Discussion Paper No. 939

KNOWLEDGE DIFFUSION AND
INDUSTRY GROWTH:
THE CASE OF JAPAN’S EARLY
COTTON SPINNING INDUSTRY

Serguey Braguinsky

June 2015

The Institute of Social and Economic Research
Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan1

Knowledge Diffusion and Industry Growth:
The Case of Japan’s Early Cotton Spinning Industry
Serguey Braguinsky
Department of Social and Decision Sciences,
Carnegie Mellon University, and NBER1

Abstract
The diffusion of technological knowledge is key to industry growth. But not all knowledge iscreated equal. I use a nanoeconomic approach to examine knowledge-diffusion based growth in
the Meiji-era Japanese cotton spinning industry, which enjoyed remarkable success after a decadeof initial failure. By tracing sources of technological knowledge to individual engineers, I find thatsuccessful technology diffusion required the right kind of human capital embodying andtransmitting knowledge, and a competitive environment that rewarded talent while weeding out
incompetence.
JEL classification numbers: O33, O14
This draft: May 2015

1

5000 Forbes Avenue, Pittsburgh, PA, 15213. E-mail: sbrag@andrew.cmu.edu Forthcoming in Industrial andCorporate Change. Part of the work on this paper was done while I was a Visiting Research Scholar at Osaka
University Institute for Social and Economic Research, whose hospitality is greatly appreciated. I am eternally grateful
to my late colleague Steven Klepper for inspiration, encouragement, and advice at the early stages of this project. I
also thank Rajshree Agarwal, David A. Hounshell, Atsushi Ohyama, an anonymous referee, and the participants at
the Klepper Memorial Conference at Carnegie Mellon University in October 2014 for helpful comments andsuggestions. All remaining errors of fact or judgment are mine and mine only.

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I.

Introduction

The rate at which new techniques are adopted and incorporated into the productive process haslong been recognized as “one of the central questions of economic growth" (Rosenberg, 1972). In
the neoclassical growth theory operating with the notion of a representative firm, technology isviewed as “disembodied,” and its diffusion as happening through an “externality” (e.g., Lucas,
1988). The same is essentially true of theories of endogenous growth, where new technologies are
generated through targeted investments but still conducted by firms that are ex ante identical andutilizing accumulated past knowledge available to them as a disembodied externality (e.g., Romer,
1990; Grossman and Helpman, 1991).
In the seminal paper in the literature on industry evolution (Gort and Klepper, 1982),
Michael Gort and Steven Klepper proposed a different approach, one that was based on“Schumpeterian dynamics, and the explicit recognition of heterogeneity among firms” (Agarwal
and Braguinsky, forthcoming). The initial focus of this approach, however, was not so much on
the process of knowledge diffusion as on industry-specific knowledge accumulation by incumbent
firms. The basic idea is formulated in Gort and Klepper (1982: 632-33) where they hypothesizethat accumulated non-transferable firm-specific knowledge becomes progressively more importantcompared to transferable individually appropriable knowledge as the industry matures. In theformal model offered by Klepper (1996), incumbents absorb innovations by new entrants
automatically and almost instantaneously, and the specific channels through which suchinnovations and their diffusion occur are not explicitly considered (see also Jovanovic and
MacDonald, 1994, for a similar approach to diffusion).
Compared to this, there has been less attention in the literature dedicated to generationand diffusion of knowledge, especially at early stages of the industry life-cycle (notable exceptions
include Agarwal and Gort, 2001; Agarwal and Bayus, 2002; Moeen and Agarwal, 2015; among
others). This is particularly true with regard to nanofactors that ensure generation and diffusion
not of any but of the right kind of knowledge. Klepper himself has certainly recognized theimportance of this factor, as witnessed by the fact that he would trace the success of wholeindustries to one exceptionally gifted (and oftentimes, as he argued, also hard-to-work-with)individual (Ransom Olds in the U.S. auto industry, William Shockley in Silicon Valley – seeKlepper, 2016, especially Chapter 3). This paper aims at contributing to this aspect of industry

3
evolution literature by employing nanoeconomic approach and historical methodology (Klepper,
2011; see also Braguinsky and Hounshell, 2015b).
One problem that presents itself when examining instances of knowledge diffusion in earlystages of the industry lifecycle is that industry evolution research has dealt almost exclusively withsuccess stories (a notable exception is Lerner, 2009). While this is understandable, it leads to a
problem well known in econometrics as “sample selection bias.” In other words, the analysis of
success cases of knowledge diffusion-based industry growth lacks a control group against whichthese cases can be meaningfully compared. The problem is even more severe at the firm level.
Tracing firms’ backgrounds requires painstaking effort and often proves impossible outside ofalready known successful clusters, such as Silicon Valley (Klepper, 2010). The data on firm
performance and even survival are often also available to a researcher only on a subset of the total
firm population of the industry, namely, those of them that had a chance to grow large enough andthus were relatively successful. But firm-level success is not always attributable solely to superior
technological knowledge; hence, examining the process of knowledge creation and diffusion on
the subsample of successful firms only may result in hindsight bias, where things that happened
are interpreted as being much more pre-determined or inevitable than they actually were (Fischoff,1980). One way of avoiding such bias is to keep full records of a nascent industry right from theoutset. While such cases in the past seem to be extremely rare, they should become more available
as more effort is dedicated to such fact-recording about new emerging industries.
While in the ideal world a researcher would like to have a “randomized” experiment totease out the factors that distinguish knowledge diffusion-based success and failure, such an
experiment is, of course, not feasible. Without it, we need to study cases where industries initiallystruggled to find their ways of diffusing the best kind of knowledge and start growing and thenfound a way to succeed through what Hausmann and Rodrik (2003) aptly called the “self-discovery”
process. The case of the Meiji-era Japanese cotton spinning industry presents a unique opportunity
in this regard. Its spectacular success, based on “super-fast” (Saxonhouse, 1974) diffusion of thebest technological and managerial practices, has been studied in the English-language literature
for at least forty years.i Less well known is the fact that this success came very closely on the heelsof an earlier failure of what at least on the surface looked like a very similar attempt. Most actors(firms and individuals) also overlapped and kept interacting with each other during both phases,
making it possible to compare directly the factors that led to success and those that led to failure.

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I employ the “nanoeconomic” database built in collaboration with Japanese colleagues,
Atsushi Ohyama, and Tetsuji Okazaki, to examine both early struggles and eventual success of
this industry, and by doing so, to advance our understanding of what it takes to generate and diffuse
the right kind of knowledge in a nascent industry. Another contribution of this paper is that it(together with other related papers mentioned below) fundamentally recasts our understanding of
the emergence of the first, globally competitive industrial sector in a non-Western country.ii The
historical circumstances surrounding this development, described in the next section, made thestory even more amazing.
Three other recent papers draw on parts of the same database. In Braguinsky, Ohyama,
Okazaki, and Syverson (2015) we examine the role played by mergers and acquisitions in
improving plant-level productivity and profitability during the industry consolidation phase in thefirst two decades of the 20th century. In Braguinsky and Hounshell (2015b) coevolution of theindustry and the firms comprising it is examined from strategic management point of view throughseveral stages of industry evolution. The paper most closely related to this one is Braguinsky andHounshell (2015a), which retells the “tale” in Gary Saxonhouse’s (1974) pioneering article oncooperative technology diffusion in the Japanese cotton spinning industry, clarifying the role
played by market competition as opposed to the industry association (Boren). The current paper
advances both the data and the analysis in Braguinsky and Hounshell (2015a). Specifically, I
examine the impact of different sources of technological knowledge and diffusion networks on
individual firms’ technological choices and outcomes. These sources are traced to individual
engineers and technologists (together with their educational background and experience) at thecenter of each network. As a result of this more detailed “nanoanalysis,” I am able to characterizemore fully than was possible before both the failure and the success parts of the story and to link
the factors that led to different outcomes in seemingly uniform environment to links between firms
and engineers they hired.
In the next section I summarize extant research in the form of several “stylized facts,” andI then describe two unresolved research questions that emerge from those and which this paper
tries to shed light on. Section III contains a brief description of the evolution of the Meiji-era cotton
spinning industry in Japan and my data sources. In Section IV I present an in-depth,“nanoeconomic” analysis of the process of knowledge accumulation and diffusion in this industry
from its early failure to the later success. By doing so I address the research questions stated in5Section II by identifying the most important factors that made the transition from failed knowledge
diffusion to a successful one possible. Such an examination is greatly facilitated by incredibly richarchival materials on the industry, which allow me to follow the universe of all firms andproduction facilities operating in the industry virtually from its inception and directly observe theirtechnological choices as well as interfirm flows of knowledge through founders’ networks andworker training. Section V concludes.

