The Great Divergence in Innovation Rates, 1700-1850
Leonard Dudleya
19 February 2016
Why during the Industrial Revolution was innovation in the West confined to a few regions, while in China
virtually no innovation occurred in comparable areas? This study extends recent research indicating that people
tend to mistrust those who speak with an accent. Here it is suggested that with the rising complexity of new
technologies, cooperation of individuals with different skill sets became increasingly necessary. Accordingly,
innovation was most likely within large networks of people with a standardized vernacular who were willing to
cooperate with one another. A model of the determinants of 117 important innovations and 201 urban regions in
Europe and North America between 1700 and 1850 supports this hypothesis. In China, however, these
conditions were not met. The high marginal cost of learning a new logographic character divided the
population into multiple disconnected networks for the written language, while the high fixed cost of preparing
a page for printing delayed the diffusion of a standardized spoken language.
Keywords: innovation, cooperation, Industrial Revolution, language standardization, Europe, China
1. Introduction
James Boswell, speaking with a strong Scottish accent, on being introduced to Samuel Johnson: “I do
indeed come from Scotland, but I cannot help it.”
Samuel Johnson: “That, Sir, I find, is what a great many of your fellow countrymen cannot help.”
James Boswell (1830, 535)
During the Middle Ages, Europeans invented few important technologies themselves, instead
borrowing or rediscovering Chinese inventions such as paper, the compass, gunpowder, cast iron
and printing with movable type (Mokyr, 1990, 215-218). Then after 1700, several regions of the
West began to develop new technologies at a rate that was unprecedented. Since in the meantime
technological innovation had virtually ceased in China (Needham, 1969, 11), the result was a
widening technological gap between East and West.
How might this East-West divergence in innovation rates be explained? Recent research in
social psychology indicates that when two strangers meet, the most important question for each is
whether the other person can be trusted (Wojciszke et al., 1998; Fiske et al., 2007). However, as
the Samuel Johnson quote suggests, people tend to mistrust a person who speaks their language
with an accent. Surprisingly, the degree of mistrust does not depend on the strength of the accent
(Lev-Ari and Keysar, 2010, 1094). If so, then the tendency for languages to drift apart over time
might help explain why Chinese innovation rates fell after the fourteenth century, especially for
those complex innovations whose development demanded the skills of two or more individuals1.
a
Correspondence concerning this paper should be addressed to Leonard Dudley, Economics Department,
Université de Montréal, Montreal, Canada H3C 3J7, [email protected].
1
As Greif and Tabellini (2010, 136) observed, because of clan loyalties, the networks of cooperation among
unrelated citizens that characterized the European city did not exist in China.
2
At the same time, successful efforts in the West to standardize the way people write and speak
would help explain its remarkable capacity to create novelty.
Can differing degrees of language standardization help explain the Great Divergence in
innovation rates? Section 2 offers a review of the relevant literature. Section 3 then presents the
research methods to be used in answering this question. An original data set of 117 important
innovations in the West between 1700 and 1850 distinguishes between cooperative and noncooperative innovations. A simple theoretical model then explains the process of cooperation to
create of new technologies. An empirical version of the model permits comparison of the
language-standardization approach with previous explanations based on differences in institutions
and factor prices.
Section 4 describes the econometric results for the West. In the simplest specification, the
coefficients of the institutional and factor-price approaches are found to be significant. However,
the model’s fit improves considerably, especially for cooperative innovations, with the inclusion
of measures of network size and degree of language standardization. The estimated signs of the
key language-network variables are quite robust to changes in specification.
Turning to China, Section 5 questions the pertinence of traditional supply and demand
approaches to innovation in this society, noting that in the seventeenth century its levels of human
capital and relative energy prices did not differ greatly from those in England. As for state policy,
rather than having too many people diverted from scientific pursuits toward literary endeavors, as
some have argued, the problem may have been that China had too few able to master the more
than 3,000 Chinese characters required to be considered literate. Moreover, increasing regional
differences in pronunciation of the koine or lingua franca, often led to problems of
comprehension and, probably, trust.
2. Literature Review
In the year 1600, there were few incentives to develop new techniques either in Europe or in
China. One important consideration was property rights. Although in both England and France
there existed a tradition for the sovereign to present requests for extraordinary taxation to
representative assemblies, there was no requirement that these bodies be convened regularly.
Moreover, there or elsewhere in the West, there was little to prevent the sovereign from
reinterpreting his or her customary rights so as to increase taxes arbitrarily without legislative
approval. In China, it was the arbitrary nature of taxation at the local level that caused
considerable uncertainty for property owners (Brandt et al., 2014, 75)
As for factor prices, in Europe the traditional sources of energy – wood, water power, and
animal and human effort – were still sufficiently inexpensive to be used efficiently in
manufacturing. The situation in China was comparable. Although the Chinese had been smelting
iron with coal since the mid-Tang dynasty, this production dropped off after the Mongol
invasions, though perhaps not as sharply as Hartwell (1967, 109, 145) described. Similarly,
neither society had a relative abundance of human capital. In England, male literacy stood around
30 percent but female literacy was under ten percent (Cressy, 1980, 177). Literacy rates were
somewhat higher in Germany but lower in France (Graff, 1991). In China, the male literacy rate
for a base level of say 400 characters of script was probably higher than that in northern Europe
(Baten et al., 2010, 353), although relatively few had mastered enough characters to be
considered fully literate (Rawski, 1979, 3).
