kyushu Spinners

Acquired

28,92040,00010,36810,36810,36831,104
14,76010,36810,36878,04097,296
10,752
19,18437,27630,72015,52810,752
10,752

169.8
143.2
3.7
85.0
259.6
-0.01
5.2
3.7
3.7

1908-2
1913-1
1909-1
1902-2-

1912-1
1905-2
1909-1
1909-2

11,136

11,904

6.9

1910-2

1910-2

22,42013,376
11,136
36,668
7,936

37,44014,528
11,13643,884
11,072

67.0
8.6
0
19.7
39.5

pre-acquisition
1912-2pre-acquisition
pre-acquisition

1907

Built

Nakatsu Spinners
Hakata Kenmen
Spinners-

1911

AcquiredKenshi Spinners

HakataTakasago
Okayama
Wakayama
Bizen
Saidaiji(3)

Acquired

Asahi Spinners &28,45631,756
11.6
pre-acquisition
pre-acquisition
Weavers
Notes: “Capacity change” shows the change rate from the initial capacity to the capacity in 1918-2. The columns “High-end yarn production” and“Downstream integration” show the timing (year-half period) when high-end spindles or looms (for textiles) were actually installed in a plant, and missing cells
mean that high-end production or downstream integration was never conducted until 1918-2. Only cotton-spinning plants are listed in the table, while thosespecialized in silk are not. Two other acquisitions are not listed in this table: Shanghai Spinners (19,840 spindles) integrated into the Hyogo plant, and Nihon
Kenmen Spinners & Weavers (20,708 spindles) integrated into the Sumoto plant.
Osaka

1913

Acquired38

Table 2. Positive assortative matching between managerial quality and plant capacity size (“two-way complementarity”)
(1)

(2)
(3)
(4)
Experienced plant
Educated and experienced plant
Manager promoted in the
VARIABLES
Educated plant manager
managermanager
futureLogged plant capacity
0.089
0.360
0.389
0.243
(0.070)(0.100)(0.078)(0.087)-0.016-2.948-3.302-2.049Constant(0.771)(1.109)(0.857)(0.943)Observations
452
452
452
452
R-squared
0.110
0.321
0.358
0.170
Half-year FEs
Yes
Yes
Yes
Yes
Plant location FEs
Yes
Yes
Yes
Yes
Mean DV
0.800
0.628
0.504
0.334
Notes: Educated manager is an indicator of whether a plant manager had higher education. Experienced plant manager is an indicator of whether aplant manager possesses prior plant manager experience. Manager promoted in the future is an indicator of whether a plant manager was promoted
to an executive position later in Kanebo. Estimation method: OLS. Robust standard errors clustered at the plant level in parentheses. Plant capacity
is measured as the total number of spindles.

Table 3. Allocation of managerial talent to newly added plants
(1)

(2)

(3)

(6)
Log(EducatedEducated andManagerLog(# of
Educated plant
Experienced
plant manager x #experienced
promoted in the
educated
managerplant managerof educatedplant manager
futureengineers)
VARIABLES
engineers)-0.001
0.143
0.189
0.330
-0.291-0.1621(First five years of a new plant)(0.089)(0.122)(0.094)(0.152)(0.230)(0.274)
0.089
0.375
0.408
0.500
0.707
0.784
Logged plant capacity(0.068)(0.090)(0.076)(0.135)(0.147)(0.215)0.134-3.982-4.268-4.973-6.823-7.524
Constant(0.786)(1.013)(0.855)(1.890)(1.988)(2.731)
Observations
452
452
452
452
452
452
R-squared
0.110
0.330
0.374
0.326
0.655
0.456
Half-year FEs
Yes
Yes
Yes
Yes
Yes
Yes
Plant location FEs
Yes
Yes
Yes
Yes
Yes
Yes
Mean DV
0.800
0.628
0.504
0.403
1.533
1.293
Notes: Estimation method: OLS. Robust standard errors clustered at the plant level in parentheses. The explanatory variable is a dummy equal to one if thefirst five years of a new plant. Plant capacity is measured as the total number of spindles. The logged dependent variables are based on the inverse hyperbolic
sine (IHS) transformation: ! = #$%& + √& ! + 1*.

39

(4)

(5)

Table 4. Three-way complementarity between managers, educated engineers/skilled workers, and specialized production machines.
(1)

(2)

(3)

(4)

(5)
Log(Educated and
Educated andLog(Educated andLog(# of educated
Log(# of internal vocational experienced plant manager X
VARIABLES
experienced plant
experienced plant manager X
engineers)school graduates)# of internal vocational
manager# of educated engineers)school graduates)
0.334
0.649
1.215
0.341
1.821
1(Specialized machines)(0.181)(0.214)(0.433)(0.123)(0.827)
0.256
0.478
0.563
0.651
1.195
Logged plant capacity(0.110)(0.165)(0.261)(0.158)(0.550)-2.172-3.481-5.095-3.070-10.749
Constant(1.049)(1.582)(2.468)(1.544)(5.153)Observations
452
452
452
291
291
R-squared
0.411
0.704
0.609
0.725
0.523
Mean DV
0.504
1.533
0.995
3.603
2.215
Notes: Estimation method: OLS. All models include half-year and plant location fixed effects. Robust standard errors clustered at the plant level in parentheses.
Specialized machines dummy is a dummy equal to one if the plant had machines for higher yarn counts and/or looms. The logged dependent variables are
based on the inverse hyperbolic sine (IHS) transformation: ! = #$%& + √& ! + 1*.