II.1 Some “stylized facts” from extant studies
The first “stylized fact” that can be distilled from past research is that the initial impetus to industry
success usually comes from experimentation by a few standout entrepreneurs (firms) or sometimes
even from one single such firm. In the U.S. auto industry it was Ransom Olds and Olds Motor
Works (Klepper, 2010), in the TV receivers industry it was Vladimir Zworykin (RCA) and Philo
Farnsworth (Klepper and Simons, 2000), in the U.S. semiconductor industry it was William
Shockley and Fairchild Semiconductor (Klepper, 2009), and in the personal computer industry it
was Steve Jobs and Apple (e.g., Isaacson, 2011). The evidence is not limited to the U.S.; in fact,
multiple similar cases have been identified across countries and historical epochs: such as Eiichi
Shibusawa and Osaka Spinning Company in Japan’s cotton spinning industry circa late 19th
century (Braguinsky and Hounshell, 2015b), Thonet Brothers and Kohn Brothers in the Austrian
bentwood furniture industry around the same time (Kiriazidou and Pesendorfer, 1999), Salmones
Antartica in the late twentieth century Chilean salmon industry (Petrobielli and Rabelotti, 2004),
and Desh Garments in recent Bangladeshi garment industry (Mostafa and Klepper, 2011).
The second “stylized fact” is that technological and managerial practices developed
through experimentation by the best firms diffuse not as a “disembodied externality” but throughmovement of individuals possessing relevant knowledge and experience. Hounshell and Smith(1988: Ch. 3) demonstrate on the example of the post-World War I U.S. dyestuff industry that evenfull access to disembodied knowledge in the form of patent descriptions was not enough to close
the gap with the German industry until Du Pont (in blatant violation of international legal
arrangements) head-hunted top German chemical engineers. Klepper’s “nanoeconomic” studies ofseveral U.S. industries noted above also clearly demonstrate that the diffusion of best practices
and firm outcomes can be unambiguously traced to such “embodied” transfer of knowledge andexperience through the human capital of firm founders.iii

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The third “stylized fact” is that many cases of spectacular growth through knowledge
diffusion tend to happen in strongly competitive and globally connected environments. In theabove-mentioned cases of the Japanese cotton spinning industry, the Austrian bentwood furnitureindustry, the Chilean salmon industry, and the Bangladeshi garment industry the environment wasthat of a nascent industry establishing its position in a globally competitive export market. Fierce
competition has been and continues to be the hallmark of Silicon Valley, and early U.S. auto andtire industries present similar pictures. Summarizing a large number of studies of industrial clusters
world-wide, Morosini (2004, p. 316) observes that “firms in industrial clusters that present a highdegree of knowledge integration and compete globally, innovate more, present stronger growth
patterns, adapt to changing environmental conditions more rapidly and have a more sustainableeconomic performance than firms in less integrated clusters that tend to compete within strictly
local geographic boundaries.”
The fourth “stylized fact” is that access to finance plays an important role in the process of
emergence of new industries, which face a rapidly changing environment with a lot of uncertainty
and risks. While self-financing in the long run can undo most of the damage caused by capital
misallocation in situations where productivity shocks are persistent, in environments characterized
by transient productivity shocks, the ability of entrepreneurs to self-finance is considerably
hampered (Moll, 2014). The latter is exactly what an emerging industry is likely to face andtherefore nascent industries normally have to rely on either the government providing the initial
market for their products or on venture capital financing (Goldfarb et al., 2012; Klepper, 2016).

II.2 Questions for further researchTwo questions that require further research emanate from the above “stylized facts.” The first suchquestion is what exactly the factors are that make possible knowledge diffusion of the right kindfrom the best firms to the rest of the industry. Existing literature, as discussed immediately below,
points to several potential factors but it is not quite clear how they interact with each other. The
second, and closely related, question is why spectacular success stories of industry growth throughknowledge diffusion are still so rare.
With respect to the first question above, Klepper himself emphasized “the role establishedfirms, particularly the leading ones, played in involuntarily training the next round of entrepreneursthat propelled their industries forward” (Braguinsky and Klepper, 2009: 29).iv The evidence from

7
employee startups in the automobile industry in Detroit, the semiconductor industry in Silicon
Valley, the U.S. laser industry, and others show that they were indeed motivated mainly by theunwillingness of their employers to aggressively pursue promising new ideas (see also Agarwal,
Echambadi, Franco, and Sarkar, 2004). Klepper and Thompson (2010) present a theory based on
disagreements stemming from differentiated ability that would account for this phenomenon.
There is another strand in the literature, however, that emphasizes knowledge diffusion
through what looks like a deliberate decision by industry-leading firms. For example, when asked
about the strategy adopted by Bell Laboratories of not just licensing their innovations but also
telling the licensees all that they knew, the firm’s vice president for electronic components
development reportedly said: “We realized that if this thing was as big as we thought, we couldn’t
keep it to ourselves and we couldn’t make all the technical contributions. It was to our interest to
spread it around. If you cast your bread on the water, sometimes it comes back angel food cake”(cited in Tilton, 1971: 75-76). In the 1950s, Bell Laboratories also “emerged as the major sourceof semiconductor scientists and engineers, and has since served as the principal training groundfor numerous semiconductor specialists.” (ibid., p. 81). v In Japan’s Meiji-era cotton spinningindustry incumbent firms proactively helped new entrants by offering training to their employees
and generally kept almost all the information about best technological and managerial practices
open (Saxonhouse, 1974). Similarly, in Austrian bentwood furniture industry “an important feature… was the free flow of information … New technical achievements and product designs were
exploited not only by the firm that invented them, but also by competitors” (Kiriazidou andPesendorfer, 1999: 157). Instances of “collective search” have also been uncovered in a large
number of other important historical cases, from the Cleveland Steel District (Allen, 1983) andCornish steam engine (Nuvolari, 2004) in England at the dawn of the industrial revolution, to flat
panel display industry (Spencer, 2003), to early personal computer industry (Meyer, 2003).
Regarding the second question, the lack of trying is certainly not the reason. On thecontrary, once successful cases became widely known, they inevitably sparked attempts to imitate
them. One early known example comes from British Dyes (Limited), a company established witha specific task to emulate the German system of innovation in the dyestuff industry. The project
went nowhere because it “has made use of entirely unskilled agents––and, as was to be expected,
the failure has been complete” (Science, Vol. 68, No. 1239, 1918, p. 314). More recent attempts
in various countries to create high-technology clusters modeled after Silicon Valley––from the

8Brittany province in France in the 1990s, to “BioValley” in Malaysia, to Australia’s BITS
(Building on Information Technology Strengths) incubator program––have all also failed due toeither bad incentives or lack of competence or both (Lerner, 2009).
Globally connected, competitive environment (“stylized fact” number 3 above) appears tobe the key in answering both above questions. With regard to employee startups as a way of
promoting innovation and diffusion, these are clearly fostered by eliminating non-competes andother anti-competitive practices as well as providing venture capital financing to new startups.
Numerous studies show that contrary to the views pushed by incumbent firms, allowing employees
to move freely and start their own firms increases both the rates of innovation and diffusion (see,e.g., Gilson, 1999; Marx, Strumsky, and Fleming, 2009).
Less obvious but nevertheless true is that innovation and diffusion through collaborationand knowledge sharing also tend to thrive in a competitive environment. The description of the
Homebrew Computer Club from which Apple emerged in Isaacson (2011) leaves no doubt thatyoung “geeks” in that club were fiercely competitive even as they shared ideas with each other.
As detailed below, fierce competition that did not allow the survival of inefficient firms was alsoa salient feature of the knowledge-sharing based success in Japanese cotton spinning. From atheory vantage point, the initial limited size of the industry (compared to the size of the potential
market) makes it possible for knowledge sharing to be privately optimal under competitive markets(see Braguinsky, Gabdrakhmanov, and Ohyama, 2007; Braguinsky and Rose, 2009).
Competitive environment, in confluence with access to initial financing (“stylized fact”
number 4 above), also likely holds the key to answering the second question posed above, as towhy success cases are so rare. There is a natural tendency to use public sources to secure initialfinancing and/or provide the initial market to a new industry with uncertain technology and noestablished best practices. Cases where this happened without interfering with global
connectedness and the principle of competition (e.g., semiconductor industry in Taiwan––see
Mathews, 1997––or Yozma program in Israel––see Avnimelech and Teubal, 2004) weresuccessful but also rare. As a general rule, maintaining competition in the face of challenges of anew, especially high-tech industry has proven to be elusive, not least because of some conventional
wisdoms about “infant industry” and intellectual monopoly protection still pervasive among
policy-makers (Boldin and Levine, 2010). It is especially important to emphasize in this context
that competition and global connectedness not only help prevent capture by special interests, but

9also allocate scarce resources to areas where there is already a basis of required knowledge andcompetence.vi The examination of the Meiji-era cotton spinning industry in Japan beginning in thenext section will present some additional insights into these issues.