3
Previous studies have argued that in the West over the course of the seventeenth century,
each of these dimensions was transformed in a manner favorable to innovation, while in China
the situation at best remained unchanged. In the case of institutions, North and Weingast (1989)
observed that the English Glorious Revolution of 1688 led to a series of reforms to protect
property rights and the enforcement of contracts. A complementary argument proposed by Mokyr
and Voth (2009) is that from the mid-eighteenth century, the European Enlightenment with its
emphasis on “useful knowledge” encouraged the application of mathematics and science to
satisfy social needs. Other hypotheses stressing the supply side of innovation are the emphasis on
Britain’s individualism, a result of culture for Landes (1998, 219) or of genetics for Clark (2007,
188). In China, in contrast, Needham (1969, 327-328) argued, a world-view incompatible with
the precise study of the laws of nature limited the application of scientific knowledge to
production technologies. Also, under the Ming and Qing, successive imperial governments
struggled to provide public services because of a declining revenue base (Brandt at al., 2014, 58).
With regard to factor prices, Pomeranz (2000, 62-63) suggested that Europe’s advantage over
China in access to inexpensive coal created a demand in the West for energy-using technologies.
Similarly, Allen (2009, 34, 97) argued that in Britain, high wage rates and cheap coal provided an
incentive to devise production processes that substituted inexpensive machinery and coal-based
energy for labor and wood-based energy. For Rosenthal and Wong (2011), China’s lower wage
rates were a result of China’s lesser degree of urbanization, itself a consequence of that
civilization’s greater internal peace compared to Europe. Finally, few would disagree with the
position advanced by Galor et al. (2009) that the formation of human capital has been a key to
economic development. Accordingly, advances in basic education to raise literacy rates are part
of the explanation of the West’s success.
There is a fundamental problem, however, with these explanations of the East-West
innovation gap in that they rely on differences at the level of societies as a whole. The difficulty
is that even in the innovating countries of the West, innovation tended to be confined to a small
number of regions. Some three-quarters of the important innovations cited by historians of
technology were developed in three narrow bands of territory containing the main cultural centers
of Britain, France and the USA. Other regions in the West with similar institutions and factor
prices, or higher levels of literacy failed to innovate during the century and a half prior to 1850
(Mokyr, 2009, 239).
This paper argues that an important element is missing from previous explanations of the
Great Divergence.2 In both Western Europe and China in the year 1600, people from different
regions had trouble understanding one another. Recent studies of the mutual intelligibility of
dialects in both Scandinavia and China provide some idea of the difficulties in communication
when strangers from different regions met each other (Gooskens et al., 2008) (Tang and van
Heuven, 2009). Because of the growing complexity of industrial technologies, innovation
increasingly required the cooperation of individuals with different skill sets. Yet if pronunciation
differences were sufficient to cause people to mistrust one another, as in the experiments of LevAri and Keysar (2010), it was unlikely the required blending could occur.
In the second half of the seventeenth century, these communication barriers began to break
down in the West. Thanks to the efforts of printers, signs of language standardization at the
national level began to appear in the principal cultural centers of the West. In England and
2
See, for example, surveys by Deng (2000, 2014).
4
France, the first monolingual dictionaries that were more than mere lists of hard words to spell
were published in 1658 and 1680 respectively. The early English dictionaries were subsequently
exported to America. It took another century or more before equivalent dictionaries were
published for the other languages of northern Europe. However, the first Chinese vernacular
dictionary with pronunciation appeared only in 1932.
In short, there is evidence to suggest that the diffusion of standardized vernaculars in the
West during the eighteenth century greatly increased the probability of successful interactions to
create novelty.
3. Research Methods
The next step is to test this hypothesis against the institutional and factor-price approaches.
This section presents the data, a theoretical model to help explain it and a corresponding
empirical specification.
The Data to Be Explained
Table 1 presents a summary of 117 innovations in the West between 1700 and 1850 as
identified by a panel of historians of technology. The authors in question were Donald Cardwell
(1991) of Britain, Maurice Daumas (1979) and his associates of France, Joel Mokyr (1990) born
in the Netherlands and living in the United States, and Akos Paulinyi (1989), born in Hungary
and residing in Germany. Of the total number of innovations, 87 were mentioned by at least two
of these authors. The 30 others were noted by only one of them but were also cited by the
Encyclopedia Britannica.
These innovations may be divided into two groups. One group comprises 54 technologies that
may be termed Cooperative Innovations (CI). In each case, the available biographical information
permits the identification of both a principal and at least one unrelated collaborator who made a
significant contribution (see Dudley, 2012, chs. 2-4). From the biographies, there is an argument
to be made that had the other individual(s) not participated in the development of these
cooperative innovations, the technology would not have been successful. Sometimes the
contribution of the other person was technical, but at other times it was entrepreneurial or
occasionally financial. These CIs tended to be relatively complex, requiring the integration of
distinct areas of specialization.
The second category comprises innovations for which only a single inventor may be
identified; that is, the Non-Cooperative Innovations (NCIs). These inventions tended to be
relatively simple conceptually, remaining within the competence of a single individual; for
example, Kay’s flying shuttle, Lenormand’s parachute, and Perkins’s machine to cut and head
nails. Like the cooperative innovators, these independent inventors depended on what Sunderland
(2007, 166) described as “inter-businessman trust” from networks of suppliers, employees and
customers. That this trust was not always forthcoming is shown by the difficulties of Kay,
Hargreaves and Cartwright in persuading users of their ideas to compensate them for their efforts.
One remarkable feature of Table 1 is that three of the ten present-day countries covered by
the study, namely, Britain, France and the United States, developed 95 percent of the innovations
studied. Even more striking is that these three countries accounted for all of the CIs, although
5
only 90 percent of the NCIs. This result suggests that there may be some factor that is more
important for cooperating innovators than for independent inventors. Although the seed drill,
porcelain and the smelting of iron with coke had been developed previously in China, almost all
of the remaining innovations identified in Table 1 were unique to the West. Moreover, neither
China nor any other country developed any other industrial technologies of similar importance
during the century and a half under study.