Table 5. Experience at a pioneering product-differentiation plant and relocation of educated engineers.
(1)
(2)
(3)
(4)
(5)
(6)
1(Transferred to a product-differentiating plant)1(Transferred to a non-product-differentiating plant)
0.021
0.135
0.098
0.008
0.017
0.017
(0.012)(0.031)(0.028)(0.014)(0.028)(0.034)0.094
0.063
Number of plants previously worked at(0.013)(0.014)0.010-0.034
Logged plant capacity(0.002)(0.005)0.027-0.026-0.178
0.035
0.031
0.363
Constant(0.005)(0.015)(0.027)(0.008)(0.013)(0.061)
Observations
1,580
1,575
1,574
1,580
1,575
1,574
R-squared
0.036
0.132
0.182
0.051
0.114
0.182
Individual FEs
No
Yes
Yes
No
Yes
Yes
Mean DV
0.037
0.037
0.037
0.039
0.039
0.039
Notes: Individual-level panel data on university- and technical college-educated engineers. Estimation method: OLS. All models include half-year and plant
location fixed effects. Robust standard errors clustered at the individual level in parentheses. Pioneering product-differentiating plants are Tokyo, Hyogo, andSumoto plants.
VARIABLES
1(Experience at a pioneering productdifferentiating plant)40

Table 6. Transition of Kanebo’s key strategies and human capital allocation
Phase I (1890s-1905)“Buy” growth strategy

Phase II (1906-10)“Build” growth strategy

Phase III (1911-18)
Balanced strategy

Number of plants(excluding silkproduction plants)

2 original plants + 8 acquired plants

10 existing plants + 1 original plant

11 existing plants + 5 acquired plants

Competitive strategy
and product type• Cost-leadership• Low-end, homogeneous yarns• Begin product differentiation in a
few of the plantso High-end yarnso (e.g., double, twisted yarns)o Textiles (downstream)• Simultaneously pursue both costleadership and product differentiation• Expand production scales of high-end
products

Growth strategy• Acquisition of cotton-spinning firms• Hiring managers using Muto’s network• Hiring university-graduated managers
and engineers• Capital investment for productdifferentiation• Purchase of high-end production
machines• Internal training school for bluecollar workers• Acquisition of cotton-spinning firms• Capital investment for productdifferentiation• Internal training school for blue-collar
workers

Human capital(re-)allocation• Allocate talented managers to large
plants (two-way complementarity) andplantsthat
neededefficiency
improvement• A few talented managers to the moststrategically important plants• Allocate educated managers andskilled engineers/workers to plants
conducting product differentiation(three-way complementarity)• Allocate educated managers toacquired plants to improve operational
efficiency (two-way complementarity)• Allocate educated managers and skilled
engineers/workers to plants conducting
product
differentiation(three-way
complementarity)• Reallocate educated managers andengineers who experienced productdifferentiation in pioneering plants toother plants also conducting productdifferentiation

41

Table 7. Managers and chief engineers of Kanebo’s plants, 1900-1905
Plant managers
# of
Share
Excl.
observations
unknown
108
1.00
0
0.00

Plant chief engineers
# of
Share
Excl.
observations
unknown
93
1.00
29
0.31

All
Retained from acquired
plants
Education:
Keio University (economics,
62
0.57
0.71
0
0.00
0.00
etc.)
Imperial
University
0
0.00
0.00
17
0.18
0.20
(engineering)High
Commerce Schools
19
0.18
0.22
0
0.00
0.00
High Technical School
6
0.06
0.07
26
0.28
0.30
Graduation cohort: 1900 or
0
0.00
0.00
13
0.14
0.15
later
No formal education
44
0.47
0.51
Unknown
21
0.19
6
0.06
Previous experience:
Worked with Muto
33
0.31
0.54
3
0.03
0.03
At Kanebo pre-1900
33
0.31
0.54
19
0.20
0.22
Mitsui network
29
0.27
0.48
2
0.02
0.02
Industry experience
50
0.46
0.82
74
0.80
0.85
Competitor experience
12
0.11
0.20
38
0.41
0.44
Unknown
47
0.44
6
0.06
Notes: The unit of observation is the individual-semiannual period. The number of individuals in charge of a plantin at least one semi-annual period is 15 managers and 18 chief engineers. Previous experience types are not mutually
exclusive, so the total does not sum up to 100 percent.

Table 8. Managers of Kanebo’s plants, 1906-1910
Plants conducting productdifferentiation
# of
Share
observations27
1.00

Plants not (yet) conducting productdifferentiation
# of
Share
observations
94
1.00

All
Education:
Keio University (economics, etc.)
23
0.85
42
0.45
High Commerce Schools
4
0.15
26
0.28
No formal education/unknown
0
0.00
26
0.28
Previous experience:
At Kanebo pre-1906
21
0.78
19
0.20
Transferred from another plant
19
0.70
66
0.70
Mitsui network
10
0.37
8
0.09
Notes: The unit of observation is the individual-semiannual period. Observations on plants conducting productdifferentiation (six managers): (i) Tokyo plant, 1906-10; (ii) Sumoto plant, 1908-10; (iii) Hyogo plant, 1909-10; (iv)
Nakatsu plant, 1909-10; (v) Hakata plant, 1910. Other observations (15 managers) on plants not (yet) conducting
product differentiation. Previous experience is not mutually exclusive, so the total does not sum up to 100 percent.

42