III. Meiji-era cotton spinning industry in Japan: overview and data
Japan opened up to foreign trade in the 1860s after 250 years of autarky. This opening up of thecountry was so “sudden and complete” that it allowed to treat this case as a “natural experiment”
to explore the empirical validity of the comparative advantage theory (Bernhofen and Brown,2004). In line with that theory, Japan’s pre-modern manufacturing production was wiped out, withcotton yarn experiencing the combination of the largest fall in relative price from autarky to thefree trade regime and the highest negative net exports (ibid., Figure 4).
Needless to say, mechanized cotton spinning was the industry that started the industrial
revolution in England, and it was the industry that each country that embarked on the path ofindustrialization during the nineteenth century tried to establish first. In 1878, the Meiji
government, deeply concerned about the growing amount of specie flowing out for imported
textile goods, started promoting the cotton spinning industry by constructing and operating “model
mills” and by purchasing British spinning machinery and reselling it to private entrepreneurs it
recruited at subsidized prices and with easy loan terms. In 1882, it created the “All Japan Cotton
Spinners Association” (known by its Japanese acronym “Boren”) with the specific aim offacilitating the transfer of best-practice spinning technology to and its diffusion within Japan.
Boren immediately began collecting and disseminating (initially through the government-issued“Official Gazette” and later through its monthly bulletin, “Geppo”) detailed firm-level data on
inputs (machine spindles in operation, number of male and female operatives, type of engines
operating the plant and its horse power, the amount of cotton yarn consumed in production, andseparately the amount of recovered waste cotton, etc.) and output (the amount of cotton yarn
produced, its main count,vii the amount of waste yarn), as well as firm-specific output prices andoperatives’ wages. These monthly tables, preserved in archival records until our time, serve as oneof the sources of the data used in this study.viii
Early studies tended to credit these efforts with helping the industry to overcome the initialdifficulties of industrialization (e,g., Smith, 1955; Landes, 1965). In reality, however, the10government-led effort by itself did not accomplish this. Figure 1 presents the earliest available dataon domestic output, imports and exports of cotton yarn in physical units (thousands of pounds).
[Figure 1 about here]
As can be seen from Figure 1, domestic output remains negligible until 1885, and while it
starts increasing beginning in 1886, it hardly puts a dent in imports until the very end of the 1880s.
More importantly, as firm-level data presented below show, the upward trend in domestic output
starting from 1886 came from knowledge sources that had nothing to do with government efforts
to promote the industry.
In 1886 the government withdrew both its support and intervention in the industry. As
documented by government’s own historians writing some fourteen years later (Enkakukiji, 1900,
Chapter 1), the main reason was poor performance of government-promoted mills contrasted tothe success of the first large private company to emerge in the industry by that time (Osaka
Spinning Company, more on this below). This abrupt policy change laid the foundation for theperiod of coexistence between former government-aided mills and new independent private
entrants, allowing me to compare the process of technology and knowledge diffusion between
these two categories operating in the same period and under the same market environment.
Figure 2 shows the dynamics of domestic output, imports and exports of cotton yarn inJapan over the 1890s. As can be seen already from Figure 1, the industry took off in the very last
few years of the 1880s and domestic output exceeded imports for the first time in 1890. Figure 2
shows that output continued to grow at a very fast rate throughout the subsequent 10 years, while
imports quickly became an afterthought and instead exports burgeoned in the second half of the
1890s. After consolidation in the early 1900s, the industry continued to grow rapidly and by the
1920s became one of the largest in the world, a major player on the global cotton yarn export
market. Almost all of this growth came from new private mills. Boren was reorganized into theassociation of independent producers in 1888 but its functions remained essentially the same, andit continued to operate as the institutional arrangement for knowledge diffusion and technology
dissemination albeit under new leadership. This, along with other evidence from the history of theindustry (see Kinugawa, 1964), shows that there was little change in the way knowledge diffusion
was organized at the industry level between the 1880s and the 1890s.
In the next section I conduct an in-depth, “nanoeconomic” investigation of the process ofknowledge diffusion in Japan’s cotton spinning industry during the first two decades of its

11existence (the 1880s and the 1890s). I compare technology choices adopted by firms that relied on
government-aided knowledge diffusion mechanism to technology choices adopted by independent
private entrants and their sources. These differences can be linked directly to very different
outcomes observed in Figures 1 and 2.
As mentioned, the data on input and output choices come from the monthly data collectedby Boren. These are supplemented by the data on dates of founding (defined as the date when theregistration of the firm was granted by the prefectural authority in charge of business registry) anddates of the actual start of operation for each firm that operated in the industry at some point fromthe early 1880s-end of the 1890s from “Enkakukiji,” the unpublished but highly influential
industry history written by the Japanese government sources at the beginning of the twentiethcentury. Enkakukiji also contains the data on the number and type (ring or mule) of spindles
installed and decommissioned by each firm in each year. The information on the backgrounds offounders, top managers and technicians comes from the seven-volume history of the industry
written in the 1930s by Taiichi Kinugawa (Kinugawa, 1964), which contains separate chapters
dedicated to each firm. Whenever possible, I verified this information and supplemented it usingfirms’ shareholder reports and firm-specific information published in Boren’s monthly bulletins(“Geppo”). Educational background of engineers was also verified through the lists of graduatesof the Imperial College of Engineering (later Imperial University Department of Engineering).
Technology data on the characteristics of machinery ordered by different firms in the 1880s and1890s used below come from the file compiled by Gary Saxonhouse and Patrick McGuire andarchived on the ICPSR web site (Wright, 2011). Braguinsky and Hounshell (2015a) describe thesedata sources in more detail.

IV.1. Knowledge diffusion and technology choices under government sponsorship
Even though first attempts to establish mechanized cotton spinning industry in Japan date back to
the 1860s, the process started in earnest in the late 1870s. As mentioned, the Meiji government,
promoted the industry by creating “model mills” and subsidizing about a dozen more bypurchasing British spinning machinery and reselling it at subsidized prices and with easy loan
terms. Despite the obvious failure of those efforts in terms of increasing domestic production andreplacing imports (see Figure 1 above), it is still possible, at least in theory, that those efforts“produced important demonstration effects and trained many skilled artisans and managers”

12(Rodrik, 2014: 83), and thus contributed to knowledge diffusion that sparked subsequent
spectacular industry growth. It is thus important to examine the available “nano” evidence aboutknowledge diffusion in early years of the industry.
Government-led knowledge dissemination conducted through early Boren was
supervised by its technology “guru,” Masatatsu Ishikawa (1825-1895). Ishikawa’s biography isdescribed in detail in Kinugawa (1964, I: Ch. 3). According to it, he was born in an educator’sfamily but was not a “samurai” (nobleman) by birth. He left home at the age of twenty to study inNagasaki, the port city in the south of Japan, which was the only one open for trade and cultural
exchange with the West at the time. The only nation to which Nagasaki was open at the time was
the Dutch, so Ishikawa learned the Dutch language. It appears that he had never learned to read orunderstand English, so when he later needed to understand a technical document about cotton
spinning written in English, he had to send it to Nagasaki to have it translated first. The translation
endeavor alone took a whole year (Kinugawa, 1964, I: 87).
Ishikawa had no training as an engineer, let alone as an engineer in cotton spinning(whatever studies in engineering he completed while at Nagasaki appear to have been on military
artillery). Nevertheless, in what was still the pre-Meiji Japan he landed a job with Nariakira
Shimazu, the Lord of Satsuma (modern day’s Kagoshima prefecture on the southern tip of thesouthernmost island of Kyushu) who awarded him the rank of a “samurai” and made him his
personal advisor.
Using its remote location to evade the rules forbidding contacts with foreign powers,
Satsuma principality was the first in pre-Meiji Japan to start exploring ways to modernize its
economic and military capacity. Pre-empting the central government’s efforts, Nariakira Shimazu
sponsored and oversaw the construction of the first hub of experimental western-style factories inJapan (“shuseikan”), right by his seaside summer residence. The factories included the firstreverberatory furnace in Japan used to cast Satsuma’s own iron cannons and a small shipbuilding
yard, but they were all destroyed by the British artillery fire during the two-day Anglo-Satsuma
War in August 1863. Satsuma-fired cannons, however, did inflict some serious damage on the
British fleet as well. The two sides quickly made peace. In 1865 Satsuma sent a mission to England,
one of the aims of which was to request assistance from Platt Brothers of Oldham in adding amodern cotton textile factory to its rebuilt industrial hub. ix Thus, the first mechanized cotton
spinning and weaving mill in Japan started operating in Kagoshima in 1867.