Why were almost all of the key technologies of the Industrial Revolution – breakthroughs
that were both technologically unprecedented and capable of multiple downstream applications –
developed in the West rather than in China? Might the explanation lie in the fact that the
development of these techniques required cooperation between strangers? If collaboration
between unrelated individuals was a necessary ingredient for the most important innovations of
the Industrial Revolution, it must be asked what conditions favored such cooperation.
A Model of Cooperating to Innovate
Milroy (1994, 20) identified two phases in the standardization of the English language.
Between 1400 and about 1650, there was an initial period of spontaneous convergence toward a
consensus of primarily phonetic norms at the regional level as people from different communities
interacted. Then for the following century and a half, from about 1650 to 1800, language norms
were imposed from above through the publication of written standards in the form of dictionaries
and grammar texts printed in the capital. As a result, the variety used by a prestige group within
London society gradually became the norm for written communication. Standardization of the
spoken language followed, although important regional differences in pronunciation persisted
until the late nineteenth century (Stein, 1994, 4-6). In China, as will be explained below, the
second of these steps did not begin until the twentieth century.
The possibility of a link between language standardization and innovation may be formalized.
Assume that there are two cities, with populations
and
respectively. Initially their dialects
are sufficiently different to prevent cooperation between their residents. Now let a standardized
language be introduced into their populations. The number of new pairings, x, made up of one
resident from each city made possible by this development is given by:
.
If the proportion π of these potential partnerships leads to a successful innovation, the total
number of innovations, y, is:
y
.
Of course, not all of these innovations need take place in city 1. More realistically, assume that
there may be congestion, represented by the parameters and , when the individuals from city 2
try to find partners in the city 1. Moreover, because of transaction costs, the probability of a
successful pairing will be a decreasing function of the distance, d, between the two cities. The
expected number of innovations in city 1 may then be expressed as:
6
,
Take logs:
or
, where
.
(1)
Equation (1) expresses the number of innovations produced after the introduction of a
standardized language as a Poisson distribution that has as explanatory variables the logarithms of
the population of two cities and the distance between them – in effect, a gravity model of
innovation.
An Empirical Specification
Consider now how to integrate the supply and demand forces examined in previous studies
into the networking model of equation (1). Look first at the dependent variable. Allen (1983),
followed by Nuvolari (2004), noted that innovations within a given country tended not to be
spread evenly across its territory but rather clustered around a few centers. This finding suggests
that the unit of observation should be a region within a state. Accordingly, the study uses count
data that measure the number of innovations that occurred in each of 201 urban regions in
Western Europe and North America during each fifty-year interval between 1700 and 1849.
If it could be assumed that the probability of an innovation in the region of a given city is
independent of the number of innovations near other cities in the same period, a Poisson
distribution could be used. However, a comparison of the variance of the dependent variable
(0.182) with its mean (0.065) suggests that zero observations are over-represented. To allow for
such over-dispersion, it is appropriate to use a variant of the Poisson, the negative binomial
specification:
y = exp (Xβ + ε + u),
(2)
where yijt is the number of innovations in city i of type j (cooperative or non-cooperative) in
period t, xijt is a vector of explanatory variables, β is a vector of parameters,
is a random
variable and exp (
) follows a gamma distribution with parameters α and 1/α.
Consider next some candidates for explanatory variables. The degree of protection of property
rights, the degree of practicality of the society’s ideology and the importance of individualism are
all difficult to measure. Since the goal of this study is not to estimate the effects of each of these
variables but rather to correct for their impact on the estimates of social-networking variables, it
will be assumed that most such influences are absorbed by the fixed-effects variables for Britain,
France, Germany, Belgium, the Netherlands, 1750 and 1800.
A second group of variables picks up the influence of demand conditions. Data for eight
urban regions and three periods permitted estimation of the following equation:
Energy price/Wage rate = 1.037 - 0.353*Coal – 0.472*Britain + 0.498*France
7
(0.054) (0.096)
(0.056)
(0.075)
- 0.172*1800, R2 = 0.917, Root MSE = 0.1683.
(0.067)
(3)
The figures in brackets are robust standard errors. Regions close to coal deposits could be
expected to have lower energy prices. There were important labor productivity differences
between England and France, and in 1800, real wages were generally higher than over the
previous century across northern Europe. From the estimated coefficients and root MSE, values
for the 579 missing observations of Relative energy prices were then imputed by the stochasticregression method.
As mentioned earlier, Galor st al. (2009) suggested that a greater stock of human capital
would stimulate innovation. The Literacy rate is an approximate measure of the abundance of this
factor. Estimates of signature rates of marrying couples at the regional level are available for 281
of the 603 observations in the data set. With these data, it was possible to estimate the following
regression:
Literacy = 41.14 + 11.96*1800 – 7.50*Catholic + 20.61*Dissent
(1.400) (2.985)
(3.414)
(2.050)
+ 0.0086*DistRome - 0.0370*DistMainz + 18.57*Male – 4.41*Rural,
(0.00073)
(0.00262)
(0.899)
(1.233)
R2=0.842, Root MSE = 10.20,
(4)
where Dissent indicates a religion other than Catholic, Lutheran or Anglican, DistRome is the
distance from Rome in kilometers, DistMainz is the distance from Mainz, and Male and Rural
indicate that the measure applies to males or rural residents respectively. As Cipolla (1969, 7273) observed, other things being equal, Catholics tended to have lower literacy rates while
members of Dissenting religions had higher rates. Among Catholic areas, those more distant from
Rome, such as the Rhineland, had higher literacy. Moreover since the printing press lowered the
cost of reading matter, literacy would have tended to increase with the diffusion of this invention
outward from Mainz (Cipolla, 1969, 50). Finally, men tended to be more literate than women and
urban residents more so than those in the countryside (Cipolla, 1969, 75, 85). The missing
observations were again imputed by the stochastic-regression method.
Now consider the determinants of the effectiveness of a language network in encouraging
innovation, as suggested by the model of equation (1). It is necessary to make an allowance for
the scale of the region of observation, expressed here by the log of the population of its main
center, City population. The log of the population of the rest of the society, Country population,
could also be expected to play an important networking role. For most cities in the sample, the
latter variable was assumed to be captured by the population within the boundaries of the
corresponding present-day state (less that of the city in question). The United States was an
exception. At the beginning of the first two sub-periods, the thirteen colonies were a part of the
British Empire. Even after the American War of Independence, the two countries remained
8
important trading partners. Accordingly, Great Britain and the United States were assumed to
form a single market.
A third possible networking variable is the degree of standardization of the country’s
language. A possible measure of standardization is Dictionary year, defined as the year of
publication of the country’s first monolingual dictionary (normalized with Britain in 1658 equal
to zero). This variable, shown in Table 2, is a measure of the time at which a standardized version
of the vernacular language first appeared.
It remains to estimate the model of equation (2) for the West and then to determine whether
the resulting estimates help explain the Great Divergence.
4. Results for the West
This section compares the estimates of equation (2) for two types of innovation: on the one
hand, relatively complex innovations which had two or more inventors and on the other, simpler
ones with a single inventor. Table 3 presents the results for the 54 Cooperative Innovations (CIs).
In column (1) is a specification capturing conventional supply and demand considerations. The
country fixed effects show that Britain and France were significantly more likely to innovate than
other Continental countries. On the demand side, Relative energy price and Literacy were also
significant. A scale variable, City population, was significantly different from zero but not
significantly greater than one, suggesting an absence of economies of agglomeration at the local
level.
This pattern of results changed in column (2), when variables representing two additional
dimensions of language networks; namely, size and degree of standardization, were inserted into
the specification. Both Country population and Dictionary year were significantly different from
zero. The coefficient estimates again suggest that Britain and France were significantly more
innovative than other Continental countries, and that the Relative energy price was an important
determinant of cooperative innovation. However, the direct impact of Literacy was no longer
statistically significant. Instead, the considerable improvement in fit suggests that rising literacy
rates were important primarily indirectly, as a facilitator of language standardization which in
turn favored cooperation. These results help explain why between 1700 and 1850 Britain, France
and the USA, where language had been standardized early, accounted for all of the cooperative
innovations.
Turn now to the non-cooperative innovations (those with a single inventor). It may be seen in
column (3) that Britain and France again performed significantly better than Belgium and the
Netherlands, although the gap with respect to Germany was no longer significant. This last result
is not surprising since Germany did have two non-cooperative innovations. Relative factor prices
were no longer important, but in this initial specification Literacy was significant, as was the
Enlightenment variable, 1750. Once again City population was significantly different from zero
but not from one.
In column (4) the other two language-network variables were added to the equation. The
country dummies and the Enlightenment variable were little changed. However, Dictionary year,
the measure of language standardization, was significantly different from zero but also
significantly less in absolute value than in equation (3). Yet with the inclusion of this variable,
Literacy and Country population, the measure of network size, were no longer significant.
9
How might these last results be explained? Some minimal level of regional linguistic
standardization appears to have been required even for non-cooperative innovations. Their
creators had to be able to write and apply contracts with their employees, suppliers and
customers. To do so did not always require that the innovators have access to the wide range of
skills that only a large nation could supply. Nevertheless, as mentioned, Britain, France and the
USA generated fully ninety percent of the non-cooperative innovations.
Methodological Issues
One of the issues that the specification of equation (2) raises is the possible endogeneity of
relative factor prices and the literacy rate. To test for the presence of feedback from the
innovation rate, the observed values of these variables were replaced by instrumental variables
estimated with equations (3) and (4) respectively. The resulting estimates (not shown here) were
almost identical to those in Table 3. The endogeneity of Dictionary year is not an issue since with
only two exceptions, this publication date preceded the year of the corresponding innovations.
As mentioned in Section 3, most of the observations for two key independent variables –
Relative energy price and Literacy – were estimated by the stochastic regression method. How
were the estimates in Table 3 affected by the resulting measurement errors? Because of this
method of estimation, the measurement errors were uncorrelated with the possibly poorlymeasured observations. As a result, the estimated coefficients in Table 3 were unbiased and
consistent. However, since the variances were not efficient, the level of significance of the
resulting coefficients will tend to be underestimated.
Yet another issue is the robustness of the estimates just presented. To assess the importance of
this problem, the explanatory variables were divided into two sets. One set comprised the seven
country and period secondary fixed effects; the second consisted of the six remaining explanatory
variables. The specifications in columns (2) and (4) were then re-estimated 64 times with the set
of fixed effects appearing in each estimate. However, each pass contained a different combination
of the second set of variables. The results, which are presented in an Appendix available from the
author on request, confirmed the pattern displayed in columns (2) and (4) of Table 3. For the
Cooperative Innovations, the Relative energy price and the three language variables were always
significant with the expected signs. As for the Non-Cooperative innovations, only the City
population and Dictionary year were significant. Complementing these robustness estimates is a
series of sensitivity tests also presented in the Appendix.