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It is not clear if Ishikawa directly oversaw the operations of this first mill, but archival
documents show that he was put in charge of the next one set up the next year by Satsuma on theland it owned in the city of Sakai, near Osaka, the heart of Japanese industry and commerce at thetime. In 1872 the new Meiji government bought the mill from Satsuma and converted it into its
first “model” mill tasked with spreading mechanized cotton spinning. Ishikawa continued to run
the mill, and became employed by the government. Subsequently, the government ordered him to
set up the Aichi National Mill, a brand-new plant that was designed to become the flagship of thegovernment-promoted cotton-spinning industry (it started operating in 1881). Seventeen more
mills either directly owned by the government (including prefectural governments) or subsidized
by the government started operating between 1882-1885. Ten of those were designed, assembled
and started operating under supervision from Ishikawa or his close associates also employed by
the government. Seven other mills, even though they also received financial support from thegovernment, were not guided by Ishikawa. Instead, they relied on engineers they themselves hired
and/or on advice obtained from another pioneering firm, founded in Tokyo in 1865 (that is, alsoprior to Meiji restoration) by a private entrepreneur Manbei Kashima (Okamoto, 1995).
To examine whether technology diffusion through government-appointed technology
advisor (Ishikawa or his subordinates, hereafter “Ishikawa” for short) helped the mills I estimate aregression where productivity metrics of mills (measured in physical units) are regressed on the
dummy equal to 1 if the firm was directed by Ishikawa and 0 otherwise, while controlling for yearmonth fixed-effects. The sample is from 1883 (when the first data become available) to 1886,
which is the last year under government promotion policies and it includes all government-aided
mills (but excludes the two mills not aided by the government, notably Osaka Spinning Company––more on this below). The estimation equation is,where

is the outcome variable,

and 0 otherwise,(1)
is the dummy equal to 1 if the firm was advised by Ishikawa

is the year-month fixed effect, andis the firm-specific random error term.

I use logged labor productivity (monthly output in weight units, divided by the total number ofworker-days employed) and logged capital productivity (monthly output in weight units, divided
by the total number of spindle-days in operation) as outcome variables. I also estimate theproduction function with logged output as a function of logged capital input, logged labor input,

14
and year-month fixed effects and use the residuals from this estimation as a measure of total factor
productivity, which is then used as the outcome variable in regression (1).x
Average capital productivity of the mills guided by Ishikawa is estimated to be about 25
percent (exp(-0.288) – 1) below that of firms that were not guided by him (statistically highly
significant); average labor productivity is lower by about 10 percent (statistically significant at the
5 percent level). Total factor productivity of Ishikawa-guided mills is about 16 percent lower on
average than that of mills he did not work with.
Productivity differentials in Table 1 show that mills that relied directly on governmentpromoted technological knowledge had worse, not better outcomes compared to other mills. In
principle, those outcomes could be due to some other factors (such as bad luck), not necessarily
inferior technologies themselves. I therefore use the detailed nature of the available data tocompare directly technology-related choices made by Ishikawa-guided mills and other mills to see
if there are systematical differences between the two.
Table 2 presents the differences in means of mill size (number of spindles installed),
power sources (water engine versus steam engine), capital to labor ratios (defined as the numberof spindles in operation, divided by the number of operative employed in each month), and theratios of female to male factory operatives (blue-collar workers). The sizes of both types of mills
are very similar (just barely above 2,000 spindles) and reflect the overall government policy ofsubsidizing 2,000-spindle mills across the country (Braguinsky and Hounshell, 2015a). But otherchoices are starkly different. Especially water engines were the main source of power in almost 70
percent of observations on mills run by Ishikawa but in less than 25 percent of observations on
mills not run by him. Relying on water engines severely limited the mills’ choices of where tolocate, creating logistic problems as a result. Given the nature of Japanese climate it also subjected
them to repeated seasonal shortages of power. Ishikawa’s well-documented insistence on hydroenergy as the main source of power is even more baffling in view of the fact that both early Satsuma
mills he had experience with were powered by steam engines. Table 2 also shows that mills
directed by Ishikawa were significantly more capital-intensive, and tended to employ more female
operatives relative to male operatives than other mills. As shown below, best practices
subsequently established in the industry involved labor-intensive production technology, coupledwith a high share of female operatives in the workforce. Ishikawa’s choice of running capital-

15
intensive mills while relying at the same time extensively on female labor thus pushed the industry
in the wrong direction.
Another wrong technological choice made by Ishikawa, which affected all governmentpromoted firms, not just those that he supervised directly, was the choice of William Higgins andSons as the machine supplier (recall that the government purchased machines from England andthen sold it to all subsidized firms). Drafting rollers in those machines were designed for mediumstapled U.S. cotton and were particularly unfit for short-stapled Japanese cotton which mills used
as inputs (Tamagawa, 1995). Again, the reason why Ishikawa recommended Higgins and Sons is
not clear – as mentioned, Satsuma had approached Platt Brothers for machines and engineers for
its first mill. Platt Brothers later became the main supplier for new private entrants into the industry,
but not before Osaka Spinning Company re-established contact with it.
It is thus apparent that all that the knowledge diffusion through the government-run
industry association could accomplish was to hammer in all the wrong choices. As summarized ina remarkably honest speech by Kokufuku Kan, the manager in charge of one of the governmentsubsidized mills, given to Boren in 1885,“Lacking capital and educated knowledge, and guided by reckless small-town quasipatriotic feeling only because we felt bad about spending 7 million yen per year on
imported cotton yarn, we unbecomingly planned a great enterprise, letting government
officials dictate things, not gauging our own ability, and almost completely failed to deliver
any progress up until this very day.” (cited in Nawa, 1937, p. 98)
To sum up, contrary to some conventional wisdoms, the knowledge transmitted throughgovernment-owned “model” mills and government-subsidized mills contributed almost nothing to
the subsequent “winning combination” of technology and management that propelled the industry
to its success starting in the late 1880s. They did not serve as the training facilities for the rightkind of managers and engineers either. The right kind of knowledge and the right kind of humancapital emerged from a totally different source and proliferated by means of a totally different
mechanism.

IV.2. Osaka Spinning Company and the emergence of new knowledge source

16
The first seed of industry success was planted around the same time as the government started its
promotion policies but was independent of those. It came from Eiichi Shibusawa, a pioneering
industrialist who is often dubbed the “father of Japanese capitalism” (see, e.g., Tai, 1993). xi
Shibusawa, whose career included serving in the inner circle of the last Prince Tokugawa, the top
military ruler of pre-Meiji Japan, as well as a stint at Meiji government’s Ministry of Finance (heleft government service in 1873 to become a private entrepreneur), had successfully launchedseveral companies, most prominently the First National Bank (the first modern bank in Japan),prior to entering cotton spinning. He started planning his mill, eventually called “Osaka Spinning
Company,” in 1879, that is, around the same time the government started building its Aichi
National Mill.
Arguably, the most important thing that Shibusawa did differently from othercontemporaneous firms was the decision to obtain his own technologically enlightened humancapital by tapping into global knowledge. Takeo Yamanobe, a 26-year-old family acquaintance
who had earlier gone to England to study economics of insurance at the University of London,
agreed to transfer to the mechanical engineering department at Kings College, Cambridge, andthen went to acquire practical skills at Rose Hill Mill in Blackburn, Lancashire, where he worked
regular eight-hour shifts alongside spinning workers (see e.g., Abe, 2004). Yamanobe returned toJapan in July 1880 and was given the task of choosing the site and designing the projected mill.
Yamanobe not only became the chief engineer and later the CEO of Osaka Spinning
Company (and its successor, Toyo Spinning), he also subsequently took over the institution ofknowledge diffusion through the industry association and filled it with the right kind of knowledge(and approach; see more on this below). During the first half of the 1880s, however, thegovernment was still in charge of the association, and this gives me an opportunity to compare
technological choices made independently by Osaka Spinning with those that proliferated throughthe extant knowledge sharing mechanism.
A key novel element in Shibusawa’s technology plan right from the beginning wasconstructing a much larger mill than the standard 2,000-spindle size of government-promoted mills(archival evidence shows that by 1880, perhaps even earlier, he had already decided that the scaleof his mill would be at least 10,000 spindles; see Takamura, 1971, I: 64). The first issue that had
to be addressed, of course, was securing enough capital. Shibusawa drew on his prior experienceas the founder of First National Bank to declare a joint stock company, and proceeded to raise