Together these results help explain why before 1850, countries without a standardized
language (Germany and Italy) or with a small number of native speakers (Austria, Belgium,
Denmark, the Netherlands and Switzerland) were unable to develop innovations requiring the
collaboration of two or more principals. The results also suggest why non-cooperative
innovations were also concentrated in Britain, France and the United States, since even
independent inventors depended on cooperation from local networks of suppliers, employees and
clients.
10
5. Implications for China
Can language-network size and degree of language standardization help explain the puzzling
decline in Chinese innovation after the fifteenth century? Unfortunately there are no data that
would allow estimation of equation (2) for China. However, if the pertinence of the results in
columns (2) and (4) of Table 3 is admitted, other historical evidence permits discussion of
possible linguistic reasons for China’s failure to innovate during the West’s Industrial Revolution.
Supply and Demand forces in China
In previous research on early-modern China, one approach has focused on the supply of
inputs to the innovation process while a second has examined the effect of factor prices on the
demand for new technologies. On the supply side, both Needham (1969) and Lin (1995)
attributed the Chinese decline to the society's inability to develop experimental methods. Whereas
Needham emphasized the incompatibility of Confucian philosophy with a mechanical view of the
world, for Lin the failure to innovate was due to the distorted incentive structure created by the
examination system for the imperial bureaucracy. In the modern period, the resulting lack of
scientifically-trained manpower became crucial, as scientific discovery became increasingly
essential for further technological innovation. Also emphasizing supply factors, Acemoglu and
Robinson (2012, 231) explained China's stagnation by its "extractive" institutions. As Khalil
(2012) has observed, however, institutions tend to be endogenous: they adapt to the demands of
the society. Initially, in the Early Modern period, as Bodde (1991, 215) pointed out, institutions in
England and France were no more favorable to technological innovation than those under the
Ming dynasty. One must therefore explain why institutions unfavorable to innovation were
replaced in the West while they persisted in China.
Could it be that potential innovators in China were reacting rationally to factor prices, but that
these prices did not favor technological innovation? Pomeranz (2000, 62-66) argued that since
China’s major coal deposits were in the north while its industry was in the south where wage
rates were low, its entrepreneurs had little incentive to develop labor-saving machinery. For
Rosenthal and Wong (2011) China’s factor prices did not favor labor-saving innovation. However
in 1700, according to Allen (2009, 101-102), the real price of coal was only 30 percent higher in
Canton than in London. Further north in Suzhou, closer to the coal fields, the difference
compared to London was likely no greater. As for wages, in the late seventeenth century, the real
wages of agricultural laborers in the Yangtze Delta and England were roughly equal (Allen,
2009a, 544). On the eve of the Industrial Revolution, then, the relative price of energy in the
Yangtze Delta was roughly comparable to that in southern England. If it may be assumed that this
approximate parity also applied to the coal-producing areas of Shanxi province compared with
the coal fields of Britain, the Chinese had as much incentive as the British to replace labor by
coal-powered machinery. As mentioned in the introduction, literacy rates in the seventeenth
century were probably comparable in England and China. In short, neither institutional
differences nor discrepancies in relative factor prices offer a convincing explanation for the
absence of innovation in China during the Industrial Revolution in the West.
11
Language Standardization in China
The results for the West indicated that cooperative invention required large networks of
people communicating in standardized languages. In sixteenth-century China, with a population
of 150 million sharing a single writing system, this condition would at first glance appear to have
been easily satisfied. However, for several reasons, such was not the case.
Consider first the size of language networks. The Chinese writing system is logographic, each
graph generally representing a morpheme, the smallest word or part of a word that still has
meaning. Most graphs are composed of a semantic element and a phonetic element. However,
because the spoken language evolved much more rapidly than the written language, by the earlymodern period, the phonetic element no longer coincided with the pronunciation of the composite
graph (Norman, 1988, 68). Another challenge for the reader was the existence of two writing
systems, one a standardized structure used for the literature of the Classical period (221 BC – 220
AD), and the other an evolving non-standardized written vernaculars used for letters, routine
documents and literature for popular consumption (Norman, 1988, 108).3 It is estimated that there
were between 1,000 and 1,600 “vulgar” graphs in use (Hannas, 1997, 20) in the written
vernacular. Even more importantly, however, the meaning of the symbols that the systems shared
often differed. As a result, each writing system would generally express a given idea with a
different set of symbols (Bodde, 1991, 21-23).
With regard to literacy, the male population was divided into distinct networks which for
simplicity may be defined by the number of characters each man could read. In the seventeenth
century perhaps one man in two may have been semi-literate, able to recognize at least 400 of the
characters that he might meet in his daily life (Baten et al., 2010, 354). A much smaller group –
mainly those who had completed the program offered by village or neighborhood schools –
would have mastered the 1,000 or more characters required to read a popular novel in the
vernacular of their region (Rawski, 1979, 3). However, probably less than one percent of the
population had passed the county-level college exams that certified their knowledge of the more
than 3,000 characters required to read Literary Chinese (Heijdra, 1998, 561).4 Finally, an elite
group of no more than a tenth of one percent of the population had passed the rigorous provincial
or metropolitan examinations based on Classical literature that were required to obtain a
government position (Hucker, 1998, 39). In effect, the steeply rising marginal cost of learning
new characters divided the population into multiple disconnected networks according to their
mastery of the written language.5
Lack of standardization was also a problem. Under the late Tang (618-907) and the Song
(960-1279) dynasties, there had been a standard koine for people who did not share the same
dialect. From the thirteenth century onward, this lingua franca was based on the dialect of the Jin
and Yuan capital of Beijing (Norman, 1988, 186-187). In conversation, educated people from
different regions communicated orally using this koine, known as guanhua, “official speech,”
3
As a result, the dominant medium for writing in China was Literary Chinese until well into the twentieth
century (Dong, 2014, 103).
4
In a comparison of 25 Chinese dynastic histories written before 1950, Cheng (2000, 111-112) found that
each contained between 4,000 and 8,000 different characters.
5
Lee et al. (1995, 253) showed that although Chinese children initially learn to read new words somewhat
more rapidly than their Americans counterparts, in the final three years of elementary school, American
children learn roughly five times as many new words as Chinese children.
12
although it was never formalized (Norman, 1988, 133). Even so, increasing differences in
pronunciation of this language of the “mandarins” often led to problems of comprehension. In
particular, the Jianghuai Mandarin spoken in parts of the lower Yangtze, including the former
capital, Nanjing, retained features that had been lost in other northern dialects (Norman, 1998,
194). In the 1720s, the Yongzheng emperor established Correct Pronunciation Academies in the
south in an attempt to standardize the Mandarin spoken by his officials. However, this effort was
later abandoned (Dong, 2014, 131).
There were seven major groups of Chinese dialects, some of which had their own regional
koines (Normal, 1988, 246). As different from each other as French, Spanish and Italian, these
regional vernaculars were not mutually intelligible (Crystal, 1997, p. 314). Within each linguistic
zone, there was a continuum of dialects across regions, between classes and from town to country
(Brook, 1998, 644). In northern China, the dialects were varieties of the vernacular spoken by the
residents of Beijing (Norman, 1988, 190). In the south, the six other linguistic groups showed
much greater variation (Norman, 1988, 187-188).
Was it more difficult for late-Ming Chinese than for their European contemporaries to
understand people from other regions of their society? The answer to this question demands some
measure of the mutual intelligibility of regional dialects in Europe and in China prior to the
Industrial Revolution. Unfortunately such information does not exist. However, if it may be
assumed that the degree of relative intelligibility of dialects spoken by rural residents has not
changed greatly over the past three centuries, then present-day measures of mutual
comprehension offer some indication of the difficulty people had in understanding one another
prior to the diffusion of standard national languages.
The first two lines of Table 4 indicate the percentage of words in 17 non-Standard
Scandinavian dialects understood by young present-day residents of Copenhagen. It may be seen
that comprehension levels average from about one-half to one-third, depending on the distance
from Copenhagen. For two strangers addressing each other in these dialects, conversation would
appear to be difficult. The last two lines of the table indicate the impact of national
standardization programs. Danes exposed to the media of neighboring Sweden and Norway were
likely have an approximate understanding of Standard Sweden and Norwegian. However, their
exposure to the formal teaching of Standard Danish results in a much higher degree of
intelligibility. This latter result provides some indication that the formal teaching of Standard
English and French in the eighteenth century would have greatly increased the mutual
intelligibility of innovators such as Boulton and Watt who came from different regions. Because
of earlier standardization, Britain, France and the United States would therefore have had a great
advantage over other Continental countries, especially in cooperative innovation.
13
Table 4. Intelligibility of Scandinavian dialects to young Danes from Copenhagen
according to distance and degree of standardization
Dialect
Distance (km)
Intelligibility (%)
Neighboring regions
301
48
Distant regions
956
35
Standard Swedish and Norwegian
628
62
Standard Danish
0
99
Source: Gooskens et al. (2008; 66, 74).
In Table 5, the first two lines present comparable present-day intelligibility rates for listeners
who were monolingual rural residents of the Yangtze prefecture of Suzhou. As one would expect,
with greater average distances between regions than in Europe, comprehension levels are lower
than in Table 4. Mutual intelligibility is better for the varieties of Mandarin spoken across the
north Chinese plain than for the less closely-related dialects of the mountainous south.
Nevertheless, it may again be inferred that conversation between strangers would be difficult. The
effect of an informally learned koine is suggested in the third line of the table. The Beijing dialect
is close to but not identical to the Standard Chinese to which most of the respondents would have
been exposed through today’s media. The 64 percent intelligibility percentage thus provides some
idea of the impact of a koine learned in non-formalized fashion. Finally, the bottom line indicates
the impact of the absence of formal teaching of the local vernacular. Residents of the Suzhou
dialect area understood varieties of their own vernacular no better than that of Beijing.
Table 5. Intelligibility of Chinese dialects to rural residents of the Suzhou area
according to distance and degree of standardization
Dialect
Distance (km)
Intelligibility (%)
Northern China
1245
37
Southern China
1001
24
Beijing
1155
64
Suzhou
approx. 50
65
Source: Tang and van Heuven (2009, 719).
In short, China had long reached the first or bottom-up stage in Milroy’s (1994, 20) analysis of
language standardization, with the emergence of “agreed norms in certain dialect areas”. Why
had China not proceeded to the second or top-down phase of standardization with the imposition
of a national standard? The answer may lie in China’s logographic writing system. Although
movable type was a Chinese invention, because of the nature of Chinese script with its thousands
of complex characters, almost all printing before the mid-nineteenth century was with handcarved wooden blocks. However, the fixed cost of carving a page of text in Chinese using block
printing was much higher than setting the same content with movable type in a European
language.6 As a result, between 1522 and 1644, there were some 457,500 different book titles
produced in Europe, but by one estimate only 6,618 in China (Chia, 2003). With a small potential
market and high production costs, publication of a vernacular dictionary was simply not
6
On the relative cost of block printing and typography, see Reed (2004, 31-32) and Angeles (2014).
14
profitable for the private publishers who dominated the Chinese publishing industry in the
seventeenth century.