17
capital from a broad and diverse array of private investors. The initial list of Osaka Spinning
Company shareholders counted 95 individuals, the largest of whom (Shibusawa himself) owned
12 percent of the total number of shares.xii
The process of choosing the power source speaks to how much even Shibusawa andYamanobe were influenced by the prevailing wisdom at Boren early on. For almost a year,
Yamanobe, accompanied by Ishikawa, toured various potential sites looking to ensure enoughhydropower for the projected mill. Only in late 1881 was the decision made to turn to the steam
engine instead (which, of course, proved to be the ultimately correct decision). Since that moment,
it appears that all the strategic technology decisions pertaining to Osaka Spinning Company’s mill
were firmly in the hands of Yamanobe who officially became the company’s chief engineer as it
was incorporated in May 1882.
Having spent only a few months at Cambridge attending cotton spinning engineeringclasses, and then less than a year at Rose Hill Mill, Yamanobe may not have yet been ready for a
fully independent role (Saxonhouse, 1974; Abe, 2004). But the contacts he established while in
Lancashire allowed him to draw on an entirely different source of knowledge than governmentaided mills. The two-story brick building that hosted the machines, which was also the first private
facility in Japan to introduce electrical lighting, became a source of admiration (and subsequentimitation) throughout the industry. Different (and superior) technological choices were made withregard to machines as well. As mentioned, Ishikawa had chosen William Higgins and Sons whosemachines did not fit the type of raw cotton available at the time to Japanese mills. Yamanobe
worked with Platt Brothers of Oldham to customize their machines so that they could be adjusted
as much as possible to handle short-staple domestically grown cotton (Farnie and Abe, 2000). This
also marked the start of a long-term partnership between Platt Brothers and Japanese cotton
spinners, something that became a central piece of the subsequent industry success (Saxonhouse,1974). Moreover, realizing the limits to his own knowledge at the time, Yamanobe invited William
Nield, a highly regarded veteran Platt Brothers engineer and millwright with vast international
experience to guide his team. One of the young Japanese assigned to help Nield in this process(and learn from him), Katsumasa Okamura, subsequently recalled that “Nield … had extremely
precise knowledge of the technology and was also a very good teacher. All previous mills had beenassembled by uneducated workers, guided by people who had worked for Satsuma’s Kagoshimamill and themselves possessed only half-baked knowledge. Ours was … the first time a Japanese18
cotton spinning mill started operating after being properly assembled and installed, and it made ahuge difference” (Toyo Boseki, 1986, Vol. I, p. 26).
Powered by global connectedness that gave it access to new knowledge and superior
technological and managerial choices, Osaka Spinning Company started operating in 1883 andquickly became the only profitable company in the industry at the time. The board of directors
distributed much of those profits as dividends to shareholders, demonstrating clear awareness of
the importance of building investors’ confidence. As a result, the company was able to triple its
capital (and the corresponding mill size) within just a few years, reaching over 30,000 spindles by1886. In that year, Osaka Spinning Company was already more than 10 times large than othermills, and it alone accounted for more than 46 percent of all cotton yarn produced by the industry.
As mentioned, it was in that year (from which, not coincidentally, the chronology of modern
economic growth in Japan starts––see Ohkawa and Rosovsky, 1965) that the government decided
to put an end to its promotion policies and to let the industry develop on its own.

IV.3. Subsequent knowledge diffusion and the role of relational networks
The emergence of Osaka Spinning Company gave the fledgling Japanese cotton spinning industry
its first standout firm. After the government withdrew, independent private entrants started coming
in. The total number of firms in the industry was 21 in 1886 but it doubled by the end of the decadeand doubled once again by 1898. Many new entrants (and even some of the extant former
government-aided firms), not surprisingly, chose to emulate Osaka Spinning Company’s choices.
The latter’s leading role in determining the overall direction of the industry was cemented in 1888
when Boren was reorganized into the association of independent producers, its headquarters weremoved to Osaka Spinning Company premises and Yamanobe became the new chairman. As noted
below, the new leadership of Boren almost immediately took several crucial steps that put theindustry on the right track, and more generally, the role of the association in diffusing the bestpractices can hardly be overestimated. At the same time, especially during the initial 5-10 years
after the industry had been privatized, the degree of uncertainty and, most crucially, the dearth ofsuitable human capital were still severe. This created the situation where technological choices
made by different firms were still quite different from each other and the eventual uniformity of
the industry’s practices emphasized, e.g., by Saxonhouse (1974) was still far from complete (seeBraguinsky and Hounshell, 2015a). This gives me an opportunity to examine how the diffusion of

19“embodied” knowledge coming from different sources affected the choices made by different
types of new entrants and how it played out in their outcomes.
Table 3 lists all firms that operated in the industry from its inception and until 1898, theirsources of technological knowledge and some other characteristics; initial mill size, year the millreached the minimum efficient size of 10,000 spindles (if at all), whether it has ever acquired
another firm (“acquirer” or not), exit year (if exited before 1920) and whether the exit happened
through acquisition (with the production facilities operating without interruption) or throughbankruptcy or other reasons that led the mill to shut down its operations. The sources oftechnological knowledge were traced to prominent engineers (technologists) with independent
backgrounds (such as the above-mentioned Masatatsu Ishikawa or Takeo Yamanobe) and assigned
to firms based on an employment contract (for example, Yamanobe was the chief engineer of
Osaka Spinning Company and Osaka Yoriito at the same time) or primary engineering advisingrole or the spinoff relationship (including former associates or apprentices).
As can be seen from Table 3, sources of technological knowledge could be traced to
specific individuals (engineers) in 73 out of 93 total cases (cases where the sources remained
unidentified are labeled “N/A” in the third column of Table 3 and mostly pertain to smaller local
firms or firms with very short histories). Among those, 36 firms derived their technologicalknowledge from engineers with formal training (including those in advisory roles); nine firms, inthe cluster centered around the city of Okayama in Western Japan were supervised or advised byTomonao Ishiguro and Shin’ichiro Arakawa and their co-workers (Ishiguro was a college-educatedengineer although in metallurgy, not cotton spinning, while Arakawa was among the first cohortof graduates of the Imperial College of Engineering), five firms were supervised or advised by
Narazo Takatsuji (Imperial College of Engineering graduate); four firms each were supervised oradvised by the already-mentioned Takeo Yamanobe and his protégé Tsunezo Saito (the Imperial
College of Engineering graduate whom Yamanobe recommended for the chief engineer positionat Mie Cotton Spinning), and by Kyozo Kikuchi and Hidehisa Shimoyama (also Imperial College
of Engineering graduates); three firms had access to services of Seizaburo Kawai (another Imperial
College of Engineering graduate), two firms each employed Takeshi Yamaguchi (Englisheducated engineer, although not in cotton spinning), Shun’ichi Hattori and Tomokichi Yoshida(the latter two Imperial College of Engineering graduates), and, finally, one firm was advised by20
England-educated Naosada Taniguchi. With the exception of Shimoyama, Kawai, and Yoshida,all the above engineers had experience in England.
Engineers mentioned above practically exhausted the list of all college-educatedengineers available to cotton spinning firms in Japan in the late 1880s–early 1890s. It is therefore
not surprising that many new entrants still had to resort to non-college-educated technicalpersonnel. The most prolific source of knowledge for such firms remained Masatatsu Ishikawa,
who retired from government service in 1887 but continued in his advisory role for the privatizedindustry, and his associates and apprentices. Thirteen new entrants relied on this source ofknowledge, bringing the total number of firms under Ishikawa’s influence to 21 and creating asituation in which it is possible to directly compare older and newer sources of knowledge incontemporaneous competition under the same market environment.
As it turns out, the new sources of knowledge generated by college-educated engineers
played by far the most important role in the industry success, both overall and individual firms. A
crucially important early instance came around 1887 when three of those (Hattori, Saito, andKikuchi, all of them Imperial College graduates, Hattori and Saito just a year apart) foundthemselves in England sent there by their respective employers for training and to place orders forspinning machines at the same time. Aided also by Senjiro Watanabe, Mitsui Trading’s
representative in London, they arrived at an innovative decision to start importing to Japan ring
spinning machines rather than mules that had been imported until that time (see, e.g., Tamagawa,1997). The subsequent speed of diffusion of ring technology in Japan was the fastest in the world(Saxonhouse and Wright, 1984). Just about a year or so later, Boren, led by Yamanobe and his
Osaka Spinning Company, organized a mission to India to study the possibility of importing Indian
cotton to replace short-stapled Japanese and Chinese cotton which was not suitable for mechanizedspinning and the use of which put severe limitations in the way of improved productivity andperformance of Japanese mills. The almost simultaneous adoption of ring spinning technology andlonger-stapled Indian (and later also the U.S.) cotton were the two most important factors thatpropelled the industry towards its spectacular success (see, e.g., Tamagawa, 1995; Braguinsky andHounshell, 2015a). The tradition of knowledge sharing established under early Boren andcontinuing under Osaka Spinning Company leadership meant that the knowledge about thesuperiority of Indian cotton and ring spinning frames diffused extremely fast. Samples of yarn
spun using Indian cotton were distributed to all firms, and Boren also negotiated a discount on

21shipping costs for imported cotton as well as the elimination of the (fairly low in any event) importtariff on raw cotton which applied equally to all its members. The actual adoption of those newtechnologies by individual firms, however, was also influenced by the sources of their individual
technological knowledge.
Ring spinning machines became the universal choice for all mills that started operating
after 1887 (the only exception was Nihon Spinning Company, which, led by Kikuchi, deliberately
imported mules to experiment with higher-quality cotton yarn). After all, it only took emulating
the orders placed by industry leaders, which did not require any particular skills (and was aided by
the fact that all such orders went through the same London office of Mitsui Trading that had been
involved in first purchases of ring machines). Employing longer-stapled Indian and U.S. cotton,
on the other hand, turned out to be more difficult for some of them. The penetration of Indian and
U.S. cotton and the phasing out of the Japanese and Chinese raw cotton took almost a decade andwas only completed by the turn of the twentieth century. Most importantly, through this process
individual mills developed their own proprietary technology of cotton mixing, something that,
according to economic historians, qualifies as the first globally important innovation to emerge inthe young cotton spinning industry in Japan and became one of the major sources of its
international competitiveness (Saxonhouse and Wright, 2010).
The earliest firm-level data on different types of cotton input used by each individual mill
are available for 1893. In that year non-Japanese and non-Chinese sources of cotton accounted forabout fifty percent of the total amount of raw cotton consumed by the industry but there was wide
variation across different mills. In Table 4 I present binary comparisons observed between thefirms whose sources of knowledge came from college-educated engineers and all other firms;
among the latter; I also distinguish between the firms that were guided by Ishikawa and all others.
The comparisons show the differences in means of the combined fraction of Indian and U.S. cottonin the total amount of raw cotton consumed by each category of mills and in the degree to which
they engaged in cotton mixing. This last metric (“cotton mixing index”) was constructed as follows.
I first assigned a dummy equal to 1 to each type of cotton if its usage in 1893 was 5 percent ormore of the total quantity of cotton consumed by the mill and zero otherwise. I then added all thesedummies together. For example, Osaka Spinning Company used 41.3 percent Chinese cotton and54.3 percent Indian cotton in 1893, while other types constituted less than 5 percent of its total
consumption, so its “cotton mixing index” assumes the value of 2, and so on.