The first Chinese vernacular dictionary, with pronunciation based on Beijing Mandarin, was
published in 1932, two decades after the revolution that overthrew the Qing imperial dynasty
(Dong, 2014, 133). In 1956, the government of the People’s Republic proclaimed Pùtōnghuà
(Common Speech) based on the Beijing vernacular to be the country's official language. In that
same year the government issued a set of 515 simplified characters along with a Romanized script
known as pinyin to indicate pronunciation. Was it simply a coincidence that in 1982, a half
century after the first vernacular dictionary, a team directed by Wang Xuan patented China’s first
important industrial innovation in five centuries – a system for laser photocomposition of Chinese
characters (An, 2006)?
6. Conclusion
This study has provided empirical support for the inclusion of language-network size and
degree of standardization among the influences that led to East-West divergence in rates of
innovation during the Industrial Revolution. In Britain, northern France and the north-eastern
United States, the emergence of networks of people able to write and speak standardized
languages proved favorable to the cooperation between strangers that was increasingly important
for innovation. In China, the high marginal cost of learning a new written character divided the
population into multiple disconnected networks in terms of the ability to read and write. In
addition the high fixed cost of preparing a page for printing delayed the standardization of the
spoken vernacular. Rapid innovation would not be possible until these barriers were lifted.
15
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18
Data Sources
City population. Population estimates for European cities were from Bairoch et al. (1988).
Estimates for New York, Philadelphia and Boston were from
Longman Publishing (n.d.), Ten Largest Cities by Population, 1700–2000, Retrieved from
http://wps.ablongman.com/wps/media/objects/244/250688/Appendix/12.pdf.
In all, there were 46 cities at or near which one or more innovations occurred. To avoid potential
endogeneity, population and literacy were determined at the beginning of each of the three halfcentury periods.
Country population. The source was Maddison (2007).
Coal. The identification of cities with coal deposits within 30 miles (50 km) was obtained from
Barraclough (1984: 201, 210-211).
Distances. The driving distance in kilometers to each city from Rome and Mainz was obtained
from Google Maps (n.d.). Retrieved from https://maps.google.com/.
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http://forums.catholic.com/showthread.php?t=640044.
Dissent. Darby and Fullard (1970, 127).
Ocean port. Hammond & Hammond (1992).
Language standardization. See Table 2. Note the dates of the first monolingual dictionary of five
countries were chosen arbitrarily. Cities in Belgium and Switzerland were assigned the dates of
French, Dutch or German dictionaries, depending on their main languages. As for Scotland, by
the year of Union with England, 1707, educated Scots were growing accustomed to using English
rather than the Scottish dialect for formal communication (Herman, 2001, p. 116). A similar
argument applies to Ireland for 1800. In the case of the United States, the first settlers spoke the
language of their home regions. However, by the year 1725, the more popular English
dictionaries were beginning to be imported (Green, 1999, 285).
Literacy. Signature rates at marriage are not necessarily an accurate measure of people’s ability to
read and write (Mitch, 1999, 244). However, unlike the national publication rates used by Baten
and Luiten van Zanden (2008), differences in signature rates provide an indication of changes in
human capital at the regional level. By country, the sources were England: Cressy (1980, 177);
Scotland: Stone (1969, 121); France: Houdaille (1977, 68); Germany: Hofmeister et al. (1998);
Italy: Reis (2005, 202); Netherlands: van der Woude (1980, 257-264); United States: Graff (1991,
249).
Wage rates. Wage rates for eight European cities in the sample were from Allen (2001, Table 2).
19
Price of coal. Coal prices for the same cities were available in Allen (2009, 99-100). To the
extent that factors were mobile within each country, these data provide sufficient information to
obtain reasonable estimates of relative factor prices by region.
20
Table 1
117 important innovations, 1700-1849
Country
Denmark
1700-1749
1750-1799
France
Loom coded with
perforated paper (1725)
Loom coded with punched
cards (1728)
Steam-powered wagon (1770)
Automatic loom (1775)
Single-action press (1781)
Two-engine steamboat (1783)
Hot-air balloon (1783)
Parachute (1783)
Press for the blind (1784)
Chlorine as bleaching agent (1785)
Sodium carbonate from salt (1790)
Visual telegraph (1793)
Vacuum sealing (1795)
Paper-making machine (1798)
Illuminating gas from wood (1799)
Germany
Porcelain (Dresden)
Lithography (1796)
Great
Britain
Seed drill (1701)
Iron smelting with coke
(1709)
Atmospheric engine
(1712)
Pottery made with flint
(1720)
Quadrant (1731)
Flying shuttle (1733)
Glass-chamber process for
sulfuric acid (1736)
Spinning machine with
rollers (1738)
Stereotyping (1739)
Lead-chamber process for
sulfuric acid (1746)
Crucible steel (1750)
Rib knitting attachment (1755)
Achromatic refracting telescope
(1757)
Breast wheel (1759)
Bimetallic strip chronometer (1760)
Spinning jenny (1764)
Creamware pottery (1765)
Cast-iron railroad (1768)
Engine using expansive steam
operation (1769)
Water frame (1769)
Efficient atmospheric steam engine
(1772)
Dividing machine (1773)
Cylinder boring machine (1775)
Carding machine (1775)
Condensing chamber for steam
engine (1776)
Steam jacket for steam engine
(1776)
Spinning mule (1779)
Reciprocating compound steam
engine (1781)
Sun and planet gear (1781)
Indicator of steam engine power
(1782)
Rolling mill (1783)
Cylinder printing press for calicoes
(1783)
Jointed levers for parallel motion
(1784)
1800-1849
Galvanometer (1819)
Automatic loom with perforated cards (1805)
Wet spinning for flax (1815)
Electromagnet (1820)
Water turbine (1824)
Single-helix propeller (1832)
Three-color textile printing
machine (1832)
Water turbine with adjustable
vanes (1837)
Photography (1838)
Multiple-phase combing
machine (1845)
Machines for tackle block
production (1800)
Illuminating gas from coal
(1802)
Steam locomotive (1804)
Compound steam engine (1805)
Winding mechanism for loom
(1805)
Arc lamp (1808)
Food canning (1810)
Rack locomotive (1811)
Mechanical printing press
(1813)
Steam locomotive on flanged
rails (1814)
Safety lamp (1816)
Circular knitting machine
(1816)
Planing machine (1817)
Large metal lathe (1817)
Gas meter (1819)
Metal power loom (1822)
Rubber fabric (1823)
Locomotive with fire-tube
boiler (1829)
Hot blast furnace (1829)
Self-acting mule (1830)
Lathe with automatic cross-feed
tool (1835)
Planing machine with pivoting
tool-rest (1835)
Even-current electric cell (1836)
21
Country
1700-1749
1750-1799
Puddling (1784)
Power loom (1785)
Speed governor (1787)
Double-acting steam engine (1787)
Threshing machine (1788)
Single-phase combing machine
(1789)
Machines for lock production
(1790)
Single-action metal printing press
(1795)
Hydraulic press (1796)
High-pressure steam engine (1797)
Slide lathe (1799)
Italy
Electric battery (1800)
Switzerland
Massive platen printing press
(1772)
Stirring process for glass (1796)
United
States
Continuous-flow production (1784)
Cotton gin (1793)
Machine to cut and head nails
(1795)
Sources: see Section 3 of text.
Underlined innovations were cooperative.
1800-1849
Electric telegraph (1837)
Riveting machine (1838)
Transatlantic steamer (1838)
Assembly-line production
(1839)
Multiple-blade propeller (1839)
Steam hammer (1842)
Iron, propellor-driven steamship
(1844)
Measuring machine (1845)
Multiple-spindle drilling
machine (1847)
Single-engine steamboat (1807)
Milling machine (1818)
Interchangeable parts (1824)
Ring spinning machine (1828)
Grain reaper (1832)
Binary-code telegraph (1845)
Sewing machine (Boston)
Rotary printing press (1847)
22
Table 2
Year of first monolingual dictionary
Country
Austria
England
Belgium (French)
Belgium (Flem.)
Denmark
France (north)
France (south)
Germany
Year
1868
1658
1680
1864
1833
1680
1815
1786
Ireland
Italy
1800
1897
Netherlands
1864
Scotland
Switzerland (Fr.)
Switzerland
(German)
United States
1707
1680
1786
1728
Author(s)
Otto Back et. al.
Edward Phillips
Christian Molbech
Pierre Richelet
Johann Christoph
Adelung
Emilio Broglio &
Giovan Battista Giorgini
Marcus and Nathan
Solomon Calisch
Nathan Bailey
Publication
Österreichisches Wörterbuch
The New World of English Words
Same as France (north)
Same as Netherlands
Dansk Ordbog
Dictionnaire français
Standardization delayeda
Grammatich-kritisches Wörterbuch
der hochdeutschen Mundart
Year of Union with England
Nòvo vocabolario della lingua
italiana secondo l'uso di Firenze
Nieuw Woordenboek der
Nederlandsche Taal
Year of Union with England
Same as France (north)
Same as Germany
An Universal Etymological English
Dictionary
a
South of a line from St. Malo to Geneva, standardization occurred through the integrating effects of
the revolutionary and Napoleonic Wars (Graff, 1991, 193).
Note: Other early dictionaries fail to reflect the existence of a standardized written vernacular.
Robert Cawdrey’s Table Alphabeticall (1604) was a list of hard words to spell. Josua Maaler’s, Die
Teütsch Spraach (1561) was devoted to Swiss and Upper German vocabulary. The Accademia della
Crusca’s dictionary of Italian (1612) was intended to provide a prescriptive norm to which writers
were advised to conform. Kornelius Kiliaan’s (1599) Etymologicum used Latin to explain Dutch
words, as did Jean Nicot’s (1606) Trésor de la langue françoise for the French language.
23
Table 3
Negative binomial regressions for Western innovations
Cooperative
Group
Variable
(1)
Britain
France
Non-cooperative
(2)
(3)
(4)
-0.018
-4.575**
0.606
-0.634
0.391
-5.378**
Fixed effects
0.268
-0.926
Germany
-17.239**
-17.128**
-1.972**
-2.073**
Belgium
-17.126**
-15.868**
-16.030**
-17.604**
Netherlands
-17.842**
-11.594**
-16.511**
-17.190**
1750
0.960
0.320
0.646**
0.674**
1800
-0.410*
-1.536
-0.358
-0.101
Demand
Relative energy price
Literacy
Ocean port
-3.285**
2.148**
-1.376
-1.103
2.777*
0.165
2.153
0.740
-0.893**
-0.596
-0.516
-0.179
Language networks
City population
1.148*
1.215**
Country population
1.736**
Dictionary year
-3.297**
1.017**
0.970**
-0.011
-0.929**
Constant
2.948**
8.465**
1.878
3.386
α
2.015**
1.204
2.131**
1.795*
Log pseudolikelihood
-80.389
-72.538
-129.858
-128.147
Time-series cross-section of 201 cities for 1700-1749, 1750-1799 and 1800-1849.
Dependent variable: number of cooperative innovations in region of city j in period t.
Number of observations: 603.
Standard errors are adjusted for five clusters in country.
* Coefficient significantly different from zero at 0.05 level, two-tailed test.
** Coefficient significantly different from zero at 0.01 level, two-tailed test.
Coefficients in bold face in column (2) are significantly different from corresponding estimates in
column (4), at 0.05 level, two-tailed test.