22
The results show that the fraction of Indian and U.S. cotton in total in 1893 was more than
twice as high in firms with college-educated engineers in charge than in all other firms (thedifference is highly statistically significant). Such firms also had on average 3.3 different types ofcotton accounting for at least 5 percent of their total cotton consumption, compared to 2.5 types
for all other firms (the difference is statistically significant at the 5 percent level). In contrast,
having Ishikawa or his associates as knowledge source did not render any advantage in terms ofadopting imported cotton and developing cotton-mixing technology, as can be seen from thecomparisons in the third row of Table 4. Thus, even in technological choices that were initiatedand diffused through Boren, it was important to have the right kind of engineering personnel incharge of individual mills to take full advantage of superior technological opportunities.
The difference in the speed with which new industry-wide technological choices diffused
across firms with different knowledge sources is also manifested in differences in capital-laborratios and female to male factory operatives. Adopting ring spinning frames and especially cottonmixing technology required a shift toward lower capital-labor ratio (caused by an increase in thenumber of workers per spindle, including those employed in preparatory stages) and a shift towards
a higher fraction of female operatives in their total number (Saxonhouse and Wright, 1984a; 2010).
Table 5 presents binary comparisons similar to Table 4 along these two dimensions. Firms deriving
their knowledge sources from college-educated engineers have markedly lower capital-labor ratiosand higher female to male operatives ratio than all other firms. Here, Ishikawa-influenced firms
can also be seen to be ahead of other firms with no college-educated engineers in charge, especiallyin the degree to which they relied on female labor force.
Differences in technological choices have a predictable impact also on firms’ performance.
In Table 6 I present the differences in mean total factor productivity and mean profitability between
firms that derived their source of knowledge from college-educated engineers, from Ishikawa andhis apprentices, and all other firms. Total factor productivity is measured as the residuals fromestimating the production function as in equation (1) above; separate estimates of labor and capital
productivity as in Table 1 above yield very similar results. For profitability, I utilize balance sheetsdata published in the association’s monthly bulletin in 1893-94. These are not available for all
firms, and the largest number of observations (on 33 firms) pertains to the first half of 1894 which
are the data I utilize in Table 6 (using all available data leads to qualitatively similar results but
introduces potential bias because of missing observations on some but not other firms). I employ

23
the “return on assets” measure, which is the amount of profit (or loss) reported by the firm, divided
by the total value of all assets on its balance sheet. It can be seen that firms with college-educatedengineers as their knowledge source economically and statistically significantly outperformed allother firms in terms of both productivity and profitability of their operations. Thus, theirtechnological choices were indeed superior. In contrast, firms that got supervision and/or advice
from Ishikawa had both lower productivity and profitability than even firms that did not havecollege-educated engineers. These results are robust to excluding firms founded before 1887, thatis, limiting the sample only to post-government promotion era entrants.
The knowledge diffusion process through the association eventually led to convergence intechnological choices across firms, despite different idiosyncratic knowledge sources. As alreadymentioned, the penetration of ring spinning frames and imported raw cotton from India and the
U.S. were by and large completed by the second half of the 1890s. In what follows I present somecomparisons across firm categories above for the year 1898 which was one of the last years of thefirst phase of industry growth (see Figure 2 above) and also the last year of operations for somefirms that went bankrupt and/or were acquired in late 1898-1899.
Table 7 confirms that technological practices were much more uniform by 1898. The
capital to labor ratios are by this time remarkably similar across all categories of firms and while
the female to male operatives ratio is statistically higher in firms with sources of knowledge fromcollege-educated engineers as compared to all other firms (and lower in Ishikawa-advised firms),
the difference is much less in magnitude than it was in 1893 (Table 5 above) and economically is
no longer significant. There is no economic or statistical difference across different firm categories
in terms of total factor productivity in 1898 either, which is also a reflection of uniform
technological practices as well as capital vintage differences (whereby newer entrants were able
to take advantage of opportunities provided by imported raw cotton and order faster and moreversatile machines for their mills––see Braguinsky, Ohyama, Okazaki, and Syverson, 2015).
Nevertheless, as shown in Table 8, the all-important profitability, mill size and exporter status
outcomes are still very different. Specifically, firms with college-educated engineers as theirknowledge source are more than 2.5 times larger than all other firms, their average returns on
assets are higher than in other firms, and they are also much more likely to be among exporting
firms. Notably, firms in the Ishikawa network also separated themselves by 1898 from the rest of
the industry––their mean returns on assets are actually economically and statistically the same as

24
in firms with college-educated engineers, while their propensity to be exporters is in-between thefirms led by college-educated engineers and the rest of the industry.

V. Some lessons
The “nanoeconomic” analysis of knowledge diffusion in the Meiji-era Japanese cotton spinningindustry revealed findings that are consistent with the stylized facts discovered by an array ofprevious studies mentioned in the Introduction.
First, even though not the first entrant, Osaka Spinning Company was the first standout
firm in the industry and the initial blueprint for success was almost entirely its creation.
Second, knowledge, both good and bad, was indeed transmitted through an “embodied”
channel. Yamanobe, the Osaka Spinning’s chief engineer’s experience and contacts in England
played the crucial role in acquiring and transmitting the right kind of knowledge, as did similar
experiences and contacts of other leading engineers (such as Saito of Mie Spinning, Kikuchi of
Hirano, Settsu, Amagasaki, and Nihon Spinning, Sanji Muto of Kanegafuchi Spinning––he wasunique in being not an engineer but a professional manager and his experience was in the U.S.).
But the government appointee (Ishikawa)’s influence led to wrong technological choices, at least
initially, and almost led the whole industry astray.
Third, good knowledge eventually won through competition. Idiosyncratic knowledge
sources continued to play an important role in individual firms, and only through competitive
pressures (exit versus expansion, including through acquisitions) did the industry eventuallyconverge to the best practices.
At the same time, the analysis above provides us with some new insights into the two
research questions posed in Section II.2, namely, what the most important factors for knowledge
diffusion are, and how to make sure that the knowledge being diffused is of the right kind.
Early on, Japanese cotton spinning industry was a typical candidate for failure––with noestablished knowledge base and no independent financing sources; the measures taken by thegovernment were misguided both in terms of incentives and, even more so, in terms of promoting
and diffusing wrong technological and managerial solutions.
The success of the industry came through private initiative. Even though Japan at the time
was a developing nation, and capital accumulation was at a very low level, a way was found to
generate access to much-needed initial finance through the system of “business promoters” whose25role was to coordinate private investors and provide monitoring services (Miyamoto, 2010, Ch. 3,
5; see also Miwa and Ramseyer, 2000). Indeed, Eiichi Shibusawa who founded Osaka Spinning
Company is widely recognized as the most prominent such promoter. Importantly, Shibusawa
elected not to rely on government assistance, but instead tapped into a global knowledge source.
This created a solid foundation for building a competitive high-tech industry for the first time.
In terms of mechanisms for knowledge diffusion process early in the industry life-cycle,
the most important lessons from the story presented in this paper can be summarized as follows.
First, a collaborative institutional arrangement by itself is not a guarantee of the diffusion of thebest practices. The Meiji government deserves credit for creating Boren as an institution forknowledge diffusion, but its first six years present a stark reminder that such an institutionalarrangement is a double-edged sword, and can easily lead the industry astray if wrong choices are
made at the outset by unqualified leadership. Second, and related, diversity of knowledge sources
and their unfettered competition (even as the firms also cooperated with each other) were the main
reason why the industry found the right way and ended up with the right kind of enlightened humancapital for successful technology adoption and diffusion.
Japan’s Meiji-era cotton spinning industry has been cited in the literature as an example
of almost seamless technology adoption through the action of the industry association acting as asingle, cooperative decision-making unit. In fact this is hardly the case. Nanoeconomic, firm- andindividual-level analysis shows that the main reason why the industry’s catch up could becompleted in what was indeed historically rather short period of time was that the environment
fostered superior human capital, whose better ideas had a chance to prove themselves and prevail
through competition in markets for inputs, output and assets (both physical and human).

26
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31
Figure 1. Japan’s domestic output, import and export of cotton yarn (1883-1890)
1,000 pounds
70,00060,00050,00040,00030,00020,00010,000
1883

1884

1885

1886

1887

1888

1889

1890

Year
Imports

Domestic output

ExportSource: For years 1887-90 “Nihon Choki Tokei Soran” (Long-Term Statistics of Japan), 1987; foryears 1883-86 my estimates based on the totals for all firms listed in “Kampo” (“Official Gaztette”).
The data for 1883 are available only for 6 months, while for 1884-85 these are available for 8
months and for 10 months in 1886. To estimate annual output I have multiplied the total output
for all available months by 2 in 1883, by 1.5 in 1884-85 and by 1.2 in 1886.

32
Figure 2. Japan’s domestic output, import and export of cotton yarn (1891-1900)
1,000 pounds
350,000
300,000
250,000
200,000
150,000
100,000
50,000
1891

1892

1893

1894

1895

1896

1897

1898

Year
Imports

Domestic output

ExportSource: “Nihon Choki Tokei Soran” (Long-Term Statistics of Japan), 1987.

1899

1900

Table 1. Productivity of government-owned and subsidized firms supervised and notsupervised by Ishikawa in 1883-86.

Coefficient
St. Error
CoefficientSupervised by
Ishikawa dummy

Constant

St. Error

Year-month dummies
Observations
Adj. R-squared

Logged laborproductivity

Logged capital
productivity

Total factor
productivity-0.101 **0.041-0.288 ***0.040-0.170 ***0.037

0.092 ***

-3.303 ***0.114 ***

0.034

0.033

0.029

371
0.226

Included
371
0.406

371
-0.028

Note: The total number of firms is 19, 11 of them supervised by Ishikawa and 8 not supervised. Available monthly
data on firm operations for years 1883-1886 (32 months total). Labor productivity is calculated as monthly output (inweight units), divided by total number of worker-days employed; capital productivity is calculated as monthly output(in weight units), divided by the total number of spindle-days in operation. Multifactor productivity is calculated as
the residual from the production function estimated by OLS with logged output as the dependent variable, and logged
spindle-days in operation, logged worker-days employed and year-month dummies as independent variables.

Table 2. Technology choices of government-owned and subsidized mills
supervised and not supervised by Ishikawa in 1883-86
Supervised by
Ishikawa
Mean
St. Error
MeanPowered by water engines St. Error
Mean
Capital to labor ratio
St. Error
Female to male operatives Mean
ratio
St. Error
Size (number of spindles
installed)

2,196
86
0.70
0.03
38.39
0.97
2.61
0.07

Not supervised by
Ishikawa
2,200
109
0.24 ***
0.03
32.36 ***
1.15
1.98 ***
0.04

Note: See Table 1. *** indicates that the difference in means is statistically significant at the 1 percent level, using adouble-sided t-test with unequal variance.

Table 3. Sources of technological knowledge
Mill name
Kagoshima
Sakai
KashimaDojima)Himeji
Aichi National
Ichikawa
Kawashima
Kuwanohara
Okayama
ShimomuraTamashima
Hiroshima
Miyagi
Osaka
Nagasaki
ShimadaToyoi
Enshu
NagoyaShimotsuke
Osaka Yoriito
Fujii
Kofu
Mie
NaniwaTenma
HiranoKanegafuchi
Kurashiki
Owari

Year started
operating
1867
1870
1872
1880
1880
1881
1882
1882
1882
1882
1882
1882
1883
1883
1883
1884
1884
1884
1885
1885
1885
1887
1888
1888
1888
1888
1888
1889
1889
1889
1889

Technological
Initial mill size Year reached
Acquirerknowledge source(spindles)
10,000 spindles
British advisors
3,648
N/A
No
Ishikawa
2,000
N/A
No
Unosuke Kashima
720
N/A
No
Yamanobe/Saito*
2,440
1891
No
Yamao
2,000
N/A
No
Ishikawa
2,000
N/A
No
Ishikawa
2,000
N/A
No
Ishikawa
2,000
N/A
No
Ishikawa
2,000
N/A
No
Ishiguro/Arakawa*
2,000
1893
Yes
Ishikawa
2,000
N/A NoIshiguro/Arakawa*
2,000
1888
No
Ishikawa
2,000 x 2
N/A
No
Kashima
2,000
N/A
No
Yamanobe/Saito*
10,500
1883
Yes
N/A
2,000
N/A
No
Ishikawa
2,000
N/A
No
Ishikawa
2,000
N/A
No
Ishikawa
2,000
N/A
No
Ishikawa
4,000
1889
No
Ishikawa
2,000
1910
No
Yamanobe/Saito*
4,608
N/A
No
Ishiguro/Arakawa*
1,136
N/A
No
Ishikawa
2,064
N/A
No
Yamanobe/Saito*
10,4401888
Yes
Suzuki
4,500
1889
No
Kawai*
6,864
1889
No
Kikuchi*
4,992
1890
Yes
Yoshida*30,356
1889
Yes
Yamaguchi*
4,472
1894
Yes
Hattori*
15,3601889
No

Exited byacquisition
1898
No
1889
No
1889
No
1896
No
1899
No
1896
No
1914
Yes
Evolved into Mie
1900
No
1907
Yes
1912
No
1898
No
N/A
N/A
1912
No
N/A
N/A
1892
No
1917
No
1895
No
1899
No
1905
Yes
1911
Yes
1903
Yes
1902
No
1907
No
N/A
N/A
1898
No
1899
Yes
1902
Yes
N/A
N/A
N/A
N/A
1905
Yes
Exit year

35
Tokyo
Wakayama
YawataSaitama
Kanakin
Shodoshima
Uwa
Amagasaki
KurumeMiike
SenshuSettsuYamashiro
Fukuyama
Iyo
Tenma Orimono
Asahi
Fukushima
Kishiwada
Koriyama
Matsuyama
Meiji
Onagigawa
Sakai
Takaoka
Wakayama
Banyo Seimai
Kashiwazaki
Kumamoto
Noda
Tsushima
Ajino
Awaji
Fushimi

1889
1889
1889
1890
1890
1890
1891
1891
1891
1891
1891
1891
1893
1893
1893
1894
1894
1894
1894
1894
1894
1894
1894
1894
1894
1895
1895
1895
1895
1895
1896
1896
1896

Kashima
Ishikawa
Suzuki
Shimoyama*
Takatsuji*
Ishiguro/Arakawa*Suzuki
Kikuchi*Ishikawa
Shimoyama*
Kawai*
Kikuchi*N/A
Ishiguro/Arakawa*
N/A
N/A
Yamaguchi*
Ishikawa
SuzukiIshikawa
N/A
HaginoShimoyama*
N/A
Kawai*
Ishikawa
N/A
N/A
Shimoyama*
N/A
Ishikawa
N/A
N/A
Takatsuji*

9,104
4,044
2,000
2,000
13,5522,016
2,064
11,136
5,160
10,36810324
19,968
1152
4608
5376
2064
14924
8256
10368
5326
4992
7668
4060
5376
5096
5696
2184
2304
4608
4992
9216
6912
5000
10864

1894
1893
N/A
N/A
1890
N/A
1896
1891
1895
1890
1891
1890
N/A
1897
N/A
1909
1894
1895
1894
1895
1907
1896
N/A
1896
1899
1910
N/A
N/A
1897
1897
1897
N/A
1898
1896

No
No
No
No
Yes
No
No
Yes
No
Yes
No
Yes
No
No
No
No
No
Yes
Yes
No
No
No
No
Yes
No
Yes
No
No
No
No
No
No
No
No

1914
1911
1890
1890
1906
1899
1902
N/A
1899
1902
1903
1918
1898
1903
1903
N/A
1898
N/A
N/A
1907
1918
1902
1903
1917
1915
N/A
1900
1901
1899
1898
1906
1903
1900
1900

Yes
Yes
No
No
Yes
Yes
No
N/A
Yes
Yes
Yes
Yes
No
Yes
Yes
N/A
No
N/A
N/A
Yes
Yes
Yes
Yes
Yes
Yes
N/A
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
35

36
HarimaHeian
Ise
Kasaoka
KuwanaKyoto
Nihon
Sanshugumi
Bizen
Hakata Kinuwata
Ichinomiya
Isechuo
Nihon Boshoku
Osaka Menshi
Saidaiji
Sanuki
Awa
Chugoku
Fuji
Kashu
Nakatsu
Nihon Hosoito
Tokyo Gasu
YamatoChita
Kunijima
Shanghai
Yawatahama

1896
1896
1896
1896
1896
1896
1896
1896
1897
1897
1897
1897
1897
1897
1897
1897
1898
1898
1898
1898
1898
1898
1898
1898
1899
1899
1899
1899

Yamao
N/A
Ishiguro/Arakawa*
Ishiguro/Arakawa*
Hattori*
Takatsuji*
Kikuchi*N/A
Ishiguro/Arakawa*
Ishiguro/Arakawa*
Hagino
N/A
N/A
N/A
N/A
N/A
Ishikawa
N/A
Taniguchi*
SuzukiN/A
HaginoHibiyaTakatsuji*
Ishikawa
Takatsuji*
Yoshida*Suzuki

10368
7156
2940
9984
3840
2304
44160
960
6144
5760
10780
15360
5712
3246
6912
10000
5376
4992
28256
10368
10368
20096
20568
11520
13056
9184
19840
5376

1896
1900
N/A
1899
1897
1897
1896
N/A
1898
1898
1897
N/A
1897
N/A
N/A
1897
N/A
1899
1898
1898
1898
1898
1898
1898
1899
N/A
1899
N/A

No
Yes
No
No
No
No
Yes
No
No
No
No
No
No
No
No
No
No
No
Yes
No
No
No
No
No
No
No
No
No

1912
1903
1900
1909
1907
1901
1916
1900
1907
1902
1907
1897
1905
1901
1898
1918
1907
1902
N/A
1898
1902
1903
1906
1902
1907
1899
1899
1903

Yes
No
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
N/A
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes

Note: N/A in the “Source of technological knowledge” means that engineers / technical advisors are unknown. N/A in “Year reached 10,000 spindles” means thatthe mill has never reached this threshold (at least as an independent firm). N/A in “Exit year” and “Exit by acquisition” means that the firm survived at least until
1920. * denotes college graduates.

36

Table 4. Knowledge sources and penetration of cotton mixing, 1893
Fraction of Indian and U.S. cotton in
Cotton mixing
Source of knowledge
total
index
3.278 **
Yes
0.458 ***
College-educated2.500
engineerNo
0.2102.385 **
Yes
0.170 ***
3.120
Ishikawa
No
0.410
2.385
Yes
0.170
No
2.714
Ishikawa*
0.285

Note: Last row excludes non-Ishikawa supervised firms whose source of knowledge came from college-educatedengineers. ***, and ** indicate that the corresponding difference is statistically significant at the 1 percent and 5
percent level, respectively, using double-sided t-test.

Table 5. Knowledge sources and capital to labor andfemale to male operatives ratios, 1893
Source of knowledge

Capital to labor ratio
Yes
14.595 ***College-educated engineer No
19.244Yes
18.119 ***Ishikawa
No
16.356Yes
18.119 ***Ishikawa
No*
21.692

Female to male operatives ratio
3.816 ***
2.828
2.987 ***
3.485
2.987 ***
2.482

Note: Last row excludes non-Ishikawa supervised firms whose source of knowledge came from college-educatedengineers. *** indicates that the corresponding difference is statistically significant at the 1 percent level, using
double-sided t-test.

Table 6. Knowledge sources and firm outcomes (1893-94)
Source of knowledge
College-educatedengineer

Mean
St. Error
Mean

Ishikawa

St. Error
Mean

Other (constant)

St. Error

Total factor productivity(1893)
0.085 ***
0.019
-0.098 ***0.016-0.0470.030

ROA (1894, first half)
0.131 *
0.014
0.078 *
0.019
0.110
0.016

Note: Total factor productivity measure is the average residuals from estimating the production function as in (1) inthe main text. ROA is return on assets, firms’ reported profits divided over the value of total assets in the balance
sheet. ***, and * indicate that the corresponding difference is statistically significant at the 1 percent and 10 percent
level, respectively, using double-sided t-test.

38
Table 7. Knowledge sources, capital to labor ratio,
female to male operatives ratio, and total factor productivity, 1898
Capital to labor
Female to male
Total factor
Source of knowledge
ratio
operatives ratio
productivity
15.7723.829***-0.008
College-educated Mean
0.240
0.065
0.015
engineer
St. Error
14.8523.423 ***
-0.005
Mean
0.368
0.062
0.018
Ishikawa
St. Error
15.9073.660 **
0.016
Mean
0.396
0.065
0.018
Other (constant)
St. Error

Note: Total factor productivity measure is the average residuals from estimating the production function as in (1) inthe main text. *** and ** indicate that the corresponding difference is statistically significant at the 1 and 5 percent
level, respectively, using double-sided t-test.

Table 8. Knowledge sources and some outcomes (1898)
Source of knowledge
College-educatedengineer
Ishikawa
Other (constant)

Mean
St. Error
Mean
St. Error
Mean
St. Error

ROA (first
half)
0.042 **
0.017
0.045 **
0.011
-0.0150.019

Size (number ofspindles)
22,527 ***3,506
8,433 *
1,622
9,855
1,458

Exporterstatus
0.516 ***
0.091
0.250
0.112
0.133
0.063

Note: ROA is return on assets, firms’ reported profits divided over the value of total assets in the balance sheet. ***,**, and * indicate that the corresponding difference is statistically significant at the 1 percent, 5 percent, and 10 percent
level, respectively, using double-sided t-test.

38

39

Endnotesi

Saxonhouse (1974) represents the first serious study of this story in English-language literature,

although some of its aspects had been mentioned (often with misinterpretations, more on which
below) by economic historians long before him (e.g., Smith, 1955; Landes, 1965). Among theJapanese historians, the two-volume treatise by Naosuke Takamura (1971) stands unsurpassed inmany aspects even today by the breadth and depth of his coverage and careful attention toimportant details. Other contributions in the English-language literature include Saxonhouse andWright (1984 and 2010), Ranis and Saxonhouse (1985), Ohyama, Braguinsky, and Murhpy (2004),
Braguinsky and Rose (2009), Braguinsky, Ohyama, Okazaki, and Syverson (2015), andBraguinsky and Hounshell (2015a and b).
ii“[T]he astonishing ascendance of Osaka over Lancashire stands as the first completely successful

instance of Asian assimilation of modern Western manufacturing techniques” (Saxonhouse, 1974:
150).
iii

See also Foster and Rosenzweig (1985), Conley and Udri (2010)iv

For example, Ransom Olds, the first great entrepreneur in the auto industry in Detroit, “probably

trained more men of prime importance to the industry to-day than any other producer” (Doolittle,
1916, cited in Klepper, 2011: 145).
v

Cynics point out that this stance might have also been related to the anti-trust litigation broughtup against AT&T, the parent of Bell Labs at the time by the U.S. authorities. Recent investigative
journalism has found that the situation may have changed in more recent years. See, e.g., Ames,
M., “Revealed: Apple and Google’s wage-fixing cartel involved dozens more companies, over one
million employees,” Pando Daily, Mar 22, 2014, http://bit.ly/1Ee1X0e. I am grateful to an
anonymous referee for drawing my attention to this. That said, there were attempts to introduce a“no recruiting rule” among competing semiconductor firms in early years too, although in practicethey did not seem to work (Tilton, 1971, p. 78). The same was true in the Japanese cotton spinningindustry circa late 19th century examined in the current paper. Examination of the actual
effectiveness of the recent Silicon Valley wage-fixing cartel is a fascinating task for future research.
vi

Lerner (2009) describes how SBIR (Small Business Innovation Research) program in the U.S.

worked only in regions where high-tech venture funds were already operating. When the need to

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listen to private venture industry was ignored in the name of “diversity,” “the program fundedfirms with inferior prospects.” (p. 130)vii

“Count” measures the thickness of the yarn, or how many yards of yarn are contained in eachpound. The higher the count, the finer the thread. Producing yarn of higher counts (which is needed
to produce higher-quality cloth) requires better-quality cotton input, more advanced technology,
and more skilled labor, and it was a big challenge facing Japan’s fledgling industry.
viii

Saxonhouse was the first Western scholar to use these data in his dissertation (Saxonhouse,1971). However, it appears that he (and many subsequent researchers) was under the erroneous
impression that the first data became available only in July 1889 (when Boren’s monthly bulletin
was first published). In fact, as mentioned above, earlier data are also available in archives, throughthe government “Official Gazette” (Kampo). I thank Naosuke Takamura for pointing this out tome. The data used in this paper were coded by myself (for the years 1883-1893) and by Tetsuji
Okazaki (for years 1894 and beyond). I thank Prof. Okazaki for generously sharing the data he has
coded.
ix

The mission also included 15 young men and boys, known as “Satsuma students,” with orders

to stay in England and learn sciences and engineering. Many of them later played key roles inmodernizing Japan (Cobbing, 2000). Satsuma became one of the two main powers in the coalition
that ushered in the new Meiji governmentx

I use work-days and spindle-days as my measures of labor and capital inputs and not work-hours

and spindle-hours because the data on hours in operation are not available for the year of 1885.
Using inputs adjusted also for hours per day the mill operated while dropping the 1885
observations leads to qualitatively very similar results.
xi

It appears that Shibusawa arrived at the idea that Japan needed its own cotton spinning industry

in order to limit the amount of money it spent on imports in 1879, about the same time as thegovernment embarked on its model mill-building policy (Geppo, 1921, September, pp. 31-32).
xii

Raising all the initially planned capital turned out to be difficult even for someone like

Shibusawa; his initial plan had been to start the mill with 12,000 spindles but he was forced to startwith 10,500 instead because of the capital constraint (Takamura, 1971, Vol. I, pp. 66-67).

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